loaders["train"] = train_loader
loaders["valid"] = valid_loader
return loaders
data_transform = Compose([
Augmentor(
dict_key="features",
augment_fn=lambda x: torch.from_numpy(x.copy().astype(np.float32) /
255.0).unsqueeze_(0),
),
Augmentor(
dict_key="features",
augment_fn=Normalize((0.5, ), (0.5, )),
),
Augmentor(
dict_key="targets",
augment_fn=lambda x: torch.from_numpy(x.copy().astype(np.float32) /
255.0).unsqueeze_(0),
),
])
loaders = get_loaders(data_transform)
# # Model
# In[ ]:
from catalyst.contrib.models.cv import Unet
def get_transforms(stage: str = None, mode: str = None):
"""
@TODO: Docs. Contribution is welcome
"""
return Compose([ToTensor(), Normalize((0.1307, ), (0.3081, ))])
# # Data
# In[ ]:
import collections
import torch
from catalyst.contrib.datasets import MNIST
from catalyst.data.cv import ToTensor, Compose, Normalize
bs = 32
num_workers = 0
data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
loaders = collections.OrderedDict()
trainset = MNIST(
"./data", train=False, download=True, transform=data_transform
)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=bs, shuffle=True, num_workers=num_workers
)
testset = MNIST("./data", train=False, download=True, transform=data_transform)
testloader = torch.utils.data.DataLoader(
testset, batch_size=bs, shuffle=False, num_workers=num_workers
)