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"""This module contains inference-related abstract class and its Torch and OpenVINO implementations."""
# 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.
from importlib.util import find_spec
from pathlib import Path
from typing import Dict, Optional, Tuple, Union
import cv2
import numpy as np
from omegaconf import DictConfig, ListConfig
from anomalib.pre_processing import PreProcessor
from .base import Inferencer
if find_spec("openvino") is not None:
from openvino.inference_engine import ( # type: ignore # pylint: disable=no-name-in-module
IECore,
)
class OpenVINOInferencer(Inferencer):
"""OpenVINO implementation for the inference.
Args:
config (DictConfig): Configurable parameters that are used
during the training stage.
path (Union[str, Path]): Path to the openvino onnx, xml or bin file.
meta_data_path (Union[str, Path], optional): Path to metadata file. Defaults to None.
"""
def __init__(
self,
config: Union[DictConfig, ListConfig],
path: Union[str, Path, Tuple[bytes, bytes]],
meta_data_path: Union[str, Path] = None,
):
self.config = config
self.input_blob, self.output_blob, self.network = self.load_model(path)
self.meta_data = super()._load_meta_data(meta_data_path)
def load_model(self, path: Union[str, Path, Tuple[bytes, bytes]]):
"""Load the OpenVINO model.
Args:
path (Union[str, Path, Tuple[bytes, bytes]]): Path to the onnx or xml and bin files
or tuple of .xml and .bin data as bytes.
Returns:
[Tuple[str, str, ExecutableNetwork]]: Input and Output blob names
together with the Executable network.
"""
ie_core = IECore()
# If tuple of bytes is passed
if isinstance(path, tuple):
network = ie_core.read_network(model=path[0], weights=path[1], init_from_buffer=True)
else:
path = path if isinstance(path, Path) else Path(path)
if path.suffix in (".bin", ".xml"):
if path.suffix == ".bin":
bin_path, xml_path = path, path.with_suffix(".xml")
elif path.suffix == ".xml":
xml_path, bin_path = path, path.with_suffix(".bin")
network = ie_core.read_network(xml_path, bin_path)
elif path.suffix == ".onnx":
network = ie_core.read_network(path)
else:
raise ValueError(f"Path must be .onnx, .bin or .xml file. Got {path.suffix}")
input_blob = next(iter(network.input_info))
output_blob = next(iter(network.outputs))
executable_network = ie_core.load_network(network=network, device_name="CPU")
return input_blob, output_blob, executable_network
def pre_process(self, image: np.ndarray) -> np.ndarray:
"""Pre process the input image by applying transformations.
Args:
image (np.ndarray): Input image.
Returns:
np.ndarray: pre-processed image.
"""
config = self.config.transform if "transform" in self.config.keys() else None
image_size = tuple(self.config.dataset.image_size)
pre_processor = PreProcessor(config, image_size)
processed_image = pre_processor(image=image)["image"]
if len(processed_image.shape) == 3:
processed_image = np.expand_dims(processed_image, axis=0)
if processed_image.shape[-1] == 3:
processed_image = processed_image.transpose(0, 3, 1, 2)
return processed_image
def forward(self, image: np.ndarray) -> np.ndarray:
"""Forward-Pass input tensor to the model.
Args:
image (np.ndarray): Input tensor.
Returns:
np.ndarray: Output predictions.
"""
return self.network.infer(inputs={self.input_blob: image})
def post_process(
self, predictions: np.ndarray, meta_data: Optional[Union[Dict, DictConfig]] = None
) -> Tuple[np.ndarray, float]:
"""Post process the output predictions.
Args:
predictions (np.ndarray): Raw output predicted by the model.
meta_data (Dict, optional): Meta data. Post-processing step sometimes requires
additional meta data such as image shape. This variable comprises such info.
Defaults to None.
Returns:
np.ndarray: Post processed predictions that are ready to be visualized.
"""
if meta_data is None:
meta_data = self.meta_data
predictions = predictions[self.output_blob]
anomaly_map = predictions.squeeze()
pred_score = anomaly_map.reshape(-1).max()
anomaly_map, pred_score = self._normalize(anomaly_map, pred_score, meta_data)
if "image_shape" in meta_data and anomaly_map.shape != meta_data["image_shape"]:
anomaly_map = cv2.resize(anomaly_map, meta_data["image_shape"])
return anomaly_map, float(pred_score)
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