64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import numpy as np
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from mmseg.core.evaluation import 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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# This func is deprecated since it's not memory efficient
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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 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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