update CAN model

.pre-commit-config.yaml

+repos:
 -   repo: https://github.com/PaddlePaddle/mirrors-yapf.git
-    sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
+    rev: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
     hooks:
     -   id: yapf
         files: \.py$
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    sha: a11d9314b22d8f8c7556443875b731ef05965464
+    rev: a11d9314b22d8f8c7556443875b731ef05965464
     hooks:
     -   id: check-merge-conflict
     -   id: check-symlinks
@@ -15,7 +16,7 @@
     -   id: trailing-whitespace
         files: \.md$
 -   repo: https://github.com/Lucas-C/pre-commit-hooks
-    sha: v1.0.1
+    rev: v1.0.1
     hooks:
     -   id: forbid-crlf
         files: \.md$

configs/rec/rec_d28_can.yml

+Global:
+  use_gpu: True
+  epoch_num: 240
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/can/
+  save_epoch_step: 1
+  # evaluation is run every 1105 iterations
+  eval_batch_step: [0, 1105]
+  cal_metric_during_train: True
+  pretrained_model: ./output/rec/can/CAN
+  checkpoints: ./output/rec/can/CAN
+  save_inference_dir: ./inference/rec_d28_can/
+  use_visualdl: False
+  infer_img: doc/imgs_hme/hme_01.jpeg
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+  max_text_length: 36
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_can.txt
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  clip_norm_global: 100.0
+  lr:
+    name: TwoStepCosine
+    learning_rate: 0.01
+    warmup_epoch: 1
+  weight_decay: 0.0001
+
+Architecture:
+  model_type: rec
+  algorithm: CAN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: DenseNet 
+    growthRate: 24
+    reduction: 0.5
+    bottleneck: True
+    use_dropout: True
+    input_channel: 1 
+    
+  Head:
+    name: CANHead
+    in_channel: 684
+    out_channel: 111
+    max_text_length: 36
+    ratio: 16
+    attdecoder:
+      is_train: True
+      input_size: 256
+      hidden_size: 256
+      encoder_out_channel: 684
+      dropout: True
+      dropout_ratio: 0.5
+      word_num: 111
+      counting_decoder_out_channel: 111
+      attention:
+        attention_dim: 512
+        word_conv_kernel: 1
+   
+Loss:
+  name: CANLoss
+
+PostProcess:
+  name: SeqLabelDecode
+  character: 111
+
+Metric:
+  name: CANMetric
+  main_indicator: exp_rate
+
+Train:
+  dataset:
+    name: HMERDataSet
+    data_dir: ./train_data/CROHME/training/images/
+    transforms:
+      - DecodeImage:
+          channel_first: False
+      - GrayImageChannelFormat: 
+          normalize: True
+          inverse: True
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+    label_file_list: ["./train_data/CROHME/training/labels.json"]
+  loader:
+    shuffle: True
+    batch_size_per_card: 2
+    drop_last: True
+    num_workers: 1
+    collate_fn: DyMaskCollator
+
+Eval:
+  dataset:
+    name: HMERDataSet
+    data_dir: ./train_data/CROHME/evaluation/images/
+    transforms: 
+      - DecodeImage:
+          channel_first: False
+      - GrayImageChannelFormat:
+          normalize: True
+          inverse: True
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+    label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1
+    num_workers: 4
+    collate_fn: DyMaskCollator

doc/doc_ch/algorithm_rec_can.md

+# 手写数学公式识别算法-ABINet
+
+- [1. 算法简介](#1)
+- [2. 环境配置](#2)
+- [3. 模型训练、评估、预测](#3)
+    - [3.1 训练](#3-1)
+    - [3.2 评估](#3-2)
+    - [3.3 预测](#3-3)
+- [4. 推理部署](#4)
+    - [4.1 Python推理](#4-1)
+    - [4.2 C++推理](#4-2)
+    - [4.3 Serving服务化部署](#4-3)
+    - [4.4 更多推理部署](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. 算法简介
+
+论文信息：
+> [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://arxiv.org/abs/2207.11463)
+> Bohan Li, Ye Yuan, Dingkang Liang, Xiao Liu, Zhilong Ji, Jinfeng Bai, Wenyu Liu, Xiang Bai
+> ECCV, 2022
+
+
+<a name="model"></a>
+`CAN`使用CROHME手写公式数据集进行训练，在对应测试集上的精度如下：
+
+|模型    |骨干网络|配置文件|ExpRate|下载链接|
+| ----- | ----- | ----- | ----- | ----- |
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)|
+
+<a name="2"></a>
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+<a name="3"></a>
+## 3. 模型训练、评估、预测
+
+<a name="3-1"></a>
+### 3.1 模型训练
+
+请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练`CAN`识别模型时需要**更换配置文件**为`CAN`的[配置文件](../../configs/rec/rec_d28_can.yml)。
+
+#### 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：
+```shell
+#单卡训练（训练周期长，不建议）
+python3 tools/train.py -c configs/rec/rec_d28_can.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_d28_can.yml
+```
+
+**注意：**
+- 我们提供的数据集，即`CROHME数据集`将手写公式存储为黑底白字的格式，若您自行准备的数据集与之相反，即以白底黑字模式存储，请在训练时做出如下修改
+```
+python3 tools/train.py -c configs/rec/rec_d28_can.yml
+-o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
+```
+
+#
+<a name="3-2"></a>
+### 3.2 评估
+
+可下载已训练完成的[模型文件](#model)，使用如下命令进行评估：
+
+```shell
+# 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_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy
+```
+
+<a name="3-3"></a>
+### 3.3 预测
+
+使用如下命令进行单张图片预测：
+```shell
+# 注意将pretrained_model的路径设置为本地路径。
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_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_hme/'。
+```
+
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 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](https://paddleocr.bj.bcebos.com/rec_d28_can_train.tar) )，可以使用如下命令进行转换：
+
+```shell
+# 注意将pretrained_model的路径设置为本地路径。
+python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
+
+# 目前的静态图模型默认的输出长度最大为36，如果您需要预测更长的序列，请在导出模型时指定其输出序列为合适的值，例如 Architecture.Head.max_text_length=72
+```
+**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ABINet的`infer_shape`。
+
+转换成功后，在目录下有三个文件：
+```
+/inference/rec_d28_can/
+    ├── 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：
+
+```shell
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_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_hme/'。
+
+# 如果您需要在白底黑字的图片上进行预测，请设置 --rec_image_inverse=False
+```
+
+![测试图片样例](../imgs_hme/hme_00.jpg)
+
+执行命令后，上面图像的预测结果（识别的文本）会打印到屏幕上，示例如下：
+```shell
+Predicts of ./doc/imgs_hme/hme_03.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []]
+```
+
+
+**注意**：
+
+- 需要注意预测图像为**黑底白字**，即手写公式部分为白色，背景为黑色的图片。
+- 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CAN的预处理为您的预处理方法。
+
+
+<a name="4-2"></a>
+### 4.2 C++推理部署
+
+由于C++预处理后处理还未支持ABINet，所以暂未支持
+
+<a name="4-3"></a>
+### 4.3 Serving服务化部署
+
+暂不支持
+
+<a name="4-4"></a>
+### 4.4 更多推理部署
+
+暂不支持
+
+<a name="5"></a>
+## 5. FAQ
+
+1. CROHME数据集来自于[CAN源repo](https://github.com/LBH1024/CAN) 。
+
+## 引用
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2207.11463,
+  doi = {10.48550/ARXIV.2207.11463},
+  url = {https://arxiv.org/abs/2207.11463},
+  author = {Li, Bohan and Yuan, Ye and Liang, Dingkang and Liu, Xiao and Ji, Zhilong and Bai, Jinfeng and Liu, Wenyu and Bai, Xiang},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```

