Bobholamovic
2 years ago
commit
d545aa8efd
102 changed files with 1773 additions and 147 deletions
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# Configurations of CDNet with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/cdnet/ |
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model: !Node |
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type: CDNet |
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# Configurations of cdnet with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/cdnet/ |
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model: !Node |
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type: CDNet |
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===========================train_params=========================== |
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model_name:cd:cdnet |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/cdnet/cdnet_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/cdnet/cdnet_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/cdnet/cdnet_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:cdnet |
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null:null |
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# Configurations of ChangeFormer with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/changeformer/ |
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model: !Node |
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type: ChangeFormer |
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# Configurations of ChangeFormer with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/changeformer/ |
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model: !Node |
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type: ChangeFormer |
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# Configurations of DSAMNet with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/dsamnet/ |
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model: !Node |
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type: DSAMNet |
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# Configurations of DSAMNet with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/dsamnet/ |
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model: !Node |
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type: DSAMNet |
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===========================train_params=========================== |
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model_name:cd:dsamnet |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/dsamnet/dsamnet_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/dsamnet/dsamnet_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/dsamnet/dsamnet_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:dsamnet |
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null:null |
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# Configurations of DSIFN with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/dsifn/ |
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model: !Node |
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type: DSIFN |
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# Configurations of DSIFN with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/dsifn/ |
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model: !Node |
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type: DSIFN |
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===========================train_params=========================== |
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model_name:cd:dsifn |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/dsifn/dsifn_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/dsifn/dsifn_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/dsifn/dsifn_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:dsifn |
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null:null |
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# Configurations of FC-EF with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/fc_ef/ |
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model: !Node |
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type: FCEarlyFusion |
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# Configurations of FC-EF with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/fc_ef/ |
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model: !Node |
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type: FCEarlyFusion |
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===========================train_params=========================== |
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model_name:cd:fc_ef |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=20 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/fc_ef/fc_ef_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/fc_ef/fc_ef_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/fc_ef/fc_ef_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:fc_ef |
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null:null |
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# Configurations of FC-Siam-conc with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/fc_siam_conc/ |
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model: !Node |
