add main model KL transformation chain
HydrogenSulfate
parent
1fe19cb701
commit
480e40eb73
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===========================train_params===========================
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model_name:GeneralRecognition_PPLCNet_x2_5
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=100
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:pact_train
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norm_train:null
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pact_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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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:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:null
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quant_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
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fpgm_export:null
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distill_export:null
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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.save_inference_dir=./deploy/models/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_rec.py -c configs/inference_rec.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16:False
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-o Global.rec_inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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null:null
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:MobileNetV3_large_x1_0
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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pact_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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fpgm_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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distill_train:null
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to_static_train:-o Global.to_static=True
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null:null
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##
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===========================eval_params===========================
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eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
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quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
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fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
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distill_export:null
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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./deploy/models/MobileNetV3_large_x1_0_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_cls.py -c configs/inference_cls.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16:False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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null:null
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===========================train_benchmark_params==========================
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batch_size:256|640
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fp_items:fp32
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epoch:1
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--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
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flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:PPHGNet_small
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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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:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
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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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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.save_inference_dir=./deploy/models/PPHGNet_small_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:True|False
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-o Global.cpu_num_threads:1|6
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-o Global.batch_size:1|16
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-o Global.use_tensorrt:True|False
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-o Global.use_fp16:True|False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:PPLCNet_x1_0
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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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:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
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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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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.save_inference_dir=./deploy/models/PPLCNet_x1_0_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_cls.py -c configs/inference_cls.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16:False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:PPLCNetV2_base
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.first_bs:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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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:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
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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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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.save_inference_dir=./deploy/models/PPLCNetV2_base_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_cls.py -c configs/inference_cls.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:True|False
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-o Global.cpu_num_threads:1|6
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-o Global.batch_size:1|16
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-o Global.use_tensorrt:True|False
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-o Global.use_fp16:True|False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:ResNet50_vd
