| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from lib.exceptions import EmptyTensorError |
| from lib.utils import interpolate_dense_features, upscale_positions |
|
|
|
|
| def process_multiscale(image, model, scales=[.5, 1, 2]): |
| b, _, h_init, w_init = image.size() |
| device = image.device |
| assert(b == 1) |
|
|
| all_keypoints = torch.zeros([3, 0]) |
| all_descriptors = torch.zeros([ |
| model.dense_feature_extraction.num_channels, 0 |
| ]) |
| all_scores = torch.zeros(0) |
|
|
| previous_dense_features = None |
| banned = None |
| for idx, scale in enumerate(scales): |
| current_image = F.interpolate( |
| image, scale_factor=scale, |
| mode='bilinear', align_corners=True |
| ) |
| _, _, h_level, w_level = current_image.size() |
|
|
| dense_features = model.dense_feature_extraction(current_image) |
| del current_image |
|
|
| _, _, h, w = dense_features.size() |
|
|
| |
| if previous_dense_features is not None: |
| dense_features += F.interpolate( |
| previous_dense_features, size=[h, w], |
| mode='bilinear', align_corners=True |
| ) |
| del previous_dense_features |
|
|
| |
| detections = model.detection(dense_features) |
| if banned is not None: |
| banned = F.interpolate(banned.float(), size=[h, w]).bool() |
| detections = torch.min(detections, ~banned) |
| banned = torch.max( |
| torch.max(detections, dim=1)[0].unsqueeze(1), banned |
| ) |
| else: |
| banned = torch.max(detections, dim=1)[0].unsqueeze(1) |
| fmap_pos = torch.nonzero(detections[0].cpu()).t() |
| del detections |
|
|
| |
| displacements = model.localization(dense_features)[0].cpu() |
| displacements_i = displacements[ |
| 0, fmap_pos[0, :], fmap_pos[1, :], fmap_pos[2, :] |
| ] |
| displacements_j = displacements[ |
| 1, fmap_pos[0, :], fmap_pos[1, :], fmap_pos[2, :] |
| ] |
| del displacements |
|
|
| mask = torch.min( |
| torch.abs(displacements_i) < 0.5, |
| torch.abs(displacements_j) < 0.5 |
| ) |
| fmap_pos = fmap_pos[:, mask] |
| valid_displacements = torch.stack([ |
| displacements_i[mask], |
| displacements_j[mask] |
| ], dim=0) |
| del mask, displacements_i, displacements_j |
|
|
| fmap_keypoints = fmap_pos[1 :, :].float() + valid_displacements |
| del valid_displacements |
|
|
| try: |
| raw_descriptors, _, ids = interpolate_dense_features( |
| fmap_keypoints.to(device), |
| dense_features[0] |
| ) |
| except EmptyTensorError: |
| continue |
| fmap_pos = fmap_pos.to(device) |
| fmap_keypoints = fmap_keypoints.to(device) |
| fmap_pos = fmap_pos[:, ids] |
| fmap_keypoints = fmap_keypoints[:, ids] |
| del ids |
|
|
| keypoints = upscale_positions(fmap_keypoints, scaling_steps=2) |
| del fmap_keypoints |
|
|
| descriptors = F.normalize(raw_descriptors, dim=0).cpu() |
| del raw_descriptors |
|
|
| keypoints[0, :] *= h_init / h_level |
| keypoints[1, :] *= w_init / w_level |
|
|
| fmap_pos = fmap_pos.cpu() |
| keypoints = keypoints.cpu() |
|
|
| keypoints = torch.cat([ |
| keypoints, |
| torch.ones([1, keypoints.size(1)]) * 1 / scale, |
| ], dim=0) |
|
|
| scores = dense_features[ |
| 0, fmap_pos[0, :], fmap_pos[1, :], fmap_pos[2, :] |
| ].cpu() / (idx + 1) |
| del fmap_pos |
|
|
| all_keypoints = torch.cat([all_keypoints, keypoints], dim=1) |
| all_descriptors = torch.cat([all_descriptors, descriptors], dim=1) |
| all_scores = torch.cat([all_scores, scores], dim=0) |
| del keypoints, descriptors |
|
|
| previous_dense_features = dense_features |
| del dense_features |
| del previous_dense_features, banned |
|
|
| keypoints = all_keypoints.t().detach().numpy() |
| del all_keypoints |
| scores = all_scores.detach().numpy() |
| del all_scores |
| descriptors = all_descriptors.t().detach().numpy() |
| del all_descriptors |
| return keypoints, scores, descriptors |
|
|