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type: FCSiamConc |
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# Configurations of FC-Siam-conc with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/fc_siam_conc/ |
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model: !Node |
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type: FCSiamConc |
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===========================train_params=========================== |
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model_name:cd:fc_siam_conc |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=20 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/fc_siam_conc/fc_siam_conc_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/fc_siam_conc/fc_siam_conc_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/fc_siam_conc/fc_siam_conc_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:fc_siam_conc |
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null:null |
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# Configurations of FC-Siam-diff with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/fc_siam_diff/ |
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model: !Node |
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type: FCSiamDiff |
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# Configurations of FC-Siam-diff with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/fc_siam_diff/ |
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model: !Node |
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type: FCSiamDiff |
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===========================train_params=========================== |
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model_name:cd:fc_siam_diff |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=20 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/fc_siam_diff/fc_siam_diff_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/fc_siam_diff/fc_siam_diff_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/fc_siam_diff/fc_siam_diff_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
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===========================infer_params=========================== |
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infer_model:null |
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infer_export:null |
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infer_quant:False |
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inference:test_tipc/infer.py |
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--device:cpu|gpu |
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--enable_mkldnn:True |
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--cpu_threads:6 |
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--batch_size:1 |
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--use_trt:False |
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--precision:fp32 |
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--model_dir:null |
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--config:null |
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--save_log_path:null |
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--benchmark:True |
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--model_name:fc_siam_diff |
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null:null |
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# Configurations of FCCDN with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/fccdn/ |
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model: !Node |
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type: FCCDN |
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learning_rate: 0.07 |
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lr_decay_power: 0.6 |
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log_interval_steps: 100 |
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save_interval_epochs: 3 |
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# Configurations of FCCDN with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/fccdn/ |
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model: !Node |
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type: FCCDN |
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# Configurations of SNUNet with AirChange dataset |
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_base_: ../_base_/airchange.yaml |
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save_dir: ./test_tipc/output/cd/snunet/ |
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model: !Node |
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type: SNUNet |
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# Configurations of SNUNet with LEVIR-CD dataset |
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_base_: ../_base_/levircd.yaml |
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save_dir: ./test_tipc/output/cd/snunet/ |
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|
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model: !Node |
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type: SNUNet |
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===========================train_params=========================== |
