steffen/Mel_Vision_Transformer_ComParE-21
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CCS intergration dataloader

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Steffen
2021-03-19 17:17:16 +01:00
parent 6ace861016
commit d4059779c4
8 changed files with 213 additions and 35 deletions
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33
multi_run.py
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@ -10,26 +10,27 @@ import itertools
if __name__ == '__main__':
# Set new values
hparams_dict = dict(seed=range(10),
hparams_dict = dict(seed=[69],
model_name=['VisualTransformer'],
batch_size=[50],
max_epochs=[250],
random_apply_chance=[0.3], # trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
loudness_ratio=[0], # trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
data_name=['CCSLibrosaDatamodule'],
batch_size=[5],
max_epochs=[200],
random_apply_chance=[0.5], # trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
loudness_ratio=[0.3], # trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
shift_ratio=[0.3], # trial.suggest_float('shift_ratio', 0.0, 0.5, step=0.1),
noise_ratio=[0.3], # trial.suggest_float('noise_ratio', 0.0, 0.5, step=0.1),
mask_ratio=[0.3], # trial.suggest_float('mask_ratio', 0.0, 0.5, step=0.1),
lr=[5e-3], # trial.suggest_uniform('lr', 1e-3, 3e-3),
lr=[1e-2], # trial.suggest_uniform('lr', 1e-3, 3e-3),
dropout=[0.2], # trial.suggest_float('dropout', 0.0, 0.3, step=0.05),
lat_dim=[32], # 2 ** trial.suggest_int('lat_dim', 1, 5, step=1),
mlp_dim=[16], # 2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
head_dim=[6], # 2 ** trial.suggest_int('head_dim', 1, 5, step=1),
lat_dim=[48], # 2 ** trial.suggest_int('lat_dim', 1, 5, step=1),
mlp_dim=[30], # 2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
head_dim=[12], # 2 ** trial.suggest_int('head_dim', 1, 5, step=1),
patch_size=[12], # trial.suggest_int('patch_size', 6, 12, step=3),
attn_depth=[10], # trial.suggest_int('attn_depth', 2, 14, step=4),
heads=[6], # trial.suggest_int('heads', 2, 16, step=2),
scheduler=['CosineAnnealingWarmRestarts'], # trial.suggest_categorical('scheduler', [None, 'LambdaLR']),
lr_scheduler_parameter=[25], # [0.98],
embedding_size=[30], # trial.suggest_int('embedding_size', 12, 64, step=12),
attn_depth=[12], # trial.suggest_int('attn_depth', 2, 14, step=4),
heads=[12], # trial.suggest_int('heads', 2, 16, step=2),
scheduler=['LambdaLR'], # trial.suggest_categorical('scheduler', [None, 'LambdaLR']),
lr_scheduler_parameter=[0.95], # [0.98],
embedding_size=[64], # trial.suggest_int('embedding_size', 12, 64, step=12),
loss=['ce_loss'],
sampler=['WeightedRandomSampler'],
# rial.suggest_categorical('sampler', [None, 'WeightedRandomSampler']),
@ -40,7 +41,7 @@ if __name__ == '__main__':
permutations_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)]
for permutations_dict in tqdm(permutations_dicts, total=len(permutations_dicts)):
# Parse comandline args, read config and get model
cmd_args, found_data_class, found_model_class = parse_comandline_args_add_defaults(
cmd_args, *data_model_seed = parse_comandline_args_add_defaults(
'_parameters.ini', overrides=permutations_dict)
hparams = dict(**cmd_args)
@ -50,6 +51,6 @@ if __name__ == '__main__':
# RUN
# ---------------------------------------
print(f'Running Loop, parameters are: {permutations_dict}')
run_lightning_loop(hparams, found_data_class, found_model_class)
run_lightning_loop(hparams, *data_model_seed)
print(f'Done, parameters were: {permutations_dict}')
pass