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main_something.py
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main_something.py
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import argparse
import os
import time
import shutil
import torch
import torchvision
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm_
from dataset import TSNDataSet
from models import TSN
from transforms import *
from opts import parser
import sys
import torch.utils.model_zoo as model_zoo
from torch.nn.init import constant_, xavier_uniform_
os.environ["CUDA_VISIBLE_DEVICES"]='4,5,6,7'
best_prec1 = 0
def main():
global args, best_prec1
args = parser.parse_args()
print("------------------------------------")
print("Environment Versions:")
print("- Python: {}".format(sys.version))
print("- PyTorch: {}".format(torch.__version__))
print("- TorchVison: {}".format(torchvision.__version__))
args_dict = args.__dict__
print("------------------------------------")
print(args.arch+" Configurations:")
for key in args_dict.keys():
print("- {}: {}".format(key, args_dict[key]))
print("------------------------------------")
print (args.mode)
if args.dataset == 'ucf101':
num_class = 101
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'hmdb51':
num_class = 51
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'kinetics':
num_class = 400
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'something':
num_class = 174
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'NTU_RGBD':
num_class = 120
rgb_read_format = "{:05d}.jpg"
elif args.dataset == 'tinykinetics':
num_class = 150
rgb_read_format = "{:05d}.jpg"
else:
raise ValueError('Unknown dataset '+args.dataset)
model = TSN(num_class, args.num_segments, args.pretrained_parts, args.modality,
base_model=args.arch,
consensus_type=args.consensus_type, dropout=args.dropout, partial_bn=not args.no_partialbn)
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
# Optimizer s also support specifying per-parameter options.
# To do this, pass in an iterable of dict s.
# Each of them will define a separate parameter group,
# and should contain a params key, containing a list of parameters belonging to it.
# Other keys should match the keyword arguments accepted by the optimizers,
# and will be used as optimization options for this group.
policies = model.get_optim_policies(args.dataset)
train_augmentation = model.get_augmentation()
model = torch.nn.DataParallel(model, device_ids=args.gpus).cuda()
model_dict = model.state_dict()
print("pretrained_parts: ", args.pretrained_parts)
if args.arch == "resnet50":
new_state_dict = {} #model_dict
div = False
roll = True
elif args.arch == "resnet34":
pretrained_dict={}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k:v})
div = False
roll = True
elif args.arch == "Res3D18":
pretrained_dict={}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k:v})
div = False
roll = True
elif args.arch == "TSM":
pretrained_dict={}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k:v})
div = True
roll = False
elif (args.arch == "MS" ):
pretrained_dict={}
new_state_dict = {} #model_dict
for k, v in model_dict.items():
if ('fc' not in k):
new_state_dict.update({k:v})
div = True
roll = False
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'])
print(("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch'])))
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
cudnn.benchmark = True
# Data loading code
if args.modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if args.modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 1
train_loader = torch.utils.data.DataLoader(
TSNDataSet("", args.train_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
mode = args.mode,
image_tmpl=args.rgb_prefix+rgb_read_format if args.modality in ["RGB", "RGBDiff"] else args.flow_prefix+rgb_read_format,
transform=torchvision.transforms.Compose([
GroupScale((240,320)),
# GroupScale(int(scale_size)),
train_augmentation,
Stack(roll=roll),
ToTorchFormatTensor(div=div),
normalize,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
TSNDataSet("", args.val_list, num_segments=args.num_segments,
new_length=data_length,
modality=args.modality,
mode =args.mode,
image_tmpl=args.rgb_prefix+rgb_read_format if args.modality in ["RGB", "RGBDiff"] else args.flow_prefix+rgb_read_format,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale((240,320)),
# GroupScale((224)),
# GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=roll),
ToTorchFormatTensor(div=div),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and optimizer
if args.loss_type == 'nll':
criterion = torch.nn.CrossEntropyLoss().cuda()
else:
raise ValueError("Unknown loss type")
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,nesterov=args.nesterov)
output_list = []
if args.evaluate:
prec1, score_tensor = validate(val_loader,model,criterion,0,temperature=100)
output_list.append(score_tensor)
save_validation_score(output_list, filename='score.pt')
print("validation score saved in {}".format('/'.join((args.val_output_folder, 'score_inf5.pt'))))
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr_steps)
# train for one epoch
temperature = train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
prec1, score_tensor = validate(val_loader, model, criterion, (epoch + 1) * len(train_loader), temperature=temperature)
output_list.append(score_tensor)
# 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)
# save validation score
save_validation_score(output_list)
print("validation score saved in {}".format('/'.join((args.val_output_folder, 'score.pt'))))
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# temperature
increase = pow(1.05, epoch)
temperature = 100 # * increase
print (temperature)
# In PyTorch 0.4, "volatile=True" is deprecated.
torch.set_grad_enabled(True)
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# discard final batch
if i == len(train_loader)-1:
break
# measure data loading time
data_time.update(time.time() - end)
# target size: [batch_size]
target = target.cuda(async=True)
input_var = input
target_var = target
output = model(input_var, temperature)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1,5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
loss.backward()
if i % args.iter_size == 0:
# scale down gradients when iter size is functioning
if args.iter_size != 1:
for g in optimizer.param_groups:
for p in g['params']:
p.grad /= args.iter_size
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))
else:
total_norm = 0
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(('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[-2]['lr'])))
# print(('Flow_Con_Loss {loss.val:.4f} ({loss.avg:.4f})'.format(loss=flow_con_losses)))
return temperature
def validate(val_loader, model, criterion, iter, temperature, logger=None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# another losses
flow_con_losses = AverageMeter()
# In PyTorch 0.4, "volatile=True" is deprecated.
torch.set_grad_enabled(False)
# torch.no_grad()
# switch to evaluate mode
model.eval()
# model.train()
output_list = []
pred_arr = []
target_arr = []
end = time.time()
for i, (input, target) in enumerate(val_loader):
# discard final batch
if i == len(val_loader)-1:
break
target = target.cuda(async=True)
# target = target.cuda(async=False)
input_var = input
target_var = target
# compute output
output= model(input_var, temperature)
# output = model(input_var, temperature)
loss = criterion(output, target_var)
# class acc
pred = torch.argmax(output.data, dim=1)
pred_arr.extend(pred)
target_arr.extend(target)
# print ('Accuracy {:.02f}%'.format(np.mean(cls_acc)*100))
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1,5))
losses.update(loss.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
output_list.append(output)
if i % args.print_freq == 0:
print(('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.4f})\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(('Flow_Con_Loss {loss.val:.4f} ({loss.avg:.4f})'.format(loss=flow_con_losses)))
output_tensor = torch.cat(output_list, dim=0)
print(('Testing Results: Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f} Loss {loss.avg:.5f} Time {batch_time.avg:.4f}'
.format(top1=top1, top5=top5, loss=losses, batch_time=batch_time)))
return top1.avg, output_tensor
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
filename = '_'.join((args.snapshot_pref, args.modality.lower(), "epoch", str(state['epoch']), filename))
torch.save(state, filename)
if is_best:
best_name = '_'.join((args.snapshot_pref, args.modality.lower(), 'model_best.pth.tar'))
shutil.copyfile(filename, best_name)
def save_validation_score(score, filename='score.pt'):
filename = '/'.join((args.val_output_folder, filename))
torch.save(score, filename)
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 every 30 epochs"""
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"""
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()