diff --git a/deploy/auto_log.log b/deploy/auto_log.log new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deploy/python/predict_cls.py b/deploy/python/predict_cls.py index b161512f7f..dc6865404e 100644 --- a/deploy/python/predict_cls.py +++ b/deploy/python/predict_cls.py @@ -27,6 +27,7 @@ from python.preprocess import create_operators from python.postprocess import build_postprocess + class ClsPredictor(Predictor): def __init__(self, config): super().__init__(config["Global"]) @@ -40,6 +41,29 @@ def __init__(self, config): if "PostProcess" in config: self.postprocess = build_postprocess(config["PostProcess"]) + # for whole_chain project to test each repo of paddle + self.benchmark = config["Global"].get("benchmark", False) + if self.benchmark: + import auto_log + import os + pid = os.getpid() + self.auto_logger = auto_log.AutoLogger( + model_name=config["Global"].get("model_name", "cls"), + model_precision='fp16' + if config["Global"]["use_fp16"] else 'fp32', + batch_size=config["Global"].get("batch_size", 1), + data_shape=[3, 224, 224], + save_path=config["Global"].get("save_log_path", + "./auto_log.log"), + inference_config=self.config, + pids=pid, + process_name=None, + gpu_ids=None, + time_keys=[ + 'preprocess_time', 'inference_time', 'postprocess_time' + ], + warmup=2) + def predict(self, images): input_names = self.paddle_predictor.get_input_names() input_tensor = self.paddle_predictor.get_input_handle(input_names[0]) @@ -48,18 +72,26 @@ def predict(self, images): output_tensor = self.paddle_predictor.get_output_handle(output_names[ 0]) + if self.benchmark: + self.auto_logger.times.start() if not isinstance(images, (list, )): images = [images] for idx in range(len(images)): for ops in self.preprocess_ops: images[idx] = ops(images[idx]) image = np.array(images) + if self.benchmark: + self.auto_logger.times.stamp() input_tensor.copy_from_cpu(image) self.paddle_predictor.run() batch_output = output_tensor.copy_to_cpu() + if self.benchmark: + self.auto_logger.times.stamp() if self.postprocess is not None: batch_output = self.postprocess(batch_output) + if self.benchmark: + self.auto_logger.times.end(stamp=True) return batch_output @@ -83,10 +115,11 @@ def main(config): batch_names.append(img_name) cnt += 1 - if cnt % config["Global"]["batch_size"] == 0 or (idx + 1) == len(image_list): - if len(batch_imgs) == 0: + if cnt % config["Global"]["batch_size"] == 0 or (idx + 1 + ) == len(image_list): + if len(batch_imgs) == 0: continue - + batch_results = cls_predictor.predict(batch_imgs) for number, result_dict in enumerate(batch_results): filename = batch_names[number] @@ -98,8 +131,11 @@ def main(config): format(filename, clas_ids, scores_str, label_names)) batch_imgs = [] batch_names = [] + if cls_predictor.benchmark: + cls_predictor.auto_logger.report() return + if __name__ == "__main__": args = config.parse_args() config = config.get_config(args.config, overrides=args.override, show=True) diff --git a/deploy/utils/predictor.py b/deploy/utils/predictor.py index 7757aa1e12..11f153071a 100644 --- a/deploy/utils/predictor.py +++ b/deploy/utils/predictor.py @@ -28,7 +28,7 @@ def __init__(self, args, inference_model_dir=None): if args.use_fp16 is True: assert args.use_tensorrt is True self.args = args - self.paddle_predictor = self.create_paddle_predictor( + self.paddle_predictor, self.config = self.create_paddle_predictor( args, inference_model_dir) def predict(self, image): @@ -59,11 +59,12 @@ def create_paddle_predictor(self, args, inference_model_dir=None): config.enable_tensorrt_engine( precision_mode=Config.Precision.Half if args.use_fp16 else Config.Precision.Float32, - max_batch_size=args.batch_size) + max_batch_size=args.batch_size, + min_subgraph_size=30) config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) - return predictor + return predictor, config diff --git a/tests/DarkNet53.txt b/tests/DarkNet53.txt new file mode 100644 index 0000000000..e5a9adb862 --- /dev/null +++ b/tests/DarkNet53.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:DarkNet53 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/DarkNet/DarkNet53.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/DarkNet/DarkNet53.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +infer_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/DarkNet53_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/HRNet_W18_C.txt b/tests/HRNet_W18_C.txt new file mode 100644 index 0000000000..08c712accc --- /dev/null +++ b/tests/HRNet_W18_C.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:HRNet_W18_C +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W18_C.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/HRNet/HRNet_W18_C.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/HRNet_W18_C_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/LeViT_128S.txt b/tests/LeViT_128S.txt new file mode 100644 index 0000000000..337d8af770 --- /dev/null +++ b/tests/LeViT_128S.