doc/doc_en/algorithm_rec_can_en.md

+# RobustScanner
+
+- [1. Introduction](#1)
+- [2. Environment](#2)
+- [3. Model Training / Evaluation / Prediction](#3)
+    - [3.1 Training](#3-1)
+    - [3.2 Evaluation](#3-2)
+    - [3.3 Prediction](#3-3)
+- [4. Inference and Deployment](#4)
+    - [4.1 Python Inference](#4-1)
+    - [4.2 C++ Inference](#4-2)
+    - [4.3 Serving](#4-3)
+    - [4.4 More](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. Introduction
+
+Paper:
+> [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://arxiv.org/abs/2207.11463)
+> Bohan Li, Ye Yuan, Dingkang Liang, Xiao Liu, Zhilong Ji, Jinfeng Bai, Wenyu Liu, Xiang Bai
+> ECCV, 2022
+
+Using CROHME handwrittem mathematical expression recognition datasets for training, and evaluating on its test sets, the algorithm reproduction effect is as follows:
+
+|Model|Backbone|config|exprate|Download link|
+| --- | --- | --- | --- | --- |
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|coming soon|
+
+<a name="2"></a>
+## 2. Environment
+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.
+
+
+<a name="3"></a>
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Tutorial](./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:
+
+```
+#Single GPU training (long training period, not recommended)
+python3 tools/train.py -c configs/rec/rec_d28_can.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_d28_can.yml
+```
+
+Evaluation:
+
+```
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy
+```
+
+Prediction:
+
+```
+# The configuration file used for prediction must match the training
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy
+```
+
+<a name="4"></a>
+## 4. Inference and Deployment
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert:
+
+```
+python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False
+```
+
+For RobustScanner text recognition model inference, the following commands can be executed:
+
+```
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_image_shape="1, 132, 519" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
+```
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+Not supported
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+Not supported
+
+<a name="4-4"></a>
+### 4.4 More
+
+Not supported
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2207.11463,
+  doi = {10.48550/ARXIV.2207.11463},
+  url = {https://arxiv.org/abs/2207.11463},
+  author = {Li, Bohan and Yuan, Ye and Liang, Dingkang and Liu, Xiao and Ji, Zhilong and Bai, Jinfeng and Liu, Wenyu and Bai, Xiang},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```

ppocr/data/__init__.py

@@ -37,6 +37,7 @@ from ppocr.data.simple_dataset import SimpleDataSet
 from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR
 from ppocr.data.pgnet_dataset import PGDataSet
 from ppocr.data.pubtab_dataset import PubTabDataSet
+from ppocr.data.hmer_dataset import HMERDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -55,7 +56,7 @@ def build_dataloader(config, mode, device, logger, seed=None):
 
     support_dict = [
         'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet',
-        'LMDBDataSetSR'
+        'LMDBDataSetSR', 'HMERDataSet'
     ]
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(

ppocr/data/collate_fn.py

@@ -70,3 +70,49 @@ class SSLRotateCollate(object):
     def __call__(self, batch):
         output = [np.concatenate(d, axis=0) for d in zip(*batch)]
         return output
+
+
+class DyMaskCollator(object):
+    """
+    batch: [
+        image [batch_size, channel, maxHinbatch, maxWinbatch]
+        image_mask [batch_size, channel, maxHinbatch, maxWinbatch]
+        label [batch_size, maxLabelLen]
+        label_mask [batch_size, maxLabelLen]
+        ...
+    ]
+    """
+
+    def __call__(self, batch):
+        max_width, max_height, max_length = 0, 0, 0
+        bs, channel = len(batch), batch[0][0].shape[0]
+        proper_items = []
+        for item in batch:
+            if item[0].shape[1] * max_width > 1600 * 320 or item[0].shape[
+                    2] * max_height > 1600 * 320:
+                continue
+            max_height = item[0].shape[1] if item[0].shape[
+                1] > max_height else max_height
+            max_width = item[0].shape[2] if item[0].shape[
+                2] > max_width else max_width
+            max_length = item[1].shape[0] if item[1].shape[
+                0] > max_length else max_length
+            proper_items.append(item)
+
+        images, image_masks = np.zeros(
+            (len(proper_items), channel, max_height, max_width),
+            dtype='float32'), np.zeros(
+                (len(proper_items), 1, max_height, max_width), dtype='float32')
+        labels, label_masks = np.zeros(
+            (len(proper_items), max_length), dtype='int64'), np.zeros(
+                (len(proper_items), max_length), dtype='int64')
+
+        for i in range(len(proper_items)):
+            _, h, w = proper_items[i][0].shape
+            images[i][:, :h, :w] = proper_items[i][0]
+            image_masks[i][:, :h, :w] = 1
+            l = proper_items[i][1].shape[0]
+            labels[i][:l] = proper_items[i][1]
+            label_masks[i][:l] = 1
+
+        return images, image_masks, labels, label_masks

ppocr/data/hmer_dataset.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+import os, json, random, traceback
+import numpy as np
+
+from PIL import Image
+from paddle.io import Dataset
+
+from .imaug import transform, create_operators
+
+
+class HMERDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(HMERDataSet, self).__init__()
+
+        self.logger = logger
+        self.seed = seed
+        self.mode = mode
+
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        self.data_dir = config[mode]['dataset']['data_dir']
+
+        label_file_list = dataset_config['label_file_list']
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+
+        self.data_lines, self.labels = self.get_image_info_list(label_file_list,
+                                                                ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if self.mode == "train" and self.do_shuffle:
+            self.shuffle_data_random()
+
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+        self.need_reset = True in [x < 1 for x in ratio_list]
+
+    def get_image_info_list(self, file_list, ratio_list):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        labels = {}
+        for idx, file in enumerate(file_list):
+            with open(file, "r") as f:
+                lines = json.load(f)
+                labels.update(lines)
+        data_lines = [name for name in labels.keys()]
+        return data_lines, labels
+
+    def shuffle_data_random(self):
+        random.seed(self.seed)
+        random.shuffle(self.data_lines)
+        return
+
+    def __len__(self):
+        return len(self.data_idx_order_list)
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_name = self.data_lines[file_idx]
+        try:
+            file_name = data_name + '.jpg'
+            img_path = os.path.join(self.data_dir, file_name)
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
+            with open(img_path, 'rb') as f:
+                img = f.read()
+
+            label = self.labels.get(data_name).split()
+            label = np.array([int(item) for item in label])
+
+            data = {'image': img, 'label': label}
+            outs = transform(data, self.ops)
+        except:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    file_name, traceback.format_exc()))
+            outs = None
+        if outs is None:
+            # during evaluation, we should fix the idx to get same results for many times of evaluation.
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)
+        return outs

ppocr/data/imaug/__init__.py

@@ -27,7 +27,7 @@ from .make_pse_gt import MakePseGt
 from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
     SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
     ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug, VLRecResizeImg, SPINRecResizeImg, RobustScannerRecResizeImg, \
-    RFLRecResizeImg
+    RFLRecResizeImg, GrayImageChannelFormat
 from .ssl_img_aug import SSLRotateResize
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste

ppocr/data/imaug/rec_img_aug.py

@@ -465,6 +465,36 @@ class RobustScannerRecResizeImg(object):
         return data
 
