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main.py
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main.py
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import argparse
import os
import sys
import time
import shutil
import torch
import torch.nn as nn
import torchvision
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm_ as clip_grad_norm
from dataset import VideoDataSet
from models import TemporalModel
from transforms import *
from opts import parser
import datasets_video
best_prec1 = 0
def main():
global args, best_prec1
args = parser.parse_args()
check_rootfolders()
categories, train_list, val_list, root_path, prefix = datasets_video.return_dataset(args.dataset,args.root_path)
num_class = len(categories)
global store_name
store_name = '_'.join([args.type, args.dataset, args.arch, 'segment%d'% args.num_segments, args.store_name])
print(('storing name: ' + store_name))
if args.dataset == 'somethingv1' or args.dataset == 'somethingv2':
# label transformation for left/right categories
# please refer to labels.json file in sometingv2 for detail.
target_transforms = {86:87,87:86,93:94,94:93,166:167,167:166}
else:
target_transforms = None
model = TemporalModel(num_class, args.num_segments, model = args.type, backbone=args.arch,
alpha = args.alpha, beta = args.beta,
dropout = args.dropout, target_transforms = target_transforms)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = get_optim_policies(model)
train_augmentation = model.get_augmentation()
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
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.module.load_state_dict(checkpoint['state_dict'])
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# Data loading code
normalize = GroupNormalize(input_mean, input_std)
train_loader = torch.utils.data.DataLoader(
VideoDataSet(root_path, train_list, num_segments=args.num_segments,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(args.arch in ['BNInception','InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception','InceptionV3'])),
normalize,
])),
batch_size=args.batch_size, shuffle=True, drop_last=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
VideoDataSet(root_path, val_list, num_segments=args.num_segments,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(args.arch in ['BNInception','InceptionV3'])),
ToTorchFormatTensor(div=(args.arch not in ['BNInception','InceptionV3'])),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and optimizer
criterion = torch.nn.CrossEntropyLoss().cuda()
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
optimizer = torch.optim.SGD(policies,
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.evaluate:
prec1 = validate(val_loader, model, criterion, 0)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': args.start_epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
return
log_training = open(os.path.join(args.checkpoint_dir,'log', '%s.csv' % store_name), 'w')
for epoch in range(args.start_epoch, args.epochs):
# adjust learning rate
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, log_training)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1 = validate(val_loader, model, criterion, (epoch + 1) * len(train_loader), log_training)
# 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,
}, is_best)
def train(train_loader, model, criterion, optimizer, epoch, log):
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)
input = input.cuda(non_blocking = True)
target = target.cuda(non_blocking=True)
# compute output
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1,5))
losses.update(loss.item(), 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()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print(("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm)))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
output = ('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\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, lr=optimizer.param_groups[-1]['lr']))
print(output)
log.write(output + '\n')
log.flush()
def validate(val_loader, model, criterion, iter, log = None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
with torch.no_grad():
for i, (input, target) in enumerate(val_loader):
input = input.cuda(non_blocking = True)
target = target.cuda(non_blocking = True)
# compute output
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1,5))
losses.update(loss.item(), 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:
output = ('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(output)
if log is not None:
log.write(output + '\n')
log.flush()
output = ('Testing Results: Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Loss {loss.avg:.5f}'
.format(top1=top1, top5=top5, loss=losses))
print(output)
output_best = '\nBest Prec@1: %.3f'%(best_prec1)
print(output_best)
if log is not None:
log.write(output + ' ' + output_best + '\n')
log.flush()
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, '%s/%s_checkpoint.pth.tar' % (os.path.join(args.checkpoint_dir,'checkpoint'), store_name))
if is_best:
shutil.copyfile('%s/%s_checkpoint.pth.tar' % (os.path.join(args.checkpoint_dir,'checkpoint'), store_name),'%s/%s_best.pth.tar' % (os.path.join(args.checkpoint_dir,'checkpoint'), store_name))
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, lr_steps):
"""Sets the learning rate to the initial LR decayed by 10 """
decay = 0.1 ** (sum(epoch >= np.array(lr_steps)))
lr = args.lr * decay
decay = args.weight_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr * param_group['lr_mult']
param_group['weight_decay'] = decay * param_group['decay_mult']
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
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,keepdim = True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def check_rootfolders():
"""Create log and model folder"""
folders_util = [args.checkpoint_dir,os.path.join(args.checkpoint_dir,'log'), os.path.join(args.checkpoint_dir,'checkpoint')]
for folder in folders_util:
if not os.path.exists(folder):
print(('creating folder ' + folder))
os.mkdir(folder)
def get_optim_policies(model):
first_conv_weight = []
first_conv_bias = []
normal_weight = []
normal_bias = []
bn = []
conv_cnt = 0
bn_cnt = 0
for m in model.modules():
if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Conv3d):
ps = list(m.parameters())
conv_cnt += 1
if conv_cnt <= 3:
first_conv_weight.append(ps[0])
if len(ps) == 2:
first_conv_bias.append(ps[1])
else:
normal_weight.append(ps[0])
if len(ps) == 2:
normal_bias.append(ps[1])
elif isinstance(m, torch.nn.Linear):
ps = list(m.parameters())
normal_weight.append(ps[0])
if len(ps) == 2:
normal_bias.append(ps[1])
elif isinstance(m,nn.BatchNorm3d):
bn_cnt += 1
bn.extend(list(m.parameters()))
elif len(m._modules) == 0:
if len(list(m.parameters())) > 0:
raise ValueError("New atomic module type: {}. Need to give it a learning policy".format(type(m)))
return [
{'params': first_conv_weight, 'lr_mult': 1, 'decay_mult': 1,
'name': "first_conv_weight"},
{'params': first_conv_bias, 'lr_mult': 2, 'decay_mult': 0,
'name': "first_conv_bias"},
{'params': normal_weight, 'lr_mult': 1, 'decay_mult': 1,
'name': "normal_weight"},
{'params': normal_bias, 'lr_mult': 2, 'decay_mult': 0,
'name': "normal_bias"},
{'params': bn, 'lr_mult': 1, 'decay_mult': 0,
'name': "BN scale/shift"},
]
if __name__ == '__main__':
main()