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remove train benchmark for dsnet

pull/2580/head
cuicheng01 2023-01-03 06:18:12 +00:00 committed by cuicheng01
parent 2f331d8588
commit 62c9d0f45d
3 changed files with 0 additions and 18 deletions
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6
test_tipc/configs/DSNet/DSNet_base_train_infer_python.txt
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@ -50,11 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
-o Global.benchmark:False
null:null
null:null
===========================train_benchmark_params==========================
batch_size:128
fp_items:fp32
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]

6
test_tipc/configs/DSNet/DSNet_small_train_infer_python.txt
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@ -50,11 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
-o Global.benchmark:False
null:null
null:null
===========================train_benchmark_params==========================
batch_size:128
fp_items:fp32
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]

6
test_tipc/configs/DSNet/DSNet_tiny_train_infer_python.txt
View File

@ -50,11 +50,5 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
-o Global.benchmark:False
null:null
null:null
===========================train_benchmark_params==========================
batch_size:128
fp_items:fp32
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]