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886 lines
34 KiB
Python
886 lines
34 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import yaml
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import cv2
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import numpy as np
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import 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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import sys
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# add deploy path of PaddleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'])))
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sys.path.insert(0, parent_path)
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from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, WarpAffine, Pad, decode_image, CULaneResize
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from .picodet_postprocess import PicoDetPostProcess
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from clrnet_postprocess import CLRNetPostProcess
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from visualize import visualize_box_mask, imshow_lanes
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from utils import Timer
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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', 'YOLOF', 'PPHGNet',
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'PPLCNet', 'DETR', 'CenterTrack', 'CLRNet'
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}
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class Detector(object):
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"""
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Args:
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pred_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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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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enable_mkldnn_bfloat16 (bool): whether to turn on mkldnn bfloat16
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output_dir (str): The path of output
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threshold (float): The threshold of score for visualization
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delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
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Used by action model.
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"""
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def __init__(self,
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model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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enable_mkldnn_bfloat16=False,
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output_dir='output',
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threshold=0.5,
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delete_shuffle_pass=False,
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use_fd_format=False):
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self.pred_config = self.set_config(
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model_dir, use_fd_format=use_fd_format)
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self.predictor, self.config = load_predictor(
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model_dir,
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self.pred_config.arch,
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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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device=device,
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use_dynamic_shape=self.pred_config.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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enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
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delete_shuffle_pass=delete_shuffle_pass)
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self.det_times = Timer()
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self.batch_size = batch_size
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self.output_dir = output_dir
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self.threshold = threshold
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self.device = device
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def set_config(self, model_dir, use_fd_format):
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return PredictConfig(model_dir, use_fd_format=use_fd_format)
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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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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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if input_names[i] == 'x':
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input_tensor.copy_from_cpu(inputs['image'])
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else:
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input_tensor.copy_from_cpu(inputs[input_names[i]])
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return inputs
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_boxes_num = result['boxes_num']
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assert isinstance(np_boxes_num, np.ndarray), \
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'`np_boxes_num` should be a `numpy.ndarray`'
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result = {k: v for k, v in result.items() if v is not None}
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return result
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def filter_box(self, result, threshold):
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np_boxes_num = result['boxes_num']
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boxes = result['boxes']
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start_idx = 0
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filter_boxes = []
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filter_num = []
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for i in range(len(np_boxes_num)):
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boxes_num = np_boxes_num[i]
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boxes_i = boxes[start_idx:start_idx + boxes_num, :]
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idx = boxes_i[:, 1] > threshold
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filter_boxes_i = boxes_i[idx, :]
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filter_boxes.append(filter_boxes_i)
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filter_num.append(filter_boxes_i.shape[0])
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start_idx += boxes_num
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boxes = np.concatenate(filter_boxes)
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filter_num = np.array(filter_num)
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filter_res = {'boxes': boxes, 'boxes_num': filter_num}
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return filter_res
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def predict(self, repeats=1, run_benchmark=False):
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'''
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Args:
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repeats (int): repeats number for prediction
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Returns:
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result (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 result include 'masks': np.ndarray:
