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Refactoring of learning code #1

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Refactoring of learning code #1

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f5ccdab
Added code to allow dataset as filename
milosobral Jan 31, 2022
b832593
Moved dataset related classes to a separate file
milosobral Jan 31, 2022
b280608
Added new file with Neural Network utils
milosobral Jan 31, 2022
0ec590d
More refactoring
milosobral Jan 31, 2022
d6498d5
Added new dataset config dictionary
milosobral Feb 1, 2022
36f14ab
Moved default configs to a better place, added json option for NN con…
milosobral Feb 1, 2022
7a32071
Added experiment config dict for hyperparameters
milosobral Feb 1, 2022
7041220
Minor details
milosobral Feb 1, 2022
6a9d275
Changed save config workings, fixed some minor issues
milosobral Feb 1, 2022
26ea691
fixed small mistake in argument parsing
milosobral Feb 1, 2022
574f657
fixed error with wandb
milosobral Feb 1, 2022
618466a
Fixed some missing references
milosobral Feb 2, 2022
0008d14
filename changes, added colab link
milosobral Feb 2, 2022
efc3980
removed old dead code
milosobral Feb 2, 2022
fc0327b
changed naming of file in pareto search imports
milosobral Feb 2, 2022
5f00482
Changed all variable names, fixed pareto search with refactored code
milosobral Feb 2, 2022
52a4af8
Fixed pareto network to comply to new configurations
milosobral Feb 3, 2022
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1 change: 0 additions & 1 deletion portiloop_software/__init__.py
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Original file line number Diff line number Diff line change
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from portiloop_software.portiloop_python.ANN.portiloop_detector_training import run_offline_unlabelled, get_final_model_config_dict
98 changes: 98 additions & 0 deletions portiloop_software/portiloop_python/ANN/configs.py
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def get_nn_dict(config_name, ablation, index, split_i):

if config_name == 'spindle_net':
return {'experiment_name': f'spindleNet_{index}', 'device_train': 'cuda:0', 'device_val':
'cuda:0', 'nb_epoch_max': 500,
'max_duration': 257400, 'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250,
'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': True, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0, 'classification': True,
'reg_balancing': 'none',
'nb_conv_layers': 5,
'seq_len': 50, 'nb_channel': 40, 'hidden_size': 100, 'seq_stride_s': 0.004, 'nb_rnn_layers': 1, 'RNN': True,
'envelope_input': True,
"batch_size": 20, "lr_adam": 0.0009,
'window_size_s': 0.250, 'stride_pool': 1, 'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 5,
'dilation_conv': 1, 'dilation_pool': 1, 'nb_out': 2, 'time_in_past': 1.55, 'estimator_size_memory': 139942400}
# put LSTM and Softmax for the occasion and add padding, not exactly the same frequency (spindleNet = 200 Hz)

