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}