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| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from .geom import gather_nd | |
| # input: [batch_size, C, H, W] | |
| # output: [batch_size, C, H, W], [batch_size, C, H, W] | |
| def peakiness_score(inputs, moving_instance_max, ksize=3, dilation=1): | |
| inputs = inputs / moving_instance_max | |
| batch_size, C, H, W = inputs.shape | |
| pad_size = ksize // 2 + (dilation - 1) | |
| kernel = torch.ones([C, 1, ksize, ksize], device=inputs.device) / (ksize * ksize) | |
| pad_inputs = F.pad(inputs, [pad_size] * 4, mode="reflect") | |
| avg_spatial_inputs = F.conv2d( | |
| pad_inputs, kernel, stride=1, dilation=dilation, padding=0, groups=C | |
| ) | |
| avg_channel_inputs = torch.mean( | |
| inputs, axis=1, keepdim=True | |
| ) # channel dimension is 1 | |
| alpha = F.softplus(inputs - avg_spatial_inputs) | |
| beta = F.softplus(inputs - avg_channel_inputs) | |
| return alpha, beta | |
| # input: score_map [batch_size, 1, H, W] | |
| # output: indices [2, k, 2], scores [2, k] | |
| def extract_kpts(score_map, k=256, score_thld=0, edge_thld=0, nms_size=3, eof_size=5): | |
| h = score_map.shape[2] | |
| w = score_map.shape[3] | |
| mask = score_map > score_thld | |
| if nms_size > 0: | |
| nms_mask = F.max_pool2d( | |
| score_map, kernel_size=nms_size, stride=1, padding=nms_size // 2 | |
| ) | |
| nms_mask = torch.eq(score_map, nms_mask) | |
| mask = torch.logical_and(nms_mask, mask) | |
| if eof_size > 0: | |
| eof_mask = torch.ones( | |
| (1, 1, h - 2 * eof_size, w - 2 * eof_size), | |
| dtype=torch.float32, | |
| device=score_map.device, | |
| ) | |
| eof_mask = F.pad(eof_mask, [eof_size] * 4, value=0) | |
| eof_mask = eof_mask.bool() | |
| mask = torch.logical_and(eof_mask, mask) | |
| if edge_thld > 0: | |
| non_edge_mask = edge_mask(score_map, 1, dilation=3, edge_thld=edge_thld) | |
| mask = torch.logical_and(non_edge_mask, mask) | |
| bs = score_map.shape[0] | |
| if bs is None: | |
| indices = torch.nonzero(mask)[0] | |
| scores = gather_nd(score_map, indices)[0] | |
| sample = torch.sort(scores, descending=True)[1][0:k] | |
| indices = indices[sample].unsqueeze(0) | |
| scores = scores[sample].unsqueeze(0) | |
| else: | |
| indices = [] | |
| scores = [] | |
| for i in range(bs): | |
| tmp_mask = mask[i][0] | |
| tmp_score_map = score_map[i][0] | |
| tmp_indices = torch.nonzero(tmp_mask) | |
| tmp_scores = gather_nd(tmp_score_map, tmp_indices) | |
| tmp_sample = torch.sort(tmp_scores, descending=True)[1][0:k] | |
| tmp_indices = tmp_indices[tmp_sample] | |
| tmp_scores = tmp_scores[tmp_sample] | |
| indices.append(tmp_indices) | |
| scores.append(tmp_scores) | |
| try: | |
| indices = torch.stack(indices, dim=0) | |
| scores = torch.stack(scores, dim=0) | |
| except: | |
| min_num = np.min([len(i) for i in indices]) | |
| indices = torch.stack([i[:min_num] for i in indices], dim=0) | |
| scores = torch.stack([i[:min_num] for i in scores], dim=0) | |
| return indices, scores | |
| def edge_mask(inputs, n_channel, dilation=1, edge_thld=5): | |
| b, c, h, w = inputs.size() | |
| device = inputs.device | |
| dii_filter = torch.tensor([[0, 1.0, 0], [0, -2.0, 0], [0, 1.0, 0]]).view(1, 1, 3, 3) | |
| dij_filter = 0.25 * torch.tensor( | |
| [[1.0, 0, -1.0], [0, 0.0, 0], [-1.0, 0, 1.0]] | |
| ).view(1, 1, 3, 3) | |
| djj_filter = torch.tensor([[0, 0, 0], [1.0, -2.0, 1.0], [0, 0, 0]]).view(1, 1, 3, 3) | |
| dii = F.conv2d( | |
| inputs.view(-1, 1, h, w), | |
| dii_filter.to(device), | |
| padding=dilation, | |
| dilation=dilation, | |
| ).view(b, c, h, w) | |
| dij = F.conv2d( | |
| inputs.view(-1, 1, h, w), | |
| dij_filter.to(device), | |
| padding=dilation, | |
| dilation=dilation, | |
| ).view(b, c, h, w) | |
| djj = F.conv2d( | |
| inputs.view(-1, 1, h, w), | |
| djj_filter.to(device), | |
| padding=dilation, | |
| dilation=dilation, | |
| ).view(b, c, h, w) | |
| det = dii * djj - dij * dij | |
| tr = dii + djj | |
| del dii, dij, djj | |
| threshold = (edge_thld + 1) ** 2 / edge_thld | |
| is_not_edge = torch.min(tr * tr / det <= threshold, det > 0) | |
| return is_not_edge | |