77 lines
3.6 KiB
Python
77 lines
3.6 KiB
Python
# Imports
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# =============================================================================
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from _paramters import main_arg_parser
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from main import run_lightning_loop
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import warnings
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from ml_lib.utils.config import Config
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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if __name__ == '__main__':
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args = main_arg_parser.parse_args()
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# Model Settings
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config = Config().read_namespace(args)
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arg_dict = dict()
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for seed in range(1):
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arg_dict.update(main_seed=seed)
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if False:
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for patch_size in [3, 5 , 9]:
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for model in ['SequentialVisualTransformer']:
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arg_dict.update(model_type=model, model_patch_size=patch_size)
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raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0,
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data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
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data_stretch=False, train_epochs=401)
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all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
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data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
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data_stretch=True, train_epochs=101)
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speed_conf = raw_conf.copy()
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speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
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data_stretch=True, train_epochs=101)
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mask_conf = raw_conf.copy()
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mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101)
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noise_conf = raw_conf.copy()
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noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101)
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shift_conf = raw_conf.copy()
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shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101)
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loudness_conf = raw_conf.copy()
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loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101)
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for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]:
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arg_dict.update(dicts)
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if True:
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for patch_size in [3, 7]:
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for lat_dim in [4, 32]:
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for heads in [2, 4]:
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for embedding_size in [32, 64]:
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for attn_depth in [1, 3]:
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for model in ['SequentialVisualTransformer', 'VisualTransformer']:
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arg_dict.update(
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model_type=model,
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model_patch_size=patch_size,
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model_lat_dim=lat_dim,
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model_heads=heads,
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model_embedding_size=embedding_size,
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model_attn_depth=attn_depth
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)
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config = config.update(arg_dict)
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version_path = config.exp_path / config.version
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if version_path.exists():
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if not (version_path / 'weights.ckpt').exists():
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shutil.rmtree(version_path)
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else:
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continue
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run_lightning_loop(config)
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