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Experiment parameters

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steffen 2020-11-23 07:40:43 +01:00
parent a079a196af
commit 98ccecac04
1 changed files with 52 additions and 38 deletions
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multi_run.py
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@ -1,4 +1,8 @@
import shutil # Imports
# =============================================================================
from _paramters import main_arg_parser
from main import run_lightning_loop
import warnings import warnings
from ml_lib.utils.config import Config from ml_lib.utils.config import Config
@ -6,12 +10,6 @@ from ml_lib.utils.config import Config
warnings.filterwarnings('ignore', category=FutureWarning) warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning) warnings.filterwarnings('ignore', category=UserWarning)
# Imports
# =============================================================================
from main import run_lightning_loop
from _paramters import main_arg_parser
if __name__ == '__main__': if __name__ == '__main__':
@ -20,43 +18,59 @@ if __name__ == '__main__':
config = Config().read_namespace(args) config = Config().read_namespace(args)
arg_dict = dict() arg_dict = dict()
for seed in range(0, 3): for seed in range(1):
arg_dict.update(main_seed=seed) arg_dict.update(main_seed=seed)
for patch_size in [3, 5 , 9]: if False:
for model in ['SequentialVisualTransformer']: for patch_size in [3, 5 , 9]:
arg_dict.update(model_type=model, model_patch_size=patch_size) for model in ['SequentialVisualTransformer']:
raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0, arg_dict.update(model_type=model, model_patch_size=patch_size)
data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0, raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0,
data_stretch=False, train_epochs=401) data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
data_stretch=False, train_epochs=401)
all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7, all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4, data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
data_stretch=True, train_epochs=101) data_stretch=True, train_epochs=101)
speed_conf = raw_conf.copy() speed_conf = raw_conf.copy()
speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7, speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
data_stretch=True, train_epochs=101) data_stretch=True, train_epochs=101)
mask_conf = raw_conf.copy() mask_conf = raw_conf.copy()
mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101) mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101)
noise_conf = raw_conf.copy() noise_conf = raw_conf.copy()
noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101) noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101)
shift_conf = raw_conf.copy() shift_conf = raw_conf.copy()
shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101) shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101)
loudness_conf = raw_conf.copy() loudness_conf = raw_conf.copy()
loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101) loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101)
for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]: for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]:
arg_dict.update(dicts) arg_dict.update(dicts)
config = config.update(arg_dict) if True:
version_path = config.exp_path / config.version for patch_size in [3, 7]:
if version_path.exists(): for lat_dim in [4, 32]:
if not (version_path / 'weights.ckpt').exists(): for heads in [2, 4]:
shutil.rmtree(version_path) for embedding_size in [32, 64]:
else: for attn_depth in [1, 3]:
continue for model in ['SequentialVisualTransformer', 'VisualTransformer']:
run_lightning_loop(config) arg_dict.update(
model_type=model,
model_patch_size=patch_size,
model_lat_dim=lat_dim,
model_heads=heads,
model_embedding_size=embedding_size,
model_attn_depth=attn_depth
)
config = config.update(arg_dict)
version_path = config.exp_path / config.version
if version_path.exists():
if not (version_path / 'weights.ckpt').exists():
shutil.rmtree(version_path)
else:
continue
run_lightning_loop(config)