 
+class GrayImageChannelFormat(object):
+    """
+    format gray scale image's channel: (3,h,w) -> (1,h,w)
+    Args:
+        normalize: True/False 
+            when True convert image dynamic range [0,255]->[0,1]
+        inverse: inverse gray image 
+    """
+
+    def __init__(self, normalize=True, inverse=False, **kwargs):
+        self.normalize = normalize
+        self.inverse = inverse
+
+    def __call__(self, data):
+        img = data['image']
+        img_single_channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img_single_channel = np.expand_dims(img_single_channel, 0)
+
+        if self.normalize:
+            img_single_channel = img_single_channel / 255.0
+
+        if self.inverse:
+            data['image'] = np.abs(img_single_channel - 1).astype('float32')
+        else:
+            data['image'] = img_single_channel.astype('float32')
+
+        data['src_image'] = img
+        return data
+
+
 def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
     imgC, imgH, imgW_min, imgW_max = image_shape
     h = img.shape[0]

ppocr/losses/__init__.py

@@ -40,6 +40,7 @@ from .rec_multi_loss import MultiLoss
 from .rec_vl_loss import VLLoss
 from .rec_spin_att_loss import SPINAttentionLoss
 from .rec_rfl_loss import RFLLoss
+from .rec_can_loss import CANLoss
 
 # cls loss
 from .cls_loss import ClsLoss
@@ -71,7 +72,7 @@ def build_loss(config):
         'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
         'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
         'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss', 'StrokeFocusLoss',
-        'SLALoss', 'CTLoss', 'RFLLoss', 'DRRGLoss'
+        'SLALoss', 'CTLoss', 'RFLLoss', 'DRRGLoss', 'CANLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/rec_can_loss.py

+import paddle
+import paddle.nn as nn
+import numpy as np
+
+
+class CANLoss(nn.Layer):
+    '''
+    CANLoss is consist of two part:
+        word_average_loss: average accuracy of the symbol
+        counting_loss: counting loss of every symbol
+    '''
+
+    def __init__(self):
+        super(CANLoss, self).__init__()
+
+        self.use_label_mask = False
+        self.out_channel = 111
+        self.cross = nn.CrossEntropyLoss(
+            reduction='none') if self.use_label_mask else nn.CrossEntropyLoss()
+        self.counting_loss = nn.SmoothL1Loss(reduction='mean')
+        self.ratio = 16
+
+    def forward(self, preds, batch):
+        word_probs = preds[0]
+        counting_preds = preds[1]
+        counting_preds1 = preds[2]
+        counting_preds2 = preds[3]
+        labels = batch[2]
+        labels_mask = batch[3]
+        counting_labels = gen_counting_label(labels, self.out_channel, True)
+        counting_loss = self.counting_loss(counting_preds1, counting_labels) + self.counting_loss(counting_preds2, counting_labels) \
+                        + self.counting_loss(counting_preds, counting_labels)
+
+        word_loss = self.cross(
+            paddle.reshape(word_probs, [-1, word_probs.shape[-1]]),
+            paddle.reshape(labels, [-1]))
+        word_average_loss = paddle.sum(
+            paddle.reshape(word_loss * labels_mask, [-1])) / (
+                paddle.sum(labels_mask) + 1e-10
+            ) if self.use_label_mask else word_loss
+        loss = word_average_loss + counting_loss
+        return {'loss': loss}
+
+
+def gen_counting_label(labels, channel, tag):
+    b, t = labels.shape
+    counting_labels = np.zeros([b, channel])
+
+    if tag:
+        ignore = [0, 1, 107, 108, 109, 110]
+    else:
+        ignore = []
+    for i in range(b):
+        for j in range(t):
+            k = labels[i][j]
+            if k in ignore:
+                continue
+            else:
+                counting_labels[i][k] += 1
+    counting_labels = paddle.to_tensor(counting_labels, dtype='float32')
+    return counting_labels

ppocr/metrics/__init__.py

@@ -22,7 +22,7 @@ import copy
 __all__ = ["build_metric"]
 
 from .det_metric import DetMetric, DetFCEMetric
-from .rec_metric import RecMetric, CNTMetric
+from .rec_metric import RecMetric, CNTMetric, CANMetric
 from .cls_metric import ClsMetric
 from .e2e_metric import E2EMetric
 from .distillation_metric import DistillationMetric
@@ -38,7 +38,7 @@ def build_metric(config):
     support_dict = [
         "DetMetric", "DetFCEMetric", "RecMetric", "ClsMetric", "E2EMetric",
         "DistillationMetric", "TableMetric", 'KIEMetric', 'VQASerTokenMetric',
-        'VQAReTokenMetric', 'SRMetric', 'CTMetric', 'CNTMetric'
+        'VQAReTokenMetric', 'SRMetric', 'CTMetric', 'CNTMetric', 'CANMetric'
     ]
 
     config = copy.deepcopy(config)

ppocr/metrics/rec_metric.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 
 from rapidfuzz.distance import Levenshtein
+from difflib import SequenceMatcher
+
+import numpy as np
 import string
 
 
@@ -106,3 +109,71 @@ class CNTMetric(object):
     def reset(self):
         self.correct_num = 0
         self.all_num = 0
+
+
+class CANMetric(object):
+    def __init__(self, main_indicator='exp_rate', **kwargs):
+        self.main_indicator = main_indicator
+        self.word_right = []
+        self.exp_right = []
+        self.word_total_length = 0
+        self.exp_total_num = 0
+        self.word_rate = 0
+        self.exp_rate = 0
+        self.reset()
+        self.epoch_reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        for k, v in kwargs.items():
+            epoch_reset = v
+            if epoch_reset:
+                self.epoch_reset()
+        word_probs = preds
+        word_label, word_label_mask = batch
+        line_right = 0
+        if word_probs is not None:
+            word_pred = word_probs.argmax(2)
+        word_pred = word_pred.cpu().detach().numpy()
+        word_scores = [
+            SequenceMatcher(
+                None,
+                s1[:int(np.sum(s3))],
+                s2[:int(np.sum(s3))],
+                autojunk=False).ratio() * (
+                    len(s1[:int(np.sum(s3))]) + len(s2[:int(np.sum(s3))])) /
+            len(s1[:int(np.sum(s3))]) / 2
+            for s1, s2, s3 in zip(word_label, word_pred, word_label_mask)
+        ]
+        batch_size = len(word_scores)
+        for i in range(batch_size):
+            if word_scores[i] == 1:
+                line_right += 1
+        self.word_rate = np.mean(word_scores)  #float
+        self.exp_rate = line_right / batch_size  #float
+        exp_length, word_length = word_label.shape[:2]
+        self.word_right.append(self.word_rate * word_length)
+        self.exp_right.append(self.exp_rate * exp_length)
+        self.word_total_length = self.word_total_length + word_length
+        self.exp_total_num = self.exp_total_num + exp_length
+
+    def get_metric(self):
+        """
+        return {
+            'word_rate': 0,
+            "exp_rate": 0,
+        }
+        """
+        cur_word_rate = sum(self.word_right) / self.word_total_length
+        cur_exp_rate = sum(self.exp_right) / self.exp_total_num
+        self.reset()
+        return {'word_rate': cur_word_rate, "exp_rate": cur_exp_rate}
+
+    def reset(self):
+        self.word_rate = 0
+        self.exp_rate = 0
+
+    def epoch_reset(self):
+        self.word_right = []
+        self.exp_right = []
+        self.word_total_length = 0
+        self.exp_total_num = 0