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shape: [N, im_h, im_w]
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'''
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# model prediction
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np_boxes_num, np_boxes, np_masks = np.array([0]), None, None
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if run_benchmark:
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for i in range(repeats):
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self.predictor.run()
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if self.device == 'GPU':
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paddle.device.cuda.synchronize()
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else:
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paddle.device.synchronize(device=self.device.lower())
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result = dict(
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boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
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return result
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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boxes_tensor = self.predictor.get_output_handle(output_names[0])
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np_boxes = boxes_tensor.copy_to_cpu()
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if len(output_names) == 1:
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# some exported model can not get tensor 'bbox_num'
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np_boxes_num = np.array([len(np_boxes)])
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else:
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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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result = dict(boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
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return result
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def merge_batch_result(self, batch_result):
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if len(batch_result) == 1:
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return batch_result[0]
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res_key = batch_result[0].keys()
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results = {k: [] for k in res_key}
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for res in batch_result:
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for k, v in res.items():
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results[k].append(v)
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for k, v in results.items():
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if k not in ['masks', 'segm']:
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results[k] = np.concatenate(v)
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return results
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def get_timer(self):
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return self.det_times
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def predict_image(self,
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image_list,
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threshold=0.5,
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visual=True):
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batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
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results = []
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for i in range(batch_loop_cnt):
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start_index = i * self.batch_size
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end_index = min((i + 1) * self.batch_size, len(image_list))
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batch_image_list = image_list[start_index:end_index]
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# preprocess
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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self.det_times.inference_time_s.start()
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result = self.predict()
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self.det_times.inference_time_s.end()
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# postprocess
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self.det_times.postprocess_time_s.start()
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result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(batch_image_list)
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if visual:
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visualize(
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batch_image_list,
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result,
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self.pred_config.labels,
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output_dir=self.output_dir,
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threshold=self.threshold)
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results.append(result)
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results = self.merge_batch_result(results)
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boxes = results['boxes']
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expect_boxes = (boxes[:, 1] > threshold) & (boxes[:, 0] > -1)
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boxes = boxes[expect_boxes, :]
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output = []
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for dt in boxes:
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clsid, box, confidence = int(dt[0]), dt[2:].tolist(), dt[1]
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output.append((clsid, box, confidence))
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return output
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class DetectorSOLOv2(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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enable_mkldnn_bfloat16 (bool): Whether to turn on mkldnn bfloat16
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output_dir (str): The path of output
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threshold (float): The threshold of score for visualization
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"""
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def __init__(self,
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model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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enable_mkldnn_bfloat16=False,
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output_dir='./',
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threshold=0.5,
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use_fd_format=False):
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super(DetectorSOLOv2, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn,
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enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
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output_dir=output_dir,
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threshold=threshold,
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use_fd_format=use_fd_format)
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def predict(self, repeats=1, run_benchmark=False):
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'''
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Args:
