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场景文本识别算法-NRTR

1. 算法简介

论文信息:

NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition Fenfen Sheng and Zhineng Chen and Bo Xu ICDAR, 2019

NRTR使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法复现效果如下:

模型 骨干网络 配置文件 Acc 下载链接
NRTR MTB rec_mtb_nrtr.yml 84.21% 训练模型

2. 环境配置

请先参考《运行环境准备》配置PaddleOCR运行环境,参考《项目克隆》克隆项目代码。

3. 模型训练、评估、预测

3.1 模型训练

请参考文本识别训练教程。PaddleOCR对代码进行了模块化,训练NRTR识别模型时需要更换配置文件NRTR配置文件

启动训练

具体地,在完成数据准备后,便可以启动训练,训练命令如下:

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#单卡训练(训练周期长,不建议)
python3 tools/train.py -c configs/rec/rec_mtb_nrtr.yml

# 多卡训练,通过--gpus参数指定卡号
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_mtb_nrtr.yml

3.2 评估

可下载已训练完成的模型文件,使用如下命令进行评估:

# 注意将pretrained_model的路径设置为本地路径。
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy

3.3 预测

使用如下命令进行单张图片预测:

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# 注意将pretrained_model的路径设置为本地路径。
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
# 预测文件夹下所有图像时,可修改infer_img为文件夹,如 Global.infer_img='./doc/imgs_words_en/'。

4. 推理部署

4.1 Python推理

首先将训练得到best模型,转换成inference model。这里以训练完成的模型为例(模型下载地址 ),可以使用如下命令进行转换:

# 注意将pretrained_model的路径设置为本地路径。
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/

注意:

  • 如果您是在自己的数据集上训练的模型,并且调整了字典文件,请注意修改配置文件中的character_dict_path是否是所需要的字典文件。
  • 如果您修改了训练时的输入大小,请修改tools/export_model.py文件中的对应NRTR的infer_shape

转换成功后,在目录下有三个文件:

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/inference/rec_mtb_nrtr/
    ├── inference.pdiparams         # 识别inference模型的参数文件
    ├── inference.pdiparams.info    # 识别inference模型的参数信息,可忽略
    └── inference.pdmodel           # 识别inference模型的program文件

执行如下命令进行模型推理:

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'
# 预测文件夹下所有图像时,可修改image_dir为文件夹,如 --image_dir='./doc/imgs_words_en/'。

img

执行命令后,上面图像的预测结果(识别的文本和得分)会打印到屏幕上,示例如下:

Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)

注意

  • 训练上述模型采用的图像分辨率是[1,32,100],需要通过参数rec_image_shape设置为您训练时的识别图像形状。
  • 在推理时需要设置参数rec_char_dict_path指定字典,如果您修改了字典,请修改该参数为您的字典文件。
  • 如果您修改了预处理方法,需修改tools/infer/predict_rec.py中NRTR的预处理为您的预处理方法。

4.2 C++推理部署

由于C++预处理后处理还未支持NRTR,所以暂未支持

4.3 Serving服务化部署

暂不支持

4.4 更多推理部署

暂不支持

5. FAQ

  1. NRTR论文中使用Beam搜索进行解码字符,但是速度较慢,这里默认未使用Beam搜索,以贪婪搜索进行解码字符。

6. 发行公告

  1. release/2.6更新NRTR代码结构,新版NRTR可加载旧版(release/2.5及之前)模型参数,使用下面示例代码将旧版模型参数转换为新版模型参数:

    详情

    params = paddle.load('path/' + '.pdparams') # 旧版本参数
    state_dict = model.state_dict() # 新版模型参数
    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. 新版相比与旧版,代码结构简洁,推理速度有所提高。

引用

@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}
}

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