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# image normalization transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) # preprocessing of images class IIITDataset(Dataset): def __init__(self, image_paths, transform): super().__init__() self.paths = image_paths self.len = len(self.paths) self.transform = transform def __len__(self): return self.len def __getitem__(self, index): path = self.paths[index] image = Image.open(path).convert('RGB') image = self.transform(image) label = int(df['class_id'].loc[df['image']==path]) #label = 0 if 'Abyssinian' in path else 1 return (image, label) # create train dataset import random # create train-test split random.shuffle(img_files) train = img_files[:3680] test = img_files[3680:] print("train size", len(train)) print("test size", len(test)) train_ds = IIITDataset(train, transform) train_dl = DataLoader(train_ds, batch_size=100) print(len(train_ds), len(train_dl)) # create test dataset test_ds = IIITDataset(test, transform) test_dl = DataLoader(test_ds, batch_size=100) print(len(test_ds), len(test_dl))
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