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- from model import ModelCT
- from matplotlib import pyplot as plt
- import torch
- # Pseudo code for visualizing filters, adapt as needed
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- # Load model
- model = ModelCT()
- model.to(device)
- model.load_state_dict(torch.load("trained_model_weights.pth"))
- model.eval()
- # Example of loading filters from the first convolutional layer (backbone.conv1)
- weights = model.backbone.conv1.weight.data.cpu().numpy() # weights.shape = (64,1,7,7) -> 64 filters, 1 channel, size 7x7
- # Visualization of 21st filter from the first convolutional layer
- plt.imshow(weights[20,0,:,:], cmap='gray')
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