代码拉取完成,页面将自动刷新
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
model_names.append('mobilenet')
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
#choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on 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=0.1, 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('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
best_prec1 = 0
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU(inplace=True)
)
def conv_dw(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
nn.ReLU(inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU(inplace=True),
)
self.model = nn.Sequential(
conv_bn( 3, 32, 2),
conv_dw( 32, 64, 1),
conv_dw( 64, 128, 2),
conv_dw(128, 128, 1),
conv_dw(128, 256, 2),
conv_dw(256, 256, 1),
conv_dw(256, 512, 2),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 512, 1),
conv_dw(512, 1024, 2),
conv_dw(1024, 1024, 1),
nn.AvgPool2d(7),
)
self.fc = nn.Linear(1024, 1000)
def forward(self, x):
x = self.model(x)
x = x.view(-1, 1024)
x = self.fc(x)
return x
def main():
global args, best_prec1
args = parser.parse_args()
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
# if args.arch == 'resnet50':
# import resnet_model
# model = resnet_model.resnet50_new(pretrained=True)
# print('save resnet50 to resnet50.weights')
# model.saveas_darknet_weights('resnet50.weights')
if args.arch == 'resnet50-pytorch':
model = models.resnet50(pretrained=True)
elif args.arch == 'resnet50-darknet':
from darknet import Darknet
model = Darknet('cfg/resnet50.cfg')
print('load weights from resnet50.weights')
model.load_weights('resnet50.weights')
elif args.arch == 'resnet50-caffe2darknet':
from darknet import Darknet
model = Darknet('resnet50-caffe2darknet.cfg')
print('load weights from resnet50-caffe2darknet.weights')
model.load_weights('resnet50-caffe2darknet.weights')
elif args.arch == 'vgg16-pytorch2darknet':
from darknet import Darknet
model = Darknet('vgg16-pytorch2darknet.cfg')
print('load weights from vgg16-pytorch2darknet.weights')
model.load_weights('vgg16-pytorch2darknet.weights')
elif args.arch == 'resnet50-pytorch2caffe':
from caffenet import CaffeNet
model = CaffeNet('resnet50-pytorch2caffe.prototxt')
print('load weights resnet50-pytorch2caffe.caffemodel')
model.load_weights('resnet50-pytorch2caffe.caffemodel')
elif args.arch == 'resnet50-pytorch2caffe.nobn':
from caffenet import CaffeNet
model = CaffeNet('resnet50-pytorch2caffe.nobn.prototxt')
print('load weights resnet50-pytorch2caffe.nobn.caffemodel')
model.load_weights('resnet50-pytorch2caffe.nobn.caffemodel')
elif args.arch == 'resnet50-darknet2caffe':
from caffenet import CaffeNet
model = CaffeNet('resnet50-darknet2caffe.prototxt')
print('load weights resnet50-darknet2caffe.caffemodel')
model.load_weights('resnet50-darknet2caffe.caffemodel')
elif args.arch == 'resnet50-kaiming':
from caffenet import CaffeNet
model = CaffeNet('ResNet-50-deploy.prototxt')
print('load weights from ResNet-50-model.caffemodel')
model.load_weights('ResNet-50-model.caffemodel')
elif args.arch == 'resnet50-kaiming-dk':
from darknet import Darknet
model = Darknet('ResNet-50-model.cfg')
print('load weights from ResNet-50-model.weights')
model.load_weights('ResNet-50-model.weights')
elif args.arch == 'resnet18-caffe':
from caffenet import CaffeNet
model = CaffeNet('cfg/resnet-18.prototxt')
print('load weights from resnet-18.caffemodel')
model.load_weights('resnet-18.caffemodel')
elif args.arch == 'resnet18-darknet':
from darknet import Darknet
model = Darknet('resnet-18.cfg')
print('load weights from resnet-18.weights')
model.load_weights('resnet-18.weights')
elif args.arch == 'resnet50-test':
from darknet import Darknet
model = Darknet('test/ResNet-50-model.cfg')
print('load weights from test/ResNet-50-model.weights')
model.load_weights('test/ResNet-50-model.weights')
else:
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
if args.arch.startswith('mobilenet'):
model = Net()
print(model)
else:
model = models.__dict__[args.arch]()
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
if args.arch != 'vgg16-pytorch2darknet':
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally 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 code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
if args.arch == 'resnet50-test' or args.arch == 'resnet50-kaiming' or args.arch == 'resnet50-kaiming-dk':
normalize = transforms.Normalize(mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0])
elif args.arch == 'resnet18-darknet' or args.arch == 'resnet18-caffe':
normalize = transforms.Normalize(mean=[104/255.0, 117/255.0, 123/255.0],
std=[1.0, 1.0, 1.0])
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
# remember 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)
def train(train_loader, model, criterion, optimizer, epoch):
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_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[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 % args.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:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(async=True)
if args.arch == 'resnet50-kaiming' or args.arch == 'resnet50-kaiming-dk' or args.arch == 'resnet18-caffe' or args.arch == 'resnet18-darknet':
input = torch.stack([input[:,2,:,:], input[:,1,:,:], input[:,0,:,:]],1)
input = input * 255
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_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:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()
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