diff --git a/README.md b/README.md
index 020d614c..de2978f1 100644
--- a/README.md
+++ b/README.md
@@ -44,6 +44,20 @@ A JavaScript implementation of the [Web Neural Network API](https://webmachinele
Open the web browser and navigate to http://localhost:8080/test
+#### Run only CTS tests in node.js.
+
+```sh
+> npm run cts
+```
+
+#### Run only CTS tests in web browser.
+
+```sh
+> npm start
+```
+
+Open the web browser and navigate to http://localhost:8080/test/cts.html
+
## Other scripts
### Build docs
diff --git a/node_setup.js b/node_setup.js
new file mode 100644
index 00000000..ce419705
--- /dev/null
+++ b/node_setup.js
@@ -0,0 +1,3 @@
+global.navigator = {};
+require('./dist/webnn-polyfill.js');
+global.chai = require('chai');
diff --git a/package.json b/package.json
index ccf5c59f..d6bd1216 100644
--- a/package.json
+++ b/package.json
@@ -18,7 +18,8 @@
"lintjs": "eslint . --no-eslintrc --config .eslintrcforjs.js --ext .js",
"lint": "npm run lintts && npm run lintjs",
"format": "clang-format --glob=\"{{src,test}/**/*.ts,{src,test}/**/*.js}\" --style=file -i",
- "test": "cross-env NODE_ENV=test mocha --require ./test/node_setup.js --exit --recursive"
+ "test": "cross-env NODE_ENV=test mocha --require ./node_setup.js --exit test/*/*.js test/cts/from_nnapi/tests/cts.js",
+ "cts": "cross-env NODE_ENV=test mocha --require ./node_setup.js --exit test/cts/from_nnapi/tests/cts.js"
},
"repository": {
"type": "git",
@@ -45,9 +46,9 @@
"@babel/polyfill": "^7.11.5",
"@babel/preset-env": "^7.11.0",
"@babel/register": "^7.11.5",
- "@tensorflow/tfjs-backend-cpu": "^2.7.0",
- "@tensorflow/tfjs-backend-webgl": "^2.7.0",
- "@tensorflow/tfjs-core": "^2.7.0",
+ "@tensorflow/tfjs-backend-cpu": "^2.8.3",
+ "@tensorflow/tfjs-backend-webgl": "^2.8.3",
+ "@tensorflow/tfjs-core": "^2.8.3",
"@typescript-eslint/eslint-plugin": "^4.0.1",
"@typescript-eslint/parser": "^4.0.1",
"babel-loader": "^8.1.0",
diff --git a/src/nn/ops/conv2d.ts b/src/nn/ops/conv2d.ts
index 20bd18e9..3e03590b 100644
--- a/src/nn/ops/conv2d.ts
+++ b/src/nn/ops/conv2d.ts
@@ -66,8 +66,8 @@ export class Conv2d extends SingleOutputOperation {
let inputChannels: number;
if (this.layout_ === OperandLayout.nchw) {
// nchw -> nhwc
- input = input.transpose([0, 2, 3, 1]);
inputChannels = input.shape[1];
+ input = input.transpose([0, 2, 3, 1]);
// nchw filter: [output_channels, input_channels/groups, height, width]
// nhwc filter: [height, width, input_channels/groups, output_channels]
filter = filter.transpose([2, 3, 1, 0]);
@@ -99,4 +99,4 @@ export class Conv2d extends SingleOutputOperation {
}
return output;
}
-}
\ No newline at end of file
+}
diff --git a/test/cts.html b/test/cts.html
new file mode 100644
index 00000000..2c652260
--- /dev/null
+++ b/test/cts.html
@@ -0,0 +1,23 @@
+
+
+
+ WebNN API Polyfill | Mocha Tests
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/test/cts/README.md b/test/cts/README.md
new file mode 100644
index 00000000..582ecd96
--- /dev/null
+++ b/test/cts/README.md
@@ -0,0 +1,5 @@
+# Compatibility Test Suite (CTS)
+
+This [cts](./) folder is for holding converted tools and tests from existed native tests of Machine Learning for WebNN API.
+
+[./from_nnapi](./from_nnapi): holds converted tests from [Android Neural Networks API (NNAPI) CTS](https://android.googlesource.com/platform/frameworks/ml/+/refs/tags/android-cts-10.0_r5/nn/runtime/test/specs/).
\ No newline at end of file
diff --git a/test/cts/from_nnapi/COPYING b/test/cts/from_nnapi/COPYING
new file mode 100644
index 00000000..bf33ab55
--- /dev/null
+++ b/test/cts/from_nnapi/COPYING
@@ -0,0 +1,18 @@
+These two Python scripts cts_generator.py and test_generator.py in
+test/generator/src folder come from those scripts in nn/tools/test_generator of
+https://android.googlesource.com/platform/frameworks/ml/
+(tags: android-cts-10.0_r5) with some modification below:
+
+1. Modify cts_generator.py to generate CTS for WebNN API.
+2. Modify test_generator.py to generate CTS for WebNN API.
+
+Copyright 2017 The Android Open Source Project
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+ http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
\ No newline at end of file
diff --git a/test/cts/from_nnapi/README.md b/test/cts/from_nnapi/README.md
new file mode 100644
index 00000000..733ecb76
--- /dev/null
+++ b/test/cts/from_nnapi/README.md
@@ -0,0 +1,101 @@
+# Converted CTS from NNAPI CTS
+
+## Test Generator Tool
+This [test_generator](./test_generator) tool is for converting existed native
+[NNAPI CTS](https://android.googlesource.com/platform/frameworks/ml/+/refs/tags/android-cts-10.0_r5/nn/runtime/test/specs/) of V1_0, V1_1 and V1_2 versions to these tests for WebNN API.
+
+### NNAPI Operations being map to WebNN API Operations Tables
+* Part I
+
+The `test_generator` tool has supported to convert those tests of following
+NNAPI operations to the tests for such WebNN API operations of first wave.
+
+| NNAPI | WebNN API (first wave ops) |
+|:------------------------------|:------------------------------|
+| RELU1 | clamp |
+| RELU6 | clamp |
+| CONCATENATION | concat |
+| ADD | add [+ relu/clamp] |
+| SUB | sub [+ relu/clamp] |
+| MUL | mul [+ relu/clamp] |
+| DIV | div [+ relu/clamp] |
+| MAXIMUM | max |
+| MINIMUM | min |
+| EXP | exp |
+| LOGISTIC | sigmoid |
+| SQRT | sqrt |
+| TANH | tanh |
+| FULLY_CONNECTED | matmul [+ add+ relu/clamp] |
+| AVERAGE_POOL_2D | averagePool2d [+ relu/clamp] |
+| MAX_POOL_2D | maxPool2d [+ relu/clamp] |
+| CONV_2D | conv2d [+ add + relu/clamp] |
+| DEPTHWISE_CONV_2D | conv2d [+ add + relu/clamp] |
+| RELU | relu |
+| RESHAPE | reshape |
+| SLICE | slice |
+| SOFTMAX | softmax |
+| SPLIT | split |
+| SQUEEZE | squeeze |
+| TRANSPOSE | transpose |
+
+* Part II
+
+And there're these following NNAPI operations which could be map to others
+WebNN API operations of next waves.
+
+| NNAPI | WebNN API (next waves ops) |
+|:------------------------------|:------------------------------|
+| ABS | abs |
+| FLOOR | floor |
+| LOG | log |
+| NEG | neg |
+| SIN | sin |
+| L2_POOL_2D | l2Pool2d [+ relu/clamp] |
+
+* Note:
+
+1. Current WebNN Polyfill API supports Float32 and Int32 two types, so these
+NNAPI CTS using Float32 and Int32 types were able to be converted, while those
+NNAPI CTS using Uint8 and Int8 types would be converted until WebNN Polyfill API
+supports Uint8 and Int8 types.
+
+2. Native NNAPI supports Float16, while there's lack of Float16 in JavaScript
+environments, so such NNAPI CTS with Float16 wouldn't be convertted.
+
+### Usage
+* Prerequisites
+ * Python3
+ * Numpy
+ * Download and unzip NNAPI CTS Specs tarball files locally by below commands
+
+ ```shell
+ cd test_generator
+ ./ready_nnapi_cts_specs.sh
+ ```
+* Generate Tests
+
+ ```shell
+ npm start
+ ```
+
+ Generated tests would be in following three folders
+[./tests/V1_0](./test/V1_0)
+[./tests/V1_1](./test/V1_1)
+[./tests/V1_2](./test/V1_2)
+and these tests could also be in all-in-one
+[./tests/cts.js](./tests/cts.js) file.
+
+
+### Accuracy for Generated Tests
+The converted tests follow these [reference accuracy](https://android.googlesource.com/platform/frameworks/ml/+/refs/tags/android-cts-10.0_r5/nn/runtime/test/TestGenerated.cpp#117):
+```cpp
+ float fpAtol = 1e-5f;
+ float fpRtol = 5.0f * 1.1920928955078125e-7f;
+```
+And for relaxed tests
+```cpp
+ // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.
+ fpAtol = 5.0f * 0.0009765625f;
+ // Set the relative tolerance to be 5ULP of the corresponding FP precision.
+ fpRtol = 5.0f * 0.0009765625f;
+```
\ No newline at end of file
diff --git a/test/cts/from_nnapi/test_generator/package.json b/test/cts/from_nnapi/test_generator/package.json
new file mode 100644
index 00000000..60234b2d
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/package.json
@@ -0,0 +1,18 @@
+{
+ "name": "test_generator",
+ "version": "0.0.1",
+ "description": "Convert native NNAPI CTS tests to WebNN API tests",
+ "license": "Apache 2.0",
+ "main": "./src/main.py",
+ "directories": {
+ "src": "src files"
+ },
+ "scripts": {
+ "start": "python3 ./src/main.py ./specs -t ../tests -c ../tests/cts.js"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "authors": [
+ "Bruce Dai "
+ ]
+}
diff --git a/test/cts/from_nnapi/test_generator/ready_nnapi_cts_specs.sh b/test/cts/from_nnapi/test_generator/ready_nnapi_cts_specs.sh
new file mode 100755
index 00000000..eb83271a
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/ready_nnapi_cts_specs.sh
@@ -0,0 +1,15 @@
+#!/bin/sh
+TAG=android-cts-10.0_r5
+NNAPI_VERSION="
+V1_0
+V1_1
+V1_2
+"
+
+# download and unzip Specs tarball files
+for version in ${NNAPI_VERSION}
+do
+ wget https://android.googlesource.com/platform/frameworks/ml/+archive/refs/tags/${TAG}/nn/runtime/test/specs/${version}.tar.gz
+ mkdir -p specs/${version}
+ tar -xvzf ${version}.tar.gz -C specs/${version}
+done
\ No newline at end of file
diff --git a/test/cts/from_nnapi/test_generator/src/cts_generator.py b/test/cts/from_nnapi/test_generator/src/cts_generator.py
new file mode 100755
index 00000000..254ff6a9
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/src/cts_generator.py
@@ -0,0 +1,727 @@
+#!/usr/bin/python3
+
+# Copyright 2018, The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import argparse
+import copy
+import math
+import os
+import sys
+import numpy as np
+
+# Stuff from test generator
+import test_generator as tg
+from test_generator import ActivationConverter
+from test_generator import BoolScalar
+from test_generator import Configuration
+from test_generator import DataTypeConverter
+from test_generator import DataLayoutConverter
+from test_generator import Example
+from test_generator import Float16Scalar
+from test_generator import Float32Scalar
+from test_generator import Float32Vector
+from test_generator import IgnoredOutput
+from test_generator import Input
+from test_generator import Int32Scalar
+from test_generator import Int32Vector
+from test_generator import Internal
+from test_generator import Model
+from test_generator import Operand
+from test_generator import Output
+from test_generator import Parameter
+from test_generator import ParameterAsInputConverter
+from test_generator import RelaxedModeConverter
+from test_generator import SmartOpen
+from test_generator import SymmPerChannelQuantParams
+from test_generator import IndentedPrint
+from test_generator import InitializeCtsTestFile
+
+import mapping_dict as md
+
+msgTemplate = 'Unsupported convert "%s" test due to "%s"'
+
+def ParseCmdLine():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("spec", help="the spec file/directory")
+ parser.add_argument(
+ "-t", "--test", help="the generated test file/directory", default="-")
+ args = parser.parse_args()
+ tg.FileNames.InitializeFileLists(args.spec, args.test)
+
+def CheckOperationWithImplicitPadding(nnapiOp, curOpInsList, nnapiOpInsList,
+ nnapiOpOptionalInsLen):
+ # Current this convert tool doesn't involve padding compute function for
+ # 'VALID', 'SAME' implicit padding, this would be an enhancement feature.
+ flag = False
+ curOpInsLen = len(curOpInsList)
+ nnapiOpInsLen = len(nnapiOpInsList)
+
+ if nnapiOp in ['AVERAGE_POOL_2D', 'MAX_POOL_2D']:
+ if curOpInsLen < nnapiOpInsLen:
+ msg = msgTemplate % (nnapiOp, "implicit padding")
+ # print(msg, file=sys.stderr)
+ flag = True
+ elif nnapiOp != 'CONCATENATION':
+ if nnapiOpOptionalInsLen != 0:
+ if curOpInsLen == nnapiOpInsLen:
+ if nnapiOp == 'CONV_2D':
+ # Layout param index of inputs with implicit padding is 7
+ if curOpInsList[7].type.type == 'BOOL':
+ msg = msgTemplate % (nnapiOp, "implicit padding")
+ # print(msg, file=sys.stderr)
+ flag = True
+ elif nnapiOp == 'DEPTHWISE_CONV_2D':
+ # Layout param index of inputs with implicit padding is 8
+ if curOpInsList[8].type.type == 'BOOL':
+ msg = msgTemplate % (nnapiOp, "implicit padding")
+ # print(msg, file=sys.stderr)
+ flag = True
+ elif curOpInsLen < nnapiOpInsLen:
+ msg = msgTemplate % (nnapiOp, "implicit padding")
+ # print(msg, file=sys.stderr)
+ flag = True
+
+ return flag
+
+def ClearMappingWebNNOpConfiguration():
+ if Configuration.successedCounter == 0:
+ Configuration.mappingWebNNOp.clear()
+
+def SupportedConvertDepthwiseConv2D(inOprand, outOprand, layout):
+ # Unsupport to convert DEPTHWISE_CONV_2D test with depth_multiplier not
+ # being equal to 1, that is, input_channels != output_channels
+ flag = True
+ inDimensions = inOprand.type.dimensions
+ outDimensions = outOprand.type.dimensions
+
+ if layout:
+ flag = inDimensions[1] == outDimensions[1]
+ else:
+ flag = inDimensions[3] == outDimensions[3]
+
+ return flag
+
+def GetOperandIndex(opInfoList, opName):
+ index = 0
+ found = False
+
+ for opInfo in opInfoList:
+ if opInfo['name'] == opName:
+ found = True
+ break
+ index += 1
+
+ return index if found else -1
+
+def GetParamOperandValue(paramsList, insList, index):
+ status = False
+ value = None
+
+ if index != -1:
+ if index < len(insList):
+ inOp = insList[index]
+ if inOp in paramsList:
+ status = True
+ value = paramsList[paramsList.index(inOp)].value
+
+ return (status, value)
+
+def GetInputOperandValue(inputsDict, insList, index):
+ status = False
+ value = None
+
+ if index != -1:
+ inOp = insList[index]
+ if inOp in inputsDict.keys():
+ status = True
+ value = inputsDict[inOp]
+
+ return (status, value)
+
+
+def SupportedConvertSoftmax(opInfoList, inOperand, paramsList, insList):
+ # Unsupport to convert SOFTMAX tests with rank of input is greater than 2,
+ # or SOFTMAX tests used 2D input with input1 as scaling factor for the
+ # exponent being not equal to 1.0 or optional inupt2 as axis being not equal
+ # to 1 or -1.
+ flag = True
+ dimLen = len(inOperand.dimensions)
+
+ if dimLen != 2:
+ flag = False
+ return flag
+
+ betaIndex = GetOperandIndex(opInfoList, 'beta')
+ status, beta = GetParamOperandValue(paramsList, insList, betaIndex)
+
+ if int(beta[0]) != 1:
+ flag = False
+ return flag
+
+ if len(insList) == 3:
+ # using optional axis parameter
+ axisIndex = GetOperandIndex(opInfoList, 'axis')
+ status, axis = GetParamOperandValue(paramsList, insList, axisIndex)
+ if axis[0] not in [-1, 1]:
+ flag = False
+ return flag
+
+ return flag
+
+def UpdateMappingWebNNOpList(actValue):
+ if Configuration.successedCounter == 0:
+ if actValue == 1:
+ Configuration.mappingWebNNOp.append('relu')
+ else:
+ Configuration.mappingWebNNOp.append('clamp')
+
+def GetReluMappedInfo(actValue):
+ info = None
+
+ if actValue == 1:
+ info = {
+ 'name': 'relu'
+ }
+ else:
+ info = {
+ 'name': 'clamp'
+ }
+ options = \
+ "{minValue: builder.constant(%d), maxValue: builder.constant(%d)}"
+ if actValue == 2:
+ # relu1
+ info['options'] = options % (-1, 1)
+ elif actValue == 3:
+ # relu6
+ info['options'] = options % (0, 6)
+
+ return info
+
+def ReorderFilterValue(operation, dimensions, values, layout):
+ valueArray = np.array(values).reshape(dimensions)
+ axes = (0, 3, 1, 2) if layout else (1, 2, 3, 0)
+
+ if operation == 'DEPTHWISE_CONV_2D':
+ axes = (3, 0, 1, 2) if layout else (1, 2, 0, 3)
+
+ return list(np.transpose(valueArray, axes).ravel())
+
+def GetWebNNFilterDimensions(operation, dimensions, layout):
+ # https://webmachinelearning.github.io/webnn/#api-modelbuilder-conv2d
+ # filter tensor:
+ # "nchw": [output_channels, input_channels/groups, height, width]
+ # "nhwc": [height, width, input_channels/groups, output_channels]
+ if operation == 'CONV_2D':
+ if layout:
+ outputChannels = dimensions[-1]
+ dimensions = dimensions[:-1]
+ dimensions.insert(1, outputChannels)
+ else:
+ outputChannels = dimensions[0]
+ dimensions = dimensions[1:]
+ dimensions.append(outputChannels)
+ elif operation == 'DEPTHWISE_CONV_2D':
+ if layout:
+ outputChannels = dimensions[-1]
+ dimensions = dimensions[:-1]
+ dimensions.insert(0, outputChannels)
+ else:
+ intputChannels = dimensions[0]
+ dimensions = dimensions[1:]
+ dimensions.insert(2, intputChannels)
+
+ return dimensions
+
+def FlattenedTo2D(in0Dims, in1Dims):
+ inputSize = in1Dims[1]
+ batchSize = int(np.product(in0Dims) / inputSize)
+ return [batchSize, inputSize]
+
+def GetWebNNOperandDesc(oprand, operation, opInsList, opInsInfoList, layout):
+ operandType = oprand.type.mappingType
+ operandDims = oprand.type.dimensions
+
+ if operation == 'FULLY_CONNECTED':
+ if opInsList.index(oprand) == 0:
+ # input0
+ if len(operandDims) > 2:
+ input1Dims = opInsList[1].dimensions
+ operandDims = FlattenedTo2D(operandDims, input1Dims)
+ elif opInsList.index(oprand) == 1:
+ operandDims = [operandDims[1], operandDims[0]]
+ else:
+ oprandName = opInsInfoList[opInsList.index(oprand)]['name']
+
+ if oprandName == 'filter':
+ operandDims = GetWebNNFilterDimensions(operation, operandDims, layout)
+ elif oprandName == 'bias':
+ if layout and len(operandDims) == 1:
+ # Update operandDims likes [x] -> [1, x, 1, 1]
+ operandDims = [1, operandDims[0], 1, 1]
+
+ operandDesc = "{type: '%s', dimensions: %s}" % (operandType, operandDims)
+ return operandDesc
+
+def PrintInputOperand(oprand, operation, opInsList, opInsInfoList, layout,
+ test):
+ opDesc = GetWebNNOperandDesc(oprand, operation, opInsList, opInsInfoList,
+ layout)
+ operand = "const %s = builder.input('%s', %s);" % (oprand, oprand, opDesc)
+ IndentedPrint(operand, indent=4, file=test)
+
+def GetOperandValue(oprand, operation, opInsList, name, layout, value=None):
+ opValue = oprand.value
+
+ if value is not None:
+ opValue = value
+
+ if name == 'filter':
+ opValue = ReorderFilterValue(operation, oprand.type.dimensions, opValue,
+ layout)
+
+ if operation == 'FULLY_CONNECTED':
+ if opInsList.index(oprand) == 1:
+ # input1
+ arrayValue = np.array(opValue).reshape(oprand.type.dimensions)
+ opValue = list(np.transpose(arrayValue, (1,0)).ravel())
+
+ return opValue
+
+def PrintInputBuffer(oprand, operation, opInsList, name, value, layout, test):
+ typedArray = oprand.type.mappingTypedArrayType
+ opValue = GetOperandValue(oprand, operation, opInsList, name, layout, value)
+ IndentedPrint('const %sBuffer = new %s(%s);' % (oprand, typedArray, opValue),
+ indent=4, file=test)
+
+def PrintConstant(oprand, operation, opInsList, opInsInfoList, name, layout,
+ test):
+ opDesc = GetWebNNOperandDesc(oprand, operation, opInsList, opInsInfoList,
+ layout)
+ opValue = GetOperandValue(oprand, operation, opInsList, name, layout)
+ operand = "const %s = builder.constant(%s, new %s(%s));" % \
+ (oprand, opDesc, oprand.type.mappingTypedArrayType, opValue)
+ IndentedPrint(operand, indent=4, file=test)
+
+def CheckDefaultParameterValue(op, inputFeedDict, paramsList):
+ flag = False
+ value = None
+
+ if op in paramsList:
+ value = paramsList[paramsList.index(op)].value
+ else:
+ value = inputFeedDict[op]
+
+ if isinstance(value, list) and (len(value) == 0 or value[0] is None):
+ flag = True
+
+ return flag
+
+def GetWebNNOperationParamsList(opInsInfoList, opInsList, inputFeedDict,
+ paramsList, operation):
+ d = {}
+ length = len(opInsList)
+
+ if operation != 'CONCATENATION':
+ counter = 0
+ for nnOpInsInfo in opInsInfoList:
+ index = nnOpInsInfo['mappingParamIndex']
+ if index != -1:
+ paramName = opInsList[counter]
+ if paramName is None:
+ break
+ value = d.get(index, None)
+ if value is None:
+ key = nnOpInsInfo.get('optionsDictKey', None)
+ if key is None:
+ value = paramName
+ else:
+ if CheckDefaultParameterValue(paramName,
+ inputFeedDict,
+ paramsList):
+ continue
+ value = {}
+ value[key] = paramName
+ d[index] = value
+ else:
+ sequenceIndex = nnOpInsInfo.get('sequenceIndex', -1)
+ if sequenceIndex != -1:
+ itemValue = value.get(nnOpInsInfo['optionsDictKey'],
+ None)
+ if itemValue is None:
+ itemValue = paramName
+ else:
+ if isinstance(itemValue, list):
+ itemValue.append(paramName)
+ else:
+ itemValue = [itemValue, paramName]
+ value[nnOpInsInfo['optionsDictKey']] = itemValue
+ else:
+ value[nnOpInsInfo['optionsDictKey']] = paramName
+ counter += 1
+ if counter == length:
+ break
+ else:
+ # Refer to
+ # https://webmachinelearning.github.io/webnn/#api-modelbuilder-concat
+ # WebNN API concat Op has sequence inputs
+ d[0] = []
+ for ins in opInsList[:-1]:
+ d[0].append(ins)
+ d[1] = opInsList[-1]
+
+ paramsList = sorted(d.items(), key = lambda item:item[0])
+ lastParam = paramsList[-1][1]
+
+ if isinstance(lastParam, dict):
+ keyList = lastParam.keys()
+ if 'padding' in keyList:
+ leftRight = lastParam['padding'][:2]
+ topBottom = lastParam['padding'][2:]
+ lastParam['padding'] = topBottom + leftRight
+ if 'strides' in keyList:
+ lastParam['strides'].reverse()
+ if 'windowDimensions' in keyList:
+ lastParam['windowDimensions'].reverse()
+ if 'dilations' in keyList:
+ lastParam['dilations'].reverse()
+ return paramsList
+
+def UpdateWebNNOperationOptionalParamValue(targetValue, kvList):
+ if isinstance(targetValue, dict):
+ for key, value in kvList:
+ if targetValue.get(key, None) is None:
+ targetValue[key] = value
+ if key == 'layout':
+ targetValue[key] = 'nchw' if value else 'nhwc'
+
+def GetWebNNParamsString(params):
+ paramsList = [p[1] for p in params]
+ paramsStr = '%s' % paramsList
+ return paramsStr[1:-1]
+
+def PrintMappedReluOpertions(fusedReluMappedInfo, outputOp, operandName):
+ mappedWebNNOpName = fusedReluMappedInfo['name']
+ options = fusedReluMappedInfo.get('options', None)
+
+ if options is None:
+ IndentedPrint("const %s = builder.%s(%s);" % \
+ (outputOp, mappedWebNNOpName, operandName),
+ indent=4, file=test)
+ else:
+ IndentedPrint("const %s = builder.%s(%s, %s);" % \
+ (outputOp, mappedWebNNOpName, operandName, options),
+ indent=4, file=test)
+
+def PrintOperations(biasOp, webnnOpType, webnnParamsStr, fusedReluMappedInfo,
+ outputOp, test):
+ if biasOp is not None:
+ IndentedPrint("const interOut0 = builder.%s(%s);" % \
+ (webnnOpType, webnnParamsStr),
+ indent=4, file=test)
+ if fusedReluMappedInfo is not None:
+ # Add 'add' operation
+ IndentedPrint("const interOut1 = builder.add(interOut0, %s);" % \
+ biasOp, indent=4, file=test)
+ # Add 'relu' or 'clamp' operation
+ PrintMappedReluOpertions(fusedReluMappedInfo[1], outputOp,
+ 'interOut1')
+ else:
+ # Add 'add' operation
+ IndentedPrint("const %s = builder.add(interOut0, %s);" % \
+ (outputOp, biasOp), indent=4, file=test)
+ else:
+ if fusedReluMappedInfo is not None:
+ if fusedReluMappedInfo[0]:
+ IndentedPrint("const interOut0 = builder.%s(%s);" % \
+ (webnnOpType, webnnParamsStr), indent=4,
+ file=test)
+ # Add 'relu' or 'clamp' operation
+ PrintMappedReluOpertions(fusedReluMappedInfo[1], outputOp,
+ 'interOut0')
+ else:
+ PrintMappedReluOpertions(fusedReluMappedInfo[1], outputOp,
+ webnnParamsStr)
+ else:
+ IndentedPrint("const %s = builder.%s(%s);" % \
+ (outputOp, webnnOpType, webnnParamsStr),
+ indent=4, file=test)
+
+# Dump Test file for Cts tests
+def DumpCtsTest(example, test):
+ model = example.model
+
+ if len(model.operations) > 1:
+ msg = 'Not convert complicated tests with multi-operations'
+ # print(msg, file=sys.stderr)
+ return
+
+ nnapiOp = model.operations[0].optype
+
+ if nnapiOp not in md.MappingDict.keys():
+ msg = msgTemplate % (nnapiOp, 'none mapped WebNN Opeartion')
+ # print(msg, file=sys.stderr)
+ return
+
+ # WebNN polyfill API cur supports 'int32' and 'float32'
+ unSupportedTypesList = ['int8', 'uint8', 'float16']
+ operandTypeList = model.GetMappedOperandTypes()
+ usedUnsupportedType = list(set(unSupportedTypesList) & set(operandTypeList))
+
+ if len(usedUnsupportedType) > 0:
+ msg = msgTemplate % \
+ (nnapiOp, 'unsupported %s Operand Type' % usedUnsupportedType)
+ # print(msg, file=sys.stderr)
+ return
+
+ mappingOpDict = md.MappingDict[nnapiOp]
+ mappedWebNNOp = mappingOpDict['webnnOperation']
+
+ if Configuration.successedCounter == 0:
+ # Update mappingWebNNOp by first time
+ Configuration.mappingWebNNOp.append(mappedWebNNOp)
+
+ nnapiOpInsList = copy.deepcopy(mappingOpDict['insList'])
+ nnapiOpOptionalInsList = mappingOpDict.get('optionalInsList', [])
+ curOpInsList = model.operations[0].ins
+
+ if CheckOperationWithImplicitPadding(nnapiOp, curOpInsList,
+ nnapiOpInsList,
+ len(nnapiOpOptionalInsList)):
+ ClearMappingWebNNOpConfiguration()
+ return
+
+ if GetOperandIndex(nnapiOpInsList, 'bias') != -1:
+ if Configuration.successedCounter == 0:
+ Configuration.mappingWebNNOp.append('add')
+
+ curInputsList = example.model.GetInputs()
+ curOutputsList = example.model.GetOutputs()
+ curParamsList = example.model.GetParameters()
+
+ fusedReluMappedInfo = None
+ actIndex = GetOperandIndex(nnapiOpInsList, 'activation')
+ actStatus, actValue = GetParamOperandValue(curParamsList, curOpInsList,
+ actIndex)
+
+ if actStatus:
+ UpdateMappingWebNNOpList(actValue[0])
+ fusedReluMappedInfo = (True, GetReluMappedInfo(actValue[0]))
+
+ if nnapiOp == 'RELU1':
+ fusedReluMappedInfo = (False, GetReluMappedInfo(2))
+
+ if nnapiOp == 'RELU6':
+ fusedReluMappedInfo = (False, GetReluMappedInfo(3))
+
+ nnapiOpInsList.extend(nnapiOpOptionalInsList)
+ layoutIndex = GetOperandIndex(nnapiOpInsList, 'layout')
+ layoutStatus, layoutValue = GetParamOperandValue(curParamsList,
+ curOpInsList,
+ layoutIndex)
+ # True: 'nchw', False: 'nhwc'
+ layout = False if not layoutStatus else layoutValue[0]
+
+ if nnapiOp == 'DEPTHWISE_CONV_2D':
+ if not SupportedConvertDepthwiseConv2D(curInputsList[0],
+ curOutputsList[0],
+ layout):
+ ClearMappingWebNNOpConfiguration()
+ return
+
+ biasOp = None
+ testIndex = 1 if len(example.feedDicts)>1 else 0
+
+ for inputFeedDict, outputFeedDict in example.feedDicts:
+ if nnapiOp == 'SOFTMAX':
+ if not SupportedConvertSoftmax(nnapiOpInsList, curInputsList[0],
+ curParamsList, curOpInsList):
+ ClearMappingWebNNOpConfiguration()
+ return
+ IndentedPrint("", file=test) # Add blank line
+ testPurpose = 'test %s converted from %s test' % \
+ (' + '.join(Configuration.mappingWebNNOp),
+ str(example.testName))
+ if testIndex > 0:
+ testPurpose = "%s/%d" % (testPurpose, testIndex)
+ IndentedPrint("it('%s', async function() {" % testPurpose,
+ indent=2, file=test)
+ IndentedPrint("// Converted test case (from: %s/%s)" % \
+ (tg.FileNames.version,
+ os.path.basename(tg.FileNames.specFile)),
+ indent=4, file=test)
+ IndentedPrint("const builder = nn.createModelBuilder();",
+ indent=4, file=test)
+ computeParamsList = []
+ # Create operand(s) by ModelBuilder.input
+ for op in curInputsList:
+ opInsDict = nnapiOpInsList[curOpInsList.index(op)]
+ mappingParamIndex = opInsDict['mappingParamIndex']
+ if mappingParamIndex != -1:
+ rule = md.MappingRule(opInsDict['mappingRuleType'])
+ if rule == md.MappingRule.OPERAND_OPERAND:
+ PrintInputOperand(op, nnapiOp, curOpInsList, nnapiOpInsList,
+ layout, test)
+ PrintInputBuffer(op, nnapiOp, curOpInsList,
+ opInsDict['name'], inputFeedDict[op],
+ layout, test)
+ computeParamsList.append("'%s': {buffer: %sBuffer}" % \
+ (op, op))
+ elif rule == md.MappingRule.OPERAND_VARIABLE:
+ varValue = inputFeedDict[op]
+ if len(varValue) != 0 and varValue[0] is not None:
+ IndentedPrint('const %s = %s;' % (op, varValue),
+ indent=4, file=test)
+ elif rule == md.MappingRule.OPERAND_ARRAY:
+ varValue = inputFeedDict[op]
+ if len(varValue) != 0:
+ IndentedPrint('const %s = %s;' % (op, varValue),
+ indent=4, file=test)
+ else:
+ if opInsDict['name'] == 'bias':
+ biasOp = op
+ PrintInputOperand(op, nnapiOp, curOpInsList, nnapiOpInsList,
+ layout, test)
+ PrintInputBuffer(op, nnapiOp, curOpInsList,
+ opInsDict['name'], inputFeedDict[op],
+ layout, test)
+ computeParamsList.append("'%s': {buffer: %sBuffer}" % \
+ (op, op))
+ # Create operand(s) by ModelBuilder.constant, or define variable(s)
+ for op in curParamsList:
+ opInsDict = nnapiOpInsList[curOpInsList.index(op)]
+ mappingParamIndex = opInsDict['mappingParamIndex']
+ if mappingParamIndex != -1:
+ rule = md.MappingRule(opInsDict['mappingRuleType'])
+ if rule == md.MappingRule.OPERAND_OPERAND:
+ PrintConstant(op, nnapiOp, curOpInsList, nnapiOpInsList,
+ opInsDict['name'], layout, test)
+ elif rule == md.MappingRule.VARIABLE_VARIABLE:
+ varValue = curParamsList[curParamsList.index(op)].value[0]
+ if opInsDict['name'] == 'layout':
+ if varValue:
+ varValue = "'nchw'"
+ else:
+ varValue = "'nhwc'"
+ IndentedPrint('const %s = %s;' % (op, varValue), indent=4,
+ file=test)
+ elif rule == md.MappingRule.OPERAND_VARIABLE:
+ varValue = curParamsList[curParamsList.index(op)].value
+ if len(varValue) != 0 and varValue[0] is not None:
+ IndentedPrint('const %s = %s;' % (op, varValue),
+ indent=4, file=test)
+ elif rule == md.MappingRule.OPERAND_ARRAY:
+ varValue = curParamsList[curParamsList.index(op)].value
+ if len(varValue) != 0:
+ IndentedPrint('const %s = %s;' % (op, varValue),
+ indent=4, file=test)
+ else:
+ if opInsDict['name'] == 'bias':
+ biasOp = op
+ PrintConstant(op, nnapiOp, curOpInsList, nnapiOpInsList,
+ opInsDict['name'], layout, test)
+ if len(curOutputsList) == 1:
+ outputOp = curOutputsList[0]
+ IndentedPrint("const expected = %s;" % outputFeedDict[outputOp],
+ indent=4, file=test)
+ elif len(curOutputsList) > 1:
+ outputOp = curOutputsList
+ expectedValueList = [outputFeedDict[k] for k in outputOp]
+ IndentedPrint("const expected = %s;" % expectedValueList, indent=4,
+ file=test)
+ # Update optional parameter value
+ optionsKeyValueList = []
+ hasLayoutOption = False
+ for optionalIns in nnapiOpOptionalInsList:
+ if optionalIns['name'] == 'layout':
+ hasLayoutOption = True
+ break
+ if hasLayoutOption:
+ if not layout:
+ # Default 'nchw' layout with WebNN API
+ optionsKeyValueList.append(('layout', False))
+ if nnapiOp == 'DEPTHWISE_CONV_2D':
+ # True: 'nchw' False: 'nhwc'
+ chanelIndex = 1 if layout else 3
+ groups = outputOp.type.dimensions[chanelIndex]
+ optionsKeyValueList.append(('groups', groups))
+ mappingParams = GetWebNNOperationParamsList(nnapiOpInsList,
+ curOpInsList,
+ inputFeedDict,
+ curParamsList,
+ nnapiOp)
+ UpdateWebNNOperationOptionalParamValue(mappingParams[-1][1],
+ optionsKeyValueList)
+ webnnParamsStr = GetWebNNParamsString(mappingParams)
+ PrintOperations(biasOp, mappedWebNNOp, webnnParamsStr,
+ fusedReluMappedInfo, outputOp, test)
+ if len(curOutputsList) == 1:
+ IndentedPrint("const model = builder.createModel({%s});" % outputOp,
+ indent=4, file=test)
+ elif len(curOutputsList) > 1:
+ outputOpNameList = [item.name for item in outputOp]
+ IndentedPrint("const model = builder.createModel({%s});" % \
+ ', '.join(outputOpNameList), indent=4, file=test)
+ IndentedPrint("const compilation = await model.compile();", indent=4,
+ file=test)
+ IndentedPrint("const outputs = await compilation.compute({%s});" % \
+ ', '.join(computeParamsList), indent=4, file=test)
+ # Check compute output
+ criteria = 'utils.ctsFp32RestrictAccuracyCriteria'
+ if model.isRelaxed:
+ criteria = 'utils.ctsFp32RelaxedAccuracyCriteria'
+ if len(curOutputsList) == 1:
+ IndentedPrint(
+ "utils.checkValue(outputs.%s.buffer, expected, %s);" % \
+ (outputOp, criteria), indent=4, file=test)
+ elif len(curOutputsList) > 1:
+ IndentedPrint('for (let i = 0; i < %d; i++) {' % \
+ len(curOutputsList), indent=4, file=test)
+ bufferStr = 'outputs[%s[i]].buffer' % ['%s' % k for k in outputOp]
+ IndentedPrint(
+ "utils.checkValue(%s, expected[i], %s);" % \
+ (bufferStr, criteria), indent=6, file=test)
+ IndentedPrint("}", indent=4, file=test)
+ IndentedPrint("});", indent=2, file=test)
+ testIndex += 1
+
+ Configuration.successedCounter += 1
+
+if __name__ == '__main__':
+ ParseCmdLine()
+ while tg.FileNames.NextFile():
+ Configuration.mappingWebNNOp = []
+ Configuration.successedCounter = 0
+ # print("Generating test(s) from spec: %s" % tg.FileNames.specFile,
+ # file=sys.stderr)
+ exec(open(tg.FileNames.specFile, "r").read())
+ testFile = tg.FileNames.testFile
+ with SmartOpen(testFile) as test:
+ InitializeCtsTestFile(test, 4)
+ Example.DumpAllExamples(DumpTest=DumpCtsTest, test=test)
+ IndentedPrint("});", file=test)
+ IndentedPrint("/* eslint-disable max-len */", file=test)
+ if Configuration.successedCounter == 0:
+ os.remove(testFile)
+ else:
+ newName = 'test_%s_converted_from_%s' % \
+ ('_'.join(Configuration.mappingWebNNOp),
+ os.path.basename(testFile))
+ renamedFile = os.path.join(os.path.dirname(testFile), newName)
+ os.rename(testFile, renamedFile)
+ # print("Successfully generated CTS test: %s" % renamedFile,
+ # file=sys.stderr)
diff --git a/test/cts/from_nnapi/test_generator/src/main.py b/test/cts/from_nnapi/test_generator/src/main.py
new file mode 100644
index 00000000..7db70427
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/src/main.py
@@ -0,0 +1,51 @@
+#!/usr/bin/python3
+
+import os
+import argparse
+from test_generator import IndentedPrint
+from test_generator import InitializeCtsTestFile
+from test_generator import SmartOpen
+
+def ParseCmdLine():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("spec", help="the spec file/directory")
+ parser.add_argument(
+ "-t", "--test", help="the generated test file/directory")
+ parser.add_argument(
+ "-c", "--cts", help="the CTS generated tests in one file cts.js")
+ args = parser.parse_args()
+ return (args.spec, args.test, args.cts)
+
+def ConvertNNAPITest(spec, test):
+ if os.path.isfile(spec):
+ cmd = cmdTemplate % (spec, test, cts)
+ os.system("python3 ./src/cts_generator.py %s -t %s" % (spec, test))
+ elif os.path.isdir(spec):
+ for version in sorted(os.listdir(spec)):
+ specDir = os.path.join(spec, version)
+ testDir = os.path.join(test, version)
+ if not os.path.exists(testDir):
+ os.makedirs(testDir)
+ os.system("python3 ./src/cts_generator.py %s -t %s" % (specDir, testDir))
+
+def DumpAllInOneCtsTest(test, cts):
+ versionList = os.listdir(test)
+ with SmartOpen(cts, mode="a") as aioTest:
+ InitializeCtsTestFile(aioTest, 3)
+ for version in sorted(versionList):
+ versionPath = os.path.join(test, version)
+ for generatedTest in sorted(os.listdir(versionPath)):
+ generatedTestPath = os.path.join(versionPath, generatedTest)
+ with SmartOpen(generatedTestPath, mode="r") as readFile:
+ fileText = readFile.readlines()
+ for (lineNum, lineText) in enumerate(fileText):
+ if lineNum in range(6, len(fileText) - 2):
+ aioTest.write(lineText)
+ IndentedPrint("});", file=aioTest)
+ IndentedPrint("/* eslint-disable max-len */", file=aioTest)
+
+if __name__ == "__main__":
+ spec, test, cts = ParseCmdLine()
+ ConvertNNAPITest(spec, test)
+ if os.path.isdir(test):
+ DumpAllInOneCtsTest(test, cts)
\ No newline at end of file
diff --git a/test/cts/from_nnapi/test_generator/src/mapping_dict.py b/test/cts/from_nnapi/test_generator/src/mapping_dict.py
new file mode 100644
index 00000000..c462271f
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/src/mapping_dict.py
@@ -0,0 +1,681 @@
+#!/usr/bin/python3
+
+from enum import IntEnum
+
+class MappingRule(IntEnum):
+ OPERAND_OPERAND = 0
+ VARIABLE_VARIABLE = 1
+ OPERAND_VARIABLE = 2
+ OPERAND_ARRAY = 3
+
+# NN-API Operations mapping WebNN API Operations
+MappingDict = {
+ 'ADD': {
+ 'webnnOperation': 'add',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ]
+ },
+ 'AVERAGE_POOL_2D': {
+ 'webnnOperation': 'averagePool2d',
+ 'insList': [ # only support for explicit paddings
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'paddingLeft',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 2, # [beginning_height, ending_height, beginning_width, ending_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingRight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 3,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingTop',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingBottom',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 1,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideWidth',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 1, # [stride_height, stride_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideHeight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'filterWidth',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'windowDimensions',
+ 'sequenceIndex': 1, # [window_height, window_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'filterHeight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'windowDimensions',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'layout',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'layout',
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'CONCATENATION': {
+ 'webnnOperation': 'concat',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'axis',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'CONV_2D': {
+ 'webnnOperation': 'conv2d',
+ 'insList': [ # only support for explicit paddings
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'filter',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'bias',
+ 'mappingParamIndex': -1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'paddingLeft',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 2, # [beginning_height, ending_height, beginning_width, ending_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingRight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 3,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingTop',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingBottom',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 1,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideWidth',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 1, # [stride_height, stride_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideHeight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'layout',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'layout',
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'dilationWidth',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'dilations',
+ 'sequenceIndex': 1, # [dilation_height, dilation_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'dilationHeight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'dilations',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'DEPTHWISE_CONV_2D': {
+ 'webnnOperation': 'conv2d',
+ 'insList': [ # only support for explicit paddings
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'filter',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'bias',
+ 'mappingParamIndex': -1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'paddingLeft',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 2, # [beginning_height, ending_height, beginning_width, ending_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingRight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 3,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingTop',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingBottom',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 1,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideWidth',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 1, # [stride_height, stride_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideHeight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'multiplier',
+ 'mappingParamIndex': -1
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'layout',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'layout',
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'dilationWidth',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'dilations',
+ 'sequenceIndex': 1, # [dilation_height, dilation_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'dilationHeight',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'dilations',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'DIV': {
+ 'webnnOperation': 'div',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ]
+ },
+ 'EXP': {
+ 'webnnOperation': 'exp',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'FULLY_CONNECTED': {
+ 'webnnOperation': 'matmul',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'bias',
+ 'mappingParamIndex': -1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ]
+ },
+ 'LOGISTIC': {
+ 'webnnOperation': 'sigmoid',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'MAX_POOL_2D': {
+ 'webnnOperation': 'maxPool2d',
+ 'insList': [ # only support for explicit paddings
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'paddingLeft',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 2, # [beginning_height, ending_height, beginning_width, ending_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingRight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 3,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingTop',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'paddingBottom',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'padding',
+ 'sequenceIndex': 1,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideWidth',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 1, # [stride_height, stride_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'strideHeight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'strides',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'filterWidth',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'windowDimensions',
+ 'sequenceIndex': 1, # [window_height, window_width]
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'filterHeight',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'windowDimensions',
+ 'sequenceIndex': 0,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'layout',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'layout',
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'MAXIMUM': {
+ 'webnnOperation': 'max',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'MINIMUM': {
+ 'webnnOperation': 'min',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'MUL': {
+ 'webnnOperation': 'mul',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ]
+ },
+ 'RELU': {
+ 'webnnOperation': 'relu',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'RELU1': {
+ 'webnnOperation': 'clamp',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'RELU6': {
+ 'webnnOperation': 'clamp',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'RESHAPE': {
+ 'webnnOperation': 'reshape',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'shape',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 2
+ }
+ ]
+ },
+ 'SLICE': {
+ 'webnnOperation': 'slice',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'starts',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 3
+ },
+ {
+ 'name': 'sizes',
+ 'mappingParamIndex': 2,
+ 'mappingRuleType': 3
+ }
+ ]
+ },
+ 'SOFTMAX': {
+ 'webnnOperation': 'softmax',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'beta',
+ 'mappingParamIndex': -1,
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'axis',
+ 'mappingParamIndex': -1,
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'SPLIT': {
+ 'webnnOperation': 'split',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'axis',
+ 'mappingParamIndex': 2,
+ 'optionsDictKey': 'axis',
+ 'mappingRuleType': 1
+ },
+ {
+ 'name': 'splitsNumber',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 1
+ }
+ ]
+ },
+ 'SQRT': {
+ 'webnnOperation': 'sqrt',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'SQUEEZE': {
+ 'webnnOperation': 'squeeze',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'axes',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'axes',
+ 'mappingRuleType': 2
+ }
+ ]
+ },
+ 'SUB': {
+ 'webnnOperation': 'sub',
+ 'insList': [
+ {
+ 'name': 'input0',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'input1',
+ 'mappingParamIndex': 1,
+ 'mappingRuleType': 0
+ },
+ {
+ 'name': 'activation',
+ 'mappingParamIndex': -1
+ }
+ ]
+ },
+ 'TANH': {
+ 'webnnOperation': 'tanh',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ]
+ },
+ 'TRANSPOSE': {
+ 'webnnOperation': 'transpose',
+ 'insList': [
+ {
+ 'name': 'input',
+ 'mappingParamIndex': 0,
+ 'mappingRuleType': 0
+ }
+ ],
+ 'optionalInsList': [
+ {
+ 'name': 'permutation',
+ 'mappingParamIndex': 1,
+ 'optionsDictKey': 'permutation',
+ 'mappingRuleType': 2
+ }
+ ]
+ }
+}
\ No newline at end of file
diff --git a/test/cts/from_nnapi/test_generator/src/test_generator.py b/test/cts/from_nnapi/test_generator/src/test_generator.py
new file mode 100755
index 00000000..80316a3c
--- /dev/null
+++ b/test/cts/from_nnapi/test_generator/src/test_generator.py
@@ -0,0 +1,1247 @@
+#!/usr/bin/python3
+
+# Copyright 2017, The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import argparse
+import copy
+from functools import reduce
+import itertools
+import math
+import os
+import re
+import struct
+import sys
+import contextlib
+import pprint
+import numpy as np
+
+def CamelCaseStr(s):
+ sList = s.split('_')
+ return sList[0] + ''.join([i.capitalize() for i in sList[1:]])
+
+def GetJointStr(l, sep=", ", method=str):
+ return sep.join([method(i) for i in l])
+
+def PrettyPrintAsFloat(x):
+ s = str(float(x))
+ if s.find(".") >= 0 or s.find("e") >= 0:
+ return s
+ else:
+ return s + ".0"
+
+# Transform from original type to float32
+def Dequantize(v, ty):
+ v -= ty.zeroPoint
+ if ty.scale != 0:
+ v *= ty.scale
+ if isinstance(ty.extraParams, SymmPerChannelQuantParams):
+ v *= ty.extraParams.GetScalesBroadcastArray(ty.dimensions)
+ return v
+
+# Transform float32 to target data type
+def Quantize(v, ty):
+ if ty.scale != 0:
+ v /= ty.scale
+ if isinstance(ty.extraParams, SymmPerChannelQuantParams):
+ v = v / ty.extraParams.GetScalesBroadcastArray(ty.dimensions)
+ v += ty.zeroPoint
+ if not ty.IsFloat():
+ v = np.round(v)
+ v = int(v) if np.isscalar(v) else v.astype(int)
+ if ty.type == "TENSOR_QUANT8_ASYMM":
+ v = np.minimum(np.maximum(v, 0), 255)
+ elif ty.type == "TENSOR_QUANT16_ASYMM":
+ v = np.minimum(np.maximum(v, 0), 65535)
+ elif ty.type == "TENSOR_QUANT8_SYMM_PER_CHANNEL":
+ v = np.minimum(np.maximum(v, -127), 127)
+ elif ty.type == "UINT32":
+ v = np.maximum(v, 0)
+ return v
+
+@contextlib.contextmanager
+def SmartOpen(filename=None, mode="w"):
+ if filename and filename != '-':
+ fh = open(filename, mode)
+ else:
+ fh = sys.stdout
+
+ try:
+ yield fh
+ finally:
+ if fh is not sys.stdout:
+ fh.close()
+
+# Tracking objects inside a model with a unique name
+class NamedObject:
+ existingNames = set()
+
+ def __init__(self, *args, sep="_", showZero=False, startsFrom=0, skipRenaming=False):
+ name = GetJointStr([i for i in args if i is not None and i != ""], sep=sep)
+ if skipRenaming:
+ self.name = name
+ return
+ # make the name unique by renaming with a suffix number
+ uniqueName = name if showZero is False else name + sep + str(startsFrom)
+ while uniqueName in self.__class__.existingNames:
+ startsFrom += 1
+ uniqueName = name + sep + str(startsFrom)
+ self.__class__.existingNames.add(uniqueName)
+ self.name = uniqueName
+
+ def __str__(self):
+ return self.name
+ __repr__ = __str__
+
+ # Since names are unique, objects with the same name are considered equal
+ def __eq__(self, other):
+ return isinstance(other, NamedObject) and self.name == other.name
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
+ def __hash__(self):
+ return hash(self.name)
+
+ def __lt__(self, other):
+ return self.name < other.name
+
+# Types, operands should all have a unique name since they share the same namespace
+class NamedVariable(NamedObject):
+ existingNames = set()
+ def __init__(self, *args, sep="_", showZero=False, startsFrom=0, skipRenaming=False):
+ NamedObject.__init__(self, *args, sep=sep, showZero=showZero,
+ startsFrom=startsFrom, skipRenaming=skipRenaming)
+
+# Global variables in the spec namespace such as CreateModel, is_ignored, and examples
+class GlobalVariable(NamedVariable):
+ def __init__(self, *args, skipRenaming=False):
+ NamedObject.__init__(self, *args, startsFrom=1, skipRenaming=skipRenaming)
+
+# Each test should have a unique name, but will not conflict with variables
+class NamedTest(NamedObject):
+ existingNames = set()
+ def __init__(self, *args, startsFrom=0, skipRenaming=False):
+ NamedObject.__init__(self, *args, startsFrom=1, skipRenaming=skipRenaming)
+
+class Type(NamedVariable):
+ typesMap = dict()
+ typeLookup = {
+ "INT32": "int32_t",
+ "UINT32": "uint32_t",
+ "FLOAT32": "float",
+ "FLOAT16": "_Float16",
+ "TENSOR_INT32": "int32_t",
+ "TENSOR_FLOAT16": "_Float16",
+ "TENSOR_FLOAT32": "float",
+ "TENSOR_QUANT8_ASYMM": "uint8_t",
+ "TENSOR_QUANT8_SYMM": "int8_t",
+ "BOOL": "bool8",
+ "TENSOR_QUANT16_ASYMM": "uint16_t",
+ "TENSOR_QUANT16_SYMM": "int16_t",
+ "TENSOR_BOOL8": "bool8",
+ "TENSOR_QUANT8_SYMM_PER_CHANNEL": "int8_t",
+# "OEM_SCALAR": this is service-defined.
