forked from PulseFocusPlatform/PulseFocusPlatform
429 lines
16 KiB
Python
429 lines
16 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import yaml
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import cv2
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import numpy as np
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import paddle
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from preprocess import preprocess
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from keypoint_postprocess import HrHRNetPostProcess, HRNetPostProcess
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from keypoint_visualize import draw_pose
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from paddle.inference import Config
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from paddle.inference import create_predictor
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from utils import argsparser, Timer, get_current_memory_mb
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from benchmark_utils import PaddleInferBenchmark
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from infer import get_test_images, print_arguments
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# Global dictionary
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KEYPOINT_SUPPORT_MODELS = {
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'HigherHRNet': 'keypoint_bottomup',
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'HRNet': 'keypoint_topdown'
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}
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class KeyPoint_Detector(object):
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"""
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Args:
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config (object): config of model, defined by `Config(model_dir)`
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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use_gpu (bool): whether use gpu
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
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use_dynamic_shape (bool): use dynamic shape or not
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
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threshold (float): threshold to reserve the result for output.
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"""
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def __init__(self,
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pred_config,
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model_dir,
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use_gpu=False,
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run_mode='fluid',
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use_dynamic_shape=False,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False):
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self.pred_config = pred_config
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self.predictor, self.config = load_predictor(
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model_dir,
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run_mode=run_mode,
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min_subgraph_size=self.pred_config.min_subgraph_size,
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use_gpu=use_gpu,
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use_dynamic_shape=use_dynamic_shape,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn)
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self.det_times = Timer()
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self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
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def preprocess(self, im):
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preprocess_ops = []
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for op_info in self.pred_config.preprocess_infos:
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new_op_info = op_info.copy()
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op_type = new_op_info.pop('type')
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preprocess_ops.append(eval(op_type)(**new_op_info))
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im, im_info = preprocess(im, preprocess_ops)
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inputs = create_inputs(im, im_info)
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return inputs
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def postprocess(self, np_boxes, np_masks, inputs, threshold=0.5):
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# postprocess output of predictor
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if KEYPOINT_SUPPORT_MODELS[
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self.pred_config.arch] == 'keypoint_bottomup':
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results = {}
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h, w = inputs['im_shape'][0]
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preds = [np_boxes]
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if np_masks is not None:
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preds += np_masks
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preds += [h, w]
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keypoint_postprocess = HrHRNetPostProcess()
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results['keypoint'] = keypoint_postprocess(*preds)
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return results
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elif KEYPOINT_SUPPORT_MODELS[
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self.pred_config.arch] == 'keypoint_topdown':
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results = {}
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imshape = inputs['im_shape'][:, ::-1]
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center = np.round(imshape / 2.)
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scale = imshape / 200.
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keypoint_postprocess = HRNetPostProcess()
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results['keypoint'] = keypoint_postprocess(np_boxes, center, scale)
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return results
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else:
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raise ValueError("Unsupported arch: {}, expect {}".format(
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self.pred_config.arch, KEYPOINT_SUPPORT_MODELS))
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def predict(self, image, threshold=0.5, warmup=0, repeats=1):
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'''
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Args:
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image (str/np.ndarray): path of image/ np.ndarray read by cv2
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threshold (float): threshold of predicted box' score
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Returns:
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results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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MaskRCNN's results include 'masks': np.ndarray:
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shape: [N, im_h, im_w]
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'''
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(image)
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np_boxes, np_masks = None, None
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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self.det_times.preprocess_time_s.end()
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for i in range(warmup):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if self.pred_config.tagmap:
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masks_tensor = self.predictor.get_output_handle(output_names[1])
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heat_k = self.predictor.get_output_handle(output_names[2])
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inds_k = self.predictor.get_output_handle(output_names[3])
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np_masks = [
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masks_tensor.copy_to_cpu(), heat_k.copy_to_cpu(),
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inds_k.copy_to_cpu()
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]
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self.det_times.inference_time_s.start()
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if self.pred_config.tagmap:
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masks_tensor = self.predictor.get_output_handle(output_names[1])
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heat_k = self.predictor.get_output_handle(output_names[2])
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inds_k = self.predictor.get_output_handle(output_names[3])
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np_masks = [
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masks_tensor.copy_to_cpu(), heat_k.copy_to_cpu(),
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inds_k.copy_to_cpu()
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]
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self.det_times.inference_time_s.end(repeats=repeats)
