97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
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import os
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import wandb
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def parse_config(cfg):
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modality = get_modality(cfg)
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cfg_filename = os.path.basename(cfg.OUTPUT_DIR)
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os.environ["WANDB__SERVICE_WAIT"] = "3000"
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cfg_filename_abbr = cfg_filename.split('_1x_')[-1]
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wandb_postfix = generate_wandb_postfix(cfg, modality)
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scheduler_name = cfg.SOLVER.SCHEDULER.SCHEDULER_NAME
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learning_rate = cfg.SOLVER.BASE_LR
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weight_decay = cfg.SOLVER.WEIGHT_DECAY
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cfg_filename = cfg_filename.replace('long_512', f'long_{cfg.MODEL.LSTR.LONG_MEMORY_SECONDS}')
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cfg_filename = cfg_filename.replace('work_8', f'work_{cfg.MODEL.LSTR.WORK_MEMORY_SECONDS}')
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cfg.OUTPUT_DIR = os.path.join(os.path.dirname(cfg.OUTPUT_DIR), cfg_filename)
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if cfg.SAVE_HPARAM:
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# save different dir containing hparam
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hparam_info = '_'.join([f"lr_{learning_rate}", f"wd_{weight_decay}", f"schd_{scheduler_name}"])
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cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR, hparam_info, modality)
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print(f"HPARAM: {hparam_info}")
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print(f"cfg.OUTPUT_DIR: {cfg.OUTPUT_DIR}")
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wandb_name = cfg_filename_abbr + '_' + wandb_postfix
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if cfg.SAVE_HPARAM:
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wandb_name += '_' + '_'.join([f"lr_{learning_rate}", f"wd_{weight_decay}", f"schd_{scheduler_name}"])
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if cfg.USE_WANDB:
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print("*** init wandb ***")
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print(f"wandb_name: {wandb_name}")
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def get_modality(cfg):
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modality = ''
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if 'motion' in cfg.INPUT.MODALITY:
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modality += 'A'
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if 'visual' in cfg.INPUT.MODALITY:
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modality += 'V'
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if 'object' in cfg.INPUT.MODALITY:
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modality += 'T'
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elif cfg['INPUT']['MODALITY'] == 'twostream':
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modality = 'AV'
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if modality == '':
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raise ValueError(f"Unknown modality: {cfg['INPUT']['MODALITY']}")
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return modality
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def generate_wandb_postfix(cfg, modality):
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visual_feature_type = '_'.join(cfg['INPUT']['VISUAL_FEATURE'].split('_')[1:])
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audio_feature_type = '_'.join(cfg['INPUT']['MOTION_FEATURE'].split('_')[1:])
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text_feature_type = '_'.join(cfg['INPUT']['OBJECT_FEATURE'].split('_')[1:])
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if cfg.MODEL.LSTR.LONG_MEMORY_USE_PE == True:
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model_name = 'LSTR_TESTRA'
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else:
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model_name = 'TESTRA'
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if '_aa' in cfg.OUTPUT_DIR:
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model_name += '_aa'
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name_parts = [
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model_name,
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cfg['DATA']['DATA_NAME'],
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modality,
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# visual_feature_type,
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# audio_feature_type,
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# text_feature_type,
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# f"epoch_{cfg['SOLVER']['NUM_EPOCHS']}"
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]
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if cfg['MODEL']['CHECKPOINT']:
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name_parts.append(f"ckpt_{os.path.basename(cfg['MODEL']['CHECKPOINT']).split('.')[0]}")
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return '_'.join(name_parts)
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def get_wandb_name(cfg, cfg_filename, modality):
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cfg_filename_abbr = cfg_filename.split('_1x_')[-1]
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wandb_postfix = generate_wandb_postfix(cfg, modality)
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scheduler_name = cfg.SOLVER.SCHEDULER.SCHEDULER_NAME
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learning_rate = cfg.SOLVER.BASE_LR
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weight_decay = cfg.SOLVER.WEIGHT_DECAY
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wandb_name = cfg_filename_abbr + '_' + wandb_postfix + '_' + '_'.join([f"lr_{learning_rate}", f"wd_{weight_decay}", f"schd_{scheduler_name}"])
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return wandb_name
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def validate_cfg(cfg):
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if cfg.DATA.NUM_CLASSES != len(cfg.DATA.CLASS_NAMES):
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print(f"len(cfg.DATA.CLASS_NAMES): {len(cfg.DATA.CLASS_NAMES)} is not equal to cfg.DATA.NUM_CLASSES: {cfg.DATA.NUM_CLASSES}")
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cfg.DATA.CLASS_NAMES = cfg.DATA.CLASS_NAMES[:cfg.DATA.NUM_CLASSES]
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print(f"Adjusted cfg.DATA.CLASS_NAMES: {cfg.DATA.CLASS_NAMES}")
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