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271 lines
10 KiB
Markdown
271 lines
10 KiB
Markdown
9 months ago
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# NRTR
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- [1. Introduction](#1)
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- [2. Environment](#2)
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- [3. Model Training / Evaluation / Prediction](#3)
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- [3.1 Training](#3-1)
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- [3.2 Evaluation](#3-2)
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- [3.3 Prediction](#3-3)
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- [4. Inference and Deployment](#4)
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- [4.1 Python Inference](#4-1)
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- [4.2 C++ Inference](#4-2)
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- [4.3 Serving](#4-3)
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- [4.4 More](#4-4)
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- [5. FAQ](#5)
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- [6. Release Note](#6)
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<a name="1"></a>
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## 1. Introduction
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Paper:
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> [NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition](https://arxiv.org/abs/1806.00926)
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> Fenfen Sheng and Zhineng Chen and Bo Xu
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> ICDAR, 2019
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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:
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|Model|Backbone|config|Acc|Download link|
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| --- | --- | --- | --- | --- |
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|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)|
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<a name="2"></a>
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## 2. Environment
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Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
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<a name="3"></a>
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## 3. Model Training / Evaluation / Prediction
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Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
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Training:
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Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
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```
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#Single GPU training (long training period, not recommended)
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python3 tools/train.py -c configs/rec/rec_mtb_nrtr.yml
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#Multi GPU training, specify the gpu number through the --gpus parameter
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_mtb_nrtr.yml
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```
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Evaluation:
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```
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# GPU evaluation
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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
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```
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Prediction:
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```
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# The configuration file used for prediction must match the training
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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
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```
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<a name="4"></a>
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## 4. Inference and Deployment
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<a name="4-1"></a>
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### 4.1 Python Inference
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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:
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```
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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
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```
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**Note:**
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- 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.
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- If you modified the input size during training, please modify the `infer_shape` corresponding to NRTR in the `tools/export_model.py` file.
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After the conversion is successful, there are three files in the directory:
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```
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/inference/rec_mtb_nrtr/
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├── inference.pdiparams
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├── inference.pdiparams.info
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└── inference.pdmodel
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```
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For NRTR text recognition model inference, the following commands can be executed:
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```
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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'
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```
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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:
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The result is as follows:
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```shell
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Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)
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```
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<a name="4-2"></a>
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### 4.2 C++ Inference
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Not supported
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<a name="4-3"></a>
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### 4.3 Serving
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Not supported
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<a name="4-4"></a>
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### 4.4 More
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Not supported
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<a name="5"></a>
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## 5. FAQ
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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.
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<a name="6"></a>
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## 6. Release Note
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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:
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```python
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params = paddle.load('path/' + '.pdparams') # the old version parameters
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state_dict = model.state_dict() # the new version model parameters
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new_state_dict = {}
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for k1, v1 in state_dict.items():
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k = k1
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if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
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k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
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v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
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new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
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elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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q = params[k_para.replace('qkv', 'conv1')]
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k = params[k_para.replace('qkv', 'conv2')]
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v = params[k_para.replace('qkv', 'conv3')]
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new_state_dict[k1] = np.concatenate([q, k, v], -1)
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elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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new_state_dict[k1] = params[k_para]
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elif 'encoder' in k and 'norm3' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')]
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elif 'encoder' in k and 'norm1' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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new_state_dict[k1] = params[k_para]
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elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
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k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
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v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
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new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
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elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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q = params[k_para.replace('qkv', 'conv1')]
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k = params[k_para.replace('qkv', 'conv2')]
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v = params[k_para.replace('qkv', 'conv3')]
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new_state_dict[k1] = np.concatenate([q, k, v], -1)
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elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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new_state_dict[k1] = params[k_para]
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elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('cross_attn', 'multihead_attn')
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q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3))
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new_state_dict[k1] = q[:, :, 0, 0]
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elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('cross_attn', 'multihead_attn')
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q = params[k_para.replace('q', 'conv1')]
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new_state_dict[k1] = q
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elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('cross_attn', 'multihead_attn')
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k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3))
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v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3))
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new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1)
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elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('cross_attn', 'multihead_attn')
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k = params[k_para.replace('kv', 'conv2')]
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v = params[k_para.replace('kv', 'conv3')]
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new_state_dict[k1] = np.concatenate([k, v], -1)
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elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('cross_attn', 'multihead_attn')
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new_state_dict[k1] = params[k_para]
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elif 'decoder' in k and 'norm' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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new_state_dict[k1] = params[k_para]
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elif 'mlp' in k and 'weight' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('fc', 'conv')
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k_para = k_para.replace('mlp.', '')
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w = params[k_para].transpose((1, 0, 2, 3))
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new_state_dict[k1] = w[:, :, 0, 0]
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elif 'mlp' in k and 'bias' in k:
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k_para = k[:13] + 'layers.' + k[13:]
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k_para = k_para.replace('fc', 'conv')
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k_para = k_para.replace('mlp.', '')
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w = params[k_para]
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new_state_dict[k1] = w
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else:
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new_state_dict[k1] = params[k1]
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if list(new_state_dict[k1].shape) != list(v1.shape):
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print(k1)
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for k, v1 in state_dict.items():
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if k not in new_state_dict.keys():
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print(1, k)
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elif list(new_state_dict[k].shape) != list(v1.shape):
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print(2, k)
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model.set_state_dict(new_state_dict)
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paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams')
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```
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2. The new version has a clean code structure and improved inference speed compared with the old version.
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## Citation
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```bibtex
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@article{Sheng2019NRTR,
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title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
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author = {Fenfen Sheng and Zhineng Chen and Bo Xu},
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booktitle = {ICDAR},
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year = {2019},
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url = {http://arxiv.org/abs/1806.00926},
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pages = {781-786}
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}
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```
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