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main.py
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main.py
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from __future__ import print_function
import argparse
import os.path
import os
import logging
import time
import datetime
import torch
import torchvision
import PIL
import torch.optim as optim
import numpy as np
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from core.datasets.image_list import ImageList
from core.models.network import ResNetFc
from core.active.active import DUC_active
from core.utils.utils import set_random_seed, mkdir, LrScheduler
from core.datasets.transforms import build_transform
from core.active.loss import EDL_Loss
from core.utils.metric_logger import MetricLogger
from core.utils.logger import setup_logger
from core.config import cfg
def test(model, test_loader):
start_test = True
model.eval()
with torch.no_grad():
for batch_idx, test_data in enumerate(test_loader):
img, labels = test_data['img0'], test_data['label']
img = img.cuda()
logits = model(img, return_feat=False)
alpha = torch.exp(logits)
total_alpha = torch.sum(alpha, dim=1, keepdim=True) # total_alpha.shape: [B, 1]
outputs = alpha / total_alpha
if start_test:
all_output = outputs.float().cpu()
all_label = labels.float()
start_test = False
else:
all_output = torch.cat((all_output, outputs.float().cpu()), 0)
all_label = torch.cat((all_label, labels.float()), 0)
_, predict = torch.max(all_output, dim=1)
acc = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0]) * 100
return acc
def train(cfg, task):
logger = logging.getLogger("main.trainer")
os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.GPU_ID)
kwargs = {'num_workers': cfg.DATALOADER.NUM_WORKERS, 'pin_memory': True}
# prepare data
source_transform = build_transform(cfg, is_train=True, choices=cfg.INPUT.SOURCE_TRANSFORMS)
target_transform = build_transform(cfg, is_train=True, choices=cfg.INPUT.TARGET_TRANSFORMS)
test_transform = build_transform(cfg, is_train=False, choices=cfg.INPUT.TEST_TRANSFORMS)
src_train_ds = ImageList(os.path.join(cfg.DATASET.ROOT, cfg.DATASET.NAME, cfg.DATASET.SOURCE_TRAIN_DOMAIN),
transform=source_transform)
src_train_loader = DataLoader(src_train_ds, batch_size=cfg.DATALOADER.SOURCE.BATCH_SIZE, shuffle=True,
drop_last=True, **kwargs)
tgt_unlabeled_ds = ImageList(os.path.join(cfg.DATASET.ROOT, cfg.DATASET.NAME, cfg.DATASET.TARGET_TRAIN_DOMAIN),
transform=target_transform)
tgt_unlabeled_loader = DataLoader(tgt_unlabeled_ds, batch_size=cfg.DATALOADER.TARGET.BATCH_SIZE, shuffle=True,
drop_last=True, **kwargs)
tgt_unlabeled_loader_full = DataLoader(tgt_unlabeled_ds, batch_size=cfg.DATALOADER.TARGET.BATCH_SIZE,
shuffle=True, drop_last=False, **kwargs)
tgt_test_ds = ImageList(os.path.join(cfg.DATASET.ROOT, cfg.DATASET.NAME, cfg.DATASET.TARGET_VAL_DOMAIN),
transform=test_transform)
tgt_test_loader = DataLoader(tgt_test_ds, batch_size=cfg.DATALOADER.TEST.BATCH_SIZE, shuffle=False, **kwargs)
# active target dataset & loader
tgt_selected_ds = ImageList(empty=True, transform=source_transform)
tgt_selected_loader = DataLoader(tgt_selected_ds, batch_size=cfg.DATALOADER.SOURCE.BATCH_SIZE,
shuffle=True, drop_last=False, **kwargs)
iter_per_epoch = max(len(src_train_loader), len(tgt_unlabeled_loader))
max_iters = cfg.TRAINER.MAX_EPOCHS * iter_per_epoch
# model
model = ResNetFc(class_num=cfg.DATASET.NUM_CLASS, cfg=cfg).cuda()
# optimizer
optimizer = optim.SGD(model.parameters_list(), lr=cfg.OPTIM.LR, momentum=0.9, weight_decay=1e-3, nesterov=True)
lr_sheduler = LrScheduler(optimizer, max_iters, init_lr=cfg.OPTIM.LR, gamma=cfg.OPTIM.GAMMA, decay_rate=cfg.OPTIM.DECAY_RATE)
# evidence deep learning loss function
edl_criterion = EDL_Loss(cfg)
# total number of target samples
totality = tgt_unlabeled_ds.__len__()
print("totality={}".format(totality))
logger.info("Start training")
meters = MetricLogger(delimiter=" ")
start_training_time = time.time()
end = time.time()
final_acc = 0.
final_model = None
best_acc = 0.
