forked from PulseFocusPlatform/PulseFocusPlatform
620 lines
23 KiB
Python
620 lines
23 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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import argparse
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import time
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import yaml
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import ast
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from functools import reduce
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import cv2
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from preprocess import preprocess, Resize, Normalize, Permute, PadStride
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from visualize import visualize_box_mask, lmk2out
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# Global dictionary
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SUPPORT_MODELS = {
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'YOLO',
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'SSD',
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'RetinaNet',
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'EfficientDet',
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'RCNN',
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'Face',
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'TTF',
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'FCOS',
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'SOLOv2',
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}
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class 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__, __params__ 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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threshold (float): threshold to reserve the result for output.
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"""
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def __init__(self,
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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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threshold=0.5,
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trt_calib_mode=False):
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self.config = config
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if self.config.use_python_inference:
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self.executor, self.program, self.fecth_targets = load_executor(
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model_dir, use_gpu=use_gpu)
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else:
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self.predictor = load_predictor(
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model_dir,
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run_mode=run_mode,
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min_subgraph_size=self.config.min_subgraph_size,
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use_gpu=use_gpu,
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trt_calib_mode=trt_calib_mode)
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def preprocess(self, im):
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preprocess_ops = []
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for op_info in self.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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if op_type == 'Resize':
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new_op_info['arch'] = self.config.arch
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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, self.config.arch)
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return inputs, im_info
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def postprocess(self, np_boxes, np_masks, np_lmk, im_info, threshold=0.5):
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# postprocess output of predictor
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results = {}
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if np_lmk is not None:
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results['landmark'] = lmk2out(np_boxes, np_lmk, im_info, threshold)
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if self.config.arch in ['SSD', 'Face']:
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w, h = im_info['origin_shape']
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np_boxes[:, 2] *= h
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np_boxes[:, 3] *= w
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np_boxes[:, 4] *= h
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np_boxes[:, 5] *= w
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expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
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np_boxes = np_boxes[expect_boxes, :]
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for box in np_boxes:
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print('class_id:{:d}, confidence:{:.4f},'
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'left_top:[{:.2f},{:.2f}],'
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' right_bottom:[{:.2f},{:.2f}]'.format(
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int(box[0]), box[1], box[2], box[3], box[4], box[5]))
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results['boxes'] = np_boxes
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if np_masks is not None:
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np_masks = np_masks[expect_boxes, :, :, :]
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results['masks'] = np_masks
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return results
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def predict(self,
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image,
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threshold=0.5,
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warmup=0,
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repeats=1,
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run_benchmark=False):
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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, class_num, mask_resolution, mask_resolution]
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'''
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inputs, im_info = self.preprocess(image)
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np_boxes, np_masks, np_lmk = None, None, None
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if self.config.use_python_inference:
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for i in range(warmup):
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outs = self.executor.run(self.program,
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feed=inputs,
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fetch_list=self.fecth_targets,
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return_numpy=False)
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t1 = time.time()
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for i in range(repeats):
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outs = self.executor.run(self.program,
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feed=inputs,
