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train_src_v1.py
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train_src_v1.py
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import os
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
from utils.util import AverageMeter, accuracy, TrackMeter
from utils.util import set_seed
from utils.config import Config, ConfigDict, DictAction
from losses import build_loss
from builder import build_optimizer
from models.build import build_model
from utils.util import format_time
from builder import build_logger
from datasets import build_dataset, build_office_home_loaders
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('config', type=str, help='config file path')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument('--resume', type=str, help='path to latest checkpoint (default: None)')
parser.add_argument('--load', type=str, help='Load init weights for fine-tune (default: None)')
parser.add_argument('--cfgname', help='specify log_file; for debug use')
parser.add_argument('--seed', default=0, type=int, help='random seed')
parser.add_argument('--cfg-options', nargs='+', action=DictAction,
help='override the config; e.g., --cfg-options port=10001 k1=a,b k2="[a,b]"'
'Note that the quotation marks are necessary and that no white space is allowed.')
args = parser.parse_args()
return args
def get_cfg(args):
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# work_dir
if args.work_dir is not None:
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
dirname = os.path.dirname(args.config).replace('configs', 'checkpoints', 1)
filename = os.path.splitext(os.path.basename(args.config))[0]
cfg.work_dir = os.path.join(dirname, filename)
os.makedirs(cfg.work_dir, exist_ok=True)
# cfgname
if args.cfgname is not None:
cfg.cfgname = args.cfgname
else:
cfg.cfgname = os.path.splitext(os.path.basename(args.config))[0]
assert cfg.cfgname is not None
# seed
if args.seed != 0:
cfg.seed = args.seed
elif not hasattr(cfg, 'seed'):
cfg.seed = 42
set_seed(cfg.seed)
# resume or load init weights
if args.resume:
cfg.resume = args.resume
if args.load:
cfg.load = args.load
assert not (cfg.resume and cfg.load)
return cfg
def adjust_lr(optimizer, it, train_iters, gamma=10, power=0.75):
decay = (1 + gamma * it / train_iters) ** (-power)
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['init_lr'] * decay
def val(val_loader, model, criterion, it, logger, writer):
"""validation"""
model.eval()
losses = AverageMeter()
top1 = AverageMeter()
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(val_loader):
images = images.cuda()
labels = labels.cuda()
bsz = labels.shape[0]
# forward
output = model(images)
loss = criterion(output, labels)
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(output, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
# writer
writer.add_scalar(f'Loss/src_val', losses.avg, it)
writer.add_scalar(f'Acc/src_val', top1.avg, it)
# logger
time2 = time.time()
val_time = format_time(time2 - time1)
logger.info(f'Iter [{it}] - val_time: {val_time}, '
f'val_loss: {losses.avg:.3f}, '
f'val_Acc@1: {top1.avg:.2f}')
return top1.avg
def main():
# args & cfg
args = parse_args()
cfg = get_cfg(args) # may modify cfg according to args
cudnn.benchmark = True
# write cfg
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = os.path.join(cfg.work_dir, f'{timestamp}.cfg')
with open(log_file, 'a') as f:
f.write(cfg.pretty_text)
# logger
logger = build_logger(cfg.work_dir, cfgname='train_source')
writer = SummaryWriter(log_dir=os.path.join(cfg.work_dir, f'tensorboard'))
'''
# -----------------------------------------
# build dataset/dataloader
# -----------------------------------------
'''
loader_dict = build_office_home_loaders(cfg, loader_list=['src_train', 'src_val'])
print(f'==> DataLoader built.')
'''
# -----------------------------------------
# build model & optimizer
# -----------------------------------------
'''
model = build_model(cfg.model)
nn.init.xavier_normal_(model.fc.weight)
model = torch.nn.DataParallel(model).cuda()
train_criterion = build_loss(cfg.loss.train).cuda()
val_criterion = build_loss(cfg.loss.val).cuda()
base_params = [v for k, v in model.named_parameters() if 'fc' not in k]
head_params = [v for k, v in model.named_parameters() if 'fc' in k]
param_groups = [{'params': base_params, 'lr': cfg.lr * 0.1},
{'params': head_params, 'lr': cfg.lr}]
optimizer = build_optimizer(cfg.optimizer, param_groups)
for param_group in optimizer.param_groups:
param_group['init_lr'] = param_group['lr']
print('==> Model built.')
'''
# -----------------------------------------
# Start source training
# -----------------------------------------
'''
print("==> Start training...")
model.train()
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
val_meter = TrackMeter()
start_iter = 1
train_iters = cfg.epochs * len(loader_dict['src_train'])
val_interval = train_iters // 10
end = time.time()
iter_source = iter(loader_dict['src_train'])
for it in range(start_iter, train_iters + 1):
adjust_lr(optimizer, it, train_iters, power=0.75)
try:
images, labels = next(iter_source)
except StopIteration:
iter_source = iter(loader_dict['src_train'])
images, labels = next(iter_source)
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
bsz = images.shape[0]
targets = torch.zeros(bsz, cfg.num_classes).cuda().scatter_(1, labels.view(-1, 1), 1)
targets = (1 - cfg.eps) * targets + cfg.eps / cfg.num_classes
# compute loss
output = model(images)
loss = train_criterion(output, targets)
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(output, labels, topk=(1, 5)) # acc use labels
top1.update(acc1[0], bsz)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# print info
if it == start_iter or it % cfg.log_interval == 0:
lr = optimizer.param_groups[0]['lr']
logger.info(f'Iter [{it}/{train_iters}] - '
f'batch_time: {batch_time.avg:.3f}, '
f'lr: {lr:.5f}, '
f'loss: {losses.avg:.3f}, '
f'train_Acc@1: {top1.avg:.2f}')
writer.add_scalar(f'lr', lr, it)
writer.add_scalar(f'Loss/src_train', losses.avg, it)
writer.add_scalar(f'Acc/src_train', top1.avg, it)
if it % val_interval == 0 or it == train_iters:
val_acc = val(loader_dict['src_val'], model, val_criterion, it, logger, writer)
if val_acc >= val_meter.max_val:
model_path = os.path.join(cfg.work_dir, f'best_val.pth')
state_dict = {
'optimizer_state': optimizer.state_dict(),
'model_state': model.state_dict(),
'iter': it
}
torch.save(state_dict, model_path)
val_meter.update(val_acc, idx=it)
logger.info(f'Best val_Acc@1: {val_meter.max_val:.2f} (iter={val_meter.max_idx}).')
model.train()
# save last
model_path = os.path.join(cfg.work_dir, 'last.pth')
state_dict = {
'optimizer_state': optimizer.state_dict(),
'model_state': model.state_dict(),
'iter': train_iters
}
torch.save(state_dict, model_path)
if __name__ == '__main__':
main()