I have succeded in creating heatmaps to annotate key points on my medical cardiograms. I want to use UNet architecture to train my model to return heatmaps for each keyponts on the image. How can i go about creating this model. I have coded like below but it has failed to run withoit errors. Please advise.
for layer in self.decoder:
if isinstance(layer, nn.ConvTranspose2d):
x = layer(x)
skip_connection = encoder_outputs.pop()
# Adjust the size of the skip connection tensor
skip_connection = F.interpolate(skip_connection, size=x.size()[2:], mode='bilinear', align_corners=False)
# Ensure the number of channels matches for concatenation
x = torch.cat([x, skip_connection], dim=1)
else:
x = layer(x)
x = self.final_conv(x)
return x
Define your model with the required arguments
model = UNet(in_channels=3, out_channels=3, channels=[64, 128, 256, 512], strides=[2, 2, 2])
Print the model architecture
print(model)
Set the model to training mode
model.train()
Define optimizer and loss function
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.MSELoss()
Cast images to torch.float before passing to the model
Training loop
num_epochs = 10
for epoch in range(num_epochs):
for images, true_heatmaps in data_loader:
optimizer.zero_grad()
# Cast images to torch.float before passing to the model
images = images.float()
# Transpose the input tensor to match the expected shape (batch_size, channels, height, width)
images = images.permute(0, 3, 1, 2) # Assuming images has shape (batch_size, height, width, channels)
# Predict
pred_heatmaps = model(images)
# Calculate loss
loss = criterion(pred_heatmaps, true_heatmaps)
# Backpropagation
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}, Loss: {loss.item()}")