from __future__ import print_function import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms import triton from torch.utils.cpp_extension import load from torch.distributions import categorical shift_cuda = load( 'shift_cuda', ['/home/philippe/development/shiftnet/kernels/shift_cuda.cpp', '/home/philippe/development/shiftnet/kernels/shift_cuda_kernel.cu'], extra_cflags=['-O3']) class shift(torch.autograd.Function): @staticmethod def forward(ctx, x, shift): ctx.save_for_backward(shift) return shift_cuda.forward(x, shift) @staticmethod def backward(ctx, grad_output): shift, = ctx.saved_tensors grad_output = shift_cuda.backward(grad_output, shift) return grad_output, None class Shift(nn.Module): def __init__(self, in_channels, kernel_size): super(Shift, self).__init__() self.channels = in_channels self.kernel_size = kernel_size if kernel_size == 3: p = torch.Tensor([0.3, 0.4, 0.3]) elif kernel_size == 5: p = torch.Tensor([0.1, 0.25, 0.3, 0.25, 0.1]) elif kernel_size == 7: p = torch.Tensor([0.075, 0.1, 0.175, 0.3, 0.175, 0.1, 0.075]) elif kernel_size == 9: p = torch.Tensor([0.05, 0.075, 0.1, 0.175, 0.2, 0.175, 0.1, 0.075, 0.05]) else: raise RuntimeError('Unsupported kernel size') shift_t = categorical.Categorical(p).sample((in_channels, 2)) - (kernel_size // 2) self.register_buffer('shift_t', shift_t.int()) def forward(self, x): if x.is_cuda: return shift.apply(x, self.shift_t) else: print('Shift only supports GPU for now..') assert False def extra_repr(self): s = ('{channels}, kernel_size={kernel_size}') return s.format(**self.__dict__) def ShiftConv2d(in_planes, out_planes, kernel_size=3, stride=1, groups=1, dilation=1): return nn.Sequential( Shift(in_planes, kernel_size), nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, groups=groups, bias=False) ) class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = ShiftConv2d(1, 32, 3, 1) self.conv2 = ShiftConv2d(32, 128, 3, 1) self.conv3 = ShiftConv2d(128, 128, 3, 2) self.bn1 = nn.BatchNorm2d(128) self.conv4 = ShiftConv2d(128, 256, 3, 2) self.bn2 = nn.BatchNorm2d(256) self.fc1 = nn.Linear(256*7*7, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.bn1(x) x = F.relu(x) x = self.conv4(x) x = self.bn2(x) x = F.relu(x) x = x.view(-1, 256*7*7) x = F.relu(self.fc1(x)) x = self.fc2(x) return F.log_softmax(x, dim=1) Net = Net() def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) def test(args, model, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) model = Net.to(device) optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) test(args, model, device, test_loader) if (args.save_model): torch.save(model.state_dict(),"mnist_cnn.pt") main()