# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import yaml from collections import OrderedDict import paddle from ppdet.data.source.category import get_categories from ppdet.utils.logger import setup_logger logger = setup_logger('ppdet.engine') # Global dictionary TRT_MIN_SUBGRAPH = { 'YOLO': 3, 'PPYOLOE': 3, 'SSD': 60, 'RCNN': 40, 'RetinaNet': 40, 'S2ANet': 80, 'EfficientDet': 40, 'Face': 3, 'TTFNet': 60, 'FCOS': 16, 'SOLOv2': 60, 'HigherHRNet': 3, 'HRNet': 3, 'DeepSORT': 3, 'ByteTrack': 10, 'CenterTrack': 5, 'JDE': 10, 'FairMOT': 5, 'GFL': 16, 'PicoDet': 3, 'CenterNet': 5, 'TOOD': 5, 'YOLOX': 8, 'YOLOF': 40, 'METRO_Body': 3, 'DETR': 3, } KEYPOINT_ARCH = ['HigherHRNet', 'TopDownHRNet'] MOT_ARCH = ['JDE', 'FairMOT', 'DeepSORT', 'ByteTrack', 'CenterTrack'] TO_STATIC_SPEC = { 'yolov3_darknet53_270e_coco': [{ 'im_id': paddle.static.InputSpec( name='im_id', shape=[-1, 1], dtype='float32'), 'is_crowd': paddle.static.InputSpec( name='is_crowd', shape=[-1, 50], dtype='float32'), 'gt_bbox': paddle.static.InputSpec( name='gt_bbox', shape=[-1, 50, 4], dtype='float32'), 'curr_iter': paddle.static.InputSpec( name='curr_iter', shape=[-1], dtype='float32'), 'image': paddle.static.InputSpec( name='image', shape=[-1, 3, -1, -1], dtype='float32'), 'im_shape': paddle.static.InputSpec( name='im_shape', shape=[-1, 2], dtype='float32'), 'scale_factor': paddle.static.InputSpec( name='scale_factor', shape=[-1, 2], dtype='float32'), 'target0': paddle.static.InputSpec( name='target0', shape=[-1, 3, 86, -1, -1], dtype='float32'), 'target1': paddle.static.InputSpec( name='target1', shape=[-1, 3, 86, -1, -1], dtype='float32'), 'target2': paddle.static.InputSpec( name='target2', shape=[-1, 3, 86, -1, -1], dtype='float32'), }], } def apply_to_static(config, model): filename = config.get('filename', None) spec = TO_STATIC_SPEC.get(filename, None) model = paddle.jit.to_static(model, input_spec=spec) logger.info("Successfully to apply @to_static with specs: {}".format(spec)) return model def _prune_input_spec(input_spec, program, targets): # try to prune static program to figure out pruned input spec # so we perform following operations in static mode device = paddle.get_device() paddle.enable_static() paddle.set_device(device) pruned_input_spec = [{}] program = program.clone() program = program._prune(targets=targets) global_block = program.global_block() for name, spec in input_spec[0].items(): try: v = global_block.var(name) pruned_input_spec[0][name] = spec except Exception: pass paddle.disable_static(place=device) return pruned_input_spec def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape): preprocess_list = [] anno_file = dataset_cfg.get_anno() clsid2catid, catid2name = get_categories(metric, anno_file, arch) label_list = [str(cat) for cat in catid2name.values()] fuse_normalize = reader_cfg.get('fuse_normalize', False) sample_transforms = reader_cfg['sample_transforms'] for st in sample_transforms[1:]: for key, value in st.items(): p = {'type': key} if key == 'Resize': if int(image_shape[1]) != -1: value['target_size'] = image_shape[1:] value['interp'] = value.get('interp', 1) # cv2.INTER_LINEAR if fuse_normalize and key == 'NormalizeImage': continue p.update(value) preprocess_list.append(p) batch_transforms = reader_cfg.get('batch_transforms', None) if batch_transforms: for bt in