424 lines
16 KiB
Python
424 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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#
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import os
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# add deploy path of PaddleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'])))
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import sys
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sys.path.insert(0, parent_path)
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import yaml
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import cv2
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import math
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import numpy as np
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import paddle
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from keypoint_preprocess import expand_crop
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from keypoint_postprocess import HrHRNetPostProcess, HRNetPostProcess
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from visualize import visualize_pose
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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 Detector, 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 KeyPointDetector(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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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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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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use_dark(bool): whether to use postprocess in DarkPose
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"""
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def __init__(self,
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model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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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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output_dir='output',
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threshold=0.5,
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use_dark=True,
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use_fd_format=False):
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super(KeyPointDetector, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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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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output_dir=output_dir,
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threshold=threshold,
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use_fd_format=use_fd_format)
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self.use_dark = use_dark
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def set_config(self, model_dir, use_fd_format):
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return PredictConfig_KeyPoint(model_dir, use_fd_format=use_fd_format)
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def get_person_from_rect(self, image, results):
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# crop the person result from image
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self.det_times.preprocess_time_s.start()
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valid_rects = results['boxes']
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rect_images = []
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new_rects = []
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org_rects = []
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for rect in valid_rects:
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rect_image, new_rect, org_rect = expand_crop(image, rect)
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if rect_image is None or rect_image.size == 0:
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continue
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rect_images.append(rect_image)
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new_rects.append(new_rect)
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org_rects.append(org_rect)
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self.det_times.preprocess_time_s.end()
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return rect_images, new_rects, org_rects
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def postprocess(self, inputs, result):
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np_heatmap = result['heatmap']
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np_masks = result['masks']
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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_heatmap]
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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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kpts, scores = keypoint_postprocess(*preds)
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results['keypoint'] = kpts
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results['score'] = scores
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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(use_dark=self.use_dark)
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kpts, scores = keypoint_postprocess(np_heatmap, center, scale)
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results['keypoint'] = kpts
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results['score'] = scores
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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, repeats=1):
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'''
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Args:
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repeats (int): repeat number for prediction
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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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# model prediction
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np_heatmap, np_masks = None, None
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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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heatmap_tensor = self.predictor.get_output_handle(output_names[0])
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np_heatmap = heatmap_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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result = dict(heatmap=np_heatmap, masks=np_masks)
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return result
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def predict_image(self,
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image_list,
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run_benchmark=False,
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repeats=1,
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visual=True):
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results = []
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batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
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for i in range(batch_loop_cnt):
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start_index = i * self.batch_size
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end_index = min((i + 1) * self.batch_size, len(image_list))
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batch_image_list = image_list[start_index:end_index]
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if run_benchmark:
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# preprocess
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inputs = self.preprocess(batch_image_list) # warmup
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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result_warmup = self.predict(repeats=repeats) # warmup
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self.det_times.inference_time_s.start()
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result = self.predict(repeats=repeats)
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self.det_times.inference_time_s.end(repeats=repeats)
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# postprocess
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result_warmup = self.postprocess(inputs, result) # warmup
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self.det_times.postprocess_time_s.start()
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result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(batch_image_list)
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cm, gm, gu = get_current_memory_mb()
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self.cpu_mem += cm
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self.gpu_mem += gm
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self.gpu_util += gu
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else:
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# preprocess
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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self.det_times.inference_time_s.start()
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result = self.predict()
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self.det_times.inference_time_s.end()
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# postprocess
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self.det_times.postprocess_time_s.start()
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result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(batch_image_list)
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if visual:
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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visualize(
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batch_image_list,
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result,
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visual_thresh=self.threshold,
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save_dir=self.output_dir)
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results.append(result)
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if visual:
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print('Test iter {}'.format(i))
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results = self.merge_batch_result(results)
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return results
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def predict_video(self, video_file, camera_id):
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video_name = 'output.mp4'
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if camera_id != -1:
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capture = cv2.VideoCapture(camera_id)
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else:
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capture = cv2.VideoCapture(video_file)
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video_name = os.path.split(video_file)[-1]
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# Get Video info : resolution, fps, frame count
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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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fps = int(capture.get(cv2.CAP_PROP_FPS))
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frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
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print("fps: %d, frame_count: %d" % (fps, frame_count))
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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out_path = os.path.join(self.output_dir, video_name)
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fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
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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 = self.predict_image([frame[:, :, ::-1]], visual=False)
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im_results = {}
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im_results['keypoint'] = [results['keypoint'], results['score']]
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im = visualize_pose(
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frame, im_results, visual_thresh=self.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 create_inputs(imgs, im_info):
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"""generate input for different model type
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Args:
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imgs (list(numpy)): list of image (np.ndarray)
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im_info (list(dict)): list of image info
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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.stack(imgs, axis=0).astype('float32')
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im_shape = []
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for e in im_info:
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im_shape.append(np.array((e['im_shape'])).astype('float32'))
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inputs['im_shape'] = np.stack(im_shape, axis=0)
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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, use_fd_format=False):
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# parsing Yaml config for Preprocess
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fd_deploy_file = os.path.join(model_dir, 'inference.yml')
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ppdet_deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
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if use_fd_format:
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if not os.path.exists(fd_deploy_file) and os.path.exists(
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ppdet_deploy_file):
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raise RuntimeError(
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"Non-FD format model detected. Please set `use_fd_format` to False."
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)
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deploy_file = fd_deploy_file
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else:
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if not os.path.exists(ppdet_deploy_file) and os.path.exists(
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fd_deploy_file):
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raise RuntimeError(
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"FD format model detected. Please set `use_fd_format` to False."
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)
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deploy_file = ppdet_deploy_file
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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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self.use_dynamic_shape = yml_conf['use_dynamic_shape']
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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 visualize(image_list, results, visual_thresh=0.6, save_dir='output'):
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im_results = {}
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for i, image_file in enumerate(image_list):
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skeletons = results['keypoint']
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scores = results['score']
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skeleton = skeletons[i:i + 1]
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score = scores[i:i + 1]
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im_results['keypoint'] = [skeleton, score]
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visualize_pose(
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image_file,
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im_results,
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visual_thresh=visual_thresh,
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save_dir=save_dir)
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def main():
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detector = KeyPointDetector(
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FLAGS.model_dir,
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device=FLAGS.device,
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run_mode=FLAGS.run_mode,
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batch_size=FLAGS.batch_size,
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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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threshold=FLAGS.threshold,
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output_dir=FLAGS.output_dir,
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use_dark=FLAGS.use_dark,
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use_fd_format=FLAGS.use_fd_format)
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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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detector.predict_video(FLAGS.video_file, 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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detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
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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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FLAGS.device = FLAGS.device.upper()
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assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
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], "device should be CPU, GPU, XPU or NPU"
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assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
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main()
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