ppocr/modeling/backbones/__init__.py

@@ -43,10 +43,12 @@ def build_backbone(config, model_type):
         from .rec_svtrnet import SVTRNet
         from .rec_vitstr import ViTSTR
         from .rec_resnet_rfl import ResNetRFL
+        from .rec_densenet import DenseNet
         support_dict = [
             'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB',
             'ResNet31', 'ResNet45', 'ResNet_ASTER', 'MicroNet',
-            'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR', 'ResNet32', 'ResNetRFL'
+            'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR', 'ResNet32', 'ResNetRFL',
+            'DenseNet'
         ]
     elif model_type == 'e2e':
         from .e2e_resnet_vd_pg import ResNet

ppocr/modeling/backbones/rec_densenet.py

+import math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class Bottleneck(nn.Layer):
+    '''
+        ratio: 16
+        growthRate: 24
+        reduction: 0.5
+        bottleneck: True
+        use_dropout: True
+    '''
+
+    def __init__(self, nChannels, growthRate, use_dropout):
+        super(Bottleneck, self).__init__()
+        interChannels = 4 * growthRate
+        self.bn1 = nn.BatchNorm2D(interChannels)
+        self.conv1 = nn.Conv2D(
+            nChannels, interChannels, kernel_size=1,
+            bias_attr=None)  # Xavier initialization
+        self.bn2 = nn.BatchNorm2D(growthRate)
+        self.conv2 = nn.Conv2D(
+            interChannels, growthRate, kernel_size=3, padding=1,
+            bias_attr=None)  # Xavier initialization
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = F.relu(self.bn2(self.conv2(out)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = paddle.concat([x, out], 1)
+        return out
+
+
+class SingleLayer(nn.Layer):
+    def __init__(self, nChannels, growthRate, use_dropout):
+        super(SingleLayer, self).__init__()
+        self.bn1 = nn.BatchNorm2D(nChannels)
+        self.conv1 = nn.Conv2D(
+            nChannels, growthRate, kernel_size=3, padding=1, bias_attr=False)
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = self.conv1(F.relu(x))
+        if self.use_dropout:
+            out = self.dropout(out)
+
+        out = paddle.concat([x, out], 1)
+        return out
+
+
+class Transition(nn.Layer):
+    def __init__(self, nChannels, out_channels, use_dropout):
+        super(Transition, self).__init__()
+        self.bn1 = nn.BatchNorm2D(out_channels)
+        self.conv1 = nn.Conv2D(
+            nChannels, out_channels, kernel_size=1, bias_attr=False)
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = F.avg_pool2d(out, 2, ceil_mode=True, exclusive=False)
+        return out
+
+
+class DenseNet(nn.Layer):
+    def __init__(self, growthRate, reduction, bottleneck, use_dropout,
+                 input_channel, **kwargs):
+        super(DenseNet, self).__init__()
+        '''
+          ratio: 16
+          growthRate: 24
+          reduction: 0.5
+        '''
+        nDenseBlocks = 16
+        nChannels = 2 * growthRate
+
+        self.conv1 = nn.Conv2D(
+            input_channel,
+            nChannels,
+            kernel_size=7,
+            padding=3,
+            stride=2,
+            bias_attr=False)
+        self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks,
+                                       bottleneck, use_dropout)
+        nChannels += nDenseBlocks * growthRate
+        out_channels = int(math.floor(nChannels * reduction))
+        self.trans1 = Transition(nChannels, out_channels, use_dropout)
+
+        nChannels = out_channels
+        self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks,
+                                       bottleneck, use_dropout)
+        nChannels += nDenseBlocks * growthRate
+        out_channels = int(math.floor(nChannels * reduction))
+        self.trans2 = Transition(nChannels, out_channels, use_dropout)
+
+        nChannels = out_channels
+        self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks,
+                                       bottleneck, use_dropout)
+        self.out_channels = out_channels
+
+    def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck,
+                    use_dropout):
+        layers = []
+        for i in range(int(nDenseBlocks)):
+            if bottleneck:
+                layers.append(Bottleneck(nChannels, growthRate, use_dropout))
+            else:
+                layers.append(SingleLayer(nChannels, growthRate, use_dropout))
+            nChannels += growthRate
+        return nn.Sequential(*layers)
+
+    def forward(self, inputs):
+        x, x_m, y = inputs
+        out = self.conv1(x)
+        out = F.relu(out)
+        out = F.max_pool2d(out, 2, ceil_mode=True)
+        out = self.dense1(out)
+        out = self.trans1(out)
+        out = self.dense2(out)
+        out = self.trans2(out)
+        out = self.dense3(out)
+        return out, x_m, y

ppocr/modeling/heads/__init__.py

@@ -40,6 +40,7 @@ def build_head(config):
     from .rec_robustscanner_head import RobustScannerHead
     from .rec_visionlan_head import VLHead
     from .rec_rfl_head import RFLHead
+    from .rec_can_head import CANHead
 
     # cls head
     from .cls_head import ClsHead
@@ -56,7 +57,7 @@ def build_head(config):
         'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead',
         'MultiHead', 'ABINetHead', 'TableMasterHead', 'SPINAttentionHead',
         'VLHead', 'SLAHead', 'RobustScannerHead', 'CT_Head', 'RFLHead',
-        'DRRGHead'
+        'DRRGHead', 'CANHead'
     ]
 