+ "TENSOR_OEM_BYTE": "uint8_t",
+ }
+ typeLookupWebnn = {
+ "INT32": "int32",
+ "UINT32": "uint32",
+ "FLOAT32": "float32",
+ "FLOAT16": "float16",
+ "TENSOR_INT32": "int32",
+ "TENSOR_FLOAT16": "float16",
+ "TENSOR_FLOAT32": "float32",
+ "TENSOR_QUANT8_ASYMM": "uint8",
+ "TENSOR_QUANT8_SYMM": "int8",
+ "BOOL": "bool8",
+ "TENSOR_QUANT16_ASYMM": "uint16",
+ "TENSOR_QUANT16_SYMM": "int16",
+ "TENSOR_QUANT8_SYMM_PER_CHANNEL": "int8",
+ "TENSOR_OEM_BYTE": "uint8",
+ }
+ typedArrayLookupWebnn = {
+ "FLOAT32": "Float32Array",
+ "INT32": "Int32Array",
+ "TENSOR_FLOAT32": "Float32Array",
+ "TENSOR_INT32": "Int32Array",
+ "TENSOR_QUANT8_ASYMM": "Uint8Array",
+ "TENSOR_QUANT8_SYMM": "Int8Array",
+ "TENSOR_QUANT8_SYMM_PER_CHANNEL": "Int8Array",
+ "TENSOR_OEM_BYTE": "Uint8Array",
+ }
+
+ # types are named as "type0", "type1", ...
+ def __init__(self, vt, dimensions, scale, zeroPoint, name="type", skipRenaming=False,
+ extraParams=None):
+ NamedVariable.__init__(self, name, sep="", showZero=True, skipRenaming=skipRenaming)
+ self.type = vt
+ self.mappingType = self.typeLookupWebnn.get(vt, None)
+ self.mappingTypedArrayType = self.typedArrayLookupWebnn.get(vt, None)
+ self.dimensions = dimensions
+ self.scale = float(scale)
+ self.zeroPoint = int(zeroPoint)
+ self.extraParams = extraParams
+
+ # Factory for Type object, only create a new Type if requested type does
+ # not have a match with all existing types
+ @staticmethod
+ def GetType(vt, dimensions, scale=0, zeroPoint=0, extraParams=None):
+ key = ",".join([vt, str(dimensions), str(scale), str(zeroPoint), str(extraParams)])
+ if key not in Type.typesMap:
+ Type.typesMap[key] = Type(vt, dimensions, scale, zeroPoint, extraParams=extraParams)
+ return Type.typesMap[key]
+
+ @staticmethod
+ def GetAllTypes():
+ # sort to ensure a stable order when dumping the code
+ return sorted(Type.typesMap.values())
+
+ # For backward-compatibility
+ @staticmethod
+ def GetTypeFromString(vt, shape, extraParams=None):
+ dimensions, scale, zeroPoint = Type.GetParsedShape(shape)
+ scale = float(scale)
+ zeroPoint = int(zeroPoint)
+ return Type.GetType(vt, dimensions, scale, zeroPoint, extraParams)
+
+ # For backward-compatibility
+ @staticmethod
+ def GetParsedShape(shape):
+ # Parse shape
+ if (shape != "" and shape != "{}"):
+ left, sep, right = shape.partition('{')
+ real_shape, sep, right = right.partition('}')
+ shape = [int(x) for x in real_shape.split(",")]
+ # left now looks like "0.0f, 127.5f, "
+ scale, sep, zero_point = right.rpartition(',')
+ if scale == "":
+ if zero_point == "":
+ return shape, "0", "0"
+ return shape, zero_point, "0"
+ left, sep, scale = scale.partition(',')
+ return shape, scale.replace("f", ""), zero_point
+ else:
+ return [], "0", "0"
+
+ def GetNumberOfElements(self):
+ return reduce(lambda x,y: x*y, self.dimensions, 1)
+
+ def GetCppTypeString(self):
+ return Type.typeLookup[self.type]
+
+ def IsFloat(self):
+ return self.GetCppTypeString() in ["float", "_Float16"]
+
+ def IsBool(self):
+ return self.GetCppTypeString() == "bool8"
+
+ def GetSignatureTuple(self):
+ return (self.type, self.dimensions, self.scale, self.zeroPoint)
+
+ def ToUnspecifiedDim(self):
+ return Type.GetType(self.type, [0] * len(self.dimensions), self.scale, self.zeroPoint)
+
+# To track implicitly convertible parameter types
+class ImplicitParameter():
+ @staticmethod
+ def ImplicitConvertion(value):
+ if isinstance(value, Operand):
+ return value
+ for implicitType in ImplicitParameter.__subclasses__():
+ if implicitType.IsCompatible(value):
+ return implicitType("param", value)
+ assert False, "%s not supported for implicit parameter"%value
+
+# ExtraParams with per-channel quantization.
+class SymmPerChannelQuantParams():
+ def __init__(self, channelDim, scales, hide = False):
+ self.channelDim = channelDim
+ self.scales = scales
+ self.hide = hide
+
+ def GetScalesBroadcastArray(self, dimensions):
+ bshape = [1] * len(dimensions)
+ bshape[self.channelDim] = len(self.scales)
+ return np.array(self.scales).reshape(bshape)
+
+ def GetConstructor(self):
+ return "SymmPerChannelQuantParams({%s},%d)" % (
+ ", ".join(str(x) + "f" for x in self.scales), self.channelDim)
+
+ def GetVtsSetter(self):
+ return "channelQuant"
+
+ def GetVtsConstructor(self):
+ return "SymmPerChannelQuantParams{.scales={%s}, .channelDim=%d}" % (
+ ", ".join(str(x) + "f" for x in self.scales), self.channelDim)
+
+# An operand that can be fed into operations. Also, an operand is always
+# declared before operations.
+class Operand(NamedVariable):
+ def __init__(self, name, opType, value, backward=None, skipRenaming=False, extraParams=None):
+ NamedVariable.__init__(self, CamelCaseStr(name), sep="", skipRenaming=skipRenaming)
+ if type(opType) is str:
+ self.type = Type.GetTypeFromString(opType, value, extraParams)
+ value = backward
+ else:
+ self.type = Type.GetType(*opType, extraParams=extraParams)
+ self.SetValue(value)
+ self.dimensions = self.type.dimensions
+ self.lifetime = "TEMPORARY_VARIABLE"
+ self.ins = []
+ self.outs = []
+
+ def SetValue(self, value):
+ self.value = value if type(value) is list or type(value) is tuple else [value]
+ return self
+
+ def SetValueFromNumpy(self, value):
+ self.value = value.flatten().tolist()
+ return self
+
+ def GetValueAsNumpy(self):
+ return np.array(self.value).reshape(self.type.dimensions)
+
+ # Print value as cpp-style list initialization
+ def GetListInitialization(self):
+ assert self.value is not None, \
+ "Trying to print operand %s with None value"%(str(self))
+ if self.type.IsFloat():
+ return "{%s}"%(GetJointStr(self.value, method=PrettyPrintAsFloat))
+ elif self.type.IsBool():
+ return "{%s}"%(GetJointStr(self.value, method=lambda v: "true" if v else "false"))
+ else:
+ return "{%s}"%(GetJointStr(self.value, method=lambda x: str(int(x))))
+
+ def ToUnspecifiedDim(self):
+ self.dimensions = self.type.dimensions
+ self.type = self.type.ToUnspecifiedDim()
+
+# Base class of user-defined input/output operand
+class InOut(Operand):
+
+ def __init__(self, name, opType, backward=None, skipRenaming=False, extraParams=None):
+ Operand.__init__(self, name, opType, backward, None, skipRenaming=skipRenaming, extraParams=extraParams)
+ self.lifetime = "MODEL_INPUT"
+ self.index = 0
+
+ def Feed(self, value):
+ self.SetValue(value[self] if type(value) is dict else value)
+ return self
+
+ def GetListInitialization(self):
+ return "{%d, %s}"%(self.index, super().GetListInitialization())
+
+# A user-declared input operand
+class Input(InOut):
+ def __init__(self, name, opType, backward=None, skipRenaming=False, extraParams=None):
+ InOut.__init__(self, name, opType, backward, skipRenaming=skipRenaming, extraParams=extraParams)
+ self.lifetime = "MODEL_INPUT"
+
+# A user-declared output operand
+class Output(InOut):
+ def __init__(self, name, opType, backward=None, skipRenaming=False):
+ InOut.__init__(self, name, opType, backward, skipRenaming=skipRenaming)
+ self.lifetime = "MODEL_OUTPUT"
+
+# An output that we don't want to compare the results
+class IgnoredOutput(Output):
+ def __init__(self, name, opType, backward=None, skipRenaming=False):
+ Output.__init__(self, name, opType, backward, skipRenaming=skipRenaming)
+ self.lifetime = "MODEL_OUTPUT"
+ def Feed(self, value):
+ numElements = reduce(lambda x,y: x*y, self.dimensions, 1)
+ self.value = [0 for x in range(numElements)]
+ return self
+
+# An explicitly declared parameter
+class Parameter(Operand):
+ def __init__(self, name, opType, value, backward=None, skipRenaming=False, extraParams=None):
+ Operand.__init__(self, name, opType, value, backward, skipRenaming=skipRenaming,
+ extraParams=extraParams)
+ self.initializer = NamedVariable(str(self) + "_init")
+ self.lifetime = "CONSTANT_REFERENCE" if Configuration.useSHM() else "CONSTANT_COPY"
+
+# A shortcut for parameters of INT32
+class Int32Scalar(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("INT32", []), int(value))
+ @staticmethod
+ def IsCompatible(value):
+ return type(value) is int
+
+# A shortcut for parameters of FLOAT16
+class Float16Scalar(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("FLOAT16", []), float(value))
+ @staticmethod
+ def IsCompatible(value):
+ return False
+
+# A shortcut for parameters of FLOAT32
+class Float32Scalar(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("FLOAT32", []), float(value))
+ @staticmethod
+ def IsCompatible(value):
+ return type(value) is float
+
+# A shortcut for parameters of BOOL
+class BoolScalar(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("BOOL", []), bool(value))
+ @staticmethod
+ def IsCompatible(value):
+ return type(value) is bool
+
+# A shortcut for parameter of 1-D TENSOR_INT32
+class Int32Vector(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("TENSOR_INT32", [len(value)]), [int(v) for v in value])
+ @staticmethod
+ def IsCompatible(value):
+ if type(value) is not list and type(value) is not tuple:
+ return False
+ return all(type(i) is int for i in value)
+
+# A shortcut for parameter of 1-D TENSOR_FLOAT32
+class Float32Vector(Parameter, ImplicitParameter):
+ def __init__(self, name, value):
+ Parameter.__init__(self, name, ("TENSOR_FLOAT32", [len(value)]), [float(v) for v in value])
+ @staticmethod
+ def IsCompatible(value):
+ if type(value) is not list and type(value) is not tuple:
+ return False
+ return all(type(i) is float for i in value)
+
+# An explicitly declared intermediate result
+class Internal(Operand):
+ def __init__(self, name, opType, backward=None, skipRenaming=False):
+ Operand.__init__(self, name, opType, backward, None, skipRenaming=skipRenaming)
+ self.lifetime = "TEMPORARY_VARIABLE"
+
+# An operation in a model, does not need a name
+class Operation:
+
+ def __init__(self, optype, ins, outs):
+ self.optype = optype
+ self.SetInputs(ins)
+ self.SetOutputs(outs)
+
+ # for the ease of debugging
+ def __str__(self):
+ insString = GetJointStr(self.ins)
+ outsString = GetJointStr(self.outs)
+ return "Operation %s: [%s] -> [%s]"%(self.optype, insString, outsString)
+ __repr__ = __str__
+
+ def SetInputs(self, ins):
+ self.ins = [ImplicitParameter.ImplicitConvertion(i) for i in ins]
+ return self
+
+ def SetOutputs(self, outs):
+ self.outs = list(outs)
+ return self
+
+ # For backward-compatibility with slicing.py
+ # Get Python-ish dump for the op
+ def PyDefinition(self):
+ py_op_string = """Operation("{optype}", {inputs}).To({outputs})"""
+ inputs = [str(x) for x in self.ins]
+ inputs = ", ".join(inputs)
+ assert len(self.outs) <= 1
+ outputs = str(self.outs[0])
+ ops = {"optype": self.optype, "inputs": inputs, "outputs": outputs}
+ return py_op_string.format(**ops)
+
+# Main interface
+class Model:
+ models = list()
+
+ def __init__(self, name=None):
+ self.name = name
+ self.operations = []
+ self.operands = []
+ self.isRelaxed = False
+ self.compiled = False
+ self.dumped = False
+ self.hasDynamicOutputShape = False
+ self.version = FileNames.version
+ Model.models.append(self)
+
+ def WithSuffix(self, *args):
+ self.createFunctionName = GlobalVariable("CreateModel", self.name, *args)
+ self.createTestFunctionName = GlobalVariable("createTestModel", self.name, *args)
+ self.isIgnoredFunctionName = GlobalVariable("is_ignored", self.name, *args)
+ return self
+
+ def AddOperation(self, operation):
+ self.operations.append(operation)
+ for i in operation.ins:
+ if i not in self.operands:
+ self.operands.append(i)
+ for o in operation.outs:
+ if o not in self.operands:
+ self.operands.append(o)
+ return self
+
+ def Operation(self, op_name, *args):
+ return self.AddOperation(Operation(op_name, args, []))
+
+ def To(self, *args):
+ assert len(self.operations) > 0
+ if type(args[0]) is tuple or type(args[0]) is list:
+ outs = args[0]
+ else:
+ outs = args
+ self.operations[-1].SetOutputs(outs)
+ for o in outs:
+ if o not in self.operands:
+ self.operands.append(o)
+ return self
+
+ def RelaxedExecution(self, isRelaxed):
+ self.isRelaxed = isRelaxed
+ return self
+
+ def TestDynamicOutputShape(self, hasDynamicOutputShape):
+ self.hasDynamicOutputShape = hasDynamicOutputShape
+ return self
+
+ # Sets the version of the model in compliance tests. Set to None to disable the test.
+ def IntroducedIn(self, ver):
+ self.version = ver
+ return self
+
+ def GetTypes(self):
+ return sorted(list(set(op.type for op in self.operands)))
+
+ def GetMappedOperandTypes(self):
+ return sorted(list(set(Type.typeLookupWebnn[op.type.type] for op in self.operands)))
+
+ def GetInputs(self):
+ return [i for i in self.operands if isinstance(i, Input)]
+
+ def GetOutputs(self):
+ return [o for o in self.operands if isinstance(o, Output)]
+
+ def GetInputsIndex(self):
+ return [i for i,op in enumerate(self.operands) if isinstance(op, Input)]
+
+ def GetOutputsIndex(self):
+ return [o for o,op in enumerate(self.operands) if isinstance(op, Output)]
+
+ def GetIndexOfOperands(self, operands):
+ return [self.operands.index(i) for i in operands]
+
+ def GetIgnoredOutputs(self):
+ return [o for o in self.operands if isinstance(o, IgnoredOutput)]
+
+ def GetParameters(self):
+ return [p for p in self.operands if isinstance(p, Parameter)]
+
+ def GetEquivalentOperands(self, targets):
+ return [self.operands[self.operands.index(t)] for t in targets]
+
+ def UpdateEquivalentOperands(self, targets):
+ for t in targets:
+ self.operands[self.operands.index(t)] = t
+ return self
+
+ def SetInputAndOutputIndex(self):
+ for ind, i in enumerate(self.GetInputs()):
+ i.index = ind
+ for ind, o in enumerate(self.GetOutputs()):
+ o.index = ind
+ return self
+
+ def SetOperandInsAndOuts(self):
+ for op in self.operands:
+ op.ins = list()
+ op.outs = list()
+ for op in self.operations:
+ op.ins = self.GetEquivalentOperands(op.ins)
+ op.outs = self.GetEquivalentOperands(op.outs)
+ for i in op.ins:
+ i.outs.append(op)
+ for o in op.outs:
+ o.ins.append(op)
+ return self
+
+ def TopologicalSortHelper(self, op, deps, visited):
+ if op in visited:
+ assert op not in deps, "Cycle detected in the graph"
+ else:
+ visited.add(op)
+ for i in deps[op]:
+ self.TopologicalSortHelper(i, deps, visited)
+ self.operations.append(op)
+ deps.pop(op)
+
+ # Topological sort of the operations, and detect if there is a cycle is the graph
+ def TopologicalSort(self):
+ deps = {op: list() for op in self.operations}
+ [deps[o].append(i) for op in self.operands for o in op.outs for i in op.ins]
+ operations = self.operations.copy()
+ self.operations = []
+ visited = set()
+ for op in operations:
+ self.TopologicalSortHelper(op, deps, visited)
+
+ def SetOutputUnspecified(self):
+ for op in self.operands:
+ op.dimensions = op.type.dimensions
+ if self.hasDynamicOutputShape:
+ for op in self.GetOutputs():
+ op.ToUnspecifiedDim()
+ return self
+
+ def Compile(self):
+ if self.compiled:
+ return self
+ self.SetInputAndOutputIndex()
+ self.SetOperandInsAndOuts()
+ self.TopologicalSort()
+ self.SetOutputUnspecified()
+ # Do not check compliance for relaxed mode and dynamic output shape tests.
+ if self.isRelaxed or self.hasDynamicOutputShape:
+ self.IntroducedIn(None)
+ self.compiled = True
+ return self
+
+# To track implicitly convertible variation types
+class ImplicitVariation:
+ @staticmethod
+ def ImplicitConvertion(value):
+ if isinstance(value, ModelVariation):
+ return value
+ for implicitType in ImplicitVariation.__subclasses__():
+ value = value if type(value) is tuple or type(value) is list else [value]
+ if implicitType.IsCompatible(value[0]):
+ var = implicitType(value[0])
+ if len(value) > 1:
+ var.Identify(*value[1:])
+ return var
+ assert False, "%s not supported for implicit variation"%value[0]
+
+# The base class for model variations
+class ModelVariation:
+
+ def __init__(self, name=None):
+ self.targetOperands = {}
+ self.name = name
+
+ def ApplyToHelper(self, model, args, feedDicts, transform):
+ opVarList = []
+ for op in model.GetEquivalentOperands(sorted(args.keys())):
+ opVar = op
+ feedDictsVar = []
+ if isinstance(op, Input) or isinstance(op, Output):
+ for feedDict in feedDicts:
+ op_tmp = copy.deepcopy(op)
+ if op_tmp in feedDict[0]:
+ opVar = transform(op_tmp.Feed(feedDict[0]), args[op_tmp])
+ elif op_tmp in feedDict[1]:
+ opVar = transform(op_tmp.Feed(feedDict[1]), args[op_tmp])
+ else:
+ assert False
+ feedDictsVar.append(opVar.value)
+ assert type(op) == type(opVar), "Can not handle %s -> %s"%(type(op), type(opVar))
+ else:
+ opVar = transform(op, args[op])
+ # handle Parameter -> Input
+ if isinstance(opVar, Input) or isinstance(opVar, Output):
+ feedDictsVar = [opVar.value] * len(feedDicts)
+ if isinstance(opVar, Input) or isinstance(opVar, Output):
+ for feedDict, feedDictVar in zip(feedDicts, feedDictsVar):
+ if opVar in feedDict[1]:
+ feedDict[1][opVar] = feedDictVar
+ else:
+ feedDict[0][opVar] = feedDictVar
+ opVarList.append(opVar)
+ return opVarList
+
+ # Make a deepcopy of the model and feedDicts, and apply the change
+ def ApplyTo(self, modelOrigin, feedDictsOrigin):
+ model, feedDicts = copy.deepcopy((modelOrigin, feedDictsOrigin))
+ model.compiled = False
+ model.dumped = False
+
+ if not self.targetOperands:
+ self.AutoIdentify(model)
+
+ # get transformed operands and update feedDicts
+ operandsVar = self.ApplyToHelper(
+ model, self.targetOperands, feedDicts, self.TransformOperand)
+
+ model = self.TransformModel(model)
+ model.UpdateEquivalentOperands(operandsVar)
+ return model, feedDicts
+
+ def IdentifyOperands(self, args=None):
+ if args is None:
+ return self
+ self.targetOperands = args if type(args) is dict else {i: None for i in args}
+ return self
+
+ def Identify(self, operandArgs=None, paramArgs=None):
+ self.IdentifyOperands(operandArgs)
+ return self
+
+ # Set variation to its default name
+ def SetToDefaultName(self):
+ self.name = ""
+ return self
+
+ # Automatically select the target operand list
+ def AutoIdentify(self, model):
+ return self
+
+ # Transform operands that are marked by IdentifyOperands()
+ def TransformOperand(self, op, arg=None):
+ return op
+
+ # Transform the model
+ def TransformModel(self, model):
+ return model
+
+# Default variation that does nothing
+class DefaultVariation(ModelVariation):
+
+ def __init__(self, name=None):
+ ModelVariation.__init__(self, name=name)
+
+# Convert operand data type
+class DataTypeConverter(ModelVariation, ImplicitVariation):
+
+ def __init__(self, targetType=None, name=None):
+ ModelVariation.__init__(self, name=name)
+ if targetType is not None:
+ assert DataTypeConverter.IsCompatible(targetType)
+ self.targetType = targetType
+
+ @staticmethod
+ def IsCompatible(value):
+ return value.lower() in ["float16", "int32"]
+
+ def SetToDefaultName(self):
+ if self.targetType is not None:
+ self.name = self.targetType.lower()
+ return self
+ # get all target types
+ targetTypes = list(zip(*self.targetOperands.values()))[0]
+ if "TENSOR_QUANT8_SYMM_PER_CHANNEL" in targetTypes:
+ self.name = "channelQuant8"
+ elif "TENSOR_QUANT8_ASYMM" in targetTypes:
+ self.name = "quant8"
+ elif "TENSOR_INT32" in targetTypes:
+ self.name = "int32"
+ elif "TENSOR_FLOAT16" in targetTypes:
+ self.name = "float16"
+ else:
+ self.name = "float32"
+ return self
+
+ def AutoIdentify(self, model):
+ if self.targetType is not None:
+ # By default, select all the float32 tensors/scalars
+ targets = {op: ["TENSOR_" + self.targetType.upper()] \
+ for op in model.operands if op.type.type == "TENSOR_FLOAT32"}
+ targets.update({op: [self.targetType.upper()] \
+ for op in model.operands if op.type.type == "FLOAT32"})
+ self.Identify(targets)
+ return self
+
+ def TransformOperand(self, op, arg=None):
+ if len(arg) == 1:
+ typeTuple = (arg[0], op.type.dimensions)
+ else:
+ typeTuple = (arg[0], op.type.dimensions, *arg[1:])
+ # To handle Internal operands
+ if op.value is None or op.type.GetNumberOfElements() == 0:
+ op.type = Type.GetType(*typeTuple)
+ else:
+ v = Dequantize(op.GetValueAsNumpy().astype(np.float32), op.type)
+ op.type = Type.GetType(*typeTuple)
+ v = Quantize(v, op.type)
+ op.SetValueFromNumpy(v)
+ return op
+
+# Convert model to turn on/off relaxed computation
+class RelaxedModeConverter(ModelVariation, ImplicitVariation):
+
+ def __init__(self, isRelaxed=True, name=None):
+ ModelVariation.__init__(self, name=name)
+ if isinstance(isRelaxed, bool):
+ self.isRelaxed = isRelaxed
+ else:
+ assert RelaxedModeConverter.IsCompatible(isRelaxed.lower())
+ self.isRelaxed = True
+
+ @staticmethod
+ def IsCompatible(value):
+ return value.lower() in ["relaxed"]
+
+ def SetToDefaultName(self):
+ self.name = "relaxed" if self.isRelaxed else "float"
+ return self
+
+ def TransformModel(self, model):
+ model.RelaxedExecution(self.isRelaxed)
+ return model
+
+# Convert data layout between "NHWC" amd "NCHW"
+class DataLayoutConverter(ModelVariation, ImplicitVariation):
+
+ def __init__(self, targetLayout="nchw", name=None):
+ ModelVariation.__init__(self, name=name)
+ self.targetLayout = targetLayout.lower()
+ assert DataLayoutConverter.IsCompatible(self.targetLayout)
+ self.perm = (0, 3, 1, 2) if self.targetLayout == "nchw" else (0, 2, 3, 1)
+ self.param = True if self.targetLayout == "nchw" else False
+
+ @staticmethod
+ def IsCompatible(value):
+ return value.lower() in ["nhwc", "nchw"]
+
+ def SetToDefaultName(self):
+ self.name = self.targetLayout
+ return self
+
+ def TransformOperand(self, op, arg=None):
+ if len(op.type.dimensions) == 4:
+ # To handle Internal operands
+ if op.value is not None and op.type.GetNumberOfElements() != 0:
+ op.SetValueFromNumpy(op.GetValueAsNumpy().transpose(self.perm))
+ newDim = [op.type.dimensions[i] for i in self.perm]
+ op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint)
+ elif len(op.type.dimensions) == 1 and len(op.value) == 4:
+ op.SetValueFromNumpy(op.GetValueAsNumpy()[list(self.perm)])
+ elif op.type.type == "BOOL":
+ op.SetValue(self.param)
+ else:
+ assert False, "%s not supported by DataLayoutConverter"%op
+ return op
+
+# Convert data by tansposing and removing axis
+class AxisConverter(ModelVariation):
+
+ def __init__(self, origin, target, dim, drop=[], name=None):
+ ModelVariation.__init__(self, name=name)
+ self.origin = origin
+ self.target = target
+ assert all(i >= -dim and i < dim for i in [self.origin, self.target])
+ self.dim = dim
+ self.perm = list(range(dim))
+ self.perm.insert(target if target >= 0 else target + dim, self.perm.pop(origin))
+ self.drop = [drop] if type(drop) is int else list(drop)
+ assert all(i >= -dim and i < dim for i in self.drop)
+ self.drop = [i if i >= 0 else i + dim for i in self.drop]
+ assert target not in self.drop and target + dim not in self.drop
+
+ def SetToDefaultName(self):
+ axis = self.target if self.target >= 0 else self.target + self.dim
+ axis -= sum(i < axis for i in self.drop)
+ neg = "" if self.target >= 0 else "_neg"
+ self.name = "dim%d_axis%d%s"%(self.dim - len(self.drop), axis, neg)
+ return self
+
+ def TransposeAxis(self, op):
+ if op.type.type == "INT32":
+ op.SetValue(self.target)
+ elif len(op.type.dimensions) == self.dim:
+ # To handle Internal operands
+ if op.value is not None:
+ op.SetValueFromNumpy(op.GetValueAsNumpy().transpose(self.perm))
+ newDim = [op.type.dimensions[i] for i in self.perm]
+ op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint)
+ else:
+ assert False, "%s not supported by AxisConverter"%op
+ return op
+
+ def RemoveAxis(self, op):
+ if op.type.type == "INT32":
+ if op.value[0] >= 0:
+ op.SetValue(op.value[0] - sum(i < op.value[0] for i in self.drop))
+ else:
+ op.SetValue(op.value[0] + sum(i > (op.value[0] + self.dim) for i in self.drop))
+ elif len(op.type.dimensions) == self.dim:
+ if op.value is not None:
+ val = op.GetValueAsNumpy()
+ for i in sorted(self.drop, reverse=True):
+ val = np.take(val, 0, axis=i)
+ op.SetValueFromNumpy(val)
+ newDim = [op.type.dimensions[i] for i in range(self.dim) if i not in self.drop]
+ op.type = Type.GetType(op.type.type, newDim, op.type.scale, op.type.zeroPoint)
+ else:
+ assert False, "%s not supported by AxisConverter"%op
+ return op
+
+ def TransformOperand(self, op, arg=None):
+ op = self.TransposeAxis(op)
+ op = self.RemoveAxis(op)
+ return op
+
+# Convert a Parameter to Input
+class ParameterAsInputConverter(ModelVariation, ImplicitVariation):
+
+ def __init__(self, arg="as_input", prefix="weight", name=None):
+ ModelVariation.__init__(self, name=name)
+ assert ParameterAsInputConverter.IsCompatible(arg.lower())
+ self.prefix = prefix
+
+ @staticmethod
+ def IsCompatible(value):
+ return value.lower() in ["as_input"]
+
+ def SetToDefaultName(self):
+ self.name = self.prefix + "_as_input"
+ return self
+
+ def TransformOperand(self, op, arg=None):
+ assert isinstance(op, Parameter), "%s cannot be converted to Input."%type(op)
+ newop = Input(op.name, op.type.GetSignatureTuple(), skipRenaming=True, extraParams=op.type.extraParams)
+ newop.SetValue(op.value)
+ return newop
+
+# Convert Output based on activation
+class ActivationConverter(ModelVariation, ImplicitVariation):
+ # (Enum, low, high)
+ actMap = {
+ "none": (0, None, None),
+ "relu": (1, 0.0, None),
+ "relu1": (2, -1.0, 1.0),
+ "relu6": (3, 0.0, 6.0),
+ }
+ def __init__(self, act="relu", name=None):
+ ModelVariation.__init__(self, name=name)
+ self.act = act.lower()
+ assert ActivationConverter.IsCompatible(self.act)
+ self.enum = ActivationConverter.actMap[self.act][0]
+ self.low = ActivationConverter.actMap[self.act][1]
+ self.high = ActivationConverter.actMap[self.act][2]
+
+ @staticmethod
+ def IsCompatible(value):
+ return value.lower() in ActivationConverter.actMap.keys()
+
+ def SetToDefaultName(self):
+ self.name = self.act
+ return self
+
+ def TransformOperand(self, op, arg=None):
+ if op.type.type == "INT32": # activation enum
+ return op.SetValue(self.enum)
+ else:
+ assert isinstance(op, Output)
+ v = op.GetValueAsNumpy()
+ if self.low is not None:
+ low = Quantize(self.low, op.type)
+ v = np.maximum(v, low)
+ if self.high is not None:
+ high = Quantize(self.high, op.type)
+ v = np.minimum(v, high)
+ return op.SetValueFromNumpy(v)
+
+class DynamicOutputShapeConverter(ModelVariation):
+ def __init__(self, name=None):
+ ModelVariation.__init__(self, name=name)
+
+ def SetToDefaultName(self):
+ self.name = "dynamic_output_shape"
+ return self
+
+ def TransformModel(self, model):
+ model.TestDynamicOutputShape(True)
+ return model
+
+# An example is always attached to a model, and could have multiple variations
+class Example:
+ examples = []
+ versionOverrides = {}
+
+ def __init__(self, *args, model=None, name=None):
+ self.model = Model.models[-1] if model is None else model
+ self.name = name
+ self.expectedMultinomialDistributionTolerance = None
+ self.feedDicts = []
+ for feedDict in args:
+ if type(feedDict) is tuple or type(feedDict) is list:
+ self.feedDicts.append(feedDict)
+ elif type(feedDict) is dict:
+ self.feedDicts.append((
+ {i: feedDict[i] for i in self.model.GetInputs()},
+ {o: feedDict[o] for o in self.model.GetOutputs()}
+ ))
+ else:
+ assert False
+ if Configuration.test_dynamic_output_shape:
+ self.variations = [[DefaultVariation(), DynamicOutputShapeConverter()]]
+ else:
+ self.variations = []
+ Example.examples.append(self)
+
+ @staticmethod
+ def SetVersion(ver, *args):
+ for name in args:
+ Example.versionOverrides[name] = ver
+
+ # Main entrance of test generator
+ @staticmethod
+ def DumpAllExamples(DumpTest=None, test=None):
+ Example.CombineAllExamples()
+ for example in Example.examples:
+ example.Dump(DumpTest, test)
+
+ # Combine examples with the same model, same name, and same set of variations
+ @staticmethod
+ def CombineAllExamples():
+ modelMap = {}
+ newExamples = []
+ for example in Example.examples:
+ if example.model.name in ["zero_sized"]:
+ continue
+ key = (example.model, example.name, tuple(tuple(e) for e in example.variations))
+ if key in modelMap:
+ modelMap[key].Combine(example)
+ else:
+ modelMap[key] = example
+ newExamples.append(example)
+ Example.examples = newExamples
+
+ def AddVariations(self, *args, includeDefault=True, defaultName=None):
+ self.variations.append([DefaultVariation(defaultName)] if includeDefault else [])
+ self.variations[-1].extend(ImplicitVariation.ImplicitConvertion(i) for i in args)
+ return self
+
+ def AddNchw(self, *args, includeDefault=True, defaultName="nhwc"):
+ var = DataLayoutConverter("nchw").Identify(args)
+ self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddRelaxed(self, isRelaxed=True, includeDefault=True, defaultName=None):
+ var = RelaxedModeConverter(isRelaxed)
+ self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddInput(self, *args, includeDefault=True, defaultName=None):
+ var = ParameterAsInputConverter().Identify(args)
+ self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddRelu(self, *args, includeDefault=True, defaultName=None):
+ var = ActivationConverter("relu").Identify(args)
+ self.AddVariations(var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddAllActivations(self, *args):
+ var = [ActivationConverter(i).Identify(args)
+ for i in sorted(ActivationConverter.actMap.keys())]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def GuessOriginalAxisAndDim(self, *args):
+ origin = None
+ dim = None
+ for arg in args:
+ if arg.type.type == "INT32":
+ origin = arg.value[0]
+ else:
+ if dim is None:
+ dim = len(arg.type.dimensions)
+ else:
+ assert dim == len(arg.type.dimensions)
+ assert dim is not None
+ origin = dim - 1 if origin is None else origin
+ origin = origin + dim if origin < 0 else origin
+ return origin, dim
+
+ def AddAxis(self, axis, *args, includeDefault=True, defaultName=None):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ axis = [axis] if type(axis) is int else list(axis)
+ var = [AxisConverter(origin, a, dim).Identify(args) for a in axis]
+ self.AddVariations(*var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddAllPositiveAxis(self, *args):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ var = [AxisConverter(origin, a, dim).Identify(args) for a in range(dim)]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def AddAllAxis(self, *args):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ var = [AxisConverter(origin, a, dim).Identify(args) for a in range(-dim, dim)]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def AddDims(self, dims, *args, includeDefault=True, defaultName=None):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ dims = [dims] if type(dims) is int else list(dims)
+ drop = list(range(dim))
+ drop.pop(origin)
+ var = [AxisConverter(origin, origin, dim, drop[0:(dim-i)]).Identify(args) for i in dims]
+ self.AddVariations(*var, includeDefault=includeDefault, defaultName=defaultName)
+ return self
+
+ def AddAllDims(self, *args):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ drop = list(range(dim))
+ drop.pop(origin)
+ var = [AxisConverter(origin, origin, dim, drop[0:i]).Identify(args) for i in range(dim)]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def AddAllDimsAndPositiveAxis(self, *args):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ var = [AxisConverter(origin, j, dim, range(i)).Identify(args) \
+ for i in range(dim) for j in range(i, dim)]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def AddAllDimsAndAxis(self, *args):
+ origin, dim = self.GuessOriginalAxisAndDim(*args)
+ var = [AxisConverter(origin, k, dim, range(i)).Identify(args) \
+ for i in range(dim) for j in range(i, dim) for k in [j, j - dim]]
+ self.AddVariations(*var, includeDefault=False)
+ return self
+
+ def Combine(self, other):
+ assert self.model is other.model, "Only examples targetting the same model can be combined"
+ assert tuple(self.variations) == tuple(other.variations), \
+ "Only examples with the same set of variations can be combined"
+ assert self.name == other.name, "Only examples with the same name can be combined"
+ self.feedDicts.extend(other.feedDicts)
+ return self
+
+ def Dump(self, DumpTest, test):
+ [v.SetToDefaultName() for vs in self.variations for v in vs if v.name is None]
+ for variationList in itertools.product(*self.variations):
+ # Apply variations
+ modelOrigin, feedDictsOrigin = self.model, self.feedDicts
+ self.model, self.feedDicts = copy.deepcopy((self.model, self.feedDicts))
+ for variation in variationList:
+ self.model, self.feedDicts = variation.ApplyTo(self.model, self.feedDicts)
+ # Concat names for test and examples
+ varNames = [v.name for v in variationList]
+ self.testName = NamedTest(FileNames.specName, self.model.name, self.name, *varNames)
+ # self.examplesName = GlobalVariable("examples", self.model.name, self.name, *varNames)
+ if str(self.testName) in Example.versionOverrides:
+ self.model.IntroducedIn(Example.versionOverrides[str(self.testName)])
+ self.model.WithSuffix(*varNames).Compile()
+ # Dump files
+ if DumpTest is not None and test is not None:
+ DumpTest(self, test)
+ # Restore model and feedDicts before variation
+ self.model = modelOrigin
+ self.feedDicts = feedDictsOrigin
+ return self
+
+ # Specifies the RANDOM_MULTINOMIAL distribution tolerance.
+ # If set to greater than zero, the input is compared as log-probabilities
+ # to the output and must be within this tolerance to pass.
+ def WithMultinomialDistributionTolerance(self, expectedTolerance):
+ self.expectedMultinomialDistributionTolerance = expectedTolerance
+ return self
+
+class FileNames:
+ specFiles = []
+ specNames = []
+ testFiles = []
+ specFile = ""
+ specName = ""
+ testFile = ""
+ version = ""
+ fileIndex = 0
+
+ @staticmethod
+ def InitializeFileLists(spec, test):
+ # get all spec files
+ if os.path.isfile(spec):
+ FileNames.specFiles = [os.path.abspath(spec)]
+ elif os.path.isdir(spec):
+ FileNames.specFiles = sorted([os.path.abspath(os.path.join(spec, f))
+ for f in os.listdir(spec) if f.endswith(".mod.py")])
+ else:
+ assert False, "%s is neither a file or a directory"%spec
+ FileNames.specNames = [re.sub(r"\..*", "", os.path.basename(f))
+ for f in FileNames.specFiles]
+ FileNames.testFiles = FileNames.ParseTargetFiles(test, ".js")
+
+ @staticmethod
+ def ParseTargetFiles(arg, ext):
+ numFiles = len(FileNames.specFiles)
+ # print('%d FileNames.specFiles %s' % (numFiles, FileNames.specFiles))
+ absPath = os.path.abspath(arg)
+ if os.path.isdir(arg):
+ target = [os.path.join(absPath, f + ext) for f in FileNames.specNames]
+ elif arg == "-":
+ target = ["-"] * numFiles
+ else:
+ target = [absPath] * numFiles
+ return target
+
+ @staticmethod
+ def NextFile():
+ if FileNames.fileIndex >= len(FileNames.specFiles):
+ return False
+ FileNames.specFile = FileNames.specFiles[FileNames.fileIndex]
+ FileNames.specName = FileNames.specNames[FileNames.fileIndex]
+ FileNames.testFile = FileNames.testFiles[FileNames.fileIndex]
+ FileNames.fileIndex += 1
+ NamedObject.existingNames = set()
+ NamedVariable.existingNames = set()
+ NamedTest.existingNames = set()
+ Type.typesMap = dict()
+ Model.models = list()
+ Example.examples = list()
+ Configuration.use_shm_for_weights = False
+
+ # Extract version from absolute file path.
