BYOL script updates

codes/scripts/byol/byol_spinenet_playground.py

@@ -53,10 +53,9 @@ def im_norm(x):
 def get_image_folder_dataloader(batch_size, num_workers):
     dataset_opt = dict_to_nonedict({
         'name': 'amalgam',
-        'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
+        'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
-        #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
         'weights': [1],
-        'target_size': 512,
+        'target_size': 256,
         'force_multiple': 32,
         'scale': 1
     })

codes/scripts/byol/byol_uresnet_playground.py

@@ -57,7 +57,7 @@ def im_norm(x):
 def get_image_folder_dataloader(batch_size, num_workers):
     dataset_opt = dict_to_nonedict({
         'name': 'amalgam',
-        'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
+        'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
         #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
         'weights': [1],
         'target_size': 256,
@@ -68,9 +68,28 @@ def get_image_folder_dataloader(batch_size, num_workers):
     return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True)
 
 
+def produce_latent_dict(model):
+    batch_size = 32
+    num_workers = 4
+    dataloader = get_image_folder_dataloader(batch_size, num_workers)
+    id = 0
+    paths = []
+    latents = []
+    for batch in tqdm(dataloader):
+        hq = batch['hq'].to('cuda')
+        l = model(hq).cpu().split(1, dim=0)
+        latents.extend(l)
+        paths.extend(batch['HQ_path'])
+        id += batch_size
+        if id > 1000:
+            print("Saving checkpoint..")
+            torch.save((latents, paths), '../results.pth')
+            id = 0
+
+
 if __name__ == '__main__':
-    pretrained_path = '../experiments/uresnet_pixpro_83k.pth'
+    pretrained_path = '../experiments/uresnet_pixpro_attempt2.pth'
-    model = UResNet50(Bottleneck, [3,4,6,3]).to('cuda')
+    model = UResNet50(Bottleneck, [3,4,6,3], out_dim=512).to('cuda')
     sd = torch.load(pretrained_path)
     resnet_sd = {}
     for k, v in sd.items():
@@ -80,5 +99,6 @@ if __name__ == '__main__':
     model.eval()
 
     with torch.no_grad():
-        find_similar_latents(model, 0, 8, structural_euc_dist)
+        #find_similar_latents(model, 0, 8, structural_euc_dist)
         #create_latent_database(model, batch_size=32)
+        produce_latent_dict(model)

codes/scripts/byol/tsne_torch.py

@@ -258,9 +258,11 @@ def plot_instance_level_results_as_image_graph():
     pyplot.savefig('tsne.pdf')
 
 
-random_coords = [(16,16), (14,14), (20,20), (24,24)]
+random_coords = [(8,8),(12,12),(18,18),(24,24)]
 def run_tsne_pixel_level():
     limit = 4000
+
+    '''  # For spinenet-style latent dicts
     latent_dict = torch.load('../results/byol_latents/latent_dict_1.pth')
     id_vals = list(latent_dict.items())
     ids, X = zip(*id_vals)
@@ -272,6 +274,22 @@ def run_tsne_pixel_level():
     for rc in random_coords:
         X_c.append(X[:, :, rc[0], rc[1]])
     X = torch.cat(X_c, dim=0)
+    '''
+
+    # For resnet-style latent tuples
+    X, files = torch.load('../results.pth')
+    zipped = list(zip(X, files))
+    shuffle(zipped)
+    X, files = zip(*zipped)
+
+    X = torch.stack(X, dim=0)[:limit//4]
+    # Unravel X into 1 latents per image, chosen from fixed points. This will serve as a psuedorandom source since these
+    # images are not aligned.
+    X_c = []
+    for rc in random_coords:
+        X_c.append(X[:, 0, :, rc[0], rc[1]])
+    X = torch.cat(X_c, dim=0)
+
     labels = np.zeros(X.shape[0])  # We don't have any labels..
 
     # confirm that x file get same number point than label file
@@ -295,21 +313,20 @@ def run_tsne_pixel_level():
 
     pyplot.scatter(Y[:, 0], Y[:, 1], 20, labels)
     pyplot.show()
-    torch.save((Y, ids[:limit//4]), "../tsne_output_pix.pth")
+    torch.save((Y, files[:limit//4]), "../tsne_output_pix.pth")
 
 
 # Uses the results from the calculation above to create a **massive** pdf plot that shows 1/8 size images on the tsne
 # spectrum.
 def plot_pixel_level_results_as_image_graph():
-    Y, ids = torch.load('../tsne_output_pix.pth')
+    Y, files = torch.load('../tsne_output_pix.pth')
-    files = torch.load('../results/byol_latents/all_paths.pth')
     fig, ax = pyplot.subplots()
     fig.set_size_inches(200,200,forward=True)
     ax.update_datalim(np.column_stack([Y[:, 0], Y[:, 1]]))
     ax.autoscale()
 
-    expansion = 32  # Should be latent_compression(=8) * image_compression_at_inference(=4)
+    expansion = 8  # Should be latent_compression(=8) * image_compression_at_inference(=1)
-    margins = 1  # Keep in mind this will be multiplied by <expansion>
+    margins = 4  # Keep in mind this will be multiplied by <expansion>
     for b in tqdm(range(Y.shape[0])):
         if b % 4 == 0:
             id = b // 4