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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))
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