batch_transforms: for key, value in bt.items(): # for deploy/infer, use PadStride(stride) instead PadBatch(pad_to_stride) if key == 'PadBatch': preprocess_list.append({ 'type': 'PadStride', 'stride': value['pad_to_stride'] }) break return preprocess_list, label_list def _parse_tracker(tracker_cfg): tracker_params = {} for k, v in tracker_cfg.items(): tracker_params.update({k: v}) return tracker_params def _dump_infer_config(config, path, image_shape, model): arch_state = False from ppdet.core.config.yaml_helpers import setup_orderdict setup_orderdict() use_dynamic_shape = True if image_shape[2] == -1 else False infer_cfg = OrderedDict({ 'mode': 'paddle', 'draw_threshold': 0.5, 'metric': config['metric'], 'use_dynamic_shape': use_dynamic_shape }) export_onnx = config.get('export_onnx', False) export_eb = config.get('export_eb', False) infer_arch = config['architecture'] if 'RCNN' in infer_arch and export_onnx: logger.warning( "Exporting RCNN model to ONNX only support batch_size = 1") infer_cfg['export_onnx'] = True infer_cfg['export_eb'] = export_eb if infer_arch in MOT_ARCH: if infer_arch == 'DeepSORT': tracker_cfg = config['DeepSORTTracker'] elif infer_arch == 'CenterTrack': tracker_cfg = config['CenterTracker'] else: tracker_cfg = config['JDETracker'] infer_cfg['tracker'] = _parse_tracker(tracker_cfg) for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items(): if arch in infer_arch: infer_cfg['arch'] = arch infer_cfg['min_subgraph_size'] = min_subgraph_size arch_state = True break if infer_arch == 'PPYOLOEWithAuxHead': infer_arch = 'PPYOLOE' if infer_arch in ['PPYOLOE', 'YOLOX', 'YOLOF']: infer_cfg['arch'] = infer_arch infer_cfg['min_subgraph_size'] = TRT_MIN_SUBGRAPH[infer_arch] arch_state = True if not arch_state: logger.error( 'Architecture: {} is not supported for exporting model now.\n'. format(infer_arch) + 'Please set TRT_MIN_SUBGRAPH in ppdet/engine/export_utils.py') os._exit(0) if 'mask_head' in config[config['architecture']] and config[config[ 'architecture']]['mask_head']: infer_cfg['mask'] = True label_arch = 'detection_arch' if infer_arch in KEYPOINT_ARCH: label_arch = 'keypoint_arch' if infer_arch in MOT_ARCH: if config['metric'] in ['COCO', 'VOC']: # MOT model run as Detector reader_cfg = config['TestReader'] dataset_cfg = config['TestDataset'] else: # 'metric' in ['MOT', 'MCMOT', 'KITTI'] label_arch = 'mot_arch' reader_cfg = config['TestMOTReader'] dataset_cfg = config['TestMOTDataset'] else: reader_cfg = config['TestReader'] dataset_cfg = config['TestDataset'] infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader( reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:]) if infer_arch == 'PicoDet': if hasattr(config, 'export') and config['export'].get( 'post_process', False) and not config['export'].get('benchmark', False): infer_cfg['arch'] = 'GFL' head_name = 'PicoHeadV2' if config['PicoHeadV2'] else 'PicoHead' infer_cfg['NMS'] = config[head_name]['nms'] # In order to speed up the prediction, the threshold of nms # is adjusted here, which can be changed in infer_cfg.yml config[head_name]['nms']["score_threshold"] = 0.3 config[head_name]['nms']["nms_threshold"] = 0.5 infer_cfg['fpn_stride'] = config[head_name]['fpn_stride'] yaml.dump(infer_cfg, open(path, 'w')) logger.info("Export inference config file to {}".format(os.path.join(path)))