| | 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=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=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} |
| |
|
| |
|
