| from torch import nn |
| import torch |
| import torch.nn.functional as F |
| from modules.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): |
| 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) |
| 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 |
|
|
| return out |
|
|
