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Arm backend: Align MobileNetV2 with MobileNetV3 unittest #9900

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Apr 4, 2025
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Arm backend: Align MobileNetV2 with MobileNetV3 unittest #9900

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142 changes: 59 additions & 83 deletions backends/arm/test/models/test_mobilenet_v2_arm.py
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# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import logging
import unittest
from typing import Tuple

import pytest

import torch
from executorch.backends.arm.test import common, conftest
from executorch.backends.arm.test import common
from executorch.backends.arm.test.tester.test_pipeline import (
EthosU55PipelineBI,
EthosU85PipelineBI,
TosaPipelineBI,
TosaPipelineMI,
)

from executorch.backends.arm.test.tester.arm_tester import ArmTester
from torchvision import models, transforms # type: ignore[import-untyped]
from torchvision.models.mobilenetv2 import ( # type: ignore[import-untyped]
MobileNet_V2_Weights,
)


logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
mv2 = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
mv2 = mv2.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

model_inputs = (normalize(torch.rand((1, 3, 224, 224))),)
input_t = Tuple[torch.Tensor]

class TestMobileNetV2(unittest.TestCase):
"""Tests MobileNetV2."""

mv2 = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
mv2 = mv2.eval()
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
def test_mv2_tosa_MI():
pipeline = TosaPipelineMI[input_t](
mv2, model_inputs, aten_op=[], exir_op=[], use_to_edge_transform_and_lower=True
)
pipeline.run()

# Used e.g. for quantization calibration and shape extraction in the tester
model_example_inputs = (normalize(torch.randn((1, 3, 224, 224))),)

def test_mv2_tosa_MI(self):
(
ArmTester(
self.mv2,
example_inputs=self.model_example_inputs,
compile_spec=common.get_tosa_compile_spec("TOSA-0.80+MI"),
)
.export()
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.run_method_and_compare_outputs()
)
def test_mv2_tosa_BI():
pipeline = TosaPipelineBI[input_t](
mv2,
model_inputs,
aten_op=[],
exir_op=[],
use_to_edge_transform_and_lower=True,
atol=0.25,
qtol=1,
)
pipeline.run()

def test_mv2_tosa_BI(self):
(
ArmTester(
self.mv2,
example_inputs=self.model_example_inputs,
compile_spec=common.get_tosa_compile_spec("TOSA-0.80+BI"),
)
.quantize()
.export()
.to_edge_transform_and_lower()
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.run_method_and_compare_outputs(rtol=0.001, atol=0.2, qtol=1)
)

@pytest.mark.slow
@pytest.mark.corstone_fvp
def test_mv2_u55_BI(self):
tester = (
ArmTester(
self.mv2,
example_inputs=self.model_example_inputs,
compile_spec=common.get_u55_compile_spec(),
)
.quantize()
.export()
.to_edge_transform_and_lower()
.to_executorch()
.serialize()
)
if conftest.is_option_enabled("corstone_fvp"):
tester.run_method_and_compare_outputs(
rtol=0.001,
atol=0.2,
qtol=1,
)
@pytest.mark.slow
@pytest.mark.corstone_fvp
@common.XfailIfNoCorstone300
def test_mv2_u55_BI():
pipeline = EthosU55PipelineBI[input_t](
mv2,
model_inputs,
aten_ops=[],
exir_ops=[],
run_on_fvp=True,
use_to_edge_transform_and_lower=True,
atol=0.25,
qtol=1,
)
pipeline.run()

@pytest.mark.slow
@pytest.mark.corstone_fvp
def test_mv2_u85_BI(self):
tester = (
ArmTester(
self.mv2,
example_inputs=self.model_example_inputs,
compile_spec=common.get_u85_compile_spec(),
)
.quantize()
.export()
.to_edge_transform_and_lower()
.to_executorch()
.serialize()
)
if conftest.is_option_enabled("corstone_fvp"):
tester.run_method_and_compare_outputs(
rtol=0.001,
atol=0.2,
qtol=1,
)

@pytest.mark.slow
@pytest.mark.corstone_fvp
@common.XfailIfNoCorstone320
def test_mv2_u85_BI():
pipeline = EthosU85PipelineBI[input_t](
mv2,
model_inputs,
aten_ops=[],
exir_ops=[],
run_on_fvp=True,
use_to_edge_transform_and_lower=True,
atol=0.25,
qtol=1,
)
pipeline.run()
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