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fetch_list=self.fecth_targets,
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return_numpy=False)
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t2 = time.time()
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ms = (t2 - t1) * 1000.0 / repeats
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print("Inference: {} ms per batch image".format(ms))
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np_boxes = np.array(outs[0])
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if self.config.mask_resolution is not None:
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np_masks = np.array(outs[1])
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else:
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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_tensor(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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for i in range(warmup):
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self.predictor.zero_copy_run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_tensor(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if self.config.mask_resolution is not None:
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masks_tensor = self.predictor.get_output_tensor(
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output_names[1])
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np_masks = masks_tensor.copy_to_cpu()
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if self.config.with_lmk is not None and self.config.with_lmk == True:
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face_index = self.predictor.get_output_tensor(output_names[
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1])
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landmark = self.predictor.get_output_tensor(output_names[2])
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prior_boxes = self.predictor.get_output_tensor(output_names[
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3])
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np_face_index = face_index.copy_to_cpu()
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np_prior_boxes = prior_boxes.copy_to_cpu()
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np_landmark = landmark.copy_to_cpu()
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np_lmk = [np_face_index, np_landmark, np_prior_boxes]
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t1 = time.time()
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for i in range(repeats):
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self.predictor.zero_copy_run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_tensor(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if self.config.mask_resolution is not None:
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masks_tensor = self.predictor.get_output_tensor(
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output_names[1])
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np_masks = masks_tensor.copy_to_cpu()
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if self.config.with_lmk is not None and self.config.with_lmk == True:
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face_index = self.predictor.get_output_tensor(output_names[
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1])
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landmark = self.predictor.get_output_tensor(output_names[2])
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prior_boxes = self.predictor.get_output_tensor(output_names[
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3])
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np_face_index = face_index.copy_to_cpu()
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np_prior_boxes = prior_boxes.copy_to_cpu()
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np_landmark = landmark.copy_to_cpu()
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np_lmk = [np_face_index, np_landmark, np_prior_boxes]
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t2 = time.time()
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ms = (t2 - t1) * 1000.0 / repeats
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print("Inference: {} ms per batch image".format(ms))
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# do not perform postprocess in benchmark mode
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results = []
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if not run_benchmark:
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if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
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print('[WARNNING] No object detected.')
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results = {'boxes': np.array([])}
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else:
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results = self.postprocess(
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np_boxes, np_masks, np_lmk, im_info, threshold=threshold)
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return results
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class DetectorSOLOv2(Detector):
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def __init__(self,
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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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threshold=0.5,
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trt_calib_mode=False):
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super(DetectorSOLOv2, self).__init__(
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config=config,
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model_dir=model_dir,
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use_gpu=use_gpu,
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run_mode=run_mode,
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threshold=threshold,
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trt_calib_mode=trt_calib_mode)
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def predict(self,
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image,
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threshold=0.5,
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warmup=0,
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repeats=1,
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run_benchmark=False):
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inputs, im_info = self.preprocess(image)
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np_label, np_score, np_segms = None, None, None
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if self.config.use_python_inference:
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for i in range(warmup):
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outs = self.executor.run(self.program,
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feed=inputs,
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fetch_list=self.fecth_targets,
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return_numpy=False)
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t1 = time.time()
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for i in range(repeats):