+ versionMatch = re.findall(r"/V\d_\d/", FileNames.specFile)
+ if len(versionMatch) == 1:
+ FileNames.version = versionMatch[0].strip('/')
+ else:
+ FileNames.version = None
+ return True
+
+class Configuration:
+ use_shm_for_weights = False
+ force_regenerate = False
+ test_dynamic_output_shape = False
+ mappingWebNNOp = []
+ successedCounter = 0
+ saveAllInOneCts = True
+
+ @staticmethod
+ def useSHM():
+ return Configuration.use_shm_for_weights
+
+def IndentedPrint(s, indent=0, *args, **kwargs):
+ print('\n'.join([" " * indent + i for i in s.split('\n')]), *args, **kwargs)
+
+def InitializeCtsTestFile(test, detph):
+ testFileHeader = """\
+'use strict';
+import * as utils from '%sutils.js';\n""" % ''.join(['../']*detph)
+ print(testFileHeader, file=test)
+ print("/* eslint-disable max-len */", file=test)
+ print("describe('CTS converted from NNAPI CTS', function() {", file=test)
+ IndentedPrint("const nn = navigator.ml.getNeuralNetworkContext();",
+ indent=2, file=test)
\ No newline at end of file
diff --git a/test/cts/from_nnapi/tests/V1_0/test_add_clamp_converted_from_add.js b/test/cts/from_nnapi/tests/V1_0/test_add_clamp_converted_from_add.js
new file mode 100644
index 00000000..2f18a27c
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_add_clamp_converted_from_add.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test add + clamp converted from add test', async function() {
+ // Converted test case (from: V1_0/add.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2]});
+ const op1Buffer = new Float32Array([1.0, 2.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2]});
+ const op2Buffer = new Float32Array([3.0, 4.0]);
+ const expected = [4.0, 6.0];
+ const interOut0 = builder.add(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_1.js
new file mode 100644
index 00000000..a86f8b5a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_1.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_1 test', async function() {
+ // Converted test case (from: V1_0/avg_pool_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const pad0 = 0;
+ const cons1 = 1;
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [pad0, pad0, pad0, pad0], 'strides': [cons1, cons1], 'windowDimensions': [cons1, cons1], 'layout': 'nhwc'});
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_2.js
new file mode 100644
index 00000000..4e7d0e27
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_2.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_2 test', async function() {
+ // Converted test case (from: V1_0/avg_pool_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const i0Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const padding = 50;
+ const stride = 5;
+ const filter = 100;
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_3.js b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_3.js
new file mode 100644
index 00000000..824d49e8
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_3.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_3 test', async function() {
+ // Converted test case (from: V1_0/avg_pool_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [1, 200, 180, 1]});
+ const i0Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 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+ const padding = 0;
+ const stride = 2;
+ const filter = 10;
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_4.js b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_4.js
new file mode 100644
index 00000000..a1894515
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_averagePool2d_clamp_converted_from_avg_pool_float_4.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_4 test', async function() {
+ // Converted test case (from: V1_0/avg_pool_float_4.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const i0Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]);
+ const padding = 50;
+ const stride = 5;
+ const filter = 100;
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_1.js
new file mode 100644
index 00000000..832aad5a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_1.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu1_float_1 test', async function() {
+ // Converted test case (from: V1_0/relu1_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [-1.0, -0.5, 0.5, 1.0];
+ const op2 = builder.clamp(op1, {minValue: builder.constant(-1), maxValue: builder.constant(1)});
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_2.js
new file mode 100644
index 00000000..ccd53f35
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu1_float_2.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu1_float_2 test', async function() {
+ // Converted test case (from: V1_0/relu1_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 30, 24, 2]});
+ const inputBuffer = new Float32Array([-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, 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1.813, -1.814, 1.815, -1.816, 1.817, -1.818, 1.819, -1.82, 1.821, -1.822, 1.823, -1.824, 1.825, -1.826, 1.827, -1.828, 1.829, -1.83, 1.831, -1.832, 1.833, -1.834, 1.835, -1.836, 1.837, -1.838, 1.839, -1.84, 1.841, -1.842, 1.843, -1.844, 1.845, -1.846, 1.847, -1.848, 1.849, -1.85, 1.851, -1.852, 1.853, -1.854, 1.855, -1.856, 1.857, -1.858, 1.859, -1.86, 1.861, -1.862, 1.863, -1.864, 1.865, -1.866, 1.867, -1.868, 1.869, -1.87, 1.871, -1.872, 1.873, -1.874, 1.875, -1.8760000000000001, 1.877, -1.8780000000000001, 1.879, -1.8800000000000001, 1.881, -1.8820000000000001, 1.883, -1.8840000000000001, 1.885, -1.8860000000000001, 1.887, -1.8880000000000001, 1.889, -1.8900000000000001, 1.891, -1.8920000000000001, 1.893, -1.8940000000000001, 1.895, -1.8960000000000001, 1.897, -1.8980000000000001, 1.899, -1.9000000000000001, 1.901, -1.9020000000000001, 1.903, -1.9040000000000001, 1.905, -1.9060000000000001, 1.907, -1.9080000000000001, 1.909, -1.9100000000000001, 1.911, -1.9120000000000001, 1.913, -1.9140000000000001, 1.915, -1.9160000000000001, 1.917, -1.9180000000000001, 1.919, -1.92, 1.921, -1.922, 1.923, -1.924, 1.925, -1.926, 1.927, -1.928, 1.929, -1.93, 1.931, -1.932, 1.933, -1.934, 1.935, -1.936, 1.937, -1.938, 1.939, -1.94, 1.941, -1.942, 1.943, -1.944, 1.945, -1.946, 1.947, -1.948, 1.949, -1.95, 1.951, -1.952, 1.953, -1.954, 1.955, -1.956, 1.957, -1.958, 1.959, -1.96, 1.961, -1.962, 1.963, -1.964, 1.965, -1.966, 1.967, -1.968, 1.969, -1.97, 1.971, -1.972, 1.973, -1.974, 1.975, -1.976, 1.977, -1.978, 1.979, -1.98, 1.981, -1.982, 1.983, -1.984, 1.985, -1.986, 1.987, -1.988, 1.989, -1.99, 1.991, -1.992, 1.993, -1.994, 1.995, -1.996, 1.997, -1.998, 1.999, -2.0, 2.001, -2.0020000000000002, 2.003, -2.004, 2.005, -2.0060000000000002, 2.007, -2.008, 2.009, -2.0100000000000002, 2.011, -2.012, 2.013, -2.0140000000000002, 2.015, -2.016, 2.017, -2.0180000000000002, 2.019, -2.02, 2.021, -2.0220000000000002, 2.023, -2.024, 2.025, -2.0260000000000002, 2.027, -2.028, 2.029, -2.0300000000000002, 2.031, -2.032, 2.033, -2.0340000000000003, 2.035, -2.036, 2.037, -2.0380000000000003, 2.039, -2.04, 2.041, -2.0420000000000003, 2.043, -2.044, 2.045, -2.0460000000000003, 2.047, -2.048, 2.049, -2.05, 2.051, -2.052, 2.053, -2.054, 2.055, -2.056, 2.057, -2.058, 2.059, -2.06, 2.061, -2.062, 2.063, -2.064, 2.065, -2.066, 2.067, -2.068, 2.069, -2.07, 2.071, -2.072, 2.073, -2.074, 2.075, -2.076, 2.077, -2.078, 2.079, -2.08, 2.081, -2.082, 2.083, -2.084, 2.085, -2.086, 2.087, -2.088, 2.089, -2.09, 2.091, -2.092, 2.093, -2.094, 2.095, -2.096, 2.097, -2.098, 2.099, -2.1, 2.101, -2.102, 2.103, -2.104, 2.105, -2.106, 2.107, -2.108, 2.109, -2.11, 2.111, -2.112, 2.113, -2.114, 2.115, -2.116, 2.117, -2.118, 2.119, -2.12, 2.121, -2.122, 2.123, -2.124, 2.125, -2.126, 2.1270000000000002, -2.128, 2.129, -2.13, 2.1310000000000002, -2.132, 2.133, -2.134, 2.1350000000000002, -2.136, 2.137, -2.138, 2.1390000000000002, -2.14, 2.141, -2.142, 2.1430000000000002, -2.144, 2.145, -2.146, 2.1470000000000002, -2.148, 2.149, -2.15, 2.1510000000000002, -2.152, 2.153, -2.154, 2.1550000000000002, -2.156, 2.157, -2.158, 2.1590000000000003, -2.16, 2.161, -2.162, 2.1630000000000003, -2.164, 2.165, -2.166, 2.1670000000000003, -2.168, 2.169, -2.17, 2.1710000000000003, -2.172, 2.173, -2.174, 2.1750000000000003, -2.176, 2.177, -2.178, 2.179, -2.18, 2.181, -2.182, 2.183, -2.184, 2.185, -2.186, 2.187, -2.188, 2.189, -2.19, 2.191, -2.192, 2.193, -2.194, 2.195, -2.196, 2.197, -2.198, 2.199, -2.2, 2.201, -2.202, 2.203, -2.204, 2.205, -2.206, 2.207, -2.208, 2.209, -2.21, 2.211, -2.212, 2.213, -2.214, 2.215, -2.216, 2.217, -2.218, 2.219, -2.22, 2.221, -2.222, 2.223, -2.224, 2.225, -2.226, 2.227, -2.228, 2.229, -2.23, 2.231, -2.232, 2.233, -2.234, 2.235, -2.236, 2.237, -2.238, 2.239, -2.24, 2.241, -2.242, 2.243, -2.244, 2.245, -2.246, 2.247, -2.248, 2.249, -2.25, 2.251, -2.2520000000000002, 2.253, -2.254, 2.255, -2.2560000000000002, 2.257, -2.258, 2.259, -2.2600000000000002, 2.261, -2.262, 2.263, -2.2640000000000002, 2.265, -2.266, 2.267, -2.2680000000000002, 2.269, -2.27, 2.271, -2.2720000000000002, 2.273, -2.274, 2.275, -2.2760000000000002, 2.277, -2.278, 2.279, -2.2800000000000002, 2.281, -2.282, 2.283, -2.2840000000000003, 2.285, -2.286, 2.287, -2.2880000000000003, 2.289, -2.29, 2.291, -2.2920000000000003, 2.293, -2.294, 2.295, -2.2960000000000003, 2.297, -2.298, 2.299, -2.3000000000000003, 2.301, -2.302, 2.303, -2.3040000000000003, 2.305, -2.306, 2.307, -2.308, 2.309, -2.31, 2.311, -2.312, 2.313, -2.314, 2.315, -2.316, 2.317, -2.318, 2.319, -2.32, 2.321, -2.322, 2.323, -2.324, 2.325, -2.326, 2.327, -2.328, 2.329, -2.33, 2.331, -2.332, 2.333, -2.334, 2.335, -2.336, 2.337, -2.338, 2.339, -2.34, 2.341, -2.342, 2.343, -2.344, 2.345, -2.346, 2.347, -2.348, 2.349, -2.35, 2.351, -2.352, 2.353, -2.354, 2.355, -2.356, 2.357, -2.358, 2.359, -2.36, 2.361, -2.362, 2.363, -2.364, 2.365, -2.366, 2.367, -2.368, 2.369, -2.37, 2.371, -2.372, 2.373, -2.374, 2.375, -2.376, 2.3770000000000002, -2.378, 2.379, -2.38, 2.3810000000000002, -2.382, 2.383, -2.384, 2.3850000000000002, -2.386, 2.387, -2.388, 2.3890000000000002, -2.39, 2.391, -2.392, 2.3930000000000002, -2.394, 2.395, -2.396, 2.3970000000000002, -2.398, 2.399, -2.4, 2.4010000000000002, -2.402, 2.403, -2.404, 2.4050000000000002, -2.406, 2.407, -2.408, 2.4090000000000003, -2.41, 2.411, -2.412, 2.4130000000000003, -2.414, 2.415, -2.416, 2.4170000000000003, -2.418, 2.419, -2.42, 2.4210000000000003, -2.422, 2.423, -2.424, 2.4250000000000003, -2.426, 2.427, -2.428, 2.4290000000000003, -2.43, 2.431, -2.432, 2.433, -2.434, 2.435, -2.436, 2.437, -2.438, 2.439, -2.44, 2.441, -2.442, 2.443, -2.444, 2.445, -2.446, 2.447, -2.448, 2.449, -2.45, 2.451, -2.452, 2.453, -2.454, 2.455, -2.456, 2.457, -2.458, 2.459, -2.46, 2.461, -2.462, 2.463, -2.464, 2.465, -2.466, 2.467, -2.468, 2.469, -2.47, 2.471, -2.472, 2.473, -2.474, 2.475, -2.476, 2.477, -2.478, 2.479, -2.48, 2.481, -2.482, 2.483, -2.484, 2.485, -2.486, 2.487, -2.488, 2.489, -2.49, 2.491, -2.492, 2.493, -2.494, 2.495, -2.496, 2.497, -2.498, 2.499, -2.5, 2.501, -2.5020000000000002, 2.503, -2.504, 2.505, -2.5060000000000002, 2.507, -2.508, 2.509, -2.5100000000000002, 2.511, -2.512, 2.513, -2.5140000000000002, 2.515, -2.516, 2.517, -2.5180000000000002, 2.519, -2.52, 2.521, -2.5220000000000002, 2.523, -2.524, 2.525, -2.5260000000000002, 2.527, -2.528, 2.529, -2.5300000000000002, 2.531, -2.532, 2.533, -2.5340000000000003, 2.535, -2.536, 2.537, -2.5380000000000003, 2.539, -2.54, 2.541, -2.5420000000000003, 2.543, -2.544, 2.545, -2.5460000000000003, 2.547, -2.548, 2.549, -2.5500000000000003, 2.551, -2.552, 2.553, -2.5540000000000003, 2.555, -2.556, 2.557, -2.5580000000000003, 2.559, -2.56, 2.561, -2.562, 2.563, -2.564, 2.565, -2.566, 2.567, -2.568, 2.569, -2.57, 2.571, -2.572, 2.573, -2.574, 2.575, -2.576, 2.577, -2.578, 2.579, -2.58, 2.581, -2.582, 2.583, -2.584, 2.585, -2.586, 2.587, -2.588, 2.589, -2.59, 2.591, -2.592, 2.593, -2.594, 2.595, -2.596, 2.597, -2.598, 2.599, -2.6, 2.601, -2.602, 2.603, -2.604, 2.605, -2.606, 2.607, -2.608, 2.609, -2.61, 2.611, -2.612, 2.613, -2.614, 2.615, -2.616, 2.617, -2.618, 2.619, -2.62, 2.621, -2.622, 2.623, -2.624, 2.625, -2.626, 2.6270000000000002, -2.628, 2.629, -2.63, 2.6310000000000002, -2.632, 2.633, -2.634, 2.6350000000000002, -2.636, 2.637, -2.638, 2.6390000000000002, -2.64, 2.641, -2.642, 2.6430000000000002, -2.644, 2.645, -2.646, 2.6470000000000002, -2.648, 2.649, -2.65, 2.6510000000000002, -2.652, 2.653, -2.654, 2.6550000000000002, -2.656, 2.657, -2.658, 2.6590000000000003, -2.66, 2.661, -2.662, 2.6630000000000003, -2.664, 2.665, -2.666, 2.6670000000000003, -2.668, 2.669, -2.67, 2.6710000000000003, -2.672, 2.673, -2.674, 2.6750000000000003, -2.676, 2.677, -2.678, 2.6790000000000003, -2.68, 2.681, -2.682, 2.6830000000000003, -2.684, 2.685, -2.686, 2.6870000000000003, -2.688, 2.689, -2.69, 2.691, -2.692, 2.693, -2.694, 2.695, -2.696, 2.697, -2.698, 2.699, -2.7, 2.701, -2.702, 2.703, -2.704, 2.705, -2.706, 2.707, -2.708, 2.709, -2.71, 2.711, -2.712, 2.713, -2.714, 2.715, -2.716, 2.717, -2.718, 2.719, -2.72, 2.721, -2.722, 2.723, -2.724, 2.725, -2.726, 2.727, -2.728, 2.729, -2.73, 2.731, -2.732, 2.733, -2.734, 2.735, -2.736, 2.737, -2.738, 2.739, -2.74, 2.741, -2.742, 2.743, -2.744, 2.745, -2.746, 2.747, -2.748, 2.749, -2.75, 2.751, -2.7520000000000002, 2.753, -2.754, 2.755, -2.7560000000000002, 2.757, -2.758, 2.759, -2.7600000000000002, 2.761, -2.762, 2.763, -2.7640000000000002, 2.765, -2.766, 2.767, -2.7680000000000002, 2.769, -2.77, 2.771, -2.7720000000000002, 2.773, -2.774, 2.775, -2.7760000000000002, 2.777, -2.778, 2.779, -2.7800000000000002, 2.781, -2.782, 2.783, -2.7840000000000003, 2.785, -2.786, 2.787, -2.7880000000000003, 2.789, -2.79, 2.791, -2.7920000000000003, 2.793, -2.794, 2.795, -2.7960000000000003, 2.797, -2.798, 2.799, -2.8000000000000003, 2.801, -2.802, 2.803, -2.8040000000000003, 2.805, -2.806, 2.807, -2.8080000000000003, 2.809, 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+ const expected = [-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, -0.10200000000000001, 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+ const output = builder.clamp(input, {minValue: builder.constant(-1), maxValue: builder.constant(1)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_1.js
new file mode 100644
index 00000000..f56dc0f0
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_1.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu6_float_1 test', async function() {
+ // Converted test case (from: V1_0/relu6_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [0.0, 0.0, 0.5, 6.0];
+ const op2 = builder.clamp(op1, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_2.js
new file mode 100644
index 00000000..4af23ae4
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_clamp_converted_from_relu6_float_2.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu6_float_2 test', async function() {
+ // Converted test case (from: V1_0/relu6_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 26, 40, 2]});
+ const inputBuffer = new Float32Array([-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, 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3.8890000000000002, 0, 3.891, 0, 3.8930000000000002, 0, 3.895, 0, 3.8970000000000002, 0, 3.899, 0, 3.9010000000000002, 0, 3.903, 0, 3.9050000000000002, 0, 3.907, 0, 3.9090000000000003, 0, 3.911, 0, 3.9130000000000003, 0, 3.915, 0, 3.9170000000000003, 0, 3.919, 0, 3.9210000000000003, 0, 3.923, 0, 3.9250000000000003, 0, 3.927, 0, 3.9290000000000003, 0, 3.931, 0, 3.9330000000000003, 0, 3.935, 0, 3.9370000000000003, 0, 3.939, 0, 3.9410000000000003, 0, 3.943, 0, 3.9450000000000003, 0, 3.947, 0, 3.9490000000000003, 0, 3.951, 0, 3.9530000000000003, 0, 3.955, 0, 3.9570000000000003, 0, 3.959, 0, 3.9610000000000003, 0, 3.963, 0, 3.9650000000000003, 0, 3.967, 0, 3.969, 0, 3.971, 0, 3.973, 0, 3.975, 0, 3.977, 0, 3.979, 0, 3.981, 0, 3.983, 0, 3.985, 0, 3.987, 0, 3.989, 0, 3.991, 0, 3.993, 0, 3.995, 0, 3.997, 0, 3.999, 0, 4.001, 0, 4.003, 0, 4.005, 0, 4.007, 0, 4.009, 0, 4.011, 0, 4.013, 0, 4.015, 0, 4.017, 0, 4.019, 0, 4.021, 0, 4.023, 0, 4.025, 0, 4.027, 0, 4.029, 0, 4.031, 0, 4.033, 0, 4.035, 0, 4.037, 0, 4.039, 0, 4.041, 0, 4.043, 0, 4.045, 0, 4.047, 0, 4.049, 0, 4.051, 0, 4.053, 0, 4.055, 0, 4.057, 0, 4.059, 0, 4.061, 0, 4.063, 0, 4.065, 0, 4.067, 0, 4.069, 0, 4.071, 0, 4.073, 0, 4.075, 0, 4.077, 0, 4.079, 0, 4.081, 0, 4.083, 0, 4.085, 0, 4.087, 0, 4.089, 0, 4.091, 0, 4.093, 0, 4.095, 0, 4.097, 0, 4.099, 0, 4.101, 0, 4.103, 0, 4.105, 0, 4.107, 0, 4.109, 0, 4.111, 0, 4.113, 0, 4.115, 0, 4.117, 0, 4.119, 0, 4.121, 0, 4.123, 0, 4.125, 0, 4.127, 0, 4.1290000000000004, 0, 4.131, 0, 4.133, 0, 4.135, 0, 4.1370000000000005, 0, 4.139, 0, 4.141, 0, 4.143, 0, 4.1450000000000005, 0, 4.147, 0, 4.149, 0, 4.151, 0, 4.1530000000000005, 0, 4.155, 0, 4.157, 0, 4.159];
+ const output = builder.clamp(input, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_1.js
new file mode 100644
index 00000000..875f3a73
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_1.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_1 test', async function() {
+ // Converted test case (from: V1_0/concat_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 3]});
+ const op2Buffer = new Float32Array([7.0, 8.0, 9.0, 10.0, 11.0, 12.0]);
+ const axis0 = 0;
+ const expected = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0];
+ const result = builder.concat([op1, op2], axis0);
+ const model = builder.createModel({result});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.result.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_2.js
new file mode 100644
index 00000000..12520ff4
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_2.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_2 test', async function() {
+ // Converted test case (from: V1_0/concat_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [52, 230]});
+ const input1Buffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 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9975, 9976, 9977, 9978, 9979, 9980, 9981, 9982, 9983, 9984, 9985, 9986, 9987, 9988, 9989, 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999, 10000, 10001, 10002, 10003, 10004, 10005, 10006, 10007, 10008, 10009, 10010, 10011, 10012, 10013, 10014, 10015, 10016, 10017, 10018, 10019, 10020, 10021, 10022, 10023, 10024, 10025, 10026, 10027, 10028, 10029, 10030, 10031, 10032, 10033, 10034, 10035, 10036, 10037, 10038, 10039, 10040, 10041, 10042, 10043, 10044, 10045, 10046, 10047, 10048, 10049, 10050, 10051, 10052, 10053, 10054, 10055, 10056, 10057, 10058, 10059, 10060, 10061, 10062, 10063, 10064, 10065, 10066, 10067, 10068, 10069, 10070, 10071, 10072, 10073, 10074, 10075, 10076, 10077, 10078, 10079, 10080, 10081, 10082, 10083, 10084, 10085, 10086, 10087, 10088, 10089, 10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099, 10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109, 10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119, 10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129, 10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139, 10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149, 10150, 10151, 10152, 10153, 10154, 10155, 10156, 10157, 10158, 10159, 10160, 10161, 10162, 10163, 10164, 10165, 10166, 10167, 10168, 10169, 10170, 10171, 10172, 10173, 10174, 10175, 10176, 10177, 10178, 10179, 10180, 10181, 10182, 10183, 10184, 10185, 10186, 10187, 10188, 10189, 10190, 10191, 10192, 10193, 10194, 10195, 10196, 10197, 10198, 10199, 10200, 10201, 10202, 10203, 10204, 10205, 10206, 10207, 10208, 10209, 10210, 10211, 10212, 10213, 10214, 10215, 10216, 10217, 10218, 10219, 10220, 10221, 10222, 10223, 10224, 10225, 10226, 10227, 10228, 10229, 10230, 10231, 10232, 10233, 10234, 10235, 10236, 10237, 10238, 10239, 10240, 10241, 10242, 10243, 10244, 10245, 10246, 10247, 10248, 10249, 10250, 10251, 10252, 10253, 10254, 10255, 10256, 10257, 10258, 10259, 10260, 10261, 10262, 10263, 10264, 10265, 10266, 10267, 10268, 10269, 10270, 10271, 10272, 10273, 10274, 10275, 10276, 10277, 10278, 10279, 10280, 10281, 10282, 10283, 10284, 10285, 10286, 10287, 10288, 10289, 10290, 10291, 10292, 10293, 10294, 10295, 10296, 10297, 10298, 10299, 10300, 10301, 10302, 10303, 10304, 10305, 10306, 10307, 10308, 10309, 10310, 10311, 10312, 10313, 10314, 10315, 10316, 10317, 10318, 10319, 10320, 10321, 10322, 10323, 10324, 10325, 10326, 10327, 10328, 10329, 10330, 10331, 10332, 10333, 10334, 10335, 10336, 10337, 10338, 10339, 10340, 10341, 10342, 10343, 10344, 10345, 10346, 10347, 10348, 10349, 10350, 10351, 10352, 10353, 10354, 10355, 10356, 10357, 10358, 10359, 10360, 10361, 10362, 10363, 10364, 10365, 10366, 10367, 10368, 10369, 10370, 10371, 10372, 10373, 10374, 10375, 10376, 10377, 10378, 10379, 10380, 10381, 10382, 10383, 10384, 10385, 10386, 10387, 10388, 10389, 10390, 10391, 10392, 10393, 10394, 10395, 10396, 10397, 10398, 10399, 10400, 10401, 10402, 10403, 10404, 10405, 10406, 10407, 10408, 10409, 10410, 10411, 10412, 10413, 10414, 10415, 10416, 10417, 10418, 10419, 10420, 10421, 10422, 10423, 10424, 10425, 10426, 10427, 10428, 10429, 10430, 10431, 10432, 10433, 10434, 10435, 10436, 10437, 10438, 10439, 10440, 10441, 10442, 10443, 10444, 10445, 10446, 10447, 10448, 10449, 10450, 10451, 10452, 10453, 10454, 10455, 10456, 10457, 10458, 10459, 10460, 10461, 10462, 10463, 10464, 10465, 10466, 10467, 10468, 10469, 10470, 10471, 10472, 10473, 10474, 10475, 10476, 10477, 10478, 10479, 10480, 10481, 10482, 10483, 10484, 10485, 10486, 10487, 10488, 10489, 10490, 10491, 10492, 10493, 10494, 10495, 10496, 10497, 10498, 10499, 10500, 10501, 10502, 10503, 10504, 10505, 10506, 10507, 10508, 10509, 10510, 10511, 10512, 10513, 10514, 10515, 10516, 10517, 10518, 10519, 10520, 10521, 10522, 10523, 10524, 10525, 10526, 10527, 10528, 10529, 10530, 10531, 10532, 10533, 10534, 10535, 10536, 10537, 10538, 10539, 10540, 10541, 10542, 10543, 10544, 10545, 10546, 10547, 10548, 10549, 10550, 10551, 10552, 10553, 10554, 10555, 10556, 10557, 10558, 10559, 10560, 10561, 10562, 10563, 10564, 10565, 10566, 10567, 10568, 10569, 10570, 10571, 10572, 10573, 10574, 10575, 10576, 10577, 10578, 10579, 10580, 10581, 10582, 10583, 10584, 10585, 10586, 10587, 10588, 10589, 10590, 10591, 10592, 10593, 10594, 10595, 10596, 10597, 10598, 10599, 10600, 10601, 10602, 10603, 10604, 10605, 10606, 10607, 10608, 10609, 10610, 10611, 10612, 10613, 10614, 10615, 10616, 10617, 10618, 10619, 10620, 10621, 10622, 10623, 10624, 10625, 10626, 10627, 10628, 10629, 10630, 10631, 10632, 10633, 10634, 10635, 10636, 10637, 10638, 10639, 10640, 10641, 10642, 10643, 10644, 10645, 10646, 10647, 10648, 10649, 10650, 10651, 10652, 10653, 10654, 10655, 10656, 10657, 10658, 10659, 10660, 10661, 10662, 10663, 10664, 10665, 10666, 10667, 10668, 10669, 10670, 10671, 10672, 10673, 10674, 10675, 10676, 10677, 10678, 10679, 10680, 10681, 10682, 10683, 10684, 10685, 10686, 10687, 10688, 10689, 10690, 10691, 10692, 10693, 10694, 10695, 10696, 10697, 10698, 10699, 10700, 10701, 10702, 10703, 10704, 10705, 10706, 10707, 10708, 10709, 10710, 10711, 10712, 10713, 10714, 10715, 10716, 10717, 10718, 10719, 10720, 10721, 10722, 10723, 10724, 10725, 10726, 10727, 10728, 10729, 10730, 10731, 10732, 10733, 10734, 10735, 10736, 10737, 10738, 10739, 10740, 10741, 10742, 10743, 10744, 10745, 10746, 10747, 10748, 10749, 10750, 10751, 10752, 10753, 10754, 10755, 10756, 10757, 10758, 10759, 10760, 10761, 10762, 10763, 10764, 10765, 10766, 10767, 10768, 10769, 10770, 10771, 10772, 10773, 10774, 10775, 10776, 10777, 10778, 10779, 10780, 10781, 10782, 10783, 10784, 10785, 10786, 10787, 10788, 10789, 10790, 10791, 10792, 10793, 10794, 10795, 10796, 10797, 10798, 10799, 10800, 10801, 10802, 10803, 10804, 10805, 10806, 10807, 10808, 10809, 10810, 10811, 10812, 10813, 10814, 10815, 10816, 10817, 10818, 10819, 10820, 10821, 10822, 10823, 10824, 10825, 10826, 10827, 10828, 10829, 10830, 10831, 10832, 10833, 10834, 10835, 10836, 10837, 10838, 10839, 10840, 10841, 10842, 10843, 10844, 10845, 10846, 10847, 10848, 10849, 10850, 10851, 10852, 10853, 10854, 10855, 10856, 10857, 10858, 10859, 10860, 10861, 10862, 10863, 10864, 10865, 10866, 10867, 10868, 10869, 10870, 10871, 10872, 10873, 10874, 10875, 10876, 10877, 10878, 10879, 10880, 10881, 10882, 10883, 10884, 10885, 10886, 10887, 10888, 10889, 10890, 10891, 10892, 10893, 10894, 10895, 10896, 10897, 10898, 10899, 10900, 10901, 10902, 10903, 10904, 10905, 10906, 10907, 10908, 10909, 10910, 10911, 10912, 10913, 10914, 10915, 10916, 10917, 10918, 10919, 10920, 10921, 10922, 10923, 10924, 10925, 10926, 10927, 10928, 10929, 10930, 10931, 10932, 10933, 10934, 10935, 10936, 10937, 10938, 10939, 10940, 10941, 10942, 10943, 10944, 10945, 10946, 10947, 10948, 10949, 10950, 10951, 10952, 10953, 10954, 10955, 10956, 10957, 10958, 10959, 10960, 10961, 10962, 10963, 10964, 10965, 10966, 10967, 10968, 10969, 10970, 10971, 10972, 10973, 10974, 10975, 10976, 10977, 10978, 10979, 10980, 10981, 10982, 10983, 10984, 10985, 10986, 10987, 10988, 10989, 10990, 10991, 10992, 10993, 10994, 10995, 10996, 10997, 10998, 10999, 11000, 11001, 11002, 11003, 11004, 11005, 11006, 11007, 11008, 11009, 11010, 11011, 11012, 11013, 11014, 11015, 11016, 11017, 11018, 11019, 11020, 11021, 11022, 11023, 11024, 11025, 11026, 11027, 11028, 11029, 11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039, 11040, 11041, 11042, 11043, 11044, 11045, 11046, 11047, 11048, 11049, 11050, 11051, 11052, 11053, 11054, 11055, 11056, 11057, 11058, 11059, 11060, 11061, 11062, 11063, 11064, 11065, 11066, 11067, 11068, 11069, 11070, 11071, 11072, 11073, 11074, 11075, 11076, 11077, 11078, 11079, 11080, 11081, 11082, 11083, 11084, 11085, 11086, 11087, 11088, 11089, 11090, 11091, 11092, 11093, 11094, 11095, 11096, 11097, 11098, 11099, 11100, 11101, 11102, 11103, 11104, 11105, 11106, 11107, 11108, 11109, 11110, 11111, 11112, 11113, 11114, 11115, 11116, 11117, 11118, 11119, 11120, 11121, 11122, 11123, 11124, 11125, 11126, 11127, 11128, 11129, 11130, 11131, 11132, 11133, 11134, 11135, 11136, 11137, 11138, 11139, 11140, 11141, 11142, 11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151, 11152, 11153, 11154, 11155, 11156, 11157, 11158, 11159, 11160, 11161, 11162, 11163, 11164, 11165, 11166, 11167, 11168, 11169, 11170, 11171, 11172, 11173, 11174, 11175, 11176, 11177, 11178, 11179, 11180, 11181, 11182, 11183, 11184, 11185, 11186, 11187, 11188, 11189, 11190, 11191, 11192, 11193, 11194, 11195, 11196, 11197, 11198, 11199, 11200, 11201, 11202, 11203, 11204, 11205, 11206, 11207, 11208, 11209, 11210, 11211, 11212, 11213, 11214, 11215, 11216, 11217, 11218, 11219, 11220, 11221, 11222, 11223, 11224, 11225, 11226, 11227, 11228, 11229, 11230, 11231, 11232, 11233, 11234, 11235, 11236, 11237, 11238, 11239, 11240, 11241, 11242, 11243, 11244, 11245, 11246, 11247, 11248, 11249, 11250, 11251, 11252, 11253, 11254, 11255, 11256, 11257, 11258, 11259, 11260, 11261, 11262, 11263, 11264, 11265, 11266, 11267, 11268, 11269, 11270, 11271, 11272, 11273, 11274, 11275, 11276, 11277, 11278, 11279, 11280, 11281, 11282, 11283, 11284, 11285, 11286, 11287, 11288, 11289, 11290, 11291, 11292, 11293, 11294, 11295, 11296, 11297, 11298, 11299, 11300, 11301, 11302, 11303, 11304, 11305, 11306, 11307, 11308, 11309, 11310, 11311, 11312, 11313, 11314, 11315, 11316, 11317, 11318, 11319, 11320, 11321, 11322, 11323, 11324, 11325, 11326, 11327, 11328, 11329, 11330, 11331, 11332, 11333, 11334, 11335, 11336, 11337, 11338, 11339, 11340, 11341, 11342, 11343, 11344, 11345, 11346, 11347, 11348, 11349, 11350, 11351, 11352, 11353, 11354, 11355, 11356, 11357, 11358, 11359, 11360, 11361, 11362, 11363, 11364, 11365, 11366, 11367, 11368, 11369, 11370, 11371, 11372, 11373, 11374, 11375, 11376, 11377, 11378, 11379, 11380, 11381, 11382, 11383, 11384, 11385, 11386, 11387, 11388, 11389, 11390, 11391, 11392, 11393, 11394, 11395, 11396, 11397, 11398, 11399, 11400, 11401, 11402, 11403, 11404, 11405, 11406, 11407, 11408, 11409, 11410, 11411, 11412, 11413, 11414, 11415, 11416, 11417, 11418, 11419, 11420, 11421, 11422, 11423, 11424, 11425, 11426, 11427, 11428, 11429, 11430, 11431, 11432, 11433, 11434, 11435, 11436, 11437, 11438, 11439, 11440, 11441, 11442, 11443, 11444, 11445, 11446, 11447, 11448, 11449, 11450, 11451, 11452, 11453, 11454, 11455, 11456, 11457, 11458, 11459, 11460, 11461, 11462, 11463, 11464, 11465, 11466, 11467, 11468, 11469, 11470, 11471, 11472, 11473, 11474, 11475, 11476, 11477, 11478, 11479, 11480, 11481, 11482, 11483, 11484, 11485, 11486, 11487, 11488, 11489, 11490, 11491, 11492, 11493, 11494, 11495, 11496, 11497, 11498, 11499, 11500, 11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 11516, 11517, 11518, 11519, 11520, 11521, 11522, 11523, 11524, 11525, 11526, 11527, 11528, 11529, 11530, 11531, 11532, 11533, 11534, 11535, 11536, 11537, 11538, 11539, 11540, 11541, 11542, 11543, 11544, 11545, 11546, 11547, 11548, 11549, 11550, 11551, 11552, 11553, 11554, 11555, 11556, 11557, 11558, 11559, 11560, 11561, 11562, 11563, 11564, 11565, 11566, 11567, 11568, 11569, 11570, 11571, 11572, 11573, 11574, 11575, 11576, 11577, 11578, 11579, 11580, 11581, 11582, 11583, 11584, 11585, 11586, 11587, 11588, 11589, 11590, 11591, 11592, 11593, 11594, 11595, 11596, 11597, 11598, 11599, 11600, 11601, 11602, 11603, 11604, 11605, 11606, 11607, 11608, 11609, 11610, 11611, 11612, 11613, 11614, 11615, 11616, 11617, 11618, 11619, 11620, 11621, 11622, 11623, 11624, 11625, 11626, 11627, 11628, 11629, 11630, 11631, 11632, 11633, 11634, 11635, 11636, 11637, 11638, 11639, 11640, 11641, 11642, 11643, 11644, 11645, 11646, 11647, 11648, 11649, 11650, 11651, 11652, 11653, 11654, 11655, 11656, 11657, 11658, 11659, 11660, 11661, 11662, 11663, 11664, 11665, 11666, 11667, 11668, 11669, 11670, 11671, 11672, 11673, 11674, 11675, 11676, 11677, 11678, 11679, 11680, 11681, 11682, 11683, 11684, 11685, 11686, 11687, 11688, 11689, 11690, 11691, 11692, 11693, 11694, 11695, 11696, 11697, 11698, 11699, 11700, 11701, 11702, 11703, 11704, 11705, 11706, 11707, 11708, 11709, 11710, 11711, 11712, 11713, 11714, 11715, 11716, 11717, 11718, 11719, 11720, 11721, 11722, 11723, 11724, 11725, 11726, 11727, 11728, 11729, 11730, 11731, 11732, 11733, 11734, 11735, 11736, 11737, 11738, 11739, 11740, 11741, 11742, 11743, 11744, 11745, 11746, 11747, 11748, 11749, 11750, 11751, 11752, 11753, 11754, 11755, 11756, 11757, 11758, 11759, 11760, 11761, 11762, 11763, 11764, 11765, 11766, 11767, 11768, 11769, 11770, 11771, 11772, 11773, 11774, 11775, 11776, 11777, 11778, 11779, 11780, 11781, 11782, 11783, 11784, 11785, 11786, 11787, 11788, 11789, 11790, 11791, 11792, 11793, 11794, 11795, 11796, 11797, 11798, 11799, 11800, 11801, 11802, 11803, 11804, 11805, 11806, 11807, 11808, 11809, 11810, 11811, 11812, 11813, 11814, 11815, 11816, 11817, 11818, 11819, 11820, 11821, 11822, 11823, 11824, 11825, 11826, 11827, 11828, 11829, 11830, 11831, 11832, 11833, 11834, 11835, 11836, 11837, 11838, 11839, 11840, 11841, 11842, 11843, 11844, 11845, 11846, 11847, 11848, 11849, 11850, 11851, 11852, 11853, 11854, 11855, 11856, 11857, 11858, 11859, 11860, 11861, 11862, 11863, 11864, 11865, 11866, 11867, 11868, 11869, 11870, 11871, 11872, 11873, 11874, 11875, 11876, 11877, 11878, 11879, 11880, 11881, 11882, 11883, 11884, 11885, 11886, 11887, 11888, 11889, 11890, 11891, 11892, 11893, 11894, 11895, 11896, 11897, 11898, 11899, 11900, 11901, 11902, 11903, 11904, 11905, 11906, 11907, 11908, 11909, 11910, 11911, 11912, 11913, 11914, 11915, 11916, 11917, 11918, 11919, 11920, 11921, 11922, 11923, 11924, 11925, 11926, 11927, 11928, 11929, 11930, 11931, 11932, 11933, 11934, 11935, 11936, 11937, 11938, 11939, 11940, 11941, 11942, 11943, 11944, 11945, 11946, 11947, 11948, 11949, 11950, 11951, 11952, 11953, 11954, 11955, 11956, 11957, 11958, 11959]);
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [40, 230]});
+ const input2Buffer = new Float32Array([11960, 11961, 11962, 11963, 11964, 11965, 11966, 11967, 11968, 11969, 11970, 11971, 11972, 11973, 11974, 11975, 11976, 11977, 11978, 11979, 11980, 11981, 11982, 11983, 11984, 11985, 11986, 11987, 11988, 11989, 11990, 11991, 11992, 11993, 11994, 11995, 11996, 11997, 11998, 11999, 12000, 12001, 12002, 12003, 12004, 12005, 12006, 12007, 12008, 12009, 12010, 12011, 12012, 12013, 12014, 12015, 12016, 12017, 12018, 12019, 12020, 12021, 12022, 12023, 12024, 12025, 12026, 12027, 12028, 12029, 12030, 12031, 12032, 12033, 12034, 12035, 12036, 12037, 12038, 12039, 12040, 12041, 12042, 12043, 12044, 12045, 12046, 12047, 12048, 12049, 12050, 12051, 12052, 12053, 12054, 12055, 12056, 12057, 12058, 12059, 12060, 12061, 12062, 12063, 12064, 12065, 12066, 12067, 12068, 12069, 12070, 12071, 12072, 12073, 12074, 12075, 12076, 12077, 12078, 12079, 12080, 12081, 12082, 12083, 12084, 12085, 12086, 12087, 12088, 12089, 12090, 12091, 12092, 12093, 12094, 12095, 12096, 12097, 12098, 12099, 12100, 12101, 12102, 12103, 12104, 12105, 12106, 12107, 12108, 12109, 12110, 12111, 12112, 12113, 12114, 12115, 12116, 12117, 12118, 12119, 12120, 12121, 12122, 12123, 12124, 12125, 12126, 12127, 12128, 12129, 12130, 12131, 12132, 12133, 12134, 12135, 12136, 12137, 12138, 12139, 12140, 12141, 12142, 12143, 12144, 12145, 12146, 12147, 12148, 12149, 12150, 12151, 12152, 12153, 12154, 12155, 12156, 12157, 12158, 12159, 12160, 12161, 12162, 12163, 12164, 12165, 12166, 12167, 12168, 12169, 12170, 12171, 12172, 12173, 12174, 12175, 12176, 12177, 12178, 12179, 12180, 12181, 12182, 12183, 12184, 12185, 12186, 12187, 12188, 12189, 12190, 12191, 12192, 12193, 12194, 12195, 12196, 12197, 12198, 12199, 12200, 12201, 12202, 12203, 12204, 12205, 12206, 12207, 12208, 12209, 12210, 12211, 12212, 12213, 12214, 12215, 12216, 12217, 12218, 12219, 12220, 12221, 12222, 12223, 12224, 12225, 12226, 12227, 12228, 12229, 12230, 12231, 12232, 12233, 12234, 12235, 12236, 12237, 12238, 12239, 12240, 12241, 12242, 12243, 12244, 12245, 12246, 12247, 12248, 12249, 12250, 12251, 12252, 12253, 12254, 12255, 12256, 12257, 12258, 12259, 12260, 12261, 12262, 12263, 12264, 12265, 12266, 12267, 12268, 12269, 12270, 12271, 12272, 12273, 12274, 12275, 12276, 12277, 12278, 12279, 12280, 12281, 12282, 12283, 12284, 12285, 12286, 12287, 12288, 12289, 12290, 12291, 12292, 12293, 12294, 12295, 12296, 12297, 12298, 12299, 12300, 12301, 12302, 12303, 12304, 12305, 12306, 12307, 12308, 12309, 12310, 12311, 12312, 12313, 12314, 12315, 12316, 12317, 12318, 12319, 12320, 12321, 12322, 12323, 12324, 12325, 12326, 12327, 12328, 12329, 12330, 12331, 12332, 12333, 12334, 12335, 12336, 12337, 12338, 12339, 12340, 12341, 12342, 12343, 12344, 12345, 12346, 12347, 12348, 12349, 12350, 12351, 12352, 12353, 12354, 12355, 12356, 12357, 12358, 12359, 12360, 12361, 12362, 12363, 12364, 12365, 12366, 12367, 12368, 12369, 12370, 12371, 12372, 12373, 12374, 12375, 12376, 12377, 12378, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390, 12391, 12392, 12393, 12394, 12395, 12396, 12397, 12398, 12399, 12400, 12401, 12402, 12403, 12404, 12405, 12406, 12407, 12408, 12409, 12410, 12411, 12412, 12413, 12414, 12415, 12416, 12417, 12418, 12419, 12420, 12421, 12422, 12423, 12424, 12425, 12426, 12427, 12428, 12429, 12430, 12431, 12432, 12433, 12434, 12435, 12436, 12437, 12438, 12439, 12440, 12441, 12442, 12443, 12444, 12445, 12446, 12447, 12448, 12449, 12450, 12451, 12452, 12453, 12454, 12455, 12456, 12457, 12458, 12459, 12460, 12461, 12462, 12463, 12464, 12465, 12466, 12467, 12468, 12469, 12470, 12471, 12472, 12473, 12474, 12475, 12476, 12477, 12478, 12479, 12480, 12481, 12482, 12483, 12484, 12485, 12486, 12487, 12488, 12489, 12490, 12491, 12492, 12493, 12494, 12495, 12496, 12497, 12498, 12499, 12500, 12501, 12502, 12503, 12504, 12505, 12506, 12507, 12508, 12509, 12510, 12511, 12512, 12513, 12514, 12515, 12516, 12517, 12518, 12519, 12520, 12521, 12522, 12523, 12524, 12525, 12526, 12527, 12528, 12529, 12530, 12531, 12532, 12533, 12534, 12535, 12536, 12537, 12538, 12539, 12540, 12541, 12542, 12543, 12544, 12545, 12546, 12547, 12548, 12549, 12550, 12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560, 12561, 12562, 12563, 12564, 12565, 12566, 12567, 12568, 12569, 12570, 12571, 12572, 12573, 12574, 12575, 12576, 12577, 12578, 12579, 12580, 12581, 12582, 12583, 12584, 12585, 12586, 12587, 12588, 12589, 12590, 12591, 12592, 12593, 12594, 12595, 12596, 12597, 12598, 12599, 12600, 12601, 12602, 12603, 12604, 12605, 12606, 12607, 12608, 12609, 12610, 12611, 12612, 12613, 12614, 12615, 12616, 12617, 12618, 12619, 12620, 12621, 12622, 12623, 12624, 12625, 12626, 12627, 12628, 12629, 12630, 12631, 12632, 12633, 12634, 12635, 12636, 12637, 12638, 12639, 12640, 12641, 12642, 12643, 12644, 12645, 12646, 12647, 12648, 12649, 12650, 12651, 12652, 12653, 12654, 12655, 12656, 12657, 12658, 12659, 12660, 12661, 12662, 12663, 12664, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12675, 12676, 12677, 12678, 12679, 12680, 12681, 12682, 12683, 12684, 12685, 12686, 12687, 12688, 12689, 12690, 12691, 12692, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, 12720, 12721, 12722, 12723, 12724, 12725, 12726, 12727, 12728, 12729, 12730, 12731, 12732, 12733, 12734, 12735, 12736, 12737, 12738, 12739, 12740, 12741, 12742, 12743, 12744, 12745, 12746, 12747, 12748, 12749, 12750, 12751, 12752, 12753, 12754, 12755, 12756, 12757, 12758, 12759, 12760, 12761, 12762, 12763, 12764, 12765, 12766, 12767, 12768, 12769, 12770, 12771, 12772, 12773, 12774, 12775, 12776, 12777, 12778, 12779, 12780, 12781, 12782, 12783, 12784, 12785, 12786, 12787, 12788, 12789, 12790, 12791, 12792, 12793, 12794, 12795, 12796, 12797, 12798, 12799, 12800, 12801, 12802, 12803, 12804, 12805, 12806, 12807, 12808, 12809, 12810, 12811, 12812, 12813, 12814, 12815, 12816, 12817, 12818, 12819, 12820, 12821, 12822, 12823, 12824, 12825, 12826, 12827, 12828, 12829, 12830, 12831, 12832, 12833, 12834, 12835, 12836, 12837, 12838, 12839, 12840, 12841, 12842, 12843, 12844, 12845, 12846, 12847, 12848, 12849, 12850, 12851, 12852, 12853, 12854, 12855, 12856, 12857, 12858, 12859, 12860, 12861, 12862, 12863, 12864, 12865, 12866, 12867, 12868, 12869, 12870, 12871, 12872, 12873, 12874, 12875, 12876, 12877, 12878, 12879, 12880, 12881, 12882, 12883, 12884, 12885, 12886, 12887, 12888, 12889, 12890, 12891, 12892, 12893, 12894, 12895, 12896, 12897, 12898, 12899, 12900, 12901, 12902, 12903, 12904, 12905, 12906, 12907, 12908, 12909, 12910, 12911, 12912, 12913, 12914, 12915, 12916, 12917, 12918, 12919, 12920, 12921, 12922, 12923, 12924, 12925, 12926, 12927, 12928, 12929, 12930, 12931, 12932, 12933, 12934, 12935, 12936, 12937, 12938, 12939, 12940, 12941, 12942, 12943, 12944, 12945, 12946, 12947, 12948, 12949, 12950, 12951, 12952, 12953, 12954, 12955, 12956, 12957, 12958, 12959, 12960, 12961, 12962, 12963, 12964, 12965, 12966, 12967, 12968, 12969, 12970, 12971, 12972, 12973, 12974, 12975, 12976, 12977, 12978, 12979, 12980, 12981, 12982, 12983, 12984, 12985, 12986, 12987, 12988, 12989, 12990, 12991, 12992, 12993, 12994, 12995, 12996, 12997, 12998, 12999, 13000, 13001, 13002, 13003, 13004, 13005, 13006, 13007, 13008, 13009, 13010, 13011, 13012, 13013, 13014, 13015, 13016, 13017, 13018, 13019, 13020, 13021, 13022, 13023, 13024, 13025, 13026, 13027, 13028, 13029, 13030, 13031, 13032, 13033, 13034, 13035, 13036, 13037, 13038, 13039, 13040, 13041, 13042, 13043, 13044, 13045, 13046, 13047, 13048, 13049, 13050, 13051, 13052, 13053, 13054, 13055, 13056, 13057, 13058, 13059, 13060, 13061, 13062, 13063, 13064, 13065, 13066, 13067, 13068, 13069, 13070, 13071, 13072, 13073, 13074, 13075, 13076, 13077, 13078, 13079, 13080, 13081, 13082, 13083, 13084, 13085, 13086, 13087, 13088, 13089, 13090, 13091, 13092, 13093, 13094, 13095, 13096, 13097, 13098, 13099, 13100, 13101, 13102, 13103, 13104, 13105, 13106, 13107, 13108, 13109, 13110, 13111, 13112, 13113, 13114, 13115, 13116, 13117, 13118, 13119, 13120, 13121, 13122, 13123, 13124, 13125, 13126, 13127, 13128, 13129, 13130, 13131, 13132, 13133, 13134, 13135, 13136, 13137, 13138, 13139, 13140, 13141, 13142, 13143, 13144, 13145, 13146, 13147, 13148, 13149, 13150, 13151, 13152, 13153, 13154, 13155, 13156, 13157, 13158, 13159, 13160, 13161, 13162, 13163, 13164, 13165, 13166, 13167, 13168, 13169, 13170, 13171, 13172, 13173, 13174, 13175, 13176, 13177, 13178, 13179, 13180, 13181, 13182, 13183, 13184, 13185, 13186, 13187, 13188, 13189, 13190, 13191, 13192, 13193, 13194, 13195, 13196, 13197, 13198, 13199, 13200, 13201, 13202, 13203, 13204, 13205, 13206, 13207, 13208, 13209, 13210, 13211, 13212, 13213, 13214, 13215, 13216, 13217, 13218, 13219, 13220, 13221, 13222, 13223, 13224, 13225, 13226, 13227, 13228, 13229, 13230, 13231, 13232, 13233, 13234, 13235, 13236, 13237, 13238, 13239, 13240, 13241, 13242, 13243, 13244, 13245, 13246, 13247, 13248, 13249, 13250, 13251, 13252, 13253, 13254, 13255, 13256, 13257, 13258, 13259, 13260, 13261, 13262, 13263, 13264, 13265, 13266, 13267, 13268, 13269, 13270, 13271, 13272, 13273, 13274, 13275, 13276, 13277, 13278, 13279, 13280, 13281, 13282, 13283, 13284, 13285, 13286, 13287, 13288, 13289, 13290, 13291, 13292, 13293, 13294, 13295, 13296, 13297, 13298, 13299, 13300, 13301, 13302, 13303, 13304, 13305, 13306, 13307, 13308, 13309, 13310, 13311, 13312, 13313, 13314, 13315, 13316, 13317, 13318, 13319, 13320, 13321, 13322, 13323, 13324, 13325, 13326, 13327, 13328, 13329, 13330, 13331, 13332, 13333, 13334, 13335, 13336, 13337, 13338, 13339, 13340, 13341, 13342, 13343, 13344, 13345, 13346, 13347, 13348, 13349, 13350, 13351, 13352, 13353, 13354, 13355, 13356, 13357, 13358, 13359, 13360, 13361, 13362, 13363, 13364, 13365, 13366, 13367, 13368, 13369, 13370, 13371, 13372, 13373, 13374, 13375, 13376, 13377, 13378, 13379, 13380, 13381, 13382, 13383, 13384, 13385, 13386, 13387, 13388, 13389, 13390, 13391, 13392, 13393, 13394, 13395, 13396, 13397, 13398, 13399, 13400, 13401, 13402, 13403, 13404, 13405, 13406, 13407, 13408, 13409, 13410, 13411, 13412, 13413, 13414, 13415, 13416, 13417, 13418, 13419, 13420, 13421, 13422, 13423, 13424, 13425, 13426, 13427, 13428, 13429, 13430, 13431, 13432, 13433, 13434, 13435, 13436, 13437, 13438, 13439, 13440, 13441, 13442, 13443, 13444, 13445, 13446, 13447, 13448, 13449, 13450, 13451, 13452, 13453, 13454, 13455, 13456, 13457, 13458, 13459, 13460, 13461, 13462, 13463, 13464, 13465, 13466, 13467, 13468, 13469, 13470, 13471, 13472, 13473, 13474, 13475, 13476, 13477, 13478, 13479, 13480, 13481, 13482, 13483, 13484, 13485, 13486, 13487, 13488, 13489, 13490, 13491, 13492, 13493, 13494, 13495, 13496, 13497, 13498, 13499, 13500, 13501, 13502, 13503, 13504, 13505, 13506, 13507, 13508, 13509, 13510, 13511, 13512, 13513, 13514, 13515, 13516, 13517, 13518, 13519, 13520, 13521, 13522, 13523, 13524, 13525, 13526, 13527, 13528, 13529, 13530, 13531, 13532, 13533, 13534, 13535, 13536, 13537, 13538, 13539, 13540, 13541, 13542, 13543, 13544, 13545, 13546, 13547, 13548, 13549, 13550, 13551, 13552, 13553, 13554, 13555, 13556, 13557, 13558, 13559, 13560, 13561, 13562, 13563, 13564, 13565, 13566, 13567, 13568, 13569, 13570, 13571, 13572, 13573, 13574, 13575, 13576, 13577, 13578, 13579, 13580, 13581, 13582, 13583, 13584, 13585, 13586, 13587, 13588, 13589, 13590, 13591, 13592, 13593, 13594, 13595, 13596, 13597, 13598, 13599, 13600, 13601, 13602, 13603, 13604, 13605, 13606, 13607, 13608, 13609, 13610, 13611, 13612, 13613, 13614, 13615, 13616, 13617, 13618, 13619, 13620, 13621, 13622, 13623, 13624, 13625, 13626, 13627, 13628, 13629, 13630, 13631, 13632, 13633, 13634, 13635, 13636, 13637, 13638, 13639, 13640, 13641, 13642, 13643, 13644, 13645, 13646, 13647, 13648, 13649, 13650, 13651, 13652, 13653, 13654, 13655, 13656, 13657, 13658, 13659, 13660, 13661, 13662, 13663, 13664, 13665, 13666, 13667, 13668, 13669, 13670, 13671, 13672, 13673, 13674, 13675, 13676, 13677, 13678, 13679, 13680, 13681, 13682, 13683, 13684, 13685, 13686, 13687, 13688, 13689, 13690, 13691, 13692, 13693, 13694, 13695, 13696, 13697, 13698, 13699, 13700, 13701, 13702, 13703, 13704, 13705, 13706, 13707, 13708, 13709, 13710, 13711, 13712, 13713, 13714, 13715, 13716, 13717, 13718, 13719, 13720, 13721, 13722, 13723, 13724, 13725, 13726, 13727, 13728, 13729, 13730, 13731, 13732, 13733, 13734, 13735, 13736, 13737, 13738, 13739, 13740, 13741, 13742, 13743, 13744, 13745, 13746, 13747, 13748, 13749, 13750, 13751, 13752, 13753, 13754, 13755, 13756, 13757, 13758, 13759, 13760, 13761, 13762, 13763, 13764, 13765, 13766, 13767, 13768, 13769, 13770, 13771, 13772, 13773, 13774, 13775, 13776, 13777, 13778, 13779, 13780, 13781, 13782, 13783, 13784, 13785, 13786, 13787, 13788, 13789, 13790, 13791, 13792, 13793, 13794, 13795, 13796, 13797, 13798, 13799, 13800, 13801, 