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import numpy as np
from mmseg.core.evaluation import eval_metrics, mean_dice, mean_iou
def get_confusion_matrix(pred_label, label, num_classes, ignore_index):
"""Intersection over Union
Args:
pred_label (np.ndarray): 2D predict map
label (np.ndarray): label 2D label map
num_classes (int): number of categories
ignore_index (int): index ignore in evaluation
"""
mask = (label != ignore_index)
pred_label = pred_label[mask]
label = label[mask]
n = num_classes
inds = n * label + pred_label
mat = np.bincount(inds, minlength=n**2).reshape(n, n)
return mat
# This func is deprecated since it's not memory efficient
def legacy_mean_iou(results, gt_seg_maps, num_classes, ignore_index):
num_imgs = len(results)
assert len(gt_seg_maps) == num_imgs
total_mat = np.zeros((num_classes, num_classes), dtype=np.float)
for i in range(num_imgs):
mat = get_confusion_matrix(
results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
acc = np.diag(total_mat) / total_mat.sum(axis=1)
iou = np.diag(total_mat) / (
total_mat.sum(axis=1) + total_mat.sum(axis=0) - np.diag(total_mat))
return all_acc, acc, iou
# This func is deprecated since it's not memory efficient
def legacy_mean_dice(results, gt_seg_maps, num_classes, ignore_index):
num_imgs = len(results)
assert len(gt_seg_maps) == num_imgs
total_mat = np.zeros((num_classes, num_classes), dtype=np.float)
for i in range(num_imgs):
mat = get_confusion_matrix(
results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
acc = np.diag(total_mat) / total_mat.sum(axis=1)
dice = 2 * np.diag(total_mat) / (
total_mat.sum(axis=1) + total_mat.sum(axis=0))
return all_acc, acc, dice
def test_metrics():
pred_size = (10, 30, 30)
num_classes = 19
ignore_index = 255
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
all_acc, acc, iou = eval_metrics(
results, label, num_classes, ignore_index, metrics='mIoU')
all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
all_acc, acc, dice = eval_metrics(
results, label, num_classes, ignore_index, metrics='mDice')
all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(dice, dice_l)
all_acc, acc, iou, dice = eval_metrics(
results, label, num_classes, ignore_index, metrics=['mIoU', 'mDice'])
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
assert np.allclose(dice, dice_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics='mIoU',
nan_to_num=-1)
assert acc[-1] == -1
assert iou[-1] == -1
all_acc, acc, dice = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics='mDice',
nan_to_num=-1)
assert acc[-1] == -1
assert dice[-1] == -1
all_acc, acc, dice, iou = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics=['mDice', 'mIoU'],
nan_to_num=-1)
assert acc[-1] == -1
assert dice[-1] == -1
assert iou[-1] == -1
def test_mean_iou():
pred_size = (10, 30, 30)
num_classes = 19
ignore_index = 255
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
all_acc, acc, iou = mean_iou(results, label, num_classes, ignore_index)
all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = mean_iou(
results, label, num_classes, ignore_index=255, nan_to_num=-1)
assert acc[-1] == -1
assert iou[-1] == -1
def test_mean_dice():
pred_size = (10, 30, 30)
num_classes = 19
ignore_index = 255
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
all_acc, acc, iou = mean_dice(results, label, num_classes, ignore_index)
all_acc_l, acc_l, iou_l = legacy_mean_dice(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = mean_dice(
results, label, num_classes, ignore_index=255, nan_to_num=-1)
assert acc[-1] == -1
assert iou[-1] == -1
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