using optimum

app.py

@@ -7,6 +7,7 @@ import numpy as np
 # import onnx
 import onnxruntime as ort
 # from onnx import helper
+from optimum.onnxruntime import ORTModel
 
 import pandas as pd
 
@@ -46,7 +47,8 @@ def load_model(model: str, activation: bool=True):
     
     sess_options = ort.SessionOptions()
     sess_options.intra_op_num_threads = 1
-    ort_session = ort.InferenceSession(model_path + '%s.onnx' % (model), sess_options)
+    # ort_session = ort.InferenceSession(model_path + '%s.onnx' % (model), sess_options)
+    ort_session = ORTModel(model_path + '%s.onnx' % (model))
     
     return ort_session
 
@@ -56,8 +58,9 @@ def get_activations(intermediate_model, image: list,
     '''Gets activations for a given input image'''
     
     
-    input_name = intermediate_model.get_inputs()[0].name
-    outputs = intermediate_model.run(None, {input_name: image})
+    # input_name = intermediate_model.get_inputs()[0].name
+    # outputs = intermediate_model.run(None, {input_name: image})
+    outputs = intermediate_model(image)
     
     output_1 = outputs[1]
     output_2 = outputs[2]

requirements.txt

@@ -4,4 +4,6 @@ matplotlib
 scipy
 onnx
 onnxruntime
-streamlit
+streamlit
+onnx2pytorch
+optimum