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| from torch import nn | |
| import torch | |
| import torch.nn.functional as F | |
| from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d | |
| from src.facerender.modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck | |
| class KPDetector(nn.Module): | |
| """ | |
| Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint. | |
| """ | |
| def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth, | |
| num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False): | |
| super(KPDetector, self).__init__() | |
| self.predictor = KPHourglass(block_expansion, in_features=image_channel, | |
| max_features=max_features, reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks) | |
| # self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3) | |
| self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1) | |
| if estimate_jacobian: | |
| self.num_jacobian_maps = 1 if single_jacobian_map else num_kp | |
| # self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3) | |
| self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1) | |
| ''' | |
| initial as: | |
| [[1 0 0] | |
| [0 1 0] | |
| [0 0 1]] | |
| ''' | |
| self.jacobian.weight.data.zero_() | |
| self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 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(image_channel, self.scale_factor) | |
| def gaussian2kp(self, heatmap): | |
| """ | |
| Extract the mean 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, 4)) | |
| 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, 9, final_shape[2], | |
| final_shape[3], final_shape[4]) | |
| heatmap = heatmap.unsqueeze(2) | |
| jacobian = heatmap * jacobian_map | |
| jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1) | |
| jacobian = jacobian.sum(dim=-1) | |
| jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3) | |
| out['jacobian'] = jacobian | |
| return out | |
| class HEEstimator(nn.Module): | |
| """ | |
| Estimating head pose and expression. | |
| """ | |
| def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True): | |
| super(HEEstimator, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2) | |
| self.norm1 = BatchNorm2d(block_expansion, affine=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1) | |
| self.norm2 = BatchNorm2d(256, affine=True) | |
| self.block1 = nn.Sequential() | |
| for i in range(3): | |
| self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1)) | |
| self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1) | |
| self.norm3 = BatchNorm2d(512, affine=True) | |
| self.block2 = ResBottleneck(in_features=512, stride=2) | |
| self.block3 = nn.Sequential() | |
| for i in range(3): | |
| self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1)) | |
| self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) | |
| self.norm4 = BatchNorm2d(1024, affine=True) | |
| self.block4 = ResBottleneck(in_features=1024, stride=2) | |
| self.block5 = nn.Sequential() | |
| for i in range(5): | |
| self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1)) | |
| self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1) | |
| self.norm5 = BatchNorm2d(2048, affine=True) | |
| self.block6 = ResBottleneck(in_features=2048, stride=2) | |
| self.block7 = nn.Sequential() | |
| for i in range(2): | |
| self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1)) | |
| self.fc_roll = nn.Linear(2048, num_bins) | |
| self.fc_pitch = nn.Linear(2048, num_bins) | |
| self.fc_yaw = nn.Linear(2048, num_bins) | |
| self.fc_t = nn.Linear(2048, 3) | |
| self.fc_exp = nn.Linear(2048, 3*num_kp) | |
| def forward(self, x): | |
| out = self.conv1(x) | |
| out = self.norm1(out) | |
| out = F.relu(out) | |
| out = self.maxpool(out) | |
| out = self.conv2(out) | |
| out = self.norm2(out) | |
| out = F.relu(out) | |
| out = self.block1(out) | |
| out = self.conv3(out) | |
| out = self.norm3(out) | |
| out = F.relu(out) | |
| out = self.block2(out) | |
| out = self.block3(out) | |
| out = self.conv4(out) | |
| out = self.norm4(out) | |
| out = F.relu(out) | |
| out = self.block4(out) | |
| out = self.block5(out) | |
| out = self.conv5(out) | |
| out = self.norm5(out) | |
| out = F.relu(out) | |
| out = self.block6(out) | |
| out = self.block7(out) | |
| out = F.adaptive_avg_pool2d(out, 1) | |
| out = out.view(out.shape[0], -1) | |
| yaw = self.fc_roll(out) | |
| pitch = self.fc_pitch(out) | |
| roll = self.fc_yaw(out) | |
| t = self.fc_t(out) | |
| exp = self.fc_exp(out) | |
| return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} | |