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inspect_weights_conv_x.py

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+import torch
+import torchvision as tv
+import matplotlib.pyplot as plt
+import os
+import glob
+from natsort import natsorted
+
+# import numpy as np
+
+layer_id: int = 3
+scale_each_inner: bool = False
+scale_each_outer: bool = False
+
+model_path: str = "trained_models"
+filename_list: list = natsorted(glob.glob(os.path.join(model_path, str("*.pt"))))
+assert len(filename_list) > 0
+model_filename: str = filename_list[-1]
+print(f"Load filename: {model_filename}")
+
+model = torch.load(model_filename, map_location=torch.device("cpu"))
+assert layer_id < len(model)
+
+print("Full network:")
+print(model)
+print("")
+print(f"Selected layer {layer_id}:")
+print(model[layer_id])
+
+# ---
+weights = model[layer_id]._parameters["weight"].data
+bias = model[layer_id]._parameters["bias"].data
+
+weight_grid = tv.utils.make_grid(
+    weights.movedim(0, 1),
+    nrow=8,
+    padding=2,
+    scale_each=scale_each_inner,
+    pad_value=float("NaN"),
+)
+weight_grid = tv.utils.make_grid(
+    weight_grid.unsqueeze(1), nrow=4, padding=2, scale_each=scale_each_outer
+)
+
+
+v_max_abs = torch.abs(weight_grid[0, ...]).max()
+
+plt.subplot(3, 1, (1, 2))
+plt.imshow(
+    weight_grid[0, ...],
+    vmin=-v_max_abs,
+    vmax=v_max_abs,
+    cmap="cool",
+)
+plt.axis("off")
+plt.colorbar()
+plt.title("Weights")
+
+plt.subplot(3, 1, 3)
+plt.plot(bias)
+plt.title("Bias")
+
+plt.show()