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| import os | |
| import importlib | |
| class DefaultEngineConfig(): | |
| def __init__(self, exp_name='default', model='aott'): | |
| model_cfg = importlib.import_module('configs.models.' + | |
| model).ModelConfig() | |
| self.__dict__.update(model_cfg.__dict__) # add model config | |
| self.EXP_NAME = exp_name + '_' + self.MODEL_NAME | |
| self.STAGE_NAME = 'YTB' | |
| self.DATASETS = ['youtubevos'] | |
| self.DATA_WORKERS = 8 | |
| self.DATA_RANDOMCROP = (465, | |
| 465) if self.MODEL_ALIGN_CORNERS else (464, | |
| 464) | |
| self.DATA_RANDOMFLIP = 0.5 | |
| self.DATA_MAX_CROP_STEPS = 10 | |
| self.DATA_SHORT_EDGE_LEN = 480 | |
| self.DATA_MIN_SCALE_FACTOR = 0.7 | |
| self.DATA_MAX_SCALE_FACTOR = 1.3 | |
| self.DATA_RANDOM_REVERSE_SEQ = True | |
| self.DATA_SEQ_LEN = 5 | |
| self.DATA_DAVIS_REPEAT = 5 | |
| self.DATA_RANDOM_GAP_DAVIS = 12 # max frame interval between two sampled frames for DAVIS (24fps) | |
| self.DATA_RANDOM_GAP_YTB = 3 # max frame interval between two sampled frames for YouTube-VOS (6fps) | |
| self.DATA_DYNAMIC_MERGE_PROB = 0.3 | |
| self.PRETRAIN = True | |
| self.PRETRAIN_FULL = False # if False, load encoder only | |
| self.PRETRAIN_MODEL = './data_wd/pretrain_model/mobilenet_v2.pth' | |
| # self.PRETRAIN_MODEL = './pretrain_models/mobilenet_v2-b0353104.pth' | |
| self.TRAIN_TOTAL_STEPS = 100000 | |
| self.TRAIN_START_STEP = 0 | |
| self.TRAIN_WEIGHT_DECAY = 0.07 | |
| self.TRAIN_WEIGHT_DECAY_EXCLUSIVE = { | |
| # 'encoder.': 0.01 | |
| } | |
| self.TRAIN_WEIGHT_DECAY_EXEMPTION = [ | |
| 'absolute_pos_embed', 'relative_position_bias_table', | |
| 'relative_emb_v', 'conv_out' | |
| ] | |
| self.TRAIN_LR = 2e-4 | |
| self.TRAIN_LR_MIN = 2e-5 if 'mobilenetv2' in self.MODEL_ENCODER else 1e-5 | |
| self.TRAIN_LR_POWER = 0.9 | |
| self.TRAIN_LR_ENCODER_RATIO = 0.1 | |
| self.TRAIN_LR_WARM_UP_RATIO = 0.05 | |
| self.TRAIN_LR_COSINE_DECAY = False | |
| self.TRAIN_LR_RESTART = 1 | |
| self.TRAIN_LR_UPDATE_STEP = 1 | |
| self.TRAIN_AUX_LOSS_WEIGHT = 1.0 | |
| self.TRAIN_AUX_LOSS_RATIO = 1.0 | |
| self.TRAIN_OPT = 'adamw' | |
| self.TRAIN_SGD_MOMENTUM = 0.9 | |
| self.TRAIN_GPUS = 4 | |
| self.TRAIN_BATCH_SIZE = 16 | |
| self.TRAIN_TBLOG = False | |
| self.TRAIN_TBLOG_STEP = 50 | |
| self.TRAIN_LOG_STEP = 20 | |
| self.TRAIN_IMG_LOG = True | |
| self.TRAIN_TOP_K_PERCENT_PIXELS = 0.15 | |
| self.TRAIN_SEQ_TRAINING_FREEZE_PARAMS = ['patch_wise_id_bank'] | |
| self.TRAIN_SEQ_TRAINING_START_RATIO = 0.5 | |
| self.TRAIN_HARD_MINING_RATIO = 0.5 | |
| self.TRAIN_EMA_RATIO = 0.1 | |
