v2

datasets/__init__.py

@@ -3,3 +3,4 @@
 ##################################################
 from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
 from .SearchDatasetWrap import SearchDataset
+from .data import get_data

datasets/data.py

@@ -0,0 +1,69 @@
+from datasets import get_datasets
+from config_utils import load_config
+import torch
+import torchvision
+
+class AddGaussianNoise(object):
+    def __init__(self, mean=0., std=0.001):
+        self.std = std
+        self.mean = mean
+                                    
+    def __call__(self, tensor):
+        return tensor + torch.randn(tensor.size()) * self.std + self.mean
+                                                    
+    def __repr__(self):
+        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
+
+
+
+
+class RepeatSampler(torch.utils.data.sampler.Sampler):
+    def __init__(self, samp, repeat):
+        self.samp = samp
+        self.repeat = repeat
+    def __iter__(self):
+        for i in self.samp:
+            for j in range(self.repeat):
+                yield i
+    def __len__(self):
+        return self.repeat*len(self.samp)
+
+
+def get_data(dataset, data_loc, trainval, batch_size, augtype, repeat, args, pin_memory=True):
+    train_data, valid_data, xshape, class_num = get_datasets(dataset, data_loc, cutout=0)
+    if augtype == 'gaussnoise':
+        train_data.transform.transforms = train_data.transform.transforms[2:]
+        train_data.transform.transforms.append(AddGaussianNoise(std=args.sigma))
+    elif augtype == 'cutout':
+        train_data.transform.transforms = train_data.transform.transforms[2:]
+        train_data.transform.transforms.append(torchvision.transforms.RandomErasing(p=0.9, scale=(0.02, 0.04)))
+    elif augtype == 'none':
+        train_data.transform.transforms = train_data.transform.transforms[2:]
+    
+    if dataset == 'cifar10':
+        acc_type = 'ori-test'
+        val_acc_type = 'x-valid'
+    
+    else:
+        acc_type = 'x-test'
+        val_acc_type = 'x-valid'
+    
+    if trainval and 'cifar10' in dataset:
+        cifar_split = load_config('config_utils/cifar-split.txt', None, None)
+        train_split, valid_split = cifar_split.train, cifar_split.valid
+        if repeat > 0:
+            train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
+                                                       num_workers=0, pin_memory=pin_memory, sampler= RepeatSampler(torch.utils.data.sampler.SubsetRandomSampler(train_split), repeat))
+        else:
+            train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
+                                                       num_workers=0, pin_memory=pin_memory, sampler= torch.utils.data.sampler.SubsetRandomSampler(train_split))
+        
+    
+    else:
+        if repeat > 0:
+            train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, #shuffle=True,
+                                                       num_workers=0, pin_memory=pin_memory, sampler= RepeatSampler(torch.utils.data.sampler.SubsetRandomSampler(range(len(train_data))), repeat))
+        else:
+            train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True,
+                                                       num_workers=0, pin_memory=pin_memory)
+    return train_loader

datasets/get_dataset_with_transform.py

@@ -16,7 +16,9 @@ from config_utils import load_config
 
 Dataset2Class = {'cifar10' : 10,
                  'cifar100': 100,
+                 'fake':10,
                  'imagenet-1k-s':1000,
+                 'imagenette2' : 10,
                  'imagenet-1k' : 1000,
                  'ImageNet16'  : 1000,
                  'ImageNet16-150': 150,
@@ -98,8 +100,13 @@ def get_datasets(name, root, cutout):
   elif name == 'cifar100':
     mean = [x / 255 for x in [129.3, 124.1, 112.4]]
     std  = [x / 255 for x in [68.2, 65.4, 70.4]]
+  elif name == 'fake':
+    mean = [x / 255 for x in [129.3, 124.1, 112.4]]
+    std  = [x / 255 for x in [68.2, 65.4, 70.4]]
   elif name.startswith('imagenet-1k'):
     mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
+  elif name.startswith('imagenette'):
+    mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
   elif name.startswith('ImageNet16'):
     mean = [x / 255 for x in [122.68, 116.66, 104.01]]
     std  = [x / 255 for x in [63.22,  61.26 , 65.09]]
@@ -113,6 +120,12 @@ def get_datasets(name, root, cutout):
     train_transform = transforms.Compose(lists)
     test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
     xshape = (1, 3, 32, 32)
+  elif name == 'fake':
+    lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
+    if cutout > 0 : lists += [CUTOUT(cutout)]
+    train_transform = transforms.Compose(lists)
+    test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
+    xshape = (1, 3, 32, 32)
   elif name.startswith('ImageNet16'):
     lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(16, padding=2), transforms.ToTensor(), transforms.Normalize(mean, std)]
     if cutout > 0 : lists += [CUTOUT(cutout)]
@@ -125,6 +138,15 @@ def get_datasets(name, root, cutout):
     train_transform = transforms.Compose(lists)
     test_transform  = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
     xshape = (1, 3, 32, 32)
+  elif name.startswith('imagenette'):
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    xlists = []
+    xlists.append( transforms.ToTensor() )
+    xlists.append( normalize )
+    #train_transform = transforms.Compose(xlists)
+    train_transform  = transforms.Compose([normalize, normalize, transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
+    test_transform  = transforms.Compose([normalize, normalize, transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
+    xshape = (1, 3, 224, 224)
   elif name.startswith('imagenet-1k'):
     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     if name == 'imagenet-1k':
@@ -156,6 +178,12 @@ def get_datasets(name, root, cutout):
     train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
     test_data  = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
     assert len(train_data) == 50000 and len(test_data) == 10000
+  elif name == 'fake':
+    train_data = dset.FakeData(size=50000, image_size=(3, 32, 32), transform=train_transform)
+    test_data = dset.FakeData(size=10000, image_size=(3, 32, 32), transform=test_transform)
+  elif name.startswith('imagenette2'):
+    train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
+    test_data  = dset.ImageFolder(osp.join(root, 'val'),   test_transform)
   elif name.startswith('imagenet-1k'):
     train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
     test_data  = dset.ImageFolder(osp.join(root, 'val'),   test_transform)