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=200
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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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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to_static_train:-o Global.to_static=True
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null:null
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##
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===========================eval_params===========================
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eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
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null:null
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##
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===========================infer_params==========================
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-o Global.save_inference_dir:./inference
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-o Global.pretrained_model:
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norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
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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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kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./deploy/models/ResNet50_vd_kl_quant_infer
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export2:null
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pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
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infer_model:../inference/
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infer_export:True
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infer_quant:Fasle
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inference:python/predict_cls.py -c configs/inference_cls.yaml
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-o Global.use_gpu:True|False
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-o Global.enable_mkldnn:False
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-o Global.cpu_num_threads:1
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-o Global.batch_size:1
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-o Global.use_tensorrt:False
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-o Global.use_fp16:False
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-o Global.inference_model_dir:../inference
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-o Global.infer_imgs:../dataset/ILSVRC2012/val
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-o Global.save_log_path:null
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-o Global.benchmark:True
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null:null
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null:null
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===========================train_benchmark_params==========================
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batch_size:128
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fp_items:fp32
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epoch:1
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--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
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flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
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===========================infer_benchmark_params==========================
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random_infer_input:[{float32,[3,224,224]}]
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===========================train_params===========================
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model_name:SwinTransformer_tiny_patch4_window7_224
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python:python3.7
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gpu_list:0
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-o Global.device:gpu
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-o Global.auto_cast:null
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-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
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-o Global.output_dir:./output/
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-o DataLoader.Train.sampler.batch_size:8
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-o Global.pretrained_model:null
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train_model_name:latest
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train_infer_img_dir:./dataset/ILSVRC2012/val
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null:null
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##
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trainer:norm_train
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norm_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
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pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Global.save_inference_dir=./deploy/models/SwinTransformer_tiny_patch4_window7_224_kl_quant_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:64|104
|
||||
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]}]
|
||||
|
|
@ -136,7 +136,7 @@ model_name=$(func_parser_value "${lines[1]}")
|
|||
model_url_value=$(func_parser_value "${lines[35]}")
|
||||
model_url_key=$(func_parser_key "${lines[35]}")
|
||||
|
||||
if [[ $FILENAME == *GeneralRecognition* ]]; then
|
||||
if [[ $model_name == *ShiTu* ]]; then
|
||||
cd dataset
|
||||
rm -rf Aliproduct
|
||||
rm -rf train_reg_all_data.txt
|
||||
|
|
@ -184,9 +184,11 @@ elif [[ ${MODE} = "whole_infer" ]] || [[ ${MODE} = "klquant_whole_infer" ]]; the
|
|||
cd ../../
|
||||
# download inference or pretrained model
|
||||
eval "wget -nc $model_url_value"
|
||||
if [[ $model_url_key == *inference* ]]; then
|
||||
rm -rf inference
|
||||
tar xf "${model_name}_infer.tar"
|
||||
if [[ ${model_url_value} =~ ".tar" ]]; then
|
||||
tar_name=$(func_get_url_file_name "${model_url_value}")
|
||||
echo $tar_name
|
||||
rm -rf {tar_name}
|
||||
tar xf ${tar_name}
|
||||
fi
|
||||
if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]]; then
|
||||
