Update eval_onnx.py (#2)

- Update eval_onnx.py (08568220b5021edb55619b43a7a55d5d0cbd5ea6)


Co-authored-by: ziheng <[email protected]>

eval_onnx.py

@@ -514,18 +514,28 @@ if __name__ == '__main__':
     data_loader = data.getEvalDataloader()
     # Load MoveNet model using ONNX runtime
     model = rt.InferenceSession(MODEL_DIR, providers=providers, provider_options=provider_options)
-
+    
     correct = 0
     total = 0
     # Loop through the data loader for evaluation
     for batch_idx, (imgs, labels, kps_mask, img_names) in enumerate(data_loader):
+        
         if batch_idx%100 == 0:
             print('Finish ',batch_idx)
+    
         imgs = imgs.detach().cpu().numpy()
-        output = model.run(['1548','1607','1665','1723'],{'blob.1':imgs})
+        imgs = imgs.transpose((0,2,3,1))
+        output = model.run(['1548_transpose','1607_transpose','1665_transpose','1723_transpose'],{'blob.1':imgs})
+        output[0] = output[0].transpose((0,3,1,2))
+        output[1] = output[1].transpose((0,3,1,2))
+        output[2] = output[2].transpose((0,3,1,2))
+        output[3] = output[3].transpose((0,3,1,2))
         pre = movenetDecode(output, kps_mask,mode='output',img_size=IMG_SIZE)
         gt = movenetDecode(labels, kps_mask,mode='label',img_size=IMG_SIZE)
+        
+        #n
         acc = myAcc(pre, gt)
+        
         correct += sum(acc)
         total += len(acc)
     # Compute and print accuracy based on evaluated data