46 lines
1,003 B
Python
46 lines
1,003 B
Python
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import torch
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import torchvision as tv
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import matplotlib.pyplot as plt
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import os
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import glob
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from natsort import natsorted
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# import numpy as np
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layer_id: int = 0
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scale_each: bool = False
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model_path: str = "trained_models"
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filename_list: list = natsorted(glob.glob(os.path.join(model_path, str("*.pt"))))
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assert len(filename_list) > 0
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model_filename: str = filename_list[-1]
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print(f"Load filename: {model_filename}")
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model = torch.load(model_filename, map_location=torch.device("cpu"))
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assert layer_id < len(model)
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# ---
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weights = model[layer_id]._parameters["weight"].data
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bias = model[layer_id]._parameters["bias"].data
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weight_grid = tv.utils.make_grid(weights, nrow=8, padding=2, scale_each=scale_each)
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v_max_abs = torch.abs(weight_grid[0, ...]).max()
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plt.subplot(3, 1, (1, 2))
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plt.imshow(
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weight_grid[0, ...],
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vmin=-v_max_abs,
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vmax=v_max_abs,
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cmap="cool",
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)
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plt.axis("off")
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plt.colorbar()
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plt.title("Weights")
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plt.subplot(3, 1, 3)
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plt.plot(bias)
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plt.title("Bias")
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plt.show()
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