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model_name:cd:snunet |
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python:python |
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gpu_list:0|0,1 |
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use_gpu:null|null |
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--precision:null |
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--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
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--save_dir:adaptive |
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--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
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--model_path:null |
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--config:lite_train_lite_infer=./test_tipc/configs/cd/snunet/snunet_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/snunet/snunet_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/snunet/snunet_levircd.yaml |
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train_model_name:best_model |
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null:null |
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## |
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trainer:norm |
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norm_train:test_tipc/run_task.py train cd |
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pact_train:null |
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fpgm_train:null |
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distill_train:null |
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null:null |
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null:null |
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## |
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===========================eval_params=========================== |
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eval:null |
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null:null |
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## |
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===========================export_params=========================== |
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--save_dir:adaptive |
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--model_dir:adaptive |
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--fixed_input_shape:[-1,3,256,256] |
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norm_export:deploy/export/export_model.py |
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quant_export:null |
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fpgm_export:null |
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distill_export:null |
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export1:null |
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export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:snunet |
||||
null:null |
||||
@ -0,0 +1,8 @@ |
||||
# Configurations of STANet with AirChange dataset |
||||
|
||||
_base_: ../_base_/airchange.yaml |
||||
|
||||
save_dir: ./test_tipc/output/cd/stanet/ |
||||
|
||||
model: !Node |
||||
type: STANet |
||||
@ -0,0 +1,8 @@ |
||||
# Configurations of STANet with LEVIR-CD dataset |
||||
|
||||
_base_: ../_base_/levircd.yaml |
||||
|
||||
save_dir: ./test_tipc/output/cd/stanet/ |
||||
|
||||
model: !Node |
||||
type: STANet |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:cd:stanet |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=5|lite_train_whole_infer=5|whole_train_whole_infer=10 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=8 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/cd/stanet/stanet_airchange.yaml|lite_train_whole_infer=./test_tipc/configs/cd/stanet/stanet_airchange.yaml|whole_train_whole_infer=./test_tipc/configs/cd/stanet/stanet_levircd.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train cd |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:stanet |
||||
null:null |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of HRNet with UCMerced dataset |
||||
|
||||
_base_: ../_base_/ucmerced.yaml |
||||
|
||||
save_dir: ./test_tipc/output/clas/hrnet/ |
||||
|
||||
model: !Node |
||||
type: HRNet_W18_C |
||||
args: |
||||
num_classes: 21 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of MobileNetV3 with UCMerced dataset |
||||
|
||||
_base_: ../_base_/ucmerced.yaml |
||||
|
||||
save_dir: ./test_tipc/output/clas/mobilenetv3/ |
||||
|
||||
model: !Node |
||||
type: MobileNetV3_small_x1_0 |
||||
args: |
||||
num_classes: 21 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:clas:mobilenetv3 |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=10 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=16|lite_train_whole_infer=16|whole_train_whole_infer=16 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/clas/mobilenetv3/mobilenetv3_ucmerced.yaml|lite_train_whole_infer=./test_tipc/configs/clas/mobilenetv3/mobilenetv3_ucmerced.yaml|whole_train_whole_infer=./test_tipc/configs/clas/mobilenetv3/mobilenetv3_ucmerced.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train clas |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:mobilenetv3 |
||||
null:null |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of ResNet50-vd with UCMerced dataset |
||||
|
||||
_base_: ../_base_/ucmerced.yaml |
||||
|
||||
