forked from PulseFocusPlatform/PulseFocusPlatform
658 lines
24 KiB
Python
658 lines
24 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import yaml
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import glob
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from functools import reduce
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import time
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import cv2
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import numpy as np
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import math
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import paddle
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from paddle.inference import Config
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from paddle.inference import create_predictor
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from benchmark_utils import PaddleInferBenchmark
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from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride
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from visualize import visualize_box_mask
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from utils import argsparser, Timer, get_current_memory_mb
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# Global dictionary
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SUPPORT_MODELS = {
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'YOLO',
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'RCNN',
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'SSD',
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'Face',
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'FCOS',
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'SOLOv2',
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'TTFNet',
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'S2ANet',
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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.pdiparams, model.pdmodel and infer_cfg.yml
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use_gpu (bool): whether use gpu
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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use_dynamic_shape (bool): use dynamic shape or not
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
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threshold (float): threshold to reserve the result for output.
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"""
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def __init__(self,
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pred_config,
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model_dir,
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use_gpu=False,
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run_mode='fluid',
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batch_size=1,
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use_dynamic_shape=False,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False):
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self.pred_config = pred_config
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self.predictor, self.config = load_predictor(
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model_dir,
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run_mode=run_mode,
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batch_size=batch_size,
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min_subgraph_size=self.pred_config.min_subgraph_size,
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use_gpu=use_gpu,
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use_dynamic_shape=use_dynamic_shape,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn)
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self.det_times = Timer()
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self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
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def preprocess(self, image_list):
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preprocess_ops = []
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for op_info in self.pred_config.preprocess_infos:
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new_op_info = op_info.copy()
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op_type = new_op_info.pop('type')
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preprocess_ops.append(eval(op_type)(**new_op_info))
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input_im_lst = []
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input_im_info_lst = []
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for im_path in image_list:
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im, im_info = preprocess(im_path, preprocess_ops)
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input_im_lst.append(im)
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input_im_info_lst.append(im_info)
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inputs = create_inputs(input_im_lst, input_im_info_lst)
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return inputs
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def postprocess(self,
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np_boxes,
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np_masks,
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inputs,
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np_boxes_num,
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threshold=0.5):
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# postprocess output of predictor
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results = {}
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results['boxes'] = np_boxes
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results['boxes_num'] = np_boxes_num
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if np_masks is not None:
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results['masks'] = np_masks
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return results
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def predict(self, image_list, threshold=0.5, warmup=0, repeats=1):
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'''
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Args:
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image_list (list): ,list of image
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threshold (float): threshold of predicted box' score
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Returns:
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results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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MaskRCNN's results include 'masks': np.ndarray:
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shape: [N, im_h, im_w]
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'''
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(image_list)
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self.det_times.preprocess_time_s.end()
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np_boxes, np_masks = None, None
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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for i in range(warmup):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if self.pred_config.mask:
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masks_tensor = self.predictor.get_output_handle(output_names[2])
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np_masks = masks_tensor.copy_to_cpu()
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self.det_times.inference_time_s.start()
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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boxes_num = self.predictor.get_output_handle(output_names[1])
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np_boxes_num = boxes_num.copy_to_cpu()
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if self.pred_config.mask:
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masks_tensor = self.predictor.get_output_handle(output_names[2])
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np_masks = masks_tensor.copy_to_cpu()
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self.det_times.inference_time_s.end(repeats=repeats)
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self.det_times.postprocess_time_s.start()
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results = []
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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([]), 'boxes_num': [0]}
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else:
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results = self.postprocess(
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np_boxes, np_masks, inputs, np_boxes_num, threshold=threshold)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(image_list)
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return results
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class DetectorSOLOv2(Detector):
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"""
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Args:
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config (object): config of model, defined by `Config(model_dir)`
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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use_gpu (bool): whether use gpu
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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use_dynamic_shape (bool): use dynamic shape or not
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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threshold (float): threshold to reserve the result for output.
