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import argparse
import os
import time
import shutil
import logging
import torch
import torchvision
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from lib.dataset import VideoDataSet
from lib.models import VideoModule
from lib.transforms import *
from lib.utils.tools import *
from lib.opts import args
from train_val import train, validate
best_metric = 0
def main():
global args, best_metric
# specify dataset
if args.dataset == 'ucf101':
num_class = 101
elif args.dataset == 'hmdb51':
num_class = 51
elif args.dataset == 'kinetics400':
num_class = 400
elif args.dataset == 'kinetics200':
num_class = 200
else:
raise ValueError('Unknown dataset '+args.dataset)
data_root = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"data/{}/access".format(args.dataset))
# create model
org_model = VideoModule(num_class=num_class,
base_model_name=args.arch,
dropout=args.dropout,
pretrained=args.pretrained,
pretrained_model=args.pretrained_model)
num_params = 0
for param in org_model.parameters():
num_params += param.reshape((-1, 1)).shape[0]
print("Model Size is {:.3f}M".format(num_params/1000000))
model = torch.nn.DataParallel(org_model).cuda()
# model = org_model
# define loss function (criterion) and optimizer
criterion = torch.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_metric = checkpoint['best_metric']
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)))
# Data loading code
## train data
train_transform = torchvision.transforms.Compose([
org_model.get_augmentation(),
Stack(mode=args.mode),
ToTorchFormatTensor(),
GroupNormalize(),
])
train_dataset = VideoDataSet(root_path=data_root,
list_file=args.train_list,
t_length=args.t_length,
t_stride=args.t_stride,
num_segments=args.num_segments,
image_tmpl=args.image_tmpl,
transform=train_transform,
phase="Train")
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size, shuffle=True, drop_last=True,
num_workers=args.workers, pin_memory=True)
## val data
val_transform = torchvision.transforms.Compose([
GroupScale(256),
GroupCenterCrop(224),
Stack(mode=args.mode),
ToTorchFormatTensor(),
GroupNormalize(),
])
val_dataset = VideoDataSet(root_path=data_root,
list_file=args.val_list,
t_length=args.t_length,
t_stride=args.t_stride,
num_segments=args.num_segments,
image_tmpl=args.image_tmpl,
transform=val_transform,
phase="Val")
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.mode != "3D":
cudnn.benchmark = True
# validate(val_loader, model, criterion, args.print_freq, args.start_epoch)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, args.lr, epoch, args.lr_steps)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args.print_freq)
# evaluate on validation set
if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1:
metric = validate(val_loader, model, criterion, args.print_freq, epoch + 1)
# remember best prec@1 and save checkpoint
is_best = metric > best_metric
best_metric = max(metric, best_metric)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_metric': best_metric,
'optimizer': optimizer.state_dict(),
}, is_best, epoch + 1, args.experiment_root)
if __name__ == '__main__':
main()