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repeats (int): repeat number for prediction
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Returns:
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result (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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np_segms, np_label, np_score, np_boxes_num = None, None, None, np.array(
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[0])
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if run_benchmark:
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for i in range(repeats):
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self.predictor.run()
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paddle.device.cuda.synchronize()
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result = dict(
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segm=np_segms,
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label=np_label,
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score=np_score,
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boxes_num=np_boxes_num)
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return result
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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_segms = self.predictor.get_output_handle(output_names[
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0]).copy_to_cpu()
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np_boxes_num = self.predictor.get_output_handle(output_names[
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1]).copy_to_cpu()
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np_label = self.predictor.get_output_handle(output_names[
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2]).copy_to_cpu()
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np_score = self.predictor.get_output_handle(output_names[
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3]).copy_to_cpu()
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result = dict(
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segm=np_segms,
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label=np_label,
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score=np_score,
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boxes_num=np_boxes_num)
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return result
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class DetectorPicoDet(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to turn on MKLDNN
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enable_mkldnn_bfloat16 (bool): whether to turn on MKLDNN_BFLOAT16
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"""
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def __init__(self,
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model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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enable_mkldnn_bfloat16=False,
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output_dir='./',
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threshold=0.5,
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use_fd_format=False):
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super(DetectorPicoDet, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn,
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enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
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output_dir=output_dir,
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threshold=threshold,
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use_fd_format=use_fd_format)
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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np_score_list = result['boxes']
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np_boxes_list = result['boxes_num']
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postprocessor = PicoDetPostProcess(
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inputs['image'].shape[2:],
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inputs['im_shape'],
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inputs['scale_factor'],
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strides=self.pred_config.fpn_stride,
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nms_threshold=self.pred_config.nms['nms_threshold'])
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np_boxes, np_boxes_num = postprocessor(np_score_list, np_boxes_list)
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result = dict(boxes=np_boxes, boxes_num=np_boxes_num)
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return result
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def predict(self, repeats=1, run_benchmark=False):
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'''
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Args:
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repeats (int): repeat number for prediction
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Returns:
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result (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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'''
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np_score_list, np_boxes_list = [], []
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if run_benchmark:
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for i in range(repeats):
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self.predictor.run()
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paddle.device.cuda.synchronize()
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result = dict(boxes=np_score_list, boxes_num=np_boxes_list)
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return result
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for i in range(repeats):
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self.predictor.run()
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np_score_list.clear()
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np_boxes_list.clear()
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output_names = self.predictor.get_output_names()
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num_outs = int(len(output_names) / 2)
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for out_idx in range(num_outs):
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np_score_list.append(
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self.predictor.get_output_handle(output_names[out_idx])
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.copy_to_cpu())
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np_boxes_list.append(
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self.predictor.get_output_handle(output_names[
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out_idx + num_outs]).copy_to_cpu())
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result = dict(boxes=np_score_list, boxes_num=np_boxes_list)
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return result
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class DetectorCLRNet(Detector):
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"""
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Args:
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|
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
|
|
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
|
|
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
|
|
batch_size (int): size of pre batch in inference
|
|
trt_min_shape (int): min shape for dynamic shape in trt
|
|
trt_max_shape (int): max shape for dynamic shape in trt
