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| import torch | |
| import numpy as np | |
| def count_parameters(model): | |
| return sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| def compute_overlap(a, b): | |
| if type(a) == torch.Tensor: | |
| if len(a.shape) == 2: | |
| area = (b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1) | |
| iw = torch.min(a[:, 2].unsqueeze(dim=1), b[:, 2]) - torch.max(a[:, 0].unsqueeze(dim=1), b[:, 0]) | |
| ih = torch.min(a[:, 3].unsqueeze(dim=1), b[:, 3]) - torch.max(a[:, 1].unsqueeze(dim=1), b[:, 1]) | |
| iw[iw<0] = 0 | |
| ih[ih<0] = 0 | |
| ua = torch.unsqueeze((a[:, 2] - a[:, 0] + 1) * (a[:, 3] - a[:, 1] + 1), dim=1) + area - iw * ih | |
| ua[ua < 1e-8] = 1e-8 | |
| intersection = iw * ih | |
| return intersection / ua | |
| elif type(a) == np.ndarray: | |
| if len(a.shape) == 2: | |
| area = np.expand_dims((b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1), axis=0) #(1, K) | |
| iw = np.minimum(np.expand_dims(a[:, 2], axis=1), np.expand_dims(b[:, 2], axis=0)) \ | |
| - np.maximum(np.expand_dims(a[:, 0], axis=1), np.expand_dims(b[:, 0], axis=0)) \ | |
| + 1 | |
| ih = np.minimum(np.expand_dims(a[:, 3], axis=1), np.expand_dims(b[:, 3], axis=0)) \ | |
| - np.maximum(np.expand_dims(a[:, 1], axis=1), np.expand_dims(b[:, 1], axis=0)) \ | |
| + 1 | |
| iw[iw<0] = 0 # (N, K) | |
| ih[ih<0] = 0 # (N, K) | |
| intersection = iw * ih | |
| ua = np.expand_dims((a[:, 2] - a[:, 0] + 1) * (a[:, 3] - a[:, 1] + 1), axis=1) + area - intersection | |
| ua[ua < 1e-8] = 1e-8 | |
| return intersection / ua | |
| elif len(a.shape) == 1: | |
| area = np.expand_dims((b[:, 2] - b[:, 0] + 1) * (b[:, 3] - b[:, 1] + 1), axis=0) #(1, K) | |
| iw = np.minimum(np.expand_dims([a[2]], axis=1), np.expand_dims(b[:, 2], axis=0)) \ | |
| - np.maximum(np.expand_dims([a[0]], axis=1), np.expand_dims(b[:, 0], axis=0)) | |
| ih = np.minimum(np.expand_dims([a[3]], axis=1), np.expand_dims(b[:, 3], axis=0)) \ | |
| - np.maximum(np.expand_dims([a[1]], axis=1), np.expand_dims(b[:, 1], axis=0)) | |
| iw[iw<0] = 0 # (N, K) | |
| ih[ih<0] = 0 # (N, K) | |
| ua = np.expand_dims([(a[2] - a[0] + 1) * (a[3] - a[1] + 1)], axis=1) + area - iw * ih | |
| ua[ua < 1e-8] = 1e-8 | |
| intersection = iw * ih | |
| return intersection / ua | |
| def _compute_ap(recall, precision): | |
| """ Compute the average precision, given the recall and precision curves. | |
| Code originally from https://github.com/rbgirshick/py-faster-rcnn. | |
| # Arguments | |
| recall: The recall curve (list). | |
| precision: The precision curve (list). | |
| # Returns | |
| The average precision as computed in py-faster-rcnn. | |
| """ | |
| # correct AP calculation | |
| # first append sentinel values at the end | |
| mrec = np.concatenate(([0.], recall, [1.])) | |
| mpre = np.concatenate(([0.], precision, [0.])) | |
| # compute the precision envelope | |
| for i in range(mpre.size - 1, 0, -1): | |
| mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) | |
| # to calculate area under PR curve, look for points | |
| # where X axis (recall) changes value | |
| i = np.where(mrec[1:] != mrec[:-1])[0] | |
| # and sum (\Delta recall) * prec | |
| ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) | |
| return ap |