# 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 import numpy as np from collections import OrderedDict import logging logger = logging.getLogger(__name__) import paddle.fluid as fluid __all__ = ['dump_infer_config', 'save_infer_model'] # Global dictionary TRT_MIN_SUBGRAPH = { 'YOLO': 3, 'SSD': 3, 'RCNN': 40, 'RetinaNet': 40, 'EfficientDet': 40, 'Face': 3, 'TTFNet': 3, 'FCOS': 33, 'SOLOv2': 60, } RESIZE_SCALE_SET = { 'RCNN', 'RetinaNet', 'FCOS', 'SOLOv2', } def parse_reader(reader_cfg, metric, arch): preprocess_list = [] image_shape = reader_cfg['inputs_def'].get('image_shape', [3, None, None]) has_shape_def = not None in image_shape dataset = reader_cfg['dataset'] anno_file = dataset.get_anno() with_background = dataset.with_background use_default_label = dataset.use_default_label if metric == 'COCO': from ppdet.utils.coco_eval import get_category_info elif metric == "VOC": from ppdet.utils.voc_eval import get_category_info elif metric == "WIDERFACE": from ppdet.utils.widerface_eval_utils import get_category_info else: raise ValueError( "metric only supports COCO, VOC, WIDERFACE, but received {}".format( metric)) clsid2catid, catid2name = get_category_info(anno_file, with_background, use_default_label) label_list = [str(cat) for cat in catid2name.values()] sample_transforms = reader_cfg['sample_transforms'] for st in sample_transforms[1:]: method = st.__class__.__name__ p = {'type': method.replace('Image', '')} params = st.__dict__ params.pop('_id') if p['type'] == 'Resize' and has_shape_def: params['target_size'] = min(image_shape[ 1:]) if arch in RESIZE_SCALE_SET else image_shape[1] params['max_size'] = max(image_shape[ 1:]) if arch in RESIZE_SCALE_SET else 0 params['image_shape'] = image_shape[1:] if 'target_dim' in params: params.pop('target_dim') if p['type'] == 'ResizeAndPad': assert has_shape_def, "missing input shape" p['type'] = 'Resize' p['target_size'] = params['target_dim'] p['max_size'] = params['target_dim'] p['interp'] = params['interp'] p['image_shape'] = image_shape[1:] preprocess_list.append(p) continue p.update(params) preprocess_list.append(p) batch_transforms = reader_cfg.get('batch_transforms', None) if batch_transforms: methods = [bt.__class__.__name__ for bt in batch_transforms] for bt in batch_transforms: method = bt.__class__.__name__ if method == 'PadBatch': preprocess_list.append({'type': 'PadStride'}) params = bt.__dict__ preprocess_list[-1].update({'stride': params['pad_to_stride']}) break return with_background, preprocess_list, label_list def dump_infer_config(FLAGS, config): arch_state = 0 cfg_name = os.path.basename(FLAGS.config).split('.')[0] save_dir = os.path.join(FLAGS.output_dir, cfg_name) if not os.path.exists(save_dir): os.makedirs(save_dir) from ppdet.core.config.yaml_helpers import setup_orderdict setup_orderdict() infer_cfg = OrderedDict({ 'use_python_inference': False, 'mode': 'fluid', 'draw_threshold': 0.5, 'metric': config['metric'] }) infer_arch = config['architecture'] 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 = 1 break if not arch_state: logger.error( 'Architecture: {} is not supported for exporting model now'.format( infer_arch)) os._exit(0) # support land mark output if 'with_lmk' in config and config['with_lmk'] == True: infer_cfg['with_lmk'] = True if 'Mask' in config['architecture']: infer_cfg['mask_resolution'] = config['MaskHead']['resolution'] infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[ 'label_list'] = parse_reader(config['TestReader'], config['metric'], infer_cfg['arch']) yaml.dump(infer_cfg, open(os.path.join(save_dir, 'infer_cfg.yml'), 'w')) logger.info("Export inference config file to {}".format( os.path.join(save_dir, 'infer_cfg.yml'))) def prune_feed_vars(feeded_var_names, target_vars, prog): """ Filter out feed variables which are not in program, pruned feed variables are only used in post processing on model output, which are not used in program, such as im_id to identify image order, im_shape to clip bbox in image. """ exist_var_names = [] prog = prog.clone() prog = prog._prune(targets=target_vars) global_block = prog.global_block() for name in feeded_var_names: try: v = global_block.var(name) exist_var_names.append(str(v.name)) except Exception: logger.info('save_inference_model pruned unused feed ' 'variables {}'.format(name)) pass return exist_var_names def save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog): cfg_name = os.path.basename(FLAGS.config).split('.')[0] save_dir = os.path.join(FLAGS.output_dir, cfg_name) feed_var_names = [var.name for var in feed_vars.values()] fetch_list = sorted(test_fetches.items(), key=lambda i: i[0]) target_vars = [var[1] for var in fetch_list] feed_var_names = prune_feed_vars(feed_var_names, target_vars, infer_prog) logger.info("Export inference model to {}, input: {}, output: " "{}...".format(save_dir, feed_var_names, [str(var.name) for var in target_vars])) fluid.io.save_inference_model( save_dir, feeded_var_names=feed_var_names, target_vars=target_vars, executor=exe, main_program=infer_prog, params_filename="__params__")