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45 lines
1.6 KiB
45 lines
1.6 KiB
"""Dataset setting and data loader for GTSRB."""
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import os
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import torch
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from torchvision import datasets, transforms
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import torch.utils.data as data
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from torch.utils.data.sampler import SubsetRandomSampler
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import numpy as np
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def get_gtsrb(dataset_root, batch_size, train):
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"""Get GTSRB datasets loader."""
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shuffle_dataset = True
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random_seed = 42
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train_size = 31367
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# image pre-processing
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pre_process = transforms.Compose([
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transforms.Resize((48, 48)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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])
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# datasets and data_loader
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gtsrb_dataset = datasets.ImageFolder(
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os.path.join(dataset_root, 'Final_Training', 'Images'), transform=pre_process)
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dataset_size = len(gtsrb_dataset)
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indices = list(range(dataset_size))
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if shuffle_dataset:
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np.random.seed(random_seed)
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np.random.shuffle(indices)
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train_indices, val_indices = indices[:train_size], indices[train_size:]
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# Creating PT data samplers and loaders:
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train_sampler = SubsetRandomSampler(train_indices)
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valid_sampler = SubsetRandomSampler(val_indices)
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if train:
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gtsrb_dataloader_train = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
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sampler=train_sampler)
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return gtsrb_dataloader_train
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else:
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gtsrb_dataloader_test = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
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sampler=valid_sampler)
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return gtsrb_dataloader_test
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