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"""MetricsManager callback."""
# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import logging
from enum import Enum
from typing import Any
import torch
from lightning.pytorch import Callback, Trainer
from lightning.pytorch.utilities.types import STEP_OUTPUT
from anomalib import TaskType
from anomalib.metrics import create_metric_collection
from anomalib.models import AnomalyModule
logger = logging.getLogger(__name__)
class Device(str, Enum):
"""Device on which to compute metrics."""
CPU = "cpu"
GPU = "gpu"
class _MetricsCallback(Callback):
"""Create image and pixel-level AnomalibMetricsCollection.
This callback creates AnomalibMetricsCollection based on the
list of strings provided for image and pixel-level metrics.
After these MetricCollections are created, the callback assigns
these to the lightning module.
Args:
task (TaskType | str): Task type of the current run.
image_metrics (list[str] | str | dict[str, dict[str, Any]] | None): List of image-level metrics.
pixel_metrics (list[str] | str | dict[str, dict[str, Any]] | None): List of pixel-level metrics.
device (str): Whether to compute metrics on cpu or gpu. Defaults to cpu.
"""
def __init__(
self,
task: TaskType | str = TaskType.SEGMENTATION,
image_metrics: list[str] | str | dict[str, dict[str, Any]] | None = None,
pixel_metrics: list[str] | str | dict[str, dict[str, Any]] | None = None,
device: Device = Device.CPU,
) -> None:
super().__init__()
self.task = TaskType(task)
self.image_metric_names = image_metrics
self.pixel_metric_names = pixel_metrics
self.device = device
def setup(
self,
trainer: Trainer,
pl_module: AnomalyModule,
stage: str | None = None,
) -> None:
"""Set image and pixel-level AnomalibMetricsCollection within Anomalib Model.
Args:
trainer (pl.Trainer): PyTorch Lightning Trainer
pl_module (AnomalyModule): Anomalib Model that inherits pl LightningModule.
stage (str | None, optional): fit, validate, test or predict. Defaults to None.
"""
del stage, trainer # this variable is not used.
image_metric_names = [] if self.image_metric_names is None else self.image_metric_names
if isinstance(image_metric_names, str):
image_metric_names = [image_metric_names]
pixel_metric_names: list[str] | dict[str, dict[str, Any]]
if self.pixel_metric_names is None:
pixel_metric_names = []
elif self.task == TaskType.CLASSIFICATION:
pixel_metric_names = []
logger.warning(
"Cannot perform pixel-level evaluation when task type is classification. "
"Ignoring the following pixel-level metrics: %s",
self.pixel_metric_names,
)
else:
pixel_metric_names = (
self.pixel_metric_names.copy()
if not isinstance(self.pixel_metric_names, str)
else [self.pixel_metric_names]
)
# create a separate metric collection for metrics that operate over the semantic segmentation mask
# (segmentation mask with a separate channel for each defect type)
semantic_pixel_metric_names: list[str] | dict[str, dict[str, Any]] = []
# currently only SPRO metric is supported as semantic segmentation metric
if "SPRO" in pixel_metric_names:
if isinstance(pixel_metric_names, list):
pixel_metric_names.remove("SPRO")
semantic_pixel_metric_names = ["SPRO"]
elif isinstance(pixel_metric_names, dict):
spro_metric = pixel_metric_names.pop("SPRO")
semantic_pixel_metric_names = {"SPRO": spro_metric}
else:
logger.warning("Unexpected type for pixel_metric_names: %s", type(pixel_metric_names))
if isinstance(pl_module, AnomalyModule):
pl_module.image_metrics = create_metric_collection(image_metric_names, "image_")
if hasattr(pl_module, "pixel_metrics"): # incase metrics are loaded from model checkpoint
new_metrics = create_metric_collection(pixel_metric_names)
for name in new_metrics:
if name not in pl_module.pixel_metrics:
pl_module.pixel_metrics.add_metrics(new_metrics[name])
else:
pl_module.pixel_metrics = create_metric_collection(pixel_metric_names, "pixel_")
pl_module.semantic_pixel_metrics = create_metric_collection(semantic_pixel_metric_names, "pixel_")
self._set_threshold(pl_module)
def on_validation_epoch_start(
self,
trainer: Trainer,
pl_module: AnomalyModule,
) -> None:
del trainer # Unused argument.
