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| # 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 numpy as np | |
| import os | |
| import sys | |
| import json | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(__dir__) | |
| sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) | |
| os.environ["FLAGS_allocator_strategy"] = 'auto_growth' | |
| import paddle | |
| from ppocr.data import create_operators, transform | |
| from ppocr.modeling.architectures import build_model | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.save_load import load_model | |
| from ppocr.utils.utility import get_image_file_list | |
| import tools.program as program | |
| def main(): | |
| global_config = config['Global'] | |
| # build post process | |
| post_process_class = build_post_process(config['PostProcess'], | |
| global_config) | |
| # build model | |
| if hasattr(post_process_class, 'character'): | |
| char_num = len(getattr(post_process_class, 'character')) | |
| if config["Architecture"]["algorithm"] in ["Distillation", | |
| ]: # distillation model | |
| for key in config["Architecture"]["Models"]: | |
| if config["Architecture"]["Models"][key]["Head"][ | |
| "name"] == 'MultiHead': # multi head | |
| out_channels_list = {} | |
| if config['PostProcess'][ | |
| 'name'] == 'DistillationSARLabelDecode': | |
| char_num = char_num - 2 | |
| if config['PostProcess'][ | |
| 'name'] == 'DistillationNRTRLabelDecode': | |
| char_num = char_num - 3 | |
| out_channels_list['CTCLabelDecode'] = char_num | |
| out_channels_list['SARLabelDecode'] = char_num + 2 | |
| out_channels_list['NRTRLabelDecode'] = char_num + 3 | |
| config['Architecture']['Models'][key]['Head'][ | |
| 'out_channels_list'] = out_channels_list | |
| else: | |
| config["Architecture"]["Models"][key]["Head"][ | |
| "out_channels"] = char_num | |
| elif config['Architecture']['Head'][ | |
| 'name'] == 'MultiHead': # multi head | |
| out_channels_list = {} | |
| char_num = len(getattr(post_process_class, 'character')) | |
| if config['PostProcess']['name'] == 'SARLabelDecode': | |
| char_num = char_num - 2 | |
| if config['PostProcess']['name'] == 'NRTRLabelDecode': | |
| char_num = char_num - 3 | |
| out_channels_list['CTCLabelDecode'] = char_num | |
| out_channels_list['SARLabelDecode'] = char_num + 2 | |
| out_channels_list['NRTRLabelDecode'] = char_num + 3 | |
| config['Architecture']['Head'][ | |
| 'out_channels_list'] = out_channels_list | |
| else: # base rec model | |
| config["Architecture"]["Head"]["out_channels"] = char_num | |
| model = build_model(config['Architecture']) | |
| load_model(config, model) | |
| # create data ops | |
| transforms = [] | |
| for op in config['Eval']['dataset']['transforms']: | |
| op_name = list(op)[0] | |
| if 'Label' in op_name: | |
| continue | |
| elif op_name in ['RecResizeImg']: | |
| op[op_name]['infer_mode'] = True | |
| elif op_name == 'KeepKeys': | |
| if config['Architecture']['algorithm'] == "SRN": | |
| op[op_name]['keep_keys'] = [ | |
| 'image', 'encoder_word_pos', 'gsrm_word_pos', | |
| 'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2' | |
| ] | |
| elif config['Architecture']['algorithm'] == "SAR": | |
| op[op_name]['keep_keys'] = ['image', 'valid_ratio'] | |
| elif config['Architecture']['algorithm'] == "RobustScanner": | |
| op[op_name][ | |
| 'keep_keys'] = ['image', 'valid_ratio', 'word_positons'] | |
| else: | |
| op[op_name]['keep_keys'] = ['image'] | |
| transforms.append(op) | |
| global_config['infer_mode'] = True | |
| ops = create_operators(transforms, global_config) | |
| save_res_path = config['Global'].get('save_res_path', | |
| "./output/rec/predicts_rec.txt") | |
| if not os.path.exists(os.path.dirname(save_res_path)): | |
| os.makedirs(os.path.dirname(save_res_path)) | |
| model.eval() | |
| with open(save_res_path, "w") as fout: | |
| for file in get_image_file_list(config['Global']['infer_img']): | |
| logger.info("infer_img: {}".format(file)) | |
| with open(file, 'rb') as f: | |
| img = f.read() | |
| data = {'image': img} | |
| batch = transform(data, ops) | |
| if config['Architecture']['algorithm'] == "SRN": | |
| encoder_word_pos_list = np.expand_dims(batch[1], axis=0) | |
| gsrm_word_pos_list = np.expand_dims(batch[2], axis=0) | |
| gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0) | |
| gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0) | |
| others = [ | |
| paddle.to_tensor(encoder_word_pos_list), | |
| paddle.to_tensor(gsrm_word_pos_list), | |
| paddle.to_tensor(gsrm_slf_attn_bias1_list), | |
| paddle.to_tensor(gsrm_slf_attn_bias2_list) | |
| ] | |
| if config['Architecture']['algorithm'] == "SAR": | |
| valid_ratio = np.expand_dims(batch[-1], axis=0) | |
| img_metas = [paddle.to_tensor(valid_ratio)] | |
| if config['Architecture']['algorithm'] == "RobustScanner": | |
| valid_ratio = np.expand_dims(batch[1], axis=0) | |
| word_positons = np.expand_dims(batch[2], axis=0) | |
| img_metas = [ | |
| paddle.to_tensor(valid_ratio), | |
| paddle.to_tensor(word_positons), | |
| ] | |
| if config['Architecture']['algorithm'] == "CAN": | |
| image_mask = paddle.ones( | |
| (np.expand_dims( | |
| batch[0], axis=0).shape), dtype='float32') | |
| label = paddle.ones((1, 36), dtype='int64') | |
| images = np.expand_dims(batch[0], axis=0) | |
| images = paddle.to_tensor(images) | |
| if config['Architecture']['algorithm'] == "SRN": | |
| preds = model(images, others) | |
| elif config['Architecture']['algorithm'] == "SAR": | |
| preds = model(images, img_metas) | |
| elif config['Architecture']['algorithm'] == "RobustScanner": | |
| preds = model(images, img_metas) | |
| elif config['Architecture']['algorithm'] == "CAN": | |
| preds = model([images, image_mask, label]) | |
| else: | |
| preds = model(images) | |
| post_result = post_process_class(preds) | |
| info = None | |
| if isinstance(post_result, dict): | |
| rec_info = dict() | |
| for key in post_result: | |
| if len(post_result[key][0]) >= 2: | |
| rec_info[key] = { | |
| "label": post_result[key][0][0], | |
| "score": float(post_result[key][0][1]), | |
| } | |
| info = json.dumps(rec_info, ensure_ascii=False) | |
| elif isinstance(post_result, list) and isinstance(post_result[0], | |
| int): | |
| # for RFLearning CNT branch | |
| info = str(post_result[0]) | |
| else: | |
| if len(post_result[0]) >= 2: | |
| info = post_result[0][0] + "\t" + str(post_result[0][1]) | |
| if info is not None: | |
| logger.info("\t result: {}".format(info)) | |
| fout.write(file + "\t" + info + "\n") | |
| logger.info("success!") | |
| if __name__ == '__main__': | |
| config, device, logger, vdl_writer = program.preprocess() | |
| main() | |