cmd="mv ${model_name}_22kto1k_pretrained.pdparams ${model_name}_pretrained.pdparams"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,180 @@
|
|||
#!/bin/bash
|
||||
FILENAME=$1
|
||||
source test_tipc/common_func.sh
|
||||
|
||||
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
|
||||
MODE=$2
|
||||
|
||||
dataline=$(cat ${FILENAME})
|
||||
|
||||
# parser params
|
||||
IFS=$'\n'
|
||||
lines=(${dataline})
|
||||
|
||||
# The training params
|
||||
model_name=$(func_parser_value "${lines[1]}")
|
||||
python=$(func_parser_value "${lines[2]}")
|
||||
gpu_list=$(func_parser_value "${lines[3]}")
|
||||
train_use_gpu_key=$(func_parser_key "${lines[4]}")
|
||||
train_use_gpu_value=$(func_parser_value "${lines[4]}")
|
||||
autocast_list=$(func_parser_value "${lines[5]}")
|
||||
autocast_key=$(func_parser_key "${lines[5]}")
|
||||
epoch_key=$(func_parser_key "${lines[6]}")
|
||||
epoch_num=$(func_parser_params "${lines[6]}")
|
||||
save_model_key=$(func_parser_key "${lines[7]}")
|
||||
train_batch_key=$(func_parser_key "${lines[8]}")
|
||||
train_batch_value=$(func_parser_value "${lines[8]}")
|
||||
pretrain_model_key=$(func_parser_key "${lines[9]}")
|
||||
pretrain_model_value=$(func_parser_value "${lines[9]}")
|
||||
train_model_name=$(func_parser_value "${lines[10]}")
|
||||
train_infer_img_dir=$(func_parser_value "${lines[11]}")
|
||||
train_param_key1=$(func_parser_key "${lines[12]}")
|
||||
train_param_value1=$(func_parser_value "${lines[12]}")
|
||||
|
||||
trainer_list=$(func_parser_value "${lines[14]}")
|
||||
|
||||
trainer_norm=$(func_parser_key "${lines[15]}")
|
||||
norm_trainer=$(func_parser_value "${lines[15]}")
|
||||
pact_key=$(func_parser_key "${lines[16]}")
|
||||
pact_trainer=$(func_parser_value "${lines[16]}")
|
||||
fpgm_key=$(func_parser_key "${lines[17]}")
|
||||
fpgm_trainer=$(func_parser_value "${lines[17]}")
|
||||
distill_key=$(func_parser_key "${lines[18]}")
|
||||
distill_trainer=$(func_parser_value "${lines[18]}")
|
||||
to_static_key=$(func_parser_key "${lines[19]}")
|
||||
to_static_trainer=$(func_parser_value "${lines[19]}")
|
||||
trainer_key2=$(func_parser_key "${lines[20]}")
|
||||
trainer_value2=$(func_parser_value "${lines[20]}")
|
||||
|
||||
eval_py=$(func_parser_value "${lines[23]}")
|
||||
eval_key1=$(func_parser_key "${lines[24]}")
|
||||
eval_value1=$(func_parser_value "${lines[24]}")
|
||||
|
||||
save_infer_key=$(func_parser_key "${lines[27]}")
|
||||
export_weight=$(func_parser_key "${lines[28]}")
|
||||
norm_export=$(func_parser_value "${lines[29]}")
|
||||
pact_export=$(func_parser_value "${lines[30]}")
|
||||
fpgm_export=$(func_parser_value "${lines[31]}")
|
||||
distill_export=$(func_parser_value "${lines[32]}")
|
||||
kl_quant_cmd_key=$(func_parser_key "${lines[33]}")
|
||||
kl_quant_cmd_value=$(func_parser_value "${lines[33]}")
|
||||
export_key2=$(func_parser_key "${lines[34]}")
|
||||
export_value2=$(func_parser_value "${lines[34]}")
|
||||
|
||||
# parser inference model
|
||||
infer_model_dir_list=$(func_parser_value "${lines[36]}")
|
||||
infer_export_flag=$(func_parser_value "${lines[37]}")
|
||||
infer_is_quant=$(func_parser_value "${lines[38]}")
|
||||
|
||||
# parser inference
|
||||
inference_py=$(func_parser_value "${lines[39]}")
|
||||
use_gpu_key=$(func_parser_key "${lines[40]}")
|
||||
use_gpu_list=$(func_parser_value "${lines[40]}")
|
||||
use_mkldnn_key=$(func_parser_key "${lines[41]}")
|
||||
use_mkldnn_list=$(func_parser_value "${lines[41]}")
|
||||
cpu_threads_key=$(func_parser_key "${lines[42]}")
|
||||
cpu_threads_list=$(func_parser_value "${lines[42]}")
|
||||
batch_size_key=$(func_parser_key "${lines[43]}")
|
||||
batch_size_list=$(func_parser_value "${lines[43]}")
|
||||
use_trt_key=$(func_parser_key "${lines[44]}")
|
||||
use_trt_list=$(func_parser_value "${lines[44]}")
|
||||
precision_key=$(func_parser_key "${lines[45]}")
|
||||
precision_list=$(func_parser_value "${lines[45]}")
|
||||
infer_model_key=$(func_parser_key "${lines[46]}")
|
||||
image_dir_key=$(func_parser_key "${lines[47]}")
|
||||
infer_img_dir=$(func_parser_value "${lines[47]}")
|
||||
save_log_key=$(func_parser_key "${lines[48]}")
|
||||
benchmark_key=$(func_parser_key "${lines[49]}")
|
||||
benchmark_value=$(func_parser_value "${lines[49]}")
|
||||
infer_key1=$(func_parser_key "${lines[50]}")
|
||||
infer_value1=$(func_parser_value "${lines[50]}")
|
||||
if [ ! $epoch_num ]; then
|
||||
epoch_num=2
|
||||
fi
|
||||
if [[ $MODE = 'benchmark_train' ]]; then
|
||||
epoch_num=1
|
||||
fi
|
||||
|
||||
LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
|
||||
mkdir -p ${LOG_PATH}
|
||||
status_log="${LOG_PATH}/results_python.log"
|
||||
|
||||
function func_inference() {
|
||||
IFS='|'
|
||||
_python=$1
|
||||
_script=$2
|
||||
_model_dir=$3
|
||||
_log_path=$4
|
||||
_img_dir=$5
|
||||
_flag_quant=$6
|
||||
# inference
|
||||
for use_gpu in ${use_gpu_list[*]}; do
|
||||
if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
|
||||
for use_mkldnn in ${use_mkldnn_list[*]}; do
|
||||
for threads in ${cpu_threads_list[*]}; do
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "../${status_log}" "${model_name}"
|
||||
done
|
||||
done
|
||||
done
|
||||
elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
|
||||
for use_trt in ${use_trt_list[*]}; do
|
||||
for precision in ${precision_list[*]}; do
|
||||
if [ ${precision} = "True" ] && [ ${use_trt} = "False" ]; then
|
||||
continue
|
||||
fi
|
||||
# if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
|
||||
# continue
|
||||
# fi
|
||||
for batch_size in ${batch_size_list[*]}; do
|
||||
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
|
||||
set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
|
||||
set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
|
||||
set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
|
||||
set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
|
||||
set_precision=$(func_set_params "${precision_key}" "${precision}")
|
||||
set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
|
||||
command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
eval "cat ${_save_log_path}"
|
||||
status_check $last_status "${command}" "../${status_log}" "${model_name}"
|
||||
done
|
||||
done
|
||||
done
|
||||
else
|
||||
echo "Does not support hardware other than CPU and GPU Currently!"
