trying to figure out how to load custom model

app.py

@@ -8,7 +8,7 @@ from PIL import Image
 from scipy import special
 import sys
 from types import SimpleNamespace
-from transformers import AutoModel
+from transformers import AutoModel, pipeline
 import torch
 
 # sys.path.insert(1, "../")
@@ -84,10 +84,10 @@ def get_activations(model, image: list, model_name: str,
     
     layer_outputs = {}
     for i in range(len(model.model.features)):
-        image = model.model.features[i](image)
+        image = model.features[i](image)
         layer_outputs[i] = image
         print(i, layer_outputs[i].shape)
-    output = model.model(image).detach().cpu().numpy()
+    output = model(image).detach().cpu().numpy()
     output_1 = activation_indices[model_name].detach().cpu().numpy()
     output_2 = activation_indices[model_name].detach().cpu().numpy()
     
@@ -197,14 +197,13 @@ def predict_and_analyze(model_name, num_channels, dim, input_channel, image):
     image = torch.from_numpy(image)
         
     assert image.shape == (1, num_channels, W, W), "Data is the wrong shape"
+    print("Data loaded")
     
-    # pipeline = pipeline(task="image-classification", model=model_path + "%s_%i_.pyt" % (model_name, num_channels))
-    
-    # model_name += '_%i' % (num_channels)
-    
+    print("Loading model")
     model_loading_name =  model_path + "%s_%i_planet_detection" % (model_name, num_channels)
     
-    print("Loading model")
+    # pipeline = pipeline(task="image-classification", model=model_loading_name)
+        
     # model = load_model(model_name, activation=True)
     model = AutoModel.from_pretrained(model_loading_name)
     print("Model loaded")