update can data loading method and tipc configs, revert precommit config

.pre-commit-config.yaml

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

configs/rec/rec_d28_can.yml

@@ -5,14 +5,14 @@ Global:
   print_batch_step: 10
   save_model_dir: ./output/rec/can/
   save_epoch_step: 1
-  # evaluation is run every 1105 iterations
+  # evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
   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/
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_hme/hme_01.jpeg
+  infer_img: doc/datasets/crohme_demo/hme_00.jpg
   # for data or label process
   character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
   max_text_length: 36
@@ -75,7 +75,7 @@ Metric:
 
 Train:
   dataset:
-    name: HMERDataSet
+    name: PGDataSet
     data_dir: ./train_data/CROHME/training/images/
     transforms:
       - DecodeImage:
@@ -83,19 +83,22 @@ Train:
       - GrayImageChannelFormat: 
           normalize: True
           inverse: True
+      - SeqLabelEncode:
+          character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+          lower: False
       - KeepKeys:
           keep_keys: ['image', 'label']
-    label_file_list: ["./train_data/CROHME/training/labels.json"]
+    label_file_list: ["./train_data/CROHME/training/labels.txt"]
   loader:
     shuffle: True
-    batch_size_per_card: 2
-    drop_last: True
-    num_workers: 1
+    batch_size_per_card: 8
+    drop_last: False
+    num_workers: 4
     collate_fn: DyMaskCollator
 
 Eval:
   dataset:
-    name: HMERDataSet
+    name: PGDataSet
     data_dir: ./train_data/CROHME/evaluation/images/
     transforms: 
       - DecodeImage:
@@ -103,9 +106,12 @@ Eval:
       - GrayImageChannelFormat:
           normalize: True
           inverse: True
+      - SeqLabelEncode:
+          character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+          lower: False
       - KeepKeys:
           keep_keys: ['image', 'label']
-    label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
+    label_file_list: ["./train_data/CROHME/evaluation/labels.txt"]
   loader:
     shuffle: False
     drop_last: False

doc/doc_ch/algorithm_rec_can.md

-# 手写数学公式识别算法-ABINet
+# 手写数学公式识别算法-CAN
 
 - [1. 算法简介](#1)
 - [2. 环境配置](#2)
@@ -27,7 +27,7 @@
 
 |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)|
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[训练模型](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
 
 <a name="2"></a>
 ## 2. 环境配置
@@ -60,16 +60,21 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs
 python3 tools/train.py -c configs/rec/rec_d28_can.yml
 -o Train.dataset.transforms.GrayImageChannelFormat.inverse=False
 ```
+- 默认每训练1个epoch（1105次iteration）进行1次评估，若您更改训练的batch_size，或更换数据集，请在训练时作出如下修改
+```
+python3 tools/train.py -c configs/rec/rec_d28_can.yml
+-o Global.eval_batch_step=[0, {length_of_dataset//batch_size}]
+```
 
 #
 <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：
+可下载已训练完成的[模型文件](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)，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
+# 注意将pretrained_model的路径设置为本地路径。若使用自行训练保存的模型，请注意修改路径和文件名为{path/to/weights}/{model_name}。
+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/CAN
 ```
 
 <a name="3-3"></a>
@@ -78,9 +83,9 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
 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
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/datasets/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
 
-# 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/'。
+# 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/datasets/crohme_demo/'。
 ```
 
 
@@ -89,17 +94,16 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
 
 <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：
+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/contribution/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
+python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/CAN 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：
 ```
@@ -112,18 +116,18 @@ python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_infe
 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"
+python3 tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.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/'。
+# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/datasets/crohme_demo/'。
 
 # 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)
+![测试图片样例](../datasets/crohme_demo/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 }', []]
+Predicts of ./doc/imgs_hme/hme_00.jpg:['x _ { k } x x _ { k } + y _ { k } y x _ { k }', []]
 ```
 
 

doc/doc_en/algorithm_rec_can_en.md

@@ -25,7 +25,7 @@ Using CROHME handwrittem mathematical expression recognition datasets for traini
 
 |Model|Backbone|config|exprate|Download link|
 | --- | --- | --- | --- | --- |
-|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|coming soon|
+|CAN|DenseNet|[rec_d28_can.yml](../../configs/rec/rec_d28_can.yml)|51.72|[trained model](https://paddleocr.bj.bcebos.com/contribution/can_train.tar)|
 
 <a name="2"></a>
 ## 2. Environment
@@ -53,14 +53,14 @@ 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
+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/CAN
 ```
 
 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
+python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/CAN
 ```
 
 <a name="4"></a>
@@ -68,16 +68,20 @@ python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.a
 
 <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:
+First, the model saved during the CAN handwritten mathematical expression 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
+
+# The default output max length of the model is 36. If you need to predict a longer sequence, please specify its output sequence as an appropriate value when exporting the model, as: Architecture.Head.max_ text_ length=72
 ```
 