|
|
trt_opt_shape (int): opt shape for dynamic shape in trt
|
|
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
|
|
calibration, trt_calib_mode need to set True
|
|
cpu_threads (int): cpu threads
|
|
enable_mkldnn (bool): whether to turn on MKLDNN
|
|
enable_mkldnn_bfloat16 (bool): whether to turn on MKLDNN_BFLOAT16
|
|
"""
|
|
|
|
def __init__(self,
|
|
model_dir,
|
|
device='CPU',
|
|
run_mode='paddle',
|
|
batch_size=1,
|
|
trt_min_shape=1,
|
|
trt_max_shape=1280,
|
|
trt_opt_shape=640,
|
|
trt_calib_mode=False,
|
|
cpu_threads=1,
|
|
enable_mkldnn=False,
|
|
enable_mkldnn_bfloat16=False,
|
|
output_dir='./',
|
|
threshold=0.5,
|
|
use_fd_format=False):
|
|
super(DetectorCLRNet, self).__init__(
|
|
model_dir=model_dir,
|
|
device=device,
|
|
run_mode=run_mode,
|
|
batch_size=batch_size,
|
|
trt_min_shape=trt_min_shape,
|
|
trt_max_shape=trt_max_shape,
|
|
trt_opt_shape=trt_opt_shape,
|
|
trt_calib_mode=trt_calib_mode,
|
|
cpu_threads=cpu_threads,
|
|
enable_mkldnn=enable_mkldnn,
|
|
enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
|
|
output_dir=output_dir,
|
|
threshold=threshold,
|
|
use_fd_format=use_fd_format)
|
|
|
|
deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
|
|
with open(deploy_file) as f:
|
|
yml_conf = yaml.safe_load(f)
|
|
self.img_w = yml_conf['img_w']
|
|
self.ori_img_h = yml_conf['ori_img_h']
|
|
self.cut_height = yml_conf['cut_height']
|
|
self.max_lanes = yml_conf['max_lanes']
|
|
self.nms_thres = yml_conf['nms_thres']
|
|
self.num_points = yml_conf['num_points']
|
|
self.conf_threshold = yml_conf['conf_threshold']
|
|
|
|
def postprocess(self, inputs, result):
|
|
# postprocess output of predictor
|
|
lanes_list = result['lanes']
|
|
postprocessor = CLRNetPostProcess(
|
|
img_w=self.img_w,
|
|
ori_img_h=self.ori_img_h,
|
|
cut_height=self.cut_height,
|
|
conf_threshold=self.conf_threshold,
|
|
nms_thres=self.nms_thres,
|
|
max_lanes=self.max_lanes,
|
|
num_points=self.num_points)
|
|
lanes = postprocessor(lanes_list)
|
|
result = dict(lanes=lanes)
|
|
return result
|
|
|
|
def predict(self, repeats=1, run_benchmark=False):
|
|
'''
|
|
Args:
|
|
repeats (int): repeat number for prediction
|
|
Returns:
|
|
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
|
|
matix element:[class, score, x_min, y_min, x_max, y_max]
|
|
'''
|
|
lanes_list = []
|
|
|
|
if run_benchmark:
|
|
for i in range(repeats):
|
|
self.predictor.run()
|
|
paddle.device.cuda.synchronize()
|
|
result = dict(lanes=lanes_list)
|
|
return result
|
|
|
|
for i in range(repeats):
|
|
# TODO: check the output of predictor
|
|
self.predictor.run()
|
|
lanes_list.clear()
|
|
output_names = self.predictor.get_output_names()
|
|
num_outs = int(len(output_names) / 2)
|
|
if num_outs == 0:
|
|
lanes_list.append([])
|
|
for out_idx in range(num_outs):
|
|
lanes_list.append(
|
|
self.predictor.get_output_handle(output_names[out_idx])
|
|
.copy_to_cpu())
|
|
result = dict(lanes=lanes_list)
|
|
return result
|
|
|
|
|
|
def create_inputs(imgs, im_info):
|
|
"""generate input for different model type
|
|
Args:
|
|
imgs (list(numpy)): list of images (np.ndarray)
|
|
im_info (list(dict)): list of image info
|
|
Returns:
|
|
inputs (dict): input of model
|
|
"""
|
|
inputs = {}
|
|
|
|
im_shape = []
|
|
scale_factor = []
|
|
if len(imgs) == 1:
|
|
inputs['image'] = np.array((imgs[0], )).astype('float32')
|
|
inputs['im_shape'] = np.array(
|
|
(im_info[0]['im_shape'], )).astype('float32')
|
|
inputs['scale_factor'] = np.array(
|
|
(im_info[0]['scale_factor'], )).astype('float32')
|
|
return inputs
|
|
|
|
for e in im_info:
|
|
im_shape.append(np.array((e['im_shape'], )).astype('float32'))
|
|
scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
|
|
|
|
inputs['im_shape'] = np.concatenate(im_shape, axis=0)
|
|
inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
|
|
|
|
imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
|
|
max_shape_h = max([e[0] for e in imgs_shape])
|
|
max_shape_w = max([e[1] for e in imgs_shape])
|
|
padding_imgs = []
|
|
for img in imgs:
|
|
im_c, im_h, im_w = img.shape[:]
|
|
padding_im = np.zeros(
|
|
(im_c, max_shape_h, max_shape_w), dtype=np.float32)
|
|
padding_im[:, :im_h, :im_w] = img
|
|
padding_imgs.append(padding_im)
|
|
inputs['image'] = np.stack(padding_imgs, axis=0)
|
|
return inputs
|
|
|
|
|
|
class PredictConfig():
|
|
"""set config of preprocess, postprocess and visualize
|
|
Args:
|
|
model_dir (str): root path of model.yml
|
|
"""
|
|
|
|
def __init__(self, model_dir, use_fd_format=False):
|
|
# parsing Yaml config for Preprocess
|
|
fd_deploy_file = os.path.join(model_dir, 'inference.yml')
|
|
ppdet_deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
|
|
if use_fd_format:
|
|
if not os.path.exists(fd_deploy_file) and os.path.exists(
|
|
ppdet_deploy_file):
|
|
raise RuntimeError(
|
|
"Non-FD format model detected. Please set `use_fd_format` to False."
|
|
)
|
|
deploy_file = fd_deploy_file
|
|
else:
|
|
if not os.path.exists(ppdet_deploy_file) and os.path.exists(
|
|
fd_deploy_file):
|
|
raise RuntimeError(
|
|
"FD format model detected. Please set `use_fd_format` to False."
|
|
)
|
|
deploy_file = ppdet_deploy_file
|
|
with open(deploy_file) as f:
|
|
yml_conf = yaml.safe_load(f)
|
|
self.check_model(yml_conf)
|
|
self.arch = yml_conf['arch']
|
|
self.preprocess_infos = yml_conf['Preprocess']
|
|
self.min_subgraph_size = yml_conf['min_subgraph_size']
|
|
self.labels = yml_conf['label_list']
|
|
self.mask = False
|
|
self.use_dynamic_shape = yml_conf['use_dynamic_shape']
|
|
if 'mask' in yml_conf:
|
|
self.mask = yml_conf['mask']
|
|
self.tracker = None
|
|
if 'tracker' in yml_conf:
|
|
self.tracker = yml_conf['tracker']
|
|
if 'NMS' in yml_conf:
|
|
self.nms = yml_conf['NMS']
|
|
if 'fpn_stride' in yml_conf:
|
|
self.fpn_stride = yml_conf['fpn_stride']
|
|
if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
|
|
print(
|
|
'The RCNN export model is used for ONNX and it only supports batch_size = 1'
|
|
)
|
|
# self.print_config()
|
|
|
|
def check_model(self, yml_conf):
|
|
"""
|
|
Raises:
|
|
ValueError: loaded model not in supported model type
|
|
"""
|
|
for support_model in SUPPORT_MODELS:
|
|
if support_model in yml_conf['arch']:
|
|
return True
|
|
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
|
|
'arch'], SUPPORT_MODELS))
|
|
|
|
def print_config(self):
|
|
print('----------- Model Configuration -----------')
|
|
print('%s: %s' % ('Model Arch', self.arch))
|
|
print('%s: ' % ('Transform Order'))
|
|
for op_info in self.preprocess_infos:
|
|
print('--%s: %s' % ('transform op', op_info['type']))
|
|
print('--------------------------------------------')
|
|
|
|
|
|
def load_predictor(model_dir,
|
|
arch,
|
|
run_mode='paddle',
|
|
batch_size=1,
|
|
device='CPU',
|
|
min_subgraph_size=3,
|
|
use_dynamic_shape=False,
|
|
trt_min_shape=1,
|
|
trt_max_shape=1280,
|
|
trt_opt_shape=640,
|
|
trt_calib_mode=False,
|
|
cpu_threads=1,
|
|
enable_mkldnn=False,
|
|
enable_mkldnn_bfloat16=False,
|
|
delete_shuffle_pass=False):
|
|
"""set AnalysisConfig, generate AnalysisPredictor
|
|
Args:
|
|
model_dir (str): root path of __model__ and __params__
|
|
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
|
|
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
|
|
use_dynamic_shape (bool): use dynamic shape or not
|
|
trt_min_shape (int): min shape for dynamic shape in trt
|
|
trt_max_shape (int): max shape for dynamic shape in trt
|
|
trt_opt_shape (int): opt shape for dynamic shape in trt
|
|
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
|
|
calibration, trt_calib_mode need to set True
|
|
delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
|
|
Used by action model.
