/
mnist_train.py
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mnist_train.py
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# encoding: utf-8
import os
import torch
import random
import argparse
import mnist_model
import data_loader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', type = int, default = 64)
parser.add_argument('--test-batch-size', type = int, default = 1000)
parser.add_argument('--epochs', type = int, default = 10)
parser.add_argument('--lr', type = float, default = 0.01)
parser.add_argument('--momentum', type=float, default = 0.5)
parser.add_argument('--no-cuda', action = 'store_true', default = False)
parser.add_argument('--seed', type = int, default = 1)
parser.add_argument('--log-interval', type = int, default = 10)
parser.add_argument('--save-interval', type = int, default = 100)
parser.add_argument('--model', required = True)
parser.add_argument('--angle', type = int, default=60)
parser.add_argument('--span_range', type = int, default = 0.9)
parser.add_argument('--grid_size', type = int, default = 4)
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
args.span_range_height = args.span_range_width = args.span_range
args.grid_height = args.grid_width = args.grid_size
args.image_height = args.image_width = 28
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
model = mnist_model.get_model(args)
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr = args.lr, momentum = args.momentum)
train_loader = data_loader.get_train_loader(args)
test_loader = data_loader.get_test_loader(args)
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]))
if batch_idx % args.save_interval == 0:
checkpoint_path = checkpoint_dir + 'epoch%03d_iter%03d.pth' % (epoch, batch_idx)
torch.save(model.cpu().state_dict(), checkpoint_path)
if args.cuda:
model.cuda()
def test(epoch):
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).data[0]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
test_loss = test_loss
test_loss /= len(test_loader) # loss function already averages over batch size
accuracy = 100. * correct / len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.02f}%)\n'.format(
test_loss, correct, len(test_loader.dataset), accuracy,
))
log_file.write('{:.02f}\n'.format(accuracy))
log_file.flush()
os.fsync(log_file)
checkpoint_dir = 'checkpoint/%s_angle%d_grid%d/' % (
args.model, args.angle, args.grid_size,
)
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
if not os.path.isdir('accuracy_log'):
os.makedirs('accuracy_log')
log_file_path = 'accuracy_log/%s_angle%d_grid%d.txt' % (
args.model, args.angle, args.grid_size,
)
with open(log_file_path, 'w') as log_file:
for epoch in range(1, args.epochs + 1):
train(epoch)
test(epoch)