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[Refactor] Refactor evaluation config, clean eval hook and metrics

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linfangjian.vendor 2022-06-08 08:22:42 +00:00 committed by zhengmiao
parent 6bf17de748
commit 02677ac2f8
23 changed files with 61 additions and 531 deletions
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3
configs/_base_/datasets/ade20k.py
View File

@ -43,3 +43,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/ade20k_640x640.py
View File

@ -43,3 +43,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/chase_db1.py
View File

@ -47,3 +47,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/cityscapes.py
View File

@ -42,3 +42,6 @@ val_dataloader = dict(
img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/cityscapes_1024x1024.py
View File

@ -20,3 +20,6 @@ test_pipeline = [
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/cityscapes_768x768.py
View File

@ -20,3 +20,6 @@ test_pipeline = [
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/cityscapes_769x769.py
View File

@ -20,3 +20,6 @@ test_pipeline = [
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/cityscapes_832x832.py
View File

@ -20,3 +20,6 @@ test_pipeline = [
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/coco-stuff10k.py
View File

@ -44,3 +44,6 @@ val_dataloader = dict(
img_path='images/test2014', seg_map_path='annotations/test2014'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/coco-stuff164k.py
View File

@ -42,3 +42,6 @@ val_dataloader = dict(
img_path='images/val2017', seg_map_path='annotations/val2017'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/drive.py
View File

@ -46,3 +46,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/hrf.py
View File

@ -46,3 +46,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/isaid.py
View File

@ -49,3 +49,6 @@ val_dataloader = dict(
data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/loveda.py
View File

@ -41,3 +41,6 @@ val_dataloader = dict(
data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/pascal_context.py
View File

@ -47,3 +47,6 @@ val_dataloader = dict(
ann_file='ImageSets/SegmentationContext/val.txt',
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/pascal_context_59.py
View File

@ -47,3 +47,6 @@ val_dataloader = dict(
ann_file='ImageSets/SegmentationContext/val.txt',
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/pascal_voc12.py
View File

@ -44,3 +44,6 @@ val_dataloader = dict(
ann_file='ImageSets/Segmentation/val.txt',
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/potsdam.py
View File

@ -41,3 +41,6 @@ val_dataloader = dict(
data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/stare.py
View File

@ -46,3 +46,6 @@ val_dataloader = dict(
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

3
configs/_base_/datasets/vaihingen.py
View File

@ -41,3 +41,6 @@ val_dataloader = dict(
data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
test_evaluator = val_evaluator

9
mmseg/core/evaluation/__init__.py
View File

@ -1,11 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .class_names import get_classes, get_palette
from .eval_hooks import DistEvalHook, EvalHook
from .metrics import (eval_metrics, intersect_and_union, mean_dice,
mean_fscore, mean_iou, pre_eval_to_metrics)
__all__ = [
'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
'eval_metrics', 'get_classes', 'get_palette', 'pre_eval_to_metrics',
'intersect_and_union'
]
__all__ = ['get_classes', 'get_palette']

128
mmseg/core/evaluation/eval_hooks.py
View File

@ -1,128 +0,0 @@
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
import torch.distributed as dist
from mmcv.runner import DistEvalHook as _DistEvalHook
from mmcv.runner import EvalHook as _EvalHook
from torch.nn.modules.batchnorm import _BatchNorm
class EvalHook(_EvalHook):
"""Single GPU EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
If set to True, it will perform by epoch. Otherwise, by iteration.
Default: False.
efficient_test (bool): Whether save the results as local numpy files to
save CPU memory during evaluation. Default: False.
pre_eval (bool): Whether to use progressive mode to evaluate model.
Default: False.
Returns:
list: The prediction results.
"""
greater_keys = ['mIoU', 'mAcc', 'aAcc']
def __init__(self,
*args,
by_epoch=False,
efficient_test=False,
pre_eval=False,
**kwargs):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.pre_eval = pre_eval
if efficient_test:
warnings.warn(
'DeprecationWarning: ``efficient_test`` for evaluation hook '
'is deprecated, the evaluation hook is CPU memory friendly '
'with ``pre_eval=True`` as argument for ``single_gpu_test()`` '
'function')
def _do_evaluate(self, runner):
"""perform evaluation and save ckpt."""
if not self._should_evaluate(runner):
return
from mmseg.apis import single_gpu_test
results = single_gpu_test(
runner.model, self.dataloader, show=False, pre_eval=self.pre_eval)
runner.log_buffer.clear()
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
self._save_ckpt(runner, key_score)
class DistEvalHook(_DistEvalHook):
"""Distributed EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epoch or by iteration.
If set to True, it will perform by epoch. Otherwise, by iteration.
Default: False.
efficient_test (bool): Whether save the results as local numpy files to
save CPU memory during evaluation. Default: False.
pre_eval (bool): Whether to use progressive mode to evaluate model.
Default: False.
Returns:
list: The prediction results.
"""
greater_keys = ['mIoU', 'mAcc', 'aAcc']
def __init__(self,
*args,
by_epoch=False,
efficient_test=False,
pre_eval=False,
**kwargs):
super().__init__(*args, by_epoch=by_epoch, **kwargs)
self.pre_eval = pre_eval
if efficient_test:
warnings.warn(
'DeprecationWarning: ``efficient_test`` for evaluation hook '
'is deprecated, the evaluation hook is CPU memory friendly '
'with ``pre_eval=True`` as argument for ``multi_gpu_test()`` '
'function')
def _do_evaluate(self, runner):
"""perform evaluation and save ckpt."""
# Synchronization of BatchNorm's buffer (running_mean
# and running_var) is not supported in the DDP of pytorch,
# which may cause the inconsistent performance of models in
# different ranks, so we broadcast BatchNorm's buffers
# of rank 0 to other ranks to avoid this.
if self.broadcast_bn_buffer:
model = runner.model
for name, module in model.named_modules():
if isinstance(module,
_BatchNorm) and module.track_running_stats:
dist.broadcast(module.running_var, 0)
dist.broadcast(module.running_mean, 0)
if not self._should_evaluate(runner):
return
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
from mmseg.apis import multi_gpu_test
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect,
pre_eval=self.pre_eval)
runner.log_buffer.clear()
if runner.rank == 0:
print('\n')
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
self._save_ckpt(runner, key_score)

