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log_eg.txt
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Sun Mar 22 00:30:40 2020
----------------- Options ---------------
BID: not-supported [default: 5_95_th]
batchsize: 16 [default: 64]
continue_train: False
dataset_dir: /home/hypo/MyProject/Ear_AU/datasets/emotion/candock_6class_60s_pad_selectlabel [default: ./datasets/sleep-edfx/]
dataset_name: preload
epochs: 150 [default: 20]
gpu_id: 1 [default: 0]
input_nc: 5 [default: 3]
k_fold: 5 [default: 0]
label: 6 [default: 5]
label_name: ['Amus', 'Neut', 'Sadn', 'Tend', 'Disg', 'Fear'] [default: auto]
lr: 0.001
model_name: multi_scale_resnet_1d [default: lstm]
network_save_freq: 1000 [default: 5]
no_cuda: False
no_cudnn: False
no_shuffle: False
pretrained: False
sample_num: not-supported [default: 20]
save_dir: ./checkpoints/EMDB_5ch_6class_last60s_pad_weightauto_selectlabel_multiscale [default: ./checkpoints/]
select_sleep_time: not-supported [default: False]
separated: False
signal_name: not-supported [default: EEG Fpz-Cz]
weight_mod: auto [default: normal]
----------------- End -------------------
network:
Multi_Scale_ResNet(
(pre_conv): Sequential(
(0): Conv1d(5, 64, kernel_size=(15,), stride=(2,), padding=(7,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): MaxPool1d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
(Route1): Route(
(block1): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block2): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 128, kernel_size=(3,), stride=(2,), padding=(1,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 128, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block3): ResidualBlock(
(conv): Sequential(
(0): Conv1d(128, 256, kernel_size=(3,), stride=(2,), padding=(1,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(128, 256, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block4): ResidualBlock(
(conv): Sequential(
(0): Conv1d(256, 512, kernel_size=(3,), stride=(2,), padding=(1,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(512, 512, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(256, 512, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool1d(output_size=1)
)
(Route2): Route(
(block1): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 64, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(64, 64, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block2): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 128, kernel_size=(5,), stride=(2,), padding=(2,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 128, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block3): ResidualBlock(
(conv): Sequential(
(0): Conv1d(128, 256, kernel_size=(5,), stride=(2,), padding=(2,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(256, 256, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(128, 256, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block4): ResidualBlock(
(conv): Sequential(
(0): Conv1d(256, 512, kernel_size=(5,), stride=(2,), padding=(2,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(256, 512, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool1d(output_size=1)
)
(Route3): Route(
(block1): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
(4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block2): ResidualBlock(
(conv): Sequential(
(0): Conv1d(64, 128, kernel_size=(7,), stride=(2,), padding=(3,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(64, 128, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block3): ResidualBlock(
(conv): Sequential(
(0): Conv1d(128, 256, kernel_size=(7,), stride=(2,), padding=(3,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
(4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(128, 256, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(block4): ResidualBlock(
(conv): Sequential(
(0): Conv1d(256, 512, kernel_size=(7,), stride=(2,), padding=(3,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(512, 512, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
(4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(shortcut): Sequential(
(0): Conv1d(256, 512, kernel_size=(1,), stride=(2,), bias=False)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool1d(output_size=1)
)
(fc): Linear(in_features=1536, out_features=6, bias=True)
)
net parameters: 8.42M
label statistics: [715 643 518 254 517 436]
Loss_weight:[0.81885882 0.87833147 0.99945861 1.39877569 1.00054139 1.09602268]
------------------------------ k-fold:1 ------------------------------
>>> per epoch cost time:99.97s
fold -> macro-prec,reca,F1,err,kappa: (0.3178, 0.3181, 0.2995, 0.6234, 0.2222)
confusion_mat:
[[64 42 4 3 16 6]
[18 82 8 5 20 5]
[24 41 21 2 16 6]
[12 18 4 0 7 2]
[ 8 24 7 0 50 10]
[ 8 28 6 2 27 12]]
------------------------------ k-fold:2 ------------------------------
>>> per epoch cost time:96.49s
fold -> macro-prec,reca,F1,err,kappa: (0.3149, 0.3155, 0.3127, 0.6464, 0.2092)
confusion_mat:
[[71 21 18 12 14 11]
[13 56 22 6 7 11]
[17 28 30 7 9 10]
[15 11 4 4 8 7]
[ 9 23 15 0 33 27]
[ 9 16 15 5 23 21]]
------------------------------ k-fold:3 ------------------------------
>>> per epoch cost time:95.27s
fold -> macro-prec,reca,F1,err,kappa: (0.3436, 0.3481, 0.3369, 0.6036, 0.2566)
confusion_mat:
[[86 17 18 8 13 2]
[16 53 24 4 15 7]
[24 20 38 3 9 5]
[15 16 14 3 7 2]
[10 18 12 4 49 12]
[11 23 16 2 20 12]]
------------------------------ k-fold:4 ------------------------------
>>> per epoch cost time:50.3s
fold -> macro-prec,reca,F1,err,kappa: (0.349, 0.354, 0.3469, 0.6102, 0.2523)
confusion_mat:
[[73 21 13 12 19 4]
[27 44 18 9 12 14]
[26 14 33 5 15 11]
[15 17 7 4 5 6]
[12 12 3 4 61 11]
[ 9 5 7 5 33 22]]
------------------------------ k-fold:5 ------------------------------
>>> per epoch cost time:49.65s
fold -> macro-prec,reca,F1,err,kappa: (0.3306, 0.3352, 0.3303, 0.6217, 0.2363)
confusion_mat:
[[68 18 18 9 20 7]
[17 61 29 5 13 12]
[18 23 30 5 14 7]
[ 9 14 13 2 5 3]
[11 14 5 3 45 19]
[ 9 17 11 3 27 24]]
------------------------------ final result ------------------------------
final -> macro-prec,reca,F1,err,kappa: (0.3299, 0.3345, 0.3284, 0.6211, 0.2357)
confusion_mat:
[[362 119 71 44 82 30]
[ 91 296 101 29 67 49]
[109 126 152 22 63 39]
[ 66 76 42 13 32 20]
[ 50 91 42 11 238 79]
[ 46 89 55 17 130 91]]