Unified, centralized and simplified code; added CPU option

experiments/plot_reconstructions.py

@@ -0,0 +1,51 @@
+from math import ceil
+
+import matplotlib.pyplot as plt
+
+
+def plot_reconstructions_single_layer(imgs,
+                                      layer_name,
+                                      filters,
+                                      n_cols=3,
+                                      cell_size=4,
+                                      save_fig=False):
+    n_rows = ceil((len(imgs)) / n_cols)
+    fig, axes = plt.subplots(n_rows,
+                             n_cols,
+                             figsize=(cell_size * n_cols, cell_size * n_rows))
+
+    for i, ax in enumerate(axes.flat):
+        ax.grid(False)
+        ax.get_xaxis().set_visible(False)
+        ax.get_yaxis().set_visible(False)
+        ax.set_title(f'fmap {filters[i]}')
+        ax.imshow(imgs[i])
+
+    fig.suptitle(f'{layer_name}', fontsize="x-large", y=1.0)
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.88)
+    save_name = layer_name.lower().replace(' ', '_')
+
+    if save_fig:
+        plt.savefig(
+            f'{save_name}_fmaps_{"_".join([str(f) for f in filters])}.png')
+
+    plt.show()
+
+    return plt
+
+
+def reconstructions_single_layer(FV,
+                                 layer,
+                                 filters,
+                                 opt_steps=20,
+                                 blur=5,
+                                 lr=1e-1,
+                                 print_losses=False):
+    return [
+        FV.visualize(layer=layer,
+                     conv_filter=filters[i],
+                     opt_steps=opt_steps,
+                     blur=blur,
+                     lr=lr,
+                     print_losses=print_losses) for i in range(len(filters))]