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self.det_times.postprocess_time_s.start()
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results = self.postprocess(
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np_boxes, np_masks, inputs, threshold=threshold)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += 1
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return results
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def create_inputs(im, im_info):
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"""generate input for different model type
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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model_arch (str): model type
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Returns:
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inputs (dict): input of model
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"""
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inputs = {}
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inputs['image'] = np.array((im, )).astype('float32')
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inputs['im_shape'] = np.array((im_info['im_shape'], )).astype('float32')
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return inputs
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class PredictConfig_KeyPoint():
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"""set config of preprocess, postprocess and visualize
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Args:
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model_dir (str): root path of model.yml
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"""
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def __init__(self, model_dir):
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# parsing Yaml config for Preprocess
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deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
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with open(deploy_file) as f:
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yml_conf = yaml.safe_load(f)
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self.check_model(yml_conf)
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self.arch = yml_conf['arch']
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self.archcls = KEYPOINT_SUPPORT_MODELS[yml_conf['arch']]
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self.preprocess_infos = yml_conf['Preprocess']
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self.min_subgraph_size = yml_conf['min_subgraph_size']
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self.labels = yml_conf['label_list']
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self.tagmap = False
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if 'keypoint_bottomup' == self.archcls:
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self.tagmap = True
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self.print_config()
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def check_model(self, yml_conf):
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"""
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Raises:
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ValueError: loaded model not in supported model type
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"""
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for support_model in KEYPOINT_SUPPORT_MODELS:
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if support_model in yml_conf['arch']:
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return True
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raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
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'arch'], KEYPOINT_SUPPORT_MODELS))
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def print_config(self):
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print('----------- Model Configuration -----------')
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print('%s: %s' % ('Model Arch', self.arch))
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print('%s: ' % ('Transform Order'))
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for op_info in self.preprocess_infos:
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print('--%s: %s' % ('transform op', op_info['type']))
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print('--------------------------------------------')
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def load_predictor(model_dir,
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run_mode='fluid',
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batch_size=1,
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use_gpu=False,
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min_subgraph_size=3,
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use_dynamic_shape=False,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False):
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"""set AnalysisConfig, generate AnalysisPredictor
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Args:
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model_dir (str): root path of __model__ and __params__
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use_gpu (bool): whether use gpu
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16/trt_int8)
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use_dynamic_shape (bool): use dynamic shape or not
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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Returns:
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predictor (PaddlePredictor): AnalysisPredictor
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Raises:
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ValueError: predict by TensorRT need use_gpu == True.
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"""
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if not use_gpu and not run_mode == 'fluid':
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raise ValueError(
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"Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
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.format(run_mode, use_gpu))
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config = Config(
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os.path.join(model_dir, 'model.pdmodel'),
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os.path.join(model_dir, 'model.pdiparams'))
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precision_map = {
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'trt_int8': Config.Precision.Int8,
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'trt_fp32': Config.Precision.Float32,
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'trt_fp16': Config.Precision.Half
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}
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if use_gpu:
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# initial GPU memory(M), device ID
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config.enable_use_gpu(200, 0)
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# optimize graph and fuse op
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config.switch_ir_optim(True)
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else:
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config.disable_gpu()
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config.set_cpu_math_library_num_threads(cpu_threads)
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if enable_mkldnn:
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try:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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except Exception as e:
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print(
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"The current environment does not support `mkldnn`, so disable mkldnn."
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)
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pass
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if run_mode in precision_map.keys():
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config.enable_tensorrt_engine(
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workspace_size=1 << 10,
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max_batch_size=batch_size,
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min_subgraph_size=min_subgraph_size,
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precision_mode=precision_map[run_mode],
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use_static=False,
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use_calib_mode=trt_calib_mode)
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if use_dynamic_shape:
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min_input_shape = {'image': [1, 3, trt_min_shape, trt_min_shape]}
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max_input_shape = {'image': [1, 3, trt_max_shape, trt_max_shape]}
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opt_input_shape = {'image': [1, 3, trt_opt_shape, trt_opt_shape]}
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config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
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opt_input_shape)
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print('trt set dynamic shape done!')
