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single_train_search_retrain_squence.py
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single_train_search_retrain_squence.py
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""" Generate commands for pre-train phase. """
import os
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject1/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_1' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=1' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject2/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_2' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=2' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject3/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_3' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=3' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject4/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_4' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=4' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject5/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_5' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=5' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject6/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_6' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=6' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject7/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_7' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=7' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject8/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_8' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=8' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)
def run_exp(lr=0.05, gamma=0.5, step_size=20, pre_batch_size=8):
max_epoch = 240
shot = 20
query = 10
way = 4
gpu = 2
base_lr = 0.01
weight_lr = 0.01
alpha_lr = 0.01
searched_structure_path = '/data00/home/xxx/BCI/Mudus/Mudus_BCI/logs/normal_search/BCI_IV_Search_batchsize32_w_lr0.01_alpha_lr0.005_gamma0.5_step20_maxepoch240_Single_Search_Formal_7_val_node_2_layer4_space_subject9/max_acc.pth'
the_command = 'python3 lauch.py' \
+ ' --pre_max_epoch=' + str(max_epoch) \
+ ' --shot=' + str(shot) \
+ ' --train_query=' + str(query) \
+ ' --way=' + str(way) \
+ ' --pre_step_size=' + str(step_size) \
+ ' --pre_gamma=' + str(gamma) \
+ ' --gpu=' + str(gpu) \
+ ' --base_lr=' + str(base_lr) \
+ ' --pre_lr=' + str(lr) \
+ ' --pre_batch_size=' + str(pre_batch_size) \
+ ' --searched_weights=' + str(searched_structure_path) \
+ ' --phase=dep_single' \
+ ' --model_type=single_retrain' \
+ ' --exp_spc=specific_retrain_formal_8_subject_9' \
+ ' --w_lr=' + str(weight_lr) \
+ ' --single_id=9' \
+ ' --alpha_lr=' + str(alpha_lr)
os.system(the_command)
run_exp(lr=0.1, gamma=0.5, step_size=20, pre_batch_size=256)