     #table head

ppocr/modeling/heads/rec_can_head.py

+from turtle import forward
+import paddle.nn as nn
+import paddle
+import math
+'''
+Counting Module
+'''
+
+
+class ChannelAtt(nn.Layer):
+    def __init__(self, channel, reduction):
+        super(ChannelAtt, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2D(1)
+
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.ReLU(), nn.Linear(channel // reduction, channel), nn.Sigmoid())
+
+    def forward(self, x):
+        b, c, _, _ = x.shape
+        y = paddle.reshape(self.avg_pool(x), [b, c])
+        y = paddle.reshape(self.fc(y), [b, c, 1, 1])
+        return x * y
+
+
+class CountingDecoder(nn.Layer):
+    def __init__(self, in_channel, out_channel, kernel_size):
+        super(CountingDecoder, self).__init__()
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+        self.trans_layer = nn.Sequential(
+            nn.Conv2D(
+                self.in_channel,
+                512,
+                kernel_size=kernel_size,
+                padding=kernel_size // 2,
+                bias_attr=False),
+            nn.BatchNorm2D(512))
+
+        self.channel_att = ChannelAtt(512, 16)
+
+        self.pred_layer = nn.Sequential(
+            nn.Conv2D(
+                512, self.out_channel, kernel_size=1, bias_attr=False),
+            nn.Sigmoid())
+
+    def forward(self, x, mask):
+        b, _, h, w = x.shape
+        x = self.trans_layer(x)
+        x = self.channel_att(x)
+        x = self.pred_layer(x)
+
+        if mask is not None:
+            x = x * mask
+        x = paddle.reshape(x, [b, self.out_channel, -1])
+        x1 = paddle.sum(x, axis=-1)
+
+        return x1, paddle.reshape(x, [b, self.out_channel, h, w])
+
+
+'''
+Attention Decoder
+'''
+
+
+class PositionEmbeddingSine(nn.Layer):
+    def __init__(self,
+                 num_pos_feats=64,
+                 temperature=10000,
+                 normalize=False,
+                 scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, x, mask):
+        y_embed = paddle.cumsum(mask, 1, dtype='float32')
+        x_embed = paddle.cumsum(mask, 2, dtype='float32')
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+        dim_t = paddle.arange(self.num_pos_feats, dtype='float32')
+        dim_d = paddle.expand(paddle.to_tensor(2), dim_t.shape)
+        dim_t = self.temperature**(2 * (dim_t / dim_d).astype('int64') /
+                                   self.num_pos_feats)
+
+        pos_x = paddle.unsqueeze(x_embed, [3]) / dim_t
+        pos_y = paddle.unsqueeze(y_embed, [3]) / dim_t
+
+        pos_x = paddle.flatten(
+            paddle.stack(
+                [
+                    paddle.sin(pos_x[:, :, :, 0::2]),
+                    paddle.cos(pos_x[:, :, :, 1::2])
+                ],
+                axis=4),
+            3)
+        pos_y = paddle.flatten(
+            paddle.stack(
+                [
+                    paddle.sin(pos_y[:, :, :, 0::2]),
+                    paddle.cos(pos_y[:, :, :, 1::2])
+                ],
+                axis=4),
+            3)
+
+        pos = paddle.transpose(
+            paddle.concat(
+                [pos_y, pos_x], axis=3), [0, 3, 1, 2])
+
+        return pos
+
+
+class AttDecoder(nn.Layer):
+    def __init__(self, ratio, is_train, input_size, hidden_size,
+                 encoder_out_channel, dropout, dropout_ratio, word_num,
+                 counting_decoder_out_channel, attention):
+        super(AttDecoder, self).__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.out_channel = encoder_out_channel
+        self.attention_dim = attention['attention_dim']
+        self.dropout_prob = dropout
+        self.ratio = ratio
+        self.word_num = word_num
+
+        self.counting_num = counting_decoder_out_channel
+        self.is_train = is_train
+
+        self.init_weight = nn.Linear(self.out_channel, self.hidden_size)
+        self.embedding = nn.Embedding(self.word_num, self.input_size)
+        self.word_input_gru = nn.GRUCell(self.input_size, self.hidden_size)
+        self.word_attention = Attention(hidden_size, attention['attention_dim'])
+
+        self.encoder_feature_conv = nn.Conv2D(
+            self.out_channel,
+            self.attention_dim,
+            kernel_size=attention['word_conv_kernel'],
+            padding=attention['word_conv_kernel'] // 2)
+
+        self.word_state_weight = nn.Linear(self.hidden_size, self.hidden_size)
+        self.word_embedding_weight = nn.Linear(self.input_size,
+                                               self.hidden_size)
+        self.word_context_weight = nn.Linear(self.out_channel, self.hidden_size)
+        self.counting_context_weight = nn.Linear(self.counting_num,
+                                                 self.hidden_size)
+        self.word_convert = nn.Linear(self.hidden_size, self.word_num)
+
+        if dropout:
+            self.dropout = nn.Dropout(dropout_ratio)
+
+    def forward(self, cnn_features, labels, counting_preds, images_mask):
+        if self.is_train:
+            _, num_steps = labels.shape
+        else:
+            num_steps = 36
+
+        batch_size, _, height, width = cnn_features.shape
+        images_mask = images_mask[:, :, ::self.ratio, ::self.ratio]
+
+        word_probs = paddle.zeros((batch_size, num_steps, self.word_num))
+        word_alpha_sum = paddle.zeros((batch_size, 1, height, width))
+
+        hidden = self.init_hidden(cnn_features, images_mask)
+        counting_context_weighted = self.counting_context_weight(counting_preds)
+        cnn_features_trans = self.encoder_feature_conv(cnn_features)
+
+        position_embedding = PositionEmbeddingSine(256, normalize=True)
+        pos = position_embedding(cnn_features_trans, images_mask[:, 0, :, :])
+
+        cnn_features_trans = cnn_features_trans + pos
+
+        word = paddle.ones([batch_size, 1], dtype='int64')  # init word as sos
+        word = word.squeeze(axis=1)
+        for i in range(num_steps):
+            word_embedding = self.embedding(word)
+            _, hidden = self.word_input_gru(word_embedding, hidden)
+            word_context_vec, _, word_alpha_sum = self.word_attention(
+                cnn_features, cnn_features_trans, hidden, word_alpha_sum,
+                images_mask)
+
+            current_state = self.word_state_weight(hidden)
+            word_weighted_embedding = self.word_embedding_weight(word_embedding)
+            word_context_weighted = self.word_context_weight(word_context_vec)
+
+            if self.dropout_prob:
+                word_out_state = self.dropout(
+                    current_state + word_weighted_embedding +
+                    word_context_weighted + counting_context_weighted)
+            else:
+                word_out_state = current_state + word_weighted_embedding + word_context_weighted + counting_context_weighted
+
+            word_prob = self.word_convert(word_out_state)
+            word_probs[:, i] = word_prob
+
+            if self.is_train:
+                word = labels[:, i]
+            else:
+                word = word_prob.argmax(1)
+                word = paddle.multiply(
+                    word, labels[:, i]
+                )  # labels are oneslike tensor in infer/predict mode
+
+        return word_probs
+
+    def init_hidden(self, features, feature_mask):
+        average = paddle.sum(paddle.sum(features * feature_mask, axis=-1),
+                             axis=-1) / paddle.sum(
+                                 (paddle.sum(feature_mask, axis=-1)), axis=-1)
+        average = self.init_weight(average)
+        return paddle.tanh(average)
+
+
+'''
+Attention Module
+'''
+
+
+class Attention(nn.Layer):
+    def __init__(self, hidden_size, attention_dim):
+        super(Attention, self).__init__()
+        self.hidden = hidden_size
+        self.attention_dim = attention_dim
+        self.hidden_weight = nn.Linear(self.hidden, self.attention_dim)
+        self.attention_conv = nn.Conv2D(
+            1, 512, kernel_size=11, padding=5, bias_attr=False)
+        self.attention_weight = nn.Linear(
+            512, self.attention_dim, bias_attr=False)
+        self.alpha_convert = nn.Linear(self.attention_dim, 1)
+
+    def forward(self,
+                cnn_features,
+                cnn_features_trans,
+                hidden,
+                alpha_sum,
+                image_mask=None):
+        query = self.hidden_weight(hidden)
+        alpha_sum_trans = self.attention_conv(alpha_sum)
+        coverage_alpha = self.attention_weight(
+            paddle.transpose(alpha_sum_trans, [0, 2, 3, 1]))
+        alpha_score = paddle.tanh(
+            paddle.unsqueeze(query, [1, 2]) + coverage_alpha + paddle.transpose(
+                cnn_features_trans, [0, 2, 3, 1]))
+        energy = self.alpha_convert(alpha_score)
+        energy = energy - energy.max()
+        energy_exp = paddle.exp(paddle.squeeze(energy, -1))
+
+        if image_mask is not None:
+            energy_exp = energy_exp * paddle.squeeze(image_mask, 1)
+        alpha = energy_exp / (paddle.unsqueeze(
+            paddle.sum(paddle.sum(energy_exp, -1), -1), [1, 2]) + 1e-10)
+        alpha_sum = paddle.unsqueeze(alpha, 1) + alpha_sum
+        context_vector = paddle.sum(
+            paddle.sum((paddle.unsqueeze(alpha, 1) * cnn_features), -1), -1)
+
+        return context_vector, alpha, alpha_sum
+
+
+class CANHead(nn.Layer):
+    def __init__(self, in_channel, out_channel, ratio, attdecoder, **kwargs):
+        super(CANHead, self).__init__()
+
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+
+        self.counting_decoder1 = CountingDecoder(self.in_channel,
+                                                 self.out_channel, 3)  # mscm
+        self.counting_decoder2 = CountingDecoder(self.in_channel,
+                                                 self.out_channel, 5)
+
+        self.decoder = AttDecoder(ratio, **attdecoder)
+
+        self.ratio = ratio
+
+    def forward(self, inputs, targets=None):
+        cnn_features, images_mask, labels = inputs
+
+        counting_mask = images_mask[:, :, ::self.ratio, ::self.ratio]
+        counting_preds1, _ = self.counting_decoder1(cnn_features, counting_mask)
+        counting_preds2, _ = self.counting_decoder2(cnn_features, counting_mask)
+        counting_preds = (counting_preds1 + counting_preds2) / 2
+
+        word_probs = self.decoder(cnn_features, labels, counting_preds,
+                                  images_mask)
+        return word_probs, counting_preds, counting_preds1, counting_preds2

