update to oxford and aircraft

correlation/NAS_Bench_201.py

@@ -71,7 +71,7 @@ def parse_arguments():
     parser.add_argument('--write_freq', type=int, default=1, help='frequency of write to file')
     parser.add_argument('--start', type=int, default=0, help='start index')
     parser.add_argument('--end', type=int, default=0, help='end index')
-    parser.add_argument('--noacc', default=False, action='store_true',
+    parser.add_argument('--noacc', default=True, action='store_true',
                         help='avoid loading NASBench2 api an instead load a pickle file with tuple (index, arch_str)')
     args = parser.parse_args()
     args.device = torch.device("cuda:" + str(args.gpu) if torch.cuda.is_available() else "cpu")
@@ -94,7 +94,14 @@ if __name__ == '__main__':
     x, y = next(iter(train_loader))
 
     cached_res = []
-    pre = 'cf' if 'cifar' in args.dataset else 'im'
+    if 'cifar' in args.dataset :
+        pre = 'cf'
+    elif 'Image' in args.dataset:
+        pre = 'im'
+    elif 'oxford' in args.dataset:
+        pre = 'ox'
+    elif 'air' in args.dataset:
+        pre = 'ai'
     pfn = f'nb2_{args.search_space}_{pre}{get_num_classes(args)}_seed{args.seed}_dl{args.dataload}_dlinfo{args.dataload_info}_initw{args.init_w_type}_initb{args.init_b_type}_{args.batch_size}.p'
     op = os.path.join(args.outdir, pfn)
 

correlation/foresight/dataset.py

@@ -18,6 +18,7 @@
 from torchvision.datasets import MNIST, CIFAR10, CIFAR100, SVHN
 from torchvision.transforms import Compose, ToTensor, Normalize
 from torchvision import transforms
+import torchvision.datasets as dset
 from torch.utils.data import TensorDataset, DataLoader
 import torch
 
@@ -44,6 +45,14 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
         mean = (0.485, 0.456, 0.406)
         std  = (0.229, 0.224, 0.225)
         #resize = 256
+    elif dataset == 'aircraft':
+        mean = (0.4785, 0.5100, 0.5338)
+        std  = (0.1845, 0.1830, 0.2060)
+        size, pad = 224, 2
+    elif dataset == 'oxford':
+        mean = (0.4811, 0.4492, 0.3957)
+        std  = (0.2260, 0.2231, 0.2249)
+        size, pad = 32, 0
     elif 'random' in dataset:
         mean = (0.5, 0.5, 0.5)
         std = (1, 1, 1)
@@ -65,6 +74,7 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
         transforms.ToTensor(),
         transforms.Normalize(mean,std),
     ])
+    root = '/nfs/data3/hanzhang/MeCo/data'
 
     if dataset == 'cifar10':
         train_dataset = CIFAR10(datadir, True, train_transform, download=True)
@@ -72,6 +82,40 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
     elif dataset == 'cifar100':
         train_dataset = CIFAR100(datadir, True, train_transform, download=True)
         test_dataset = CIFAR100(datadir, False, test_transform, download=True)
+    elif dataset == 'aircraft': 
+        lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        # if resize != None : 
+        #     print(resize)
+        #     lists += [CUTOUT(resize)]
+        train_transform = transforms.Compose(lists)
+        test_transform  = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
+        train_data = dset.ImageFolder(os.path.join(root, 'train_sorted_images'), train_transform)
+        test_data  = dset.ImageFolder(os.path.join(root, 'test_sorted_images'),  test_transform)
+    elif dataset == 'oxford':
+        lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        # if resize != None : 
+        #     print(resize)
+        #     lists += [CUTOUT(resize)]
+        train_transform = transforms.Compose(lists)
+        test_transform  = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
+
+        train_data = torch.load(os.path.join(root, 'train85.pth'))
+        test_data  = torch.load(os.path.join(root, 'test15.pth'))
+
+        train_tensor_data = [(image, label) for image, label in train_data]
+        test_tensor_data = [(image, label) for image, label in test_data]
+        sum_data = train_tensor_data + test_tensor_data
+
+        train_images = [image for image, label in train_tensor_data]
+        train_labels = torch.tensor([label for image, label in train_tensor_data])
+        test_images = [image for image, label in test_tensor_data]
+        test_labels = torch.tensor([label for image, label in test_tensor_data])
+
+        train_tensors = torch.stack([train_transform(image) for image in train_images])
+        test_tensors = torch.stack([test_transform(image) for image in test_images])
+
+        train_dataset = TensorDataset(train_tensors, train_labels)
+        test_dataset = TensorDataset(test_tensors, test_labels)
     elif dataset == 'svhn':
         train_dataset = SVHN(datadir, split='train', transform=train_transform, download=True)
         test_dataset = SVHN(datadir, split='test', transform=test_transform, download=True)
@@ -97,8 +141,6 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
         shuffle=False,
         num_workers=num_workers,
         pin_memory=True)
-
-
     return train_loader, test_loader