13802, 13803, 13804, 13805, 13806, 13807, 13808, 13809, 13810, 13811, 13812, 13813, 13814, 13815, 13816, 13817, 13818, 13819, 13820, 13821, 13822, 13823, 13824, 13825, 13826, 13827, 13828, 13829, 13830, 13831, 13832, 13833, 13834, 13835, 13836, 13837, 13838, 13839, 13840, 13841, 13842, 13843, 13844, 13845, 13846, 13847, 13848, 13849, 13850, 13851, 13852, 13853, 13854, 13855, 13856, 13857, 13858, 13859, 13860, 13861, 13862, 13863, 13864, 13865, 13866, 13867, 13868, 13869, 13870, 13871, 13872, 13873, 13874, 13875, 13876, 13877, 13878, 13879, 13880, 13881, 13882, 13883, 13884, 13885, 13886, 13887, 13888, 13889, 13890, 13891, 13892, 13893, 13894, 13895, 13896, 13897, 13898, 13899, 13900, 13901, 13902, 13903, 13904, 13905, 13906, 13907, 13908, 13909, 13910, 13911, 13912, 13913, 13914, 13915, 13916, 13917, 13918, 13919, 13920, 13921, 13922, 13923, 13924, 13925, 13926, 13927, 13928, 13929, 13930, 13931, 13932, 13933, 13934, 13935, 13936, 13937, 13938, 13939, 13940, 13941, 13942, 13943, 13944, 13945, 13946, 13947, 13948, 13949, 13950, 13951, 13952, 13953, 13954, 13955, 13956, 13957, 13958, 13959, 13960, 13961, 13962, 13963, 13964, 13965, 13966, 13967, 13968, 13969, 13970, 13971, 13972, 13973, 13974, 13975, 13976, 13977, 13978, 13979, 13980, 13981, 13982, 13983, 13984, 13985, 13986, 13987, 13988, 13989, 13990, 13991, 13992, 13993, 13994, 13995, 13996, 13997, 13998, 13999, 14000, 14001, 14002, 14003, 14004, 14005, 14006, 14007, 14008, 14009, 14010, 14011, 14012, 14013, 14014, 14015, 14016, 14017, 14018, 14019, 14020, 14021, 14022, 14023, 14024, 14025, 14026, 14027, 14028, 14029, 14030, 14031, 14032, 14033, 14034, 14035, 14036, 14037, 14038, 14039, 14040, 14041, 14042, 14043, 14044, 14045, 14046, 14047, 14048, 14049, 14050, 14051, 14052, 14053, 14054, 14055, 14056, 14057, 14058, 14059, 14060, 14061, 14062, 14063, 14064, 14065, 14066, 14067, 14068, 14069, 14070, 14071, 14072, 14073, 14074, 14075, 14076, 14077, 14078, 14079, 14080, 14081, 14082, 14083, 14084, 14085, 14086, 14087, 14088, 14089, 14090, 14091, 14092, 14093, 14094, 14095, 14096, 14097, 14098, 14099, 14100, 14101, 14102, 14103, 14104, 14105, 14106, 14107, 14108, 14109, 14110, 14111, 14112, 14113, 14114, 14115, 14116, 14117, 14118, 14119, 14120, 14121, 14122, 14123, 14124, 14125, 14126, 14127, 14128, 14129, 14130, 14131, 14132, 14133, 14134, 14135, 14136, 14137, 14138, 14139, 14140, 14141, 14142, 14143, 14144, 14145, 14146, 14147, 14148, 14149, 14150, 14151, 14152, 14153, 14154, 14155, 14156, 14157, 14158, 14159, 14160, 14161, 14162, 14163, 14164, 14165, 14166, 14167, 14168, 14169, 14170, 14171, 14172, 14173, 14174, 14175, 14176, 14177, 14178, 14179, 14180, 14181, 14182, 14183, 14184, 14185, 14186, 14187, 14188, 14189, 14190, 14191, 14192, 14193, 14194, 14195, 14196, 14197, 14198, 14199, 14200, 14201, 14202, 14203, 14204, 14205, 14206, 14207, 14208, 14209, 14210, 14211, 14212, 14213, 14214, 14215, 14216, 14217, 14218, 14219, 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, 14229, 14230, 14231, 14232, 14233, 14234, 14235, 14236, 14237, 14238, 14239, 14240, 14241, 14242, 14243, 14244, 14245, 14246, 14247, 14248, 14249, 14250, 14251, 14252, 14253, 14254, 14255, 14256, 14257, 14258, 14259, 14260, 14261, 14262, 14263, 14264, 14265, 14266, 14267, 14268, 14269, 14270, 14271, 14272, 14273, 14274, 14275, 14276, 14277, 14278, 14279, 14280, 14281, 14282, 14283, 14284, 14285, 14286, 14287, 14288, 14289, 14290, 14291, 14292, 14293, 14294, 14295, 14296, 14297, 14298, 14299, 14300, 14301, 14302, 14303, 14304, 14305, 14306, 14307, 14308, 14309, 14310, 14311, 14312, 14313, 14314, 14315, 14316, 14317, 14318, 14319, 14320, 14321, 14322, 14323, 14324, 14325, 14326, 14327, 14328, 14329, 14330, 14331, 14332, 14333, 14334, 14335, 14336, 14337, 14338, 14339, 14340, 14341, 14342, 14343, 14344, 14345, 14346, 14347, 14348, 14349, 14350, 14351, 14352, 14353, 14354, 14355, 14356, 14357, 14358, 14359, 14360, 14361, 14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 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19533, 19534, 19535, 19536, 19537, 19538, 19539, 19540, 19541, 19542, 19543, 19544, 19545, 19546, 19547, 19548, 19549, 19550, 19551, 19552, 19553, 19554, 19555, 19556, 19557, 19558, 19559, 19560, 19561, 19562, 19563, 19564, 19565, 19566, 19567, 19568, 19569, 19570, 19571, 19572, 19573, 19574, 19575, 19576, 19577, 19578, 19579, 19580, 19581, 19582, 19583, 19584, 19585, 19586, 19587, 19588, 19589, 19590, 19591, 19592, 19593, 19594, 19595, 19596, 19597, 19598, 19599, 19600, 19601, 19602, 19603, 19604, 19605, 19606, 19607, 19608, 19609, 19610, 19611, 19612, 19613, 19614, 19615, 19616, 19617, 19618, 19619, 19620, 19621, 19622, 19623, 19624, 19625, 19626, 19627, 19628, 19629, 19630, 19631, 19632, 19633, 19634, 19635, 19636, 19637, 19638, 19639, 19640, 19641, 19642, 19643, 19644, 19645, 19646, 19647, 19648, 19649, 19650, 19651, 19652, 19653, 19654, 19655, 19656, 19657, 19658, 19659, 19660, 19661, 19662, 19663, 19664, 19665, 19666, 19667, 19668, 19669, 19670, 19671, 19672, 19673, 19674, 19675, 19676, 19677, 19678, 19679, 19680, 19681, 19682, 19683, 19684, 19685, 19686, 19687, 19688, 19689, 19690, 19691, 19692, 19693, 19694, 19695, 19696, 19697, 19698, 19699, 19700, 19701, 19702, 19703, 19704, 19705, 19706, 19707, 19708, 19709, 19710, 19711, 19712, 19713, 19714, 19715, 19716, 19717, 19718, 19719, 19720, 19721, 19722, 19723, 19724, 19725, 19726, 19727, 19728, 19729, 19730, 19731, 19732, 19733, 19734, 19735, 19736, 19737, 19738, 19739, 19740, 19741, 19742, 19743, 19744, 19745, 19746, 19747, 19748, 19749, 19750, 19751, 19752, 19753, 19754, 19755, 19756, 19757, 19758, 19759, 19760, 19761, 19762, 19763, 19764, 19765, 19766, 19767, 19768, 19769, 19770, 19771, 19772, 19773, 19774, 19775, 19776, 19777, 19778, 19779, 19780, 19781, 19782, 19783, 19784, 19785, 19786, 19787, 19788, 19789, 19790, 19791, 19792, 19793, 19794, 19795, 19796, 19797, 19798, 19799, 19800, 19801, 19802, 19803, 19804, 19805, 19806, 19807, 19808, 19809, 19810, 19811, 19812, 19813, 19814, 19815, 19816, 19817, 19818, 19819, 19820, 19821, 19822, 19823, 19824, 19825, 19826, 19827, 19828, 19829, 19830, 19831, 19832, 19833, 19834, 19835, 19836, 19837, 19838, 19839, 19840, 19841, 19842, 19843, 19844, 19845, 19846, 19847, 19848, 19849, 19850, 19851, 19852, 19853, 19854, 19855, 19856, 19857, 19858, 19859, 19860, 19861, 19862, 19863, 19864, 19865, 19866, 19867, 19868, 19869, 19870, 19871, 19872, 19873, 19874, 19875, 19876, 19877, 19878, 19879, 19880, 19881, 19882, 19883, 19884, 19885, 19886, 19887, 19888, 19889, 19890, 19891, 19892, 19893, 19894, 19895, 19896, 19897, 19898, 19899, 19900, 19901, 19902, 19903, 19904, 19905, 19906, 19907, 19908, 19909, 19910, 19911, 19912, 19913, 19914, 19915, 19916, 19917, 19918, 19919, 19920, 19921, 19922, 19923, 19924, 19925, 19926, 19927, 19928, 19929, 19930, 19931, 19932, 19933, 19934, 19935, 19936, 19937, 19938, 19939, 19940, 19941, 19942, 19943, 19944, 19945, 19946, 19947, 19948, 19949, 19950, 19951, 19952, 19953, 19954, 19955, 19956, 19957, 19958, 19959, 19960, 19961, 19962, 19963, 19964, 19965, 19966, 19967, 19968, 19969, 19970, 19971, 19972, 19973, 19974, 19975, 19976, 19977, 19978, 19979, 19980, 19981, 19982, 19983, 19984, 19985, 19986, 19987, 19988, 19989, 19990, 19991, 19992, 19993, 19994, 19995, 19996, 19997, 19998, 19999, 20000, 20001, 20002, 20003, 20004, 20005, 20006, 20007, 20008, 20009, 20010, 20011, 20012, 20013, 20014, 20015, 20016, 20017, 20018, 20019, 20020, 20021, 20022, 20023, 20024, 20025, 20026, 20027, 20028, 20029, 20030, 20031, 20032, 20033, 20034, 20035, 20036, 20037, 20038, 20039, 20040, 20041, 20042, 20043, 20044, 20045, 20046, 20047, 20048, 20049, 20050, 20051, 20052, 20053, 20054, 20055, 20056, 20057, 20058, 20059, 20060, 20061, 20062, 20063, 20064, 20065, 20066, 20067, 20068, 20069, 20070, 20071, 20072, 20073, 20074, 20075, 20076, 20077, 20078, 20079, 20080, 20081, 20082, 20083, 20084, 20085, 20086, 20087, 20088, 20089, 20090, 20091, 20092, 20093, 20094, 20095, 20096, 20097, 20098, 20099, 20100, 20101, 20102, 20103, 20104, 20105, 20106, 20107, 20108, 20109, 20110, 20111, 20112, 20113, 20114, 20115, 20116, 20117, 20118, 20119, 20120, 20121, 20122, 20123, 20124, 20125, 20126, 20127, 20128, 20129, 20130, 20131, 20132, 20133, 20134, 20135, 20136, 20137, 20138, 20139, 20140, 20141, 20142, 20143, 20144, 20145, 20146, 20147, 20148, 20149, 20150, 20151, 20152, 20153, 20154, 20155, 20156, 20157, 20158, 20159, 20160, 20161, 20162, 20163, 20164, 20165, 20166, 20167, 20168, 20169, 20170, 20171, 20172, 20173, 20174, 20175, 20176, 20177, 20178, 20179, 20180, 20181, 20182, 20183, 20184, 20185, 20186, 20187, 20188, 20189, 20190, 20191, 20192, 20193, 20194, 20195, 20196, 20197, 20198, 20199, 20200, 20201, 20202, 20203, 20204, 20205, 20206, 20207, 20208, 20209, 20210, 20211, 20212, 20213, 20214, 20215, 20216, 20217, 20218, 20219, 20220, 20221, 20222, 20223, 20224, 20225, 20226, 20227, 20228, 20229, 20230, 20231, 20232, 20233, 20234, 20235, 20236, 20237, 20238, 20239, 20240, 20241, 20242, 20243, 20244, 20245, 20246, 20247, 20248, 20249, 20250, 20251, 20252, 20253, 20254, 20255, 20256, 20257, 20258, 20259, 20260, 20261, 20262, 20263, 20264, 20265, 20266, 20267, 20268, 20269, 20270, 20271, 20272, 20273, 20274, 20275, 20276, 20277, 20278, 20279, 20280, 20281, 20282, 20283, 20284, 20285, 20286, 20287, 20288, 20289, 20290, 20291, 20292, 20293, 20294, 20295, 20296, 20297, 20298, 20299, 20300, 20301, 20302, 20303, 20304, 20305, 20306, 20307, 20308, 20309, 20310, 20311, 20312, 20313, 20314, 20315, 20316, 20317, 20318, 20319, 20320, 20321, 20322, 20323, 20324, 20325, 20326, 20327, 20328, 20329, 20330, 20331, 20332, 20333, 20334, 20335, 20336, 20337, 20338, 20339, 20340, 20341, 20342, 20343, 20344, 20345, 20346, 20347, 20348, 20349, 20350, 20351, 20352, 20353, 20354, 20355, 20356, 20357, 20358, 20359, 20360, 20361, 20362, 20363, 20364, 20365, 20366, 20367, 20368, 20369, 20370, 20371, 20372, 20373, 20374, 20375, 20376, 20377, 20378, 20379, 20380, 20381, 20382, 20383, 20384, 20385, 20386, 20387, 20388, 20389, 20390, 20391, 20392, 20393, 20394, 20395, 20396, 20397, 20398, 20399, 20400, 20401, 20402, 20403, 20404, 20405, 20406, 20407, 20408, 20409, 20410, 20411, 20412, 20413, 20414, 20415, 20416, 20417, 20418, 20419, 20420, 20421, 20422, 20423, 20424, 20425, 20426, 20427, 20428, 20429, 20430, 20431, 20432, 20433, 20434, 20435, 20436, 20437, 20438, 20439, 20440, 20441, 20442, 20443, 20444, 20445, 20446, 20447, 20448, 20449, 20450, 20451, 20452, 20453, 20454, 20455, 20456, 20457, 20458, 20459, 20460, 20461, 20462, 20463, 20464, 20465, 20466, 20467, 20468, 20469, 20470, 20471, 20472, 20473, 20474, 20475, 20476, 20477, 20478, 20479, 20480, 20481, 20482, 20483, 20484, 20485, 20486, 20487, 20488, 20489, 20490, 20491, 20492, 20493, 20494, 20495, 20496, 20497, 20498, 20499, 20500, 20501, 20502, 20503, 20504, 20505, 20506, 20507, 20508, 20509, 20510, 20511, 20512, 20513, 20514, 20515, 20516, 20517, 20518, 20519, 20520, 20521, 20522, 20523, 20524, 20525, 20526, 20527, 20528, 20529, 20530, 20531, 20532, 20533, 20534, 20535, 20536, 20537, 20538, 20539, 20540, 20541, 20542, 20543, 20544, 20545, 20546, 20547, 20548, 20549, 20550, 20551, 20552, 20553, 20554, 20555, 20556, 20557, 20558, 20559, 20560, 20561, 20562, 20563, 20564, 20565, 20566, 20567, 20568, 20569, 20570, 20571, 20572, 20573, 20574, 20575, 20576, 20577, 20578, 20579, 20580, 20581, 20582, 20583, 20584, 20585, 20586, 20587, 20588, 20589, 20590, 20591, 20592, 20593, 20594, 20595, 20596, 20597, 20598, 20599, 20600, 20601, 20602, 20603, 20604, 20605, 20606, 20607, 20608, 20609, 20610, 20611, 20612, 20613, 20614, 20615, 20616, 20617, 20618, 20619, 20620, 20621, 20622, 20623, 20624, 20625, 20626, 20627, 20628, 20629, 20630, 20631, 20632, 20633, 20634, 20635, 20636, 20637, 20638, 20639, 20640, 20641, 20642, 20643, 20644, 20645, 20646, 20647, 20648, 20649, 20650, 20651, 20652, 20653, 20654, 20655, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20682, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 20700, 20701, 20702, 20703, 20704, 20705, 20706, 20707, 20708, 20709, 20710, 20711, 20712, 20713, 20714, 20715, 20716, 20717, 20718, 20719, 20720, 20721, 20722, 20723, 20724, 20725, 20726, 20727, 20728, 20729, 20730, 20731, 20732, 20733, 20734, 20735, 20736, 20737, 20738, 20739, 20740, 20741, 20742, 20743, 20744, 20745, 20746, 20747, 20748, 20749, 20750, 20751, 20752, 20753, 20754, 20755, 20756, 20757, 20758, 20759, 20760, 20761, 20762, 20763, 20764, 20765, 20766, 20767, 20768, 20769, 20770, 20771, 20772, 20773, 20774, 20775, 20776, 20777, 20778, 20779, 20780, 20781, 20782, 20783, 20784, 20785, 20786, 20787, 20788, 20789, 20790, 20791, 20792, 20793, 20794, 20795, 20796, 20797, 20798, 20799, 20800, 20801, 20802, 20803, 20804, 20805, 20806, 20807, 20808, 20809, 20810, 20811, 20812, 20813, 20814, 20815, 20816, 20817, 20818, 20819, 20820, 20821, 20822, 20823, 20824, 20825, 20826, 20827, 20828, 20829, 20830, 20831, 20832, 20833, 20834, 20835, 20836, 20837, 20838, 20839, 20840, 20841, 20842, 20843, 20844, 20845, 20846, 20847, 20848, 20849, 20850, 20851, 20852, 20853, 20854, 20855, 20856, 20857, 20858, 20859, 20860, 20861, 20862, 20863, 20864, 20865, 20866, 20867, 20868, 20869, 20870, 20871, 20872, 20873, 20874, 20875, 20876, 20877, 20878, 20879, 20880, 20881, 20882, 20883, 20884, 20885, 20886, 20887, 20888, 20889, 20890, 20891, 20892, 20893, 20894, 20895, 20896, 20897, 20898, 20899, 20900, 20901, 20902, 20903, 20904, 20905, 20906, 20907, 20908, 20909, 20910, 20911, 20912, 20913, 20914, 20915, 20916, 20917, 20918, 20919, 20920, 20921, 20922, 20923, 20924, 20925, 20926, 20927, 20928, 20929, 20930, 20931, 20932, 20933, 20934, 20935, 20936, 20937, 20938, 20939, 20940, 20941, 20942, 20943, 20944, 20945, 20946, 20947, 20948, 20949, 20950, 20951, 20952, 20953, 20954, 20955, 20956, 20957, 20958, 20959, 20960, 20961, 20962, 20963, 20964, 20965, 20966, 20967, 20968, 20969, 20970, 20971, 20972, 20973, 20974, 20975, 20976, 20977, 20978, 20979, 20980, 20981, 20982, 20983, 20984, 20985, 20986, 20987, 20988, 20989, 20990, 20991, 20992, 20993, 20994, 20995, 20996, 20997, 20998, 20999, 21000, 21001, 21002, 21003, 21004, 21005, 21006, 21007, 21008, 21009, 21010, 21011, 21012, 21013, 21014, 21015, 21016, 21017, 21018, 21019, 21020, 21021, 21022, 21023, 21024, 21025, 21026, 21027, 21028, 21029, 21030, 21031, 21032, 21033, 21034, 21035, 21036, 21037, 21038, 21039, 21040, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21048, 21049, 21050, 21051, 21052, 21053, 21054, 21055, 21056, 21057, 21058, 21059, 21060, 21061, 21062, 21063, 21064, 21065, 21066, 21067, 21068, 21069, 21070, 21071, 21072, 21073, 21074, 21075, 21076, 21077, 21078, 21079, 21080, 21081, 21082, 21083, 21084, 21085, 21086, 21087, 21088, 21089, 21090, 21091, 21092, 21093, 21094, 21095, 21096, 21097, 21098, 21099, 21100, 21101, 21102, 21103, 21104, 21105, 21106, 21107, 21108, 21109, 21110, 21111, 21112, 21113, 21114, 21115, 21116, 21117, 21118, 21119, 21120, 21121, 21122, 21123, 21124, 21125, 21126, 21127, 21128, 21129, 21130, 21131, 21132, 21133, 21134, 21135, 21136, 21137, 21138, 21139, 21140, 21141, 21142, 21143, 21144, 21145, 21146, 21147, 21148, 21149, 21150, 21151, 21152, 21153, 21154, 21155, 21156, 21157, 21158, 21159]);
+ const axis0 = 0;
+ const expected = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 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21136, 21137, 21138, 21139, 21140, 21141, 21142, 21143, 21144, 21145, 21146, 21147, 21148, 21149, 21150, 21151, 21152, 21153, 21154, 21155, 21156, 21157, 21158, 21159];
+ const output = builder.concat([input1, input2], axis0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}, 'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_3.js b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_3.js
new file mode 100644
index 00000000..36c6ac64
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_concat_converted_from_concat_float_3.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_3 test', async function() {
+ // Converted test case (from: V1_0/concat_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [212, 60]});
+ const input1Buffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 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12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719]);
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [212, 30]});
+ const input2Buffer = new Float32Array([0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32, -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46, -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60, -61, -62, -63, -64, -65, -66, -67, -68, -69, -70, -71, -72, -73, -74, -75, -76, -77, -78, -79, -80, -81, -82, -83, -84, -85, -86, -87, -88, -89, -90, -91, -92, -93, -94, -95, -96, -97, -98, -99, -100, -101, -102, -103, -104, -105, -106, -107, -108, -109, -110, -111, -112, -113, -114, -115, -116, -117, -118, -119, -120, -121, -122, -123, -124, -125, -126, -127, -128, -129, -130, -131, -132, -133, -134, -135, -136, -137, -138, -139, -140, -141, -142, -143, -144, -145, -146, -147, -148, -149, -150, -151, -152, -153, -154, -155, -156, -157, -158, -159, -160, -161, -162, -163, -164, -165, -166, -167, -168, -169, -170, -171, -172, -173, -174, -175, -176, -177, 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-842, -843, -844, -845, -846, -847, -848, -849, -850, -851, -852, -853, -854, -855, -856, -857, -858, -859, -860, -861, -862, -863, -864, -865, -866, -867, -868, -869, -870, -871, -872, -873, -874, -875, -876, -877, -878, -879, -880, -881, -882, -883, -884, -885, -886, -887, -888, -889, -890, -891, -892, -893, -894, -895, -896, -897, -898, -899, -900, -901, -902, -903, -904, -905, -906, -907, -908, -909, -910, -911, -912, -913, -914, -915, -916, -917, -918, -919, -920, -921, -922, -923, -924, -925, -926, -927, -928, -929, -930, -931, -932, -933, -934, -935, -936, -937, -938, -939, -940, -941, -942, -943, -944, -945, -946, -947, -948, -949, -950, -951, -952, -953, -954, -955, -956, -957, -958, -959, -960, -961, -962, -963, -964, -965, -966, -967, -968, -969, -970, -971, -972, -973, -974, -975, -976, -977, -978, -979, -980, -981, -982, -983, -984, -985, -986, -987, -988, -989, -990, -991, -992, -993, -994, -995, -996, -997, -998, -999, -1000, -1001, -1002, -1003, -1004, -1005, -1006, -1007, -1008, -1009, -1010, -1011, -1012, -1013, -1014, -1015, -1016, -1017, -1018, -1019, -1020, -1021, -1022, -1023, -1024, -1025, -1026, -1027, -1028, -1029, -1030, -1031, -1032, -1033, -1034, -1035, -1036, -1037, -1038, -1039, -1040, -1041, -1042, -1043, -1044, -1045, -1046, -1047, -1048, -1049, -1050, -1051, -1052, -1053, -1054, -1055, -1056, -1057, -1058, -1059, -1060, -1061, -1062, -1063, -1064, -1065, -1066, -1067, -1068, -1069, -1070, -1071, -1072, -1073, -1074, -1075, -1076, -1077, -1078, -1079, -1080, -1081, -1082, -1083, -1084, -1085, -1086, -1087, -1088, -1089, -1090, -1091, -1092, -1093, -1094, -1095, -1096, -1097, -1098, -1099, -1100, -1101, -1102, -1103, -1104, -1105, -1106, -1107, -1108, -1109, -1110, -1111, -1112, -1113, -1114, -1115, -1116, -1117, -1118, -1119, -1120, -1121, -1122, -1123, -1124, -1125, -1126, -1127, -1128, -1129, -1130, -1131, -1132, -1133, -1134, -1135, -1136, -1137, -1138, -1139, -1140, -1141, -1142, -1143, -1144, -1145, -1146, -1147, -1148, -1149, 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+ const axis1 = 1;
+ const expected = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, -30, -31, -32, -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46, -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 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11914, 11915, 11916, 11917, 11918, 11919, 11920, 11921, 11922, 11923, 11924, 11925, 11926, 11927, 11928, 11929, 11930, 11931, 11932, 11933, 11934, 11935, 11936, 11937, 11938, 11939, -5940, -5941, -5942, -5943, -5944, -5945, -5946, -5947, -5948, -5949, -5950, -5951, -5952, -5953, -5954, -5955, -5956, -5957, -5958, -5959, -5960, -5961, -5962, -5963, -5964, -5965, -5966, -5967, -5968, -5969, 11940, 11941, 11942, 11943, 11944, 11945, 11946, 11947, 11948, 11949, 11950, 11951, 11952, 11953, 11954, 11955, 11956, 11957, 11958, 11959, 11960, 11961, 11962, 11963, 11964, 11965, 11966, 11967, 11968, 11969, 11970, 11971, 11972, 11973, 11974, 11975, 11976, 11977, 11978, 11979, 11980, 11981, 11982, 11983, 11984, 11985, 11986, 11987, 11988, 11989, 11990, 11991, 11992, 11993, 11994, 11995, 11996, 11997, 11998, 11999, -5970, -5971, -5972, -5973, -5974, -5975, -5976, -5977, -5978, -5979, -5980, -5981, -5982, -5983, -5984, -5985, -5986, -5987, -5988, -5989, -5990, -5991, -5992, -5993, -5994, -5995, -5996, 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-6126, -6127, -6128, -6129, -6130, -6131, -6132, -6133, -6134, -6135, -6136, -6137, -6138, -6139, -6140, -6141, -6142, -6143, -6144, -6145, -6146, -6147, -6148, -6149, 12300, 12301, 12302, 12303, 12304, 12305, 12306, 12307, 12308, 12309, 12310, 12311, 12312, 12313, 12314, 12315, 12316, 12317, 12318, 12319, 12320, 12321, 12322, 12323, 12324, 12325, 12326, 12327, 12328, 12329, 12330, 12331, 12332, 12333, 12334, 12335, 12336, 12337, 12338, 12339, 12340, 12341, 12342, 12343, 12344, 12345, 12346, 12347, 12348, 12349, 12350, 12351, 12352, 12353, 12354, 12355, 12356, 12357, 12358, 12359, -6150, -6151, -6152, -6153, -6154, -6155, -6156, -6157, -6158, -6159, -6160, -6161, -6162, -6163, -6164, -6165, -6166, -6167, -6168, -6169, -6170, -6171, -6172, -6173, -6174, -6175, -6176, -6177, -6178, -6179, 12360, 12361, 12362, 12363, 12364, 12365, 12366, 12367, 12368, 12369, 12370, 12371, 12372, 12373, 12374, 12375, 12376, 12377, 12378, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390, 12391, 12392, 12393, 12394, 12395, 12396, 12397, 12398, 12399, 12400, 12401, 12402, 12403, 12404, 12405, 12406, 12407, 12408, 12409, 12410, 12411, 12412, 12413, 12414, 12415, 12416, 12417, 12418, 12419, -6180, -6181, -6182, -6183, -6184, -6185, -6186, -6187, -6188, -6189, -6190, -6191, -6192, -6193, -6194, -6195, -6196, -6197, -6198, -6199, -6200, -6201, -6202, -6203, -6204, -6205, -6206, -6207, -6208, -6209, 12420, 12421, 12422, 12423, 12424, 12425, 12426, 12427, 12428, 12429, 12430, 12431, 12432, 12433, 12434, 12435, 12436, 12437, 12438, 12439, 12440, 12441, 12442, 12443, 12444, 12445, 12446, 12447, 12448, 12449, 12450, 12451, 12452, 12453, 12454, 12455, 12456, 12457, 12458, 12459, 12460, 12461, 12462, 12463, 12464, 12465, 12466, 12467, 12468, 12469, 12470, 12471, 12472, 12473, 12474, 12475, 12476, 12477, 12478, 12479, -6210, -6211, -6212, -6213, -6214, -6215, -6216, -6217, -6218, -6219, -6220, -6221, -6222, -6223, -6224, -6225, -6226, -6227, -6228, -6229, -6230, -6231, -6232, -6233, -6234, -6235, -6236, -6237, -6238, -6239, 12480, 12481, 12482, 12483, 12484, 12485, 12486, 12487, 12488, 12489, 12490, 12491, 12492, 12493, 12494, 12495, 12496, 12497, 12498, 12499, 12500, 12501, 12502, 12503, 12504, 12505, 12506, 12507, 12508, 12509, 12510, 12511, 12512, 12513, 12514, 12515, 12516, 12517, 12518, 12519, 12520, 12521, 12522, 12523, 12524, 12525, 12526, 12527, 12528, 12529, 12530, 12531, 12532, 12533, 12534, 12535, 12536, 12537, 12538, 12539, -6240, -6241, -6242, -6243, -6244, -6245, -6246, -6247, -6248, -6249, -6250, -6251, -6252, -6253, -6254, -6255, -6256, -6257, -6258, -6259, -6260, -6261, -6262, -6263, -6264, -6265, -6266, -6267, -6268, -6269, 12540, 12541, 12542, 12543, 12544, 12545, 12546, 12547, 12548, 12549, 12550, 12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560, 12561, 12562, 12563, 12564, 12565, 12566, 12567, 12568, 12569, 12570, 12571, 12572, 12573, 12574, 12575, 12576, 12577, 12578, 12579, 12580, 12581, 12582, 12583, 12584, 12585, 12586, 12587, 12588, 12589, 12590, 12591, 12592, 12593, 12594, 12595, 12596, 12597, 12598, 12599, -6270, -6271, -6272, -6273, -6274, -6275, -6276, -6277, -6278, -6279, -6280, -6281, -6282, -6283, -6284, -6285, -6286, -6287, -6288, -6289, -6290, -6291, -6292, -6293, -6294, -6295, -6296, -6297, -6298, -6299, 12600, 12601, 12602, 12603, 12604, 12605, 12606, 12607, 12608, 12609, 12610, 12611, 12612, 12613, 12614, 12615, 12616, 12617, 12618, 12619, 12620, 12621, 12622, 12623, 12624, 12625, 12626, 12627, 12628, 12629, 12630, 12631, 12632, 12633, 12634, 12635, 12636, 12637, 12638, 12639, 12640, 12641, 12642, 12643, 12644, 12645, 12646, 12647, 12648, 12649, 12650, 12651, 12652, 12653, 12654, 12655, 12656, 12657, 12658, 12659, -6300, -6301, -6302, -6303, -6304, -6305, -6306, -6307, -6308, -6309, -6310, -6311, -6312, -6313, -6314, -6315, -6316, -6317, -6318, -6319, -6320, -6321, -6322, -6323, -6324, -6325, -6326, -6327, -6328, -6329, 12660, 12661, 12662, 12663, 12664, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12675, 12676, 12677, 12678, 12679, 12680, 12681, 12682, 12683, 12684, 12685, 12686, 12687, 12688, 12689, 12690, 12691, 12692, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, -6330, -6331, -6332, -6333, -6334, -6335, -6336, -6337, -6338, -6339, -6340, -6341, -6342, -6343, -6344, -6345, -6346, -6347, -6348, -6349, -6350, -6351, -6352, -6353, -6354, -6355, -6356, -6357, -6358, -6359];
+ const output = builder.concat([input1, input2], axis1);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}, 'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float.js
new file mode 100644
index 00000000..0fb16078
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float test', async function() {
+ // Converted test case (from: V1_0/conv_float.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels.js
new file mode 100644
index 00000000..b77baf86
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_channels test', async function() {
+ // Converted test case (from: V1_0/conv_float_channels.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op1Buffer = new Float32Array([99.0, 99.0, 99.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [297.0, 594.0, 891.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs.js
new file mode 100644
index 00000000..6145b075
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_channels_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/conv_float_channels_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op1Buffer = new Float32Array([99.0, 99.0, 99.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op2Buffer = new Float32Array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [3]});
+ const op3Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [297.0, 594.0, 891.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large.js
new file mode 100644
index 00000000..3e1f0062
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_large test', async function() {
+ // Converted test case (from: V1_0/conv_float_large.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs.js
new file mode 100644
index 00000000..41573d6e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_large_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/conv_float_large_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op2Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [3]});
+ const op3Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs.js
new file mode 100644
index 00000000..2ed4cb4e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/conv_float_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large.js
new file mode 100644
index 00000000..4e2ecf1b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large test', async function() {
+ // Converted test case (from: V1_0/depthwise_conv2d_float_large.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op1Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 2]}, new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [2]}, new Float32Array([100, 200]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 2});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2.js
new file mode 100644
index 00000000..0e9a423c
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_2 test', async function() {
+ // Converted test case (from: V1_0/depthwise_conv2d_float_large_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op1Buffer = new Float32Array([10, 21, 100, 0, 10, 22, 200, 0, 10, 23, 300, 0, 10, 24, 400, 0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 4]}, new Float32Array([0.25, 0.0, 10.0, 100.0, 0.25, 1.0, 20.0, 100.0, 0.25, 0.0, 30.0, 100.0, 0.25, 1.0, 40.0, 100.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [4]}, new Float32Array([600000, 700000, 800000, 900000]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [600010, 700046, 830000, 900000];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 4});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs.js
new file mode 100644
index 00000000..36788e71
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_2_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/depthwise_conv2d_float_large_2_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op1Buffer = new Float32Array([10, 21, 100, 0, 10, 22, 200, 0, 10, 23, 300, 0, 10, 24, 400, 0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 4]});
+ const op2Buffer = new Float32Array([0.25, 0.0, 10.0, 100.0, 0.25, 1.0, 20.0, 100.0, 0.25, 0.0, 30.0, 100.0, 0.25, 1.0, 40.0, 100.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [4]});
+ const op3Buffer = new Float32Array([600000, 700000, 800000, 900000]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [600010, 700046, 830000, 900000];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 4});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs.js
new file mode 100644
index 00000000..18cac7df
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/depthwise_conv2d_float_large_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op1Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [2]});
+ const op3Buffer = new Float32Array([100, 200]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 2});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float.js
new file mode 100644
index 00000000..bca88c2e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1]}, new Float32Array([2]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([4]));
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_3.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_3.js
new file mode 100644
index 00000000..6f6cd7c6
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_3.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_3 test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 2]});
+ const op1Buffer = new Float32Array([1, 2, 2, 1]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 1]}, new Float32Array([2, 4]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([1]));
+ const expected = [11, 9];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large.js
new file mode 100644
index 00000000..afdcd5b9
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_large test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float_large.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 5]});
+ const op1Buffer = new Float32Array([1, 10, 100, 1000, 10000]);
+ const op2 = builder.constant({type: 'float32', dimensions: [5, 1]}, new Float32Array([2, 3, 4, 5, 6]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([900000]));
+ const expected = [965432];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs.js
new file mode 100644
index 00000000..0653ff52
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_large_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float_large_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 5]});
+ const op1Buffer = new Float32Array([1, 10, 100, 1000, 10000]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [5, 1]});
+ const op2Buffer = new Float32Array([2, 3, 4, 5, 6]);
+ const b0 = builder.input('b0', {type: 'float32', dimensions: [1]});
+ const b0Buffer = new Float32Array([900000]);
+ const expected = [965432];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'b0': {buffer: b0Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs.js
new file mode 100644
index 00000000..a4081803
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1]});
+ const op2Buffer = new Float32Array([2]);
+ const b0 = builder.input('b0', {type: 'float32', dimensions: [1]});
+ const b0Buffer = new Float32Array([4]);
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'b0': {buffer: b0Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_matmul_add_relu_converted_from_fully_connected_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_relu_converted_from_fully_connected_float_2.js
new file mode 100644
index 00000000..57cbeb4f
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_matmul_add_relu_converted_from_fully_connected_float_2.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + relu converted from fully_connected_float_2 test', async function() {
+ // Converted test case (from: V1_0/fully_connected_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 8]});
+ const op1Buffer = new Float32Array([0.503691, 0.196961, 0.521017, 0.554248, 0.288678, 0.792476, 0.561653, 0.46223, 0.650736, 0.163132, 0.029658, 0.411544, 0.470539, 0.57239, 0.538755, 0.21203]);
+ const op2 = builder.constant({type: 'float32', dimensions: [8, 16]}, new Float32Array([0.091327, 0.063031, 0.109758, 0.266455, 0.096086, 0.142238, -0.104321, -0.252595, -0.304651, -0.128762, -0.339283, 0.103532, -0.139061, -0.241619, 0.265148, 0.302309, 0.103366, 0.19167, 0.008307, 0.051517, 0.131157, 0.08654, -0.176035, -0.16175, -0.070958, -0.03578, 0.333071, -0.034284, -0.048873, 0.357835, 0.141627, -0.041084, -0.316505, -0.062001, -0.062657, -0.123448, 0.031164, -0.139154, -0.208587, -0.136403, 0.054598, 0.117262, 0.180827, 0.093299, 0.067557, 0.135762, 0.02012, 0.146334, -0.08312, -0.061504, -0.060962, 0.322464, 0.100638, 0.174268, -0.001019, 0.008308, 0.147113, 0.017177, 0.287583, -0.145361, 0.139038, -0.306764, 0.083815, -0.061511, 0.149366, -0.275581, -0.049782, 0.043282, -0.312191, -0.073161, -0.162032, 0.00571, -0.139112, 0.263335, 0.06635, 0.054001, 0.324106, -0.125982, -0.124556, -0.232605, -0.196636, 0.059388, -0.106719, -0.173782, -0.080923, 0.080072, 0.080824, 0.0966, -0.072798, -0.176612, -0.197947, 0.25057, 0.227041, 0.091916, -0.100124, 0.281324, -0.123672, -0.118497, -0.319482, -0.190381, -0.101318, 0.006874, -0.025021, 0.289839, -0.163335, 0.262961, -0.114449, 0.15701, 0.037793, 0.266587, -0.048159, 0.145408, 0.0628, -0.079224, -0.10365, 0.002013, -0.116614, 0.229382, 0.07446, 0.218816, -0.167863, -0.093654, -0.236035, -0.14348, -0.225747, 0.030135, 0.181172, -0.221897]));
+ const b0 = builder.constant({type: 'float32', dimensions: [16]}, new Float32Array([-0.160594, 0.20577, -0.078307, -0.077984, 0.001937, 0.01586, 0.03681, 0.012346, 0.001028, 0.038551, 0.075415, 0.020804, 0.048478, -0.03227, 0.175688, -0.085662]));
+ const expected = [0, 0.0732134, 0, 0, 0, 0.280859, 0, 0.128927, 0, 0.0777251, 0, 0.270268, 0.271435, 0.0173503, 0.335465, 0.235562, 0, 0.0745866, 0, 0.051611, 0, 0.253876, 0, 0.0814873, 0, 0.104104, 0, 0.248529, 0.264194, 0, 0.302973, 0.166252];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.relu(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_1.js
new file mode 100644
index 00000000..a947a41a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_1.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_1 test', async function() {
+ // Converted test case (from: V1_0/max_pool_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const pad0 = 0;
+ const cons1 = 1;
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [pad0, pad0, pad0, pad0], 'strides': [cons1, cons1], 'windowDimensions': [cons1, cons1], 'layout': 'nhwc'});
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_2.js
new file mode 100644
index 00000000..4ef91093
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_2.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_2 test', async function() {
+ // Converted test case (from: V1_0/max_pool_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const i0Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const padding = 0;
+ const stride = 20;
+ const filter = 20;
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_3.js b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_3.js
new file mode 100644
index 00000000..de110a7f
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_maxPool2d_clamp_converted_from_max_pool_float_3.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_3 test', async function() {
+ // Converted test case (from: V1_0/max_pool_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const i0Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 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14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const padding = 0;
+ const stride = 20;
+ const filter = 20;
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_mul_clamp_converted_from_mul.js b/test/cts/from_nnapi/tests/V1_0/test_mul_clamp_converted_from_mul.js
new file mode 100644
index 00000000..4d480873
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_mul_clamp_converted_from_mul.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test mul + clamp converted from mul test', async function() {
+ // Converted test case (from: V1_0/mul.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2, -4, 8, -16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([32, -16, -8, 4]);
+ const expected = [64, 64, -64, -64];
+ const interOut0 = builder.mul(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_mul_relu_converted_from_mul_relu.js b/test/cts/from_nnapi/tests/V1_0/test_mul_relu_converted_from_mul_relu.js
new file mode 100644
index 00000000..26b3da24
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_mul_relu_converted_from_mul_relu.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test mul + relu converted from mul_relu test', async function() {
+ // Converted test case (from: V1_0/mul_relu.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2, -4, 8, -16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([32, -16, -8, 4]);
+ const expected = [64, 64, 0, 0];
+ const interOut0 = builder.mul(op1, op2);
+ const op3 = builder.relu(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_1.js
new file mode 100644
index 00000000..acf92b03
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_1.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test relu converted from relu_float_1 test', async function() {
+ // Converted test case (from: V1_0/relu_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [0.0, 0.0, 0.5, 10.0];
+ const op2 = builder.relu(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_2.js
new file mode 100644
index 00000000..d48cb7a0
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_relu_converted_from_relu_float_2.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test relu converted from relu_float_2 test', async function() {
+ // Converted test case (from: V1_0/relu_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 64, 40, 2]});
+ const inputBuffer = new Float32Array([-0.0, 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0, 9.0, -10.0, 11.0, -12.0, 13.0, -14.0, 15.0, -16.0, 17.0, -18.0, 19.0, -20.0, 21.0, -22.0, 23.0, -24.0, 25.0, -26.0, 27.0, -28.0, 29.0, -30.0, 31.0, -32.0, 33.0, -34.0, 35.0, -36.0, 37.0, -38.0, 39.0, -40.0, 41.0, -42.0, 43.0, -44.0, 45.0, -46.0, 47.0, -48.0, 49.0, -50.0, 51.0, -52.0, 53.0, -54.0, 55.0, -56.0, 57.0, -58.0, 59.0, -60.0, 61.0, -62.0, 63.0, -64.0, 65.0, -66.0, 67.0, -68.0, 69.0, -70.0, 71.0, -72.0, 73.0, -74.0, 75.0, -76.0, 77.0, -78.0, 79.0, -80.0, 81.0, -82.0, 83.0, -84.0, 85.0, -86.0, 87.0, -88.0, 89.0, -90.0, 91.0, -92.0, 93.0, -94.0, 95.0, -96.0, 97.0, -98.0, 99.0, -100.0, 101.0, -102.0, 103.0, -104.0, 105.0, -106.0, 107.0, -108.0, 109.0, -110.0, 111.0, -112.0, 113.0, -114.0, 115.0, -116.0, 117.0, -118.0, 119.0, -120.0, 121.0, -122.0, 123.0, -124.0, 125.0, -126.0, 127.0, -128.0, 129.0, -130.0, 131.0, -132.0, 133.0, -134.0, 135.0, -136.0, 137.0, -138.0, 139.0, -140.0, 141.0, 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+ const output = builder.relu(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape.js b/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape.js
new file mode 100644
index 00000000..85b17fca
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test reshape converted from reshape test', async function() {
+ // Converted test case (from: V1_0/reshape.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op2 = [-1];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9];
+ const op3 = builder.reshape(op1, op2);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape_weights_as_inputs.js b/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape_weights_as_inputs.js
new file mode 100644
index 00000000..2f0eeb30
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_reshape_converted_from_reshape_weights_as_inputs.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test reshape converted from reshape_weights_as_inputs test', async function() {
+ // Converted test case (from: V1_0/reshape_weights_as_inputs.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op2 = [-1];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9];
+ const op3 = builder.reshape(op1, op2);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_1.js b/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_1.js
new file mode 100644
index 00000000..98b8c5cd
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_1.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sigmoid converted from logistic_float_1 test', async function() {
+ // Converted test case (from: V1_0/logistic_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 4.0, 8.0]);
+ const expected = [0.7310585975646973, 0.8807970285415649, 0.9820137619972229, 0.9996646642684937];
+ const op3 = builder.sigmoid(op1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_2.js
new file mode 100644
index 00000000..e6baa10a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_sigmoid_converted_from_logistic_float_2.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sigmoid converted from logistic_float_2 test', async function() {
+ // Converted test case (from: V1_0/logistic_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 32, 40, 2]});
+ const inputBuffer = new Float32Array([0.0, 1.0, 510.0, 3.0, 508.0, 5.0, 506.0, 7.0, 504.0, 9.0, 502.0, 11.0, 500.0, 13.0, 498.0, 15.0, 496.0, 17.0, 494.0, 19.0, 492.0, 21.0, 490.0, 23.0, 488.0, 25.0, 486.0, 27.0, 484.0, 29.0, 482.0, 31.0, 480.0, 33.0, 478.0, 35.0, 476.0, 37.0, 474.0, 39.0, 472.0, 41.0, 470.0, 43.0, 468.0, 45.0, 466.0, 47.0, 464.0, 49.0, 462.0, 51.0, 460.0, 53.0, 458.0, 55.0, 456.0, 57.0, 454.0, 59.0, 452.0, 61.0, 450.0, 63.0, 448.0, 65.0, 446.0, 67.0, 444.0, 69.0, 442.0, 71.0, 440.0, 73.0, 438.0, 75.0, 436.0, 77.0, 434.0, 79.0, 432.0, 81.0, 430.0, 83.0, 428.0, 85.0, 426.0, 87.0, 424.0, 89.0, 422.0, 91.0, 420.0, 93.0, 418.0, 95.0, 416.0, 97.0, 414.0, 99.0, 412.0, 101.0, 410.0, 103.0, 408.0, 105.0, 406.0, 107.0, 404.0, 109.0, 402.0, 111.0, 400.0, 113.0, 398.0, 115.0, 396.0, 117.0, 394.0, 119.0, 392.0, 121.0, 390.0, 123.0, 388.0, 125.0, 386.0, 127.0, 384.0, 129.0, 382.0, 131.0, 380.0, 133.0, 378.0, 135.0, 376.0, 137.0, 374.0, 139.0, 372.0, 141.0, 370.0, 143.0, 368.0, 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0.8807970779778823, 1.0];
+ const output = builder.sigmoid(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_softmax_converted_from_softmax_float_2.js b/test/cts/from_nnapi/tests/V1_0/test_softmax_converted_from_softmax_float_2.js
new file mode 100644
index 00000000..cdc1fa20
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_softmax_converted_from_softmax_float_2.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test softmax converted from softmax_float_2 test', async function() {
+ // Converted test case (from: V1_0/softmax_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 5]});
+ const inputBuffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, -1.0, -2.0, -3.0, -4.0, -5.0]);
+ const expected = [0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647, 0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231];
+ const output = builder.softmax(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_0/test_tanh_converted_from_tanh.js b/test/cts/from_nnapi/tests/V1_0/test_tanh_converted_from_tanh.js
new file mode 100644
index 00000000..9b3728a9
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_0/test_tanh_converted_from_tanh.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test tanh converted from tanh test', async function() {
+ // Converted test case (from: V1_0/tanh.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-1, 0, 1, 10]);
