| from torch import nn | |
| import torch | |
| import torch.nn.functional as F | |
| from models.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d | |
| class KPDetector(nn.Module): | |
| """ | |
| Detecting a keypoints. Return keypoint position and jacobian near each keypoint. | |
| """ | |
| def __init__(self, block_expansion, num_kp, num_channels, max_features, | |
| num_blocks, temperature, estimate_jacobian=False, scale_factor=1, | |
| single_jacobian_map=False, pad=0): | |
| super(KPDetector, self).__init__() | |
| self.predictor = Hourglass(block_expansion, in_features=num_channels, | |
| max_features=max_features, num_blocks=num_blocks) | |
| self.kp = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=(7, 7), | |
| padding=pad) | |
| if estimate_jacobian: | |
| self.num_jacobian_maps = 1 if single_jacobian_map else num_kp | |
| self.jacobian = nn.Conv2d(in_channels=self.predictor.out_filters, | |
| out_channels=4 * self.num_jacobian_maps, kernel_size=(7, 7), padding=pad) | |
| self.jacobian.weight.data.zero_() | |
| self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) | |
| else: | |
| self.jacobian = None | |
| self.temperature = temperature | |
| self.scale_factor = scale_factor | |
| if self.scale_factor != 1: | |
| self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) | |
| def gaussian2kp(self, heatmap): | |
| """ | |
| Extract the mean and from a heatmap | |
| """ | |
| shape = heatmap.shape | |
| heatmap = heatmap.unsqueeze(-1) | |
| grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) | |
| value = (heatmap * grid).sum(dim=(2, 3)) | |
| kp = {'value': value} | |
| return kp | |
| def forward(self, x,with_feature = False): | |
| if self.scale_factor != 1: | |
| x = self.down(x) | |
| feature_map = self.predictor(x) | |
| prediction = self.kp(feature_map) | |
| final_shape = prediction.shape | |
| heatmap = prediction.view(final_shape[0], final_shape[1], -1) | |
| heatmap = F.softmax(heatmap / self.temperature, dim=2) | |
| heatmap = heatmap.view(*final_shape) | |
| out = self.gaussian2kp(heatmap) | |
| if self.jacobian is not None: | |
| jacobian_map = self.jacobian(feature_map) | |
| out["jacobian_map"] = jacobian_map | |
| jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 4, final_shape[2], | |
| final_shape[3]) | |
| heatmap = heatmap.unsqueeze(2) | |
| jacobian = heatmap * jacobian_map | |
| jacobian = jacobian.view(final_shape[0], final_shape[1], 4, -1) | |
| jacobian = jacobian.sum(dim=-1) | |
| jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 2, 2) | |
| out['jacobian'] = jacobian | |
| out["pred_feature"] = prediction | |
| if with_feature: | |
| out["feature_map"] = feature_map | |
| return out | |