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
if [[ ${MODE} = "whole_infer" ]]; then
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ]; then
|
||||
env="export CUDA_VISIBLE_DEVICES=0"
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
# set CUDA_VISIBLE_DEVICES
|
||||
eval $env
|
||||
export Count=0
|
||||
cd deploy
|
||||
for infer_model in ${infer_model_dir_list[*]}; do
|
||||
#run inference
|
||||
is_quant=${infer_quant_flag[Count]}
|
||||
echo "is_quant: ${is_quant}"
|
||||
func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
Count=$(($Count + 1))
|
||||
done
|
||||
cd ..
|
||||
|
|
@ -161,51 +161,52 @@ function func_inference(){
|
|||
done
|
||||
}
|
||||
|
||||
if [[ ${MODE} = "whole_infer" ]] || [[ ${MODE} = "klquant_whole_infer" ]]; then
|
||||
IFS="|"
|
||||
infer_export_flag=(${infer_export_flag})
|
||||
if [ ${infer_export_flag} != "null" ] && [ ${infer_export_flag} != "False" ]; then
|
||||
rm -rf ${infer_model_dir_list/..\//}
|
||||
export_cmd="${python} ${norm_export} -o Global.pretrained_model=${model_name}_pretrained -o Global.save_inference_dir=${infer_model_dir_list/..\//}"
|
||||
eval $export_cmd
|
||||
fi
|
||||
fi
|
||||
# if [[ ${MODE} = "whole_infer" ]] || [[ ${MODE} = "klquant_whole_infer" ]]; then
|
||||
# IFS="|"
|
||||
# infer_export_flag=(${infer_export_flag})
|
||||
# if [ ${infer_export_flag} != "null" ] && [ ${infer_export_flag} != "False" ]; then
|
||||
# rm -rf ${infer_model_dir_list/..\//}
|
||||
# export_cmd="${python} ${norm_export} -o Global.pretrained_model=${model_name}_pretrained -o Global.save_inference_dir=${infer_model_dir_list/..\//}"
|
||||
# eval $export_cmd
|
||||
# fi
|
||||
# fi
|
||||
|
||||
# if [[ ${MODE} = "whole_infer" ]]; then
|
||||
# GPUID=$3
|
||||
# if [ ${#GPUID} -le 0 ]; then
|
||||
# env=" "
|
||||
# else
|
||||
# env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
# fi
|
||||
# # set CUDA_VISIBLE_DEVICES
|
||||
# eval $env
|
||||
# export Count=0
|
||||
# cd deploy
|
||||
# for infer_model in ${infer_model_dir_list[*]}; do
|
||||
# #run inference
|
||||
# is_quant=${infer_quant_flag[Count]}
|
||||
# echo "is_quant: ${is_quant}"
|
||||
# func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
# Count=$(($Count + 1))
|
||||
# done
|
||||
# cd ..
|
||||
|
||||
if [[ ${MODE} = "whole_infer" ]]; then
|
||||
GPUID=$3
|
||||
if [ ${#GPUID} -le 0 ];then
|
||||
env=" "
|
||||
else
|
||||
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
|
||||
fi
|
||||
# set CUDA_VISIBLE_DEVICES
|
||||
eval $env
|
||||
export Count=0
|
||||
cd deploy
|
||||
for infer_model in ${infer_model_dir_list[*]}; do
|
||||
#run inference
|
||||
is_quant=${infer_quant_flag[Count]}
|
||||
echo "is_quant: ${is_quant}"
|
||||
func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
Count=$(($Count + 1))
|
||||
done
|
||||
cd ..
|
||||
|
||||
elif [[ ${MODE} = "klquant_whole_infer" ]]; then
|
||||
# for kl_quant
|
||||
if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
|
||||
echo "kl_quant"
|
||||
command="${python} ${kl_quant_cmd_value}"
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${command}" "${status_log}" "${model_name}"
|
||||
cd inference/quant_post_static_model
|
||||
ln -s __model__ inference.pdmodel
|
||||
ln -s __params__ inference.pdiparams
|
||||
cd ../../deploy
|
||||
is_quant=True
|
||||
func_inference "${python}" "${inference_py}" "${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
cd ..
|
||||
echo "kl_quant"
|
||||
command="${python} ${kl_quant_cmd_value}"
|
||||
echo ${command}
|
||||
eval $command
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${command}" "${status_log}" "${model_name}"
|
||||
# cd inference/quant_post_static_model
|
||||
# ln -s __model__ inference.pdmodel
|
||||
# ln -s __params__ inference.pdiparams
|
||||
# cd ../../deploy
|
||||
# is_quant=True
|
||||
# func_inference "${python}" "${inference_py}" "${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
|
||||
# cd ..
|
||||
fi
|
||||
else
|
||||
IFS="|"
|
||||
|
|
|
|||
Loading…
Reference in New Issue