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add terminal notifications

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s486797 2024-08-23 15:49:27 +02:00
parent f82b25ee68
commit 84eabfae0e
1 changed files with 4 additions and 0 deletions
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neural_style_app/app.py
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@ -76,6 +76,7 @@ def visualize():
conv_layers = [] conv_layers = []
for module in pretrained_model.children(): for module in pretrained_model.children():
if isinstance(module, nn.Conv2d): if isinstance(module, nn.Conv2d):
print("Adding module to the layers... ")
conv_layers.append(module) conv_layers.append(module)
# Pass the resulting image through the convolutional layers and capture feature maps # Pass the resulting image through the convolutional layers and capture feature maps
@ -85,12 +86,14 @@ def visualize():
for i, layer in enumerate(conv_layers): for i, layer in enumerate(conv_layers):
input_image = layer(input_image) input_image = layer(input_image)
print("Passing through feature maps - layer " , i)
feature_maps.append(input_image) feature_maps.append(input_image)
layer_names.append(f"Layer {i + 1}: {str(layer)}") layer_names.append(f"Layer {i + 1}: {str(layer)}")
# Process and feature maps # Process and feature maps
processed_feature_maps = [] processed_feature_maps = []
for feature_map in feature_maps: for feature_map in feature_maps:
print("Processing feature map...")
feature_map = feature_map.squeeze(0) # Remove the batch dimension feature_map = feature_map.squeeze(0) # Remove the batch dimension
mean_feature_map = torch.mean(feature_map, dim=0).cpu().detach().numpy() # Compute mean across channels mean_feature_map = torch.mean(feature_map, dim=0).cpu().detach().numpy() # Compute mean across channels
processed_feature_maps.append(mean_feature_map) processed_feature_maps.append(mean_feature_map)
@ -98,6 +101,7 @@ def visualize():
# Plot the feature maps # Plot the feature maps
fig = plt.figure(figsize=(20, 20)) fig = plt.figure(figsize=(20, 20))
for i, fm in enumerate(processed_feature_maps): for i, fm in enumerate(processed_feature_maps):
print("Plotting feature maps... now at map number ", i)
ax = fig.add_subplot(4, 4, i + 1) # Adjust grid size as needed ax = fig.add_subplot(4, 4, i + 1) # Adjust grid size as needed
ax.imshow(fm, cmap='viridis') # Display feature map as image ax.imshow(fm, cmap='viridis') # Display feature map as image
ax.axis("off") ax.axis("off")