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01bd570 | 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 | # Copyright (c) MONAI Consortium
# 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 __future__ import annotations
from collections.abc import Callable
from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import PanopticQualityMetric
from monai.utils import MetricReduction
class PanopticQuality(IgniteMetricHandler):
"""
Computes Panoptic quality from full size Tensor and collects average over batch, class-channels, iterations.
"""
def __init__(
self,
num_classes: int,
metric_name: str = "pq",
reduction: MetricReduction | str = MetricReduction.MEAN_BATCH,
match_iou_threshold: float = 0.5,
smooth_numerator: float = 1e-6,
output_transform: Callable = lambda x: x,
save_details: bool = True,
) -> None:
"""
Args:
num_classes: number of classes. The number should not count the background.
metric_name: output metric. The value can be "pq", "sq" or "rq".
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
match_iou_threshold: IOU threshold to determine the pairing between `y_pred` and `y`. Usually,
it should >= 0.5, the pairing between instances of `y_pred` and `y` are identical.
If set `match_iou_threshold` < 0.5, this function uses Munkres assignment to find the
maximal amount of unique pairing.
smooth_numerator: a small constant added to the numerator to avoid zero.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: panoptic quality of
every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
See also:
:py:meth:`monai.metrics.panoptic_quality.compute_panoptic_quality`
"""
metric_fn = PanopticQualityMetric(
num_classes=num_classes,
metric_name=metric_name,
reduction=reduction,
match_iou_threshold=match_iou_threshold,
smooth_numerator=smooth_numerator,
)
super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)
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