add save rec res
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05975f84b5
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: True
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save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt
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Optimizer:
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: True
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# save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
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Optimizer:
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_ic15.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
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Optimizer:
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt
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Optimizer:
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name: Adam
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
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Optimizer:
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@ -19,6 +19,7 @@ Global:
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max_text_length: 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt
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Optimizer:
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name: Adam
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@ -20,6 +20,7 @@ Global:
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num_heads: 8
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_srn.txt
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Optimizer:
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@ -73,35 +73,45 @@ def main():
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global_config['infer_mode'] = True
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ops = create_operators(transforms, global_config)
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save_res_path = config['Global'].get('save_res_path',
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"./output/rec/predicts_rec.txt")
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if not os.path.exists(os.path.dirname(save_res_path)):
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os.makedirs(os.path.dirname(save_res_path))
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model.eval()
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for file in get_image_file_list(config['Global']['infer_img']):
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logger.info("infer_img: {}".format(file))
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with open(file, 'rb') as f:
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img = f.read()
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data = {'image': img}
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batch = transform(data, ops)
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if config['Architecture']['algorithm'] == "SRN":
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encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
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gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
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gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
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gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
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others = [
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paddle.to_tensor(encoder_word_pos_list),
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paddle.to_tensor(gsrm_word_pos_list),
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paddle.to_tensor(gsrm_slf_attn_bias1_list),
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paddle.to_tensor(gsrm_slf_attn_bias2_list)
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]
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with open(save_res_path, "w") as fout:
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for file in get_image_file_list(config['Global']['infer_img']):
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logger.info("infer_img: {}".format(file))
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with open(file, 'rb') as f:
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img = f.read()
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data = {'image': img}
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batch = transform(data, ops)
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if config['Architecture']['algorithm'] == "SRN":
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encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
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gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
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gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
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gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
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images = np.expand_dims(batch[0], axis=0)
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images = paddle.to_tensor(images)
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if config['Architecture']['algorithm'] == "SRN":
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preds = model(images, others)
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else:
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preds = model(images)
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post_result = post_process_class(preds)
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for rec_reuslt in post_result:
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logger.info('\t result: {}'.format(rec_reuslt))
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others = [
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paddle.to_tensor(encoder_word_pos_list),
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paddle.to_tensor(gsrm_word_pos_list),
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paddle.to_tensor(gsrm_slf_attn_bias1_list),
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paddle.to_tensor(gsrm_slf_attn_bias2_list)
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]
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images = np.expand_dims(batch[0], axis=0)
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images = paddle.to_tensor(images)
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if config['Architecture']['algorithm'] == "SRN":
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preds = model(images, others)
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else:
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preds = model(images)
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post_result = post_process_class(preds)
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for rec_reuslt in post_result:
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logger.info('\t result: {}'.format(rec_reuslt))
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if len(rec_reuslt) >= 2:
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fout.write(file + "\t" + rec_reuslt[0] + "\t" + str(
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rec_reuslt[1]) + "\n")
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logger.info("success!")
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