-For RobustScanner text recognition model inference, the following commands can be executed:
+For CAN handwritten mathematical expression 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, 100, 100" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"
+python3 tools/infer/predict_rec.py --image_dir="./doc/crohme_demo/hme_00.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"
+
+# If you need to predict on a picture with black characters on a white background, please set: -- rec_ image_ inverse=False
 ```
 
 <a name="4-2"></a>

ppocr/data/__init__.py

@@ -37,7 +37,6 @@ 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']
 
@@ -56,7 +55,7 @@ def build_dataloader(config, mode, device, logger, seed=None):
 
     support_dict = [
         'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet',
-        'LMDBDataSetSR', 'HMERDataSet'
+        'LMDBDataSetSR'
     ]
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(

ppocr/data/collate_fn.py

@@ -95,8 +95,8 @@ class DyMaskCollator(object):
                 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
+            max_length = len(item[1]) if len(item[
+                1]) > max_length else max_length
             proper_items.append(item)
 
         images, image_masks = np.zeros(
@@ -111,7 +111,7 @@ class DyMaskCollator(object):
             _, 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]
+            l = len(proper_items[i][1])
             labels[i][:l] = proper_items[i][1]
             label_masks[i][:l] = 1
 

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/label_ops.py

@@ -1476,4 +1476,33 @@ class CTLabelEncode(object):
 
         data['polys'] = boxes
         data['texts'] = txts
-        return data
\ No newline at end of file
+        return data
+
+
+class SeqLabelEncode(BaseRecLabelEncode):
+    def __init__(self,
+                 character_dict_path,
+                 max_text_length=100,
+                 use_space_char=False,
+                 lower=True,
+                 **kwargs):
+        super(SeqLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char, lower)
+
+    def encode(self, text_seq):
+        text_seq_encoded = []
+        for text in text_seq:
+            if text not in self.character:
+                continue
+            text_seq_encoded.append(self.dict.get(text))
+        if len(text_seq_encoded) == 0:
+            return None
+        return text_seq_encoded
+
+    def __call__(self, data):
+        label = data['label']
+        if isinstance(label, str):
+            label = label.strip().split()
+        label.append(self.end_str)
+        data['label'] = self.encode(label)
+        return data

test_tipc/configs/rec_d28_can/rec_d28_can.yml

@@ -5,14 +5,14 @@ Global:
   print_batch_step: 10
   save_model_dir: ./output/rec/can/
   save_epoch_step: 1
-  # evaluation is run every 1105 iterations
+  # evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
   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/
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_hme/hme_01.jpeg
+  infer_img: doc/datasets/crohme_demo/hme_00.jpg
   # for data or label process
   character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
   max_text_length: 36
@@ -75,37 +75,43 @@ Metric:
 
 Train:
   dataset:
-    name: HMERDataSet
-    data_dir: ./train_data/CROHME/training/images/
+    name: PGDataSet
+    data_dir: ./train_data/CROHME_lite/training/images/
     transforms:
       - DecodeImage:
           channel_first: False
       - GrayImageChannelFormat: 
           normalize: True
           inverse: True
+      - SeqLabelEncode:
+          character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+          lower: False
       - KeepKeys:
           keep_keys: ['image', 'label']
-    label_file_list: ["./train_data/CROHME/training/labels.json"]
+    label_file_list: ["./train_data/CROHME_lite/training/labels.txt"]
   loader:
     shuffle: True
-    batch_size_per_card: 2
-    drop_last: True
-    num_workers: 1
+    batch_size_per_card: 8
+    drop_last: False
+    num_workers: 4
     collate_fn: DyMaskCollator
 
 Eval:
   dataset:
-    name: HMERDataSet
-    data_dir: ./train_data/CROHME/evaluation/images/
+    name: PGDataSet
+    data_dir: ./train_data/CROHME_lite/evaluation/images/
     transforms: 
       - DecodeImage:
           channel_first: False
       - GrayImageChannelFormat:
           normalize: True
           inverse: True
+      - SeqLabelEncode:
+          character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+          lower: False
       - KeepKeys:
           keep_keys: ['image', 'label']
-    label_file_list: ["./train_data/CROHME/evaluation/labels.json"]
+    label_file_list: ["./train_data/CROHME_lite/evaluation/labels.txt"]
   loader:
     shuffle: False
     drop_last: False

test_tipc/configs/rec_d28_can/train_infer_python.txt

 ===========================train_params===========================
 model_name:rec_d28_can
-python:python
+python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
 Global.auto_cast:null
@@ -9,7 +9,7 @@ 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
+train_infer_img_dir:./doc/datasets/crohme_demo
 null:null
 ##
 trainer:norm_train
@@ -37,15 +37,15 @@ 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"
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict/latex_symbol_dict.txt --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
+--rec_model_dir:
+--image_dir:./doc/datasets/crohme_demo
 --save_log_path:./test/output/
 --benchmark:True
 null:null

test_tipc/prepare.sh

@@ -257,6 +257,13 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_r32_gaspin_bilstm_att_train.tar --no-check-certificate
         cd ./pretrain_models/ && tar xf rec_r32_gaspin_bilstm_att_train.tar && cd ../
     fi
+    if [ ${model_name} == "rec_d28_can" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/contribution/can_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf can_train.tar && cd ../
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/CROHME_lite.tar --no-check-certificate
+        cd ./train_data/ && tar xf CROHME_lite.tar && cd ../
+
+    fi
     if [ ${model_name} == "layoutxlm_ser" ]; then
         ${python_name} -m pip install -r ppstructure/kie/requirements.txt
         ${python_name} -m pip install opencv-python -U

test_tipc/readme.md

@@ -44,6 +44,7 @@
 | SAST   |det_r50_vd_sast_totaltext_v2.0 | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | - |
 | Rosetta|rec_mv3_none_none_ctc_v2.0     | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
 | Rosetta|rec_r34_vd_none_none_ctc_v2.0  | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
+| CAN    |rec_d28_can                    | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
 | CRNN   |rec_mv3_none_bilstm_ctc_v2.0   | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
 | CRNN   |rec_r34_vd_none_bilstm_ctc_v2.0| 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |
 | StarNet|rec_mv3_tps_bilstm_ctc_v2.0    | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | - |

tools/program.py

@@ -544,7 +544,7 @@ def eval(model,
             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)
+                eval_class(preds[0], batch_numpy[2:], epoch_reset=(idx == 0))
             else:
                 post_result = post_process_class(preds, batch_numpy[1])
                 eval_class(post_result, batch_numpy)