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