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:LeViT_128S +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_128S.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/LeViT/LeViT_128S.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +infer_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/LeViT_128S_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|Fasle +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/MobileNetV1.txt b/tests/MobileNetV1.txt new file mode 100644 index 0000000000..784d6f3083 --- /dev/null +++ b/tests/MobileNetV1.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:MobileNetV1 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.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/MobileNetV1/MobileNetV1.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/MobileNetV1_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/MobileNetV2.txt b/tests/MobileNetV2.txt new file mode 100644 index 0000000000..c622100fea --- /dev/null +++ b/tests/MobileNetV2.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:MobileNetV2 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.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/MobileNetV2/MobileNetV2.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +infer_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/MobileNetV2_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/MobileNetV3_large_x1_0.txt b/tests/MobileNetV3_large_x1_0.txt new file mode 100644 index 0000000000..2bc4ec43fc --- /dev/null +++ b/tests/MobileNetV3_large_x1_0.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:MobileNetV3_large_x1_0 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml +pact_train:deploy/slim/slim.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml +fpgm_train:deploy/slim/slim.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.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/MobileNetV3/MobileNetV3_large_x1_0.yaml +quant_export:deploy/slim/slim.py -m export -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantalization.yaml +fpgm_export:deploy/slim/slim.py -m export -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml +distill_export:null +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/MobileNetV3_large_x1_0_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/ResNeXt101_vd_64x4d.txt b/tests/ResNeXt101_vd_64x4d.txt new file mode 100644 index 0000000000..5a4a088d73 --- /dev/null +++ b/tests/ResNeXt101_vd_64x4d.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:ResNeXt101_vd_64x4d +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.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/ResNeXt/ResNeXt101_vd_64x4d.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNeXt101_64x4d_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/ResNet50_vd.txt b/tests/ResNet50_vd.txt new file mode 100644 index 0000000000..da02c8894b --- /dev/null +++ b/tests/ResNet50_vd.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:ResNet50_vd +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml +pact_train:deploy/slim/slim.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml +fpgm_train:deploy/slim/slim.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.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/ResNet/ResNet50_vd.yaml +quant_export:deploy/slim/slim.py -m export -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml +fpgm_export:deploy/slim/slim.py -m export -c ppcls/configs/slim/ResNet50_vd_prune.yaml +distill_export:null +export1:null +export2:null +infer_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/ShuffleNetV2_x1_0.txt b/tests/ShuffleNetV2_x1_0.txt new file mode 100644 index 0000000000..08964a2f0f --- /dev/null +++ b/tests/ShuffleNetV2_x1_0.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:ShuffleNetV2_x1_0 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.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/ShuffleNet/ShuffleNetV2_x1_0.yaml +quant_export:null +fpgm_export:null +distill_export:null +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ShuffleNetV2_x1_0_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/SwinTransformer_tiny_patch4_window7_224.txt b/tests/SwinTransformer_tiny_patch4_window7_224.txt new file mode 100644 index 0000000000..a358d191a0 --- /dev/null +++ b/tests/SwinTransformer_tiny_patch4_window7_224.txt @@ -0,0 +1,51 @@ +===========================train_params=========================== +model_name:SwinTransformer_tiny_patch4_window7_224 +python:python3.7 +gpu_list:0|0,1 +-o Global.device:gpu +-o Global.auto_cast:null +-o Global.epochs:lite_train_infer=2|whole_train_infer=120 +-o Global.output_dir:./output/ +-o DataLoader.Train.sampler.batch_size:8 +-o Global.pretrained_model:null +train_model_name:latest +train_infer_img_dir:./dataset/ILSVRC2012/val +null:null +## +trainer:norm_train +norm_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml +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 +export1:null +export2:null +inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/SwinTransformer_tiny_patch4_window7_224_inference.tar +infer_model:../inference/ +infer_export:null +infer_quant:Fasle +inference:python/predict_cls.py -c configs/inference_cls.yaml +-o Global.use_gpu:True|False +-o Global.enable_mkldnn:True|False +-o Global.cpu_num_threads:1|6 +-o Global.batch_size:1 +-o Global.use_tensorrt:True|False +-o Global.use_fp16:True|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 diff --git a/tests/prepare.sh b/tests/prepare.sh new file mode 100644 index 0000000000..55e1f2c7f0 --- /dev/null +++ b/tests/prepare.sh @@ -0,0 +1,60 @@ +#!/bin/bash +FILENAME=$1 +# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer'] +MODE=$2 + +dataline=$(cat ${FILENAME}) +# parser params +IFS=$'\n' +lines=(${dataline}) +function func_parser_value(){ + strs=$1 + IFS=":" + array=(${strs}) + if [ ${#array[*]} = 2 ]; then + echo ${array[1]} + else + IFS="|" + tmp="${array[1]}:${array[2]}" + echo ${tmp} + fi +} +model_name=$(func_parser_value "${lines[1]}") +inference_model_url=$(func_parser_value "${lines[35]}") + +if [ ${MODE} = "lite_train_infer" ] || [ ${MODE} = "whole_infer" ];then + # pretrain lite train data + cd dataset + rm -rf ILSVRC2012 + wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_little_train.tar + tar xf whole_chain_little_train.tar + ln -s whole_chain_little_train ILSVRC2012 + cd ILSVRC2012 + mv train.txt train_list.txt + mv val.txt val_list.txt + cd ../../ +elif [ ${MODE} = "infer" ];then + # download data + cd dataset + rm -rf ILSVRC2012 + wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar + tar xf whole_chain_infer.tar + ln -s whole_chain_infer ILSVRC2012 + cd ILSVRC2012 + mv val.txt val_list.txt + cd ../../ + # download inference model + eval "wget -nc $inference_model_url" + tar xf "${model_name}_inference.tar" + +elif [ ${MODE} = "whole_train_infer" ];then + cd dataset + rm -rf ILSVRC2012 + wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_CIFAR100.tar + tar xf whole_chain_CIFAR100.tar + ln -s whole_chain_CIFAR100 ILSVRC2012 + cd ILSVRC2012 + mv train.txt train_list.txt + mv val.txt val_list.txt + cd ../../ +fi diff --git a/tests/test.sh b/tests/test.sh new file mode 100644 index 0000000000..7259b24c4f --- /dev/null +++ b/tests/test.sh @@ -0,0 +1,363 @@ +#!/bin/bash +FILENAME=$1 +# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer'] +MODE=$2 + +dataline=$(cat ${FILENAME}) + +# parser params +IFS=$'\n' +lines=(${dataline}) + +function func_parser_key(){ + strs=$1 + IFS=":" + array=(${strs}) + tmp=${array[0]} + echo ${tmp} +} +function func_parser_value(){ + strs=$1 + IFS=":" + array=(${strs}) + tmp=${array[1]} + echo ${tmp} +} +function func_set_params(){ + key=$1 + value=$2 + if [ ${key} = "null" ];then + echo " " + elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then + echo " " + else + echo "${key}=${value}" + fi +} +function func_parser_params(){ + strs=$1 + IFS=":" + array=(${strs}) + key=${array[0]} + tmp=${array[1]} + IFS="|" + res="" + for _params in ${tmp[*]}; do + IFS="=" + array=(${_params}) + mode=${array[0]} + value=${array[1]} + if [[ ${mode} = ${MODE} ]]; then + IFS="|" + #echo $(func_set_params "${mode}" "${value}") + echo $value + break + fi + IFS="|" + done + echo ${res} +} +function status_check(){ + last_status=$1 # the exit code + run_command=$2 + run_log=$3 + if [ $last_status -eq 0 ]; then + echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log} + else + echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log} + fi +} + +IFS=$'\n' +# 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=$(func_parser_params "${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]}") +trainer_key1=$(func_parser_key "${lines[19]}") +trainer_value1=$(func_parser_value "${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]}") +export_key1=$(func_parser_key "${lines[33]}") +export_value1=$(func_parser_value "${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_list=$(func_parser_value "${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]}") + +LOG_PATH="./tests/output" +mkdir -p ${LOG_PATH} +status_log="${LOG_PATH}/results.