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# disable print log when predict
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config.disable_glog_info()
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# enable shared memory
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config.enable_memory_optim()
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# disable feed, fetch OP, needed by zero_copy_run
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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return predictor, config
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def predict_image(detector, image_list):
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for i, img_file in enumerate(image_list):
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if FLAGS.run_benchmark:
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detector.predict(img_file, FLAGS.threshold, warmup=10, repeats=10)
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cm, gm, gu = get_current_memory_mb()
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detector.cpu_mem += cm
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detector.gpu_mem += gm
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detector.gpu_util += gu
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print('Test iter {}, file name:{}'.format(i, img_file))
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else:
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results = detector.predict(img_file, FLAGS.threshold)
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if not os.path.exists(FLAGS.output_dir):
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os.makedirs(FLAGS.output_dir)
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draw_pose(
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img_file,
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results,
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visual_thread=FLAGS.threshold,
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save_dir=FLAGS.output_dir)
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def predict_video(detector, camera_id):
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if camera_id != -1:
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capture = cv2.VideoCapture(camera_id)
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video_name = 'output.mp4'
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else:
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capture = cv2.VideoCapture(FLAGS.video_file)
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video_name = os.path.splitext(os.path.basename(FLAGS.video_file))[
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0] + '.mp4'
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fps = 30
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width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# yapf: disable
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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# yapf: enable
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if not os.path.exists(FLAGS.output_dir):
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os.makedirs(FLAGS.output_dir)
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out_path = os.path.join(FLAGS.output_dir, video_name + '.mp4')
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writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
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index = 1
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while (1):
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ret, frame = capture.read()
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if not ret:
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break
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print('detect frame:%d' % (index))
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index += 1
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results = detector.predict(frame, FLAGS.threshold)
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im = draw_pose(
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frame, results, visual_thread=FLAGS.threshold, returnimg=True)
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writer.write(im)
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if camera_id != -1:
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cv2.imshow('Mask Detection', im)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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writer.release()
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def main():
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pred_config = PredictConfig_KeyPoint(FLAGS.model_dir)
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detector = KeyPoint_Detector(
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pred_config,
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FLAGS.model_dir,
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use_gpu=FLAGS.use_gpu,
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run_mode=FLAGS.run_mode,
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use_dynamic_shape=FLAGS.use_dynamic_shape,
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trt_min_shape=FLAGS.trt_min_shape,
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trt_max_shape=FLAGS.trt_max_shape,
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trt_opt_shape=FLAGS.trt_opt_shape,
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trt_calib_mode=FLAGS.trt_calib_mode,
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cpu_threads=FLAGS.cpu_threads,
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enable_mkldnn=FLAGS.enable_mkldnn)
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# predict from video file or camera video stream
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if FLAGS.video_file is not None or FLAGS.camera_id != -1:
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predict_video(detector, FLAGS.camera_id)
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else:
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# predict from image
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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predict_image(detector, img_list)
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if not FLAGS.run_benchmark:
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detector.det_times.info(average=True)
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else:
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mems = {
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'cpu_rss_mb': detector.cpu_mem / len(img_list),
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'gpu_rss_mb': detector.gpu_mem / len(img_list),
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'gpu_util': detector.gpu_util * 100 / len(img_list)
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}
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perf_info = detector.det_times.report(average=True)
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model_dir = FLAGS.model_dir
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mode = FLAGS.run_mode
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model_info = {
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'model_name': model_dir.strip('/').split('/')[-1],
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'precision': mode.split('_')[-1]
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}
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data_info = {
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'batch_size': 1,
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'shape': "dynamic_shape",
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'data_num': perf_info['img_num']
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}
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det_log = PaddleInferBenchmark(detector.config, model_info,
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data_info, perf_info, mems)
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det_log('KeyPoint')
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if __name__ == '__main__':
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paddle.enable_static()
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parser = argsparser()
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FLAGS = parser.parse_args()
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print_arguments(FLAGS)
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main()
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