262 lines
9.6 KiB
Python
262 lines
9.6 KiB
Python
# Copyright (c) 2022 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 copy
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from paddle_serving_server.web_service import WebService, Op
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from paddle_serving_server.proto import general_model_config_pb2 as m_config
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import google.protobuf.text_format
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import os
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import numpy as np
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import base64
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from PIL import Image
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import io
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from preprocess_ops import Compose
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from postprocess_ops import HRNetPostProcess
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from argparse import ArgumentParser, RawDescriptionHelpFormatter
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import yaml
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# Global dictionary
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SUPPORT_MODELS = {
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'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
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'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
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'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet'
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}
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GLOBAL_VAR = {}
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class ArgsParser(ArgumentParser):
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def __init__(self):
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super(ArgsParser, self).__init__(
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formatter_class=RawDescriptionHelpFormatter)
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self.add_argument(
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"-c",
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"--config",
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default="deploy/serving/python/config.yml",
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help="configuration file to use")
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self.add_argument(
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"--model_dir",
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type=str,
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default=None,
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help=("Directory include:'model.pdiparams', 'model.pdmodel', "
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"'infer_cfg.yml', created by tools/export_model.py."),
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required=True)
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self.add_argument(
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"-o", "--opt", nargs='+', help="set configuration options")
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def parse_args(self, argv=None):
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args = super(ArgsParser, self).parse_args(argv)
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assert args.config is not None, \
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"Please specify --config=configure_file_path."
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args.service_config = self._parse_opt(args.opt, args.config)
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args.model_config = PredictConfig(args.model_dir)
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return args
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def _parse_helper(self, v):
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if v.isnumeric():
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if "." in v:
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v = float(v)
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else:
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v = int(v)
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elif v == "True" or v == "False":
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v = (v == "True")
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return v
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def _parse_opt(self, opts, conf_path):
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f = open(conf_path)
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config = yaml.load(f, Loader=yaml.Loader)
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if not opts:
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return config
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for s in opts:
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s = s.strip()
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k, v = s.split('=')
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v = self._parse_helper(v)
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if "devices" in k:
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v = str(v)
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print(k, v, type(v))
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cur = config
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parent = cur
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for kk in k.split("."):
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if kk not in cur:
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cur[kk] = {}
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parent = cur
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cur = cur[kk]
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else:
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parent = cur
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cur = cur[kk]
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parent[k.split(".")[-1]] = v
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return config
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class PredictConfig(object):
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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 infer_cfg.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.preprocess_infos = yml_conf['Preprocess']
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self.min_subgraph_size = yml_conf['min_subgraph_size']
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self.label_list = yml_conf['label_list']
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self.use_dynamic_shape = yml_conf['use_dynamic_shape']
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self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
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self.mask = yml_conf.get("mask", False)
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self.tracker = yml_conf.get("tracker", None)
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self.nms = yml_conf.get("NMS", None)
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self.fpn_stride = yml_conf.get("fpn_stride", None)
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if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
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print(
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'The RCNN export model is used for ONNX and it only supports batch_size = 1'
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)
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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 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'], 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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class DetectorOp(Op):
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def init_op(self):
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self.preprocess_pipeline = Compose(GLOBAL_VAR['preprocess_ops'])
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def preprocess(self, input_dicts, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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inputs = []
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for key, data in input_dict.items():
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data = base64.b64decode(data.encode('utf8'))
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byte_stream = io.BytesIO(data)
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img = Image.open(byte_stream).convert("RGB")
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inputs.append(self.preprocess_pipeline(img))
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inputs = self.collate_inputs(inputs)
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return inputs, False, None, ""
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def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
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(_, input_dict), = input_dicts.items()
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if GLOBAL_VAR['model_config'].arch in ["HRNet"]:
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result = self.parse_keypoint_result(input_dict, fetch_dict)
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else:
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result = self.parse_detection_result(input_dict, fetch_dict)
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return result, None, ""
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def collate_inputs(self, inputs):
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collate_inputs = {k: [] for k in inputs[0].keys()}
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for info in inputs:
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for k in collate_inputs.keys():
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collate_inputs[k].append(info[k])
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return {
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k: np.stack(v)
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for k, v in collate_inputs.items() if k in GLOBAL_VAR['feed_vars']
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}
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def parse_detection_result(self, input_dict, fetch_dict):
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bboxes = fetch_dict[GLOBAL_VAR['fetch_vars'][0]]
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bboxes_num = fetch_dict[GLOBAL_VAR['fetch_vars'][1]]
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if GLOBAL_VAR['model_config'].mask:
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masks = fetch_dict[GLOBAL_VAR['fetch_vars'][2]]
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idx = 0
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results = {}
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for img_name, num in zip(input_dict.keys(), bboxes_num):
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if num == 0:
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results[img_name] = 'No object detected!'
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else:
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result = []
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bbox = bboxes[idx:idx + num]
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for line in bbox:
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if line[0] > -1 and line[1] > GLOBAL_VAR[
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'model_config'].draw_threshold:
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result.append(
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f"{int(line[0])} {line[1]} "
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f"{line[2]} {line[3]} {line[4]} {line[5]}")
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if len(result) == 0:
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result = 'No object detected!'
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results[img_name] = result
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idx += num
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return results
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def parse_keypoint_result(self, input_dict, fetch_dict):
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heatmap = fetch_dict["conv2d_441.tmp_1"]
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keypoint_postprocess = HRNetPostProcess()
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im_shape = []
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for key, data in input_dict.items():
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data = base64.b64decode(data.encode('utf8'))
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byte_stream = io.BytesIO(data)
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img = Image.open(byte_stream).convert("RGB")
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im_shape.append([img.width, img.height])
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im_shape = np.array(im_shape)
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center = np.round(im_shape / 2.)
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scale = im_shape / 200.
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kpts, scores = keypoint_postprocess(heatmap, center, scale)
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results = {"keypoint": kpts, "scores": scores}
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return results
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class DetectorService(WebService):
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def get_pipeline_response(self, read_op):
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return DetectorOp(name="ppdet", input_ops=[read_op])
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def get_model_vars(model_dir, service_config):
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serving_server_dir = os.path.join(model_dir, "serving_server")
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# rewrite model_config
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service_config['op']['ppdet']['local_service_conf'][
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'model_config'] = serving_server_dir
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serving_server_conf = os.path.join(serving_server_dir,
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"serving_server_conf.prototxt")
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with open(serving_server_conf, 'r') as f:
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model_var = google.protobuf.text_format.Merge(
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str(f.read()), m_config.GeneralModelConfig())
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feed_vars = [var.name for var in model_var.feed_var]
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fetch_vars = [var.name for var in model_var.fetch_var]
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return feed_vars, fetch_vars
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if __name__ == '__main__':
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# load config and prepare the service
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FLAGS = ArgsParser().parse_args()
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feed_vars, fetch_vars = get_model_vars(FLAGS.model_dir,
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FLAGS.service_config)
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GLOBAL_VAR['feed_vars'] = feed_vars
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GLOBAL_VAR['fetch_vars'] = fetch_vars
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GLOBAL_VAR['preprocess_ops'] = FLAGS.model_config.preprocess_infos
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GLOBAL_VAR['model_config'] = FLAGS.model_config
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print(FLAGS)
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# define the service
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uci_service = DetectorService(name="ppdet")
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uci_service.prepare_pipeline_config(yml_dict=FLAGS.service_config)
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# start the service
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uci_service.run_service()
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