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dataloader.py
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dataloader.py
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from torchvision import datasets, transforms
from torch.utils.data import DataLoader, Subset
from TrucksDataset import TrucksDataset
def get_train_val_dataloader(train_csv_path='dataset/train.csv', val_csv_path='dataset/val.csv', batch_size=4, input_size=224):
train_transformer = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Resize((input_size, input_size)),
transforms.RandomHorizontalFlip(0.2),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
val_transformer = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Resize((input_size, input_size)),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dataset = TrucksDataset(train_csv_path, train_transformer)
valid_dataset = TrucksDataset(val_csv_path, val_transformer)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, num_workers=4, drop_last=False, shuffle=True)
val_dataloader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=4, drop_last=False, shuffle=True)
return {'train': train_dataloader, 'validation': val_dataloader}
def get_test_dataloader(test_csv_path='dataset/test.csv', batch_size=4, input_size=224):
test_transformer = transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Resize((input_size, input_size)),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_dataset = TrucksDataset(test_csv_path, test_transformer)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, num_workers=4, shuffle=True, drop_last=False)
return test_dataloader