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vaeVelocity.py
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vaeVelocity.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from dataloader import PoseDataset, getFrameBoundaries
from torch.utils.data import DataLoader
class Encoder(nn.Module):
def __init__(self, input_size, hidden_size, latent_dim):
super(Encoder, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size) # t is included in input_Size
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, hidden_size)
self.fc4_mean = nn.Linear(hidden_size, latent_dim)
self.fc4_logvar = nn.Linear(hidden_size, latent_dim)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.3)
def forward(self, x):
h = self.dropout(self.relu(self.fc1(x)))
h = self.dropout(self.relu(self.fc2(h)))
h = self.dropout(self.relu(self.fc3(h)))
return self.fc4_mean(h), self.fc4_logvar(h)
class Decoder(nn.Module):
def __init__(self, latent_dim, hidden_size, output_size):
super(Decoder, self).__init__()
self.fc1 = nn.Linear(latent_dim, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, hidden_size)
self.fc4 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.3)
def forward(self, x):
h = self.dropout(self.relu(self.fc1(x)))
h = self.dropout(self.relu(self.fc2(h)))
h = self.dropout(self.relu(self.fc3(h)))
return self.fc4(h)
class VAE(nn.Module):
def __init__(self, input_size, hidden_size, latent_dim, output_size, n):
super(VAE, self).__init__()
self.n = n
self.encoder = Encoder(input_size, hidden_size, latent_dim)
self.decoder = Decoder(latent_dim, hidden_size, output_size)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, x, t):
x = torch.cat([t, x], dim=1) # concatenate t with x here
mu, logvar = self.encoder(x)
z = self.reparameterize(mu, logvar)
return self.decoder(z), mu, logvar, z
def new_loss(output, t, X, velocity):
# Jacobian of position output with respect to t to get velocity
velocity_hat = torch.autograd.functional.jacobian(lambda x: output, t).squeeze(0)
# print("pos", output.shape) # torch.Size([32, 165])
# print("velocity", velocity.shape) #torch.Size([32, 165])
# print("vel hat", velocity_hat.shape) #torch.Size([32, 165, 32, 1])
# print("X", X)
# Loss incorporating velocity
loss = ((X - output) ** 2).sum() + 0.01 * ((velocity - velocity_hat) ** 2).sum()
return loss
def train(model, dataloader, optimizer, epoch, scheduler=None):
model.train()
train_loss = 0
for batch_idx, (input_frame, output_frame, input_velocity, idx) in enumerate(dataloader):
# print(input_frame)
# print(input_velocity)
optimizer.zero_grad()
# Extract the time values from input_frame
t = input_frame[:, :1] # Extract the time/frame number (first dimension of each frame)
t.requires_grad_()
# print("T", t.shape)
# Exclude time values from input_frame and reshape
x = input_frame[:, model.n:].reshape(input_frame.size(0), -1)
# print("X", x.shape)
# print("Output", output_frame[:, 1:].shape)
# print("V", input_velocity.shape)
recon_batch, mu, logvar, z = model(x, t)
loss = new_loss(recon_batch, t, output_frame[:, 1:], input_velocity)
loss.backward()
train_loss += loss.item()
optimizer.step()
print(f'====> Epoch: {epoch} Average loss: {train_loss / len(dataloader.dataset):.4f}')
if scheduler:
scheduler.step()
if __name__ == "__main__":
csv_file_path = 'D:/Claire/CMUsmplx/CMU/01/merged_poses_with_frames_normalized.csv'
npz_files = [
'D:/Claire/CMUsmplx/CMU/01/01_01_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_02_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_03_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_05_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_06_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_07_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_08_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_09_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_10_stageii.npz',
'D:/Claire/CMUsmplx/CMU/01/01_11_stageii.npz',
]
frame_boundaries = getFrameBoundaries(npz_files)
n = 2
pose_dataset = PoseDataset(csv_file_path, frame_boundaries, n)
pose_dataloader = DataLoader(pose_dataset, batch_size=32, shuffle=False)
input_size = 165 * n + 1 # Joint positions and time/frame number
hidden_size = 256
latent_dim = 5
output_size = 165 # Joint positions
model = VAE(input_size, hidden_size, latent_dim, output_size, n)
optimizer = optim.Adam(model.parameters(), lr=0.001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
def init_weights(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0)
model.apply(init_weights)
epochs = 50
for epoch in range(1, epochs + 1):
train(model, pose_dataloader, optimizer, epoch, scheduler)