395
mmseg/core/evaluation/metrics.py
View File

@ -1,395 +0,0 @@
# Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
import mmcv
import numpy as np
import torch
def f_score(precision, recall, beta=1):
"""calculate the f-score value.
Args:
precision (float | torch.Tensor): The precision value.
recall (float | torch.Tensor): The recall value.
beta (int): Determines the weight of recall in the combined score.
Default: False.
Returns:
[torch.tensor]: The f-score value.
"""
score = (1 + beta**2) * (precision * recall) / (
(beta**2 * precision) + recall)
return score
def intersect_and_union(pred_label,
label,
num_classes,
ignore_index,
label_map=dict(),
reduce_zero_label=False):
"""Calculate intersection and Union.
Args:
pred_label (ndarray | str): Prediction segmentation map
or predict result filename.
label (ndarray | str): Ground truth segmentation map
or label filename.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
label_map (dict): Mapping old labels to new labels. The parameter will
work only when label is str. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. The parameter will
work only when label is str. Default: False.
Returns:
torch.Tensor: The intersection of prediction and ground truth
histogram on all classes.
torch.Tensor: The union of prediction and ground truth histogram on
all classes.
torch.Tensor: The prediction histogram on all classes.
torch.Tensor: The ground truth histogram on all classes.
"""
if isinstance(pred_label, str):
pred_label = torch.from_numpy(np.load(pred_label))
else:
pred_label = torch.from_numpy((pred_label))
if isinstance(label, str):
label = torch.from_numpy(
mmcv.imread(label, flag='unchanged', backend='pillow'))
else:
label = torch.from_numpy(label)
if label_map is not None:
for old_id, new_id in label_map.items():
label[label == old_id] = new_id
if reduce_zero_label:
label[label == 0] = 255
label = label - 1
label[label == 254] = 255
mask = (label != ignore_index)
pred_label = pred_label[mask]
label = label[mask]
intersect = pred_label[pred_label == label]
area_intersect = torch.histc(
intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
area_pred_label = torch.histc(
pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
area_label = torch.histc(
label.float(), bins=(num_classes), min=0, max=num_classes - 1)
area_union = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def total_intersect_and_union(results,
gt_seg_maps,
num_classes,
ignore_index,
label_map=dict(),
reduce_zero_label=False):
"""Calculate Total Intersection and Union.
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str] | Iterables): list of ground
truth segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. Default: False.
Returns:
ndarray: The intersection of prediction and ground truth histogram
on all classes.
ndarray: The union of prediction and ground truth histogram on all
classes.
ndarray: The prediction histogram on all classes.
ndarray: The ground truth histogram on all classes.
"""
total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
for result, gt_seg_map in zip(results, gt_seg_maps):
area_intersect, area_union, area_pred_label, area_label = \
intersect_and_union(
result, gt_seg_map, num_classes, ignore_index,
label_map, reduce_zero_label)
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, \
total_area_label
def mean_iou(results,
gt_seg_maps,
num_classes,
ignore_index,
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False):
"""Calculate Mean Intersection and Union (mIoU)
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str]): list of ground truth
segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. Default: False.
Returns:
dict[str, float | ndarray]:
<aAcc> float: Overall accuracy on all images.
<Acc> ndarray: Per category accuracy, shape (num_classes, ).
<IoU> ndarray: Per category IoU, shape (num_classes, ).
"""
iou_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
metrics=['mIoU'],
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label)
return iou_result
def mean_dice(results,
gt_seg_maps,
num_classes,
ignore_index,
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False):
"""Calculate Mean Dice (mDice)
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str]): list of ground truth
segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. Default: False.
Returns:
dict[str, float | ndarray]: Default metrics.
<aAcc> float: Overall accuracy on all images.
<Acc> ndarray: Per category accuracy, shape (num_classes, ).
<Dice> ndarray: Per category dice, shape (num_classes, ).
"""
dice_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
metrics=['mDice'],
