interactive run all models

interactive_CA/interactive.py

@@ -139,7 +139,7 @@ class Interactive:
             if counter in [40, 60, 100, 150, 200, 400]:
                 self.save_cell(x_eval, cur_path)
             # Quadratic erasing at 51
-            # elif counter == 51:
+            # if counter == 51:
             #     # record loss before dmg
             #     before_loss = self.net.loss(x_eval, self.pad_target)
             #     # For lower half:
@@ -176,9 +176,9 @@ class Interactive:
             image = np.asarray(Image.open(self.imgpath))
             self.game_update(surface, image, cellsize)
             time.sleep(0.00)  # update delay
-            if counter == 2000:
+            counter += 1
+            pygame.display.update()
+            if counter == 400:
                 print('Reached 400 iterations. Shutting down...')
                 pygame.quit()
                 sys.exit()
-            counter += 1
-            pygame.display.update()

interactive_CA/main.py

@@ -46,49 +46,54 @@ models = [adam_nonsample_models, adam_sample_models,
 
 if __name__ == '__main__':
     # pick model [Index of model types][index of 9x9 or 15x15 rabbit or carrot]
-    model = models[1][2]  # change only this or size
+    # model = models[1][2]  # change only this or size
 
     # Auto determined
-    load_model = model[0]
-    emoji = model[1]
-    emoji_size = model[2]  # size of training image usually 9 or 15
-    es = model[3]
+    # load_model = model[0]
+    # emoji = model[1]
+    # emoji_size = model[2]  # size of training image usually 9 or 15
+    # es = model[3]
 
     # canvas size
-    size = emoji_size
+    # size = emoji_size
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--img", type=str, default=emoji,
+    parser.add_argument("-i", "--img", type=str, default='',
                         metavar="🐰", help="The emoji to train on")
     parser.add_argument("-s", "--size", type=int,
-                        default=size, help="Image size")
+                        default=9, help="Image size")
     parser.add_argument("--logdir", type=str, default="interactive_CA/logs",
                         help="Logging folder for new model")
     parser.add_argument("-l", "--load_model_path", type=str,
-                        default=load_model, help="Path to pre trained model")
+                        default='', help="Path to pre trained model")
     parser.add_argument("--n_channels", type=int, default=16,
                         help="Number of channels of the input tensor")
     parser.add_argument("--hidden_size", type=int, default=32,
                         help="Number of hidden channels")
     parser.add_argument("--fire_rate", type=float, default=0.5,
                         metavar=0.5, help="Cell fire rate")
-    parser.add_argument("--es", type=bool, default=es,
+    parser.add_argument("-e", "--es", type=str, default='',
                         metavar=True, help="ES or adam")
     parser.add_argument("--eps", type=float, default=0.007,
                         help="Epsilon scales the amount of damage done from adversarial attacks")
 
     args = parser.parse_args()
-    args.emoji_size = emoji_size
+    args.emoji_size = args.size
 
     if not os.path.isdir(args.logdir):
         raise Exception(
             "Logging directory '%s' not found in base folder" % args.logdir)
 
-    match es:
-        case True: method = 'ES'
-        case False: method = 'ADAM'
+    match args.es:
+        case 'True': 
+            method = 'ES'
+            args.es = True
+        case 'False': 
+            method = 'ADAM'
+            args.es = False
         
-    args.logdir = "%s/%s-%s-%s_%s" % (args.logdir, emoji_size, method,
+
+    args.logdir = "%s/%s-%s-%s_%s" % (args.logdir, args.emoji_size, method,
                                       unicodedata.name(args.img), time.strftime("%d-%m-%Y_%H-%M-%S"))
     os.mkdir(args.logdir)
 

interactive_CA/run_all.py

+import subprocess
+
+adam_nonsample_models = [
+    ['final_models\\Adam\\NonSamplePools\\9-CARROT-train_05-05-2022_17-55-17\\models\\model_19500.pt', '🥕', 9, False],
+    ['final_models\\Adam\\NonSamplePools\\9-RABBIT-FACE-train_05-05-2022_16-07-21\\models\\model_19500.pt', '🐰', 9, False],
+    ['final_models\\Adam\\NonSamplePools\\15-CARROT-train_05-05-2022_17-31-03\\models\\model_19500.pt', '🥕', 15, False],
+    ['final_models\\Adam\\NonSamplePools\\15-RABBIT-FACE-train_05-05-2022_14-39-24\\models\\model_19500.pt', '🐰', 15, False]
+]
+
+adam_sample_models = [
+    ['final_models\\Adam\\SamplePools\\9-CARROT-train_05-05-2022_17-04-48\\models\\model_19500.pt', '🥕', 9, False],
+    ['final_models\\Adam\\SamplePools\\9-RABBIT-FACE-train_05-05-2022_11-43-14\\models\\model_19500.pt', '🐰', 9, False],
+    ['final_models\\Adam\\SamplePools\\15-CARROT-train_05-05-2022_17-00-31\\models\\model_19500.pt', '🥕', 15, False],
+    ['final_models\\Adam\\SamplePools\\15-RABBIT-FACE-train_05-05-2022_13-44-43\\models\\model_19500.pt', '🐰', 15, False]
+]
+
+es_nonsample_models = [
+    ['final_models\\ES\\NonSamplePools\\9-CARROT-train_05-05-2022_09-14-06\\models\\model_2212000', '🥕', 9, True],
+    ['final_models\\ES\\NonSamplePools\\9-RABBIT-FACE-train_06-05-2022_12-18-56\\models\\model_1999000', '🐰', 9, True ],
+    ['final_models\\ES\\NonSamplePools\\15-CARROT-train_06-05-2022_11-06-58\\models\\model_1999000', '🥕', 15, True],
+    ['final_models\\ES\\NonSamplePools\\15-RABBIT-FACE-train_06-05-2022_12-21-00\\models\\model_1999000', '🐰', 15, True]
+]
+
+es_sample_models = [
+    ['final_models\\ES\\SamplePools\\9-CARROT-train_29-04-2022_11-33-16\\models\\model_1036000', '🥕', 9, True],
+    ['final_models\\ES\\SamplePools\\9-RABBIT-FACE-train_01-05-2022_10-32-24\\models\\model_1157000', '🐰', 9, True],
+    ['final_models\\ES\\SamplePools\\15-CARROT-train_29-04-2022_11-18-06\\models\\model_1105000', '🥕', 15, True],
+    ['final_models\\ES\\SamplePools\\15-RABBIT-FACE-train_01-05-2022_10-32-40\\models\\model_1126000', '🐰', 15, True]
+]
+
+models = [adam_nonsample_models, adam_sample_models,
+          es_nonsample_models, es_sample_models]
+
+if __name__ == '__main__':
+    for i in models:
+        for model in i:
+            load_model = model[0]
+            emoji = model[1]
+            size = model[2]
+            es = model[3]
+            command = "python .\interactive_CA\main.py -i %s -s %i -l %s -e %r" % (emoji, size, load_model, es)
+            subprocess.run(command)
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