pl_module.image_metrics.reset()
pl_module.pixel_metrics.reset()
pl_module.semantic_pixel_metrics.reset()
def on_validation_batch_end(
self,
trainer: Trainer,
pl_module: AnomalyModule,
outputs: STEP_OUTPUT | None,
batch: Any, # noqa: ANN401
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
del trainer, batch, batch_idx, dataloader_idx # Unused arguments.
if outputs is not None:
self._outputs_to_device(outputs)
self._update_metrics(pl_module, outputs)
def on_validation_epoch_end(
self,
trainer: Trainer,
pl_module: AnomalyModule,
) -> None:
del trainer # Unused argument.
self._set_threshold(pl_module)
self._log_metrics(pl_module)
def on_test_epoch_start(
self,
trainer: Trainer,
pl_module: AnomalyModule,
) -> None:
del trainer # Unused argument.
pl_module.image_metrics.reset()
pl_module.pixel_metrics.reset()
pl_module.semantic_pixel_metrics.reset()
def on_test_batch_end(
self,
trainer: Trainer,
pl_module: AnomalyModule,
outputs: STEP_OUTPUT | None,
batch: Any, # noqa: ANN401
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
del trainer, batch, batch_idx, dataloader_idx # Unused arguments.
if outputs is not None:
self._outputs_to_device(outputs)
self._update_metrics(pl_module, outputs)
def on_test_epoch_end(
self,
trainer: Trainer,
pl_module: AnomalyModule,
) -> None:
del trainer # Unused argument.
self._log_metrics(pl_module)
def _set_threshold(self, pl_module: AnomalyModule) -> None:
pl_module.image_metrics.set_threshold(pl_module.image_threshold.value.item())
pl_module.pixel_metrics.set_threshold(pl_module.pixel_threshold.value.item())
pl_module.semantic_pixel_metrics.set_threshold(pl_module.pixel_threshold.value.item())
def _update_metrics(
self,
pl_module: AnomalyModule,
output: STEP_OUTPUT,
) -> None:
pl_module.image_metrics.to(self.device)
pl_module.image_metrics.update(output["pred_scores"], output["label"].int())
if "mask" in output and "anomaly_maps" in output:
pl_module.pixel_metrics.to(self.device)
pl_module.pixel_metrics.update(torch.squeeze(output["anomaly_maps"]), torch.squeeze(output["mask"].int()))
if "semantic_mask" in output and "anomaly_maps" in output:
pl_module.semantic_pixel_metrics.to(self.device)
pl_module.semantic_pixel_metrics.update(torch.squeeze(output["anomaly_maps"]), output["semantic_mask"])
def _outputs_to_device(self, output: STEP_OUTPUT) -> STEP_OUTPUT | dict[str, Any]:
if isinstance(output, dict):
for key, value in output.items():
output[key] = self._outputs_to_device(value)
elif isinstance(output, torch.Tensor):
output = output.to(self.device)
elif isinstance(output, list):
for i, value in enumerate(output):
output[i] = self._outputs_to_device(value)
return output
@staticmethod
def _log_metrics(pl_module: AnomalyModule) -> None:
"""Log computed performance metrics."""
pl_module.log_dict(pl_module.image_metrics, prog_bar=True)
if pl_module.pixel_metrics.update_called:
pl_module.log_dict(pl_module.pixel_metrics, prog_bar=False)
if pl_module.semantic_pixel_metrics.update_called:
pl_module.log_dict(pl_module.semantic_pixel_metrics, prog_bar=False)
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