+ const expected = [-0.761594156, 0, 0.761594156, 0.999999996];
+ const op2 = builder.tanh(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_add_clamp_converted_from_add_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_add_clamp_converted_from_add_relaxed.js
new file mode 100644
index 00000000..6122bfdf
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_add_clamp_converted_from_add_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test add + clamp converted from add_relaxed test', async function() {
+ // Converted test case (from: V1_1/add_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2]});
+ const op1Buffer = new Float32Array([1.0, 2.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2]});
+ const op2Buffer = new Float32Array([3.0, 4.0]);
+ const expected = [4.0, 6.0];
+ const interOut0 = builder.add(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_1_relaxed.js
new file mode 100644
index 00000000..64b7ddfd
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_1_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/avg_pool_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const pad0 = 0;
+ const cons1 = 1;
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [pad0, pad0, pad0, pad0], 'strides': [cons1, cons1], 'windowDimensions': [cons1, cons1], 'layout': 'nhwc'});
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_2_relaxed.js
new file mode 100644
index 00000000..5d7a7525
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_2_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/avg_pool_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const i0Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const padding = 30;
+ const stride = 5;
+ const filter = 35;
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_3_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_3_relaxed.js
new file mode 100644
index 00000000..24e6be9c
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_3_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_3_relaxed test', async function() {
+ // Converted test case (from: V1_1/avg_pool_float_3_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [1, 200, 180, 1]});
+ const i0Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 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+ const padding = 0;
+ const stride = 2;
+ const filter = 10;
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_4_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_4_relaxed.js
new file mode 100644
index 00000000..988bd5d2
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_averagePool2d_clamp_converted_from_avg_pool_float_4_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_float_4_relaxed test', async function() {
+ // Converted test case (from: V1_1/avg_pool_float_4_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const i0Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]);
+ const padding = 50;
+ const stride = 5;
+ const filter = 100;
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_1_relaxed.js
new file mode 100644
index 00000000..fbac316e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_1_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu1_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu1_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [-1.0, -0.5, 0.5, 1.0];
+ const op2 = builder.clamp(op1, {minValue: builder.constant(-1), maxValue: builder.constant(1)});
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_2_relaxed.js
new file mode 100644
index 00000000..3e7dc7f6
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu1_float_2_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu1_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu1_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 30, 24, 2]});
+ const inputBuffer = new Float32Array([-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, 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1.813, -1.814, 1.815, -1.816, 1.817, -1.818, 1.819, -1.82, 1.821, -1.822, 1.823, -1.824, 1.825, -1.826, 1.827, -1.828, 1.829, -1.83, 1.831, -1.832, 1.833, -1.834, 1.835, -1.836, 1.837, -1.838, 1.839, -1.84, 1.841, -1.842, 1.843, -1.844, 1.845, -1.846, 1.847, -1.848, 1.849, -1.85, 1.851, -1.852, 1.853, -1.854, 1.855, -1.856, 1.857, -1.858, 1.859, -1.86, 1.861, -1.862, 1.863, -1.864, 1.865, -1.866, 1.867, -1.868, 1.869, -1.87, 1.871, -1.872, 1.873, -1.874, 1.875, -1.8760000000000001, 1.877, -1.8780000000000001, 1.879, -1.8800000000000001, 1.881, -1.8820000000000001, 1.883, -1.8840000000000001, 1.885, -1.8860000000000001, 1.887, -1.8880000000000001, 1.889, -1.8900000000000001, 1.891, -1.8920000000000001, 1.893, -1.8940000000000001, 1.895, -1.8960000000000001, 1.897, -1.8980000000000001, 1.899, -1.9000000000000001, 1.901, -1.9020000000000001, 1.903, -1.9040000000000001, 1.905, -1.9060000000000001, 1.907, -1.9080000000000001, 1.909, -1.9100000000000001, 1.911, -1.9120000000000001, 1.913, -1.9140000000000001, 1.915, -1.9160000000000001, 1.917, -1.9180000000000001, 1.919, -1.92, 1.921, -1.922, 1.923, -1.924, 1.925, -1.926, 1.927, -1.928, 1.929, -1.93, 1.931, -1.932, 1.933, -1.934, 1.935, -1.936, 1.937, -1.938, 1.939, -1.94, 1.941, -1.942, 1.943, -1.944, 1.945, -1.946, 1.947, -1.948, 1.949, -1.95, 1.951, -1.952, 1.953, -1.954, 1.955, -1.956, 1.957, -1.958, 1.959, -1.96, 1.961, -1.962, 1.963, -1.964, 1.965, -1.966, 1.967, -1.968, 1.969, -1.97, 1.971, -1.972, 1.973, -1.974, 1.975, -1.976, 1.977, -1.978, 1.979, -1.98, 1.981, -1.982, 1.983, -1.984, 1.985, -1.986, 1.987, -1.988, 1.989, -1.99, 1.991, -1.992, 1.993, -1.994, 1.995, -1.996, 1.997, -1.998, 1.999, -2.0, 2.001, -2.0020000000000002, 2.003, -2.004, 2.005, -2.0060000000000002, 2.007, -2.008, 2.009, -2.0100000000000002, 2.011, -2.012, 2.013, -2.0140000000000002, 2.015, -2.016, 2.017, -2.0180000000000002, 2.019, -2.02, 2.021, -2.0220000000000002, 2.023, -2.024, 2.025, -2.0260000000000002, 2.027, -2.028, 2.029, -2.0300000000000002, 2.031, -2.032, 2.033, -2.0340000000000003, 2.035, -2.036, 2.037, -2.0380000000000003, 2.039, -2.04, 2.041, -2.0420000000000003, 2.043, -2.044, 2.045, -2.0460000000000003, 2.047, -2.048, 2.049, -2.05, 2.051, -2.052, 2.053, -2.054, 2.055, -2.056, 2.057, -2.058, 2.059, -2.06, 2.061, -2.062, 2.063, -2.064, 2.065, -2.066, 2.067, -2.068, 2.069, -2.07, 2.071, -2.072, 2.073, -2.074, 2.075, -2.076, 2.077, -2.078, 2.079, -2.08, 2.081, -2.082, 2.083, -2.084, 2.085, -2.086, 2.087, -2.088, 2.089, -2.09, 2.091, -2.092, 2.093, -2.094, 2.095, -2.096, 2.097, -2.098, 2.099, -2.1, 2.101, -2.102, 2.103, -2.104, 2.105, -2.106, 2.107, -2.108, 2.109, -2.11, 2.111, -2.112, 2.113, -2.114, 2.115, -2.116, 2.117, -2.118, 2.119, -2.12, 2.121, -2.122, 2.123, -2.124, 2.125, -2.126, 2.1270000000000002, -2.128, 2.129, -2.13, 2.1310000000000002, -2.132, 2.133, -2.134, 2.1350000000000002, -2.136, 2.137, -2.138, 2.1390000000000002, -2.14, 2.141, -2.142, 2.1430000000000002, -2.144, 2.145, -2.146, 2.1470000000000002, -2.148, 2.149, -2.15, 2.1510000000000002, -2.152, 2.153, -2.154, 2.1550000000000002, -2.156, 2.157, -2.158, 2.1590000000000003, -2.16, 2.161, -2.162, 2.1630000000000003, -2.164, 2.165, -2.166, 2.1670000000000003, -2.168, 2.169, -2.17, 2.1710000000000003, -2.172, 2.173, -2.174, 2.1750000000000003, -2.176, 2.177, -2.178, 2.179, -2.18, 2.181, -2.182, 2.183, -2.184, 2.185, -2.186, 2.187, -2.188, 2.189, -2.19, 2.191, -2.192, 2.193, -2.194, 2.195, -2.196, 2.197, -2.198, 2.199, -2.2, 2.201, -2.202, 2.203, -2.204, 2.205, -2.206, 2.207, -2.208, 2.209, -2.21, 2.211, -2.212, 2.213, -2.214, 2.215, -2.216, 2.217, -2.218, 2.219, -2.22, 2.221, -2.222, 2.223, -2.224, 2.225, -2.226, 2.227, -2.228, 2.229, -2.23, 2.231, -2.232, 2.233, -2.234, 2.235, -2.236, 2.237, -2.238, 2.239, -2.24, 2.241, -2.242, 2.243, -2.244, 2.245, -2.246, 2.247, -2.248, 2.249, -2.25, 2.251, -2.2520000000000002, 2.253, -2.254, 2.255, -2.2560000000000002, 2.257, -2.258, 2.259, -2.2600000000000002, 2.261, -2.262, 2.263, -2.2640000000000002, 2.265, -2.266, 2.267, -2.2680000000000002, 2.269, -2.27, 2.271, -2.2720000000000002, 2.273, -2.274, 2.275, -2.2760000000000002, 2.277, -2.278, 2.279, -2.2800000000000002, 2.281, -2.282, 2.283, -2.2840000000000003, 2.285, -2.286, 2.287, -2.2880000000000003, 2.289, -2.29, 2.291, -2.2920000000000003, 2.293, -2.294, 2.295, -2.2960000000000003, 2.297, -2.298, 2.299, -2.3000000000000003, 2.301, -2.302, 2.303, -2.3040000000000003, 2.305, -2.306, 2.307, -2.308, 2.309, -2.31, 2.311, -2.312, 2.313, -2.314, 2.315, -2.316, 2.317, -2.318, 2.319, -2.32, 2.321, -2.322, 2.323, -2.324, 2.325, -2.326, 2.327, -2.328, 2.329, -2.33, 2.331, -2.332, 2.333, -2.334, 2.335, -2.336, 2.337, -2.338, 2.339, -2.34, 2.341, -2.342, 2.343, -2.344, 2.345, -2.346, 2.347, -2.348, 2.349, -2.35, 2.351, -2.352, 2.353, -2.354, 2.355, -2.356, 2.357, -2.358, 2.359, -2.36, 2.361, -2.362, 2.363, -2.364, 2.365, -2.366, 2.367, -2.368, 2.369, -2.37, 2.371, -2.372, 2.373, -2.374, 2.375, -2.376, 2.3770000000000002, -2.378, 2.379, -2.38, 2.3810000000000002, -2.382, 2.383, -2.384, 2.3850000000000002, -2.386, 2.387, -2.388, 2.3890000000000002, -2.39, 2.391, -2.392, 2.3930000000000002, -2.394, 2.395, -2.396, 2.3970000000000002, -2.398, 2.399, -2.4, 2.4010000000000002, -2.402, 2.403, -2.404, 2.4050000000000002, -2.406, 2.407, -2.408, 2.4090000000000003, -2.41, 2.411, -2.412, 2.4130000000000003, -2.414, 2.415, -2.416, 2.4170000000000003, -2.418, 2.419, -2.42, 2.4210000000000003, -2.422, 2.423, -2.424, 2.4250000000000003, -2.426, 2.427, -2.428, 2.4290000000000003, -2.43, 2.431, -2.432, 2.433, -2.434, 2.435, -2.436, 2.437, -2.438, 2.439, -2.44, 2.441, -2.442, 2.443, -2.444, 2.445, -2.446, 2.447, -2.448, 2.449, -2.45, 2.451, -2.452, 2.453, -2.454, 2.455, -2.456, 2.457, -2.458, 2.459, -2.46, 2.461, -2.462, 2.463, -2.464, 2.465, -2.466, 2.467, -2.468, 2.469, -2.47, 2.471, -2.472, 2.473, -2.474, 2.475, -2.476, 2.477, -2.478, 2.479, -2.48, 2.481, -2.482, 2.483, -2.484, 2.485, -2.486, 2.487, -2.488, 2.489, -2.49, 2.491, -2.492, 2.493, -2.494, 2.495, -2.496, 2.497, -2.498, 2.499, -2.5, 2.501, -2.5020000000000002, 2.503, -2.504, 2.505, -2.5060000000000002, 2.507, -2.508, 2.509, -2.5100000000000002, 2.511, -2.512, 2.513, -2.5140000000000002, 2.515, -2.516, 2.517, -2.5180000000000002, 2.519, -2.52, 2.521, -2.5220000000000002, 2.523, -2.524, 2.525, -2.5260000000000002, 2.527, -2.528, 2.529, -2.5300000000000002, 2.531, -2.532, 2.533, -2.5340000000000003, 2.535, -2.536, 2.537, -2.5380000000000003, 2.539, -2.54, 2.541, -2.5420000000000003, 2.543, -2.544, 2.545, -2.5460000000000003, 2.547, -2.548, 2.549, -2.5500000000000003, 2.551, -2.552, 2.553, -2.5540000000000003, 2.555, -2.556, 2.557, -2.5580000000000003, 2.559, -2.56, 2.561, -2.562, 2.563, -2.564, 2.565, -2.566, 2.567, -2.568, 2.569, -2.57, 2.571, -2.572, 2.573, -2.574, 2.575, -2.576, 2.577, -2.578, 2.579, -2.58, 2.581, -2.582, 2.583, -2.584, 2.585, -2.586, 2.587, -2.588, 2.589, -2.59, 2.591, -2.592, 2.593, -2.594, 2.595, -2.596, 2.597, -2.598, 2.599, -2.6, 2.601, -2.602, 2.603, -2.604, 2.605, -2.606, 2.607, -2.608, 2.609, -2.61, 2.611, -2.612, 2.613, -2.614, 2.615, -2.616, 2.617, -2.618, 2.619, -2.62, 2.621, -2.622, 2.623, -2.624, 2.625, -2.626, 2.6270000000000002, -2.628, 2.629, -2.63, 2.6310000000000002, -2.632, 2.633, -2.634, 2.6350000000000002, -2.636, 2.637, -2.638, 2.6390000000000002, -2.64, 2.641, -2.642, 2.6430000000000002, -2.644, 2.645, -2.646, 2.6470000000000002, -2.648, 2.649, -2.65, 2.6510000000000002, -2.652, 2.653, -2.654, 2.6550000000000002, -2.656, 2.657, -2.658, 2.6590000000000003, -2.66, 2.661, -2.662, 2.6630000000000003, -2.664, 2.665, -2.666, 2.6670000000000003, -2.668, 2.669, -2.67, 2.6710000000000003, -2.672, 2.673, -2.674, 2.6750000000000003, -2.676, 2.677, -2.678, 2.6790000000000003, -2.68, 2.681, -2.682, 2.6830000000000003, -2.684, 2.685, -2.686, 2.6870000000000003, -2.688, 2.689, -2.69, 2.691, -2.692, 2.693, -2.694, 2.695, -2.696, 2.697, -2.698, 2.699, -2.7, 2.701, -2.702, 2.703, -2.704, 2.705, -2.706, 2.707, -2.708, 2.709, -2.71, 2.711, -2.712, 2.713, -2.714, 2.715, -2.716, 2.717, -2.718, 2.719, -2.72, 2.721, -2.722, 2.723, -2.724, 2.725, -2.726, 2.727, -2.728, 2.729, -2.73, 2.731, -2.732, 2.733, -2.734, 2.735, -2.736, 2.737, -2.738, 2.739, -2.74, 2.741, -2.742, 2.743, -2.744, 2.745, -2.746, 2.747, -2.748, 2.749, -2.75, 2.751, -2.7520000000000002, 2.753, -2.754, 2.755, -2.7560000000000002, 2.757, -2.758, 2.759, -2.7600000000000002, 2.761, -2.762, 2.763, -2.7640000000000002, 2.765, -2.766, 2.767, -2.7680000000000002, 2.769, -2.77, 2.771, -2.7720000000000002, 2.773, -2.774, 2.775, -2.7760000000000002, 2.777, -2.778, 2.779, -2.7800000000000002, 2.781, -2.782, 2.783, -2.7840000000000003, 2.785, -2.786, 2.787, -2.7880000000000003, 2.789, -2.79, 2.791, -2.7920000000000003, 2.793, -2.794, 2.795, -2.7960000000000003, 2.797, -2.798, 2.799, -2.8000000000000003, 2.801, -2.802, 2.803, -2.8040000000000003, 2.805, -2.806, 2.807, -2.8080000000000003, 2.809, 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+ const expected = [-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, -0.10200000000000001, 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+ const output = builder.clamp(input, {minValue: builder.constant(-1), maxValue: builder.constant(1)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_1_relaxed.js
new file mode 100644
index 00000000..b03582b4
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_1_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu6_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu6_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [0.0, 0.0, 0.5, 6.0];
+ const op2 = builder.clamp(op1, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_2_relaxed.js
new file mode 100644
index 00000000..cd0dc9c7
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_clamp_converted_from_relu6_float_2_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test clamp converted from relu6_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu6_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 26, 40, 2]});
+ const inputBuffer = new Float32Array([-0.0, 0.001, -0.002, 0.003, -0.004, 0.005, -0.006, 0.007, -0.008, 0.009000000000000001, -0.01, 0.011, -0.012, 0.013000000000000001, -0.014, 0.015, -0.016, 0.017, -0.018000000000000002, 0.019, -0.02, 0.021, -0.022, 0.023, -0.024, 0.025, -0.026000000000000002, 0.027, -0.028, 0.029, -0.03, 0.031, -0.032, 0.033, -0.034, 0.035, -0.036000000000000004, 0.037, -0.038, 0.039, -0.04, 0.041, -0.042, 0.043000000000000003, -0.044, 0.045, -0.046, 0.047, -0.048, 0.049, -0.05, 0.051000000000000004, -0.052000000000000005, 0.053, -0.054, 0.055, -0.056, 0.057, -0.058, 0.059000000000000004, -0.06, 0.061, -0.062, 0.063, -0.064, 0.065, -0.066, 0.067, -0.068, 0.069, -0.07, 0.07100000000000001, -0.07200000000000001, 0.073, -0.074, 0.075, -0.076, 0.077, -0.078, 0.079, -0.08, 0.081, -0.082, 0.083, -0.084, 0.085, -0.08600000000000001, 0.08700000000000001, -0.088, 0.089, -0.09, 0.091, -0.092, 0.093, -0.094, 0.095, -0.096, 0.097, -0.098, 0.099, -0.1, 0.101, 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4.037, 0, 4.039, 0, 4.041, 0, 4.043, 0, 4.045, 0, 4.047, 0, 4.049, 0, 4.051, 0, 4.053, 0, 4.055, 0, 4.057, 0, 4.059, 0, 4.061, 0, 4.063, 0, 4.065, 0, 4.067, 0, 4.069, 0, 4.071, 0, 4.073, 0, 4.075, 0, 4.077, 0, 4.079, 0, 4.081, 0, 4.083, 0, 4.085, 0, 4.087, 0, 4.089, 0, 4.091, 0, 4.093, 0, 4.095, 0, 4.097, 0, 4.099, 0, 4.101, 0, 4.103, 0, 4.105, 0, 4.107, 0, 4.109, 0, 4.111, 0, 4.113, 0, 4.115, 0, 4.117, 0, 4.119, 0, 4.121, 0, 4.123, 0, 4.125, 0, 4.127, 0, 4.1290000000000004, 0, 4.131, 0, 4.133, 0, 4.135, 0, 4.1370000000000005, 0, 4.139, 0, 4.141, 0, 4.143, 0, 4.1450000000000005, 0, 4.147, 0, 4.149, 0, 4.151, 0, 4.1530000000000005, 0, 4.155, 0, 4.157, 0, 4.159];
+ const output = builder.clamp(input, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_1_relaxed.js
new file mode 100644
index 00000000..41273fc3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_1_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/concat_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 3]});
+ const op2Buffer = new Float32Array([7.0, 8.0, 9.0, 10.0, 11.0, 12.0]);
+ const axis0 = 0;
+ const expected = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0];
+ const result = builder.concat([op1, op2], axis0);
+ const model = builder.createModel({result});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.result.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_2_relaxed.js
new file mode 100644
index 00000000..ac836f6f
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_2_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/concat_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [52, 230]});
+ const input1Buffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 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9975, 9976, 9977, 9978, 9979, 9980, 9981, 9982, 9983, 9984, 9985, 9986, 9987, 9988, 9989, 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998, 9999, 10000, 10001, 10002, 10003, 10004, 10005, 10006, 10007, 10008, 10009, 10010, 10011, 10012, 10013, 10014, 10015, 10016, 10017, 10018, 10019, 10020, 10021, 10022, 10023, 10024, 10025, 10026, 10027, 10028, 10029, 10030, 10031, 10032, 10033, 10034, 10035, 10036, 10037, 10038, 10039, 10040, 10041, 10042, 10043, 10044, 10045, 10046, 10047, 10048, 10049, 10050, 10051, 10052, 10053, 10054, 10055, 10056, 10057, 10058, 10059, 10060, 10061, 10062, 10063, 10064, 10065, 10066, 10067, 10068, 10069, 10070, 10071, 10072, 10073, 10074, 10075, 10076, 10077, 10078, 10079, 10080, 10081, 10082, 10083, 10084, 10085, 10086, 10087, 10088, 10089, 10090, 10091, 10092, 10093, 10094, 10095, 10096, 10097, 10098, 10099, 10100, 10101, 10102, 10103, 10104, 10105, 10106, 10107, 10108, 10109, 10110, 10111, 10112, 10113, 10114, 10115, 10116, 10117, 10118, 10119, 10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129, 10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139, 10140, 10141, 10142, 10143, 10144, 10145, 10146, 10147, 10148, 10149, 10150, 10151, 10152, 10153, 10154, 10155, 10156, 10157, 10158, 10159, 10160, 10161, 10162, 10163, 10164, 10165, 10166, 10167, 10168, 10169, 10170, 10171, 10172, 10173, 10174, 10175, 10176, 10177, 10178, 10179, 10180, 10181, 10182, 10183, 10184, 10185, 10186, 10187, 10188, 10189, 10190, 10191, 10192, 10193, 10194, 10195, 10196, 10197, 10198, 10199, 10200, 10201, 10202, 10203, 10204, 10205, 10206, 10207, 10208, 10209, 10210, 10211, 10212, 10213, 10214, 10215, 10216, 10217, 10218, 10219, 10220, 10221, 10222, 10223, 10224, 10225, 10226, 10227, 10228, 10229, 10230, 10231, 10232, 10233, 10234, 10235, 10236, 10237, 10238, 10239, 10240, 10241, 10242, 10243, 10244, 10245, 10246, 10247, 10248, 10249, 10250, 10251, 10252, 10253, 10254, 10255, 10256, 10257, 10258, 10259, 10260, 10261, 10262, 10263, 10264, 10265, 10266, 10267, 10268, 10269, 10270, 10271, 10272, 10273, 10274, 10275, 10276, 10277, 10278, 10279, 10280, 10281, 10282, 10283, 10284, 10285, 10286, 10287, 10288, 10289, 10290, 10291, 10292, 10293, 10294, 10295, 10296, 10297, 10298, 10299, 10300, 10301, 10302, 10303, 10304, 10305, 10306, 10307, 10308, 10309, 10310, 10311, 10312, 10313, 10314, 10315, 10316, 10317, 10318, 10319, 10320, 10321, 10322, 10323, 10324, 10325, 10326, 10327, 10328, 10329, 10330, 10331, 10332, 10333, 10334, 10335, 10336, 10337, 10338, 10339, 10340, 10341, 10342, 10343, 10344, 10345, 10346, 10347, 10348, 10349, 10350, 10351, 10352, 10353, 10354, 10355, 10356, 10357, 10358, 10359, 10360, 10361, 10362, 10363, 10364, 10365, 10366, 10367, 10368, 10369, 10370, 10371, 10372, 10373, 10374, 10375, 10376, 10377, 10378, 10379, 10380, 10381, 10382, 10383, 10384, 10385, 10386, 10387, 10388, 10389, 10390, 10391, 10392, 10393, 10394, 10395, 10396, 10397, 10398, 10399, 10400, 10401, 10402, 10403, 10404, 10405, 10406, 10407, 10408, 10409, 10410, 10411, 10412, 10413, 10414, 10415, 10416, 10417, 10418, 10419, 10420, 10421, 10422, 10423, 10424, 10425, 10426, 10427, 10428, 10429, 10430, 10431, 10432, 10433, 10434, 10435, 10436, 10437, 10438, 10439, 10440, 10441, 10442, 10443, 10444, 10445, 10446, 10447, 10448, 10449, 10450, 10451, 10452, 10453, 10454, 10455, 10456, 10457, 10458, 10459, 10460, 10461, 10462, 10463, 10464, 10465, 10466, 10467, 10468, 10469, 10470, 10471, 10472, 10473, 10474, 10475, 10476, 10477, 10478, 10479, 10480, 10481, 10482, 10483, 10484, 10485, 10486, 10487, 10488, 10489, 10490, 10491, 10492, 10493, 10494, 10495, 10496, 10497, 10498, 10499, 10500, 10501, 10502, 10503, 10504, 10505, 10506, 10507, 10508, 10509, 10510, 10511, 10512, 10513, 10514, 10515, 10516, 10517, 10518, 10519, 10520, 10521, 10522, 10523, 10524, 10525, 10526, 10527, 10528, 10529, 10530, 10531, 10532, 10533, 10534, 10535, 10536, 10537, 10538, 10539, 10540, 10541, 10542, 10543, 10544, 10545, 10546, 10547, 10548, 10549, 10550, 10551, 10552, 10553, 10554, 10555, 10556, 10557, 10558, 10559, 10560, 10561, 10562, 10563, 10564, 10565, 10566, 10567, 10568, 10569, 10570, 10571, 10572, 10573, 10574, 10575, 10576, 10577, 10578, 10579, 10580, 10581, 10582, 10583, 10584, 10585, 10586, 10587, 10588, 10589, 10590, 10591, 10592, 10593, 10594, 10595, 10596, 10597, 10598, 10599, 10600, 10601, 10602, 10603, 10604, 10605, 10606, 10607, 10608, 10609, 10610, 10611, 10612, 10613, 10614, 10615, 10616, 10617, 10618, 10619, 10620, 10621, 10622, 10623, 10624, 10625, 10626, 10627, 10628, 10629, 10630, 10631, 10632, 10633, 10634, 10635, 10636, 10637, 10638, 10639, 10640, 10641, 10642, 10643, 10644, 10645, 10646, 10647, 10648, 10649, 10650, 10651, 10652, 10653, 10654, 10655, 10656, 10657, 10658, 10659, 10660, 10661, 10662, 10663, 10664, 10665, 10666, 10667, 10668, 10669, 10670, 10671, 10672, 10673, 10674, 10675, 10676, 10677, 10678, 10679, 10680, 10681, 10682, 10683, 10684, 10685, 10686, 10687, 10688, 10689, 10690, 10691, 10692, 10693, 10694, 10695, 10696, 10697, 10698, 10699, 10700, 10701, 10702, 10703, 10704, 10705, 10706, 10707, 10708, 10709, 10710, 10711, 10712, 10713, 10714, 10715, 10716, 10717, 10718, 10719, 10720, 10721, 10722, 10723, 10724, 10725, 10726, 10727, 10728, 10729, 10730, 10731, 10732, 10733, 10734, 10735, 10736, 10737, 10738, 10739, 10740, 10741, 10742, 10743, 10744, 10745, 10746, 10747, 10748, 10749, 10750, 10751, 10752, 10753, 10754, 10755, 10756, 10757, 10758, 10759, 10760, 10761, 10762, 10763, 10764, 10765, 10766, 10767, 10768, 10769, 10770, 10771, 10772, 10773, 10774, 10775, 10776, 10777, 10778, 10779, 10780, 10781, 10782, 10783, 10784, 10785, 10786, 10787, 10788, 10789, 10790, 10791, 10792, 10793, 10794, 10795, 10796, 10797, 10798, 10799, 10800, 10801, 10802, 10803, 10804, 10805, 10806, 10807, 10808, 10809, 10810, 10811, 10812, 10813, 10814, 10815, 10816, 10817, 10818, 10819, 10820, 10821, 10822, 10823, 10824, 10825, 10826, 10827, 10828, 10829, 10830, 10831, 10832, 10833, 10834, 10835, 10836, 10837, 10838, 10839, 10840, 10841, 10842, 10843, 10844, 10845, 10846, 10847, 10848, 10849, 10850, 10851, 10852, 10853, 10854, 10855, 10856, 10857, 10858, 10859, 10860, 10861, 10862, 10863, 10864, 10865, 10866, 10867, 10868, 10869, 10870, 10871, 10872, 10873, 10874, 10875, 10876, 10877, 10878, 10879, 10880, 10881, 10882, 10883, 10884, 10885, 10886, 10887, 10888, 10889, 10890, 10891, 10892, 10893, 10894, 10895, 10896, 10897, 10898, 10899, 10900, 10901, 10902, 10903, 10904, 10905, 10906, 10907, 10908, 10909, 10910, 10911, 10912, 10913, 10914, 10915, 10916, 10917, 10918, 10919, 10920, 10921, 10922, 10923, 10924, 10925, 10926, 10927, 10928, 10929, 10930, 10931, 10932, 10933, 10934, 10935, 10936, 10937, 10938, 10939, 10940, 10941, 10942, 10943, 10944, 10945, 10946, 10947, 10948, 10949, 10950, 10951, 10952, 10953, 10954, 10955, 10956, 10957, 10958, 10959, 10960, 10961, 10962, 10963, 10964, 10965, 10966, 10967, 10968, 10969, 10970, 10971, 10972, 10973, 10974, 10975, 10976, 10977, 10978, 10979, 10980, 10981, 10982, 10983, 10984, 10985, 10986, 10987, 10988, 10989, 10990, 10991, 10992, 10993, 10994, 10995, 10996, 10997, 10998, 10999, 11000, 11001, 11002, 11003, 11004, 11005, 11006, 11007, 11008, 11009, 11010, 11011, 11012, 11013, 11014, 11015, 11016, 11017, 11018, 11019, 11020, 11021, 11022, 11023, 11024, 11025, 11026, 11027, 11028, 11029, 11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039, 11040, 11041, 11042, 11043, 11044, 11045, 11046, 11047, 11048, 11049, 11050, 11051, 11052, 11053, 11054, 11055, 11056, 11057, 11058, 11059, 11060, 11061, 11062, 11063, 11064, 11065, 11066, 11067, 11068, 11069, 11070, 11071, 11072, 11073, 11074, 11075, 11076, 11077, 11078, 11079, 11080, 11081, 11082, 11083, 11084, 11085, 11086, 11087, 11088, 11089, 11090, 11091, 11092, 11093, 11094, 11095, 11096, 11097, 11098, 11099, 11100, 11101, 11102, 11103, 11104, 11105, 11106, 11107, 11108, 11109, 11110, 11111, 11112, 11113, 11114, 11115, 11116, 11117, 11118, 11119, 11120, 11121, 11122, 11123, 11124, 11125, 11126, 11127, 11128, 11129, 11130, 11131, 11132, 11133, 11134, 11135, 11136, 11137, 11138, 11139, 11140, 11141, 11142, 11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151, 11152, 11153, 11154, 11155, 11156, 11157, 11158, 11159, 11160, 11161, 11162, 11163, 11164, 11165, 11166, 11167, 11168, 11169, 11170, 11171, 11172, 11173, 11174, 11175, 11176, 11177, 11178, 11179, 11180, 11181, 11182, 11183, 11184, 11185, 11186, 11187, 11188, 11189, 11190, 11191, 11192, 11193, 11194, 11195, 11196, 11197, 11198, 11199, 11200, 11201, 11202, 11203, 11204, 11205, 11206, 11207, 11208, 11209, 11210, 11211, 11212, 11213, 11214, 11215, 11216, 11217, 11218, 11219, 11220, 11221, 11222, 11223, 11224, 11225, 11226, 11227, 11228, 11229, 11230, 11231, 11232, 11233, 11234, 11235, 11236, 11237, 11238, 11239, 11240, 11241, 11242, 11243, 11244, 11245, 11246, 11247, 11248, 11249, 11250, 11251, 11252, 11253, 11254, 11255, 11256, 11257, 11258, 11259, 11260, 11261, 11262, 11263, 11264, 11265, 11266, 11267, 11268, 11269, 11270, 11271, 11272, 11273, 11274, 11275, 11276, 11277, 11278, 11279, 11280, 11281, 11282, 11283, 11284, 11285, 11286, 11287, 11288, 11289, 11290, 11291, 11292, 11293, 11294, 11295, 11296, 11297, 11298, 11299, 11300, 11301, 11302, 11303, 11304, 11305, 11306, 11307, 11308, 11309, 11310, 11311, 11312, 11313, 11314, 11315, 11316, 11317, 11318, 11319, 11320, 11321, 11322, 11323, 11324, 11325, 11326, 11327, 11328, 11329, 11330, 11331, 11332, 11333, 11334, 11335, 11336, 11337, 11338, 11339, 11340, 11341, 11342, 11343, 11344, 11345, 11346, 11347, 11348, 11349, 11350, 11351, 11352, 11353, 11354, 11355, 11356, 11357, 11358, 11359, 11360, 11361, 11362, 11363, 11364, 11365, 11366, 11367, 11368, 11369, 11370, 11371, 11372, 11373, 11374, 11375, 11376, 11377, 11378, 11379, 11380, 11381, 11382, 11383, 11384, 11385, 11386, 11387, 11388, 11389, 11390, 11391, 11392, 11393, 11394, 11395, 11396, 11397, 11398, 11399, 11400, 11401, 11402, 11403, 11404, 11405, 11406, 11407, 11408, 11409, 11410, 11411, 11412, 11413, 11414, 11415, 11416, 11417, 11418, 11419, 11420, 11421, 11422, 11423, 11424, 11425, 11426, 11427, 11428, 11429, 11430, 11431, 11432, 11433, 11434, 11435, 11436, 11437, 11438, 11439, 11440, 11441, 11442, 11443, 11444, 11445, 11446, 11447, 11448, 11449, 11450, 11451, 11452, 11453, 11454, 11455, 11456, 11457, 11458, 11459, 11460, 11461, 11462, 11463, 11464, 11465, 11466, 11467, 11468, 11469, 11470, 11471, 11472, 11473, 11474, 11475, 11476, 11477, 11478, 11479, 11480, 11481, 11482, 11483, 11484, 11485, 11486, 11487, 11488, 11489, 11490, 11491, 11492, 11493, 11494, 11495, 11496, 11497, 11498, 11499, 11500, 11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 11516, 11517, 11518, 11519, 11520, 11521, 11522, 11523, 11524, 11525, 11526, 11527, 11528, 11529, 11530, 11531, 11532, 11533, 11534, 11535, 11536, 11537, 11538, 11539, 11540, 11541, 11542, 11543, 11544, 11545, 11546, 11547, 11548, 11549, 11550, 11551, 11552, 11553, 11554, 11555, 11556, 11557, 11558, 11559, 11560, 11561, 11562, 11563, 11564, 11565, 11566, 11567, 11568, 11569, 11570, 11571, 11572, 11573, 11574, 11575, 11576, 11577, 11578, 11579, 11580, 11581, 11582, 11583, 11584, 11585, 11586, 11587, 11588, 11589, 11590, 11591, 11592, 11593, 11594, 11595, 11596, 11597, 11598, 11599, 11600, 11601, 11602, 11603, 11604, 11605, 11606, 11607, 11608, 11609, 11610, 11611, 11612, 11613, 11614, 11615, 11616, 11617, 11618, 11619, 11620, 11621, 11622, 11623, 11624, 11625, 11626, 11627, 11628, 11629, 11630, 11631, 11632, 11633, 11634, 11635, 11636, 11637, 11638, 11639, 11640, 11641, 11642, 11643, 11644, 11645, 11646, 11647, 11648, 11649, 11650, 11651, 11652, 11653, 11654, 11655, 11656, 11657, 11658, 11659, 11660, 11661, 11662, 11663, 11664, 11665, 11666, 11667, 11668, 11669, 11670, 11671, 11672, 11673, 11674, 11675, 11676, 11677, 11678, 11679, 11680, 11681, 11682, 11683, 11684, 11685, 11686, 11687, 11688, 11689, 11690, 11691, 11692, 11693, 11694, 11695, 11696, 11697, 11698, 11699, 11700, 11701, 11702, 11703, 11704, 11705, 11706, 11707, 11708, 11709, 11710, 11711, 11712, 11713, 11714, 11715, 11716, 11717, 11718, 11719, 11720, 11721, 11722, 11723, 11724, 11725, 11726, 11727, 11728, 11729, 11730, 11731, 11732, 11733, 11734, 11735, 11736, 11737, 11738, 11739, 11740, 11741, 11742, 11743, 11744, 11745, 11746, 11747, 11748, 11749, 11750, 11751, 11752, 11753, 11754, 11755, 11756, 11757, 11758, 11759, 11760, 11761, 11762, 11763, 11764, 11765, 11766, 11767, 11768, 11769, 11770, 11771, 11772, 11773, 11774, 11775, 11776, 11777, 11778, 11779, 11780, 11781, 11782, 11783, 11784, 11785, 11786, 11787, 11788, 11789, 11790, 11791, 11792, 11793, 11794, 11795, 11796, 11797, 11798, 11799, 11800, 11801, 11802, 11803, 11804, 11805, 11806, 11807, 11808, 11809, 11810, 11811, 11812, 11813, 11814, 11815, 11816, 11817, 11818, 11819, 11820, 11821, 11822, 11823, 11824, 11825, 11826, 11827, 11828, 11829, 11830, 11831, 11832, 11833, 11834, 11835, 11836, 11837, 11838, 11839, 11840, 11841, 11842, 11843, 11844, 11845, 11846, 11847, 11848, 11849, 11850, 11851, 11852, 11853, 11854, 11855, 11856, 11857, 11858, 11859, 11860, 11861, 11862, 11863, 11864, 11865, 11866, 11867, 11868, 11869, 11870, 11871, 11872, 11873, 11874, 11875, 11876, 11877, 11878, 11879, 11880, 11881, 11882, 11883, 11884, 11885, 11886, 11887, 11888, 11889, 11890, 11891, 11892, 11893, 11894, 11895, 11896, 11897, 11898, 11899, 11900, 11901, 11902, 11903, 11904, 11905, 11906, 11907, 11908, 11909, 11910, 11911, 11912, 11913, 11914, 11915, 11916, 11917, 11918, 11919, 11920, 11921, 11922, 11923, 11924, 11925, 11926, 11927, 11928, 11929, 11930, 11931, 11932, 11933, 11934, 11935, 11936, 11937, 11938, 11939, 11940, 11941, 11942, 11943, 11944, 11945, 11946, 11947, 11948, 11949, 11950, 11951, 11952, 11953, 11954, 11955, 11956, 11957, 11958, 11959]);
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [40, 230]});
+ const input2Buffer = new Float32Array([11960, 11961, 11962, 11963, 11964, 11965, 11966, 11967, 11968, 11969, 11970, 11971, 11972, 11973, 11974, 11975, 11976, 11977, 11978, 11979, 11980, 11981, 11982, 11983, 11984, 11985, 11986, 11987, 11988, 11989, 11990, 11991, 11992, 11993, 11994, 11995, 11996, 11997, 11998, 11999, 12000, 12001, 12002, 12003, 12004, 12005, 12006, 12007, 12008, 12009, 12010, 12011, 12012, 12013, 12014, 12015, 12016, 12017, 12018, 12019, 12020, 12021, 12022, 12023, 12024, 12025, 12026, 12027, 12028, 12029, 12030, 12031, 12032, 12033, 12034, 12035, 12036, 12037, 12038, 12039, 12040, 12041, 12042, 12043, 12044, 12045, 12046, 12047, 12048, 12049, 12050, 12051, 12052, 12053, 12054, 12055, 12056, 12057, 12058, 12059, 12060, 12061, 12062, 12063, 12064, 12065, 12066, 12067, 12068, 12069, 12070, 12071, 12072, 12073, 12074, 12075, 12076, 12077, 12078, 12079, 12080, 12081, 12082, 12083, 12084, 12085, 12086, 12087, 12088, 12089, 12090, 12091, 12092, 12093, 12094, 12095, 12096, 12097, 12098, 12099, 12100, 12101, 12102, 12103, 12104, 12105, 12106, 12107, 12108, 12109, 12110, 12111, 12112, 12113, 12114, 12115, 12116, 12117, 12118, 12119, 12120, 12121, 12122, 12123, 12124, 12125, 12126, 12127, 12128, 12129, 12130, 12131, 12132, 12133, 12134, 12135, 12136, 12137, 12138, 12139, 12140, 12141, 12142, 12143, 12144, 12145, 12146, 12147, 12148, 12149, 12150, 12151, 12152, 12153, 12154, 12155, 12156, 12157, 12158, 12159, 12160, 12161, 12162, 12163, 12164, 12165, 12166, 12167, 12168, 12169, 12170, 12171, 12172, 12173, 12174, 12175, 12176, 12177, 12178, 12179, 12180, 12181, 12182, 12183, 12184, 12185, 12186, 12187, 12188, 12189, 12190, 12191, 12192, 12193, 12194, 12195, 12196, 12197, 12198, 12199, 12200, 12201, 12202, 12203, 12204, 12205, 12206, 12207, 12208, 12209, 12210, 12211, 12212, 12213, 12214, 12215, 12216, 12217, 12218, 12219, 12220, 12221, 12222, 12223, 12224, 12225, 12226, 12227, 12228, 12229, 12230, 12231, 12232, 12233, 12234, 12235, 12236, 12237, 12238, 12239, 12240, 12241, 12242, 12243, 12244, 12245, 12246, 12247, 12248, 12249, 12250, 12251, 12252, 12253, 12254, 12255, 12256, 12257, 12258, 12259, 12260, 12261, 12262, 12263, 12264, 12265, 12266, 12267, 12268, 12269, 12270, 12271, 12272, 12273, 12274, 12275, 12276, 12277, 12278, 12279, 12280, 12281, 12282, 12283, 12284, 12285, 12286, 12287, 12288, 12289, 12290, 12291, 12292, 12293, 12294, 12295, 12296, 12297, 12298, 12299, 12300, 12301, 12302, 12303, 12304, 12305, 12306, 12307, 12308, 12309, 12310, 12311, 12312, 12313, 12314, 12315, 12316, 12317, 12318, 12319, 12320, 12321, 12322, 12323, 12324, 12325, 12326, 12327, 12328, 12329, 12330, 12331, 12332, 12333, 12334, 12335, 12336, 12337, 12338, 12339, 12340, 12341, 12342, 12343, 12344, 12345, 12346, 12347, 12348, 12349, 12350, 12351, 12352, 12353, 12354, 12355, 12356, 12357, 12358, 12359, 12360, 12361, 12362, 12363, 12364, 12365, 12366, 12367, 12368, 12369, 12370, 12371, 12372, 12373, 12374, 12375, 12376, 12377, 12378, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390, 12391, 12392, 12393, 12394, 12395, 12396, 12397, 12398, 12399, 12400, 12401, 12402, 12403, 12404, 12405, 12406, 12407, 12408, 12409, 12410, 12411, 12412, 12413, 12414, 12415, 12416, 12417, 12418, 12419, 12420, 12421, 12422, 12423, 12424, 12425, 12426, 12427, 12428, 12429, 12430, 12431, 12432, 12433, 12434, 12435, 12436, 12437, 12438, 12439, 12440, 12441, 12442, 12443, 12444, 12445, 12446, 12447, 12448, 12449, 12450, 12451, 12452, 12453, 12454, 12455, 12456, 12457, 12458, 12459, 12460, 12461, 12462, 12463, 12464, 12465, 12466, 12467, 12468, 12469, 12470, 12471, 12472, 12473, 12474, 12475, 12476, 12477, 12478, 12479, 12480, 12481, 12482, 12483, 12484, 12485, 12486, 12487, 12488, 12489, 12490, 12491, 12492, 12493, 12494, 12495, 12496, 12497, 12498, 12499, 12500, 12501, 12502, 12503, 12504, 12505, 12506, 12507, 12508, 12509, 12510, 12511, 12512, 12513, 12514, 12515, 12516, 12517, 12518, 12519, 12520, 12521, 12522, 12523, 12524, 12525, 12526, 12527, 12528, 12529, 12530, 12531, 12532, 12533, 12534, 12535, 12536, 12537, 12538, 12539, 12540, 12541, 12542, 12543, 12544, 12545, 12546, 12547, 12548, 12549, 12550, 12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560, 12561, 12562, 12563, 12564, 12565, 12566, 12567, 12568, 12569, 12570, 12571, 12572, 12573, 12574, 12575, 12576, 12577, 12578, 12579, 12580, 12581, 12582, 12583, 12584, 12585, 12586, 12587, 12588, 12589, 12590, 12591, 12592, 12593, 12594, 12595, 12596, 12597, 12598, 12599, 12600, 12601, 12602, 12603, 12604, 12605, 12606, 12607, 12608, 12609, 12610, 12611, 12612, 12613, 12614, 12615, 12616, 12617, 12618, 12619, 12620, 12621, 12622, 12623, 12624, 12625, 12626, 12627, 12628, 12629, 12630, 12631, 12632, 12633, 12634, 12635, 12636, 12637, 12638, 12639, 12640, 12641, 12642, 12643, 12644, 12645, 12646, 12647, 12648, 12649, 12650, 12651, 12652, 12653, 12654, 12655, 12656, 12657, 12658, 12659, 12660, 12661, 12662, 12663, 12664, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12675, 12676, 12677, 12678, 12679, 12680, 12681, 12682, 12683, 12684, 12685, 12686, 12687, 12688, 12689, 12690, 12691, 12692, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, 12720, 12721, 12722, 12723, 12724, 12725, 12726, 12727, 12728, 12729, 12730, 12731, 12732, 12733, 12734, 12735, 12736, 12737, 12738, 12739, 12740, 12741, 12742, 12743, 12744, 12745, 12746, 12747, 12748, 12749, 12750, 12751, 12752, 12753, 12754, 12755, 12756, 12757, 12758, 12759, 12760, 12761, 12762, 12763, 12764, 12765, 12766, 12767, 12768, 12769, 12770, 12771, 12772, 12773, 12774, 12775, 12776, 12777, 12778, 12779, 12780, 12781, 12782, 12783, 12784, 12785, 12786, 12787, 12788, 12789, 12790, 12791, 12792, 12793, 12794, 12795, 12796, 12797, 12798, 12799, 12800, 12801, 12802, 12803, 12804, 12805, 12806, 12807, 12808, 12809, 12810, 12811, 12812, 12813, 12814, 12815, 12816, 12817, 12818, 12819, 12820, 12821, 12822, 12823, 12824, 12825, 12826, 12827, 12828, 12829, 12830, 12831, 12832, 12833, 12834, 12835, 12836, 12837, 12838, 12839, 12840, 12841, 12842, 12843, 12844, 12845, 12846, 12847, 12848, 12849, 12850, 12851, 12852, 12853, 12854, 12855, 12856, 12857, 12858, 12859, 12860, 12861, 12862, 12863, 12864, 12865, 12866, 12867, 12868, 12869, 12870, 12871, 12872, 12873, 12874, 12875, 12876, 12877, 12878, 12879, 12880, 12881, 12882, 12883, 12884, 12885, 12886, 12887, 12888, 12889, 12890, 12891, 12892, 12893, 12894, 12895, 12896, 12897, 12898, 12899, 12900, 12901, 12902, 12903, 12904, 12905, 12906, 12907, 12908, 12909, 12910, 12911, 12912, 12913, 12914, 12915, 12916, 12917, 12918, 12919, 12920, 12921, 12922, 12923, 12924, 12925, 12926, 12927, 12928, 12929, 12930, 12931, 12932, 12933, 12934, 12935, 12936, 12937, 12938, 12939, 12940, 12941, 12942, 12943, 12944, 12945, 12946, 12947, 12948, 12949, 12950, 12951, 12952, 12953, 12954, 12955, 12956, 12957, 12958, 12959, 12960, 12961, 12962, 12963, 12964, 12965, 12966, 12967, 12968, 12969, 12970, 12971, 12972, 12973, 12974, 12975, 12976, 12977, 12978, 12979, 12980, 12981, 12982, 12983, 12984, 12985, 12986, 12987, 12988, 12989, 12990, 12991, 12992, 12993, 12994, 12995, 12996, 12997, 12998, 12999, 13000, 13001, 13002, 13003, 13004, 13005, 13006, 13007, 13008, 13009, 13010, 13011, 13012, 13013, 13014, 13015, 13016, 13017, 13018, 13019, 13020, 13021, 13022, 13023, 13024, 13025, 13026, 13027, 13028, 13029, 13030, 13031, 13032, 13033, 13034, 13035, 13036, 13037, 13038, 13039, 13040, 13041, 13042, 13043, 13044, 13045, 13046, 13047, 13048, 13049, 13050, 13051, 13052, 13053, 13054, 13055, 13056, 13057, 13058, 13059, 13060, 13061, 13062, 13063, 13064, 13065, 13066, 13067, 13068, 13069, 13070, 13071, 13072, 13073, 13074, 13075, 13076, 13077, 13078, 13079, 13080, 13081, 13082, 13083, 13084, 13085, 13086, 13087, 13088, 13089, 13090, 13091, 13092, 13093, 13094, 13095, 13096, 13097, 13098, 13099, 13100, 13101, 13102, 13103, 13104, 13105, 13106, 13107, 13108, 13109, 13110, 13111, 13112, 13113, 13114, 13115, 13116, 13117, 13118, 13119, 13120, 13121, 13122, 13123, 13124, 13125, 13126, 13127, 13128, 13129, 13130, 13131, 13132, 13133, 13134, 13135, 13136, 13137, 13138, 13139, 13140, 13141, 13142, 13143, 13144, 13145, 13146, 13147, 13148, 13149, 13150, 13151, 13152, 13153, 13154, 13155, 13156, 13157, 13158, 13159, 13160, 13161, 13162, 13163, 13164, 13165, 13166, 13167, 13168, 13169, 13170, 13171, 13172, 13173, 13174, 13175, 13176, 13177, 13178, 13179, 13180, 13181, 13182, 13183, 13184, 13185, 13186, 13187, 13188, 13189, 13190, 13191, 13192, 13193, 13194, 13195, 13196, 13197, 13198, 13199, 13200, 13201, 13202, 13203, 13204, 13205, 13206, 13207, 13208, 13209, 13210, 13211, 13212, 13213, 13214, 13215, 13216, 13217, 13218, 13219, 13220, 13221, 13222, 13223, 13224, 13225, 13226, 13227, 13228, 13229, 13230, 13231, 13232, 13233, 13234, 13235, 13236, 13237, 13238, 13239, 13240, 13241, 13242, 13243, 13244, 13245, 13246, 13247, 13248, 13249, 13250, 13251, 13252, 13253, 13254, 13255, 13256, 13257, 13258, 13259, 13260, 13261, 13262, 13263, 13264, 13265, 13266, 13267, 13268, 13269, 13270, 13271, 13272, 13273, 13274, 13275, 13276, 13277, 13278, 13279, 13280, 13281, 13282, 13283, 13284, 13285, 13286, 13287, 13288, 13289, 13290, 13291, 13292, 13293, 13294, 13295, 13296, 13297, 13298, 13299, 13300, 13301, 13302, 13303, 13304, 13305, 13306, 13307, 13308, 13309, 13310, 13311, 13312, 13313, 13314, 13315, 13316, 13317, 13318, 13319, 13320, 13321, 13322, 13323, 13324, 13325, 13326, 13327, 13328, 13329, 13330, 13331, 13332, 13333, 13334, 13335, 13336, 13337, 13338, 13339, 13340, 13341, 13342, 13343, 13344, 13345, 13346, 13347, 13348, 13349, 13350, 13351, 13352, 13353, 13354, 13355, 13356, 13357, 13358, 13359, 13360, 13361, 13362, 13363, 13364, 13365, 13366, 13367, 13368, 13369, 13370, 13371, 13372, 13373, 13374, 13375, 13376, 13377, 13378, 13379, 13380, 13381, 13382, 13383, 13384, 13385, 13386, 13387, 13388, 13389, 13390, 13391, 13392, 13393, 13394, 13395, 13396, 13397, 13398, 13399, 13400, 13401, 13402, 13403, 13404, 13405, 13406, 13407, 13408, 13409, 13410, 13411, 13412, 13413, 13414, 13415, 13416, 13417, 13418, 13419, 13420, 13421, 13422, 13423, 13424, 13425, 13426, 13427, 13428, 13429, 13430, 13431, 13432, 13433, 13434, 13435, 13436, 13437, 13438, 13439, 13440, 13441, 13442, 13443, 13444, 13445, 13446, 13447, 13448, 13449, 13450, 13451, 13452, 13453, 13454, 13455, 13456, 13457, 13458, 13459, 13460, 13461, 13462, 13463, 13464, 13465, 13466, 13467, 13468, 13469, 13470, 13471, 13472, 13473, 13474, 13475, 13476, 13477, 13478, 13479, 13480, 13481, 13482, 13483, 13484, 13485, 13486, 13487, 13488, 13489, 13490, 13491, 13492, 13493, 13494, 13495, 13496, 13497, 13498, 13499, 13500, 13501, 13502, 13503, 13504, 13505, 13506, 13507, 13508, 13509, 13510, 13511, 13512, 13513, 13514, 13515, 13516, 13517, 13518, 13519, 13520, 13521, 13522, 13523, 13524, 13525, 13526, 13527, 13528, 13529, 13530, 13531, 13532, 13533, 13534, 13535, 13536, 13537, 13538, 13539, 13540, 13541, 13542, 13543, 13544, 13545, 13546, 13547, 13548, 13549, 13550, 13551, 13552, 13553, 13554, 13555, 13556, 13557, 13558, 13559, 13560, 13561, 13562, 13563, 13564, 13565, 13566, 13567, 13568, 13569, 13570, 13571, 13572, 13573, 13574, 13575, 13576, 13577, 13578, 13579, 13580, 13581, 13582, 13583, 13584, 13585, 13586, 13587, 13588, 13589, 13590, 13591, 13592, 13593, 13594, 13595, 13596, 13597, 13598, 13599, 13600, 13601, 13602, 13603, 13604, 13605, 13606, 13607, 13608, 13609, 13610, 13611, 13612, 13613, 13614, 13615, 13616, 13617, 13618, 13619, 13620, 13621, 13622, 13623, 13624, 13625, 13626, 13627, 13628, 13629, 13630, 13631, 13632, 13633, 13634, 13635, 13636, 13637, 13638, 13639, 13640, 13641, 13642, 13643, 13644, 13645, 13646, 13647, 13648, 13649, 13650, 13651, 13652, 13653, 13654, 13655, 13656, 13657, 13658, 13659, 13660, 13661, 13662, 13663, 13664, 13665, 13666, 13667, 13668, 13669, 13670, 13671, 13672, 13673, 13674, 13675, 13676, 13677, 13678, 13679, 13680, 13681, 13682, 13683, 13684, 13685, 13686, 13687, 13688, 13689, 13690, 13691, 13692, 13693, 13694, 13695, 13696, 13697, 13698, 13699, 13700, 13701, 13702, 13703, 13704, 13705, 13706, 13707, 13708, 13709, 13710, 13711, 13712, 13713, 13714, 13715, 13716, 13717, 13718, 13719, 13720, 13721, 13722, 13723, 13724, 13725, 13726, 13727, 13728, 13729, 13730, 13731, 13732, 13733, 13734, 13735, 13736, 13737, 13738, 13739, 13740, 13741, 13742, 13743, 13744, 13745, 13746, 13747, 13748, 13749, 13750, 13751, 13752, 13753, 13754, 13755, 13756, 13757, 13758, 13759, 13760, 13761, 13762, 13763, 13764, 13765, 13766, 13767, 13768, 13769, 13770, 13771, 13772, 13773, 13774, 13775, 13776, 13777, 13778, 13779, 13780, 13781, 13782, 13783, 13784, 13785, 13786, 13787, 13788, 13789, 13790, 13791, 13792, 13793, 13794, 13795, 13796, 13797, 13798, 13799, 13800, 13801, 13802, 13803, 13804, 13805, 13806, 13807, 13808, 13809, 13810, 13811, 13812, 13813, 13814, 13815, 13816, 13817, 13818, 13819, 13820, 13821, 13822, 13823, 13824, 13825, 13826, 13827, 13828, 13829, 13830, 13831, 13832, 13833, 13834, 13835, 13836, 13837, 13838, 13839, 13840, 13841, 13842, 13843, 13844, 13845, 13846, 13847, 13848, 13849, 13850, 13851, 13852, 13853, 13854, 13855, 13856, 13857, 13858, 13859, 13860, 13861, 13862, 13863, 13864, 13865, 13866, 13867, 13868, 13869, 13870, 13871, 13872, 13873, 13874, 13875, 13876, 13877, 13878, 13879, 13880, 13881, 13882, 13883, 13884, 13885, 13886, 13887, 13888, 13889, 13890, 13891, 13892, 13893, 13894, 13895, 13896, 13897, 13898, 13899, 13900, 13901, 13902, 13903, 13904, 13905, 13906, 13907, 13908, 13909, 13910, 13911, 13912, 13913, 13914, 13915, 13916, 13917, 13918, 13919, 13920, 13921, 13922, 13923, 13924, 13925, 13926, 13927, 13928, 13929, 13930, 13931, 13932, 13933, 13934, 13935, 13936, 13937, 13938, 13939, 13940, 13941, 13942, 13943, 13944, 13945, 13946, 13947, 13948, 13949, 13950, 13951, 13952, 13953, 13954, 13955, 13956, 13957, 13958, 13959, 13960, 13961, 13962, 13963, 13964, 13965, 13966, 13967, 13968, 13969, 