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train_mnist.py
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train_mnist.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
import sys
sys.path.append('../')
from net2net import *
import copy
# 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=100, metavar='N',
help='how many batches to wait before logging 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('./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)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, x.size(1)*x.size(2)*x.size(3))
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
def net2net_wider(self):
self.conv1, self.conv2, _ = wider(self.conv1, self.conv2, 15, noise_var=0.01)
self.conv2, self.fc1, _ = wider(self.conv2, self.fc1, 30, noise_var=0.01)
print(self)
def net2net_deeper(self):
s = deeper(self.conv1, nn.ReLU, bnorm_flag=False)
self.conv1 = s
s = deeper(self.conv2, nn.ReLU, bnorm_flag=False)
self.conv2 = s
print(self)
def define_wider(self):
self.conv1 = nn.Conv2d(1, 15, kernel_size=5)
self.conv2 = nn.Conv2d(15, 30, kernel_size=5)
self.fc1 = nn.Linear(480, 50)
def define_wider_deeper(self):
self.conv1 = nn.Sequential(nn.Conv2d(1, 15, kernel_size=5),
nn.ReLU(),
nn.Conv2d(15, 15, kernel_size=5, padding=2))
self.conv2 = nn.Sequential(nn.Conv2d(15, 30, kernel_size=5),
nn.ReLU(),
nn.Conv2d(30, 30, kernel_size=5, padding=2))
self.fc1 = nn.Linear(480, 50)
model = Net()
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):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
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)))
return 100. * correct / len(test_loader.dataset)
print("\n\n > Teacher training ... ")
# treacher training
for epoch in range(1, args.epochs + 1):
train(epoch)
teacher_accu = test()
# wider student training
print("\n\n > Wider Student training ... ")
model_ = Net()
model_ = copy.deepcopy(model)
del model
model = model_
model.net2net_wider()
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, args.epochs + 1):
train(epoch)
wider_accu = test()
# wider + deeper student training
print("\n\n > Wider+Deeper Student training ... ")
model_ = Net()
model_ = copy.deepcopy(model)
del model
model = model_
model.net2net_deeper()
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, args.epochs + 1):
train(epoch)
deeper_accu = test()
# wider teacher training
print("\n\n > Wider teacher training ... ")
model_ = Net()
del model
model = model_
model.define_wider()
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, 2*(args.epochs) + 1):
train(epoch)
wider_teacher_accu = test()
# wider deeper teacher training
print("\n\n > Wider+Deeper teacher training ... ")
model_ = Net()
del model
model = model_
model.define_wider_deeper()
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(1, 3*(args.epochs) + 1):
train(epoch)
wider_deeper_teacher_accu = test()
print(" -> Teacher:\t{}".format(teacher_accu))
print(" -> Wider model:\t{}".format(wider_accu))
print(" -> Deeper-Wider model:\t{}".format(deeper_accu))
print(" -> Wider teacher:\t{}".format(wider_teacher_accu))
print(" -> Deeper-Wider teacher:\t{}".format(wider_deeper_teacher_accu))