ppocr/optimizer/learning_rate.py

@@ -18,7 +18,7 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 from paddle.optimizer import lr
-from .lr_scheduler import CyclicalCosineDecay, OneCycleDecay
+from .lr_scheduler import CyclicalCosineDecay, OneCycleDecay, TwoStepCosineDecay
 
 
 class Linear(object):
@@ -386,3 +386,44 @@ class MultiStepDecay(object):
                 end_lr=self.learning_rate,
                 last_epoch=self.last_epoch)
         return learning_rate
+
+
+class TwoStepCosine(object):
+    """
+    Cosine learning rate decay
+    lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1)
+    Args:
+        lr(float): initial learning rate
+        step_each_epoch(int): steps each epoch
+        epochs(int): total training epochs
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 step_each_epoch,
+                 epochs,
+                 warmup_epoch=0,
+                 last_epoch=-1,
+                 **kwargs):
+        super(TwoStepCosine, self).__init__()
+        self.learning_rate = learning_rate
+        self.T_max1 = step_each_epoch * 200
+        self.T_max2 = step_each_epoch * epochs
+        self.last_epoch = last_epoch
+        self.warmup_epoch = round(warmup_epoch * step_each_epoch)
+
+    def __call__(self):
+        learning_rate = TwoStepCosineDecay(
+            learning_rate=self.learning_rate,
+            T_max1=self.T_max1,
+            T_max2=self.T_max2,
+            last_epoch=self.last_epoch)
+        if self.warmup_epoch > 0:
+            learning_rate = lr.LinearWarmup(
+                learning_rate=learning_rate,
+                warmup_steps=self.warmup_epoch,
+                start_lr=0.0,
+                end_lr=self.learning_rate,
+                last_epoch=self.last_epoch)
+        return learning_rate

ppocr/optimizer/lr_scheduler.py

@@ -160,3 +160,63 @@ class OneCycleDecay(LRScheduler):
             start_step = phase['end_step']
 
         return computed_lr
+
+
+class TwoStepCosineDecay(LRScheduler):
+    def __init__(self,
+                 learning_rate,
+                 T_max1,
+                 T_max2,
+                 eta_min=0,
+                 last_epoch=-1,
+                 verbose=False):
+        if not isinstance(T_max1, int):
+            raise TypeError(
+                "The type of 'T_max1' in 'CosineAnnealingDecay' must be 'int', but received %s."
+                % type(T_max1))
+        if not isinstance(T_max2, int):
+            raise TypeError(
+                "The type of 'T_max2' in 'CosineAnnealingDecay' must be 'int', but received %s."
+                % type(T_max2))
+        if not isinstance(eta_min, (float, int)):
+            raise TypeError(
+                "The type of 'eta_min' in 'CosineAnnealingDecay' must be 'float, int', but received %s."
+                % type(eta_min))
+        assert T_max1 > 0 and isinstance(
+            T_max1, int), " 'T_max1' must be a positive integer."
+        assert T_max2 > 0 and isinstance(
+            T_max2, int), " 'T_max1' must be a positive integer."
+        self.T_max1 = T_max1
+        self.T_max2 = T_max2
+        self.eta_min = float(eta_min)
+        super(TwoStepCosineDecay, self).__init__(learning_rate, last_epoch,
+                                                 verbose)
+
+    def get_lr(self):
+
+        if self.last_epoch <= self.T_max1:
+            if self.last_epoch == 0:
+                return self.base_lr
+            elif (self.last_epoch - 1 - self.T_max1) % (2 * self.T_max1) == 0:
+                return self.last_lr + (self.base_lr - self.eta_min) * (
+                    1 - math.cos(math.pi / self.T_max1)) / 2
+
+            return (1 + math.cos(math.pi * self.last_epoch / self.T_max1)) / (
+                1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max1)) * (
+                    self.last_lr - self.eta_min) + self.eta_min
+        else:
+            if (self.last_epoch - 1 - self.T_max2) % (2 * self.T_max2) == 0:
+                return self.last_lr + (self.base_lr - self.eta_min) * (
+                    1 - math.cos(math.pi / self.T_max2)) / 2
+
+            return (1 + math.cos(math.pi * self.last_epoch / self.T_max2)) / (
+                1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max2)) * (
+                    self.last_lr - self.eta_min) + self.eta_min
+
+    def _get_closed_form_lr(self):
+        if self.last_epoch <= self.T_max1:
+            return self.eta_min + (self.base_lr - self.eta_min) * (1 + math.cos(
+                math.pi * self.last_epoch / self.T_max1)) / 2
+        else:
+            return self.eta_min + (self.base_lr - self.eta_min) * (1 + math.cos(
+                math.pi * self.last_epoch / self.T_max2)) / 2

ppocr/postprocess/__init__.py

@@ -37,6 +37,7 @@ from .table_postprocess import TableMasterLabelDecode, TableLabelDecode
 from .picodet_postprocess import PicoDetPostProcess
 from .ct_postprocess import CTPostProcess
 from .drrg_postprocess import DRRGPostprocess
+from .rec_postprocess import SeqLabelDecode
 
