Created
September 26, 2024 17:35
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Image Prep
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| image_width, image_height = 600, 450 | |
| data_transforms = { | |
| 'train': transforms.Compose([ | |
| transforms.Resize((image_width, image_height)), | |
| transforms.RandomHorizontalFlip(), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) | |
| ]), | |
| 'test': transforms.Compose([ | |
| transforms.Resize((image_width, image_height)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) | |
| ]), | |
| } | |
| # directory where all the train and test folders are | |
| data_dir = 'mel-nv' | |
| image_datasets = { | |
| 'train': datasets.ImageFolder(root=f'{data_dir}/train', transform=data_transforms['train']), | |
| 'test': datasets.ImageFolder(root=f'{data_dir}/test', transform=data_transforms['test']) | |
| } | |
| # DataLoader for batching | |
| batch_size = 8 | |
| dataloaders = { | |
| 'train': DataLoader(image_datasets['train'], batch_size=batch_size, shuffle=True), | |
| 'test': DataLoader(image_datasets['test'], batch_size=batch_size, shuffle=False) | |
| } | |
| class_names = image_datasets['train'].classes | |
| print(class_names) |
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