def __init__(
self,
cfg_file_path,
model_weight_url,
detect_rate,
common_cate,
device,
exclude_class=[],
):
# TODO: add exclude class
cfg = base_cfg.clone()
cfg.merge_from_file(cfg_file_path)
cfg.MODEL.WEIGHT = model_weight_url
cfg.IA_STRUCTURE.MEMORY_RATE *= detect_rate
if common_cate:
cfg.MODEL.ROI_ACTION_HEAD.NUM_CLASSES = 15
cfg.MODEL.ROI_ACTION_HEAD.NUM_PERSON_MOVEMENT_CLASSES = 6
cfg.MODEL.ROI_ACTION_HEAD.NUM_OBJECT_MANIPULATION_CLASSES = 5
cfg.MODEL.ROI_ACTION_HEAD.NUM_PERSON_INTERACTION_CLASSES = 4
cfg.freeze()
self.cfg = cfg
self.model = build_detection_model(cfg)
self.model.eval()
self.model.to(device)
save_dir = cfg.OUTPUT_DIR
checkpointer = ActionCheckpointer(cfg, self.model, save_dir=save_dir)
self.mem_pool = MemoryPool()
self.object_pool = MemoryPool()
self.timestamps = []
_ = checkpointer.load(cfg.MODEL.WEIGHT)
self.transforms, self.person_transforms, self.object_transforms = self.build_transform(
)
self.device = device
self.cpu_device = torch.device("cpu")
self.exclude_class = exclude_class
def main():
args = parse_args()
update_config(cfg, args)
logger, final_output_dir, tb_log_dir = create_logger(
cfg, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(cfg)
t_checkpoints = cfg.KD.TEACHER #注意是在student配置文件中修改
train_type = cfg.KD.TRAIN_TYPE #注意是在student配置文件中修改
train_type = get_train_type(train_type, t_checkpoints)
logger.info('=> train type is {} '.format(train_type))
if train_type == 'FPD':
cfg_name = 'student_' + os.path.basename(args.cfg).split('.')[0]
else:
cfg_name = os.path.basename(args.cfg).split('.')[0]
save_yaml_file(cfg_name, cfg, final_output_dir)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
model = eval('models.' + cfg.MODEL.NAME + '.get_pose_net')(cfg,
is_train=True)
# fpd method, default NORMAL
if train_type == 'FPD':
tcfg = cfg.clone()
tcfg.defrost()
tcfg.merge_from_file(args.tcfg)
tcfg.freeze()
tcfg_name = 'teacher_' + os.path.basename(args.tcfg).split('.')[0]
save_yaml_file(tcfg_name, tcfg, final_output_dir)
# teacher model
tmodel = eval('models.' + tcfg.MODEL.NAME + '.get_pose_net')(
tcfg, is_train=False)
load_checkpoint(t_checkpoints,
tmodel,
strict=True,
model_info='teacher_' + tcfg.MODEL.NAME)
tmodel = torch.nn.DataParallel(tmodel, device_ids=cfg.GPUS).cuda()
# define kd_pose loss function (criterion) and optimizer
kd_pose_criterion = JointsMSELoss(
use_target_weight=tcfg.LOSS.USE_TARGET_WEIGHT).cuda()
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'),
final_output_dir)
# logger.info(pprint.pformat(model))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model, (dump_input, ))
logger.info(get_model_summary(model, dump_input))
if cfg.TRAIN.CHECKPOINT:
load_checkpoint(cfg.TRAIN.CHECKPOINT,
model,
strict=True,
model_info='student_' + cfg.MODEL.NAME)
model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()
# you can choose or replace pose_loss and kd_pose_loss type, including mse,kl,ohkm loss ect
# define pose loss function (criterion) and optimizer
pose_criterion = JointsMSELoss(
use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda()
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
best_perf = 0.0
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.TRAIN.LR_STEP,
cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
# evaluate on validation set
validate(cfg, valid_loader, valid_dataset, tmodel, pose_criterion,
final_output_dir, tb_log_dir, writer_dict)
validate(cfg, valid_loader, valid_dataset, model, pose_criterion,
final_output_dir, tb_log_dir, writer_dict)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
lr_scheduler.step()
# fpd method, default NORMAL
if train_type == 'FPD':
# train for one epoch
fpd_train(cfg, train_loader, model, tmodel, pose_criterion,
kd_pose_criterion, optimizer, epoch, final_output_dir,
tb_log_dir, writer_dict)
else:
# train for one epoch
train(cfg, train_loader, model, pose_criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
# evaluate on validation set
perf_indicator = validate(cfg, valid_loader, valid_dataset, model,
pose_criterion, final_output_dir, tb_log_dir,
writer_dict)
if perf_indicator >= best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir, 'final_state.pth')
logger.info(
'=> saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
import pprint
from config import cfg
if __name__ == "__main__":
cfg.merge_from_file("config.yaml")
cfg.freeze()
cfg2 = cfg.clone()
cfg2.defrost()
cfg2.TRAIN.SCALES = (8, 16, 32)
cfg2.freeze()
print("cfg:")
pprint.pprint(cfg)
print("cfg2:")
pprint.pprint(cfg2)
os.makedirs(output_base_path, exist_ok=True)
print("Moving dataset to target directory...")
train_path = utils.abs_or_offset_from(cfg.train_data.loader.path,
cfg_dir)
val_path = utils.abs_or_offset_from(cfg.valid_data.loader.path,
cfg_dir)
test_path = utils.abs_or_offset_from(cfg.test_data.loader.path,
cfg_dir)
shutil.copy(train_path, output_base_path)
if train_path != val_path:
shutil.copy(val_path, output_base_path)
if test_path != val_path:
shutil.copy(test_path, output_base_path)
cfg_new = cfg.clone()
cfg_new.train_data.loader.path = os.path.basename(
cfg.train_data.loader.path)
cfg_new.valid_data.loader.path = os.path.basename(
cfg.valid_data.loader.path)
cfg_new.test_data.loader.path = os.path.basename(
cfg.test_data.loader.path)
cfg_new.output.base_path = "."
cfg_new.skip_copy = True
print("Writing new configuration file...")
with open(os.path.join(output_base_path, "config.yaml"), "w") as f:
f.write(cfg_new.dump())
print("Writing normallization file...")
norm_node = CfgNode()