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outs = self.executor.run(self.program,
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feed=inputs,
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fetch_list=self.fecth_targets,
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return_numpy=False)
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t2 = time.time()
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ms = (t2 - t1) * 1000.0 / repeats
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print("Inference: {} ms per batch image".format(ms))
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np_label, np_score, np_segms = np.array(outs[0]), np.array(outs[
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1]), np.array(outs[2])
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else:
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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_tensor(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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for i in range(warmup):
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self.predictor.zero_copy_run()
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output_names = self.predictor.get_output_names()
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np_label = self.predictor.get_output_tensor(output_names[
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0]).copy_to_cpu()
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np_score = self.predictor.get_output_tensor(output_names[
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1]).copy_to_cpu()
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np_segms = self.predictor.get_output_tensor(output_names[
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2]).copy_to_cpu()
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t1 = time.time()
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for i in range(repeats):
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self.predictor.zero_copy_run()
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output_names = self.predictor.get_output_names()
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np_label = self.predictor.get_output_tensor(output_names[
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0]).copy_to_cpu()
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np_score = self.predictor.get_output_tensor(output_names[
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1]).copy_to_cpu()
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np_segms = self.predictor.get_output_tensor(output_names[
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2]).copy_to_cpu()
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t2 = time.time()
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ms = (t2 - t1) * 1000.0 / repeats
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print("Inference: {} ms per batch image".format(ms))
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# do not perform postprocess in benchmark mode
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results = []
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if not run_benchmark:
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return dict(segm=np_segms, label=np_label, score=np_score)
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return results
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def create_inputs(im, im_info, model_arch='YOLO'):
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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'] = im
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origin_shape = list(im_info['origin_shape'])
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resize_shape = list(im_info['resize_shape'])
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pad_shape = list(im_info['pad_shape']) if im_info[
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'pad_shape'] is not None else list(im_info['resize_shape'])
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scale_x, scale_y = im_info['scale']
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if 'YOLO' in model_arch:
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im_size = np.array([origin_shape]).astype('int32')
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inputs['im_size'] = im_size
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elif 'RetinaNet' in model_arch or 'EfficientDet' in model_arch:
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scale = scale_x
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im_info = np.array([pad_shape + [scale]]).astype('float32')
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inputs['im_info'] = im_info
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elif ('RCNN' in model_arch) or ('FCOS' in model_arch):
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scale = scale_x
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im_info = np.array([pad_shape + [scale]]).astype('float32')
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im_shape = np.array([origin_shape + [1.]]).astype('float32')
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inputs['im_info'] = im_info
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inputs['im_shape'] = im_shape
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elif 'TTF' in model_arch:
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scale_factor = np.array([scale_x, scale_y] * 2).astype('float32')
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inputs['scale_factor'] = scale_factor
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elif 'SOLOv2' in model_arch:
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scale = scale_x
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im_info = np.array([resize_shape + [scale]]).astype('float32')
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inputs['im_info'] = im_info
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return inputs
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class Config():
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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.preprocess_infos = yml_conf['Preprocess']
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self.use_python_inference = yml_conf['use_python_inference']
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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.mask_resolution = None
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if 'mask_resolution' in yml_conf:
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self.mask_resolution = yml_conf['mask_resolution']
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self.with_lmk = None
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if 'with_lmk' in yml_conf:
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self.with_lmk = yml_conf['with_lmk']
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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: %s' % ('Use Paddle Executor', self.use_python_inference))
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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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trt_calib_mode=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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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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precision_map = {