save_dir: ./test_tipc/output/clas/resnet50_vd/ |
||||
|
||||
model: !Node |
||||
type: ResNet50_vd |
||||
args: |
||||
num_classes: 21 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:clas:resnet50_vd |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=10 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=16|lite_train_whole_infer=16|whole_train_whole_infer=16 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/clas/resnet50_vd/resnet50_vd_ucmerced.yaml|lite_train_whole_infer=./test_tipc/configs/clas/resnet50_vd/resnet50_vd_ucmerced.yaml|whole_train_whole_infer=./test_tipc/configs/clas/resnet50_vd/resnet50_vd_ucmerced.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train clas |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:resnet50_vd |
||||
null:null |
||||
@ -0,0 +1,72 @@ |
||||
# Basic configurations of RSOD dataset |
||||
|
||||
datasets: |
||||
train: !Node |
||||
type: VOCDetDataset |
||||
args: |
||||
data_dir: ./test_tipc/data/rsod/ |
||||
file_list: ./test_tipc/data/rsod/train.txt |
||||
label_list: ./test_tipc/data/rsod/labels.txt |
||||
shuffle: True |
||||
eval: !Node |
||||
type: VOCDetDataset |
||||
args: |
||||
data_dir: ./test_tipc/data/rsod/ |
||||
file_list: ./test_tipc/data/rsod/val.txt |
||||
label_list: ./test_tipc/data/rsod/labels.txt |
||||
shuffle: False |
||||
transforms: |
||||
train: |
||||
- !Node |
||||
type: DecodeImg |
||||
- !Node |
||||
type: RandomDistort |
||||
- !Node |
||||
type: RandomExpand |
||||
- !Node |
||||
type: RandomCrop |
||||
- !Node |
||||
type: RandomHorizontalFlip |
||||
- !Node |
||||
type: BatchRandomResize |
||||
args: |
||||
target_sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] |
||||
interp: RANDOM |
||||
- !Node |
||||
type: Normalize |
||||
args: |
||||
mean: [0.485, 0.456, 0.406] |
||||
std: [0.229, 0.224, 0.225] |
||||
- !Node |
||||
type: ArrangeDetector |
||||
args: ['train'] |
||||
eval: |
||||
- !Node |
||||
type: DecodeImg |
||||
- !Node |
||||
type: Resize |
||||
args: |
||||
target_size: 608 |
||||
interp: CUBIC |
||||
- !Node |
||||
type: Normalize |
||||
args: |
||||
mean: [0.485, 0.456, 0.406] |
||||
std: [0.229, 0.224, 0.225] |
||||
- !Node |
||||
type: ArrangeDetector |
||||
args: ['eval'] |
||||
download_on: False |
||||
|
||||
num_epochs: 10 |
||||
train_batch_size: 4 |
||||
save_interval_epochs: 10 |
||||
log_interval_steps: 4 |
||||
save_dir: ./test_tipc/output/det/ |
||||
learning_rate: 0.0001 |
||||
use_vdl: False |
||||
resume_checkpoint: '' |
||||
train: |
||||
pretrain_weights: COCO |
||||
warmup_steps: 0 |
||||
warmup_start_lr: 0.0 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of Faster R-CNN with RSOD dataset |
||||
|
||||
_base_: ../_base_/rsod.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/faster_rcnn/ |
||||
|
||||
model: !Node |
||||
type: FasterRCNN |
||||
args: |
||||
num_classes: 4 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of Faster R-CNN with SARShip dataset |
||||
|
||||
_base_: ../_base_/sarship.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/faster_rcnn/ |
||||
|
||||
model: !Node |
||||
type: FasterRCNN |
||||
args: |
||||
num_classes: 1 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:det:faster_rcnn |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=10 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/det/faster_rcnn/faster_rcnn_sarship.yaml|lite_train_whole_infer=./test_tipc/configs/det/faster_rcnn/faster_rcnn_sarship.yaml|whole_train_whole_infer=./test_tipc/configs/det/faster_rcnn/faster_rcnn_rsod.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train det |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,608,608] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:faster_rcnn |
||||
null:null |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of PP-YOLO with RSOD dataset |
||||
|
||||
_base_: ../_base_/rsod.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/ppyolo/ |
||||
|
||||
model: !Node |
||||
type: PPYOLO |
||||
args: |
||||
num_classes: 4 |
||||
@ -1,4 +1,4 @@ |
||||
# Basic configurations of PP-YOLO |
||||
# Configurations of PP-YOLO with SARShip dataset |
||||
|
||||
_base_: ../_base_/sarship.yaml |
||||
|
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of PP-YOLO Tiny with RSOD dataset |
||||
|
||||
_base_: ../_base_/rsod.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/ppyolo_tiny/ |
||||
|
||||
model: !Node |
||||
type: PPYOLOTiny |
||||
args: |
||||
num_classes: 4 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of PP-YOLO Tiny with SARShip dataset |
||||
|
||||
_base_: ../_base_/sarship.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/ppyolo_tiny/ |
||||
|
||||
model: !Node |
||||
type: PPYOLOTiny |
||||
args: |
||||
num_classes: 1 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:det:ppyolo_tiny |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/det/ppyolo_tiny/ppyolo_tiny_sarship.yaml|lite_train_whole_infer=./test_tipc/configs/det/ppyolo_tiny/ppyolo_tiny_sarship.yaml|whole_train_whole_infer=./test_tipc/configs/det/ppyolo_tiny/ppyolo_tiny_rsod.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train det |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,608,608] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:ppyolo_tiny |
||||
null:null |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of PP-YOLOv2 with RSOD dataset |
||||
|
