def get_data_loaders(train_batch_size, val_batch_size):
data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
train_loader = DataLoader(
MNIST(download=True, root=".", transform=data_transform, train=True), batch_size=train_batch_size, shuffle=True)
val_loader = DataLoader(
MNIST(download=False, root=".", transform=data_transform, train=False), batch_size=val_batch_size, shuffle=False)
return train_loader, val_loader
def val_dataloader(self):
# TODO: do a real train/val split
dataset = MNIST(os.getcwd(),
train=False,
download=True,
transform=transforms.ToTensor())
loader = DataLoader(dataset, batch_size=32, num_workers=4)
return loader
def train_dataloader(self):
dataset = MNIST(os.getcwd(),
train=True,
download=True,
transform=transforms.ToTensor())
return DataLoader(dataset, batch_size=32, num_workers=4, shuffle=True)
# The fastai DataLoader is a drop-in replacement for Pytorch's;
# no code changes are required other than changing the import line
from fastai.data.load import DataLoader
import os,torch
from torch.nn import functional as F
from catalyst import dl
from catalyst.data.cv import ToTensor
from catalyst.contrib.datasets import MNIST
from catalyst.utils import metrics
model = torch.nn.Linear(28 * 28, 10)
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)
loaders = {
"train": DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32),
"valid": DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32),
}
class CustomRunner(dl.Runner):
def predict_batch(self, batch): return self.model(batch[0].to(self.device).view(batch[0].size(0), -1))
def _handle_batch(self, batch):
x, y = batch
y_hat = self.model(x.view(x.size(0), -1))
loss = F.cross_entropy(y_hat, y)
accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
self.batch_metrics.update(
{"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}
)