13970, 13971, 13972, 13973, 13974, 13975, 13976, 13977, 13978, 13979, 13980, 13981, 13982, 13983, 13984, 13985, 13986, 13987, 13988, 13989, 13990, 13991, 13992, 13993, 13994, 13995, 13996, 13997, 13998, 13999, 14000, 14001, 14002, 14003, 14004, 14005, 14006, 14007, 14008, 14009, 14010, 14011, 14012, 14013, 14014, 14015, 14016, 14017, 14018, 14019, 14020, 14021, 14022, 14023, 14024, 14025, 14026, 14027, 14028, 14029, 14030, 14031, 14032, 14033, 14034, 14035, 14036, 14037, 14038, 14039, 14040, 14041, 14042, 14043, 14044, 14045, 14046, 14047, 14048, 14049, 14050, 14051, 14052, 14053, 14054, 14055, 14056, 14057, 14058, 14059, 14060, 14061, 14062, 14063, 14064, 14065, 14066, 14067, 14068, 14069, 14070, 14071, 14072, 14073, 14074, 14075, 14076, 14077, 14078, 14079, 14080, 14081, 14082, 14083, 14084, 14085, 14086, 14087, 14088, 14089, 14090, 14091, 14092, 14093, 14094, 14095, 14096, 14097, 14098, 14099, 14100, 14101, 14102, 14103, 14104, 14105, 14106, 14107, 14108, 14109, 14110, 14111, 14112, 14113, 14114, 14115, 14116, 14117, 14118, 14119, 14120, 14121, 14122, 14123, 14124, 14125, 14126, 14127, 14128, 14129, 14130, 14131, 14132, 14133, 14134, 14135, 14136, 14137, 14138, 14139, 14140, 14141, 14142, 14143, 14144, 14145, 14146, 14147, 14148, 14149, 14150, 14151, 14152, 14153, 14154, 14155, 14156, 14157, 14158, 14159, 14160, 14161, 14162, 14163, 14164, 14165, 14166, 14167, 14168, 14169, 14170, 14171, 14172, 14173, 14174, 14175, 14176, 14177, 14178, 14179, 14180, 14181, 14182, 14183, 14184, 14185, 14186, 14187, 14188, 14189, 14190, 14191, 14192, 14193, 14194, 14195, 14196, 14197, 14198, 14199, 14200, 14201, 14202, 14203, 14204, 14205, 14206, 14207, 14208, 14209, 14210, 14211, 14212, 14213, 14214, 14215, 14216, 14217, 14218, 14219, 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, 14229, 14230, 14231, 14232, 14233, 14234, 14235, 14236, 14237, 14238, 14239, 14240, 14241, 14242, 14243, 14244, 14245, 14246, 14247, 14248, 14249, 14250, 14251, 14252, 14253, 14254, 14255, 14256, 14257, 14258, 14259, 14260, 14261, 14262, 14263, 14264, 14265, 14266, 14267, 14268, 14269, 14270, 14271, 14272, 14273, 14274, 14275, 14276, 14277, 14278, 14279, 14280, 14281, 14282, 14283, 14284, 14285, 14286, 14287, 14288, 14289, 14290, 14291, 14292, 14293, 14294, 14295, 14296, 14297, 14298, 14299, 14300, 14301, 14302, 14303, 14304, 14305, 14306, 14307, 14308, 14309, 14310, 14311, 14312, 14313, 14314, 14315, 14316, 14317, 14318, 14319, 14320, 14321, 14322, 14323, 14324, 14325, 14326, 14327, 14328, 14329, 14330, 14331, 14332, 14333, 14334, 14335, 14336, 14337, 14338, 14339, 14340, 14341, 14342, 14343, 14344, 14345, 14346, 14347, 14348, 14349, 14350, 14351, 14352, 14353, 14354, 14355, 14356, 14357, 14358, 14359, 14360, 14361, 14362, 14363, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 14373, 14374, 14375, 14376, 14377, 14378, 14379, 14380, 14381, 14382, 14383, 14384, 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19533, 19534, 19535, 19536, 19537, 19538, 19539, 19540, 19541, 19542, 19543, 19544, 19545, 19546, 19547, 19548, 19549, 19550, 19551, 19552, 19553, 19554, 19555, 19556, 19557, 19558, 19559, 19560, 19561, 19562, 19563, 19564, 19565, 19566, 19567, 19568, 19569, 19570, 19571, 19572, 19573, 19574, 19575, 19576, 19577, 19578, 19579, 19580, 19581, 19582, 19583, 19584, 19585, 19586, 19587, 19588, 19589, 19590, 19591, 19592, 19593, 19594, 19595, 19596, 19597, 19598, 19599, 19600, 19601, 19602, 19603, 19604, 19605, 19606, 19607, 19608, 19609, 19610, 19611, 19612, 19613, 19614, 19615, 19616, 19617, 19618, 19619, 19620, 19621, 19622, 19623, 19624, 19625, 19626, 19627, 19628, 19629, 19630, 19631, 19632, 19633, 19634, 19635, 19636, 19637, 19638, 19639, 19640, 19641, 19642, 19643, 19644, 19645, 19646, 19647, 19648, 19649, 19650, 19651, 19652, 19653, 19654, 19655, 19656, 19657, 19658, 19659, 19660, 19661, 19662, 19663, 19664, 19665, 19666, 19667, 19668, 19669, 19670, 19671, 19672, 19673, 19674, 19675, 19676, 19677, 19678, 19679, 19680, 19681, 19682, 19683, 19684, 19685, 19686, 19687, 19688, 19689, 19690, 19691, 19692, 19693, 19694, 19695, 19696, 19697, 19698, 19699, 19700, 19701, 19702, 19703, 19704, 19705, 19706, 19707, 19708, 19709, 19710, 19711, 19712, 19713, 19714, 19715, 19716, 19717, 19718, 19719, 19720, 19721, 19722, 19723, 19724, 19725, 19726, 19727, 19728, 19729, 19730, 19731, 19732, 19733, 19734, 19735, 19736, 19737, 19738, 19739, 19740, 19741, 19742, 19743, 19744, 19745, 19746, 19747, 19748, 19749, 19750, 19751, 19752, 19753, 19754, 19755, 19756, 19757, 19758, 19759, 19760, 19761, 19762, 19763, 19764, 19765, 19766, 19767, 19768, 19769, 19770, 19771, 19772, 19773, 19774, 19775, 19776, 19777, 19778, 19779, 19780, 19781, 19782, 19783, 19784, 19785, 19786, 19787, 19788, 19789, 19790, 19791, 19792, 19793, 19794, 19795, 19796, 19797, 19798, 19799, 19800, 19801, 19802, 19803, 19804, 19805, 19806, 19807, 19808, 19809, 19810, 19811, 19812, 19813, 19814, 19815, 19816, 19817, 19818, 19819, 19820, 19821, 19822, 19823, 19824, 19825, 19826, 19827, 19828, 19829, 19830, 19831, 19832, 19833, 19834, 19835, 19836, 19837, 19838, 19839, 19840, 19841, 19842, 19843, 19844, 19845, 19846, 19847, 19848, 19849, 19850, 19851, 19852, 19853, 19854, 19855, 19856, 19857, 19858, 19859, 19860, 19861, 19862, 19863, 19864, 19865, 19866, 19867, 19868, 19869, 19870, 19871, 19872, 19873, 19874, 19875, 19876, 19877, 19878, 19879, 19880, 19881, 19882, 19883, 19884, 19885, 19886, 19887, 19888, 19889, 19890, 19891, 19892, 19893, 19894, 19895, 19896, 19897, 19898, 19899, 19900, 19901, 19902, 19903, 19904, 19905, 19906, 19907, 19908, 19909, 19910, 19911, 19912, 19913, 19914, 19915, 19916, 19917, 19918, 19919, 19920, 19921, 19922, 19923, 19924, 19925, 19926, 19927, 19928, 19929, 19930, 19931, 19932, 19933, 19934, 19935, 19936, 19937, 19938, 19939, 19940, 19941, 19942, 19943, 19944, 19945, 19946, 19947, 19948, 19949, 19950, 19951, 19952, 19953, 19954, 19955, 19956, 19957, 19958, 19959, 19960, 19961, 19962, 19963, 19964, 19965, 19966, 19967, 19968, 19969, 19970, 19971, 19972, 19973, 19974, 19975, 19976, 19977, 19978, 19979, 19980, 19981, 19982, 19983, 19984, 19985, 19986, 19987, 19988, 19989, 19990, 19991, 19992, 19993, 19994, 19995, 19996, 19997, 19998, 19999, 20000, 20001, 20002, 20003, 20004, 20005, 20006, 20007, 20008, 20009, 20010, 20011, 20012, 20013, 20014, 20015, 20016, 20017, 20018, 20019, 20020, 20021, 20022, 20023, 20024, 20025, 20026, 20027, 20028, 20029, 20030, 20031, 20032, 20033, 20034, 20035, 20036, 20037, 20038, 20039, 20040, 20041, 20042, 20043, 20044, 20045, 20046, 20047, 20048, 20049, 20050, 20051, 20052, 20053, 20054, 20055, 20056, 20057, 20058, 20059, 20060, 20061, 20062, 20063, 20064, 20065, 20066, 20067, 20068, 20069, 20070, 20071, 20072, 20073, 20074, 20075, 20076, 20077, 20078, 20079, 20080, 20081, 20082, 20083, 20084, 20085, 20086, 20087, 20088, 20089, 20090, 20091, 20092, 20093, 20094, 20095, 20096, 20097, 20098, 20099, 20100, 20101, 20102, 20103, 20104, 20105, 20106, 20107, 20108, 20109, 20110, 20111, 20112, 20113, 20114, 20115, 20116, 20117, 20118, 20119, 20120, 20121, 20122, 20123, 20124, 20125, 20126, 20127, 20128, 20129, 20130, 20131, 20132, 20133, 20134, 20135, 20136, 20137, 20138, 20139, 20140, 20141, 20142, 20143, 20144, 20145, 20146, 20147, 20148, 20149, 20150, 20151, 20152, 20153, 20154, 20155, 20156, 20157, 20158, 20159, 20160, 20161, 20162, 20163, 20164, 20165, 20166, 20167, 20168, 20169, 20170, 20171, 20172, 20173, 20174, 20175, 20176, 20177, 20178, 20179, 20180, 20181, 20182, 20183, 20184, 20185, 20186, 20187, 20188, 20189, 20190, 20191, 20192, 20193, 20194, 20195, 20196, 20197, 20198, 20199, 20200, 20201, 20202, 20203, 20204, 20205, 20206, 20207, 20208, 20209, 20210, 20211, 20212, 20213, 20214, 20215, 20216, 20217, 20218, 20219, 20220, 20221, 20222, 20223, 20224, 20225, 20226, 20227, 20228, 20229, 20230, 20231, 20232, 20233, 20234, 20235, 20236, 20237, 20238, 20239, 20240, 20241, 20242, 20243, 20244, 20245, 20246, 20247, 20248, 20249, 20250, 20251, 20252, 20253, 20254, 20255, 20256, 20257, 20258, 20259, 20260, 20261, 20262, 20263, 20264, 20265, 20266, 20267, 20268, 20269, 20270, 20271, 20272, 20273, 20274, 20275, 20276, 20277, 20278, 20279, 20280, 20281, 20282, 20283, 20284, 20285, 20286, 20287, 20288, 20289, 20290, 20291, 20292, 20293, 20294, 20295, 20296, 20297, 20298, 20299, 20300, 20301, 20302, 20303, 20304, 20305, 20306, 20307, 20308, 20309, 20310, 20311, 20312, 20313, 20314, 20315, 20316, 20317, 20318, 20319, 20320, 20321, 20322, 20323, 20324, 20325, 20326, 20327, 20328, 20329, 20330, 20331, 20332, 20333, 20334, 20335, 20336, 20337, 20338, 20339, 20340, 20341, 20342, 20343, 20344, 20345, 20346, 20347, 20348, 20349, 20350, 20351, 20352, 20353, 20354, 20355, 20356, 20357, 20358, 20359, 20360, 20361, 20362, 20363, 20364, 20365, 20366, 20367, 20368, 20369, 20370, 20371, 20372, 20373, 20374, 20375, 20376, 20377, 20378, 20379, 20380, 20381, 20382, 20383, 20384, 20385, 20386, 20387, 20388, 20389, 20390, 20391, 20392, 20393, 20394, 20395, 20396, 20397, 20398, 20399, 20400, 20401, 20402, 20403, 20404, 20405, 20406, 20407, 20408, 20409, 20410, 20411, 20412, 20413, 20414, 20415, 20416, 20417, 20418, 20419, 20420, 20421, 20422, 20423, 20424, 20425, 20426, 20427, 20428, 20429, 20430, 20431, 20432, 20433, 20434, 20435, 20436, 20437, 20438, 20439, 20440, 20441, 20442, 20443, 20444, 20445, 20446, 20447, 20448, 20449, 20450, 20451, 20452, 20453, 20454, 20455, 20456, 20457, 20458, 20459, 20460, 20461, 20462, 20463, 20464, 20465, 20466, 20467, 20468, 20469, 20470, 20471, 20472, 20473, 20474, 20475, 20476, 20477, 20478, 20479, 20480, 20481, 20482, 20483, 20484, 20485, 20486, 20487, 20488, 20489, 20490, 20491, 20492, 20493, 20494, 20495, 20496, 20497, 20498, 20499, 20500, 20501, 20502, 20503, 20504, 20505, 20506, 20507, 20508, 20509, 20510, 20511, 20512, 20513, 20514, 20515, 20516, 20517, 20518, 20519, 20520, 20521, 20522, 20523, 20524, 20525, 20526, 20527, 20528, 20529, 20530, 20531, 20532, 20533, 20534, 20535, 20536, 20537, 20538, 20539, 20540, 20541, 20542, 20543, 20544, 20545, 20546, 20547, 20548, 20549, 20550, 20551, 20552, 20553, 20554, 20555, 20556, 20557, 20558, 20559, 20560, 20561, 20562, 20563, 20564, 20565, 20566, 20567, 20568, 20569, 20570, 20571, 20572, 20573, 20574, 20575, 20576, 20577, 20578, 20579, 20580, 20581, 20582, 20583, 20584, 20585, 20586, 20587, 20588, 20589, 20590, 20591, 20592, 20593, 20594, 20595, 20596, 20597, 20598, 20599, 20600, 20601, 20602, 20603, 20604, 20605, 20606, 20607, 20608, 20609, 20610, 20611, 20612, 20613, 20614, 20615, 20616, 20617, 20618, 20619, 20620, 20621, 20622, 20623, 20624, 20625, 20626, 20627, 20628, 20629, 20630, 20631, 20632, 20633, 20634, 20635, 20636, 20637, 20638, 20639, 20640, 20641, 20642, 20643, 20644, 20645, 20646, 20647, 20648, 20649, 20650, 20651, 20652, 20653, 20654, 20655, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20682, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 20700, 20701, 20702, 20703, 20704, 20705, 20706, 20707, 20708, 20709, 20710, 20711, 20712, 20713, 20714, 20715, 20716, 20717, 20718, 20719, 20720, 20721, 20722, 20723, 20724, 20725, 20726, 20727, 20728, 20729, 20730, 20731, 20732, 20733, 20734, 20735, 20736, 20737, 20738, 20739, 20740, 20741, 20742, 20743, 20744, 20745, 20746, 20747, 20748, 20749, 20750, 20751, 20752, 20753, 20754, 20755, 20756, 20757, 20758, 20759, 20760, 20761, 20762, 20763, 20764, 20765, 20766, 20767, 20768, 20769, 20770, 20771, 20772, 20773, 20774, 20775, 20776, 20777, 20778, 20779, 20780, 20781, 20782, 20783, 20784, 20785, 20786, 20787, 20788, 20789, 20790, 20791, 20792, 20793, 20794, 20795, 20796, 20797, 20798, 20799, 20800, 20801, 20802, 20803, 20804, 20805, 20806, 20807, 20808, 20809, 20810, 20811, 20812, 20813, 20814, 20815, 20816, 20817, 20818, 20819, 20820, 20821, 20822, 20823, 20824, 20825, 20826, 20827, 20828, 20829, 20830, 20831, 20832, 20833, 20834, 20835, 20836, 20837, 20838, 20839, 20840, 20841, 20842, 20843, 20844, 20845, 20846, 20847, 20848, 20849, 20850, 20851, 20852, 20853, 20854, 20855, 20856, 20857, 20858, 20859, 20860, 20861, 20862, 20863, 20864, 20865, 20866, 20867, 20868, 20869, 20870, 20871, 20872, 20873, 20874, 20875, 20876, 20877, 20878, 20879, 20880, 20881, 20882, 20883, 20884, 20885, 20886, 20887, 20888, 20889, 20890, 20891, 20892, 20893, 20894, 20895, 20896, 20897, 20898, 20899, 20900, 20901, 20902, 20903, 20904, 20905, 20906, 20907, 20908, 20909, 20910, 20911, 20912, 20913, 20914, 20915, 20916, 20917, 20918, 20919, 20920, 20921, 20922, 20923, 20924, 20925, 20926, 20927, 20928, 20929, 20930, 20931, 20932, 20933, 20934, 20935, 20936, 20937, 20938, 20939, 20940, 20941, 20942, 20943, 20944, 20945, 20946, 20947, 20948, 20949, 20950, 20951, 20952, 20953, 20954, 20955, 20956, 20957, 20958, 20959, 20960, 20961, 20962, 20963, 20964, 20965, 20966, 20967, 20968, 20969, 20970, 20971, 20972, 20973, 20974, 20975, 20976, 20977, 20978, 20979, 20980, 20981, 20982, 20983, 20984, 20985, 20986, 20987, 20988, 20989, 20990, 20991, 20992, 20993, 20994, 20995, 20996, 20997, 20998, 20999, 21000, 21001, 21002, 21003, 21004, 21005, 21006, 21007, 21008, 21009, 21010, 21011, 21012, 21013, 21014, 21015, 21016, 21017, 21018, 21019, 21020, 21021, 21022, 21023, 21024, 21025, 21026, 21027, 21028, 21029, 21030, 21031, 21032, 21033, 21034, 21035, 21036, 21037, 21038, 21039, 21040, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21048, 21049, 21050, 21051, 21052, 21053, 21054, 21055, 21056, 21057, 21058, 21059, 21060, 21061, 21062, 21063, 21064, 21065, 21066, 21067, 21068, 21069, 21070, 21071, 21072, 21073, 21074, 21075, 21076, 21077, 21078, 21079, 21080, 21081, 21082, 21083, 21084, 21085, 21086, 21087, 21088, 21089, 21090, 21091, 21092, 21093, 21094, 21095, 21096, 21097, 21098, 21099, 21100, 21101, 21102, 21103, 21104, 21105, 21106, 21107, 21108, 21109, 21110, 21111, 21112, 21113, 21114, 21115, 21116, 21117, 21118, 21119, 21120, 21121, 21122, 21123, 21124, 21125, 21126, 21127, 21128, 21129, 21130, 21131, 21132, 21133, 21134, 21135, 21136, 21137, 21138, 21139, 21140, 21141, 21142, 21143, 21144, 21145, 21146, 21147, 21148, 21149, 21150, 21151, 21152, 21153, 21154, 21155, 21156, 21157, 21158, 21159]);
+ const axis0 = 0;
+ const expected = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 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21136, 21137, 21138, 21139, 21140, 21141, 21142, 21143, 21144, 21145, 21146, 21147, 21148, 21149, 21150, 21151, 21152, 21153, 21154, 21155, 21156, 21157, 21158, 21159];
+ const output = builder.concat([input1, input2], axis0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}, 'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_3_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_3_relaxed.js
new file mode 100644
index 00000000..ff9300d0
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_concat_converted_from_concat_float_3_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test concat converted from concat_float_3_relaxed test', async function() {
+ // Converted test case (from: V1_1/concat_float_3_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [212, 60]});
+ const input1Buffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 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12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719]);
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [212, 30]});
+ const input2Buffer = new Float32Array([0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, -30, -31, -32, -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46, -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, -60, -61, -62, -63, -64, -65, -66, -67, -68, -69, -70, -71, -72, -73, -74, -75, -76, -77, -78, -79, -80, -81, -82, -83, -84, -85, -86, -87, -88, -89, -90, -91, -92, -93, -94, -95, -96, -97, -98, -99, -100, -101, -102, -103, -104, -105, -106, -107, -108, -109, -110, -111, -112, -113, -114, -115, -116, -117, -118, -119, -120, -121, -122, -123, -124, -125, -126, -127, -128, -129, -130, -131, -132, -133, -134, -135, -136, -137, -138, -139, -140, -141, -142, -143, -144, -145, -146, -147, -148, -149, -150, -151, -152, -153, -154, -155, -156, -157, -158, -159, -160, -161, -162, -163, -164, -165, -166, -167, -168, -169, -170, -171, -172, -173, -174, -175, -176, -177, 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-842, -843, -844, -845, -846, -847, -848, -849, -850, -851, -852, -853, -854, -855, -856, -857, -858, -859, -860, -861, -862, -863, -864, -865, -866, -867, -868, -869, -870, -871, -872, -873, -874, -875, -876, -877, -878, -879, -880, -881, -882, -883, -884, -885, -886, -887, -888, -889, -890, -891, -892, -893, -894, -895, -896, -897, -898, -899, -900, -901, -902, -903, -904, -905, -906, -907, -908, -909, -910, -911, -912, -913, -914, -915, -916, -917, -918, -919, -920, -921, -922, -923, -924, -925, -926, -927, -928, -929, -930, -931, -932, -933, -934, -935, -936, -937, -938, -939, -940, -941, -942, -943, -944, -945, -946, -947, -948, -949, -950, -951, -952, -953, -954, -955, -956, -957, -958, -959, -960, -961, -962, -963, -964, -965, -966, -967, -968, -969, -970, -971, -972, -973, -974, -975, -976, -977, -978, -979, -980, -981, -982, -983, -984, -985, -986, -987, -988, -989, -990, -991, -992, -993, -994, -995, -996, -997, -998, -999, -1000, -1001, -1002, -1003, -1004, -1005, -1006, -1007, -1008, -1009, -1010, -1011, -1012, -1013, -1014, -1015, -1016, -1017, -1018, -1019, -1020, -1021, -1022, -1023, -1024, -1025, -1026, -1027, -1028, -1029, -1030, -1031, -1032, -1033, -1034, -1035, -1036, -1037, -1038, -1039, -1040, -1041, -1042, -1043, -1044, -1045, -1046, -1047, -1048, -1049, -1050, -1051, -1052, -1053, -1054, -1055, -1056, -1057, -1058, -1059, -1060, -1061, -1062, -1063, -1064, -1065, -1066, -1067, -1068, -1069, -1070, -1071, -1072, -1073, -1074, -1075, -1076, -1077, -1078, -1079, -1080, -1081, -1082, -1083, -1084, -1085, -1086, -1087, -1088, -1089, -1090, -1091, -1092, -1093, -1094, -1095, -1096, -1097, -1098, -1099, -1100, -1101, -1102, -1103, -1104, -1105, -1106, -1107, -1108, -1109, -1110, -1111, -1112, -1113, -1114, -1115, -1116, -1117, -1118, -1119, -1120, -1121, -1122, -1123, -1124, -1125, -1126, -1127, -1128, -1129, -1130, -1131, -1132, -1133, -1134, -1135, -1136, -1137, -1138, -1139, -1140, -1141, -1142, -1143, -1144, -1145, -1146, -1147, -1148, -1149, 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+ const axis1 = 1;
+ const expected = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19, -20, -21, -22, -23, -24, -25, -26, -27, -28, -29, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, -30, -31, -32, -33, -34, -35, -36, -37, -38, -39, -40, -41, -42, -43, -44, -45, -46, -47, -48, -49, -50, -51, -52, -53, -54, -55, -56, -57, -58, -59, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 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11914, 11915, 11916, 11917, 11918, 11919, 11920, 11921, 11922, 11923, 11924, 11925, 11926, 11927, 11928, 11929, 11930, 11931, 11932, 11933, 11934, 11935, 11936, 11937, 11938, 11939, -5940, -5941, -5942, -5943, -5944, -5945, -5946, -5947, -5948, -5949, -5950, -5951, -5952, -5953, -5954, -5955, -5956, -5957, -5958, -5959, -5960, -5961, -5962, -5963, -5964, -5965, -5966, -5967, -5968, -5969, 11940, 11941, 11942, 11943, 11944, 11945, 11946, 11947, 11948, 11949, 11950, 11951, 11952, 11953, 11954, 11955, 11956, 11957, 11958, 11959, 11960, 11961, 11962, 11963, 11964, 11965, 11966, 11967, 11968, 11969, 11970, 11971, 11972, 11973, 11974, 11975, 11976, 11977, 11978, 11979, 11980, 11981, 11982, 11983, 11984, 11985, 11986, 11987, 11988, 11989, 11990, 11991, 11992, 11993, 11994, 11995, 11996, 11997, 11998, 11999, -5970, -5971, -5972, -5973, -5974, -5975, -5976, -5977, -5978, -5979, -5980, -5981, -5982, -5983, -5984, -5985, -5986, -5987, -5988, -5989, -5990, -5991, -5992, -5993, -5994, -5995, -5996, 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12110, 12111, 12112, 12113, 12114, 12115, 12116, 12117, 12118, 12119, -6030, -6031, -6032, -6033, -6034, -6035, -6036, -6037, -6038, -6039, -6040, -6041, -6042, -6043, -6044, -6045, -6046, -6047, -6048, -6049, -6050, -6051, -6052, -6053, -6054, -6055, -6056, -6057, -6058, -6059, 12120, 12121, 12122, 12123, 12124, 12125, 12126, 12127, 12128, 12129, 12130, 12131, 12132, 12133, 12134, 12135, 12136, 12137, 12138, 12139, 12140, 12141, 12142, 12143, 12144, 12145, 12146, 12147, 12148, 12149, 12150, 12151, 12152, 12153, 12154, 12155, 12156, 12157, 12158, 12159, 12160, 12161, 12162, 12163, 12164, 12165, 12166, 12167, 12168, 12169, 12170, 12171, 12172, 12173, 12174, 12175, 12176, 12177, 12178, 12179, -6060, -6061, -6062, -6063, -6064, -6065, -6066, -6067, -6068, -6069, -6070, -6071, -6072, -6073, -6074, -6075, -6076, -6077, -6078, -6079, -6080, -6081, -6082, -6083, -6084, -6085, -6086, -6087, -6088, -6089, 12180, 12181, 12182, 12183, 12184, 12185, 12186, 12187, 12188, 12189, 12190, 12191, 12192, 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-6126, -6127, -6128, -6129, -6130, -6131, -6132, -6133, -6134, -6135, -6136, -6137, -6138, -6139, -6140, -6141, -6142, -6143, -6144, -6145, -6146, -6147, -6148, -6149, 12300, 12301, 12302, 12303, 12304, 12305, 12306, 12307, 12308, 12309, 12310, 12311, 12312, 12313, 12314, 12315, 12316, 12317, 12318, 12319, 12320, 12321, 12322, 12323, 12324, 12325, 12326, 12327, 12328, 12329, 12330, 12331, 12332, 12333, 12334, 12335, 12336, 12337, 12338, 12339, 12340, 12341, 12342, 12343, 12344, 12345, 12346, 12347, 12348, 12349, 12350, 12351, 12352, 12353, 12354, 12355, 12356, 12357, 12358, 12359, -6150, -6151, -6152, -6153, -6154, -6155, -6156, -6157, -6158, -6159, -6160, -6161, -6162, -6163, -6164, -6165, -6166, -6167, -6168, -6169, -6170, -6171, -6172, -6173, -6174, -6175, -6176, -6177, -6178, -6179, 12360, 12361, 12362, 12363, 12364, 12365, 12366, 12367, 12368, 12369, 12370, 12371, 12372, 12373, 12374, 12375, 12376, 12377, 12378, 12379, 12380, 12381, 12382, 12383, 12384, 12385, 12386, 12387, 12388, 12389, 12390, 12391, 12392, 12393, 12394, 12395, 12396, 12397, 12398, 12399, 12400, 12401, 12402, 12403, 12404, 12405, 12406, 12407, 12408, 12409, 12410, 12411, 12412, 12413, 12414, 12415, 12416, 12417, 12418, 12419, -6180, -6181, -6182, -6183, -6184, -6185, -6186, -6187, -6188, -6189, -6190, -6191, -6192, -6193, -6194, -6195, -6196, -6197, -6198, -6199, -6200, -6201, -6202, -6203, -6204, -6205, -6206, -6207, -6208, -6209, 12420, 12421, 12422, 12423, 12424, 12425, 12426, 12427, 12428, 12429, 12430, 12431, 12432, 12433, 12434, 12435, 12436, 12437, 12438, 12439, 12440, 12441, 12442, 12443, 12444, 12445, 12446, 12447, 12448, 12449, 12450, 12451, 12452, 12453, 12454, 12455, 12456, 12457, 12458, 12459, 12460, 12461, 12462, 12463, 12464, 12465, 12466, 12467, 12468, 12469, 12470, 12471, 12472, 12473, 12474, 12475, 12476, 12477, 12478, 12479, -6210, -6211, -6212, -6213, -6214, -6215, -6216, -6217, -6218, -6219, -6220, -6221, -6222, -6223, -6224, -6225, -6226, -6227, -6228, -6229, -6230, -6231, -6232, -6233, -6234, -6235, -6236, -6237, -6238, -6239, 12480, 12481, 12482, 12483, 12484, 12485, 12486, 12487, 12488, 12489, 12490, 12491, 12492, 12493, 12494, 12495, 12496, 12497, 12498, 12499, 12500, 12501, 12502, 12503, 12504, 12505, 12506, 12507, 12508, 12509, 12510, 12511, 12512, 12513, 12514, 12515, 12516, 12517, 12518, 12519, 12520, 12521, 12522, 12523, 12524, 12525, 12526, 12527, 12528, 12529, 12530, 12531, 12532, 12533, 12534, 12535, 12536, 12537, 12538, 12539, -6240, -6241, -6242, -6243, -6244, -6245, -6246, -6247, -6248, -6249, -6250, -6251, -6252, -6253, -6254, -6255, -6256, -6257, -6258, -6259, -6260, -6261, -6262, -6263, -6264, -6265, -6266, -6267, -6268, -6269, 12540, 12541, 12542, 12543, 12544, 12545, 12546, 12547, 12548, 12549, 12550, 12551, 12552, 12553, 12554, 12555, 12556, 12557, 12558, 12559, 12560, 12561, 12562, 12563, 12564, 12565, 12566, 12567, 12568, 12569, 12570, 12571, 12572, 12573, 12574, 12575, 12576, 12577, 12578, 12579, 12580, 12581, 12582, 12583, 12584, 12585, 12586, 12587, 12588, 12589, 12590, 12591, 12592, 12593, 12594, 12595, 12596, 12597, 12598, 12599, -6270, -6271, -6272, -6273, -6274, -6275, -6276, -6277, -6278, -6279, -6280, -6281, -6282, -6283, -6284, -6285, -6286, -6287, -6288, -6289, -6290, -6291, -6292, -6293, -6294, -6295, -6296, -6297, -6298, -6299, 12600, 12601, 12602, 12603, 12604, 12605, 12606, 12607, 12608, 12609, 12610, 12611, 12612, 12613, 12614, 12615, 12616, 12617, 12618, 12619, 12620, 12621, 12622, 12623, 12624, 12625, 12626, 12627, 12628, 12629, 12630, 12631, 12632, 12633, 12634, 12635, 12636, 12637, 12638, 12639, 12640, 12641, 12642, 12643, 12644, 12645, 12646, 12647, 12648, 12649, 12650, 12651, 12652, 12653, 12654, 12655, 12656, 12657, 12658, 12659, -6300, -6301, -6302, -6303, -6304, -6305, -6306, -6307, -6308, -6309, -6310, -6311, -6312, -6313, -6314, -6315, -6316, -6317, -6318, -6319, -6320, -6321, -6322, -6323, -6324, -6325, -6326, -6327, -6328, -6329, 12660, 12661, 12662, 12663, 12664, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12675, 12676, 12677, 12678, 12679, 12680, 12681, 12682, 12683, 12684, 12685, 12686, 12687, 12688, 12689, 12690, 12691, 12692, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, -6330, -6331, -6332, -6333, -6334, -6335, -6336, -6337, -6338, -6339, -6340, -6341, -6342, -6343, -6344, -6345, -6346, -6347, -6348, -6349, -6350, -6351, -6352, -6353, -6354, -6355, -6356, -6357, -6358, -6359];
+ const output = builder.concat([input1, input2], axis1);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}, 'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_relaxed.js
new file mode 100644
index 00000000..91add3f1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_channels_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_channels_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op1Buffer = new Float32Array([99.0, 99.0, 99.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [297.0, 594.0, 891.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..a75263fc
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_channels_weights_as_inputs_relaxed.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_channels_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_channels_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op1Buffer = new Float32Array([99.0, 99.0, 99.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op2Buffer = new Float32Array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [3]});
+ const op3Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [297.0, 594.0, 891.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_relaxed.js
new file mode 100644
index 00000000..c03c3561
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_large_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_large_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..36250cf6
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_large_weights_as_inputs_relaxed.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_large_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_large_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op2Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [3]});
+ const op3Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_relaxed.js
new file mode 100644
index 00000000..a0a9e06d
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..4f4409bf
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_conv_float_weights_as_inputs_relaxed.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv_float_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/conv_float_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc'});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_relaxed.js
new file mode 100644
index 00000000..f3bc862f
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/depthwise_conv2d_float_large_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op1Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 4]}, new Float32Array([0.25, 0.0, 10.0, 100.0, 0.25, 1.0, 20.0, 100.0, 0.25, 0.0, 30.0, 100.0, 0.25, 1.0, 40.0, 100.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [4]}, new Float32Array([6000, 7000, 8000, 9000]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 4});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..f721384b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_2_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/depthwise_conv2d_float_large_2_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op1Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 4]});
+ const op2Buffer = new Float32Array([0.25, 0.0, 10.0, 100.0, 0.25, 1.0, 20.0, 100.0, 0.25, 0.0, 30.0, 100.0, 0.25, 1.0, 40.0, 100.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [4]});
+ const op3Buffer = new Float32Array([6000, 7000, 8000, 9000]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 4});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_relaxed.js
new file mode 100644
index 00000000..90f4821a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_relaxed test', async function() {
+ // Converted test case (from: V1_1/depthwise_conv2d_float_large_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op1Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 2]}, new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]));
+ const op3 = builder.constant({type: 'float32', dimensions: [2]}, new Float32Array([100, 200]));
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 2});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..3020566e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_conv2d_add_clamp_converted_from_depthwise_conv2d_float_large_weights_as_inputs_relaxed.js
@@ -0,0 +1,29 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_float_large_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/depthwise_conv2d_float_large_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op1Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [2]});
+ const op3Buffer = new Float32Array([100, 200]);
+ const pad0 = 0;
+ const stride = 1;
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [pad0, pad0, pad0, pad0], 'strides': [stride, stride], 'layout': 'nhwc', 'groups': 2});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div.js b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div.js
new file mode 100644
index 00000000..f45ecb83
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test div + clamp converted from div test', async function() {
+ // Converted test case (from: V1_1/div.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2.0, -4.0, 8.0, -16.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([2.0, -2.0, -4.0, 4.0]);
+ const expected = [1.0, 2.0, -2.0, -4.0];
+ const interOut0 = builder.div(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float.js b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float.js
new file mode 100644
index 00000000..ad2ba749
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test div + clamp converted from div_broadcast_float test', async function() {
+ // Converted test case (from: V1_1/div_broadcast_float.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2]});
+ const op1Buffer = new Float32Array([1, 2]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2]});
+ const op2Buffer = new Float32Array([1, 1, 2, 2]);
+ const expected = [1, 2, 0.5, 1];
+ const interOut0 = builder.div(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float_relaxed.js
new file mode 100644
index 00000000..1fe8eb42
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_broadcast_float_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test div + clamp converted from div_broadcast_float_relaxed test', async function() {
+ // Converted test case (from: V1_1/div_broadcast_float_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2]});
+ const op1Buffer = new Float32Array([1, 2]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2]});
+ const op2Buffer = new Float32Array([1, 1, 2, 2]);
+ const expected = [1, 2, 0.5, 1];
+ const interOut0 = builder.div(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_relaxed.js
new file mode 100644
index 00000000..c85907c3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_div_clamp_converted_from_div_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test div + clamp converted from div_relaxed test', async function() {
+ // Converted test case (from: V1_1/div_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2.0, -4.0, 8.0, -16.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([2.0, -2.0, -4.0, 4.0]);
+ const expected = [1.0, 2.0, -2.0, -4.0];
+ const interOut0 = builder.div(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple.js
new file mode 100644
index 00000000..f923e2ab
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_4d_simple test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_4d_simple.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 10]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, -9, -10, 1, 2, 3, 4, 5, 6, 7, -8, 9, -10]);
+ const op2 = builder.constant({type: 'float32', dimensions: [10, 3]}, new Float32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10]));
+ const b0 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([1, 2, 3]));
+ const expected = [24, 25, 26, 58, 59, 60];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple_relaxed.js
new file mode 100644
index 00000000..e0e7b131
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_4d_simple_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_4d_simple_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_4d_simple_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 10]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, -9, -10, 1, 2, 3, 4, 5, 6, 7, -8, 9, -10]);
+ const op2 = builder.constant({type: 'float32', dimensions: [10, 3]}, new Float32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10]));
+ const b0 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([1, 2, 3]));
+ const expected = [24, 25, 26, 58, 59, 60];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_relaxed.js
new file mode 100644
index 00000000..400f9887
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_large_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_large_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 5]});
+ const op1Buffer = new Float32Array([1, 10, 100, 500, 1000]);
+ const op2 = builder.constant({type: 'float32', dimensions: [5, 1]}, new Float32Array([2, 3, 4, 5, 6]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([900]));
+ const expected = [9832];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..a8dec6eb
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_large_weights_as_inputs_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_large_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_large_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 5]});
+ const op1Buffer = new Float32Array([1, 10, 100, 500, 1000]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [5, 1]});
+ const op2Buffer = new Float32Array([2, 3, 4, 5, 6]);
+ const b0 = builder.input('b0', {type: 'float32', dimensions: [1]});
+ const b0Buffer = new Float32Array([900]);
+ const expected = [9832];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'b0': {buffer: b0Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_relaxed.js
new file mode 100644
index 00000000..33cd33bc
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1]}, new Float32Array([2]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([4]));
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..ddfe8149
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_clamp_converted_from_fully_connected_float_weights_as_inputs_relaxed.js
@@ -0,0 +1,27 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_float_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1]});
+ const op2Buffer = new Float32Array([2]);
+ const b0 = builder.input('b0', {type: 'float32', dimensions: [1]});
+ const b0Buffer = new Float32Array([4]);
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'b0': {buffer: b0Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_matmul_add_relu_converted_from_fully_connected_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_relu_converted_from_fully_connected_float_2_relaxed.js
new file mode 100644
index 00000000..177d4356
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_matmul_add_relu_converted_from_fully_connected_float_2_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + relu converted from fully_connected_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/fully_connected_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 8]});
+ const op1Buffer = new Float32Array([0.503691, 0.196961, 0.521017, 0.554248, 0.288678, 0.792476, 0.561653, 0.46223, 0.650736, 0.163132, 0.029658, 0.411544, 0.470539, 0.57239, 0.538755, 0.21203]);
+ const op2 = builder.constant({type: 'float32', dimensions: [8, 16]}, new Float32Array([0.091327, 0.063031, 0.109758, 0.266455, 0.096086, 0.142238, -0.104321, -0.252595, -0.304651, -0.128762, -0.339283, 0.103532, -0.139061, -0.241619, 0.265148, 0.302309, 0.103366, 0.19167, 0.008307, 0.051517, 0.131157, 0.08654, -0.176035, -0.16175, -0.070958, -0.03578, 0.333071, -0.034284, -0.048873, 0.357835, 0.141627, -0.041084, -0.316505, -0.062001, -0.062657, -0.123448, 0.031164, -0.139154, -0.208587, -0.136403, 0.054598, 0.117262, 0.180827, 0.093299, 0.067557, 0.135762, 0.02012, 0.146334, -0.08312, -0.061504, -0.060962, 0.322464, 0.100638, 0.174268, -0.001019, 0.008308, 0.147113, 0.017177, 0.287583, -0.145361, 0.139038, -0.306764, 0.083815, -0.061511, 0.149366, -0.275581, -0.049782, 0.043282, -0.312191, -0.073161, -0.162032, 0.00571, -0.139112, 0.263335, 0.06635, 0.054001, 0.324106, -0.125982, -0.124556, -0.232605, -0.196636, 0.059388, -0.106719, -0.173782, -0.080923, 0.080072, 0.080824, 0.0966, -0.072798, -0.176612, -0.197947, 0.25057, 0.227041, 0.091916, -0.100124, 0.281324, -0.123672, -0.118497, -0.319482, -0.190381, -0.101318, 0.006874, -0.025021, 0.289839, -0.163335, 0.262961, -0.114449, 0.15701, 0.037793, 0.266587, -0.048159, 0.145408, 0.0628, -0.079224, -0.10365, 0.002013, -0.116614, 0.229382, 0.07446, 0.218816, -0.167863, -0.093654, -0.236035, -0.14348, -0.225747, 0.030135, 0.181172, -0.221897]));
+ const b0 = builder.constant({type: 'float32', dimensions: [16]}, new Float32Array([-0.160594, 0.20577, -0.078307, -0.077984, 0.001937, 0.01586, 0.03681, 0.012346, 0.001028, 0.038551, 0.075415, 0.020804, 0.048478, -0.03227, 0.175688, -0.085662]));
+ const expected = [0, 0.0732134, 0, 0, 0, 0.280859, 0, 0.128927, 0, 0.0777251, 0, 0.270268, 0.271435, 0.0173503, 0.335465, 0.235562, 0, 0.0745866, 0, 0.051611, 0, 0.253876, 0, 0.0814873, 0, 0.104104, 0, 0.248529, 0.264194, 0, 0.302973, 0.166252];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.relu(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_1_relaxed.js
new file mode 100644
index 00000000..628362ae
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_1_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/max_pool_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const pad0 = 0;
+ const cons1 = 1;
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [pad0, pad0, pad0, pad0], 'strides': [cons1, cons1], 'windowDimensions': [cons1, cons1], 'layout': 'nhwc'});
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_2_relaxed.js
new file mode 100644
index 00000000..184d343b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_2_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/max_pool_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const i0Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const padding = 0;
+ const stride = 20;
+ const filter = 20;
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_3_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_3_relaxed.js
new file mode 100644
index 00000000..b04b2ee8
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_maxPool2d_clamp_converted_from_max_pool_float_3_relaxed.js
@@ -0,0 +1,25 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_float_3_relaxed test', async function() {
+ // Converted test case (from: V1_1/max_pool_float_3_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const i0 = builder.input('i0', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const i0Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const padding = 0;
+ const stride = 20;
+ const filter = 20;
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(i0, {'padding': [padding, padding, padding, padding], 'strides': [stride, stride], 'windowDimensions': [filter, filter], 'layout': 'nhwc'});
+ const output = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'i0': {buffer: i0Buffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_mul_clamp_converted_from_mul_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_mul_clamp_converted_from_mul_relaxed.js
new file mode 100644
index 00000000..b88e33ed
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_mul_clamp_converted_from_mul_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test mul + clamp converted from mul_relaxed test', async function() {
+ // Converted test case (from: V1_1/mul_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2, -4, 8, -16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([32, -16, -8, 4]);
+ const expected = [64, 64, -64, -64];
+ const interOut0 = builder.mul(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_mul_relu_converted_from_mul_relu_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_mul_relu_converted_from_mul_relu_relaxed.js
new file mode 100644
index 00000000..0eb5dfc8
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_mul_relu_converted_from_mul_relu_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test mul + relu converted from mul_relu_relaxed test', async function() {
+ // Converted test case (from: V1_1/mul_relu_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2, -4, 8, -16]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([32, -16, -8, 4]);
+ const expected = [64, 64, 0, 0];
+ const interOut0 = builder.mul(op1, op2);
+ const op3 = builder.relu(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_1_relaxed.js
new file mode 100644
index 00000000..af43d9c3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_1_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test relu converted from relu_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-10.0, -0.5, 0.5, 10.0]);
+ const expected = [0.0, 0.0, 0.5, 10.0];
+ const op2 = builder.relu(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_2_relaxed.js
new file mode 100644
index 00000000..80a05693
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_relu_converted_from_relu_float_2_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test relu converted from relu_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/relu_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 64, 40, 2]});
+ const inputBuffer = new Float32Array([-0.0, 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0, 9.0, -10.0, 11.0, -12.0, 13.0, -14.0, 15.0, -16.0, 17.0, -18.0, 19.0, -20.0, 21.0, -22.0, 23.0, -24.0, 25.0, -26.0, 27.0, -28.0, 29.0, -30.0, 31.0, -32.0, 33.0, -34.0, 35.0, -36.0, 37.0, -38.0, 39.0, -40.0, 41.0, -42.0, 43.0, -44.0, 45.0, -46.0, 47.0, -48.0, 49.0, -50.0, 51.0, -52.0, 53.0, -54.0, 55.0, -56.0, 57.0, -58.0, 59.0, -60.0, 61.0, -62.0, 63.0, -64.0, 65.0, -66.0, 67.0, -68.0, 69.0, -70.0, 71.0, -72.0, 73.0, -74.0, 75.0, -76.0, 77.0, -78.0, 79.0, -80.0, 81.0, -82.0, 83.0, -84.0, 85.0, -86.0, 87.0, -88.0, 89.0, -90.0, 91.0, -92.0, 93.0, -94.0, 95.0, -96.0, 97.0, -98.0, 99.0, -100.0, 101.0, -102.0, 103.0, -104.0, 105.0, -106.0, 107.0, -108.0, 109.0, -110.0, 111.0, -112.0, 113.0, -114.0, 115.0, -116.0, 117.0, -118.0, 119.0, -120.0, 121.0, -122.0, 123.0, -124.0, 125.0, -126.0, 127.0, -128.0, 129.0, -130.0, 131.0, -132.0, 133.0, -134.0, 135.0, -136.0, 137.0, -138.0, 139.0, -140.0, 141.0, 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+ const output = builder.relu(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_relaxed.js
new file mode 100644
index 00000000..e301c502
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test reshape converted from reshape_relaxed test', async function() {
+ // Converted test case (from: V1_1/reshape_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op2 = [-1];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9];
+ const op3 = builder.reshape(op1, op2);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_weights_as_inputs_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_weights_as_inputs_relaxed.js
new file mode 100644
