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import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self):
"""
Pose + Shape discriminator proposed in HMR
"""
super(Discriminator, self).__init__()
self.num_joints = 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.num_joints):
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.num_joints, 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: torch.Tensor, betas: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the discriminator.
Args:
poses (torch.Tensor): Tensor of shape (B, 23, 3, 3) containing a batch of SMPL body poses (excluding the global orientation).
betas (torch.Tensor): Tensor of shape (B, 10) containign a batch of SMPL beta coefficients.
Returns:
torch.Tensor: Discriminator output with shape (B, 25)
"""
#import ipdb; ipdb.set_trace()
#bn = poses.shape[0]
# poses B x 207
#poses = poses.reshape(bn, -1)
# poses B x num_joints x 1 x 9
poses = poses.reshape(-1, self.num_joints, 1, 9)
bn = poses.shape[0]
# poses B x 9 x num_joints x 1
poses = poses.permute(0, 3, 1, 2).contiguous()
# poses_alone
poses = self.D_conv1(poses)
poses = self.relu(poses)
poses = self.D_conv2(poses)
poses = self.relu(poses)
poses_out = []
for i in range(self.num_joints):
poses_out_ = self.pose_out[i](poses[:, :, i, 0])
poses_out.append(poses_out_)
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 = poses.reshape(bn,-1)
poses_all = self.D_alljoints_fc1(poses)
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), 1)
return disc_out
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