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