index 00000000..3d5683c3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_reshape_converted_from_reshape_weights_as_inputs_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test reshape converted from reshape_weights_as_inputs_relaxed test', async function() {
+ // Converted test case (from: V1_1/reshape_weights_as_inputs_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op2 = [-1];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9];
+ const op3 = builder.reshape(op1, op2);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_1_relaxed.js
new file mode 100644
index 00000000..9a417ed9
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_1_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sigmoid converted from logistic_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/logistic_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 4.0, 8.0]);
+ const expected = [0.7310585975646973, 0.8807970285415649, 0.9820137619972229, 0.9996646642684937];
+ const op3 = builder.sigmoid(op1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_2_relaxed.js
new file mode 100644
index 00000000..deb8a67e
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sigmoid_converted_from_logistic_float_2_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sigmoid converted from logistic_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/logistic_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 32, 40, 2]});
+ const inputBuffer = new Float32Array([0.0, 1.0, 510.0, 3.0, 508.0, 5.0, 506.0, 7.0, 504.0, 9.0, 502.0, 11.0, 500.0, 13.0, 498.0, 15.0, 496.0, 17.0, 494.0, 19.0, 492.0, 21.0, 490.0, 23.0, 488.0, 25.0, 486.0, 27.0, 484.0, 29.0, 482.0, 31.0, 480.0, 33.0, 478.0, 35.0, 476.0, 37.0, 474.0, 39.0, 472.0, 41.0, 470.0, 43.0, 468.0, 45.0, 466.0, 47.0, 464.0, 49.0, 462.0, 51.0, 460.0, 53.0, 458.0, 55.0, 456.0, 57.0, 454.0, 59.0, 452.0, 61.0, 450.0, 63.0, 448.0, 65.0, 446.0, 67.0, 444.0, 69.0, 442.0, 71.0, 440.0, 73.0, 438.0, 75.0, 436.0, 77.0, 434.0, 79.0, 432.0, 81.0, 430.0, 83.0, 428.0, 85.0, 426.0, 87.0, 424.0, 89.0, 422.0, 91.0, 420.0, 93.0, 418.0, 95.0, 416.0, 97.0, 414.0, 99.0, 412.0, 101.0, 410.0, 103.0, 408.0, 105.0, 406.0, 107.0, 404.0, 109.0, 402.0, 111.0, 400.0, 113.0, 398.0, 115.0, 396.0, 117.0, 394.0, 119.0, 392.0, 121.0, 390.0, 123.0, 388.0, 125.0, 386.0, 127.0, 384.0, 129.0, 382.0, 131.0, 380.0, 133.0, 378.0, 135.0, 376.0, 137.0, 374.0, 139.0, 372.0, 141.0, 370.0, 143.0, 368.0, 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418.0, 95.0, 416.0, 97.0, 414.0, 99.0, 412.0, 101.0, 410.0, 103.0, 408.0, 105.0, 406.0, 107.0, 404.0, 109.0, 402.0, 111.0, 400.0, 113.0, 398.0, 115.0, 396.0, 117.0, 394.0, 119.0, 392.0, 121.0, 390.0, 123.0, 388.0, 125.0, 386.0, 127.0, 384.0, 129.0, 382.0, 131.0, 380.0, 133.0, 378.0, 135.0, 376.0, 137.0, 374.0, 139.0, 372.0, 141.0, 370.0, 143.0, 368.0, 145.0, 366.0, 147.0, 364.0, 149.0, 362.0, 151.0, 360.0, 153.0, 358.0, 155.0, 356.0, 157.0, 354.0, 159.0, 352.0, 161.0, 350.0, 163.0, 348.0, 165.0, 346.0, 167.0, 344.0, 169.0, 342.0, 171.0, 340.0, 173.0, 338.0, 175.0, 336.0, 177.0, 334.0, 179.0, 332.0, 181.0, 330.0, 183.0, 328.0, 185.0, 326.0, 187.0, 324.0, 189.0, 322.0, 191.0, 320.0, 193.0, 318.0, 195.0, 316.0, 197.0, 314.0, 199.0, 312.0, 201.0, 310.0, 203.0, 308.0, 205.0, 306.0, 207.0, 304.0, 209.0, 302.0, 211.0, 300.0, 213.0, 298.0, 215.0, 296.0, 217.0, 294.0, 219.0, 292.0, 221.0, 290.0, 223.0, 288.0, 225.0, 286.0, 227.0, 284.0, 229.0, 282.0, 231.0, 280.0, 233.0, 278.0, 235.0, 276.0, 237.0, 274.0, 239.0, 272.0, 241.0, 270.0, 243.0, 268.0, 245.0, 266.0, 247.0, 264.0, 249.0, 262.0, 251.0, 260.0, 253.0, 258.0, 255.0, 256.0, 257.0, 254.0, 259.0, 252.0, 261.0, 250.0, 263.0, 248.0, 265.0, 246.0, 267.0, 244.0, 269.0, 242.0, 271.0, 240.0, 273.0, 238.0, 275.0, 236.0, 277.0, 234.0, 279.0, 232.0, 281.0, 230.0, 283.0, 228.0, 285.0, 226.0, 287.0, 224.0, 289.0, 222.0, 291.0, 220.0, 293.0, 218.0, 295.0, 216.0, 297.0, 214.0, 299.0, 212.0, 301.0, 210.0, 303.0, 208.0, 305.0, 206.0, 307.0, 204.0, 309.0, 202.0, 311.0, 200.0, 313.0, 198.0, 315.0, 196.0, 317.0, 194.0, 319.0, 192.0, 321.0, 190.0, 323.0, 188.0, 325.0, 186.0, 327.0, 184.0, 329.0, 182.0, 331.0, 180.0, 333.0, 178.0, 335.0, 176.0, 337.0, 174.0, 339.0, 172.0, 341.0, 170.0, 343.0, 168.0, 345.0, 166.0, 347.0, 164.0, 349.0, 162.0, 351.0, 160.0, 353.0, 158.0, 355.0, 156.0, 357.0, 154.0, 359.0, 152.0, 361.0, 150.0, 363.0, 148.0, 365.0, 146.0, 367.0, 144.0, 369.0, 142.0, 371.0, 140.0, 373.0, 138.0, 375.0, 136.0, 377.0, 134.0, 379.0, 132.0, 381.0, 130.0, 383.0, 128.0, 385.0, 126.0, 387.0, 124.0, 389.0, 122.0, 391.0, 120.0, 393.0, 118.0, 395.0, 116.0, 397.0, 114.0, 399.0, 112.0, 401.0, 110.0, 403.0, 108.0, 405.0, 106.0, 407.0, 104.0, 409.0, 102.0, 411.0, 100.0, 413.0, 98.0, 415.0, 96.0, 417.0, 94.0, 419.0, 92.0, 421.0, 90.0, 423.0, 88.0, 425.0, 86.0, 427.0, 84.0, 429.0, 82.0, 431.0, 80.0, 433.0, 78.0, 435.0, 76.0, 437.0, 74.0, 439.0, 72.0, 441.0, 70.0, 443.0, 68.0, 445.0, 66.0, 447.0, 64.0, 449.0, 62.0, 451.0, 60.0, 453.0, 58.0, 455.0, 56.0, 457.0, 54.0, 459.0, 52.0, 461.0, 50.0, 463.0, 48.0, 465.0, 46.0, 467.0, 44.0, 469.0, 42.0, 471.0, 40.0, 473.0, 38.0, 475.0, 36.0, 477.0, 34.0, 479.0, 32.0, 481.0, 30.0, 483.0, 28.0, 485.0, 26.0, 487.0, 24.0, 489.0, 22.0, 491.0, 20.0, 493.0, 18.0, 495.0, 16.0, 497.0, 14.0, 499.0, 12.0, 501.0, 10.0, 503.0, 8.0, 505.0, 6.0, 507.0, 4.0, 509.0, 2.0, 511.0, 0.0, 1.0, 510.0, 3.0, 508.0, 5.0, 506.0, 7.0, 504.0, 9.0, 502.0, 11.0, 500.0, 13.0, 498.0, 15.0, 496.0, 17.0, 494.0, 19.0, 492.0, 21.0, 490.0, 23.0, 488.0, 25.0, 486.0, 27.0, 484.0, 29.0, 482.0, 31.0, 480.0, 33.0, 478.0, 35.0, 476.0, 37.0, 474.0, 39.0, 472.0, 41.0, 470.0, 43.0, 468.0, 45.0, 466.0, 47.0, 464.0, 49.0, 462.0, 51.0, 460.0, 53.0, 458.0, 55.0, 456.0, 57.0, 454.0, 59.0, 452.0, 61.0, 450.0, 63.0, 448.0, 65.0, 446.0, 67.0, 444.0, 69.0, 442.0, 71.0, 440.0, 73.0, 438.0, 75.0, 436.0, 77.0, 434.0, 79.0, 432.0, 81.0, 430.0, 83.0, 428.0, 85.0, 426.0, 87.0, 424.0, 89.0, 422.0, 91.0, 420.0, 93.0, 418.0, 95.0, 416.0, 97.0, 414.0, 99.0, 412.0, 101.0, 410.0, 103.0, 408.0, 105.0, 406.0, 107.0, 404.0, 109.0, 402.0, 111.0, 400.0, 113.0, 398.0, 115.0, 396.0, 117.0, 394.0, 119.0, 392.0, 121.0, 390.0, 123.0, 388.0, 125.0, 386.0, 127.0, 384.0, 129.0, 382.0, 131.0, 380.0, 133.0, 378.0, 135.0, 376.0, 137.0, 374.0, 139.0, 372.0, 141.0, 370.0, 143.0, 368.0, 145.0, 366.0, 147.0, 364.0, 149.0, 362.0, 151.0, 360.0, 153.0, 358.0, 155.0, 356.0, 157.0, 354.0, 159.0, 352.0, 161.0, 350.0, 163.0, 348.0, 165.0, 346.0, 167.0, 344.0, 169.0, 342.0, 171.0, 340.0, 173.0, 338.0, 175.0, 336.0, 177.0, 334.0, 179.0, 332.0, 181.0, 330.0, 183.0, 328.0, 185.0, 326.0, 187.0, 324.0, 189.0, 322.0, 191.0, 320.0, 193.0, 318.0, 195.0, 316.0, 197.0, 314.0, 199.0, 312.0, 201.0, 310.0, 203.0, 308.0, 205.0, 306.0, 207.0, 304.0, 209.0, 302.0, 211.0, 300.0, 213.0, 298.0, 215.0, 296.0, 217.0, 294.0, 219.0, 292.0, 221.0, 290.0, 223.0, 288.0, 225.0, 286.0, 227.0, 284.0, 229.0, 282.0, 231.0, 280.0, 233.0, 278.0, 235.0, 276.0, 237.0, 274.0, 239.0, 272.0, 241.0, 270.0, 243.0, 268.0, 245.0, 266.0, 247.0, 264.0, 249.0, 262.0, 251.0, 260.0, 253.0, 258.0, 255.0, 256.0, 257.0, 254.0, 259.0, 252.0, 261.0, 250.0, 263.0, 248.0, 265.0, 246.0, 267.0, 244.0, 269.0, 242.0, 271.0, 240.0, 273.0, 238.0, 275.0, 236.0, 277.0, 234.0, 279.0, 232.0, 281.0, 230.0, 283.0, 228.0, 285.0, 226.0, 287.0, 224.0, 289.0, 222.0, 291.0, 220.0, 293.0, 218.0, 295.0, 216.0, 297.0, 214.0, 299.0, 212.0, 301.0, 210.0, 303.0, 208.0, 305.0, 206.0, 307.0, 204.0, 309.0, 202.0, 311.0, 200.0, 313.0, 198.0, 315.0, 196.0, 317.0, 194.0, 319.0, 192.0, 321.0, 190.0, 323.0, 188.0, 325.0, 186.0, 327.0, 184.0, 329.0, 182.0, 331.0, 180.0, 333.0, 178.0, 335.0, 176.0, 337.0, 174.0, 339.0, 172.0, 341.0, 170.0, 343.0, 168.0, 345.0, 166.0, 347.0, 164.0, 349.0, 162.0, 351.0, 160.0, 353.0, 158.0, 355.0, 156.0, 357.0, 154.0, 359.0, 152.0, 361.0, 150.0, 363.0, 148.0, 365.0, 146.0, 367.0, 144.0, 369.0, 142.0, 371.0, 140.0, 373.0, 138.0, 375.0, 136.0, 377.0, 134.0, 379.0, 132.0, 381.0, 130.0, 383.0, 128.0, 385.0, 126.0, 387.0, 124.0, 389.0, 122.0, 391.0, 120.0, 393.0, 118.0, 395.0, 116.0, 397.0, 114.0, 399.0, 112.0, 401.0, 110.0, 403.0, 108.0, 405.0, 106.0, 407.0, 104.0, 409.0, 102.0, 411.0, 100.0, 413.0, 98.0, 415.0, 96.0, 417.0, 94.0, 419.0, 92.0, 421.0, 90.0, 423.0, 88.0, 425.0, 86.0, 427.0, 84.0, 429.0, 82.0, 431.0, 80.0, 433.0, 78.0, 435.0, 76.0, 437.0, 74.0, 439.0, 72.0, 441.0, 70.0, 443.0, 68.0, 445.0, 66.0, 447.0, 64.0, 449.0, 62.0, 451.0, 60.0, 453.0, 58.0, 455.0, 56.0, 457.0, 54.0, 459.0, 52.0, 461.0, 50.0, 463.0, 48.0, 465.0, 46.0, 467.0, 44.0, 469.0, 42.0, 471.0, 40.0, 473.0, 38.0, 475.0, 36.0, 477.0, 34.0, 479.0, 32.0, 481.0, 30.0, 483.0, 28.0, 485.0, 26.0, 487.0, 24.0, 489.0, 22.0, 491.0, 20.0, 493.0, 18.0, 495.0, 16.0, 497.0, 14.0, 499.0, 12.0, 501.0, 10.0, 503.0, 8.0, 505.0, 6.0, 507.0, 4.0, 509.0, 2.0, 511.0, 0.0, 1.0, 510.0, 3.0, 508.0, 5.0, 506.0, 7.0, 504.0, 9.0, 502.0, 11.0, 500.0, 13.0, 498.0, 15.0, 496.0, 17.0, 494.0, 19.0, 492.0, 21.0, 490.0, 23.0, 488.0, 25.0, 486.0, 27.0, 484.0, 29.0, 482.0, 31.0, 480.0, 33.0, 478.0, 35.0, 476.0, 37.0, 474.0, 39.0, 472.0, 41.0, 470.0, 43.0, 468.0, 45.0, 466.0, 47.0, 464.0, 49.0, 462.0, 51.0, 460.0, 53.0, 458.0, 55.0, 456.0, 57.0, 454.0, 59.0, 452.0, 61.0, 450.0, 63.0, 448.0, 65.0, 446.0, 67.0, 444.0, 69.0, 442.0, 71.0, 440.0, 73.0, 438.0, 75.0, 436.0, 77.0, 434.0, 79.0, 432.0, 81.0, 430.0, 83.0, 428.0, 85.0, 426.0, 87.0, 424.0, 89.0, 422.0, 91.0, 420.0, 93.0, 418.0, 95.0, 416.0, 97.0, 414.0, 99.0, 412.0, 101.0, 410.0, 103.0, 408.0, 105.0, 406.0, 107.0, 404.0, 109.0, 402.0, 111.0, 400.0, 113.0, 398.0, 115.0, 396.0, 117.0, 394.0, 119.0, 392.0, 121.0, 390.0, 123.0, 388.0, 125.0, 386.0, 127.0, 384.0, 129.0, 382.0, 131.0, 380.0, 133.0, 378.0, 135.0, 376.0, 137.0, 374.0, 139.0, 372.0, 141.0, 370.0, 143.0, 368.0, 145.0, 366.0, 147.0, 364.0, 149.0, 362.0, 151.0, 360.0, 153.0, 358.0, 155.0, 356.0, 157.0, 354.0, 159.0, 352.0, 161.0, 350.0, 163.0, 348.0, 165.0, 346.0, 167.0, 344.0, 169.0, 342.0, 171.0, 340.0, 173.0, 338.0, 175.0, 336.0, 177.0, 334.0, 179.0, 332.0, 181.0, 330.0, 183.0, 328.0, 185.0, 326.0, 187.0, 324.0, 189.0, 322.0, 191.0, 320.0, 193.0, 318.0, 195.0, 316.0, 197.0, 314.0, 199.0, 312.0, 201.0, 310.0, 203.0, 308.0, 205.0, 306.0, 207.0, 304.0, 209.0, 302.0, 211.0, 300.0, 213.0, 298.0, 215.0, 296.0, 217.0, 294.0, 219.0, 292.0, 221.0, 290.0, 223.0, 288.0, 225.0, 286.0, 227.0, 284.0, 229.0, 282.0, 231.0, 280.0, 233.0, 278.0, 235.0, 276.0, 237.0, 274.0, 239.0, 272.0, 241.0, 270.0, 243.0, 268.0, 245.0, 266.0, 247.0, 264.0, 249.0, 262.0, 251.0, 260.0, 253.0, 258.0, 255.0, 256.0, 257.0, 254.0, 259.0, 252.0, 261.0, 250.0, 263.0, 248.0, 265.0, 246.0, 267.0, 244.0, 269.0, 242.0, 271.0, 240.0, 273.0, 238.0, 275.0, 236.0, 277.0, 234.0, 279.0, 232.0, 281.0, 230.0, 283.0, 228.0, 285.0, 226.0, 287.0, 224.0, 289.0, 222.0, 291.0, 220.0, 293.0, 218.0, 295.0, 216.0, 297.0, 214.0, 299.0, 212.0, 301.0, 210.0, 303.0, 208.0, 305.0, 206.0, 307.0, 204.0, 309.0, 202.0, 311.0, 200.0, 313.0, 198.0, 315.0, 196.0, 317.0, 194.0, 319.0, 192.0, 321.0, 190.0, 323.0, 188.0, 325.0, 186.0, 327.0, 184.0, 329.0, 182.0, 331.0, 180.0, 333.0, 178.0, 335.0, 176.0, 337.0, 174.0, 339.0, 172.0, 341.0, 170.0, 343.0, 168.0, 345.0, 166.0, 347.0, 164.0, 349.0, 162.0, 351.0, 160.0, 353.0, 158.0, 355.0, 156.0, 357.0, 154.0, 359.0, 152.0, 361.0, 150.0, 363.0, 148.0, 365.0, 146.0, 367.0, 144.0, 369.0, 142.0, 371.0, 140.0, 373.0, 138.0, 375.0, 136.0, 377.0, 134.0, 379.0, 132.0, 381.0, 130.0, 383.0, 128.0, 385.0, 126.0, 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0.9990889488055994, 1.0, 0.9998766054240137, 1.0, 0.999983298578152, 1.0, 0.999997739675702, 1.0, 0.999999694097773, 1.0, 0.9999999586006244, 1.0, 0.9999999943972036, 1.0, 0.9999999992417439, 1.0, 0.9999999998973812, 1.0, 0.999999999986112, 1.0, 0.9999999999981204, 1.0, 0.9999999999997455, 1.0, 0.9999999999999656, 1.0, 0.9999999999999953, 1.0, 0.9999999999999993, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9999999999999998, 1.0, 0.9999999999999982, 1.0, 0.9999999999999873, 1.0, 0.9999999999999065, 1.0, 0.9999999999993086, 1.0, 0.999999999994891, 1.0, 0.9999999999622486, 1.0, 0.9999999997210531, 1.0, 0.9999999979388463, 1.0, 0.9999999847700205, 1.0, 0.9999998874648379, 1.0, 0.9999991684719722, 1.0, 0.9999938558253978, 1.0, 0.9999546021312976, 1.0, 0.9996646498695336, 1.0, 0.9975273768433653, 1.0, 0.9820137900379085, 1.0, 0.8807970779778823, 1.0];
+ const output = builder.sigmoid(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_softmax_converted_from_softmax_float_2_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_softmax_converted_from_softmax_float_2_relaxed.js
new file mode 100644
index 00000000..2a34a827
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_softmax_converted_from_softmax_float_2_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test softmax converted from softmax_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_1/softmax_float_2_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 5]});
+ const inputBuffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, -1.0, -2.0, -3.0, -4.0, -5.0]);
+ const expected = [0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647, 0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231];
+ const output = builder.softmax(input);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze.js b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze.js
new file mode 100644
index 00000000..d4c31e5b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test squeeze converted from squeeze test', async function() {
+ // Converted test case (from: V1_1/squeeze.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [4, 1, 1, 2]});
+ const inputBuffer = new Float32Array([1.4, 2.3, 3.2, 4.1, 5.4, 6.3, 7.2, 8.1]);
+ const squeezeDims = [1, 2];
+ const expected = [1.4, 2.3, 3.2, 4.1, 5.4, 6.3, 7.2, 8.1];
+ const output = builder.squeeze(input, {'axes': squeezeDims});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1.js b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1.js
new file mode 100644
index 00000000..e38da944
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test squeeze converted from squeeze_float_1 test', async function() {
+ // Converted test case (from: V1_1/squeeze_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [1, 24, 1]});
+ const inputBuffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]);
+ const squeezeDims = [2];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24];
+ const output = builder.squeeze(input, {'axes': squeezeDims});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1_relaxed.js
new file mode 100644
index 00000000..823499d3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_float_1_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test squeeze converted from squeeze_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/squeeze_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [1, 24, 1]});
+ const inputBuffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]);
+ const squeezeDims = [2];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24];
+ const output = builder.squeeze(input, {'axes': squeezeDims});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_relaxed.js
new file mode 100644
index 00000000..b790aac2
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_squeeze_converted_from_squeeze_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test squeeze converted from squeeze_relaxed test', async function() {
+ // Converted test case (from: V1_1/squeeze_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [4, 1, 1, 2]});
+ const inputBuffer = new Float32Array([1.4, 2.3, 3.2, 4.1, 5.4, 6.3, 7.2, 8.1]);
+ const squeezeDims = [1, 2];
+ const expected = [1.4, 2.3, 3.2, 4.1, 5.4, 6.3, 7.2, 8.1];
+ const output = builder.squeeze(input, {'axes': squeezeDims});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub.js b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub.js
new file mode 100644
index 00000000..84177613
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sub + clamp converted from sub test', async function() {
+ // Converted test case (from: V1_1/sub.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2.0, -4.0, 8.0, -16.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([2.0, -2.0, -4.0, 4.0]);
+ const expected = [0.0, -2.0, 12.0, -20.0];
+ const interOut0 = builder.sub(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float.js b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float.js
new file mode 100644
index 00000000..b61f9e41
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sub + clamp converted from sub_broadcast_float test', async function() {
+ // Converted test case (from: V1_1/sub_broadcast_float.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2]});
+ const op1Buffer = new Float32Array([1, 2]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2]});
+ const op2Buffer = new Float32Array([1, 2, 3, 4]);
+ const expected = [0, 0, -2, -2];
+ const interOut0 = builder.sub(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float_relaxed.js
new file mode 100644
index 00000000..6b485f8a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_broadcast_float_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sub + clamp converted from sub_broadcast_float_relaxed test', async function() {
+ // Converted test case (from: V1_1/sub_broadcast_float_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2]});
+ const op1Buffer = new Float32Array([1, 2]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2]});
+ const op2Buffer = new Float32Array([1, 2, 3, 4]);
+ const expected = [0, 0, -2, -2];
+ const interOut0 = builder.sub(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_relaxed.js
new file mode 100644
index 00000000..5312b651
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_sub_clamp_converted_from_sub_relaxed.js
@@ -0,0 +1,24 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test sub + clamp converted from sub_relaxed test', async function() {
+ // Converted test case (from: V1_1/sub_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([2.0, -4.0, 8.0, -16.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op2Buffer = new Float32Array([2.0, -2.0, -4.0, 4.0]);
+ const expected = [0.0, -2.0, 12.0, -20.0];
+ const interOut0 = builder.sub(op1, op2);
+ const op3 = builder.clamp(interOut0);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_tanh_converted_from_tanh_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_tanh_converted_from_tanh_relaxed.js
new file mode 100644
index 00000000..1808131a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_tanh_converted_from_tanh_relaxed.js
@@ -0,0 +1,21 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test tanh converted from tanh_relaxed test', async function() {
+ // Converted test case (from: V1_1/tanh_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([-1, 0, 1, 10]);
+ const expected = [-0.761594156, 0, 0.761594156, 0.999999996];
+ const op2 = builder.tanh(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose.js b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose.js
new file mode 100644
index 00000000..29383fd1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test transpose converted from transpose test', async function() {
+ // Converted test case (from: V1_1/transpose.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const inputBuffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const perms = [0, 2, 1, 3];
+ const expected = [1.0, 3.0, 2.0, 4.0];
+ const output = builder.transpose(input, {'permutation': perms});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1.js b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1.js
new file mode 100644
index 00000000..3c3b116a
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test transpose converted from transpose_float_1 test', async function() {
+ // Converted test case (from: V1_1/transpose_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 3, 4, 5]});
+ const inputBuffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]);
+ const perms = [2, 0, 1, 3];
+ const expected = [0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119];
+ const output = builder.transpose(input, {'permutation': perms});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1_relaxed.js
new file mode 100644
index 00000000..366c5dfd
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_float_1_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test transpose converted from transpose_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_1/transpose_float_1_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [2, 3, 4, 5]});
+ const inputBuffer = new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]);
+ const perms = [2, 0, 1, 3];
+ const expected = [0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119];
+ const output = builder.transpose(input, {'permutation': perms});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_relaxed.js b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_relaxed.js
new file mode 100644
index 00000000..d17a18e9
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_1/test_transpose_converted_from_transpose_relaxed.js
@@ -0,0 +1,22 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test transpose converted from transpose_relaxed test', async function() {
+ // Converted test case (from: V1_1/transpose_relaxed.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const inputBuffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const perms = [0, 2, 1, 3];
+ const expected = [1.0, 3.0, 2.0, 4.0];
+ const output = builder.transpose(input, {'permutation': perms});
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_averagePool2d_clamp_converted_from_avg_pool_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_averagePool2d_clamp_converted_from_avg_pool_v1_2.js
new file mode 100644
index 00000000..b38bdea7
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_averagePool2d_clamp_converted_from_avg_pool_v1_2.js
@@ -0,0 +1,376 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nhwc';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nhwc';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nchw';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nchw';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.averagePool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_2 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const op11Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+ const param9 = 30;
+ const param10 = 30;
+ const param11 = 30;
+ const param12 = 30;
+ const param13 = 5;
+ const param14 = 5;
+ const param15 = 35;
+ const param16 = 35;
+ const layout = 'nhwc';
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const op11Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+ const param9 = 30;
+ const param10 = 30;
+ const param11 = 30;
+ const param12 = 30;
+ const param13 = 5;
+ const param14 = 5;
+ const param15 = 35;
+ const param16 = 35;
+ const layout = 'nhwc';
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_2 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 3, 52, 60]});
+ const op11Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+ const param9 = 30;
+ const param10 = 30;
+ const param11 = 30;
+ const param12 = 30;
+ const param13 = 5;
+ const param14 = 5;
+ const param15 = 35;
+ const param16 = 35;
+ const layout = 'nchw';
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 3, 52, 60]});
+ const op11Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
+ const param9 = 30;
+ const param10 = 30;
+ const param11 = 30;
+ const param12 = 30;
+ const param13 = 5;
+ const param14 = 5;
+ const param15 = 35;
+ const param16 = 35;
+ const layout = 'nchw';
+ const expected = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+ const interOut0 = builder.averagePool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_3 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 200, 180, 1]});
+ const op12Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 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+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 2;
+ const param23 = 2;
+ const param24 = 10;
+ const param25 = 10;
+ const layout = 'nhwc';
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_relaxed_3 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 200, 180, 1]});
+ const op12Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 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+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 2;
+ const param23 = 2;
+ const param24 = 10;
+ const param25 = 10;
+ const layout = 'nhwc';
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_3 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 200, 180]});
+ const op12Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 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+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 2;
+ const param23 = 2;
+ const param24 = 10;
+ const param25 = 10;
+ const layout = 'nchw';
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_relaxed_3 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 200, 180]});
+ const op12Buffer = new Float32Array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 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+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 2;
+ const param23 = 2;
+ const param24 = 10;
+ const param25 = 10;
+ const layout = 'nchw';
+ const expected = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 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+ const interOut0 = builder.averagePool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_4 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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+ const param27 = 50;
+ const param28 = 50;
+ const param29 = 50;
+ const param30 = 50;
+ const param31 = 5;
+ const param32 = 5;
+ const param33 = 100;
+ const param34 = 100;
+ const layout = 'nhwc';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(op13, {'padding': [param29, param30, param27, param28], 'strides': [param32, param31], 'windowDimensions': [param34, param33], 'layout': layout});
+ const op43 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nhwc_relaxed_4 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [5, 52, 60, 3]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]);
+ const param27 = 50;
+ const param28 = 50;
+ const param29 = 50;
+ const param30 = 50;
+ const param31 = 5;
+ const param32 = 5;
+ const param33 = 100;
+ const param34 = 100;
+ const layout = 'nhwc';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(op13, {'padding': [param29, param30, param27, param28], 'strides': [param32, param31], 'windowDimensions': [param34, param33], 'layout': layout});
+ const op43 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_4 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [5, 3, 52, 60]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]);
+ const param27 = 50;
+ const param28 = 50;
+ const param29 = 50;
+ const param30 = 50;
+ const param31 = 5;
+ const param32 = 5;
+ const param33 = 100;
+ const param34 = 100;
+ const layout = 'nchw';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(op13, {'padding': [param29, param30, param27, param28], 'strides': [param32, param31], 'windowDimensions': [param34, param33], 'layout': layout});
+ const op43 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test averagePool2d + clamp converted from avg_pool_v1_2_nchw_relaxed_4 test', async function() {
+ // Converted test case (from: V1_2/avg_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [5, 3, 52, 60]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]);
+ const param27 = 50;
+ const param28 = 50;
+ const param29 = 50;
+ const param30 = 50;
+ const param31 = 5;
+ const param32 = 5;
+ const param33 = 100;
+ const param34 = 100;
+ const layout = 'nchw';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.averagePool2d(op13, {'padding': [param29, param30, param27, param28], 'strides': [param32, param31], 'windowDimensions': [param34, param33], 'layout': layout});
+ const op43 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_dilation.js b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_dilation.js
new file mode 100644
index 00000000..d7452ec1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_dilation.js
@@ -0,0 +1,440 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const param7 = 1;
+ const param8 = 1;
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout, 'dilations': [param8, param7]});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 9, 9, 1]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]));
+ const op31 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nhwc';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 9, 9, 1]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]));
+ const op31 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nhwc';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_weight_as_input_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 9, 9, 1]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.input('op21', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op21Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op31 = builder.input('op31', {type: 'float32', dimensions: [1]});
+ const op31Buffer = new Float32Array([0]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nhwc';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}, 'op21': {buffer: op21Buffer}, 'op31': {buffer: op31Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nhwc_weight_as_input_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 9, 9, 1]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.input('op21', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op21Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op31 = builder.input('op31', {type: 'float32', dimensions: [1]});
+ const op31Buffer = new Float32Array([0]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nhwc';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}, 'op21': {buffer: op21Buffer}, 'op31': {buffer: op31Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 1, 9, 9]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]));
+ const op31 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nchw';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 1, 9, 9]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]));
+ const op31 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nchw';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_weight_as_input_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 1, 9, 9]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.input('op21', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op21Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op31 = builder.input('op31', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op31Buffer = new Float32Array([0]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nchw';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}, 'op21': {buffer: op21Buffer}, 'op31': {buffer: op31Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_dilation_nchw_weight_as_input_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/conv2d_dilation.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [1, 1, 9, 9]});
+ const op11Buffer = new Float32Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+ const op21 = builder.input('op21', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op21Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+ const op31 = builder.input('op31', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op31Buffer = new Float32Array([0]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 1;
+ const param14 = 1;
+ const layout = 'nchw';
+ const param16 = 3;
+ const param17 = 3;
+ const expected = [5, 5, 5, 5, 5, 5, 5, 5, 5];
+ const interOut0 = builder.conv2d(op11, op21, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'layout': layout, 'dilations': [param17, param16]});
+ const interOut1 = builder.add(interOut0, op31);
+ const op41 = builder.clamp(interOut1);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}, 'op21': {buffer: op21Buffer}, 'op31': {buffer: op31Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_v1_2.js
new file mode 100644
index 00000000..94429ecd
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_conv2d_v1_2.js
@@ -0,0 +1,608 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nhwc test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 1]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nhwc_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nhwc_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 3, 3, 1]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [2, 2, 1, 1]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nhwc';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nchw test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25]));
+ const op3 = builder.constant({type: 'float32', dimensions: [1, 1, 1, 1]}, new Float32Array([0]));
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nchw_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_nchw_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op1Buffer = new Float32Array([1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0]);
+ const op2 = builder.input('op2', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op2Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25]);
+ const op3 = builder.input('op3', {type: 'float32', dimensions: [1, 1, 1, 1]});
+ const op3Buffer = new Float32Array([0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const layout = 'nchw';
+ const expected = [0.875, 0.875, 0.875, 0.875];
+ const interOut0 = builder.conv2d(op1, op2, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op3);
+ const op4 = builder.clamp(interOut1);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}, 'op2': {buffer: op2Buffer}, 'op3': {buffer: op3Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nhwc test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([0.5, 2.0, 3.5, 1.0, 2.5, 4.0, 1.5, 3.0, 4.5]));
+ const op32 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nhwc';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([0.5, 2.0, 3.5, 1.0, 2.5, 4.0, 1.5, 3.0, 4.5]));
+ const op32 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nhwc';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nhwc_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op22Buffer = new Float32Array([0.5, 2.0, 3.5, 1.0, 2.5, 4.0, 1.5, 3.0, 4.5]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [3]});
+ const op32Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nhwc';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nhwc_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 1, 1, 3]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op22Buffer = new Float32Array([0.5, 2.0, 3.5, 1.0, 2.5, 4.0, 1.5, 3.0, 4.5]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [3]});
+ const op32Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nhwc';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nchw test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]));
+ const op32 = builder.constant({type: 'float32', dimensions: [1, 3, 1, 1]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nchw';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]));
+ const op32 = builder.constant({type: 'float32', dimensions: [1, 3, 1, 1]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nchw';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nchw_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op22Buffer = new Float32Array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op32Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nchw';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_channel_nchw_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op12Buffer = new Float32Array([5.0, 5.0, 5.0]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op22Buffer = new Float32Array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op32Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 1;
+ const param16 = 1;
+ const layout = 'nchw';
+ const expected = [15.0, 37.5, 60.0];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param13, param14, param11, param12], 'strides': [param16, param15], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nhwc test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op13Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nhwc';
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op13Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [3]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nhwc';
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nhwc_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op13Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op23Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [3]});
+ const op33Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nhwc';
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nhwc_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 3, 3]});
+ const op13Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [1, 1, 3, 3]});
+ const op23Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [3]});
+ const op33Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nhwc';
+ const expected = [30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nchw test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 3, 2, 3]});
+ const op13Buffer = new Float32Array([1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([1.0, 4.0, 7.0, 2.0, 5.0, 8.0, 3.0, 6.0, 9.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [1, 3, 1, 1]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nchw';
+ const expected = [30.0, 66.0, 102.0, 138.0, 174.0, 210.0, 36.0, 81.0, 126.0, 171.0, 216.0, 261.0, 42.0, 96.0, 150.0, 204.0, 258.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 3, 2, 3]});
+ const op13Buffer = new Float32Array([1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [3, 3, 1, 1]}, new Float32Array([1.0, 4.0, 7.0, 2.0, 5.0, 8.0, 3.0, 6.0, 9.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [1, 3, 1, 1]}, new Float32Array([0.0, 0.0, 0.0]));
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nchw';
+ const expected = [30.0, 66.0, 102.0, 138.0, 174.0, 210.0, 36.0, 81.0, 126.0, 171.0, 216.0, 261.0, 42.0, 96.0, 150.0, 204.0, 258.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nchw_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 3, 2, 3]});
+ const op13Buffer = new Float32Array([1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op23Buffer = new Float32Array([1.0, 4.0, 7.0, 2.0, 5.0, 8.0, 3.0, 6.0, 9.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op33Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nchw';
+ const expected = [30.0, 66.0, 102.0, 138.0, 174.0, 210.0, 36.0, 81.0, 126.0, 171.0, 216.0, 261.0, 42.0, 96.0, 150.0, 204.0, 258.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from conv2d_v1_2_large_nchw_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 3, 2, 3]});
+ const op13Buffer = new Float32Array([1.0, 4.0, 7.0, 10.0, 13.0, 16.0, 2.0, 5.0, 8.0, 11.0, 14.0, 17.0, 3.0, 6.0, 9.0, 12.0, 15.0, 18.0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [3, 3, 1, 1]});
+ const op23Buffer = new Float32Array([1.0, 4.0, 7.0, 2.0, 5.0, 8.0, 3.0, 6.0, 9.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [1, 3, 1, 1]});
+ const op33Buffer = new Float32Array([0.0, 0.0, 0.0]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 1;
+ const param23 = 1;
+ const layout = 'nchw';
+ const expected = [30.0, 66.0, 102.0, 138.0, 174.0, 210.0, 36.0, 81.0, 126.0, 171.0, 216.0, 261.0, 42.0, 96.0, 150.0, 204.0, 258.0, 312.0];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'layout': layout});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_depthwise_conv2d_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_depthwise_conv2d_v1_2.js
new file mode 100644
index 00000000..a537b5c1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_conv2d_add_clamp_converted_from_depthwise_conv2d_v1_2.js
@@ -0,0 +1,408 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op22 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 2]}, new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]));
+ const op32 = builder.constant({type: 'float32', dimensions: [2]}, new Float32Array([100, 200]));
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nhwc';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op22 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 2]}, new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]));
+ const op32 = builder.constant({type: 'float32', dimensions: [2]}, new Float32Array([100, 200]));
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nhwc';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [2, 2, 1, 2]});
+ const op22Buffer = new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [2]});
+ const op32Buffer = new Float32Array([100, 200]);
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nhwc';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 21, 10, 22, 10, 23, 10, 24]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [2, 2, 1, 2]});
+ const op22Buffer = new Float32Array([0.25, 0.0, 0.25, 1.0, 0.25, 0.0, 0.25, 1.0]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [2]});
+ const op32Buffer = new Float32Array([100, 200]);
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nhwc';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24]);
+ const op22 = builder.constant({type: 'float32', dimensions: [2, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0]));
+ const op32 = builder.constant({type: 'float32', dimensions: [1, 2, 1, 1]}, new Float32Array([100, 200]));
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nchw';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24]);
+ const op22 = builder.constant({type: 'float32', dimensions: [2, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0]));
+ const op32 = builder.constant({type: 'float32', dimensions: [1, 2, 1, 1]}, new Float32Array([100, 200]));
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nchw';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_weight_as_input test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [2, 1, 2, 2]});
+ const op22Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [1, 2, 1, 1]});
+ const op32Buffer = new Float32Array([100, 200]);
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nchw';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_weight_as_input_relaxed test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [1, 2, 2, 2]});
+ const op12Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24]);
+ const op22 = builder.input('op22', {type: 'float32', dimensions: [2, 1, 2, 2]});
+ const op22Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0]);
+ const op32 = builder.input('op32', {type: 'float32', dimensions: [1, 2, 1, 1]});
+ const op32Buffer = new Float32Array([100, 200]);
+ const param13 = 0;
+ const param14 = 0;
+ const param15 = 0;
+ const param16 = 0;
+ const param17 = 1;
+ const param18 = 1;
+ const layout = 'nchw';
+ const expected = [110, 246];
+ const interOut0 = builder.conv2d(op12, op22, {'padding': [param15, param16, param13, param14], 'strides': [param18, param17], 'layout': layout, 'groups': 2});
+ const interOut1 = builder.add(interOut0, op32);
+ const op42 = builder.clamp(interOut1);
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}, 'op22': {buffer: op22Buffer}, 'op32': {buffer: op32Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op13Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 4]}, new Float32Array([0.25, 0.0, 10.0, 50.0, 0.25, 1.0, 20.0, 50.0, 0.25, 0.0, 30.0, 50.0, 0.25, 1.0, 40.0, 50.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [4]}, new Float32Array([6000, 7000, 8000, 9000]));
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nhwc';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op13Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [2, 2, 1, 4]}, new Float32Array([0.25, 0.0, 10.0, 50.0, 0.25, 1.0, 20.0, 50.0, 0.25, 0.0, 30.0, 50.0, 0.25, 1.0, 40.0, 50.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [4]}, new Float32Array([6000, 7000, 8000, 9000]));
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nhwc';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_weight_as_input_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op13Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [2, 2, 1, 4]});
+ const op23Buffer = new Float32Array([0.25, 0.0, 10.0, 50.0, 0.25, 1.0, 20.0, 50.0, 0.25, 0.0, 30.0, 50.0, 0.25, 1.0, 40.0, 50.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [4]});
