2-dl-container/Container-Root/job/resnet/compile_model-gpu.py [19:38]:
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img_cat = Image.open("data/cat.png").convert('RGB')

#
# Create a preprocessing pipeline
#
preprocess = transforms.Compose([
    transforms.Resize(image_size),
    transforms.CenterCrop(image_size),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )])

#
# Pass the image for preprocessing and the image preprocessed
#
img_cat_preprocessed = preprocess(img_cat)
img_cat_preprocessed_unsqueeze = torch.unsqueeze(img_cat_preprocessed, 0)
batch_img_cat_tensor = torch.cat([img_cat_preprocessed_unsqueeze] * batch_size)
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2-dl-container/Container-Root/job/resnet/compile_model-inf.py [25:44]:
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img_cat = Image.open("data/cat.png").convert('RGB')

#
# Create a preprocessing pipeline
#
preprocess = transforms.Compose([
    transforms.Resize(image_size),
    transforms.CenterCrop(image_size),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )])

#
# Pass the image for preprocessing and the image preprocessed
#
img_cat_preprocessed = preprocess(img_cat)
img_cat_preprocessed_unsqueeze = torch.unsqueeze(img_cat_preprocessed, 0)
batch_img_cat_tensor = torch.cat([img_cat_preprocessed_unsqueeze] * batch_size)
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