text stringlengths 1 93.6k |
|---|
def bin_mnist_transform(x):
|
return torch.bernoulli(x.permute(1, 2, 0).contiguous()).int()
|
def bin_mnist_cts_transform(x):
|
return torch.bernoulli(x.permute(1, 2, 0).contiguous()) - 0.5
|
def rgb_image_transform(x, num_bins=256):
|
return quantize((x * 2) - 1, num_bins).permute(1, 2, 0).contiguous()
|
class MyLambda(torchvision.transforms.Lambda):
|
def __init__(self, lambd, arg1):
|
super().__init__(lambd)
|
self.arg1 = arg1
|
def __call__(self, x):
|
return self.lambd(x, self.arg1)
|
class CIFAR10(torchvision.datasets.CIFAR10):
|
def __getitem__(self, idx):
|
return super().__getitem__(idx)[0]
|
class MNIST(torchvision.datasets.MNIST):
|
def __getitem__(self, idx):
|
return super().__getitem__(idx)[0]
|
def make_datasets(cfg: DictConfig) -> tuple[Dataset, Dataset, Dataset]:
|
"""
|
Mandatory keys: dataset (must be cifar10, mnist, bin_mnist, bin_mnist_cts or text8), data_dir
|
Optional for vision: num_bins (default 256), val_frac (default 0.01), horizontal_flip (default: False)
|
Mandatory for text: seq_len
|
"""
|
num_bins = cfg.get("num_bins", 256)
|
if cfg.dataset == "cifar10":
|
train_transform_list = [transforms.ToTensor()]
|
if cfg.get("horizontal_flip", False):
|
train_transform_list.append(transforms.RandomHorizontalFlip())
|
train_transform_list.append(MyLambda(rgb_image_transform, num_bins))
|
train_transform = transforms.Compose(train_transform_list)
|
test_transform = transforms.Compose([transforms.ToTensor(), MyLambda(rgb_image_transform, num_bins)])
|
train_set = CIFAR10(root=cfg.data_dir, train=True, download=True, transform=train_transform)
|
val_set = CIFAR10(root=cfg.data_dir, train=True, download=True, transform=test_transform)
|
test_set = CIFAR10(root=cfg.data_dir, train=False, download=True, transform=test_transform)
|
elif cfg.dataset == "mnist":
|
transform = transforms.Compose(
|
[
|
transforms.ToTensor(),
|
MyLambda(rgb_image_transform, num_bins),
|
]
|
)
|
train_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
val_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
test_set = MNIST(root=cfg.data_dir, train=False, download=True, transform=transform)
|
elif cfg.dataset == "bin_mnist":
|
transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(bin_mnist_transform)])
|
train_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
val_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
test_set = MNIST(root=cfg.data_dir, train=False, download=True, transform=transform)
|
elif cfg.dataset == "bin_mnist_cts":
|
transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(bin_mnist_cts_transform)])
|
train_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
val_set = MNIST(root=cfg.data_dir, train=True, download=True, transform=transform)
|
test_set = MNIST(root=cfg.data_dir, train=False, download=True, transform=transform)
|
elif cfg.dataset == "text8":
|
train_set = Text8Dataset(cfg.data_dir, "train", download=True, seq_len=cfg.seq_len)
|
val_set = Text8Dataset(cfg.data_dir, "val", download=True, seq_len=cfg.seq_len)
|
test_set = Text8Dataset(cfg.data_dir, "test", download=True, seq_len=cfg.seq_len)
|
else:
|
raise NotImplementedError(cfg.dataset)
|
if cfg.dataset != "text8":
|
# For vision datasets we split the train set into train and val
|
val_frac = cfg.get("val_frac", 0.01)
|
train_val_split = [1.0 - val_frac, val_frac]
|
seed = 2147483647
|
train_set = random_split(train_set, train_val_split, generator=torch.Generator().manual_seed(seed))[0]
|
val_set = random_split(val_set, train_val_split, generator=torch.Generator().manual_seed(seed))[1]
|
return train_set, val_set, test_set
|
def prepare_text8(data_dir: pathlib.Path):
|
data_dir.mkdir(parents=True, exist_ok=True)
|
data_url = "http://mattmahoney.net/dc/text8.zip"
|
with open(data_dir / "text8.zip", "wb") as f:
|
print("Downloading text8")
|
f.write(requests.get(data_url).content)
|
print("Done")
|
with zipfile.ZipFile(data_dir / "text8.zip") as f:
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.