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"""Tests for Torch and OpenVINO inferencers."""
# Copyright (C) 2020 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.
import os
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Union
import pytest
import torch
from omegaconf import DictConfig, ListConfig
from pytorch_lightning import Trainer
from anomalib.config import get_configurable_parameters
from anomalib.data import get_datamodule
from anomalib.deploy import OpenVINOInferencer, TorchInferencer, export_convert
from anomalib.models import get_model
from tests.helpers.dataset import TestDataset, get_dataset_path
from tests.helpers.inference import MockImageLoader, get_meta_data
def get_model_config(
model_name: str, project_path: str, dataset_path: str, category: str
) -> Union[DictConfig, ListConfig]:
model_config = get_configurable_parameters(model_name=model_name)
model_config.project.path = project_path
model_config.dataset.path = dataset_path
model_config.dataset.category = category
model_config.trainer.max_epochs = 1
return model_config
class TestInferencers:
@pytest.mark.parametrize(
"model_name",
["padim", "stfpm", "patchcore", "dfm", "dfkde", "ganomaly", "cflow"],
)
@TestDataset(num_train=20, num_test=1, path=get_dataset_path(), use_mvtec=False)
def test_torch_inference(self, model_name: str, category: str = "shapes", path: str = "./datasets/MVTec"):
"""Tests Torch inference.
Model is not trained as this checks that the inferencers are working.
Args:
model_name (str): Name of the model
"""
with TemporaryDirectory() as project_path:
model_config = get_model_config(
model_name=model_name, dataset_path=path, category=category, project_path=project_path
)
model = get_model(model_config)
trainer = Trainer(logger=False, **model_config.trainer)
datamodule = get_datamodule(model_config)
trainer.fit(model=model, datamodule=datamodule)
model.eval()
# Test torch inferencer
torch_inferencer = TorchInferencer(model_config, model)
torch_dataloader = MockImageLoader(model_config.dataset.image_size, total_count=1)
meta_data = get_meta_data(model, model_config.dataset.image_size)
with torch.no_grad():
for image in torch_dataloader():
torch_inferencer.predict(image, superimpose=False, meta_data=meta_data)
@pytest.mark.parametrize(
"model_name",
[
"padim",
"stfpm",
"dfm",
"ganomaly",
],
)
@TestDataset(num_train=20, num_test=1, path=get_dataset_path(), use_mvtec=False)
def test_openvino_inference(self, model_name: str, category: str = "shapes", path: str = "./datasets/MVTec"):
"""Tests OpenVINO inference.
Model is not trained as this checks that the inferencers are working.
Args:
model_name (str): Name of the model
"""
with TemporaryDirectory() as project_path:
model_config = get_model_config(
model_name=model_name, dataset_path=path, category=category, project_path=project_path
)
export_path = Path(project_path)
model = get_model(model_config)
trainer = Trainer(logger=False, **model_config.trainer)
datamodule = get_datamodule(model_config)
trainer.fit(model=model, datamodule=datamodule)
export_convert(
model=model,
input_size=model_config.dataset.image_size,
onnx_path=export_path / "model.onnx",
export_path=export_path,
)
# Test OpenVINO inferencer
openvino_inferencer = OpenVINOInferencer(model_config, export_path / "model.xml")
openvino_dataloader = MockImageLoader(model_config.dataset.image_size, total_count=1)
meta_data = get_meta_data(model, model_config.dataset.image_size)
for image in openvino_dataloader():
openvino_inferencer.predict(image, superimpose=False, meta_data=meta_data)
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