+ const op33Buffer = new Float32Array([6000, 7000, 8000, 9000]);
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nhwc';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nhwc_weight_as_input_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 2, 2, 4]});
+ const op13Buffer = new Float32Array([10, 21, 10, 0, 10, 22, 20, 0, 10, 23, 30, 0, 10, 24, 40, 0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [2, 2, 1, 4]});
+ const op23Buffer = new Float32Array([0.25, 0.0, 10.0, 50.0, 0.25, 1.0, 20.0, 50.0, 0.25, 0.0, 30.0, 50.0, 0.25, 1.0, 40.0, 50.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [4]});
+ const op33Buffer = new Float32Array([6000, 7000, 8000, 9000]);
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nhwc';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 4, 2, 2]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24, 10, 20, 30, 40, 0, 0, 0, 0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [4, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0, 10.0, 20.0, 30.0, 40.0, 50.0, 50.0, 50.0, 50.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [1, 4, 1, 1]}, new Float32Array([6000, 7000, 8000, 9000]));
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nchw';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 4, 2, 2]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24, 10, 20, 30, 40, 0, 0, 0, 0]);
+ const op23 = builder.constant({type: 'float32', dimensions: [4, 1, 2, 2]}, new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0, 10.0, 20.0, 30.0, 40.0, 50.0, 50.0, 50.0, 50.0]));
+ const op33 = builder.constant({type: 'float32', dimensions: [1, 4, 1, 1]}, new Float32Array([6000, 7000, 8000, 9000]));
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nchw';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_weight_as_input_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 4, 2, 2]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24, 10, 20, 30, 40, 0, 0, 0, 0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [4, 1, 2, 2]});
+ const op23Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0, 10.0, 20.0, 30.0, 40.0, 50.0, 50.0, 50.0, 50.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [1, 4, 1, 1]});
+ const op33Buffer = new Float32Array([6000, 7000, 8000, 9000]);
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nchw';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test conv2d + add + clamp converted from depthwise_conv2d_v1_2_large_nchw_weight_as_input_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/depthwise_conv2d_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op13 = builder.input('op13', {type: 'float32', dimensions: [1, 4, 2, 2]});
+ const op13Buffer = new Float32Array([10, 10, 10, 10, 21, 22, 23, 24, 10, 20, 30, 40, 0, 0, 0, 0]);
+ const op23 = builder.input('op23', {type: 'float32', dimensions: [4, 1, 2, 2]});
+ const op23Buffer = new Float32Array([0.25, 0.25, 0.25, 0.25, 0.0, 1.0, 0.0, 1.0, 10.0, 20.0, 30.0, 40.0, 50.0, 50.0, 50.0, 50.0]);
+ const op33 = builder.input('op33', {type: 'float32', dimensions: [1, 4, 1, 1]});
+ const op33Buffer = new Float32Array([6000, 7000, 8000, 9000]);
+ const param21 = 0;
+ const param22 = 0;
+ const param23 = 0;
+ const param24 = 0;
+ const param25 = 1;
+ const param26 = 1;
+ const layout = 'nchw';
+ const expected = [6010, 7046, 11000, 9000];
+ const interOut0 = builder.conv2d(op13, op23, {'padding': [param23, param24, param21, param22], 'strides': [param26, param25], 'layout': layout, 'groups': 4});
+ const interOut1 = builder.add(interOut0, op33);
+ const op43 = builder.clamp(interOut1);
+ const model = builder.createModel({op43});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op13': {buffer: op13Buffer}, 'op23': {buffer: op23Buffer}, 'op33': {buffer: op33Buffer}});
+ utils.checkValue(outputs.op43.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_exp_converted_from_exp.js b/test/cts/from_nnapi/tests/V1_2/test_exp_converted_from_exp.js
new file mode 100644
index 00000000..4be5e36b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_exp_converted_from_exp.js
@@ -0,0 +1,34 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test exp converted from exp test', async function() {
+ // Converted test case (from: V1_2/exp.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [1, 2, 3, 4, 5]});
+ const input0Buffer = new Float32Array([-6.0, -5.9, -5.8, -5.7, -5.6, -5.5, -5.4, -5.3, -5.2, -5.1, -5.0, -4.9, -4.8, -4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4.0, -3.9, -3.8, -3.7, -3.6, -3.5, -3.4, -3.3, -3.2, -3.1, -3.0, -2.9, -2.8, -2.7, -2.6, -2.5, -2.4, -2.3, -2.2, -2.1, -2.0, -1.9, -1.8, -1.7, -1.6, -1.5, -1.4, -1.3, -1.2, -1.1, -1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9]);
+ const expected = [0.0024787521766663585, 0.0027394448187683684, 0.0030275547453758153, 0.003345965457471272, 0.003697863716482932, 0.004086771438464067, 0.004516580942612666, 0.004991593906910217, 0.0055165644207607716, 0.006096746565515638, 0.006737946999085467, 0.007446583070924338, 0.00822974704902003, 0.009095277101695816, 0.010051835744633586, 0.011108996538242306, 0.012277339903068436, 0.013568559012200934, 0.014995576820477703, 0.016572675401761255, 0.01831563888873418, 0.02024191144580439, 0.0223707718561656, 0.024723526470339388, 0.02732372244729256, 0.0301973834223185, 0.03337326996032608, 0.036883167401240015, 0.04076220397836621, 0.0450492023935578, 0.049787068367863944, 0.05502322005640723, 0.06081006262521797, 0.06720551273974976, 0.07427357821433388, 0.0820849986238988, 0.09071795328941251, 0.10025884372280375, 0.11080315836233387, 0.1224564282529819, 0.1353352832366127, 0.14956861922263506, 0.16529888822158653, 0.18268352405273466, 0.20189651799465538, 0.22313016014842982, 0.2465969639416065, 0.2725317930340126, 0.30119421191220214, 0.33287108369807955, 0.36787944117144233, 0.4065696597405991, 0.44932896411722156, 0.4965853037914095, 0.5488116360940264, 0.6065306597126334, 0.6703200460356393, 0.7408182206817179, 0.8187307530779818, 0.9048374180359595, 1.0, 1.1051709180756477, 1.2214027581601699, 1.3498588075760032, 1.4918246976412703, 1.6487212707001282, 1.8221188003905089, 2.0137527074704766, 2.225540928492468, 2.45960311115695, 2.718281828459045, 3.0041660239464334, 3.3201169227365472, 3.6692966676192444, 4.0551999668446745, 4.4816890703380645, 4.953032424395115, 5.4739473917272, 6.0496474644129465, 6.6858944422792685, 7.38905609893065, 8.166169912567652, 9.025013499434122, 9.974182454814718, 11.023176380641601, 12.182493960703473, 13.463738035001692, 14.879731724872837, 16.444646771097048, 18.17414536944306, 20.085536923187668, 22.197951281441636, 24.532530197109352, 27.112638920657883, 29.96410004739701, 33.11545195869231, 36.59823444367799, 40.4473043600674, 44.701184493300815, 49.40244910553017, 54.598150033144236, 60.34028759736195, 66.68633104092515, 73.69979369959579, 81.45086866496814, 90.01713130052181, 99.48431564193378, 109.94717245212352, 121.51041751873485, 134.28977968493552, 148.4131591025766, 164.0219072999017, 181.27224187515122, 200.33680997479166, 221.40641620418717, 244.69193226422038, 270.42640742615254, 298.8674009670603, 330.2995599096486, 365.0374678653289];
+ const output0 = builder.exp(input0);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test exp converted from exp_relaxed test', async function() {
+ // Converted test case (from: V1_2/exp.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [1, 2, 3, 4, 5]});
+ const input0Buffer = new Float32Array([-6.0, -5.9, -5.8, -5.7, -5.6, -5.5, -5.4, -5.3, -5.2, -5.1, -5.0, -4.9, -4.8, -4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4.0, -3.9, -3.8, -3.7, -3.6, -3.5, -3.4, -3.3, -3.2, -3.1, -3.0, -2.9, -2.8, -2.7, -2.6, -2.5, -2.4, -2.3, -2.2, -2.1, -2.0, -1.9, -1.8, -1.7, -1.6, -1.5, -1.4, -1.3, -1.2, -1.1, -1.0, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9]);
+ const expected = [0.0024787521766663585, 0.0027394448187683684, 0.0030275547453758153, 0.003345965457471272, 0.003697863716482932, 0.004086771438464067, 0.004516580942612666, 0.004991593906910217, 0.0055165644207607716, 0.006096746565515638, 0.006737946999085467, 0.007446583070924338, 0.00822974704902003, 0.009095277101695816, 0.010051835744633586, 0.011108996538242306, 0.012277339903068436, 0.013568559012200934, 0.014995576820477703, 0.016572675401761255, 0.01831563888873418, 0.02024191144580439, 0.0223707718561656, 0.024723526470339388, 0.02732372244729256, 0.0301973834223185, 0.03337326996032608, 0.036883167401240015, 0.04076220397836621, 0.0450492023935578, 0.049787068367863944, 0.05502322005640723, 0.06081006262521797, 0.06720551273974976, 0.07427357821433388, 0.0820849986238988, 0.09071795328941251, 0.10025884372280375, 0.11080315836233387, 0.1224564282529819, 0.1353352832366127, 0.14956861922263506, 0.16529888822158653, 0.18268352405273466, 0.20189651799465538, 0.22313016014842982, 0.2465969639416065, 0.2725317930340126, 0.30119421191220214, 0.33287108369807955, 0.36787944117144233, 0.4065696597405991, 0.44932896411722156, 0.4965853037914095, 0.5488116360940264, 0.6065306597126334, 0.6703200460356393, 0.7408182206817179, 0.8187307530779818, 0.9048374180359595, 1.0, 1.1051709180756477, 1.2214027581601699, 1.3498588075760032, 1.4918246976412703, 1.6487212707001282, 1.8221188003905089, 2.0137527074704766, 2.225540928492468, 2.45960311115695, 2.718281828459045, 3.0041660239464334, 3.3201169227365472, 3.6692966676192444, 4.0551999668446745, 4.4816890703380645, 4.953032424395115, 5.4739473917272, 6.0496474644129465, 6.6858944422792685, 7.38905609893065, 8.166169912567652, 9.025013499434122, 9.974182454814718, 11.023176380641601, 12.182493960703473, 13.463738035001692, 14.879731724872837, 16.444646771097048, 18.17414536944306, 20.085536923187668, 22.197951281441636, 24.532530197109352, 27.112638920657883, 29.96410004739701, 33.11545195869231, 36.59823444367799, 40.4473043600674, 44.701184493300815, 49.40244910553017, 54.598150033144236, 60.34028759736195, 66.68633104092515, 73.69979369959579, 81.45086866496814, 90.01713130052181, 99.48431564193378, 109.94717245212352, 121.51041751873485, 134.28977968493552, 148.4131591025766, 164.0219072999017, 181.27224187515122, 200.33680997479166, 221.40641620418717, 244.69193226422038, 270.42640742615254, 298.8674009670603, 330.2995599096486, 365.0374678653289];
+ const output0 = builder.exp(input0);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_matmul_add_clamp_converted_from_fully_connected_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_matmul_add_clamp_converted_from_fully_connected_v1_2.js
new file mode 100644
index 00000000..9a5c9098
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_matmul_add_clamp_converted_from_fully_connected_v1_2.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test matmul + add + clamp converted from fully_connected_v1_2 test', async function() {
+ // Converted test case (from: V1_2/fully_connected_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1]}, new Float32Array([2]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([4]));
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test matmul + add + clamp converted from fully_connected_v1_2_relaxed test', async function() {
+ // Converted test case (from: V1_2/fully_connected_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [3, 1]});
+ const op1Buffer = new Float32Array([2, 32, 16]);
+ const op2 = builder.constant({type: 'float32', dimensions: [1, 1]}, new Float32Array([2]));
+ const b0 = builder.constant({type: 'float32', dimensions: [1]}, new Float32Array([4]));
+ const expected = [8, 68, 36];
+ const interOut0 = builder.matmul(op1, op2);
+ const interOut1 = builder.add(interOut0, b0);
+ const op3 = builder.clamp(interOut1);
+ const model = builder.createModel({op3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_maxPool2d_clamp_converted_from_max_pool_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_maxPool2d_clamp_converted_from_max_pool_v1_2.js
new file mode 100644
index 00000000..d595d4b1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_maxPool2d_clamp_converted_from_max_pool_v1_2.js
@@ -0,0 +1,284 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nhwc';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc_relaxed test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 2, 2, 1]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nhwc';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nchw';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw_relaxed test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [1, 1, 2, 2]});
+ const op1Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0]);
+ const param = 0;
+ const param1 = 0;
+ const param2 = 0;
+ const param3 = 0;
+ const param4 = 1;
+ const param5 = 1;
+ const param6 = 1;
+ const param7 = 1;
+ const layout = 'nchw';
+ const expected = [1.0, 2.0, 3.0, 4.0];
+ const interOut0 = builder.maxPool2d(op1, {'padding': [param2, param3, param, param1], 'strides': [param5, param4], 'windowDimensions': [param7, param6], 'layout': layout});
+ const op4 = builder.clamp(interOut0);
+ const model = builder.createModel({op4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc_2 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const op11Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 20;
+ const param14 = 20;
+ const param15 = 20;
+ const param16 = 20;
+ const layout = 'nhwc';
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const op11Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 20;
+ const param14 = 20;
+ const param15 = 20;
+ const param16 = 20;
+ const layout = 'nhwc';
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw_2 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 3, 50, 70]});
+ const op11Buffer = new Float32Array([1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 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11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 20;
+ const param14 = 20;
+ const param15 = 20;
+ const param16 = 20;
+ const layout = 'nchw';
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op11 = builder.input('op11', {type: 'float32', dimensions: [5, 3, 50, 70]});
+ const op11Buffer = new Float32Array([1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 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11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20]);
+ const param9 = 0;
+ const param10 = 0;
+ const param11 = 0;
+ const param12 = 0;
+ const param13 = 20;
+ const param14 = 20;
+ const param15 = 20;
+ const param16 = 20;
+ const layout = 'nchw';
+ const expected = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20];
+ const interOut0 = builder.maxPool2d(op11, {'padding': [param11, param12, param9, param10], 'strides': [param14, param13], 'windowDimensions': [param16, param15], 'layout': layout});
+ const op41 = builder.clamp(interOut0);
+ const model = builder.createModel({op41});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op11': {buffer: op11Buffer}});
+ utils.checkValue(outputs.op41.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc_3 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const op12Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 20;
+ const param23 = 20;
+ const param24 = 20;
+ const param25 = 20;
+ const layout = 'nhwc';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nhwc_relaxed_3 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [5, 50, 70, 3]});
+ const op12Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 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18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 20;
+ const param23 = 20;
+ const param24 = 20;
+ const param25 = 20;
+ const layout = 'nhwc';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw_3 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [5, 3, 50, 70]});
+ const op12Buffer = new Float32Array([1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 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11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 20;
+ const param23 = 20;
+ const param24 = 20;
+ const param25 = 20;
+ const layout = 'nchw';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test maxPool2d + clamp converted from max_pool_v1_2_nchw_relaxed_3 test', async function() {
+ // Converted test case (from: V1_2/max_pool_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op12 = builder.input('op12', {type: 'float32', dimensions: [5, 3, 50, 70]});
+ const op12Buffer = new Float32Array([1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 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11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20]);
+ const param18 = 0;
+ const param19 = 0;
+ const param20 = 0;
+ const param21 = 0;
+ const param22 = 20;
+ const param23 = 20;
+ const param24 = 20;
+ const param25 = 20;
+ const layout = 'nchw';
+ const expected = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6];
+ const interOut0 = builder.maxPool2d(op12, {'padding': [param20, param21, param18, param19], 'strides': [param23, param22], 'windowDimensions': [param25, param24], 'layout': layout});
+ const op42 = builder.clamp(interOut0, {minValue: builder.constant(0), maxValue: builder.constant(6)});
+ const model = builder.createModel({op42});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op12': {buffer: op12Buffer}});
+ utils.checkValue(outputs.op42.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_max_converted_from_maximum.js b/test/cts/from_nnapi/tests/V1_2/test_max_converted_from_maximum.js
new file mode 100644
index 00000000..f26d634c
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_max_converted_from_maximum.js
@@ -0,0 +1,98 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test max converted from maximum_simple test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Float32Array([1.0, 0.0, -1.0, 11.0, -2.0, -1.44]);
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Float32Array([-1.0, 0.0, 1.0, 12.0, -3.0, -1.43]);
+ const expected = [1.0, 0.0, 1.0, 12.0, -2.0, -1.43];
+ const output0 = builder.max(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test max converted from maximum_simple_relaxed test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Float32Array([1.0, 0.0, -1.0, 11.0, -2.0, -1.44]);
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Float32Array([-1.0, 0.0, 1.0, 12.0, -3.0, -1.43]);
+ const expected = [1.0, 0.0, 1.0, 12.0, -2.0, -1.43];
+ const output0 = builder.max(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test max converted from maximum_simple_int32 test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Int32Array([1, 0, -1, 11, -2, -1]);
+ const input1 = builder.input('input1', {type: 'int32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Int32Array([-1, 0, 1, 12, -3, -1]);
+ const expected = [1, 0, 1, 12, -2, -1];
+ const output0 = builder.max(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test max converted from maximum_broadcast test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'float32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Float32Array([1.0, 0.0, -1.0, -2.0, -1.44, 11.0]);
+ const input11 = builder.input('input11', {type: 'float32', dimensions: [2]});
+ const input11Buffer = new Float32Array([0.5, 2.0]);
+ const expected = [1.0, 2.0, 0.5, 2.0, 0.5, 11.0];
+ const output01 = builder.max(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test max converted from maximum_broadcast_relaxed test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'float32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Float32Array([1.0, 0.0, -1.0, -2.0, -1.44, 11.0]);
+ const input11 = builder.input('input11', {type: 'float32', dimensions: [2]});
+ const input11Buffer = new Float32Array([0.5, 2.0]);
+ const expected = [1.0, 2.0, 0.5, 2.0, 0.5, 11.0];
+ const output01 = builder.max(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test max converted from maximum_broadcast_int32 test', async function() {
+ // Converted test case (from: V1_2/maximum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'int32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Int32Array([1, 0, -1, -2, -1, 11]);
+ const input11 = builder.input('input11', {type: 'int32', dimensions: [2]});
+ const input11Buffer = new Int32Array([0, 2]);
+ const expected = [1, 2, 0, 2, 0, 11];
+ const output01 = builder.max(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_min_converted_from_minimum.js b/test/cts/from_nnapi/tests/V1_2/test_min_converted_from_minimum.js
new file mode 100644
index 00000000..c548f375
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_min_converted_from_minimum.js
@@ -0,0 +1,98 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test min converted from minimum_simple test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Float32Array([1.0, 0.0, -1.0, 11.0, -2.0, -1.44]);
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Float32Array([-1.0, 0.0, 1.0, 12.0, -3.0, -1.43]);
+ const expected = [-1.0, 0.0, -1.0, 11.0, -3.0, -1.44];
+ const output0 = builder.min(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test min converted from minimum_simple_relaxed test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Float32Array([1.0, 0.0, -1.0, 11.0, -2.0, -1.44]);
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Float32Array([-1.0, 0.0, 1.0, 12.0, -3.0, -1.43]);
+ const expected = [-1.0, 0.0, -1.0, 11.0, -3.0, -1.44];
+ const output0 = builder.min(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test min converted from minimum_simple_int32 test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [3, 1, 2]});
+ const input0Buffer = new Int32Array([1, 0, -1, 11, -2, -1]);
+ const input1 = builder.input('input1', {type: 'int32', dimensions: [3, 1, 2]});
+ const input1Buffer = new Int32Array([-1, 0, 1, 12, -3, -1]);
+ const expected = [-1, 0, -1, 11, -3, -1];
+ const output0 = builder.min(input0, input1);
+ const model = builder.createModel({output0});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}, 'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output0.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test min converted from minimum_broadcast test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'float32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Float32Array([1.0, 0.0, -1.0, -2.0, -1.44, 11.0]);
+ const input11 = builder.input('input11', {type: 'float32', dimensions: [2]});
+ const input11Buffer = new Float32Array([0.5, 2.0]);
+ const expected = [0.5, 0.0, -1.0, -2.0, -1.44, 2.0];
+ const output01 = builder.min(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test min converted from minimum_broadcast_relaxed test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'float32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Float32Array([1.0, 0.0, -1.0, -2.0, -1.44, 11.0]);
+ const input11 = builder.input('input11', {type: 'float32', dimensions: [2]});
+ const input11Buffer = new Float32Array([0.5, 2.0]);
+ const expected = [0.5, 0.0, -1.0, -2.0, -1.44, 2.0];
+ const output01 = builder.min(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test min converted from minimum_broadcast_int32 test', async function() {
+ // Converted test case (from: V1_2/minimum.mod.py)
+ const builder = nn.createModelBuilder();
+ const input01 = builder.input('input01', {type: 'int32', dimensions: [3, 1, 2]});
+ const input01Buffer = new Int32Array([1, 0, -1, -2, -1, 11]);
+ const input11 = builder.input('input11', {type: 'int32', dimensions: [2]});
+ const input11Buffer = new Int32Array([0, 2]);
+ const expected = [0, 0, -1, -2, -1, 2];
+ const output01 = builder.min(input01, input11);
+ const model = builder.createModel({output01});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input01': {buffer: input01Buffer}, 'input11': {buffer: input11Buffer}});
+ utils.checkValue(outputs.output01.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_slice_converted_from_slice.js b/test/cts/from_nnapi/tests/V1_2/test_slice_converted_from_slice.js
new file mode 100644
index 00000000..16077930
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_slice_converted_from_slice.js
@@ -0,0 +1,263 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test slice converted from slice test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [4]});
+ const inputBuffer = new Float32Array([1, 2, 3, 4]);
+ const begin = [1];
+ const size = [2];
+ const expected = [2, 3];
+ const output = builder.slice(input, begin, size);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input = builder.input('input', {type: 'float32', dimensions: [4]});
+ const inputBuffer = new Float32Array([1, 2, 3, 4]);
+ const begin = [1];
+ const size = [2];
+ const expected = [2, 3];
+ const output = builder.slice(input, begin, size);
+ const model = builder.createModel({output});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input': {buffer: inputBuffer}});
+ utils.checkValue(outputs.output.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_2 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [2, 3]});
+ const input1Buffer = new Float32Array([1, 2, 3, 4, 5, 6]);
+ const begin1 = [1, 0];
+ const size1 = [1, 2];
+ const expected = [4, 5];
+ const output1 = builder.slice(input1, begin1, size1);
+ const model = builder.createModel({output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output1.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_2 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input1 = builder.input('input1', {type: 'float32', dimensions: [2, 3]});
+ const input1Buffer = new Float32Array([1, 2, 3, 4, 5, 6]);
+ const begin1 = [1, 0];
+ const size1 = [1, 2];
+ const expected = [4, 5];
+ const output1 = builder.slice(input1, begin1, size1);
+ const model = builder.createModel({output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input1': {buffer: input1Buffer}});
+ utils.checkValue(outputs.output1.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_3 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [2, 3, 2]});
+ const input2Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+ const begin2 = [0, 0, 0];
+ const size2 = [2, 3, 2];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
+ const output2 = builder.slice(input2, begin2, size2);
+ const model = builder.createModel({output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_3 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input2 = builder.input('input2', {type: 'float32', dimensions: [2, 3, 2]});
+ const input2Buffer = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+ const begin2 = [0, 0, 0];
+ const size2 = [2, 3, 2];
+ const expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
+ const output2 = builder.slice(input2, begin2, size2);
+ const model = builder.createModel({output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input2': {buffer: input2Buffer}});
+ utils.checkValue(outputs.output2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_4 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input3 = builder.input('input3', {type: 'float32', dimensions: [4, 1, 1, 1]});
+ const input3Buffer = new Float32Array([1, 2, 3, 4]);
+ const begin3 = [1, 0, 0, 0];
+ const size3 = [3, 1, 1, 1];
+ const expected = [2, 3, 4];
+ const output3 = builder.slice(input3, begin3, size3);
+ const model = builder.createModel({output3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input3': {buffer: input3Buffer}});
+ utils.checkValue(outputs.output3.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_4 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input3 = builder.input('input3', {type: 'float32', dimensions: [4, 1, 1, 1]});
+ const input3Buffer = new Float32Array([1, 2, 3, 4]);
+ const begin3 = [1, 0, 0, 0];
+ const size3 = [3, 1, 1, 1];
+ const expected = [2, 3, 4];
+ const output3 = builder.slice(input3, begin3, size3);
+ const model = builder.createModel({output3});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input3': {buffer: input3Buffer}});
+ utils.checkValue(outputs.output3.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_5 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input4 = builder.input('input4', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input4Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin4 = [1, 0, 0, 0];
+ const size4 = [1, 1, 3, 1];
+ const expected = [3, 3, 3];
+ const output4 = builder.slice(input4, begin4, size4);
+ const model = builder.createModel({output4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input4': {buffer: input4Buffer}});
+ utils.checkValue(outputs.output4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_5 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input4 = builder.input('input4', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input4Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin4 = [1, 0, 0, 0];
+ const size4 = [1, 1, 3, 1];
+ const expected = [3, 3, 3];
+ const output4 = builder.slice(input4, begin4, size4);
+ const model = builder.createModel({output4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input4': {buffer: input4Buffer}});
+ utils.checkValue(outputs.output4.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_float16_5 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input4 = builder.input('input4', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input4Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin4 = [1, 0, 0, 0];
+ const size4 = [1, 1, 3, 1];
+ const expected = [3, 3, 3];
+ const output4 = builder.slice(input4, begin4, size4);
+ const model = builder.createModel({output4});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input4': {buffer: input4Buffer}});
+ utils.checkValue(outputs.output4.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_6 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input5 = builder.input('input5', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input5Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin5 = [1, 0, 0, 0];
+ const size5 = [2, 1, 3, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output5 = builder.slice(input5, begin5, size5);
+ const model = builder.createModel({output5});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input5': {buffer: input5Buffer}});
+ utils.checkValue(outputs.output5.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_6 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input5 = builder.input('input5', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input5Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin5 = [1, 0, 0, 0];
+ const size5 = [2, 1, 3, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output5 = builder.slice(input5, begin5, size5);
+ const model = builder.createModel({output5});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input5': {buffer: input5Buffer}});
+ utils.checkValue(outputs.output5.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_float16_6 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input5 = builder.input('input5', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input5Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin5 = [1, 0, 0, 0];
+ const size5 = [2, 1, 3, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output5 = builder.slice(input5, begin5, size5);
+ const model = builder.createModel({output5});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input5': {buffer: input5Buffer}});
+ utils.checkValue(outputs.output5.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_8 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input7 = builder.input('input7', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input7Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin7 = [1, 0, 0, 0];
+ const size7 = [2, 1, -1, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output7 = builder.slice(input7, begin7, size7);
+ const model = builder.createModel({output7});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input7': {buffer: input7Buffer}});
+ utils.checkValue(outputs.output7.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_relaxed_8 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input7 = builder.input('input7', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input7Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin7 = [1, 0, 0, 0];
+ const size7 = [2, 1, -1, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output7 = builder.slice(input7, begin7, size7);
+ const model = builder.createModel({output7});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input7': {buffer: input7Buffer}});
+ utils.checkValue(outputs.output7.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test slice converted from slice_float16_8 test', async function() {
+ // Converted test case (from: V1_2/slice.mod.py)
+ const builder = nn.createModelBuilder();
+ const input7 = builder.input('input7', {type: 'int32', dimensions: [3, 2, 3, 1]});
+ const input7Buffer = new Int32Array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]);
+ const begin7 = [1, 0, 0, 0];
+ const size7 = [2, 1, -1, 1];
+ const expected = [3, 3, 3, 5, 5, 5];
+ const output7 = builder.slice(input7, begin7, size7);
+ const model = builder.createModel({output7});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input7': {buffer: input7Buffer}});
+ utils.checkValue(outputs.output7.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_softmax_converted_from_softmax_v1_2.js b/test/cts/from_nnapi/tests/V1_2/test_softmax_converted_from_softmax_v1_2.js
new file mode 100644
index 00000000..2bfa0d3d
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_softmax_converted_from_softmax_v1_2.js
@@ -0,0 +1,60 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test softmax converted from softmax_v1_2_axis_dim2_axis1 test', async function() {
+ // Converted test case (from: V1_2/softmax_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 5]});
+ const op1Buffer = new Float32Array([17.0, 16.0, 15.0, 14.0, 1.0, -1.0, -2.0, -3.0, -4.0, -17.0]);
+ const expected = [0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08, 0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08];
+ const op2 = builder.softmax(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test softmax converted from softmax_v1_2_axis_dim2_axis1_neg test', async function() {
+ // Converted test case (from: V1_2/softmax_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 5]});
+ const op1Buffer = new Float32Array([17.0, 16.0, 15.0, 14.0, 1.0, -1.0, -2.0, -3.0, -4.0, -17.0]);
+ const expected = [0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08, 0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08];
+ const op2 = builder.softmax(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RestrictAccuracyCriteria);
+ });
+
+ it('test softmax converted from softmax_v1_2_axis_relaxed_dim2_axis1 test', async function() {
+ // Converted test case (from: V1_2/softmax_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 5]});
+ const op1Buffer = new Float32Array([17.0, 16.0, 15.0, 14.0, 1.0, -1.0, -2.0, -3.0, -4.0, -17.0]);
+ const expected = [0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08, 0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08];
+ const op2 = builder.softmax(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+
+ it('test softmax converted from softmax_v1_2_axis_relaxed_dim2_axis1_neg test', async function() {
+ // Converted test case (from: V1_2/softmax_v1_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const op1 = builder.input('op1', {type: 'float32', dimensions: [2, 5]});
+ const op1Buffer = new Float32Array([17.0, 16.0, 15.0, 14.0, 1.0, -1.0, -2.0, -3.0, -4.0, -17.0]);
+ const expected = [0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08, 0.643914213228014, 0.236882800924671, 0.087144312427294, 0.032058600957022, 7.246299848982885e-08];
+ const op2 = builder.softmax(op1);
+ const model = builder.createModel({op2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'op1': {buffer: op1Buffer}});
+ utils.checkValue(outputs.op2.buffer, expected, utils.ctsFp32RelaxedAccuracyCriteria);
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_1.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_1.js
new file mode 100644
index 00000000..a72719f3
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_1.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_float_1 test', async function() {
+ // Converted test case (from: V1_2/split_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [6]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 0;
+ const numSplits = 3;
+ const expected = [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_float_1_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_float_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [6]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 0;
+ const numSplits = 3;
+ const expected = [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_2.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_2.js
new file mode 100644
index 00000000..3c8bf1c1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_2.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_float_2 test', async function() {
+ // Converted test case (from: V1_2/split_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 3]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 0;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_float_2_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_float_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 3]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 0;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_3.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_3.js
new file mode 100644
index 00000000..848bb15f
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_3.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_float_3 test', async function() {
+ // Converted test case (from: V1_2/split_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 3]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 1;
+ const numSplits = 3;
+ const expected = [[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_float_3_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_float_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 3]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+ const axis = 1;
+ const numSplits = 3;
+ const expected = [[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_4.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_4.js
new file mode 100644
index 00000000..074669d4
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_4.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_float_4 test', async function() {
+ // Converted test case (from: V1_2/split_float_4.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]);
+ const axis = 1;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 5.0, 6.0], [3.0, 4.0, 7.0, 8.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_float_4_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_float_4.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]);
+ const axis = 1;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 5.0, 6.0], [3.0, 4.0, 7.0, 8.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_5.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_5.js
new file mode 100644
index 00000000..34f21bfb
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_float_5.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_float_5 test', async function() {
+ // Converted test case (from: V1_2/split_float_5.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]);
+ const axis = -2;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 5.0, 6.0], [3.0, 4.0, 7.0, 8.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_float_5_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_float_5.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'float32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Float32Array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]);
+ const axis = -2;
+ const numSplits = 2;
+ const expected = [[1.0, 2.0, 5.0, 6.0], [3.0, 4.0, 7.0, 8.0]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_1.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_1.js
new file mode 100644
index 00000000..1999ae8b
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_1.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_int32_1 test', async function() {
+ // Converted test case (from: V1_2/split_int32_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [6]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 0;
+ const numSplits = 3;
+ const expected = [[1, 2], [3, 4], [5, 6]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_int32_1_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_int32_1.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [6]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 0;
+ const numSplits = 3;
+ const expected = [[1, 2], [3, 4], [5, 6]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_2.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_2.js
new file mode 100644
index 00000000..212725e1
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_2.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_int32_2 test', async function() {
+ // Converted test case (from: V1_2/split_int32_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 3]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 0;
+ const numSplits = 2;
+ const expected = [[1, 2, 3], [4, 5, 6]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_int32_2_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_int32_2.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 3]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 0;
+ const numSplits = 2;
+ const expected = [[1, 2, 3], [4, 5, 6]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_3.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_3.js
new file mode 100644
index 00000000..cab596cb
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_3.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_int32_3 test', async function() {
+ // Converted test case (from: V1_2/split_int32_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 3]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 1;
+ const numSplits = 3;
+ const expected = [[1, 4], [2, 5], [3, 6]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_int32_3_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_int32_3.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 3]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6]);
+ const axis = 1;
+ const numSplits = 3;
+ const expected = [[1, 4], [2, 5], [3, 6]];
+ const [output0, output1, output2] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1, output2});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 3; i++) {
+ utils.checkValue(outputs[['output0', 'output1', 'output2'][i]].buffer, expected[i], utils.ctsFp32RelaxedAccuracyCriteria);
+ }
+ });
+});
+/* eslint-disable max-len */
diff --git a/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_4.js b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_4.js
new file mode 100644
index 00000000..5823bc13
--- /dev/null
+++ b/test/cts/from_nnapi/tests/V1_2/test_split_converted_from_split_int32_4.js
@@ -0,0 +1,42 @@
+'use strict';
+import * as utils from '../../../../utils.js';
+
+/* eslint-disable max-len */
+describe('CTS converted from NNAPI CTS', function() {
+ const nn = navigator.ml.getNeuralNetworkContext();
+
+ it('test split converted from split_int32_4 test', async function() {
+ // Converted test case (from: V1_2/split_int32_4.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6, 7, 8]);
+ const axis = 1;
+ const numSplits = 2;
+ const expected = [[1, 2, 5, 6], [3, 4, 7, 8]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i < 2; i++) {
+ utils.checkValue(outputs[['output0', 'output1'][i]].buffer, expected[i], utils.ctsFp32RestrictAccuracyCriteria);
+ }
+ });
+
+ it('test split converted from split_int32_4_relaxed test', async function() {
+ // Converted test case (from: V1_2/split_int32_4.mod.py)
+ const builder = nn.createModelBuilder();
+ const input0 = builder.input('input0', {type: 'int32', dimensions: [2, 2, 2]});
+ const input0Buffer = new Int32Array([1, 2, 3, 4, 5, 6, 7, 8]);
+ const axis = 1;
+ const numSplits = 2;
+ const expected = [[1, 2, 5, 6], [3, 4, 7, 8]];
+ const [output0, output1] = builder.split(input0, numSplits, {'axis': axis});
+ const model = builder.createModel({output0, output1});
+ const compilation = await model.compile();
+ const outputs = await compilation.compute({'input0': {buffer: input0Buffer}});
+ for (let i = 0; i <