 
 def build_post_process(config, global_config=None):
@@ -51,7 +52,7 @@ def build_post_process(config, global_config=None):
         'TableMasterLabelDecode', 'SPINLabelDecode',
         'DistillationSerPostProcess', 'DistillationRePostProcess',
         'VLLabelDecode', 'PicoDetPostProcess', 'CTPostProcess',
-        'RFLLabelDecode', 'DRRGPostprocess'
+        'RFLLabelDecode', 'DRRGPostprocess', 'SeqLabelDecode'
     ]
 
     if config['name'] == 'PSEPostProcess':

ppocr/postprocess/rec_postprocess.py

@@ -897,3 +897,36 @@ class VLLabelDecode(BaseRecLabelDecode):
             return text
         label = self.decode(label)
         return text, label
+
+
+class SeqLabelDecode(BaseRecLabelDecode):
+    """ Convert between latex-symbol and symbol-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(SeqLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+
+    def decode(self, text_index, preds_prob=None):
+        result_list = []
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            seq_end = text_index[batch_idx].argmin(0)
+            idx_list = text_index[batch_idx][:seq_end].tolist()
+            symbol_list = [self.character[idx] for idx in idx_list]
+            probs = []
+            if preds_prob is not None:
+                probs = preds_prob[batch_idx][:len(symbol_list)].tolist()
+
+            result_list.append([' '.join(symbol_list), probs])
+        return result_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        pred_prob, _, _, _ = preds
+        preds_idx = pred_prob.argmax(axis=2)
+
+        text = self.decode(preds_idx)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label

ppocr/utils/dict/latex_symbol_dict.txt

+eos
+sos
+!
+'
+(
+)
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+<
+=
+>
+A
+B
+C
+E
+F
+G
+H
+I
+L
+M
+N
+P
+R
+S
+T
+V
+X
+Y
+[
+\Delta
+\alpha
+\beta
+\cdot
+\cdots
+\cos
+\div
+\exists
+\forall
+\frac
+\gamma
+\geq
+\in
+\infty
+\int
+\lambda
+\ldots
+\leq
+\lim
+\log
+\mu
+\neq
+\phi
+\pi
+\pm
+\prime
+\rightarrow
+\sigma
+\sin
+\sqrt
+\sum
+\tan
+\theta
+\times
+]
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+\{
+|
+\}
+{
+}
+^
+_
\ No newline at end of file

test_tipc/configs/rec_d28_can/rec_d28_can.yml

+Global:
+  use_gpu: True
+  epoch_num: 240
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/can/
+  save_epoch_step: 1
+  # evaluation is run every 1105 iterations
+  eval_batch_step: [0, 1105]
+  cal_metric_during_train: True
+  pretrained_model: ./output/rec/can/CAN
+  checkpoints: ./output/rec/can/CAN
+  save_inference_dir: ./inference/rec_d28_can/
+  use_visualdl: False
+  infer_img: doc/imgs_hme/hme_01.jpeg
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+  max_text_length: 36
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_can.txt
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  clip_norm_global: 100.0
+  lr:
+    name: TwoStepCosine
+    learning_rate: 0.01
+    warmup_epoch: 1
+  weight_decay: 0.0001
+
+Architecture:
+  model_type: rec
+  algorithm: CAN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: DenseNet 
+    growthRate: 24
+    reduction: 0.5
+    bottleneck: True
+    use_dropout: True
+    input_channel: 1 
+    
+  Head:
+    name: CANHead
+    in_channel: 684
+    out_channel: 111
+    max_text_length: 36
+    ratio: 16
+    attdecoder:
+      is_train: True
+      input_size: 256
+      hidden_size: 256
+      encoder_out_channel: 684
+      dropout: True
+      dropout_ratio: 0.5
+      word_num: 111
+      counting_decoder_out_channel: 111
+      attention:
+        attention_dim: 512
+        word_conv_kernel: 1
+   
+Loss:
+  name: CANLoss
+
+PostProcess:
+  name: SeqLabelDecode
+  character: 111
+
+Metric:
+  name: CANMetric
+  main_indicator: exp_rate
+
+Train:
+  dataset:
+    name: HMERDataSet
+    data_dir: ./train_data/CROHME/training/images/
+    transforms:
+      - DecodeImage:
+          channel_first: False
+      - GrayImageChannelFormat: 
+          normalize: True
+          inverse: True
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+    label_file_list: ["./train_data/CROHME/training/labels.json"]
+  loader:
+    shuffle: True
+    batch_size_per_card: 2
+    drop_last: True
+    num_workers: 1
+    collate_fn: DyMaskCollator
+
+Eval:
+  dataset:
+    name: HMERDataSet
+    data_dir: ./train_data/CROHME/evaluation/images/
+    transforms: 
+      - DecodeImage:
+          channel_first: False
+      - GrayImageChannelFormat:
+          normalize: True
+          inverse: True
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+    label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1
+    num_workers: 4
+    collate_fn: DyMaskCollator

test_tipc/configs/rec_d28_can/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_d28_can
+python:python
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=240
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=8
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./doc/imgs_hme
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.checkpoints:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/rec_d28_can_train/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/configs/rec_d28_can/rec_d28_can.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --rec_image_shape="1,100,100" --rec_algorithm="CAN"
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--rec_model_dir:./output/
+--image_dir:./doc/imgs_hme
+--save_log_path:./test/output/
+--benchmark:True
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[1,100,100]}]

tools/eval.py

@@ -74,7 +74,9 @@ def main():
             config['Architecture']["Head"]['out_channels'] = char_num
 
     model = build_model(config['Architecture'])
-    extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR", "VisionLAN", "RobustScanner"]
+    extra_input_models = [
+        "SRN", "NRTR", "SAR", "SEED", "SVTR", "VisionLAN", "RobustScanner"
+    ]
     extra_input = False
     if config['Architecture']['algorithm'] == 'Distillation':
         for key in config['Architecture']["Models"]:
@@ -83,7 +85,10 @@ def main():
     else:
         extra_input = config['Architecture']['algorithm'] in extra_input_models
     if "model_type" in config['Architecture'].keys():
-        model_type = config['Architecture']['model_type']
+        if config['Architecture']['algorithm'] == 'CAN':
+            model_type = 'can'
+        else:
+            model_type = config['Architecture']['model_type']
     else:
         model_type = None
 
@@ -92,7 +97,7 @@ def main():
     # amp
     use_amp = config["Global"].get("use_amp", False)
     amp_level = config["Global"].get("amp_level", 'O2')
-    amp_custom_black_list = config['Global'].get('amp_custom_black_list',[])
+    amp_custom_black_list = config['Global'].get('amp_custom_black_list', [])
     if use_amp:
         AMP_RELATED_FLAGS_SETTING = {
             'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
@@ -120,7 +125,8 @@ def main():
 
