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Functions107 in github.com/dilabproj/PAW

⨍ Functions 107◇ Types & classes 33

↓ 6 callersMethod__init__

(self, n_in_chans,n_blocks, n_filters, kernel_size, drop_prob, activation='relu')

Code/PAW/Adaptation Phase/model_eegtcnet.py:109

↓ 6 callersMethod__init__

(self, n_in_chans,n_blocks, n_filters, kernel_size, drop_prob, activation='relu')

Code/PAW/Training Phase/model_eegtcnet.py:106

↓ 4 callersMethod__init__

( self, in_chans, n_classes, input_window_samples, pool_mode="mean",

Code/PAW/Adaptation Phase/model_EEGNet.py:18

↓ 3 callersFunctionfixed_random_seed

Code/PAW/Training Phase/utils.py:13

↓ 3 callersFunctionget_dataset_setting

Code/PAW/Training Phase/utils.py:20

↓ 3 callersFunctionget_source_repre

Code/PAW/Adaptation Phase/utils.py:144

↓ 2 callersMethod__init__

( self, in_chans, n_classes, input_window_samples, pool_mode="mean",

Code/PAW/Training Phase/model_EEGNet.py:6

↓ 2 callersFunction_glorot_weight_zero_bias

Initalize parameters of all modules by initializing weights with glorot uniform/xavier initialization, and setting biases to zero. Weights fr

Code/PAW/Training Phase/model_eegtcnet.py:253

↓ 2 callersMethodforward

Code/PAW/Adaptation Phase/model_EEGNet.py:79

↓ 2 callersFunctionmetrics_computation

(x, y, model, args)

Code/PAW/Training Phase/utils.py:162

↓ 2 callersFunctionprepare_features

(path,subject, training, start_t, end_t, reject_artifact=False)

Code/Get Data/BCIIV2a/2a_raw_to_saved_data.py:62

↓ 1 callersMethod__init__

(self, args , apply_wn=False)

Code/PAW/Adaptation Phase/network.py:7

↓ 1 callersFunction_glorot_weight_zero_bias

Initalize parameters of all modules by initializing weights with glorot uniform/xavier initialization, and setting biases to zero. Weights fr

Code/PAW/Adaptation Phase/model_eegtcnet.py:255

↓ 1 callersFunction_glorot_weight_zero_bias

Initalize parameters of all modules by initializing weights with glorot uniform/xavier initialization, and setting biases to zero. Weights fr

Code/PAW/Adaptation Phase/model_EEGNet.py:96

↓ 1 callersFunction_glorot_weight_zero_bias

Initalize parameters of all modules by initializing weights with glorot uniform/xavier initialization, and setting biases to zero. Weights fr

Code/PAW/Training Phase/model_EEGNet.py:80

↓ 1 callersFunctioncal_acc_multi

(loader, netF_list, netB_list, netC_list, netQ, args)

Code/PAW/Adaptation Phase/adaptation.py:341

↓ 1 callersFunctionfixed_random_seed

Code/PAW/Adaptation Phase/utils.py:9

↓ 1 callersMethodforward

Forward pass. Parameters ---------- x: torch.Tensor Batch of EEG windows of shape (batch_size, n_channels, n_time

Code/PAW/Adaptation Phase/model_eegtcnet.py:130

↓ 1 callersMethodforward

Forward pass. Parameters ---------- x: torch.Tensor Batch of EEG windows of shape (batch_size, n_channels, n_time

Code/PAW/Training Phase/model_eegtcnet.py:128

↓ 1 callersMethodforward

Code/PAW/Training Phase/model_EEGNet.py:67

↓ 1 callersFunctionget_data

(data_file_dir, labels_file_dir)

Code/Get Data/BCIIV2b/2b_raw_to_saved_data.py:55

↓ 1 callersFunctionget_data

Loads the dataset 2a of the BCI Competition IV available on http://bnci-horizon-2020.eu/database/data-sets Keyword arguments: subject --

Code/Get Data/BCIIV2a/2a_raw_to_saved_data.py:5

↓ 1 callersFunctionget_dataset_setting

Code/PAW/Adaptation Phase/utils.py:16

↓ 1 callersFunctionget_lambda

(epoch, max_epoch)

Code/PAW/Training Phase/utils.py:175

↓ 1 callersFunctionget_model

Code/PAW/Adaptation Phase/utils.py:119

↓ 1 callersFunctionget_model

Code/PAW/Training Phase/utils.py:72

↓ 1 callersFunctionget_partial_subjects_dataloaders

Code/PAW/Adaptation Phase/utils.py:58

↓ 1 callersFunctionget_subjects_data_list

Code/PAW/Training Phase/utils.py:136

↓ 1 callersFunctionget_train_loader

取得subject的dataloader，可設定是否使用session number

Code/PAW/Training Phase/utils.py:101

↓ 1 callersFunctionlr_scheduler

(optimizer, iter_num, max_iter, gamma=10, power=0.75)

Code/PAW/Adaptation Phase/adaptation.py:24

↓ 1 callersFunctionobtain_pseudo_label

(loader, netF_list, netC_list, netQ, args)

Code/PAW/Adaptation Phase/adaptation.py:240

↓ 1 callersFunctionop_copy

Code/PAW/Adaptation Phase/adaptation.py:18

↓ 1 callersFunctionprepare_all_data_feature

(path, start_t, end_t,reject_artifact)

Code/Get Data/BCIIV2a/2a_raw_to_saved_data.py:78

↓ 1 callersMethodprint_type_info

Code/Get Data/BCIIV2b/2b_raw_to_saved_data.py:36

↓ 1 callersFunctiontrain_target

Code/PAW/Adaptation Phase/adaptation.py:36

↓ 1 callersFunctionweak_aug

Code/PAW/Adaptation Phase/augmentation.py:21

FunctionEntropy

Code/PAW/Adaptation Phase/loss.py:4

Method__getitem__

Code/PAW/Adaptation Phase/load_data.py:15

Method__getitem__

Code/PAW/Training Phase/load_data.py:12

Method__getitem__

Code/PAW/Training Phase/load_data.py:27

(self, file_name)

Code/Get Data/BCIIV2b/2b_raw_to_saved_data.py:17

(self, n_channels_in, reduction_ratio)

Code/PAW/Adaptation Phase/network.py:35

(self, probability=0.5, min=-10, max=10)

Code/PAW/Adaptation Phase/augmentation.py:7

(self,nb_classes,Chans=64, Samples=128, layers=3, kernel_s=10, filt=10, dropout=0, activation

Code/PAW/Adaptation Phase/model_eegtcnet.py:9

( self, in_chans, input_window_samples=None, pool_mode="mean", F1=8,

Code/PAW/Adaptation Phase/model_eegtcnet.py:30

(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, drop_prob, activation)

Code/PAW/Adaptation Phase/model_eegtcnet.py:147

(self, chomp_size)

Code/PAW/Adaptation Phase/model_eegtcnet.py:190

(self, *args, max_norm=1, **kwargs)

Code/PAW/Adaptation Phase/model_eegtcnet.py:202

(self, expression_fn)

Code/PAW/Adaptation Phase/model_eegtcnet.py:229

(self, *args, max_norm=1, **kwargs)

Code/PAW/Adaptation Phase/model_EEGNet.py:6

(self, chomp_size)

Code/PAW/Adaptation Phase/model_EEGNet.py:121

(self, expression_fn)

Code/PAW/Adaptation Phase/model_EEGNet.py:160

(self, data, label, if_test=False)

Code/PAW/Adaptation Phase/load_data.py:5

(self, input_size=512, num_classes=1)

Code/PAW/Training Phase/domain_discriminator.py:7

(self,nb_classes,Chans=64, Samples=128, layers=3, kernel_s=10, filt=10, dropout=0, activation

Code/PAW/Training Phase/model_eegtcnet.py:9

( self, in_chans, input_window_samples=None, pool_mode="mean", F1=8,

Code/PAW/Training Phase/model_eegtcnet.py:27

(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, drop_prob, activation)

Code/PAW/Training Phase/model_eegtcnet.py:145

(self, chomp_size)

Code/PAW/Training Phase/model_eegtcnet.py:188

(self, *args, max_norm=1, **kwargs)

Code/PAW/Training Phase/model_eegtcnet.py:200

(self, expression_fn)

Code/PAW/Training Phase/model_eegtcnet.py:227

(self, *args, max_norm=1, **kwargs)

Code/PAW/Training Phase/model_EEGNet.py:105

(self, expression_fn)

Code/PAW/Training Phase/model_EEGNet.py:132

(self, data, label)

Code/PAW/Training Phase/load_data.py:4

(self, data, label, domain_label)

Code/PAW/Training Phase/load_data.py:18

return the size of dataset

Code/PAW/Adaptation Phase/load_data.py:11

return the size of dataset

Code/PAW/Training Phase/load_data.py:8

return the size of dataset

Code/PAW/Training Phase/load_data.py:23

Code/PAW/Adaptation Phase/model_eegtcnet.py:236

Code/PAW/Adaptation Phase/model_EEGNet.py:167

Code/PAW/Training Phase/model_eegtcnet.py:234

Code/PAW/Training Phase/model_EEGNet.py:139

Function_transpose_1_0

Code/PAW/Adaptation Phase/model_EEGNet.py:91

Function_transpose_to_b_1_c_0

Code/PAW/Adaptation Phase/model_eegtcnet.py:252

Function_transpose_to_b_1_c_0

Code/PAW/Adaptation Phase/model_EEGNet.py:87

Function_transpose_to_b_1_c_0

Code/PAW/Training Phase/model_eegtcnet.py:250

Function_transpose_to_b_1_c_0

Code/PAW/Training Phase/model_EEGNet.py:75

Methodextra_repr

Code/PAW/Adaptation Phase/model_eegtcnet.py:194

Methodextra_repr

Code/PAW/Adaptation Phase/model_EEGNet.py:125

Methodextra_repr

Code/PAW/Training Phase/model_eegtcnet.py:192

Code/PAW/Adaptation Phase/network.py:17

Code/PAW/Adaptation Phase/network.py:48

Code/PAW/Adaptation Phase/augmentation.py:13

Code/PAW/Adaptation Phase/model_eegtcnet.py:21

Code/PAW/Adaptation Phase/model_eegtcnet.py:83

Code/PAW/Adaptation Phase/model_eegtcnet.py:175

Code/PAW/Adaptation Phase/model_eegtcnet.py:197

Code/PAW/Adaptation Phase/model_eegtcnet.py:206

Code/PAW/Adaptation Phase/model_eegtcnet.py:213

Code/PAW/Adaptation Phase/model_eegtcnet.py:233

Code/PAW/Adaptation Phase/model_EEGNet.py:10

Code/PAW/Adaptation Phase/model_EEGNet.py:128

Code/PAW/Adaptation Phase/model_EEGNet.py:144

Code/PAW/Adaptation Phase/model_EEGNet.py:164

Code/PAW/Training Phase/domain_discriminator.py:18

Code/PAW/Training Phase/model_eegtcnet.py:19

Code/PAW/Training Phase/model_eegtcnet.py:80

Code/PAW/Training Phase/model_eegtcnet.py:173

Code/PAW/Training Phase/model_eegtcnet.py:195

Code/PAW/Training Phase/model_eegtcnet.py:204

Code/PAW/Training Phase/model_eegtcnet.py:211

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