|
|
Returns:
|
|
predictor (PaddlePredictor): AnalysisPredictor
|
|
Raises:
|
|
ValueError: predict by TensorRT need device == 'GPU'.
|
|
"""
|
|
if device != 'GPU' and run_mode != 'paddle':
|
|
raise ValueError(
|
|
"Predict by TensorRT mode: {}, expect device=='GPU', but device == {}"
|
|
.format(run_mode, device))
|
|
|
|
if paddle.__version__ >= '3.0.0' or paddle.__version__ == '0.0.0':
|
|
model_path = model_dir
|
|
model_prefix = 'model'
|
|
infer_param = os.path.join(model_dir, 'model.pdiparams')
|
|
if not os.path.exists(infer_param):
|
|
if paddle.framework.use_pir_api():
|
|
infer_model = os.path.join(model_dir, 'inference.pdmodel')
|
|
else:
|
|
infer_model = os.path.join(model_dir, 'inference.json')
|
|
if not os.path.exists(infer_model):
|
|
raise ValueError(
|
|
"Cannot find any inference model in dir: {}.".format(model_dir))
|
|
|
|
config = Config(model_path, model_prefix)
|
|
|
|
else:
|
|
infer_model = os.path.join(model_dir, 'model.pdmodel')
|
|
infer_params = os.path.join(model_dir, 'model.pdiparams')
|
|
if not os.path.exists(infer_model):
|
|
infer_model = os.path.join(model_dir, 'inference.pdmodel')
|
|
infer_params = os.path.join(model_dir, 'inference.pdiparams')
|
|
if not os.path.exists(infer_model):
|
|
raise ValueError(
|
|
"Cannot find any inference model in dir: {},".format(model_dir))
|
|
config = Config(infer_model, infer_params)
|
|
|
|
if device == 'GPU':
|
|
# initial GPU memory(M), device ID
|
|
config.enable_use_gpu(200, 0)
|
|
# optimize graph and fuse op
|
|
config.switch_ir_optim(True)
|
|
else:
|
|
config.disable_gpu()
|
|
config.set_cpu_math_library_num_threads(cpu_threads)
|
|
if enable_mkldnn:
|
|
try:
|
|
# cache 10 different shapes for mkldnn to avoid memory leak
|
|
config.set_mkldnn_cache_capacity(10)
|
|
config.enable_mkldnn()
|
|
if enable_mkldnn_bfloat16:
|
|
config.enable_mkldnn_bfloat16()
|
|
except Exception as e:
|
|
print(
|
|
"The current environment does not support `mkldnn`, so disable mkldnn."
|
|
)
|
|
pass
|
|
|
|
precision_map = {
|
|
'trt_int8': Config.Precision.Int8,
|
|
'trt_fp32': Config.Precision.Float32,
|
|
'trt_fp16': Config.Precision.Half
|
|
}
|
|
if run_mode in precision_map.keys():
|
|
config.enable_tensorrt_engine(
|
|
workspace_size=(1 << 25) * batch_size,
|
|
max_batch_size=batch_size,
|
|
min_subgraph_size=min_subgraph_size,
|
|
precision_mode=precision_map[run_mode],
|
|
use_static=False,
|
|
use_calib_mode=trt_calib_mode)
|
|
if os.path.exists('shape_range_info.pbtxt'):
|
|
print('Use dynamic shape file: shape_range_info.pbtxt for TRT...')