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"""
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def __init__(self,
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pred_config,
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model_dir,
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use_gpu=False,
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run_mode='fluid',
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batch_size=1,
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use_dynamic_shape=False,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False):
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self.pred_config = pred_config
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self.predictor, self.config = load_predictor(
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model_dir,
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run_mode=run_mode,
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batch_size=batch_size,
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min_subgraph_size=self.pred_config.min_subgraph_size,
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use_gpu=use_gpu,
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use_dynamic_shape=use_dynamic_shape,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn)
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self.det_times = Timer()
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def predict(self, image, threshold=0.5, warmup=0, repeats=1):
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'''
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Args:
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image (str/np.ndarray): path of image/ np.ndarray read by cv2
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threshold (float): threshold of predicted box' score
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Returns:
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results (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
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'cate_label': label of segm, shape:[N]
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'cate_score': confidence score of segm, shape:[N]
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'''
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(image)
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self.det_times.preprocess_time_s.end()
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np_label, np_score, np_segms = None, None, None
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(input_names[i])
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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for i in range(warmup):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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np_label = self.predictor.get_output_handle(output_names[
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1]).copy_to_cpu()
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np_score = self.predictor.get_output_handle(output_names[
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2]).copy_to_cpu()
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np_segms = self.predictor.get_output_handle(output_names[
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3]).copy_to_cpu()
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self.det_times.inference_time_s.start()
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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np_label = self.predictor.get_output_handle(output_names[
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1]).copy_to_cpu()
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np_score = self.predictor.get_output_handle(output_names[
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2]).copy_to_cpu()
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np_segms = self.predictor.get_output_handle(output_names[
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3]).copy_to_cpu()
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self.det_times.inference_time_s.end(repeats=repeats)
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self.det_times.img_num += 1
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return dict(segm=np_segms, label=np_label, score=np_score)
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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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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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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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im_shape = []
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scale_factor = []
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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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scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
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inputs['im_shape'] = np.concatenate(im_shape, axis=0)
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inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
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imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
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max_shape_h = max([e[0] for e in imgs_shape])
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max_shape_w = max([e[1] for e in imgs_shape])
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padding_imgs = []
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for img in imgs:
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im_c, im_h, im_w = img.shape[:]
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padding_im = np.zeros(
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(im_c, max_shape_h, max_shape_w), dtype=np.float32)
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padding_im[:, :im_h, :im_w] = img
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padding_imgs.append(padding_im)
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inputs['image'] = np.stack(padding_imgs, axis=0)
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return inputs
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class PredictConfig():
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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.min_subgraph_size = yml_conf['min_subgraph_size']
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self.labels = yml_conf['label_list']
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self.mask = False
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if 'mask' in yml_conf:
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self.mask = yml_conf['mask']
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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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def load_predictor(model_dir,
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run_mode='fluid',
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batch_size=1,
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use_gpu=False,
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min_subgraph_size=3,
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use_dynamic_shape=False,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False):
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"""set AnalysisConfig, generate AnalysisPredictor
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Args:
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model_dir (str): root path of __model__ and __params__
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use_gpu (bool): whether use gpu
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run_mode (str): mode of running(fluid/trt_fp32/trt_fp16/trt_int8)
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use_dynamic_shape (bool): use dynamic shape or not
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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Returns:
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predictor (PaddlePredictor): AnalysisPredictor
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Raises:
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ValueError: predict by TensorRT need use_gpu == True.
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"""
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if not use_gpu and not run_mode == 'fluid':
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raise ValueError(
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"Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
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.format(run_mode, use_gpu))
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config = Config(
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os.path.join(model_dir, 'model.pdmodel'),
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os.path.join(model_dir, 'model.pdiparams'))
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precision_map = {
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'trt_int8': Config.Precision.Int8,
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'trt_fp32': Config.Precision.Float32,
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'trt_fp16': Config.Precision.Half
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}
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if use_gpu:
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# initial GPU memory(M), device ID
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config.enable_use_gpu(200, 0)
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# optimize graph and fuse op
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config.switch_ir_optim(True)
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else:
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config.disable_gpu()
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config.set_cpu_math_library_num_threads(cpu_threads)
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if enable_mkldnn:
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try:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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except Exception as e:
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print(
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"The current environment does not support `mkldnn`, so disable mkldnn."
