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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
#
try:
import torch
import torch.nn as nn
import torch.nn.functional as functional
except ModuleNotFoundError:
print("WARNING: Unable to import torch. Torch may not be installed")
import os
import pytest
import shutil
import tempfile
from tlt.datasets import dataset_factory
from tlt.models import model_factory
from tlt.utils.file_utils import download_and_extract_tar_file
try:
from tlt.models.image_anomaly_detection.pytorch_image_anomaly_detection_model import extract_features
except ModuleNotFoundError:
print("WARNING: Unable to import torch. Torch may not be installed")
@pytest.mark.integration
@pytest.mark.pytorch
class TestImageAnomalyDetectionCustomDataset:
"""
Tests for PyTorch image anomaly detection using a custom dataset using the flowers dataset
"""
@classmethod
def setup_class(cls):
os.makedirs('/tmp/data', exist_ok=True)
temp_dir = tempfile.mkdtemp(dir='/tmp/data')
custom_dataset_path = os.path.join(temp_dir, "flower_photos")
if not os.path.exists(custom_dataset_path):
download_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
download_and_extract_tar_file(download_url, temp_dir)
# Rename daisy to "good" and delete all but one other kind to make the dataset small
os.rename(os.path.join(custom_dataset_path, 'daisy'), os.path.join(custom_dataset_path, 'good'))
for flower in ['dandelion', 'roses', 'sunflowers']:
shutil.rmtree(os.path.join(custom_dataset_path, flower))
os.makedirs('/tmp/output', exist_ok=True)
cls._output_dir = tempfile.mkdtemp(dir='/tmp/output')
os.environ["TORCH_HOME"] = cls._output_dir
cls._temp_dir = temp_dir
cls._dataset_dir = custom_dataset_path
@classmethod
def teardown_class(cls):
# remove directories
for dir in [cls._output_dir, cls._temp_dir]:
if os.path.exists(dir):
print("Deleting test directory:", dir)
shutil.rmtree(dir)
@pytest.mark.parametrize('model_name',
['resnet18'])
def test_custom_dataset_workflow(self, model_name):
"""
Tests the workflow for PYT image anomaly detection using a custom dataset
"""
framework = 'pytorch'
use_case = 'image_anomaly_detection'
# Get the dataset
dataset = dataset_factory.load_dataset(self._dataset_dir, use_case=use_case, framework=framework,
shuffle_files=False)
assert ['tulips'] == dataset.defect_names
assert ['bad', 'good'] == dataset.class_names
# Get the model
model = model_factory.get_model(model_name, framework, use_case)
# Preprocess the dataset and split to get small subsets for training and validation
dataset.preprocess(model.image_size, 32)
dataset.shuffle_split(train_pct=0.5, val_pct=0.5, seed=10)
# Train for 1 epoch
pca_components, trained_model = model.train(dataset, self._output_dir,
layer_name='layer3', seed=10, simsiam=False)
# Extract features
images, labels = dataset.get_batch(subset='validation')
features = extract_features(trained_model, images, layer_name='layer3', pooling=['avg', 2])
assert len(features) == 32
# Evaluate
threshold, auroc = model.evaluate(dataset, pca_components)
assert isinstance(auroc, float)
# Predict with a batch
predictions = model.predict(images, pca_components)
assert len(predictions) == 32
def test_custom_model_workflow(self):
"""
Tests the workflow for PYT image anomaly detection using a custom model and custom dataset
"""
framework = 'pytorch'
use_case = 'image_anomaly_detection'
# Get the dataset
dataset = dataset_factory.load_dataset(self._dataset_dir, use_case=use_case, framework=framework,
shuffle_files=False)
# Define a model
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(functional.relu(self.conv1(x)))
x = self.pool(functional.relu(self.conv2(x)))
x = torch.flatten(x, 1)
x = functional.relu(self.fc1(x))
x = functional.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
# Load the model
model = model_factory.load_model('custom_model', net, framework=framework, use_case=use_case)
model.list_layers()
# Preprocess the dataset and split to get small subsets for training and validation
dataset.preprocess(image_size=224, batch_size=32)
dataset.shuffle_split(train_pct=0.5, val_pct=0.5, seed=10)
# Train for 1 epoch
pca_components, trained_model = model.train(dataset, self._output_dir,
layer_name='conv2', seed=10, simsiam=False)
# Extract features
images, labels = dataset.get_batch(subset='validation')
features = extract_features(trained_model, images, layer_name='conv2', pooling=['avg', 2])
assert len(features) == 32
# Evaluate
threshold, auroc = model.evaluate(dataset, pca_components)
assert isinstance(auroc, float)
# Predict with a batch
predictions = model.predict(images, pca_components)
assert len(predictions) == 32
@pytest.mark.parametrize('model_name',
['resnet18'])
def test_simsiam_workflow(self, model_name):
"""
Tests the workflow for PYT image anomaly detection using a custom dataset
and simsiam feature extractor enabled
"""
framework = 'pytorch'
use_case = 'image_anomaly_detection'
# Get the dataset
dataset = dataset_factory.load_dataset(self._dataset_dir, use_case=use_case, framework=framework,
shuffle_files=False)
assert ['tulips'] == dataset.defect_names
assert ['bad', 'good'] == dataset.class_names
# Get the model
model = model_factory.get_model(model_name, framework, use_case)
# Preprocess the dataset and split to get small subsets for training and validation
dataset.preprocess(model.image_size, 32)
dataset.shuffle_split(train_pct=0.5, val_pct=0.5, seed=10)
# Train for 1 epoch
pca_components, trained_model = model.train(dataset, self._output_dir, epochs=1,
layer_name='layer3', feature_dim=1000, pred_dim=250,
seed=10, simsiam=True, initial_checkpoints=None)
# Evaluate
threshold, auroc = model.evaluate(dataset, pca_components)
assert isinstance(auroc, float)
# Predict with a batch
images, labels = dataset.get_batch(subset='validation')
predictions = model.predict(images, pca_components)
assert len(predictions) == 32
@pytest.mark.parametrize('model_name',
['resnet18'])
def test_cutpaste_workflow(self, model_name):
"""
Tests the workflow for PYT image anomaly detection using a custom dataset
and cutpaste feature extractor enabled
"""
framework = 'pytorch'
use_case = 'image_anomaly_detection'
# Get the dataset
dataset = dataset_factory.load_dataset(self._dataset_dir, use_case=use_case, framework=framework,
shuffle_files=False)
assert ['tulips'] == dataset.defect_names
assert ['bad', 'good'] == dataset.class_names
# Get the model
model = model_factory.get_model(model_name, framework, use_case)
# Preprocess the dataset and split to get small subsets for training and validation
dataset.preprocess(model.image_size, 32)
dataset.shuffle_split(train_pct=0.5, val_pct=0.25, test_pct=0.25, seed=10)
# Train for 1 epoch
pca_components, trained_model = model.train(dataset, self._output_dir, epochs=1,
layer_name='layer3', optim='sgd', freeze_resnet=20,
head_layer=2, cutpaste_type='normal', seed=10,
cutpaste=True)
# Evaluate
threshold, auroc = model.evaluate(dataset, pca_components, use_test_set=True)
assert isinstance(auroc, float)
# Predict with a batch
images, labels = dataset.get_batch(subset='test')
predictions = model.predict(images, pca_components)
assert len(predictions) == 32
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