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"""Anomaly Map Generator for CFlow model implementation."""
# 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 typing import List, Tuple, Union, cast
import torch
import torch.nn.functional as F
from omegaconf import ListConfig
from torch import Tensor
class AnomalyMapGenerator:
"""Generate Anomaly Heatmap."""
def __init__(
self,
image_size: Union[ListConfig, Tuple],
pool_layers: List[str],
):
self.distance = torch.nn.PairwiseDistance(p=2, keepdim=True)
self.image_size = image_size if isinstance(image_size, tuple) else tuple(image_size)
self.pool_layers: List[str] = pool_layers
def compute_anomaly_map(
self, distribution: Union[List[Tensor], List[List]], height: List[int], width: List[int]
) -> Tensor:
"""Compute the layer map based on likelihood estimation.
Args:
distribution: Probability distribution for each decoder block
height: blocks height
width: blocks width
Returns:
Final Anomaly Map
"""
test_map: List[Tensor] = []
for layer_idx in range(len(self.pool_layers)):
test_norm = torch.tensor(distribution[layer_idx], dtype=torch.double) # pylint: disable=not-callable
test_norm -= torch.max(test_norm) # normalize likelihoods to (-Inf:0] by subtracting a constant
test_prob = torch.exp(test_norm) # convert to probs in range [0:1]
test_mask = test_prob.reshape(-1, height[layer_idx], width[layer_idx])
# upsample
test_map.append(
F.interpolate(
test_mask.unsqueeze(1), size=self.image_size, mode="bilinear", align_corners=True
).squeeze()
)
# score aggregation
score_map = torch.zeros_like(test_map[0])
for layer_idx in range(len(self.pool_layers)):
score_map += test_map[layer_idx]
score_mask = score_map
# invert probs to anomaly scores
anomaly_map = score_mask.max() - score_mask
return anomaly_map
def __call__(self, **kwargs: Union[List[Tensor], List[int], List[List]]) -> Tensor:
"""Returns anomaly_map.
Expects `distribution`, `height` and 'width' keywords to be passed explicitly
Example
>>> anomaly_map_generator = AnomalyMapGenerator(image_size=tuple(hparams.model.input_size),
>>> pool_layers=pool_layers)
>>> output = self.anomaly_map_generator(distribution=dist, height=height, width=width)
Raises:
ValueError: `distribution`, `height` and 'width' keys are not found
Returns:
torch.Tensor: anomaly map
"""
if not ("distribution" in kwargs and "height" in kwargs and "width" in kwargs):
raise KeyError(f"Expected keys `distribution`, `height` and `width`. Found {kwargs.keys()}")
# placate mypy
distribution: List[Tensor] = cast(List[Tensor], kwargs["distribution"])
height: List[int] = cast(List[int], kwargs["height"])
width: List[int] = cast(List[int], kwargs["width"])
return self.compute_anomaly_map(distribution, height, width)
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