||||
_base_: ../_base_/rsod.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/ppyolov2/ |
||||
|
||||
model: !Node |
||||
type: PPYOLOv2 |
||||
args: |
||||
num_classes: 4 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of PP-YOLOv2 with SARShip dataset |
||||
|
||||
_base_: ../_base_/sarship.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/ppyolov2/ |
||||
|
||||
model: !Node |
||||
type: PPYOLOv2 |
||||
args: |
||||
num_classes: 1 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:det:ppyolov2 |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/det/ppyolov2/ppyolov2_sarship.yaml|lite_train_whole_infer=./test_tipc/configs/det/ppyolov2/ppyolov2_sarship.yaml|whole_train_whole_infer=./test_tipc/configs/det/ppyolov2/ppyolov2_rsod.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train det |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,608,608] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:ppyolov2 |
||||
null:null |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:det:yolov3 |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=10 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/det/yolov3/yolov3_sarship.yaml|lite_train_whole_infer=./test_tipc/configs/det/yolov3/yolov3_sarship.yaml|whole_train_whole_infer=./test_tipc/configs/det/yolov3/yolov3_rsod.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train det |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,608,608] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:yolov3 |
||||
null:null |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of YOLOv3 with RSOD dataset |
||||
|
||||
_base_: ../_base_/rsod.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/yolov3/ |
||||
|
||||
model: !Node |
||||
type: YOLOv3 |
||||
args: |
||||
num_classes: 4 |
||||
@ -0,0 +1,10 @@ |
||||
# Configurations of YOLOv3 with SARShip dataset |
||||
|
||||
_base_: ../_base_/sarship.yaml |
||||
|
||||
save_dir: ./test_tipc/output/det/yolov3/ |
||||
|
||||
model: !Node |
||||
type: YOLOv3 |
||||
args: |
||||
num_classes: 1 |
||||
@ -0,0 +1,72 @@ |
||||
# Basic configurations of RSSR dataset |
||||
|
||||
datasets: |
||||
train: !Node |
||||
type: ResDataset |
||||
args: |
||||
data_dir: ./test_tipc/data/rssr/ |
||||
file_list: ./test_tipc/data/rssr/train.txt |
||||
num_workers: 0 |
||||
shuffle: True |
||||
sr_factor: 4 |
||||
eval: !Node |
||||
type: ResDataset |
||||
args: |
||||
data_dir: ./test_tipc/data/rssr/ |
||||
file_list: ./test_tipc/data/rssr/val.txt |
||||
num_workers: 0 |
||||
shuffle: False |
||||
sr_factor: 4 |
||||
transforms: |
||||
train: |
||||
- !Node |
||||
type: DecodeImg |
||||
- !Node |
||||
type: RandomCrop |
||||
args: |
||||
crop_size: 32 |
||||
- !Node |
||||
type: RandomHorizontalFlip |
||||
args: |
||||
prob: 0.5 |
||||
- !Node |
||||
type: RandomVerticalFlip |
||||
args: |
||||
prob: 0.5 |
||||
- !Node |
||||
type: Normalize |
||||
args: |
||||
mean: [0.0, 0.0, 0.0] |
||||
std: [1.0, 1.0, 1.0] |
||||
- !Node |
||||
type: ArrangeRestorer |
||||
args: ['train'] |
||||
eval: |
||||
- !Node |
||||
type: DecodeImg |
||||
- !Node |
||||
type: Resize |
||||
args: |
||||
target_size: 256 |
||||
- !Node |
||||
type: Normalize |
||||
args: |
||||
mean: [0.0, 0.0, 0.0] |
||||
std: [1.0, 1.0, 1.0] |
||||
- !Node |
||||
type: ArrangeRestorer |
||||
args: ['eval'] |
||||
download_on: False |
||||
download_url: https://paddlers.bj.bcebos.com/datasets/rssr.zip |
||||
download_path: ./test_tipc/data/ |
||||
|
||||
num_epochs: 10 |
||||
train_batch_size: 4 |
||||
save_interval_epochs: 10 |
||||
log_interval_steps: 10 |
||||
save_dir: ./test_tipc/output/res/ |
||||
learning_rate: 0.0005 |
||||
early_stop: False |
||||
early_stop_patience: 5 |
||||
use_vdl: False |
||||
resume_checkpoint: '' |
||||
@ -0,0 +1,8 @@ |
||||
# Configurations of DRN with RSSR dataset |
||||
|
||||
_base_: ../_base_/rssr.yaml |
||||
|
||||
save_dir: ./test_tipc/output/res/drn/ |
||||
|
||||
model: !Node |
||||
type: DRN |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:res:drn |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/res/drn/drn_rssr.yaml|lite_train_whole_infer=./test_tipc/configs/res/drn/drn_rssr.yaml|whole_train_whole_infer=./test_tipc/configs/res/drn/drn_rssr.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train res |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:drn |
||||
null:null |
||||
@ -0,0 +1,8 @@ |
||||
# Configurations of ESRGAN with RSSR dataset |
||||
|
||||
_base_: ../_base_/rssr.yaml |
||||
|
||||
save_dir: ./test_tipc/output/res/esrgan/ |
||||
|
||||
model: !Node |
||||
type: ESRGAN |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:res:esrgan |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/res/esrgan/esrgan_rssr.yaml|lite_train_whole_infer=./test_tipc/configs/res/esrgan/esrgan_rssr.yaml|whole_train_whole_infer=./test_tipc/configs/res/esrgan/esrgan_rssr.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train res |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:esrgan |
||||
null:null |
||||
@ -0,0 +1,8 @@ |
||||
# Configurations of LESRCNN with RSSR dataset |
||||
|
||||
_base_: ../_base_/rssr.yaml |
||||
|
||||
save_dir: ./test_tipc/output/res/lesrcnn/ |
||||
|
||||
model: !Node |
||||