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label)
return dice_result
def mean_fscore(results,
gt_seg_maps,
num_classes,
ignore_index,
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False,
beta=1):
"""Calculate Mean Intersection and Union (mIoU)
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str]): list of ground truth
segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. Default: False.
beta (int): Determines the weight of recall in the combined score.
Default: False.
Returns:
dict[str, float | ndarray]: Default metrics.
<aAcc> float: Overall accuracy on all images.
<Fscore> ndarray: Per category recall, shape (num_classes, ).
<Precision> ndarray: Per category precision, shape (num_classes, ).
<Recall> ndarray: Per category f-score, shape (num_classes, ).
"""
fscore_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
metrics=['mFscore'],
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label,
beta=beta)
return fscore_result
def eval_metrics(results,
gt_seg_maps,
num_classes,
ignore_index,
metrics=['mIoU'],
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False,
beta=1):
"""Calculate evaluation metrics
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str] | Iterables): list of ground
truth segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Whether ignore zero label. Default: False.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category evaluation metrics, shape (num_classes, ).
"""
total_area_intersect, total_area_union, total_area_pred_label, \
total_area_label = total_intersect_and_union(
results, gt_seg_maps, num_classes, ignore_index, label_map,
reduce_zero_label)
ret_metrics = total_area_to_metrics(total_area_intersect, total_area_union,
total_area_pred_label,
total_area_label, metrics, nan_to_num,
beta)
return ret_metrics
def pre_eval_to_metrics(pre_eval_results,
metrics=['mIoU'],
nan_to_num=None,
beta=1):
"""Convert pre-eval results to metrics.
Args:
pre_eval_results (list[tuple[torch.Tensor]]): per image eval results
for computing evaluation metric
metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category evaluation metrics, shape (num_classes, ).
"""
# convert list of tuples to tuple of lists, e.g.
# [(A_1, B_1, C_1, D_1), ..., (A_n, B_n, C_n, D_n)] to
# ([A_1, ..., A_n], ..., [D_1, ..., D_n])
pre_eval_results = tuple(zip(*pre_eval_results))
assert len(pre_eval_results) == 4
total_area_intersect = sum(pre_eval_results[0])
total_area_union = sum(pre_eval_results[1])
total_area_pred_label = sum(pre_eval_results[2])
total_area_label = sum(pre_eval_results[3])
ret_metrics = total_area_to_metrics(total_area_intersect, total_area_union,
total_area_pred_label,
total_area_label, metrics, nan_to_num,
beta)
return ret_metrics
def total_area_to_metrics(total_area_intersect,
total_area_union,
total_area_pred_label,
total_area_label,
metrics=['mIoU'],
nan_to_num=None,
beta=1):
"""Calculate evaluation metrics
Args:
total_area_intersect (ndarray): The intersection of prediction and
ground truth histogram on all classes.
total_area_union (ndarray): The union of prediction and ground truth
histogram on all classes.
total_area_pred_label (ndarray): The prediction histogram on all
classes.
total_area_label (ndarray): The ground truth histogram on all classes.
metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category evaluation metrics, shape (num_classes, ).
"""
if isinstance(metrics, str):
metrics = [metrics]
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if not set(metrics).issubset(set(allowed_metrics)):
raise KeyError('metrics {} is not supported'.format(metrics))
all_acc = total_area_intersect.sum() / total_area_label.sum()
ret_metrics = OrderedDict({'aAcc': all_acc})
for metric in metrics:
if metric == 'mIoU':
iou = total_area_intersect / total_area_union
acc = total_area_intersect / total_area_label
ret_metrics['IoU'] = iou
ret_metrics['Acc'] = acc
elif metric == 'mDice':
dice = 2 * total_area_intersect / (
total_area_pred_label + total_area_label)
acc = total_area_intersect / total_area_label
ret_metrics['Dice'] = dice
ret_metrics['Acc'] = acc
elif metric == 'mFscore':
precision = total_area_intersect / total_area_pred_label
recall = total_area_intersect / total_area_label
f_value = torch.tensor(
[f_score(x[0], x[1], beta) for x in zip(precision, recall)])
ret_metrics['Fscore'] = f_value
ret_metrics['Precision'] = precision
ret_metrics['Recall'] = recall
ret_metrics = {
metric: value.numpy()
for metric, value in ret_metrics.items()
}
if nan_to_num is not None:
ret_metrics = OrderedDict({
metric: np.nan_to_num(metric_value, nan=nan_to_num)
for metric, metric_value in ret_metrics.items()
})
return ret_metrics