PyTorch代码学习-ImageNET训练

PyTorch代码学习-ImageNET训练

# -*- coding: utf-8 -*-
import argparse  # 命令行解释器相关程序，命令行解释器
import os        # 操作系统文件相关
import shutil    # 文件高级操作
import time      # 调用时间模块

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn        # gpu 使用
import torch.distributed as dist            # 分布式（pytorch 0.2)
import torch.optim                          # 优化器
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models

# name中若为小写且不以‘——’开头，则对其进行升序排列
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
# callable功能为判断返回对象是否可调用（即某种功能）。

# 创建argparse.ArgumentParser对象
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# 添加命令行元素
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')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
help='distributed backend')

# 定义参数
best_prec1 = 0

# 定义主函数main()
def main():
global args, best_prec1
# 使用函数parse_args()进行参数解析，输入默认是sys.argv[1:]，
# 返回值是一个包含命令参数的Namespace，所有参数以属性的形式存在，比如args.myoption。
args = parser.parse_args()

########## 使用多播地址进行初始化
args.distributed = args.world_size > 1

if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)

##### step1: create model and set GPU
# 导入pretrained model 或者创建model
if args.pretrained:
# format 格式化表达字符串，上述默认arch为resnet18
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
# 分布式运行，可实现在多块GPU上运行
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
# 批处理，多GPU默认用dataparallel使用在多块gpu上
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
# Wrap model in DistributedDataParallel (CUDA only for the moment)
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)

##### step2: define loss function (criterion) and optimizer
# 使用交叉熵损失函数
criterion = nn.CrossEntropyLoss().cuda()
# optimizer 使用 SGD + momentum
# 动量，默认设置为0.9
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
# 权值衰减，默认为1e-4
weight_decay=args.weight_decay)

# 恢复模型（详见模型存取与恢复）
####step3：optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):                                 # 判断返回的是不是文件
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)                        # load 一个save的对象
args.start_epoch = checkpoint['epoch']                      # default = 90
best_prec1 = checkpoint['best_prec1']                       # best_prec1 = 0
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])          # load_state_dict:恢复模型
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))

cudnn.benchmark = True

##### step4: Data loading code base of dataset(have downloaded) and normalize
# 从 train、val文件中导入数据
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
# 数据预处理：normalize: - mean / std
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])

# ImageFolder 一个通用的数据加载器
train_dataset = datasets.ImageFolder(
traindir,
# 对数据进行预处理
transforms.Compose([                      # 将几个transforms 组合在一起
transforms.RandomSizedCrop(224),      # 随机切再resize成给定的size大小
transforms.RandomHorizontalFlip(),    # 概率为0.5，随机水平翻转。
transforms.ToTensor(),                # 把一个取值范围是[0,255]或者shape为(H,W,C)的numpy.ndarray，
# 转换成形状为[C,H,W]，取值范围是[0,1.0]的torch.FloadTensor
normalize,
]))

#######
if args.distributed:
# Use a DistributedSampler to restrict each process to a distinct subset of the dataset.
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
######

# train 数据下载及预处理
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)

val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
# 重新改变大小为`size`，若：height>width`,则：(size*height/width, size)
transforms.Scale(256),
# 将给定的数据进行中心切割，得到给定的size。
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)         # default workers = 4

##### step5: 验证函数
if args.evaluate:
validate(val_loader, model, criterion)             # 自定义的validate函数，见下
return

##### step6:开始训练模型
for epoch in range(args.start_epoch, args.epochs):
# Use .set_epoch() method to reshuffle the dataset partition at every iteration
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)      # adjust_learning_rate 自定义的函数，见下

# 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)

# 定义相关函数
# def train 函数
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)
# criterion 为定义过的损失函数
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()

# 每十步输出一次
if i % args.print_freq == 0:     # default=10
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)
# 这是一种用来包裹张量并记录应用的操作
"""
Attributes:
data: 任意类型的封装好的张量。
grad: 保存与data类型和位置相匹配的梯度，此属性难以分配并且不能重新分配。
requires_grad: 标记变量是否已经由一个需要调用到此变量的子图创建的bool值。只能在叶子变量上进行修改。
volatile: 标记变量是否能在推理模式下应用（如不保存历史记录）的bool值。只能在叶变量上更改。
is_leaf: 标记变量是否是图叶子(如由用户创建的变量)的bool值.
grad_fn: Gradient function graph trace.

Parameters:
data (any tensor class): 要包装的张量.
requires_grad (bool): bool型的标记值. **Keyword only.**
volatile (bool): bool型的标记值. **Keyword only.**
"""
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

# 保存当前节点
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')

# 计算并存储参数当前值或平均值
class AverageMeter(object):
# Computes and stores the average and current value
"""
batch_time = AverageMeter()
即 self = batch_time
则 batch_time 具有__init__，reset，update三个属性，
直接使用batch_time.update()调用
功能为：batch_time.update(time.time() - end)

b0bf
仅一个参数，则直接保存参数值
对应定义：def update(self, val, n=1)
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
这些有两个参数则求参数val的均值，保存在avg中##不确定##

"""
def __init__(self):
self.reset()       # __init__():reset parameters

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

# 更新 learning_rate ：每30步，学习率降至前的10分之1
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))            # args.lr = 0.1 , 即每30步，lr = lr /10
for param_group in optimizer.param_groups:       # 将更新的lr 送入优化器 optimizer 中，进行下一次优化
param_group['lr'] = lr

# 计算准确度
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
"""
maxk = max(topk)
# size函数：总元素的个数
batch_size = target.size(0)

# topk函数选取output前k大个数
_, pred = output.topk(maxk, 1, True, True)
##########不了解t()
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, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res

if __name__ == '__main__':
main()