type: LESRCNN |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:res:lesrcnn |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=20 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/res/lesrcnn/lesrcnn_rssr.yaml|lite_train_whole_infer=./test_tipc/configs/res/lesrcnn/lesrcnn_rssr.yaml|whole_train_whole_infer=./test_tipc/configs/res/lesrcnn/lesrcnn_rssr.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train res |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,3,256,256] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:lesrcnn |
||||
null:null |
||||
@ -0,0 +1,11 @@ |
||||
# Configurations of DeepLab V3+ with RSSeg dataset |
||||
|
||||
_base_: ../_base_/rsseg.yaml |
||||
|
||||
save_dir: ./test_tipc/output/seg/deeplabv3p/ |
||||
|
||||
model: !Node |
||||
type: DeepLabV3P |
||||
args: |
||||
in_channels: 10 |
||||
num_classes: 5 |
||||
@ -0,0 +1,53 @@ |
||||
===========================train_params=========================== |
||||
model_name:seg:deeplabv3p |
||||
python:python |
||||
gpu_list:0|0,1 |
||||
use_gpu:null|null |
||||
--precision:null |
||||
--num_epochs:lite_train_lite_infer=3|lite_train_whole_infer=3|whole_train_whole_infer=30 |
||||
--save_dir:adaptive |
||||
--train_batch_size:lite_train_lite_infer=4|lite_train_whole_infer=4|whole_train_whole_infer=4 |
||||
--model_path:null |
||||
--config:lite_train_lite_infer=./test_tipc/configs/seg/deeplabv3p/deeplabv3p_rsseg.yaml|lite_train_whole_infer=./test_tipc/configs/seg/deeplabv3p/deeplabv3p_rsseg.yaml|whole_train_whole_infer=./test_tipc/configs/seg/deeplabv3p/deeplabv3p_rsseg.yaml |
||||
train_model_name:best_model |
||||
null:null |
||||
## |
||||
trainer:norm |
||||
norm_train:test_tipc/run_task.py train seg |
||||
pact_train:null |
||||
fpgm_train:null |
||||
distill_train:null |
||||
null:null |
||||
null:null |
||||
## |
||||
===========================eval_params=========================== |
||||
eval:null |
||||
null:null |
||||
## |
||||
===========================export_params=========================== |
||||
--save_dir:adaptive |
||||
--model_dir:adaptive |
||||
--fixed_input_shape:[-1,10,512,512] |
||||
norm_export:deploy/export/export_model.py |
||||
quant_export:null |
||||
fpgm_export:null |
||||
distill_export:null |
||||
export1:null |
||||
export2:null |
||||
===========================infer_params=========================== |
||||
infer_model:null |
||||
infer_export:null |
||||
infer_quant:False |
||||
inference:test_tipc/infer.py |
||||
--device:cpu|gpu |
||||
--enable_mkldnn:True |
||||
--cpu_threads:6 |
||||
--batch_size:1 |
||||
--use_trt:False |
||||
--precision:fp32 |
||||
--model_dir:null |
||||
--config:null |
||||
--save_log_path:null |
||||
--benchmark:True |
||||
--model_name:deeplabv3p |
||||
null:null |
||||
@ -1,11 +0,0 @@ |
||||
# Basic configurations of UNet |
||||
|
||||
_base_: ../_base_/rsseg.yaml |
||||
|
||||
save_dir: ./test_tipc/output/seg/unet/ |
||||
|
||||
model: !Node |
||||
type: UNet |
||||
args: |
||||
in_channels: 10 |
||||
num_classes: 5 |
||||
@ -0,0 +1,11 @@ |
||||
# Configurations of UNet with RSSeg dataset |
||||
|
||||
_base_: ../_base_/rsseg.yaml |
||||
|
||||
save_dir: ./test_tipc/output/seg/unet/ |
||||
|
||||
model: !Node |
||||
type: UNet |
||||
args: |
||||
in_channels: 10 |
||||
num_classes: 5 |
||||
@ -1 +0,0 @@ |
||||
#!/usr/bin/env bash |
||||
@ -0,0 +1,56 @@ |
||||
#!/usr/bin/env python |
||||
|
||||
import random |
||||
import os.path as osp |
||||
from functools import reduce, partial |
||||
|
||||
from common import (get_default_parser, get_path_tuples, create_file_list, |
||||
link_dataset, random_split, create_label_list) |
||||
|
||||
CLASSES = ('aircraft', 'oiltank', 'overpass', 'playground') |
||||
SUBSETS = ('train', 'val', 'test') |
||||
SUBDIRS = ('JPEGImages', osp.sep.join(['Annotation', 'xml'])) |
||||
FILE_LIST_PATTERN = "{subset}.txt" |
||||
LABEL_LIST_NAME = "labels.txt" |
||||
URL = "" |
||||
|
||||
if __name__ == '__main__': |
||||
parser = get_default_parser() |
||||
parser.add_argument('--seed', type=int, default=None, help="Random seed.") |
||||
parser.add_argument( |
||||
'--ratios', |
||||
type=float, |
||||
nargs='+', |
||||
default=(0.7, 0.2, 0.1), |
||||
help="Ratios of each subset (train/val or train/val/test).") |
||||
args = parser.parse_args() |
||||
|
||||
if args.seed is not None: |
||||
random.seed(args.seed) |
||||
|
||||
if len(args.ratios) not in (2, 3): |
||||
raise ValueError("Wrong number of ratios!") |
||||
|
||||
out_dir = osp.join(args.out_dataset_dir, |
||||
osp.basename(osp.normpath(args.in_dataset_dir))) |
||||
|
||||
link_dataset(args.in_dataset_dir, args.out_dataset_dir) |
||||
|
||||
splits_list = [] |
||||
for cls in CLASSES: |
||||
path_tuples = get_path_tuples( |
||||
*(osp.join(out_dir, cls, subdir) for subdir in SUBDIRS), |
||||
data_dir=args.out_dataset_dir) |
||||
splits = random_split(path_tuples, ratios=args.ratios) |
||||
splits_list.append(splits) |
||||
splits = map(partial(reduce, list.__add__), zip(*splits_list)) |
||||
|
||||
for subset, split in zip(SUBSETS, splits): |
||||
file_list = osp.join( |
||||
args.out_dataset_dir, FILE_LIST_PATTERN.format(subset=subset)) |
||||
create_file_list(file_list, split) |
||||
print(f"Write file list to {file_list}.") |
||||
|
||||
label_list = osp.join(args.out_dataset_dir, LABEL_LIST_NAME) |
||||
create_label_list(label_list, CLASSES) |
||||
print(f"Write label list to {label_list}.") |
||||
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Reference in new issue