table rec code

configs/table/SLANet.yml

@@ -101,7 +101,7 @@ Train:
           size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
+          keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
   loader:
     shuffle: True
     batch_size_per_card: 48
@@ -137,7 +137,7 @@ Eval:
           size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
+          keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
   loader:
     shuffle: False
     drop_last: False

configs/table/SLANet_ch.yml

@@ -97,7 +97,7 @@ Train:
           size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
+          keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
   loader:
     shuffle: True
     batch_size_per_card: 48
@@ -133,7 +133,7 @@ Eval:
           size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
+          keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape']
   loader:
     shuffle: False
     drop_last: False

configs/table/SLANet_lcnetv2.yml

@@ -0,0 +1,139 @@
+Global:
+  use_gpu: true
+  epoch_num: 50
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/SLANet_lcnetv2
+  save_epoch_step: 20
+  # evaluation is run every 1000 iterations after the 0th iteration
+  eval_batch_step: [0, 1000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir: ./SLANet_lcnetv2_infer
+  use_visualdl: False
+  infer_img: ppstructure/docs/table/table.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/table_structure_dict.txt
+  character_type: en
+  max_text_length: &max_text_length 500
+  box_format: &box_format 'xyxy' # 'xywh', 'xyxy', 'xyxyxyxy'
+  infer_mode: False
+  use_sync_bn: True
+  save_res_path: 'output/infer'
+  d2s_train_image_shape: [3, -1, -1]
+  amp_custom_white_list: ['concat', 'elementwise_sub', 'set_value']
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 5.0
+  lr:
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00000
+
+Architecture:
+  model_type: table
+  algorithm: SLANet
+  Backbone:
+    name: PPLCNetV2_base
+  Neck:
+    name: CSPPAN
+    out_channels: 96
+  Head:
+    name: SLAHead
+    hidden_size: 256
+    max_text_length: *max_text_length
+    loc_reg_num: &loc_reg_num 4
+
+Loss:
+  name: SLALoss
+  structure_weight: 1.0
+  loc_weight: 2.0
+  loc_loss: smooth_l1
+
+PostProcess:
+  name: TableLabelDecode
+  merge_no_span_structure: &merge_no_span_structure True
+
+Metric:
+  name: TableMetric
+  main_indicator: acc
+  compute_bbox_metric: False
+  loc_reg_num: *loc_reg_num
+  box_format: *box_format
+
+Train:
+  dataset:
+    name: PubTabDataSet
+    data_dir: ../table_data/pubtabnet/train/
+    label_file_list: [../table_data/pubtabnet/PubTabNet_2.0.0_train.jsonl]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - TableLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: *merge_no_span_structure
+          replace_empty_cell_token: False
+          loc_reg_num: *loc_reg_num
+          max_text_length: *max_text_length
+      - TableBoxEncode:
+          in_box_format: *box_format
+          out_box_format: *box_format
+      - ResizeTableImage:
+          max_len: 488
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - PaddingTableImage:
+          size: [488, 488]
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'length', 'shape' ]
+  loader:
+    shuffle: True
+    batch_size_per_card: 24
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: PubTabDataSet
+    data_dir: ../table_data/pubtabnet/val/
+    label_file_list: [../table_data/pubtabnet/PubTabNet_2.0.0_val.jsonl]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - TableLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: *merge_no_span_structure
+          replace_empty_cell_token: False
+          loc_reg_num: *loc_reg_num
+          max_text_length: *max_text_length
+      - TableBoxEncode:
+          in_box_format: *box_format
+          out_box_format: *box_format
+      - ResizeTableImage:
+          max_len: 488
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - PaddingTableImage:
+          size: [488, 488]
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 48
+    num_workers: 4

