print statements

app.py

@@ -125,7 +125,7 @@ def get_activations(model, image: list, model_name: str,
             print(i, layer_outputs[i].shape)
         if i == max(activation_indices[model_name]):
             break
-    output = model.model(image).detach().cpu().numpy()
+    output = model(image).detach().cpu().numpy()
     
     image = image.detach().cpu().numpy()
     output_1 = layer_outputs[activation_indices[model_name][0]].detach().cpu().numpy()
@@ -312,6 +312,7 @@ 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)
     
     if output[0][0] < output[0][1]:
         output = 'Planet predicted with %.3f percent confidence' % (100*output[0][1])

model_utils/efficientnet_config.py

@@ -320,14 +320,17 @@ class EfficientNetPreTrained(PreTrainedModel):
         # not all will have weights and biases
         try:
             module.weight.data.normal_(mean=0.0)
-        except Exception:
+        except Exception as e:
+            print('weight 1', e)
             try:
                 module.weight.data.fill_(1.0)
-            except Exception:
+            except Exception as e:
+                print('weight 2', e)
                 _ = None
         try:
             module.bias.data.zero_()
-        except AttributeError:
+        except AttributeError as e:
+            print('bias', e)
             _ = None