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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2022 Intel Corporation
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
#
import os
import pytest
import shutil
import tempfile
import uuid
from pathlib import Path
from unittest.mock import patch, MagicMock
from tlt.models import model_factory
from tlt.datasets.image_classification.pytorch_custom_image_classification_dataset import PyTorchCustomImageClassificationDataset # noqa: E501
try:
# Do PyTorch specific imports in a try/except to prevent pytest test loading from failing when running in a TF env
from tlt.models.image_classification.torchvision_image_classification_model import TorchvisionImageClassificationModel # noqa: F401, E501
except ModuleNotFoundError:
print("WARNING: Unable to import TorchvisionImageClassificationModel. PyTorch or torchvision may not be installed")
@pytest.mark.pytorch
def test_torchvision_image_classification_optimize_graph_not_implemented():
"""
Verifies the error that gets raise if graph optimization is attempted with a PyTorch model
"""
try:
output_dir = tempfile.mkdtemp()
saved_model_dir = tempfile.mkdtemp()
dummy_config_file = os.path.join(output_dir, "config.yaml")
Path(dummy_config_file).touch()
model = model_factory.get_model('resnet50', 'pytorch')
# The torchvision model is not present until training, so call _get_hub_model()
model._get_hub_model(3)
# Graph optimization is not enabled for PyTorch, so this should fail
with patch('neural_compressor.experimental.Graph_Optimization'):
with pytest.raises(NotImplementedError):
model.optimize_graph(output_dir)
# Verify that the installed version of Intel Neural Compressor throws a SystemError
from neural_compressor.experimental import Graph_Optimization, common
# set_backend API is no longer available in Neural Compressor v2.0
# from neural_compressor.experimental.common.model import set_backend
# set_backend('pytorch')
graph_optimizer = Graph_Optimization()
with pytest.raises(AssertionError):
graph_optimizer.model = common.Model(model._model)
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
if os.path.exists(saved_model_dir):
shutil.rmtree(saved_model_dir)
@pytest.mark.pytorch
@patch('tlt.models.image_classification.torchvision_image_classification_model.ModelDownloader')
@patch('tlt.models.pytorch_model.quantization.fit')
def test_pyt_image_classification_quantize_overwrite_saved_model(mock_quantization_fit, mock_model_downloader):
"""
Given a valid directory for the output dir, test the quantize function with the actual Intel Neural
Compressor call mocked out. Tests that the model will be overwritten or not using the overwrite_model flag.
"""
# tlt imports
from tlt.datasets.image_classification.pytorch_custom_image_classification_dataset \
import PyTorchCustomImageClassificationDataset
from tlt.models import model_factory
try:
# Specify a directory for output
output_dir = tempfile.mkdtemp()
model = model_factory.get_model(model_name='efficientnet_b0', framework='pytorch')
# Mock the dataset
mock_dataset = MagicMock()
mock_dataset.__class__ = PyTorchCustomImageClassificationDataset
mock_dataset.get_inc_dataloaders.return_value = 1, 2
# Method to create a dummy model.pt file in the specified directory
def create_dummy_file(output_dir):
with open(os.path.join(output_dir, 'model.pt'), 'w') as _:
pass
# Mock an INC quantized model that will create a dummy file when saved
mock_quantized_model = MagicMock()
mock_quantized_model.save.side_effect = create_dummy_file
# Mock the INC quantization.fit method
def mock_fit(**args):
return mock_quantized_model
mock_quantization_fit.side_effect = mock_fit
# Call quantize when a model does not exist
model.quantize(output_dir=output_dir, dataset=mock_dataset, overwrite_model=False)
# Call quantize when the model exists, but overwrite_model=True
model.quantize(output_dir=output_dir, dataset=mock_dataset, overwrite_model=True)
model.quantize(output_dir=output_dir, dataset=mock_dataset, overwrite_model=True)
with pytest.raises(FileExistsError): # Model exists, so this should be true
model.quantize(output_dir=output_dir, dataset=mock_dataset, overwrite_model=False)
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
@pytest.mark.pytorch
def test_pyt_image_classification_quantization():
"""
Given a valid directory for output dir, test the quantization function with the actual INC called mocked out.
"""
try:
output_dir = tempfile.mkdtemp()
model = model_factory.get_model('efficientnet_b0', 'pytorch')
with patch('tlt.datasets.image_classification.pytorch_custom_image_classification_dataset.PyTorchCustomImageClassificationDataset') as mock_dataset: # noqa: E501
with patch('neural_compressor.quantization.fit') as mock_q:
mock_dataset.dataset_dir = "/tmp/data/my_photos"
mock_dataset.__class__ = PyTorchCustomImageClassificationDataset
mock_dataset.get_inc_dataloaders.return_value = (1, 2)
model.quantize(output_dir, mock_dataset)
mock_q.assert_called_once()
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
@pytest.mark.pytorch
def test_pyt_image_classification_benchmark_model_does_not_exist():
"""
Verifies the error that gets raise if benchmarking is done with a model that does not exist
"""
try:
output_dir = tempfile.mkdtemp()
model = model_factory.get_model('efficientnet_b0', 'pytorch')
with patch('tlt.datasets.image_classification.pytorch_custom_image_classification_dataset.PyTorchCustomImageClassificationDataset') as mock_dataset: # noqa: E501
mock_dataset.dataset_dir = "/tmp/data/my_photos"
mock_dataset.__class__ = PyTorchCustomImageClassificationDataset
random_dir = str(uuid.uuid4())
saved_model_dir = tempfile.mkdtemp()
with patch('neural_compressor.experimental.Benchmark'):
# It's not a directory, so we expect an error
with pytest.raises(NotADirectoryError):
model.benchmark(mock_dataset, saved_model_dir=random_dir)
# An empty directory with no saved model should also generate an error
with pytest.raises(FileNotFoundError):
model.benchmark(mock_dataset, saved_model_dir=saved_model_dir)
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
if os.path.exists(saved_model_dir):
shutil.rmtree(saved_model_dir)
@pytest.mark.pytorch
def test_pyt_image_classification_inc_benchmark():
"""
Verifies that if we have a valid model and dataset, benchmarking is called. The actual benchmarking calls to INC
are mocked out.
"""
model = model_factory.get_model('efficientnet_b0', 'pytorch')
with patch('tlt.datasets.image_classification.pytorch_custom_image_classification_dataset.PyTorchCustomImageClassificationDataset') as mock_dataset: # noqa: E501
with patch('neural_compressor.benchmark.fit') as mock_bench:
mock_dataset.dataset_dir = "/tmp/data/my_photos"
mock_dataset.__class__ = PyTorchCustomImageClassificationDataset
mock_dataset.get_inc_dataloaders.return_value = (1, 2)
model.benchmark(mock_dataset)
mock_bench.assert_called_once()