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, model_type, extra_input, scaler, amp_level, amp_custom_black_list)
+                          eval_class, model_type, extra_input, scaler,
+                          amp_level, amp_custom_black_list)
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

tools/export_model.py

@@ -123,6 +123,17 @@ def export_single_model(model,
                 ]
         ]
         model = to_static(model, input_spec=other_shape)
+    elif arch_config["algorithm"] == "CAN":
+        other_shape = [[
+            paddle.static.InputSpec(
+                shape=[None, 1, None, None],
+                dtype="float32"), paddle.static.InputSpec(
+                    shape=[None, 1, None, None], dtype="float32"),
+            paddle.static.InputSpec(
+                shape=[None, arch_config['Head']['max_text_length']],
+                dtype="int64")
+        ]]
+        model = to_static(model, input_spec=other_shape)
     elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
         input_spec = [
             paddle.static.InputSpec(

tools/infer/predict_rec.py

@@ -108,6 +108,13 @@ class TextRecognizer(object):
             }
         elif self.rec_algorithm == "PREN":
             postprocess_params = {'name': 'PRENLabelDecode'}
+        elif self.rec_algorithm == "CAN":
+            self.inverse = args.rec_image_inverse
+            postprocess_params = {
+                'name': 'SeqLabelDecode',
+                "character_dict_path": args.rec_char_dict_path,
+                "use_space_char": args.use_space_char
+            }
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             utility.create_predictor(args, 'rec', logger)
@@ -351,6 +358,30 @@ class TextRecognizer(object):
 
         return resized_image
 
+    def norm_img_can(self, img, image_shape):
+
+        img = cv2.cvtColor(
+            img, cv2.COLOR_BGR2GRAY)  # CAN only predict gray scale image
+
+        if self.inverse:
+            img = 255 - img
+
+        if self.rec_image_shape[0] == 1:
+            h, w = img.shape
+            _, imgH, imgW = self.rec_image_shape
+            if h < imgH or w < imgW:
+                padding_h = max(imgH - h, 0)
+                padding_w = max(imgW - w, 0)
+                img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
+                                    'constant',
+                                    constant_values=(255))
+                img = img_padded
+
+        img = np.expand_dims(img, 0) / 255.0  # h,w,c -> c,h,w
+        img = img.astype('float32')
+
+        return img
+
     def __call__(self, img_list):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
@@ -430,6 +461,17 @@ class TextRecognizer(object):
                     word_positions = np.array(range(0, 40)).astype('int64')
                     word_positions = np.expand_dims(word_positions, axis=0)
                     word_positions_list.append(word_positions)
+                elif self.rec_algorithm == "CAN":
+                    norm_img = self.norm_img_can(img_list[indices[ino]],
+                                                 max_wh_ratio)
+                    norm_img = norm_img[np.newaxis, :]
+                    norm_img_batch.append(norm_img)
+                    norm_image_mask = np.ones(norm_img.shape, dtype='float32')
+                    word_label = np.ones([1, 36], dtype='int64')
+                    norm_img_mask_batch = []
+                    word_label_list = []
+                    norm_img_mask_batch.append(norm_image_mask)
+                    word_label_list.append(word_label)
                 else:
                     norm_img = self.resize_norm_img(img_list[indices[ino]],
                                                     max_wh_ratio)
@@ -527,6 +569,33 @@ class TextRecognizer(object):
                     if self.benchmark:
                         self.autolog.times.stamp()
                     preds = outputs[0]
+            elif self.rec_algorithm == "CAN":
+                norm_img_mask_batch = np.concatenate(norm_img_mask_batch)
+                word_label_list = np.concatenate(word_label_list)
+                inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]
+                if self.use_onnx:
+                    input_dict = {}
+                    input_dict[self.input_tensor.name] = norm_img_batch
+                    outputs = self.predictor.run(self.output_tensors,
+                                                 input_dict)
+                    preds = outputs
+                else:
+                    input_names = self.predictor.get_input_names()
+                    input_tensor = []
+                    for i in range(len(input_names)):
+                        input_tensor_i = self.predictor.get_input_handle(
+                            input_names[i])
+                        input_tensor_i.copy_from_cpu(inputs[i])
+                        input_tensor.append(input_tensor_i)
+                    self.input_tensor = input_tensor
+                    self.predictor.run()
+                    outputs = []
+                    for output_tensor in self.output_tensors:
+                        output = output_tensor.copy_to_cpu()
+                        outputs.append(output)
+                    if self.benchmark:
+                        self.autolog.times.stamp()
+                    preds = outputs
             else:
                 if self.use_onnx:
                     input_dict = {}

tools/infer/utility.py

@@ -84,6 +84,7 @@ def init_args():
     # params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='SVTR_LCNet')
     parser.add_argument("--rec_model_dir", type=str)
+    parser.add_argument("--rec_image_inverse", type=str2bool, default=True)
     parser.add_argument("--rec_image_shape", type=str, default="3, 48, 320")
     parser.add_argument("--rec_batch_num", type=int, default=6)
     parser.add_argument("--max_text_length", type=int, default=25)

tools/infer_rec.py

@@ -141,6 +141,11 @@ def main():
                     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":
@@ -149,6 +154,8 @@ def main():
                 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)

tools/program.py

@@ -273,6 +273,8 @@ def train(config,
                         preds = model(images, data=batch[1:])
                     elif model_type in ["kie"]:
                         preds = model(batch)
+                    elif algorithm in ['CAN']:
+                        preds = model(batch[:3])
                     else:
                         preds = model(images)
                 preds = to_float32(preds)
@@ -286,6 +288,8 @@ def train(config,
                     preds = model(images, data=batch[1:])
                 elif model_type in ["kie", 'sr']:
                     preds = model(batch)
+                elif algorithm in ['CAN']:
+                    preds = model(batch[:3])
                 else:
                     preds = model(images)
                 loss = loss_class(preds, batch)
@@ -302,6 +306,9 @@ def train(config,
                 elif model_type in ['table']:
                     post_result = post_process_class(preds, batch)
                     eval_class(post_result, batch)
+                elif algorithm in ['CAN']:
+                    model_type = 'can'
+                    eval_class(preds[0], batch[2:], epoch_reset=(idx == 0))
                 else:
                     if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2'
                                                   ]:  # for multi head loss
@@ -496,6 +503,8 @@ def eval(model,
                         preds = model(images, data=batch[1:])
                     elif model_type in ["kie"]:
                         preds = model(batch)
+                    elif model_type in ['can']:
+                        preds = model(batch[:3])
                     elif model_type in ['sr']:
                         preds = model(batch)
                         sr_img = preds["sr_img"]
@@ -508,6 +517,8 @@ def eval(model,
                     preds = model(images, data=batch[1:])
                 elif model_type in ["kie"]:
                     preds = model(batch)
+                elif model_type in ['can']:
+                    preds = model(batch[:3])
                 elif model_type in ['sr']:
                     preds = model(batch)
                     sr_img = preds["sr_img"]
@@ -532,6 +543,8 @@ def eval(model,
                     eval_class(post_result, batch_numpy)
             elif model_type in ['sr']:
                 eval_class(preds, batch_numpy)
+            elif model_type in ['can']:
+                eval_class(preds[0], batch_numpy[2:], epoch_reset=False)
             else:
                 post_result = post_process_class(preds, batch_numpy[1])
                 eval_class(post_result, batch_numpy)
@@ -629,7 +642,7 @@ def preprocess(is_train=False):
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
         'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
         'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
-        'Gestalt', 'SLANet', 'RobustScanner', 'CT', 'RFL', 'DRRG'
+        'Gestalt', 'SLANet', 'RobustScanner', 'CT', 'RFL', 'DRRG', 'CAN'
     ]
 
     if use_xpu: