# 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': # for multi head out_channels_list = {} if config['PostProcess'][ 'name'] == 'DistillationSARLabelDecode': char_num = char_num - 2 out_channels_list['CTCLabelDecode'] = char_num out_channels_list['SARLabelDecode'] = char_num + 2 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': # for multi head loss out_channels_list = {} if config['PostProcess']['name'] == 'SARLabelDecode': char_num = char_num - 2 out_channels_list['CTCLabelDecode'] = char_num out_channels_list['SARLabelDecode'] = char_num + 2 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()