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)
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pass
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if run_mode in precision_map.keys():
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config.enable_tensorrt_engine(
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workspace_size=1 << 10,
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max_batch_size=batch_size,
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min_subgraph_size=min_subgraph_size,
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precision_mode=precision_map[run_mode],
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use_static=False,
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use_calib_mode=trt_calib_mode)
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if use_dynamic_shape:
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min_input_shape = {'image': [1, 3, trt_min_shape, trt_min_shape]}
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max_input_shape = {'image': [1, 3, trt_max_shape, trt_max_shape]}
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opt_input_shape = {'image': [1, 3, trt_opt_shape, trt_opt_shape]}
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config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
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opt_input_shape)
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print('trt set dynamic shape done!')
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# disable print log when predict
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config.disable_glog_info()
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# enable shared memory
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config.enable_memory_optim()
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# disable feed, fetch OP, needed by zero_copy_run
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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return predictor, config
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def get_test_images(infer_dir, infer_img):
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"""
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Get image path list in TEST mode
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"""
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assert infer_img is not None or infer_dir is not None, \
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"--infer_img or --infer_dir should be set"
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assert infer_img is None or os.path.isfile(infer_img), \
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"{} is not a file".format(infer_img)
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assert infer_dir is None or os.path.isdir(infer_dir), \
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"{} is not a directory".format(infer_dir)
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# infer_img has a higher priority
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if infer_img and os.path.isfile(infer_img):
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return [infer_img]
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images = set()
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infer_dir = os.path.abspath(infer_dir)
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assert os.path.isdir(infer_dir), \
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"infer_dir {} is not a directory".format(infer_dir)
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exts = ['jpg', 'jpeg', 'png', 'bmp']
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exts += [ext.upper() for ext in exts]
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for ext in exts:
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images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
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images = list(images)
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assert len(images) > 0, "no image found in {}".format(infer_dir)
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print("Found {} inference images in total.".format(len(images)))
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return images
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def visualize(image_list, results, labels, output_dir='output/', threshold=0.5):
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# visualize the predict result
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start_idx = 0
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for idx, image_file in enumerate(image_list):
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im_bboxes_num = results['boxes_num'][idx]
|
|
im_results = {}
|
|
if 'boxes' in results:
|
|
try:
|
|
im_results['boxes'] = results['boxes'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
except:
|
|
pass
|
|
