got rid of more print statements

app.py

@@ -122,18 +122,17 @@ def get_activations(model, image: list, model_name: str,
         temp_image = model.features[i](temp_image)
         if i in activation_indices[model_name]:
             layer_outputs[i] = image
-            print(i, layer_outputs[i].shape)
+            # print(i, layer_outputs[i].shape)
         if i == max(activation_indices[model_name]):
             break
     output = model(image).detach().cpu().numpy()
-    print(output)
     # print(model(image), model.model(image))
     
     image = image.detach().cpu().numpy()
     output_1 = layer_outputs[activation_indices[model_name][0]].detach().cpu().numpy()
     output_2 = layer_outputs[activation_indices[model_name][1]].detach().cpu().numpy()
     
-    print(image.shape, output.shape, output_1.shape, output_2.shape)
+    # print(image.shape, output.shape, output_1.shape, output_2.shape)
     
     # get activations
     # output_1 = outputs[1]
@@ -142,6 +141,7 @@ def get_activations(model, image: list, model_name: str,
     # get prediction
     # output = outputs[0][0]
     output = special.softmax(output)
+    print(output)
     
     # sum over velocity channels
     if channel == 0:
@@ -231,7 +231,7 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
 
     print("Running %s for %i channels" % (model_name, num_channels))
     print("Loading data")
-    print(image)
+    # print(image)
     
     image = np.load(image.name, allow_pickle=True)
     image = image.astype(np.float32)
@@ -296,7 +296,7 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
     # print(model_url)
     
     loaded = torch.load(model_url, map_location='cpu', )
-    print(loaded.keys())
+    # print(loaded.keys())
     
     model.load_state_dict(loaded['state_dict'])
     # print(model)
@@ -317,13 +317,15 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
                                                                       channel=input_channel,
                                                                       sub_mean=True)
     print("Activations and predictions finished")
-    print(output)
+    # print(output)
     
     if output[0][0] < output[0][1]:
         output = 'Planet predicted with %.3f percent confidence' % (100*output[0][1])
     else:
         output = 'No planet predicted with %.3f percent confidence' % (100*output[0][0])
         
+    print(output)
+        
     input_image = normalize_array(input_image)
     activation_1 = normalize_array(activation_1)
     activation_2 = normalize_array(activation_2)