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demo.py
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demo.py
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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
from torch.autograd import Variable
from deform_conv import DeformConv2D
from time import time
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=32, metavar='N',
help='input batch size for training (default: 32)')
parser.add_argument('--test-batch-size', type=int, default=32, metavar='N',
help='input batch size for testing (default: 32)')
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')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./MNIST', 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('./MNIST', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
class DeformNet(nn.Module):
def __init__(self):
super(DeformNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.offsets = nn.Conv2d(128, 18, kernel_size=3, padding=1)
self.conv4 = DeformConv2D(128, 128, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(128)
self.classifier = nn.Linear(128, 10)
def forward(self, x):
# convs
x = F.relu(self.conv1(x))
x = self.bn1(x)
x = F.relu(self.conv2(x))
x = self.bn2(x)
x = F.relu(self.conv3(x))
x = self.bn3(x)
# deformable convolution
offsets = self.offsets(x)
x = F.relu(self.conv4(x, offsets))
x = self.bn4(x)
x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1)
x = self.classifier(x)
return F.log_softmax(x, dim=1)
class PlainNet(nn.Module):
def __init__(self):
super(PlainNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(128)
self.classifier = nn.Linear(128, 10)
def forward(self, x):
# convs
x = F.relu(self.conv1(x))
x = self.bn1(x)
x = F.relu(self.conv2(x))
x = self.bn2(x)
x = F.relu(self.conv3(x))
x = self.bn3(x)
x = F.relu(self.conv4(x))
x = self.bn4(x)
x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1)
x = self.classifier(x)
return F.log_softmax(x, dim=1)
model = DeformNet()
def init_weights(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_uniform(m.weight, gain=nn.init.calculate_gain('relu'))
if m.bias is not None:
m.bias.data = torch.FloatTensor(m.bias.shape[0]).zero_()
def init_conv_offset(m):
m.weight.data = torch.zeros_like(m.weight.data)
if m.bias is not None:
m.bias.data = torch.FloatTensor(m.bias.shape[0]).zero_()
model.apply(init_weights)
model.offsets.apply(init_conv_offset)
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data), Variable(target)
if args.cuda:
data, target = data.cuda(), target.cuda()
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.data[0]))
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
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)))
for epoch in range(1, args.epochs + 1):
since = time()
train(epoch)
iter = time() - since
print("Spends {}s for each training epoch".format(iter/args.epochs))
test()