if 'masks' in results:
|
|
im_results['masks'] = results['masks'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
if 'segm' in results:
|
|
im_results['segm'] = results['segm'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
start_idx += im_bboxes_num
|
|
im = visualize_box_mask(
|
|
image_file, im_results, labels, threshold=threshold)
|
|
try:
|
|
txt = [list(i) for i in im_results['boxes'] if i[1]>=0.3]
|
|
except:
|
|
pass
|
|
img_name = os.path.split(image_file)[-1]
|
|
try:
|
|
if len(txt) >=1:
|
|
img_name = img_name.strip('.jpg')
|
|
for i in txt:
|
|
img_name = img_name+'_'+str(int(i[0]))
|
|
img_name = img_name+'.jpg'
|
|
except:
|
|
pass
|
|
if not os.path.exists(output_dir):
|
|
os.makedirs(output_dir)
|
|
out_path = os.path.join(output_dir, img_name)
|
|
print(out_path)
|
|
try:
|
|
im.save(out_path, quality=95)
|
|
except Exception as e:
|
|
print(out_path)
|
|
print('7'*100)
|
|
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, image_list, batch_size=1):
|
|
batch_loop_cnt = math.ceil(float(len(image_list)) / batch_size)
|
|
for i in range(batch_loop_cnt):
|
|
start_index = i * batch_size
|
|
end_index = min((i + 1) * batch_size, len(image_list))
|
|
batch_image_list = image_list[start_index:end_index]
|
|
if FLAGS.run_benchmark:
|
|
detector.predict(
|
|
batch_image_list, FLAGS.threshold, warmup=10, repeats=10)
|
|
cm, gm, gu = get_current_memory_mb()
|
|
detector.cpu_mem += cm
|
|
detector.gpu_mem += gm
|
|
detector.gpu_util += gu
|
|
print('Test iter {}'.format(i))
|
|
else:
|
|
results = detector.predict(batch_image_list, FLAGS.threshold)
|
|
visualize(
|
|
batch_image_list,
|
|
results,
|
|
detector.pred_config.labels,
|
|
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 = 5
|
|
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
print('frame_count', frame_count)
|
|
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 = 0.2
|
|
while (1):
|
|
capture.set(cv2.CAP_PROP_POS_MSEC, 1 * 1000 * index)
|
|
ret, frame = capture.read()
|
|
if not ret:
|
|
break
|
|
print('detect frame:%d' % (index))
|
|
index += 1
|
|
results = detector.predict([frame], FLAGS.threshold)
|
|
#print(results)
|
|
im = visualize_box_mask(
|
|
frame,
|
|
results,
|
|
detector.pred_config.labels,
|
|
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():
|
|
pred_config = PredictConfig(FLAGS.model_dir)
|
|
detector = Detector(
|
|
pred_config,
|
|
FLAGS.model_dir,
|
|
use_gpu=FLAGS.use_gpu,
|
|
run_mode=FLAGS.run_mode,
|
|
batch_size=FLAGS.batch_size,
|
|
use_dynamic_shape=FLAGS.use_dynamic_shape,
|
|
trt_min_shape=FLAGS.trt_min_shape,
|
|
trt_max_shape=FLAGS.trt_max_shape,
|
|
trt_opt_shape=FLAGS.trt_opt_shape,
|
|
trt_calib_mode=FLAGS.trt_calib_mode,
|
|
cpu_threads=FLAGS.cpu_threads,
|
|
enable_mkldnn=FLAGS.enable_mkldnn)
|
|
if pred_config.arch == 'SOLOv2':
|
|
detector = DetectorSOLOv2(
|
|
pred_config,
|
|
FLAGS.model_dir,
|
|
use_gpu=FLAGS.use_gpu,
|
|
run_mode=FLAGS.run_mode,
|
|
batch_size=FLAGS.batch_size,
|
|
use_dynamic_shape=FLAGS.use_dynamic_shape,
|
|
trt_min_shape=FLAGS.trt_min_shape,
|
|
trt_max_shape=FLAGS.trt_max_shape,
|
|
trt_opt_shape=FLAGS.trt_opt_shape,
|
|
trt_calib_mode=FLAGS.trt_calib_mode,
|
|
cpu_threads=FLAGS.cpu_threads,
|
|
enable_mkldnn=FLAGS.enable_mkldnn)
|
|
|
|
# predict from video file or camera video stream
|
|
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
|
|
predict_video(detector, FLAGS.camera_id)
|
|
else:
|
|
# predict from image
|
|
if FLAGS.image_dir is None and FLAGS.image_file is not None:
|
|
assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None"
|
|
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
|
|
if len(img_list)>1:
|
|
img_path = ''
|
|
print('8'*100,img_path)
|
|
print(img_path)
|
|
img_list = [i.replace(img_path,'') for i in img_list]
|
|
print(img_list)
|
|
img_list = [i.split('/')[-1] for i in img_list]
|
|
img_list = [i.strip('.jpg') for i in img_list]
|
|
img_list.sort()
|
|
img_list=[img_path+str(i)+'.jpg' for i in img_list]
|
|
print(img_list)
|
|
predict_image(detector, img_list, FLAGS.batch_size)
|
|
if not FLAGS.run_benchmark:
|
|
detector.det_times.info(average=True)
|
|
else:
|
|
mems = {
|
|
'cpu_rss_mb': detector.cpu_mem / len(img_list),
|
|
'gpu_rss_mb': detector.gpu_mem / len(img_list),
|
|
'gpu_util': detector.gpu_util * 100 / len(img_list)
|
|
}
|
|
|
|
perf_info = detector.det_times.report(average=True)
|
|
model_dir = FLAGS.model_dir
|
|
mode = FLAGS.run_mode
|
|
model_info = {
|
|
'model_name': model_dir.strip('/').split('/')[-1],
|
|
'precision': mode.split('_')[-1]
|
|
}
|
|
data_info = {
|
|
'batch_size': FLAGS.batch_size,
|
|
'shape': "dynamic_shape",
|
|
'data_num': perf_info['img_num']
|
|
}
|
|
det_log = PaddleInferBenchmark(detector.config, model_info,
|
|
data_info, perf_info, mems)
|
|
det_log('Det')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
parser = argsparser()
|
|
FLAGS = parser.parse_args()
|
|
print(FLAGS)
|
|
print_arguments(FLAGS)
|
|
main()
|