--- comments: true --- # NRTR ## 1. Introduction Paper: > [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://arxiv.org/abs/1806.00926) > Fenfen Sheng and Zhineng Chen and Bo Xu > ICDAR, 2019 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: |Model|Backbone|config|Acc|Download link| | --- | --- | --- | --- | --- | |NRTR|MTB|[rec_mtb_nrtr.yml](../../configs/rec/rec_mtb_nrtr.yml)|84.21%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)| ## 2. Environment Please refer to ["Environment Preparation"](../../ppocr/environment.en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](../../ppocr/blog/clone.en.md)to clone the project code. ## 3. Model Training / Evaluation / Prediction Please refer to [Text Recognition Tutorial](../../ppocr/model_train/recognition.en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. ### Training Specifically, after the data preparation is completed, the training can be started. The training command is as follows: ```bash linenums="1" # Single GPU training (long training period, not recommended) python3 tools/train.py -c configs/rec/rec_mtb_nrtr.yml # Multi GPU training, specify the gpu number through the --gpus parameter python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_mtb_nrtr.yml ``` ### Evaluation ```bash linenums="1" # GPU evaluation python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model={path/to/weights}/best_accuracy ``` ### Prediction ```bash linenums="1" # The configuration file used for prediction must match the training python3 tools/infer_rec.py -c configs/rec/rec_mtb_nrtr.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy ``` ## 4. Inference and Deployment ### 4.1 Python Inference First, the model saved during the NRTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)) ), you can use the following command to convert: ```bash linenums="1" python3 tools/export_model.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy Global.save_inference_dir=./inference/rec_mtb_nrtr ``` **Note:** - If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. - If you modified the input size during training, please modify the `infer_shape` corresponding to NRTR in the `tools/export_model.py` file. After the conversion is successful, there are three files in the directory: ```text linenums="1" /inference/rec_mtb_nrtr/ ├── inference.pdiparams ├── inference.pdiparams.info └── inference.pdmodel ``` For NRTR text recognition model inference, the following commands can be executed: ```bash linenums="1" python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_mtb_nrtr/' --rec_algorithm='NRTR' --rec_image_shape='1,32,100' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt' ``` ![img](./images/word_10.png) After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: ```bash linenums="1" Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392) ``` ### 4.2 C++ Inference Not supported ### 4.3 Serving Not supported ### 4.4 More Not supported ## 5. FAQ 1. In the `NRTR` paper, Beam search is used to decode characters, but the speed is slow. Beam search is not used by default here, and greedy search is used to decode characters. ## 6. Release Note 1. The release/2.6 version updates the NRTR code structure. The new version of NRTR can load the model parameters of the old version (release/2.5 and before), and you may use the following code to convert the old version model parameters to the new version model parameters:
Click to expand ```python linenums="1" params = paddle.load('path/' + '.pdparams') # the old version parameters state_dict = model.state_dict() # the new version model parameters new_state_dict = {} for k1, v1 in state_dict.items(): k = k1 if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k: k_para = k[:13] + 'layers.' + k[13:] q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3)) k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3)) v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3)) new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1) elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k: k_para = k[:13] + 'layers.' + k[13:] q = params[k_para.replace('qkv', 'conv1')] k = params[k_para.replace('qkv', 'conv2')] v = params[k_para.replace('qkv', 'conv3')] new_state_dict[k1] = np.concatenate([q, k, v], -1) elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k: k_para = k[:13] + 'layers.' + k[13:] new_state_dict[k1] = params[k_para] elif 'encoder' in k and 'norm3' in k: k_para = k[:13] + 'layers.' + k[13:] new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')] elif 'encoder' in k and 'norm1' in k: k_para = k[:13] + 'layers.' + k[13:] new_state_dict[k1] = params[k_para] elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k: k_para = k[:13] + 'layers.' + k[13:] q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3)) k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3)) v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3)) new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1) elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k: k_para = k[:13] + 'layers.' + k[13:] q = params[k_para.replace('qkv', 'conv1')] k = params[k_para.replace('qkv', 'conv2')] v = params[k_para.replace('qkv', 'conv3')] new_state_dict[k1] = np.concatenate([q, k, v], -1) elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k: k_para = k[:13] + 'layers.' + k[13:] new_state_dict[k1] = params[k_para] elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('cross_attn', 'multihead_attn') q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3)) new_state_dict[k1] = q[:, :, 0, 0] elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('cross_attn', 'multihead_attn') q = params[k_para.replace('q', 'conv1')] new_state_dict[k1] = q elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('cross_attn', 'multihead_attn') k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3)) v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3)) new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1) elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('cross_attn', 'multihead_attn') k = params[k_para.replace('kv', 'conv2')] v = params[k_para.replace('kv', 'conv3')] new_state_dict[k1] = np.concatenate([k, v], -1) elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('cross_attn', 'multihead_attn') new_state_dict[k1] = params[k_para] elif 'decoder' in k and 'norm' in k: k_para = k[:13] + 'layers.' + k[13:] new_state_dict[k1] = params[k_para] elif 'mlp' in k and 'weight' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('fc', 'conv') k_para = k_para.replace('mlp.', '') w = params[k_para].transpose((1, 0, 2, 3)) new_state_dict[k1] = w[:, :, 0, 0] elif 'mlp' in k and 'bias' in k: k_para = k[:13] + 'layers.' + k[13:] k_para = k_para.replace('fc', 'conv') k_para = k_para.replace('mlp.', '') w = params[k_para] new_state_dict[k1] = w else: new_state_dict[k1] = params[k1] if list(new_state_dict[k1].shape) != list(v1.shape): print(k1) for k, v1 in state_dict.items(): if k not in new_state_dict.keys(): print(1, k) elif list(new_state_dict[k].shape) != list(v1.shape): print(2, k) model.set_state_dict(new_state_dict) paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams') ```
2. The new version has a clean code structure and improved inference speed compared with the old version. ## Citation ```bibtex @article{Sheng2019NRTR, title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition}, author = {Fenfen Sheng and Zhineng Chen and Bo Xu}, booktitle = {ICDAR}, year = {2019}, url = {http://arxiv.org/abs/1806.00926}, pages = {781-786} } ```