def trainmain(): sys.argv = [ 'train.py', "--use_gpu=true", "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=256", "--class_dim=10", "--image_shape=3,32,32", "--lr=0.1", "--lr_strategy=piecewise_decay", "--lr_steps=6000,12000,18000", #"--lr_epoch=30, 60, 90", #"--l2_decay=5e-4", "--display_iter_step=10", "--total_iter_num=20000", "--test_iter_step=500", "--save_iter_step=2000", "--loss_name=softmax", #"--pretrained_model=cifar_pretrained" "--train_datasetfile=dataset/cifar10/cifar10_train.data", "--train_labelfile=dataset/cifar10/cifar10_train.label", "--val_datasetfile=dataset/cifar10/cifar10_test.data", "--val_labelfile=dataset/cifar10/cifar10_test.label", ] trainmodule.main()
def trainmain(): defaultargv = [ 'train.py', #"--use_gpu=false", #"--checkpoint=output/L2Net/12000/", #"--pretrained_model=pretrained_model", "--input_dtype=uint8", #"--model=L2Net", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=64", "--class_dim=500000", "--image_shape=1,32,32", "--lr=0.1", "--lr_strategy=cosine_decay", #"--lr_strategy=cosine_decay_with_warmup", #"--warmup_iter_num=6000", "--display_iter_step=5", "--total_iter_num=60000", "--test_iter_step=100", "--save_iter_step=3000", "--loss_name=arcmargin", "--arc_scale=80", "--arc_margin=0.2", "--train_datasetfile=dataset/samepatch_train/samepatch_train.data", "--train_labelfile=dataset/samepatch_train/samepatch_train_500000.label", "--val_datasetfile=dataset/samepatch_train/samepatch_train.data", "--val_labelfile=dataset/samepatch_train/samepatch_test_44803.label", ] update_argv(defaultargv) trainmodule.main()
def trainmain(): sys.argv = [ 'train.py', #"--use_gpu=false", #"--checkpoint=output/L2Net/12000/", "--input_dtype=uint8", #"--model=L2Net", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=64", "--class_dim=500000", "--image_shape=1,32,32", "--lr=0.1", "--lr_strategy=cosine_decay_with_warmup", "--warmup_iter_num=6000", "--display_iter_step=5", "--total_iter_num=18000", "--test_iter_step=500", "--save_iter_step=3000", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.5", ] trainmodule.main()
def trainmain(): bigargv = [ 'train.py', "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=256", "--class_dim=80000", "--image_shape=3,112,112", "--lr=0.1", "--lr_strategy=cosine_decay_with_warmup", "--warmup_iter_num=12000", "--display_iter_step=10", "--total_iter_num=36000", "--test_iter_step=500", "--save_iter_step=6000", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.5", ] smallargv = [ 'train.py', "--use_gpu=false", "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=256", "--test_batch_size=64", "--embedding_size=256", "--class_dim=1000", "--image_shape=3,112,112", "--lr=0.1", "--lr_strategy=cosine_decay_with_warmup", "--warmup_iter_num=1200", "--display_iter_step=10", "--total_iter_num=3600", "--test_iter_step=500", "--save_iter_step=600", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.5", ] sys.argv = smallargv trainmodule.main()
def trainmain(): bigargv = [ 'train.py', "--model_save_dir=outputface", "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=256", "--class_dim=80000", "--image_shape=3,112,112", "--lr=0.1", #"--lr_strategy=cosine_decay_with_warmup", "--lr_strategy=cosine_decay", #"--warmup_iter_num=12000", "--display_iter_step=10", "--total_iter_num=36000", "--test_iter_step=500", "--save_iter_step=6000", #"--loss_name=softmax", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.5", "--train_datasetfile=dataset/face_ms1m/ms1m_train.data", "--train_labelfile=dataset/face_ms1m/ms1m_train_80000.label", "--val_datasetfile=dataset/face_ms1m/ms1m_train.data", "--val_labelfile=dataset/face_ms1m/ms1m_train_5164.label", ] bigargvkaibin = [ 'train.py', "--model_save_dir=outputface", "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=512", "--test_batch_size=64", "--embedding_size=512", "--class_dim=85164", "--image_shape=3,112,112", "--lr=0.1", "--lr_strategy=piecewise_decay", "--lr_steps=1000,2000,3000,4000,100000,140000,160000, 200000", "--lr_steps_values=0.01,0.05,0.1,0.5,1,0.1,0.01,0.001,0.0001", "--display_iter_step=10", "--total_iter_num=200000", "--test_iter_step=500", "--save_iter_step=5000", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.3", "--train_datasetfile=dataset/face_ms1m/ms1m_train.data", "--train_labelfile=dataset/face_ms1m/ms1m_train.label", "--val_datasetfile=dataset/face_ms1m/ms1m_train.data", "--val_labelfile=dataset/face_ms1m/ms1m_train_5164.label", ] smallargv = [ 'train.py', #"--checkpoint=2400", #"--pretrained_model=softmaxface600", "--model_save_dir=outputface", "--use_gpu=true", "--input_dtype=uint8", "--model=ResNet18", "--train_batch_size=400", "--test_batch_size=64", "--embedding_size=256", "--class_dim=1000", "--image_shape=3,112,112", "--lr=0.1", #"--lr_strategy=cosine_decay_with_warmup", "--lr_strategy=cosine_decay", "--display_iter_step=1", "--total_iter_num=500", "--test_iter_step=100", "--save_iter_step=100", #"--loss_name=softmax", "--loss_name=arcmargin", "--arc_scale=64", "--arc_margin=0.5", "--train_datasetfile=dataset/face_ms1m_small/train.data", "--train_labelfile=dataset/face_ms1m_small/train.label", "--val_datasetfile=dataset/face_ms1m_small/train.data", "--val_labelfile=dataset/face_ms1m_small/train.label", #"--val_datasetfile=dataset/face_ms1m_small/test.data", #"--val_labelfile=dataset/face_ms1m_small/test.label", ] update_argv(bigargvkaibin) #update_argv(bigargv) trainmodule.main()