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| from lib.kits.basic import * | |
| class HSMRDiscriminator(nn.Module): | |
| def __init__(self): | |
| ''' | |
| Pose + Shape discriminator proposed in HMR | |
| ''' | |
| super(HSMRDiscriminator, self).__init__() | |
| self.n_poses = 23 | |
| # poses_alone | |
| self.D_conv1 = nn.Conv2d(9, 32, kernel_size=1) | |
| nn.init.xavier_uniform_(self.D_conv1.weight) | |
| nn.init.zeros_(self.D_conv1.bias) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.D_conv2 = nn.Conv2d(32, 32, kernel_size=1) | |
| nn.init.xavier_uniform_(self.D_conv2.weight) | |
| nn.init.zeros_(self.D_conv2.bias) | |
| pose_out = [] | |
| for i in range(self.n_poses): | |
| pose_out_temp = nn.Linear(32, 1) | |
| nn.init.xavier_uniform_(pose_out_temp.weight) | |
| nn.init.zeros_(pose_out_temp.bias) | |
| pose_out.append(pose_out_temp) | |
| self.pose_out = nn.ModuleList(pose_out) | |
| # betas | |
| self.betas_fc1 = nn.Linear(10, 10) | |
| nn.init.xavier_uniform_(self.betas_fc1.weight) | |
| nn.init.zeros_(self.betas_fc1.bias) | |
| self.betas_fc2 = nn.Linear(10, 5) | |
| nn.init.xavier_uniform_(self.betas_fc2.weight) | |
| nn.init.zeros_(self.betas_fc2.bias) | |
| self.betas_out = nn.Linear(5, 1) | |
| nn.init.xavier_uniform_(self.betas_out.weight) | |
| nn.init.zeros_(self.betas_out.bias) | |
| # poses_joint | |
| self.D_alljoints_fc1 = nn.Linear(32*self.n_poses, 1024) | |
| nn.init.xavier_uniform_(self.D_alljoints_fc1.weight) | |
| nn.init.zeros_(self.D_alljoints_fc1.bias) | |
| self.D_alljoints_fc2 = nn.Linear(1024, 1024) | |
| nn.init.xavier_uniform_(self.D_alljoints_fc2.weight) | |
| nn.init.zeros_(self.D_alljoints_fc2.bias) | |
| self.D_alljoints_out = nn.Linear(1024, 1) | |
| nn.init.xavier_uniform_(self.D_alljoints_out.weight) | |
| nn.init.zeros_(self.D_alljoints_out.bias) | |
| def forward(self, poses_body: torch.Tensor, betas: torch.Tensor) -> torch.Tensor: | |
| ''' | |
| Forward pass of the discriminator. | |
| ### Args | |
| - poses: torch.Tensor, shape (B, 23, 9) | |
| - Matrix representation of the SKEL poses excluding the global orientation. | |
| - betas: torch.Tensor, shape (B, 10) | |
| ### Returns | |
| - torch.Tensor, shape (B, 25) | |
| ''' | |
| poses_body = poses_body.reshape(-1, self.n_poses, 1, 9) # (B, n_poses, 1, 9) | |
| B = poses_body.shape[0] | |
| poses_body = poses_body.permute(0, 3, 1, 2).contiguous() # (B, 9, n_poses, 1) | |
| # poses_alone | |
| poses_body = self.D_conv1(poses_body) | |
| poses_body = self.relu(poses_body) | |
| poses_body = self.D_conv2(poses_body) | |
| poses_body = self.relu(poses_body) | |
| poses_out = [] | |
| for i in range(self.n_poses): | |
| poses_out_i = self.pose_out[i](poses_body[:, :, i, 0]) | |
| poses_out.append(poses_out_i) | |
| poses_out = torch.cat(poses_out, dim=1) | |
| # betas | |
| betas = self.betas_fc1(betas) | |
| betas = self.relu(betas) | |
| betas = self.betas_fc2(betas) | |
| betas = self.relu(betas) | |
| betas_out = self.betas_out(betas) | |
| # poses_joint | |
| poses_body = poses_body.reshape(B, -1) | |
| poses_all = self.D_alljoints_fc1(poses_body) | |
| poses_all = self.relu(poses_all) | |
| poses_all = self.D_alljoints_fc2(poses_all) | |
| poses_all = self.relu(poses_all) | |
| poses_all_out = self.D_alljoints_out(poses_all) | |
| disc_out = torch.cat((poses_out, betas_out, poses_all_out), dim=1) | |
| return disc_out | |