from fastai.vision.all import *
from fastai.distributed import *
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

class Net(nn.Sequential):
    def __init__(self):
        super().__init__(
            nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
            nn.Conv2d(32, 64, 3, 1), nn.MaxPool2d(2), nn.Dropout2d(0.25),
            Flatten(), nn.Linear(9216, 128), nn.ReLU(), nn.Dropout2d(0.5),
            nn.Linear(128, 10), nn.LogSoftmax(dim=1) )

batch_size,test_batch_size = 256,512
epochs,lr = 5,1e-2

kwargs = {'num_workers': 1, 'pin_memory': True}
transform=transforms.Compose([transforms.ToTensor(),
                              transforms.Normalize((0.1307,), (0.3081,))])
train_loader = DataLoader(
    datasets.MNIST('../data', train=True, download=True, transform=transform),
                   batch_size=batch_size, shuffle=True, **kwargs)
test_loader = DataLoader(
    datasets.MNIST('../data', train=False, transform=transform),
                   batch_size=test_batch_size, shuffle=True, **kwargs)

if __name__ == '__main__':
    data = DataLoaders(train_loader, test_loader)
    learn = Learner(data, Net(), loss_func=F.nll_loss, opt_func=Adam, metrics=accuracy)
    with learn.distrib_ctx(): learn.fit_one_cycle(epochs, lr)