| self.TRAIN_CLIP_GRAD_NORM = 5. | |
| self.TRAIN_SAVE_STEP = 5000 | |
| self.TRAIN_MAX_KEEP_CKPT = 8 | |
| self.TRAIN_RESUME = False | |
| self.TRAIN_RESUME_CKPT = None | |
| self.TRAIN_RESUME_STEP = 0 | |
| self.TRAIN_AUTO_RESUME = True | |
| self.TRAIN_DATASET_FULL_RESOLUTION = False | |
| self.TRAIN_ENABLE_PREV_FRAME = False | |
| self.TRAIN_ENCODER_FREEZE_AT = 2 | |
| self.TRAIN_LSTT_EMB_DROPOUT = 0. | |
| self.TRAIN_LSTT_ID_DROPOUT = 0. | |
| self.TRAIN_LSTT_DROPPATH = 0.1 | |
| self.TRAIN_LSTT_DROPPATH_SCALING = False | |
| self.TRAIN_LSTT_DROPPATH_LST = False | |
| self.TRAIN_LSTT_LT_DROPOUT = 0. | |
| self.TRAIN_LSTT_ST_DROPOUT = 0. | |
| self.TEST_GPU_ID = 0 | |
| self.TEST_GPU_NUM = 1 | |
| self.TEST_FRAME_LOG = False | |
| self.TEST_DATASET = 'youtubevos' | |
| self.TEST_DATASET_FULL_RESOLUTION = False | |
| self.TEST_DATASET_SPLIT = 'val' | |
| self.TEST_CKPT_PATH = None | |
| # if "None", evaluate the latest checkpoint. | |
| self.TEST_CKPT_STEP = None | |
| self.TEST_FLIP = False | |
| self.TEST_MULTISCALE = [1] | |
| self.TEST_MAX_SHORT_EDGE = None | |
| self.TEST_MAX_LONG_EDGE = 800 * 1.3 | |
| self.TEST_WORKERS = 4 | |
| # GPU distribution | |
| self.DIST_ENABLE = True | |
| self.DIST_BACKEND = "nccl" # "gloo" | |
| self.DIST_URL = "tcp://127.0.0.1:13241" | |
| self.DIST_START_GPU = 0 | |
| def init_dir(self): | |
| self.DIR_DATA = '../VOS02/datasets'#'./datasets' | |
| self.DIR_DAVIS = os.path.join(self.DIR_DATA, 'DAVIS') | |
| self.DIR_YTB = os.path.join(self.DIR_DATA, 'YTB') | |
| self.DIR_STATIC = os.path.join(self.DIR_DATA, 'Static') | |
| self.DIR_ROOT = './'#'./data_wd/youtube_vos_jobs' | |
| self.DIR_RESULT = os.path.join(self.DIR_ROOT, 'result', self.EXP_NAME, | |
| self.STAGE_NAME) | |
| self.DIR_CKPT = os.path.join(self.DIR_RESULT, 'ckpt') | |
| self.DIR_EMA_CKPT = os.path.join(self.DIR_RESULT, 'ema_ckpt') | |
| self.DIR_LOG = os.path.join(self.DIR_RESULT, 'log') | |
| self.DIR_TB_LOG = os.path.join(self.DIR_RESULT, 'log', 'tensorboard') | |
| # self.DIR_IMG_LOG = os.path.join(self.DIR_RESULT, 'log', 'img') | |
| # self.DIR_EVALUATION = os.path.join(self.DIR_RESULT, 'eval') | |
| self.DIR_IMG_LOG = './img_logs' | |
| self.DIR_EVALUATION = './results' | |
| for path in [ | |
| self.DIR_RESULT, self.DIR_CKPT, self.DIR_EMA_CKPT, | |
| self.DIR_LOG, self.DIR_EVALUATION, self.DIR_IMG_LOG, | |
| self.DIR_TB_LOG | |
| ]: | |
| if not os.path.isdir(path): | |
| try: | |
| os.makedirs(path) | |
| except Exception as inst: | |
| print(inst) | |
| print('Failed to make dir: {}.'.format(path)) | |