elif config_name == 'ablation_portiloop':
return {'experiment_name': f'ABLATION_{ablation}_test_v11_implemented_on_portiloop_{index}', 'device_train': 'cuda:0', 'device_val':
'cuda:0', 'nb_epoch_max': 500,
'max_duration': 257400, 'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250,
'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': False, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0, 'classification': True,
'reg_balancing': 'none',
'nb_conv_layers': 4,
'seq_len': 50, 'nb_channel': 26, 'hidden_size': 7, 'seq_stride_s': 0.044, 'nb_rnn_layers': 2, 'RNN': True,
'envelope_input': True,
"batch_size": 256, "lr_adam": 0.0009,
'window_size_s': 0.234, 'stride_pool': 1, 'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 9,
'dilation_conv': 1, 'dilation_pool': 1, 'nb_out': 2, 'time_in_past': 1.55, 'estimator_size_memory': 139942400,
'split_idx': split_i, 'validation_network_stride': 1}
elif config_name == 'pareto_search_small_seq':
return {'experiment_name': f'pareto_search_15_35_v5_small_seq_{index}', 'device_train': 'cuda:0', 'device_val': 'cuda:0', 'nb_epoch_max': 150,
'max_duration':
257400,
'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250, 'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': False, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0, 'classification': True,
'reg_balancing': 'none',
'split_idx': split_i, 'validation_network_stride': 1, 'nb_conv_layers': 3, 'seq_len': 50, 'nb_channel': 31, 'hidden_size': 7,
'seq_stride_s': 0.02,
'nb_rnn_layers': 1, 'RNN': True, 'envelope_input': False, 'lr_adam': 0.0005, 'batch_size': 256, 'window_size_s': 0.218,
'stride_pool': 1,
'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 7, 'dilation_conv': 1, 'dilation_pool': 1, 'nb_out': 18, 'time_in_past': 8.5,
'estimator_size_memory': 188006400}
elif config_name == '2_inputs_network':
return {'experiment_name': f'ABLATION_{ablation}_2inputs_network_{index}', 'device_train': 'cuda:0', 'device_val':
'cuda:0', 'nb_epoch_max': 500,
'max_duration': 257400, 'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250,
'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': False, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0, 'classification': True,
'reg_balancing': 'none',
'nb_conv_layers': 4,
'seq_len': 50, 'nb_channel': 26, 'hidden_size': 7, 'seq_stride_s': 0.044, 'nb_rnn_layers': 2, 'RNN': True,
'envelope_input': True,
"batch_size": 256, "lr_adam": 0.0009,
'window_size_s': 0.234, 'stride_pool': 1, 'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 9,
'dilation_conv': 1, 'dilation_pool': 1, 'nb_out': 2, 'time_in_past': 1.55, 'estimator_size_memory': 139942400,
'split_idx': split_i, 'validation_network_stride': 1}
elif config_name == 'pareto_search_v6':
return {'experiment_name': f'pareto_search_15_35_v6_{index}', 'device_train': 'cpu', 'device_val': 'cpu', 'nb_epoch_max': 500,
'max_duration':
257400,
'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250, 'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': False, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0, 'classification': True,
'reg_balancing': 'none',
'split_idx': split_i, 'validation_network_stride': 1, 'nb_conv_layers': 3, 'seq_len': 50, 'nb_channel': 31, 'hidden_size': 7,
'seq_stride_s': 0.17,
'nb_rnn_layers': 1, 'RNN': True, 'envelope_input': False, 'lr_adam': 0.0005, 'batch_size': 256, 'window_size_s': 0.218,
'stride_pool': 1,
'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 7, 'dilation_conv': 1, 'dilation_pool': 1, 'nb_out': 18, 'time_in_past': 8.5,
'estimator_size_memory': 188006400}
elif config_name == 'pareto_search_v4':
return {'experiment_name': f'pareto_search_15_35_v4_{index}', 'device_train': 'cpu', 'device_val': 'cpu',
'device_inference': 'cpu', 'nb_epoch_max': 150, 'max_duration': 257400,
'nb_epoch_early_stopping_stop': 100, 'early_stopping_smoothing_factor': 0.1, 'fe': 250,
'nb_batch_per_epoch': 1000,
'first_layer_dropout': False,
'power_features_input': False, 'dropout': 0.5, 'adam_w': 0.01, 'distribution_mode': 0,
'classification': True,
'reg_balancing': 'none',
'split_idx': split_i, 'validation_network_stride': 1, 'nb_conv_layers': 3, 'seq_len': 50,
'nb_channel': 31, 'hidden_size': 7,
'seq_stride_s': 0.170,
'nb_rnn_layers': 1, 'RNN': True, 'envelope_input': False, 'lr_adam': 0.0005, 'batch_size': 256,
'window_size_s': 0.218,
'stride_pool': 1,
'stride_conv': 1, 'kernel_conv': 7, 'kernel_pool': 7, 'dilation_conv': 1, 'dilation_pool': 1,
'nb_out': 18, 'time_in_past': 8.5,
'estimator_size_memory': 188006400}
else:
raise ValueError(
'Given config dict must either be a json file with .json extension or one of the default names of configuration.')
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