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()