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 + if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then + continue + fi + 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}" + 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} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${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}" + + done + done + done + else + echo "Does not support hardware other than CPU and GPU Currently!" + fi + done +} + +if [ ${MODE} = "infer" ]; then + GPUID=$3 + if [ ${#GPUID} -le 0 ];then + env=" " + else + env="export CUDA_VISIBLE_DEVICES=${GPUID}" + fi + # set CUDA_VISIBLE_DEVICES + eval $env + export Count=0 + IFS="|" + infer_run_exports=(${infer_export_list}) + infer_quant_flag=(${infer_is_quant}) + cd deploy + for infer_model in ${infer_model_dir_list[*]}; do + # run export + if [ ${infer_run_exports[Count]} != "null" ];then + set_export_weight=$(func_set_params "${export_weight}" "${infer_model}") + set_save_infer_key=$(func_set_params "${save_infer_key}" "${infer_model}") + export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}" + eval $export_cmd + status_export=$? + if [ ${status_export} = 0 ];then + status_check $status_export "${export_cmd}" "../${status_log}" + fi + fi + #run inference + is_quant=${infer_quant_flag[Count]} + echo "is_quant: ${is_quant}" + func_inference "${python}" "${inference_py}" "${infer_model}" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant} + Count=$(($Count + 1)) + done + cd .. + +else + IFS="|" + export Count=0 + USE_GPU_KEY=(${train_use_gpu_value}) + for gpu in ${gpu_list[*]}; do + use_gpu=${USE_GPU_KEY[Count]} + Count=$(($Count + 1)) + if [ ${gpu} = "-1" ];then + env="" + elif [ ${#gpu} -le 1 ];then + env="export CUDA_VISIBLE_DEVICES=${gpu}" + eval ${env} + elif [ ${#gpu} -le 15 ];then + IFS="," + array=(${gpu}) + env="export CUDA_VISIBLE_DEVICES=${array[0]}" + IFS="|" + else + IFS=";" + array=(${gpu}) + ips=${array[0]} + gpu=${array[1]} + IFS="|" + env=" " + fi + for autocast in ${autocast_list[*]}; do + for trainer in ${trainer_list[*]}; do + flag_quant=False + if [ ${trainer} = ${pact_key} ]; then + run_train=${pact_trainer} + run_export=${pact_export} + flag_quant=True + elif [ ${trainer} = "${fpgm_key}" ]; then + run_train=${fpgm_trainer} + run_export=${fpgm_export} + elif [ ${trainer} = "${distill_key}" ]; then + run_train=${distill_trainer} + run_export=${distill_export} + elif [ ${trainer} = ${trainer_key1} ]; then + run_train=${trainer_value1} + run_export=${export_value1} + elif [[ ${trainer} = ${trainer_key2} ]]; then + run_train=${trainer_value2} + run_export=${export_value2} + else + run_train=${norm_trainer} + run_export=${norm_export} + fi + + if [ ${run_train} = "null" ]; then + continue + fi + + set_autocast=$(func_set_params "${autocast_key}" "${autocast}") + set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}") + set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}") + set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}") + set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}") + set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}") + save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}" + + # load pretrain from norm training if current trainer is pact or fpgm trainer + if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then + set_pretrain="${load_norm_train_model}" + fi + + set_save_model=$(func_set_params "${save_model_key}" "${save_log}") + if [ ${#gpu} -le 2 ];then # train with cpu or single gpu + cmd="${python} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} " + elif [ ${#gpu} -le 15 ];then # train with multi-gpu + cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}" + else # train with multi-machine + cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}" + fi + # run train + eval "unset CUDA_VISIBLE_DEVICES" + eval $cmd + status_check $? "${cmd}" "${status_log}" + + set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${$model_name}/${train_model_name}") + # save norm trained models to set pretrain for pact training and fpgm training + if [ ${trainer} = ${trainer_norm} ]; then + load_norm_train_model=${set_eval_pretrain} + fi + # run eval + if [ ${eval_py} != "null" ]; then + set_eval_params1=$(func_set_params "${eval_key1}" "${eval_value1}") + eval_cmd="${python} ${eval_py} ${set_eval_pretrain} ${set_use_gpu} ${set_eval_params1}" + eval $eval_cmd + status_check $? "${eval_cmd}" "${status_log}" + fi + # run export model + if [ ${run_export} != "null" ]; then + # run export model + save_infer_path="${save_log}" + set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}") + set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}") + export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}" + eval $export_cmd + status_check $? "${export_cmd}" "${status_log}" + + #run inference + eval $env + save_infer_path="${save_log}" + cd deploy + func_inference "${python}" "${inference_py}" "../${save_infer_path}" "../${LOG_PATH}" "${infer_img_dir}" "${flag_quant}" + cd .. + fi + eval "unset CUDA_VISIBLE_DEVICES" + done # done with: for trainer in ${trainer_list[*]}; do + done # done with: for autocast in ${autocast_list[*]}; do + done # done with: for gpu in ${gpu_list[*]}; do +fi # end if [ ${MODE} = "infer" ]; then