import argparse
import os
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
from models import *
from data_loader import data_loader
from helper import AverageMeter, save_checkpoint, accuracy, adjust_learning_rate
model_names = [
'alexnet', 'squeezenet1_0', 'squeezenet1_1', 'densenet121',
'densenet169', 'densenet201', 'densenet201', 'densenet161',
'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19', 'vgg19_bn', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152'
]
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='alexnet', choices=model_names,
help='model architecture: ' + ' | '.join(model_names) + ' (default: alexnet)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='numer of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful to restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N',
help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.01, type=float, metavar='LR',
help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='Weight decay (default: 1e-4)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('-m', '--pin-memory', dest='pin_memory', action='store_true',
help='use pin memory')
parser.add_argument('-p', '--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--print-freq', '-f', default=10, type=int, metavar='N',
help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoitn, (default: None)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
best_prec1 = 0.0
def main():
global args, best_prec1
args = parser.parse_args()
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
else:
print("=> creating model '{}'".format(args.arch))
if args.arch == 'alexnet':
model = alexnet(pretrained=args.pretrained)
elif args.arch == 'squeezenet1_0':
model = squeezenet1_0(pretrained=args.pretrained)
elif args.arch == 'squeezenet1_1':
model = squeezenet1_1(pretrained=args.pretrained)
elif args.arch == 'densenet121':
model = densenet121(pretrained=args.pretrained)
elif args.arch == 'densenet169':
model = densenet169(pretrained=args.pretrained)
elif args.arch == 'densenet201':
model = densenet201(pretrained=args.pretrained)
elif args.arch == 'densenet161':
model = densenet161(pretrained=args.pretrained)
elif args.arch == 'vgg11':
model = vgg11(pretrained=args.pretrained)
elif args.arch == 'vgg13':
model = vgg13(pretrained=args.pretrained)
elif args.arch == 'vgg16':
model = vgg16(pretrained=args.pretrained)
elif args.arch == 'vgg19':
model = vgg19(pretrained=args.pretrained)
elif args.arch == 'vgg11_bn':
model = vgg11_bn(pretrained=args.pretrained)
elif args.arch == 'vgg13_bn':
model = vgg13_bn(pretrained=args.pretrained)
elif args.arch == 'vgg16_bn':
model = vgg16_bn(pretrained=args.pretrained)
elif args.arch == 'vgg19_bn':
model = vgg19_bn(pretrained=args.pretrained)
elif args.arch == 'resnet18':
model = resnet18(pretrained=args.pretrained)
elif args.arch == 'resnet34':
model = resnet34(pretrained=args.pretrained)
elif args.arch == 'resnet50':
model = resnet50(pretrained=args.pretrained)
elif args.arch == 'resnet101':
model = resnet101(pretrained=args.pretrained)
elif args.arch == 'resnet152':
model = resnet152(pretrained=args.pretrained)
else:
raise NotImplementedError
# use cuda
model.cuda()
# model = torch.nn.parallel.DistributedDataParallel(model)
# define loss and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionlly resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# cudnn.benchmark = True
# Data loading
train_loader, val_loader = data_loader(args.data, args.batch_size, args.workers, args.pin_memory)
if args.evaluate:
validate(val_loader, model, criterion, args.print_freq)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args.print_freq)
# evaluate on validation set
prec1, prec5 = validate(val_loader, model, criterion, args.print_freq)
# remember the best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict()
}, is_best, args.arch + '.pth')
def train(train_loader, model, criterion, optimizer, epoch, print_freq):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(async=True)
input = input.cuda(async=True)
# compute output
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec1[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg