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'trt_int8': fluid.core.AnalysisConfig.Precision.Int8,
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'trt_fp32': fluid.core.AnalysisConfig.Precision.Float32,
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'trt_fp16': fluid.core.AnalysisConfig.Precision.Half
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}
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config = fluid.core.AnalysisConfig(
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os.path.join(model_dir, '__model__'),
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os.path.join(model_dir, '__params__'))
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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(100, 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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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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# 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 = fluid.core.create_paddle_predictor(config)
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return predictor
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def load_executor(model_dir, use_gpu=False):
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if use_gpu:
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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program, feed_names, fetch_targets = fluid.io.load_inference_model(
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dirname=model_dir,
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executor=exe,
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model_filename='__model__',
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params_filename='__params__')
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return exe, program, fetch_targets
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def visualize(image_file,
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results,
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labels,
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mask_resolution=14,
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output_dir='output/',
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threshold=0.5):
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# visualize the predict result
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im = visualize_box_mask(
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image_file,
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results,
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labels,
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mask_resolution=mask_resolution,
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threshold=threshold)
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img_name = os.path.split(image_file)[-1]
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
|
|
out_path = os.path.join(output_dir, img_name)
|
|
im.save(out_path, quality=95)
|
|
print("save result to: " + out_path)
|
|
|
|
|
|
def print_arguments(args):
|
|
print('----------- Running Arguments -----------')
|
|
for arg, value in sorted(vars(args).items()):
|
|
print('%s: %s' % (arg, value))
|
|
print('------------------------------------------')
|
|
|
|
|
|
def predict_image(detector):
|
|
if FLAGS.run_benchmark:
|
|
detector.predict(
|
|
FLAGS.image_file,
|
|
FLAGS.threshold,
|
|
warmup=100,
|
|
repeats=100,
|
|
run_benchmark=True)
|
|
else:
|
|
results = detector.predict(FLAGS.image_file, FLAGS.threshold)
|
|
visualize(
|
|
FLAGS.image_file,
|
|
results,
|
|
detector.config.labels,
|
|
mask_resolution=detector.config.mask_resolution,
|
|
output_dir=FLAGS.output_dir,
|
|
threshold=FLAGS.threshold)
|
|
|
|
|
|
def predict_video(detector, camera_id):
|
|
if camera_id != -1:
|
|
capture = cv2.VideoCapture(camera_id)
|
|
video_name = 'output.mp4'
|
|
else:
|
|
capture = cv2.VideoCapture(FLAGS.video_file)
|
|
video_name = os.path.split(FLAGS.video_file)[-1]
|
|
fps = 30
|
|
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
# yapf: disable
|
|
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
|
# yapf: enable
|
|
if not os.path.exists(FLAGS.output_dir):
|
|
os.makedirs(FLAGS.output_dir)
|
|
out_path = os.path.join(FLAGS.output_dir, video_name)
|
|
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
|
|
index = 1
|
|
while (1):
|
|
ret, frame = capture.read()
|
|
if not ret:
|
|
break
|
|
print('detect frame:%d' % (index))
|
|
index += 1
|
|
results = detector.predict(frame, FLAGS.threshold)
|
|
im = visualize_box_mask(
|
|
frame,
|
|
results,
|
|
detector.config.labels,
|
|
mask_resolution=detector.config.mask_resolution,
|
|
threshold=FLAGS.threshold)
|
|
im = np.array(im)
|
|
writer.write(im)
|
|
if camera_id != -1:
|
|
cv2.imshow('Mask Detection', im)
|
|
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
break
|
|
writer.release()
|
|
|
|
|
|
def main():
|
|
config = Config(FLAGS.model_dir)
|
|
detector = Detector(
|
|
config,
|
|
FLAGS.model_dir,
|
|
use_gpu=FLAGS.use_gpu,
|
|
run_mode=FLAGS.run_mode,
|
|
trt_calib_mode=FLAGS.trt_calib_mode)
|
|
if config.arch == 'SOLOv2':
|
|
detector = DetectorSOLOv2(
|
|
config,
|
|
FLAGS.model_dir,
|
|
use_gpu=FLAGS.use_gpu,
|
|
run_mode=FLAGS.run_mode,
|
|
trt_calib_mode=FLAGS.trt_calib_mode)
|
|
# predict from image
|
|
if FLAGS.image_file != '':
|
|
predict_image(detector)
|
|
# predict from video file or camera video stream
|
|
if FLAGS.video_file != '' or FLAGS.camera_id != -1:
|
|
predict_video(detector, FLAGS.camera_id)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
try:
|
|
paddle.enable_static()
|
|
except:
|
|
pass
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument(
|
|
"--model_dir",
|
|
type=str,
|
|
default=None,
|
|
help=("Directory include:'__model__', '__params__', "
|
|
"'infer_cfg.yml', created by tools/export_model.py."),
|
|
required=True)
|
|
parser.add_argument(
|
|
"--image_file", type=str, default='', help="Path of image file.")
|
|
parser.add_argument(
|
|
"--video_file", type=str, default='', help="Path of video file.")
|
|
parser.add_argument(
|
|
"--camera_id",
|
|
type=int,
|
|
default=-1,
|
|
help="device id of camera to predict.")
|
|
parser.add_argument(
|
|
"--run_mode",
|
|
type=str,
|
|
default='fluid',
|
|
help="mode of running(fluid/trt_fp32/trt_fp16/trt_int8)")
|
|
parser.add_argument(
|
|
"--use_gpu",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Whether to predict with GPU.")
|
|
parser.add_argument(
|
|
"--run_benchmark",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Whether to predict a image_file repeatedly for benchmark")
|
|
parser.add_argument(
|
|
"--threshold", type=float, default=0.5, help="Threshold of score.")
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
type=str,
|
|
default="output",
|
|
help="Directory of output visualization files.")
|
|
parser.add_argument(
|
|
"--trt_calib_mode",
|
|
type=bool,
|
|
default=False,
|
|
help="If the model is produced by TRT offline quantitative "
|
|
"calibration, trt_calib_mode need to set True.")
|
|
|
|
FLAGS = parser.parse_args()
|
|
print_arguments(FLAGS)
|
|
if FLAGS.image_file != '' and FLAGS.video_file != '':
|
|
assert "Cannot predict image and video at the same time"
|
|
|
|
main()
|