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SqueezeNet_int_NHWC.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54c81613ca567e51a68ca37f2f728f3b52549f45116a13e3c63c2e0efd82bf12
+size 5008442

eval_onnx.py

@@ -68,7 +68,6 @@ def accuracy(output: torch.Tensor,
     output: Prediction of the model.
     target: Ground truth labels.
     topk: Topk accuracy to compute.
-
   Returns:
     Accuracy results according to 'topk'.
   """
@@ -91,13 +90,11 @@ def prepare_data_loader(data_dir: str,
                         batch_size: int = 100,
                         workers: int = 8) -> torch.utils.data.DataLoader:
   """Returns a validation data loader of ImageNet by given `data_dir`.
-
   Args:
     data_dir: Directory where images stores. There must be a subdirectory named
       'validation' that stores the validation set of ImageNet.
     batch_size: Batch size of data loader.
     workers: How many subprocesses to use for data loading.
-
   Returns:
     An object of torch.utils.data.DataLoader.
   """
@@ -144,7 +141,7 @@ def val_imagenet():
   val_loader = tqdm(val_loader, file=sys.stdout)
   with torch.no_grad():
     for batch_idx, (images, targets) in enumerate(val_loader):
-      inputs, targets = images.numpy(), targets
+      inputs, targets = images.numpy().transpose(0, 2, 3, 1), targets
       ort_inputs = {ort_session.get_inputs()[0].name: inputs}
 
       outputs = ort_session.run(None, ort_inputs)