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finetune.py
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finetune.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 torch.utils.data import DataLoader
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
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)
# 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)
# work_dir
if args.work_dir is not None:
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
cfg.work_dir = os.path.dirname(cfg.load)
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)
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 update_batch_stats(model, flag):
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.update_batch_stats = flag
def test(test_loader, model, criterion, it, logger, writer):
""" test target """
model.eval()
losses = AverageMeter()
top1 = AverageMeter()
all_pred = []
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(test_loader):
images = images.float().cuda()
labels = labels.cuda()
bsz = labels.shape[0]
# forward
logits = model(images)
loss = criterion(logits, labels)
pred = F.softmax(logits, dim=1)
all_pred.append(pred.detach())
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(logits, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
all_pred = torch.cat(all_pred)
mean_ent = (-all_pred * torch.log(all_pred + 1e-5)).sum(dim=1).mean().item() / np.log(all_pred.size(0))
pred_max = all_pred.max(dim=1).indices
# writer
writer.add_scalar(f'Loss/ft_tgt_test', losses.avg, it)
writer.add_scalar(f'Entropy/ft_tgt_test', mean_ent, it)
writer.add_scalar(f'Acc/ft_tgt_test', top1.avg, it)
# logger
time2 = time.time()
test_time = format_time(time2 - time1)
logger.info(f'Test at iter [{it}] - test_time: {test_time}, '
f'test_loss: {losses.avg:.3f}, '
f'test_entropy: {mean_ent:.3f}, '
f'test_Acc@1: {top1.avg:.2f}')
return top1.avg, mean_ent, pred_max
def test_class_acc(test_loader, model, criterion, it, logger, writer, cfg):
""" test target """
model.eval()
losses = AverageMeter()
top1 = AverageMeter()
all_pred, all_labels = [], []
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(test_loader):
images = images.float().cuda()
labels = labels.cuda()
all_labels.append(labels)
bsz = labels.shape[0]
# forward
logits = model(images)
loss = criterion(logits, labels)
pred = F.softmax(logits, dim=1)
all_pred.append(pred.detach())
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(logits, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
all_labels = torch.cat(all_labels)
all_pred = torch.cat(all_pred)
mean_ent = (-all_pred * torch.log(all_pred + 1e-5)).sum(dim=1).mean().item() / np.log(all_pred.size(0))
pred_max = all_pred.max(dim=1).indices
# class-wise acc
class_accs = []
all_eq = pred_max == all_labels
for c in range(cfg.num_classes):
mask_c = all_labels == c
acc_c = all_eq[mask_c].float().mean().item()
class_accs.append(round(acc_c * 100, 2))
avg_acc = round(sum(class_accs) / len(class_accs), 2)
# writer
writer.add_scalar(f'Loss/ft_tgt_test', losses.avg, it)
writer.add_scalar(f'Entropy/ft_tgt_test', mean_ent, it)
writer.add_scalar(f'Acc/ft_tgt_test', top1.avg, it)
# logger
time2 = time.time()
test_time = format_time(time2 - time1)
logger.info(f'Test at iter [{it}] - test_time: {test_time}, '
f'test_loss: {losses.avg:.3f}, '
f'test_entropy: {mean_ent:.3f}, '
f'test_Acc@1: {top1.avg:.2f}')
logger.info(f'per class acc: {str(class_accs)}, avg_acc: {avg_acc}')
return top1.avg, mean_ent, pred_max
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=f'finetune')
writer = SummaryWriter(log_dir=os.path.join(cfg.work_dir, f'tensorboard'))
'''
# -----------------------------------------
# build dataset/dataloader
# -----------------------------------------
'''
# loader_dict = build_office_home_loaders(cfg, tgt=cfg.tgt, loader_list=['tgt_train', 'tgt_test'])
loader_dict = {}
train_set = build_dataset(cfg.data.train)
test_set = build_dataset(cfg.data.test)
loader_dict['tgt_train'] = DataLoader(train_set, batch_size=cfg.batch_size,
shuffle=True, num_workers=cfg.num_workers, drop_last=True)
loader_dict['tgt_test'] = DataLoader(test_set, batch_size=cfg.batch_size,
shuffle=False, num_workers=cfg.num_workers, drop_last=False)
print(f'==> DataLoader built.')
'''
# -----------------------------------------
# build model & optimizer
# -----------------------------------------
'''
# build target model & load weights
model = build_model(cfg.tgt_model)
model.fc = build_model(cfg.tgt_head)
model = torch.nn.DataParallel(model).cuda()
print(f'==> Loading checkpoint "{cfg.load}"')
ckpt = torch.load(cfg.load, map_location='cuda')
model.load_state_dict(ckpt['model_state'] if 'model_state' in ckpt.keys() else ckpt['model1_state'])
test_criterion = build_loss(cfg.loss.test).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.')
'''
# -----------------------------------------
# Test distilled model before finetune
# -----------------------------------------
'''
if cfg.get('test_class_acc', False):
test_class_acc(loader_dict['tgt_test'], model, test_criterion, 0, logger, writer, cfg)
else:
test(loader_dict['tgt_test'], model, test_criterion, 0, logger, writer)
'''
# -----------------------------------------
# Start target training (finetune)
# -----------------------------------------
'''
print("==> Start training...")
model.train()
batch_time = AverageMeter()
losses = AverageMeter()
test_meter = TrackMeter()
start_iter = 1
train_iters = cfg.epochs * len(loader_dict['tgt_train'])
test_interval = train_iters // 10
last_pred = -1
end = time.time()
iter_source = iter(loader_dict['tgt_train'])
for it in range(start_iter, train_iters + 1):
# train
adjust_lr(optimizer, it, train_iters, power=0.75)
try:
images, labels = next(iter_source)
except StopIteration:
iter_source = iter(loader_dict['tgt_train'])
images, labels = next(iter_source)
images = images.cuda(non_blocking=True)
bsz = images.shape[0]
# forward
logits = model(images)
pred_tgt = F.softmax(logits, dim=1)
loss_entropy = (-pred_tgt * torch.log(pred_tgt + 1e-5)).sum(dim=1).mean()
pred_mean = pred_tgt.mean(dim=0)
loss_gentropy = torch.sum(-pred_mean * torch.log(pred_mean + 1e-5))
loss_entropy -= loss_gentropy
loss = loss_entropy
# update metric
losses.update(loss.item(), bsz)
# backward1
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}')
writer.add_scalar(f'lr/ft_tgt', lr, it)
writer.add_scalar(f'Loss/ft_tgt_train', losses.avg, it)
if it % test_interval == 0 or it == train_iters:
if cfg.get('test_class_acc', False):
test_acc, mean_ent, pred_max = \
test_class_acc(loader_dict['tgt_test'], model, test_criterion, it, logger, writer, cfg)
else:
test_acc, mean_ent, pred_max = \
test(loader_dict['tgt_test'], model, test_criterion, it, logger, writer)
test_meter.update(test_acc, idx=it)
model.train()
if torch.abs(pred_max - last_pred).sum() == 0:
break
last_pred = pred_max
# We print the best test_acc but report test_acc of the last epoch.
logger.info(f'Best test_Acc@1: {test_meter.max_val:.2f} (iter={test_meter.max_idx}).')
# save last
model_path = os.path.join(cfg.work_dir, 'ft_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()