add legacy GTSRB dataloader for comparison.

datasets/gtsrb_legacy.py

@@ -0,0 +1,45 @@
+"""Dataset setting and data loader for GTSRB."""
+
+import os
+import torch
+from torchvision import datasets, transforms
+import torch.utils.data as data
+from torch.utils.data.sampler import SubsetRandomSampler
+import numpy as np
+
+def get_gtsrb(dataset_root, batch_size, train):
+    """Get GTSRB datasets loader."""
+    shuffle_dataset = True
+    random_seed = 42
+    train_size = 31367
+
+    # image pre-processing
+    pre_process = transforms.Compose([
+        transforms.Resize((40, 40)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+    ])
+
+    # datasets and data_loader
+    gtsrb_dataset = datasets.ImageFolder(
+        os.path.join(dataset_root, 'Final_Training', 'Images'), transform=pre_process)
+
+    dataset_size = len(gtsrb_dataset)
+    indices = list(range(dataset_size))
+    if shuffle_dataset:
+        np.random.seed(random_seed)
+        np.random.shuffle(indices)
+    train_indices, val_indices = indices[:train_size], indices[train_size:]
+
+    # Creating PT data samplers and loaders:
+    train_sampler = SubsetRandomSampler(train_indices)
+    valid_sampler = SubsetRandomSampler(val_indices)
+
+    if train:
+        gtsrb_dataloader_train = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
+                                               sampler=train_sampler)
+        return gtsrb_dataloader_train
+    else:
+        gtsrb_dataloader_test = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
+                                                    sampler=valid_sampler)
+        return gtsrb_dataloader_test