Spaces:
Build error
Build error
File size: 5,958 Bytes
c8c12e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
"""This module contains Torch inference 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 pathlib import Path
from typing import Dict, Optional, Tuple, Union
import cv2
import numpy as np
import torch
from omegaconf import DictConfig, ListConfig
from torch import Tensor
from anomalib.deploy.optimize import get_model_metadata
from anomalib.models import get_model
from anomalib.models.components import AnomalyModule
from anomalib.pre_processing import PreProcessor
from .base import Inferencer
class TorchInferencer(Inferencer):
"""PyTorch implementation for the inference.
Args:
config (DictConfig): Configurable parameters that are used
during the training stage.
model_source (Union[str, Path, AnomalyModule]): Path to the model ckpt file or the Anomaly model.
meta_data_path (Union[str, Path], optional): Path to metadata file. If none, it tries to load the params
from the model state_dict. Defaults to None.
"""
def __init__(
self,
config: Union[DictConfig, ListConfig],
model_source: Union[str, Path, AnomalyModule],
meta_data_path: Union[str, Path] = None,
):
self.config = config
if isinstance(model_source, AnomalyModule):
self.model = model_source
else:
self.model = self.load_model(model_source)
self.meta_data = self._load_meta_data(meta_data_path)
def _load_meta_data(self, path: Optional[Union[str, Path]] = None) -> Union[Dict, DictConfig]:
"""Load metadata from file or from model state dict.
Args:
path (Optional[Union[str, Path]], optional): Path to metadata file. If none, it tries to load the params
from the model state_dict. Defaults to None.
Returns:
Dict: Dictionary containing the meta_data.
"""
meta_data: Union[DictConfig, Dict[str, Union[float, Tensor, np.ndarray]]]
if path is None:
meta_data = get_model_metadata(self.model)
else:
meta_data = super()._load_meta_data(path)
return meta_data
def load_model(self, path: Union[str, Path]) -> AnomalyModule:
"""Load the PyTorch model.
Args:
path (Union[str, Path]): Path to model ckpt file.
Returns:
(AnomalyModule): PyTorch Lightning model.
"""
model = get_model(self.config)
model.load_state_dict(torch.load(path)["state_dict"])
model.eval()
return model
def pre_process(self, image: np.ndarray) -> Tensor:
"""Pre process the input image by applying transformations.
Args:
image (np.ndarray): Input image
Returns:
Tensor: 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) == 3:
processed_image = processed_image.unsqueeze(0)
return processed_image
def forward(self, image: Tensor) -> Tensor:
"""Forward-Pass input tensor to the model.
Args:
image (Tensor): Input tensor.
Returns:
Tensor: Output predictions.
"""
return self.model(image)
def post_process(
self, predictions: Tensor, meta_data: Optional[Union[Dict, DictConfig]] = None
) -> Tuple[np.ndarray, float]:
"""Post process the output predictions.
Args:
predictions (Tensor): 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
if isinstance(predictions, Tensor):
anomaly_map = predictions
pred_score = anomaly_map.reshape(-1).max()
else:
# NOTE: Patchcore `forward`` returns heatmap and score.
# We need to add the following check to ensure the variables
# are properly assigned. Without this check, the code
# throws an error regarding type mismatch torch vs np.
if isinstance(predictions[1], (Tensor)):
anomaly_map, pred_score = predictions
pred_score = pred_score.detach()
else:
anomaly_map, pred_score = predictions
pred_score = pred_score.detach().numpy()
anomaly_map = anomaly_map.squeeze()
anomaly_map, pred_score = self._normalize(anomaly_map, pred_score, meta_data)
if isinstance(anomaly_map, Tensor):
anomaly_map = anomaly_map.detach().cpu().numpy()
if "image_shape" in meta_data and anomaly_map.shape != meta_data["image_shape"]:
image_height = meta_data["image_shape"][0]
image_width = meta_data["image_shape"][1]
anomaly_map = cv2.resize(anomaly_map, (image_width, image_height))
return anomaly_map, float(pred_score)
|