|
|
config.enable_tuned_tensorrt_dynamic_shape(
|
|
'shape_range_info.pbtxt', True)
|
|
|
|
if use_dynamic_shape:
|
|
min_input_shape = {
|
|
'image': [batch_size, 3, trt_min_shape, trt_min_shape],
|
|
'scale_factor': [batch_size, 2]
|
|
}
|
|
max_input_shape = {
|
|
'image': [batch_size, 3, trt_max_shape, trt_max_shape],
|
|
'scale_factor': [batch_size, 2]
|
|
}
|
|
opt_input_shape = {
|
|
'image': [batch_size, 3, trt_opt_shape, trt_opt_shape],
|
|
'scale_factor': [batch_size, 2]
|
|
}
|
|
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
|
|
opt_input_shape)
|
|
print('trt set dynamic shape done!')
|
|
|
|
# disable print log when predict
|
|
config.disable_glog_info()
|
|
# enable shared memory
|
|
config.enable_memory_optim()
|
|
# disable feed, fetch OP, needed by zero_copy_run
|
|
config.switch_use_feed_fetch_ops(False)
|
|
if delete_shuffle_pass:
|
|
config.delete_pass("shuffle_channel_detect_pass")
|
|
predictor = create_predictor(config)
|
|
return predictor, config
|
|
|
|
|
|
def visualize(image_list, result, labels, output_dir='output/', threshold=0.5):
|
|
# visualize the predict result
|
|
if 'lanes' in result:
|
|
for idx, image_file in enumerate(image_list):
|
|
lanes = result['lanes'][idx]
|
|
img = cv2.imread(image_file)
|
|
out_file = os.path.join(output_dir, os.path.basename(image_file))
|
|
# hard code
|
|
lanes = [lane.to_array([], ) for lane in lanes]
|
|
imshow_lanes(img, lanes, out_file=out_file)
|
|
return
|
|
start_idx = 0
|
|
for idx, image_file in enumerate(image_list):
|
|
im_bboxes_num = result['boxes_num'][idx]
|
|
im_results = {}
|
|
if 'boxes' in result:
|
|
im_results['boxes'] = result['boxes'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
if 'masks' in result:
|
|
im_results['masks'] = result['masks'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
if 'segm' in result:
|
|
im_results['segm'] = result['segm'][start_idx:start_idx +
|
|
im_bboxes_num, :]
|
|
if 'label' in result:
|
|
im_results['label'] = result['label'][start_idx:start_idx +
|
|
im_bboxes_num]
|
|
if 'score' in result:
|
|
im_results['score'] = result['score'][start_idx:start_idx +
|
|
im_bboxes_num]
|
|
|
|
start_idx += im_bboxes_num
|
|
im = visualize_box_mask(
|
|
image_file, im_results, labels, threshold=threshold)
|
|
img_name = os.path.split(image_file)[-1]
|
|
if not os.path.exists(output_dir):
|
|
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)
|
|
|
|
|
|
class Pipeline(object):
|
|
|
|
def __init__(self, model_dir):
|
|
deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
|
|
with open(deploy_file) as f:
|
|
yml_conf = yaml.safe_load(f)
|
|
arch = yml_conf['arch']
|
|
detector_func = 'Detector'
|
|
if arch == 'SOLOv2':
|
|
detector_func = 'DetectorSOLOv2'
|
|
elif arch == 'PicoDet':
|
|
detector_func = 'DetectorPicoDet'
|
|
elif arch == "CLRNet":
|
|
detector_func = 'DetectorCLRNet'
|
|
|
|
self.detector = eval(detector_func)(
|
|
model_dir,
|
|
device='GPU',
|
|
run_mode='paddle',
|
|
batch_size=1,
|
|
trt_min_shape=1,
|
|
trt_max_shape=1280,
|
|
trt_opt_shape=640,
|
|
trt_calib_mode=False,
|
|
cpu_threads=1,
|
|
enable_mkldnn=False,
|
|
enable_mkldnn_bfloat16=False,
|
|
threshold=0.5,
|
|
output_dir='output',
|
|
use_fd_format=False)
|
|
|
|
def __call__(self, image):
|
|
if isinstance(image, np.ndarray):
|
|
image = [image]
|
|
results = self.detector.predict_image(
|
|
image,
|
|
visual=False)
|
|
return results
|
|
|
|
|
|
paddle.enable_static()
|