The default branch to run experiments of shallownet and eegnet.

.gitignore

@@ -153,9 +153,15 @@ dmypy.json
 
 # Results folders
 **/results/
+**/data
+**/wandb
 
 # Log folders
 **/log/
 
 # Mac OS
-.DS_Store
+.DS_Store
+
+EEGNet_BNCI2014001_*
+ShallowConvNet_BNCI2014001_hopt*
+finetune_*

benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/EEGNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 862 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2014001"
+prefix_name: 'EEGNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/BNCI2014001/ShallowConvNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 1 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 881 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2014001"
+prefix_name: 'ShallowNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/EEGNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 2 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 796 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100 
 lr: 0.0001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2014004"
+prefix_name: 'EEGNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/BNCI2014004/ShallowConvNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 11 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 262 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100 
 lr: 0.005 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2014004"
+prefix_name: 'ShallowNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/EEGNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 760 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2015001"
+prefix_name: 'EEGNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/BNCI2015001/ShallowConvNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 9 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 976 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "BNCI2015001"
+prefix_name: 'ShallowNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/EEGNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 15 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 821 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "Lee2019_MI"
+prefix_name: 'EEGNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/Lee2019_MI/ShallowConvNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 12 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 922 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "Lee2019_MI"
+prefix_name: 'ShallowNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/Zhou2016/EEGNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 10 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 741 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "Zhou2016"
+prefix_name: 'EEGNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/hparams/MotorImagery/Zhou2016/ShallowConvNet.yaml

@@ -47,12 +47,12 @@ cm: !name:sklearn.metrics.confusion_matrix
 metrics:
     f1: !ref <f1>
     acc: !ref <acc>
-    cm: !ref <cm>
+    # cm: !ref <cm>
 # TRAINING HPARS
 n_train_examples: 100  # it will be replaced in the train script
 # checkpoints to average
 avg_models: 13 # @orion_step1: --avg_models~"uniform(1, 15,discrete=True)"
-number_of_epochs: 955 # @orion_step1: --number_of_epochs~"uniform(250, 1000, discrete=True)"
+number_of_epochs: 100
 lr: 0.001 # @orion_step1: --lr~"choices([0.01, 0.005, 0.001, 0.0005, 0.0001])"
 # Learning rate scheduling (cyclic learning rate is used here)
 max_lr: !ref <lr> # Upper bound of the cycle (max value of the lr)
@@ -120,6 +120,13 @@ augment: !new:speechbrain.augment.augmenter.Augmenter
         !ref <time_shift>,
         !ref <add_noise_white>]
 
+# WEIGHTS AND BIASES Configuration
+project: "Zhou2016"
+prefix_name: 'ShallowNet'
+mode: "disabled"
+entity: "neuro-gflow"
+sweep_run: False # to indicate if you are running sweeps or normal wandb runs
+
 # DATA NORMALIZATION
 dims_to_normalize: 1 # 1 (time) or 2 (EEG channels)
 normalize: !name:speechbrain.processing.signal_processing.mean_std_norm

benchmarks/MOABB/run_hparam_opt_EEGNet.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+#SBATCH --job-name=finetune
+#SBATCH --output=finetune_output_%j.txt
+#SBATCH --error=finetune_error_%j.txt
+#SBATCH --ntasks=1
+#SBATCH --time=5:00:00
+#SBATCH --partition=unkillable
+#SBATCH --cpus-per-task=6
+#SBATCH --mem=32G
+#SBATCH --gres=gpu:a100l:1
+
+bash run_hparam_optimization.sh --exp_name 'EEGNet_BNCI2014001_hopt' \
+                            --output_folder results/BNCI2014001/EEGNet/hopt \
+                            --data_folder data \
+                            --cached_data_folder data \
+                            --hparams hparams/MotorImagery/BNCI2014001/EEGNet.yaml \
+                            --nsbj_hpsearch 9 \
+                            --nsess_hpsearch 2 \
+                            --nsbj 9 \
+                            --nsess 2 \
+                            --nruns 1 \
+                            --nruns_eval 10 \
+                            --eval_metric acc \
+                            --train_mode leave-one-subject-out \
+                            --exp_max_trials 50 \
+                            --store_all True \
+                            --device 'cuda' \
+                            --project 'EEGNet_BNCI2014001_hopt' \
+                            --mode 'online'

benchmarks/MOABB/run_hparam_opt_shallownet.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+#SBATCH --job-name=finetune
+#SBATCH --output=finetune_output_%j.txt
+#SBATCH --error=finetune_error_%j.txt
+#SBATCH --ntasks=1
+#SBATCH --time=10:00:00
+#SBATCH --partition=main
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=48G
+#SBATCH --gres=gpu:a100l:1
+
+bash run_hparam_optimization.sh --exp_name 'ShallowConvNet_BNCI2014001_hopt' \
+                            --output_folder results/BNCI2014001/ShallowConvNet/hopt \
+                            --data_folder data \
+                            --cached_data_folder data \
+                            --hparams hparams/MotorImagery/BNCI2014001/ShallowConvNet.yaml \
+                            --nsbj_hpsearch 9 \
+                            --nsess_hpsearch 2 \
+                            --nsbj 9 \
+                            --nsess 2 \
+                            --nruns 1 \
+                            --nruns_eval 10 \
+                            --eval_metric acc \
+                            --train_mode leave-one-subject-out \
+                            --exp_max_trials 50 \
+                            --store_all True \
+                            --device 'cuda' \
+                            --project 'ShallowConvNet_BNCI2014001_hopt' \
+                            --mode 'online'