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frankwhzhangfrankwhzhang
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add norm 2.0 api, test=develop (#26465)
* add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop * add norm 2.0 api, test=develop
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‎python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py‎

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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from op_test import OpTest, _set_use_system_allocator
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from paddle.fluid.framework import grad_var_name
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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import paddle
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class TestBatchNorm(unittest.TestCase):
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def test_name(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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with fluid.dygraph.guard(p):
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batch_norm1d = paddle.nn.BatchNorm1d(1, name="test")
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def test_error(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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#paddle.disable_static()
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x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
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x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
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def error1d():
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x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
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batch_norm1d = paddle.nn.BatchNorm1d(1)
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batch_norm1d(fluid.dygraph.to_variable(x_data_4))
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def error2d():
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x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
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batch_norm2d = paddle.nn.BatchNorm2d(1)
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batch_norm2d(fluid.dygraph.to_variable(x_data_3))
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def error3d():
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x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
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batch_norm3d = paddle.nn.BatchNorm3d(1)
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batch_norm3d(fluid.dygraph.to_variable(x_data_4))
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with fluid.dygraph.guard(p):
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self.assertRaises(ValueError, error1d)
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self.assertRaises(ValueError, error2d)
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self.assertRaises(ValueError, error3d)
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def test_dygraph(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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shape = [4, 10, 4, 4]
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def compute_v1(x, is_test, trainable_statistics):
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with fluid.dygraph.guard(p):
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bn = fluid.dygraph.BatchNorm(
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shape[1],
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is_test=is_test,
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trainable_statistics=trainable_statistics)
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y = bn(fluid.dygraph.to_variable(x))
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return y.numpy()
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def compute_v2(x):
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with fluid.dygraph.guard(p):
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bn = paddle.nn.BatchNorm2d(shape[1])
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y = bn(fluid.dygraph.to_variable(x))
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return y.numpy()
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x, False, False)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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def test_static(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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exe = fluid.Executor(p)
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shape = [4, 10, 16, 16]
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def compute_v1(x_np, is_test, trainable_statistics):
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with program_guard(Program(), Program()):
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bn = fluid.dygraph.BatchNorm(
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shape[1],
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is_test=is_test,
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trainable_statistics=trainable_statistics)
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = bn(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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def compute_v2(x_np):
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with program_guard(Program(), Program()):
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bn = paddle.nn.BatchNorm2d(shape[1])
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = bn(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x, False, False)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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if __name__ == '__main__':
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unittest.main()

‎python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py‎

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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from op_test import OpTest, _set_use_system_allocator
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from paddle.fluid.framework import grad_var_name
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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import paddle
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class TestDygraphGroupNormv2(unittest.TestCase):
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def test_dygraph(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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shape = [2, 6, 2, 2]
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def compute_v1(x):
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with fluid.dygraph.guard(p):
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gn = fluid.dygraph.GroupNorm(channels=2, groups=2)
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y = gn(fluid.dygraph.to_variable(x))
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return y.numpy()
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def compute_v2(x):
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with fluid.dygraph.guard(p):
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gn = paddle.nn.GroupNorm(num_channels=2, num_groups=2)
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y = gn(fluid.dygraph.to_variable(x))
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return y.numpy()
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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def test_static(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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exe = fluid.Executor(p)
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shape = [2, 6, 2, 2]
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def compute_v1(x_np):
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with program_guard(Program(), Program()):
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gn = fluid.dygraph.GroupNorm(channels=2, groups=2)
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = gn(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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def compute_v2(x_np):
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with program_guard(Program(), Program()):
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gn = paddle.nn.GroupNorm(num_channels=2, num_groups=2)
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = gn(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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if __name__ == '__main__':
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unittest.main()

‎python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py‎

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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from op_test import OpTest, _set_use_system_allocator
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from paddle.fluid.framework import grad_var_name
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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import paddle
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class TestInstanceNorm(unittest.TestCase):
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def test_error(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(
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"instance_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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def error1d():
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x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
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instance_norm1d = paddle.nn.InstanceNorm1d(1)
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instance_norm1d(fluid.dygraph.to_variable(x_data_4))
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def error2d():
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x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
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instance_norm2d = paddle.nn.InstanceNorm2d(1)
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instance_norm2d(fluid.dygraph.to_variable(x_data_3))
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def error3d():
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x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
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instance_norm3d = paddle.nn.BatchNorm3d(1)
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instance_norm3d(fluid.dygraph.to_variable(x_data_4))
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with fluid.dygraph.guard(p):
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self.assertRaises(ValueError, error1d)
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self.assertRaises(ValueError, error2d)
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self.assertRaises(ValueError, error3d)
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def test_dygraph(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(
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"instance_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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shape = [4, 10, 4, 4]
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def compute_v1(x):
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with fluid.dygraph.guard(p):
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bn = fluid.dygraph.InstanceNorm(shape[1])
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y = bn(fluid.dygraph.to_variable(x))
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return y.numpy()
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def compute_v2(x):
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with fluid.dygraph.guard(p):
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bn = paddle.nn.InstanceNorm2d(shape[1])
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y = bn(fluid.dygraph.to_variable(x))
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return y.numpy()
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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def test_static(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(
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"instance_norm"):
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places.append(fluid.CUDAPlace(0))
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for p in places:
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exe = fluid.Executor(p)
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shape = [4, 10, 16, 16]
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def compute_v1(x_np):
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with program_guard(Program(), Program()):
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ins = fluid.dygraph.InstanceNorm(shape[1])
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = ins(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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def compute_v2(x_np):
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with program_guard(Program(), Program()):
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ins = paddle.nn.InstanceNorm2d(shape[1])
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x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
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y = ins(x)
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exe.run(fluid.default_startup_program())
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r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
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return r
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x = np.random.randn(*shape).astype("float32")
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y1 = compute_v1(x)
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y2 = compute_v2(x)
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self.assertTrue(np.allclose(y1, y2))
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if __name__ == '__main__':
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unittest.main()

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