best_model = None
all_epoch_result = []
all_selected_images = None
active_round = 1
ckt_path = os.path.join(cfg.OUTPUT_DIR, cfg.DATASET.NAME, task)
mkdir(ckt_path)
for epoch in range(1, cfg.TRAINER.MAX_EPOCHS + 1):
model.train()
for batch_idx in range(iter_per_epoch):
data_time = time.time() - end
if batch_idx % len(src_train_loader) == 0:
src_iter = iter(src_train_loader)
if batch_idx % len(tgt_unlabeled_loader) == 0:
tgt_unlabeled_iter = iter(tgt_unlabeled_loader)
if not tgt_selected_ds.empty:
if batch_idx % len(tgt_selected_loader) == 0:
tgt_selected_iter = iter(tgt_selected_loader)
src_data = src_iter.next()
tgt_unlabeled_data = tgt_unlabeled_iter.next()
src_img, src_lbl = src_data['img0'], src_data['label']
src_img, src_lbl = src_img.cuda(), src_lbl.cuda()
tgt_unlabeled_img = tgt_unlabeled_data['img']
tgt_unlabeled_img = tgt_unlabeled_img.cuda()
lr_sheduler.step()
optimizer.zero_grad()
total_loss = 0
# evidence deep learning loss on labeled source data
src_out = model(src_img, return_feat=False)
Loss_nll_s, Loss_KL_s = edl_criterion(src_out, src_lbl)
Loss_KL_s = Loss_KL_s / cfg.DATASET.NUM_CLASS
if cfg.DATASET.NAME == 'visda2017' and epoch > 1:
Loss_nll_s = 0.5 * Loss_nll_s
Loss_KL_s = 0.5 * Loss_KL_s
total_loss += Loss_nll_s
meters.update(Loss_nll_s=Loss_nll_s.item())
total_loss += Loss_KL_s
meters.update(Loss_KL_s=Loss_KL_s.item())
if cfg.TRAINER.BETA > 0:
# uncertainty reduction loss on unlabeled target data
tgt_unlabeled_out = model(tgt_unlabeled_img, return_feat=False)
alpha_t = torch.exp(tgt_unlabeled_out)
total_alpha_t = torch.sum(alpha_t, dim=1, keepdim=True) # total_alpha.shape: [B, 1]
expected_p_t = alpha_t / total_alpha_t
eps = 1e-7
point_entropy_t = - torch.sum(expected_p_t * torch.log(expected_p_t + eps), dim=1)
data_uncertainty_t = torch.sum((alpha_t / total_alpha_t) * (torch.digamma(total_alpha_t + 1) - torch.digamma(alpha_t + 1)), dim=1)
loss_Udis = torch.sum(point_entropy_t - data_uncertainty_t) / tgt_unlabeled_out.shape[0]
loss_Udata = torch.sum(data_uncertainty_t) / tgt_unlabeled_out.shape[0]
total_loss += cfg.TRAINER.BETA * loss_Udis
meters.update(loss_Udis=(loss_Udis).item())
total_loss += cfg.TRAINER.LAMBDA * loss_Udata
meters.update(loss_Udata=(loss_Udata).item())
# evidence deep learning loss on selected target data
if not tgt_selected_ds.empty:
tgt_selected_data = tgt_selected_iter.next()
tgt_selected_img, tgt_selected_lbl = tgt_selected_data['img0'], tgt_selected_data['label']
tgt_selected_img, tgt_selected_lbl = tgt_selected_img.cuda(), tgt_selected_lbl.cuda()
if tgt_selected_img.size(0) == 1:
# avoid bs=1, can't pass through BN layer
tgt_selected_img = torch.cat((tgt_selected_img, tgt_selected_img), dim=0)
tgt_selected_lbl = torch.cat((tgt_selected_lbl, tgt_selected_lbl), dim=0)
tgt_selected_out = model(tgt_selected_img, return_feat=False)
selected_Loss_nll_t, selected_Loss_KL_t = edl_criterion(tgt_selected_out, tgt_selected_lbl)
selected_Loss_KL_t = selected_Loss_KL_t / cfg.DATASET.NUM_CLASS
total_loss += selected_Loss_nll_t
meters.update(selected_Loss_nll_t=selected_Loss_nll_t.item())
total_loss += selected_Loss_KL_t
meters.update(selected_Loss_KL_t=selected_Loss_KL_t.item())
total_loss.backward()
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (iter_per_epoch * cfg.TRAINER.MAX_EPOCHS - batch_idx * epoch)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if batch_idx % cfg.TRAIN.PRINT_FREQ == 0:
logger.info(
meters.delimiter.join(
[
"eta: {eta}",
"task: {task}",
"epoch: {epoch}",
f"[iter: {batch_idx}/{iter_per_epoch}]",
"{meters}",
"max mem: {memory:.2f} GB",
]
).format(
task=task,
eta=eta_string,
epoch=epoch,
meters=str(meters),
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 / 1024.0,
)
)
# test every 5 epoch
if cfg.DATASET.NAME == 'visda2017':
if epoch % 2 == 0:
testacc = test(model, tgt_test_loader)
logger.info('Task: {} Test Epoch: {} testacc: {:.2f}'.format(task, epoch, testacc))
all_epoch_result.append({'epoch': epoch, 'acc': testacc})
if epoch == cfg.TRAINER.MAX_EPOCHS:
final_model = model.state_dict()
final_acc = testacc
if testacc > best_acc:
best_acc = testacc
if cfg.SAVE:
torch.save(model.state_dict(), os.path.join(ckt_path, "best_model_{}.pth".format(task)))
else:
if epoch % 5 == 0:
testacc = test(model, tgt_test_loader)
logger.info('Task: {} Test Epoch: {} testacc: {:.2f}'.format(task, epoch, testacc))
all_epoch_result.append({'epoch': epoch, 'acc': testacc})
if epoch == cfg.TRAINER.MAX_EPOCHS:
final_model = model.state_dict()
final_acc = testacc
if testacc > best_acc:
best_acc = testacc
if cfg.SAVE:
torch.save(model.state_dict(), os.path.join(ckt_path, "best_model_{}.pth".format(task)))
# active selection rounds
if epoch in cfg.TRAINER.ACTIVE_ROUND:
logger.info('Task: {} Active Epoch: {}'.format(task, epoch))
active_samples = DUC_active(tgt_unlabeled_loader_full=tgt_unlabeled_loader_full,
tgt_unlabeled_ds=tgt_unlabeled_ds,
tgt_selected_ds=tgt_selected_ds,
active_ratio=0.01,
totality=totality,
model=model,
cfg=cfg,
logger=logger)
active_round += 1
# record all selected target images
if all_selected_images is None:
all_selected_images = active_samples
else:
all_selected_images = np.concatenate((all_selected_images, active_samples), axis=0)
if all_selected_images is not None:
logger.info("totality*0.05={} all_selected_images.shape={}".format(totality * 0.05, all_selected_images.shape))
logger.info(all_selected_images)
# record all selected images
if cfg.SAVE:
# if all_selected_images is not None:
# torch.save(all_selected_images, os.path.join(ckt_path, "all_selected_images.pth"))
torch.save(final_model, os.path.join(ckt_path, "final_model_{}.pth".format(task)))
# record results for test epochs
best_acc = 0.0
best_epoch = 0
with open(os.path.join(ckt_path, 'all_epoch_result.csv'), 'w') as handle:
for i, rec in enumerate(all_epoch_result):
keys_list = list(rec.keys())
if rec[keys_list[1]] > best_acc:
best_acc = rec[keys_list[1]]
best_epoch = rec[keys_list[0]]
if i == 0:
handle.write(','.join(list(rec.keys())) + '\n')
line = [str(rec[key]) for key in rec.keys()]
handle.write(','.join(line) + '\n')
handle.write(','.join(['best epoch', 'best acc']) + '\n')
line = [str(best_epoch), str(best_acc)]
handle.write(','.join(line) + '\n')
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f} s / ep)".format(
total_time_str, total_training_time / cfg.TRAINER.MAX_EPOCHS
)
)
return task, final_acc, best_acc
def main():
parser = argparse.ArgumentParser(description='PyTorch Activate Domain Adaptation')
parser.add_argument('--cfg',
default='',
metavar='FILE',
help='path to config file',
type=str)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.cfg)
cfg.merge_from_list(args.opts)
output_dir = os.path.join(cfg.OUTPUT_DIR, cfg.DATASET.NAME)
if output_dir:
mkdir(output_dir)
logger = setup_logger("main", output_dir, 0, filename=cfg.LOG_NAME)
logger.info("PTL.version = {}".format(PIL.__version__))
logger.info("torch.version = {}".format(torch.__version__))
logger.info("torchvision.version = {}".format(torchvision.__version__))
logger.info("Loaded configuration file {}".format(args.cfg))
logger.info("Running with config:\n{}".format(cfg))
if cfg.SEED >= 0:
print('Setting fixed seed: {}'.format(cfg.SEED))
set_random_seed(cfg.SEED)
torch.multiprocessing.set_sharing_strategy('file_system')
cudnn.deterministic = True
cudnn.benchmark = True
all_task_result = []
for source in cfg.DATASET.SOURCE_DOMAINS:
for target in cfg.DATASET.TARGET_DOMAINS:
if source != target:
cfg.DATASET.SOURCE_TRAIN_DOMAIN = os.path.join(source + '_train.txt')
cfg.DATASET.TARGET_TRAIN_DOMAIN = os.path.join(target + '_train.txt')
cfg.DATASET.TARGET_VAL_DOMAIN = os.path.join(target + '_test.txt')
print("{}2{}: cfg.OPTIM.LR={}".format(source, target, cfg.OPTIM.LR))
logger.info("{}2{}: cfg.OPTIM.LR={}".format(source, target, cfg.OPTIM.LR))
cfg.freeze()
task, final_acc, best_acc = train(cfg, task=source + '2' + target)
all_task_result.append({'task': task, 'final_acc': final_acc, 'best_acc': best_acc})
cfg.defrost()
# record all results for all tasks
with open(os.path.join(output_dir, 'all_task_result.csv'), 'w') as handle:
for i, rec in enumerate(all_task_result):
if i == 0:
handle.write(','.join(list(rec.keys())) + '\n')
line = [str(rec[key]) for key in rec.keys()]
handle.write(','.join(line) + '\n')
if __name__ == '__main__':
main()