doc/doc_ch/algorithm_table_slanet.md

@@ -0,0 +1,111 @@
+# 表格识别算法-SLANet-LCNetV2
+
+- [1. 算法简介](#1-算法简介)
+- [2. 环境配置](#2-环境配置)
+- [3. 模型训练、评估、预测](#3-模型训练评估预测)
+- [4. 推理部署](#4-推理部署)
+  - [4.1 Python推理](#41-python推理)
+  - [4.2 C++推理部署](#42-c推理部署)
+  - [4.3 Serving服务化部署](#43-serving服务化部署)
+  - [4.4 更多推理部署](#44-更多推理部署)
+- [5. FAQ](#5-faq)
+
+<a name="1"></a>
+## 1. 算法简介
+
+PaddleOCR 算法模型挑战赛 - 赛题二：通用表格识别任务排行榜第一算法。核心思路：
+
+- 1. 改善推理过程，至EOS停止，速度提升3倍
+- 2. 升级Backbone为LCNetV2（SSLD版本）
+- 3. 行列特征增强模块
+- 4. 提升分辨率488至512
+- 5. 三阶段训练策略
+
+在PubTabNet表格识别公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|acc|
+| --- | --- | --- | --- |
+|SLANet|LCNetV2|[configs/table/SLANet_lcnetv2.yml](../../configs/table/SLANet_lcnetv2.yml)|76.67%|
+
+
+<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
+
+上述SLANet_LCNetv2模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](./dataset/table_datasets.md)。
+
+### 启动训练
+
+数据下载完成后，请参考[文本识别教程](./recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。
+
+训练命令如下：
+```shell
+# stage1
+python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7'  tools/train.py -c configs/table/SLANet_lcnetv2.yml
+# stage2 加载stage1的best model作为预训练模型，学习率调整为0.0001;
+# stage3 加载stage2的best model作为预训练模型，不调整学习率，将配置文件中所有的488修改为512.
+```
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 4.1 Python推理
+将训练得到best模型，转换成inference model，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/table/SLANet_lcnetv2.yml -o Global.pretrained_model=path/best_accuracy Global.save_inference_dir=./inference/slanet_lcnetv2_infer
+```
+
+**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`是否为所正确的字典文件。
+
+转换成功后，在目录下有三个文件：
+```
+./inference/slanet_lcnetv2_infer/
+    ├── 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
+cd ppstructure/
+python3.7 table/predict_structure.py --table_model_dir=../inference/slanet_lcnetv2_infer/ --table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt --image_dir=docs/table/table.jpg --output=../output/table_slanet_lcnetv2 --use_gpu=False --benchmark=True --enable_mkldnn=True
+# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。
+```
+
+执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下：
+结果如下：
+```shell
+[2022/06/16 13:06:54] ppocr INFO: result: ['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344],
+...
+[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]]
+[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg
+[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246
+```
+
+<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SLANet
+
+<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

ppocr/data/imaug/label_ops.py

@@ -708,7 +708,7 @@ def __call__(self, data):
         structure = self.encode(new_structure)
         if structure is None:
             return None
-
+        data["length"] = len(structure)
         structure = [self.start_idx] + structure + [self.end_idx]  # add sos abd eos
         structure = structure + [self.pad_idx] * (
             self._max_text_len - len(structure)

ppocr/losses/table_att_loss.py

@@ -69,7 +69,8 @@ def __init__(self, structure_weight, loc_weight, loc_loss="mse", **kwargs):
     def forward(self, predicts, batch):
         structure_probs = predicts["structure_probs"]
         structure_targets = batch[1].astype("int64")
-        structure_targets = structure_targets[:, 1:]
+        max_len = batch[-2].max()
+        structure_targets = structure_targets[:, 1 : max_len + 2]
 
         structure_loss = self.loss_func(structure_probs, structure_targets)
 
@@ -78,8 +79,8 @@ def forward(self, predicts, batch):
         loc_preds = predicts["loc_preds"]
         loc_targets = batch[2].astype("float32")
         loc_targets_mask = batch[3].astype("float32")
-        loc_targets = loc_targets[:, 1:, :]
-        loc_targets_mask = loc_targets_mask[:, 1:, :]
+        loc_targets = loc_targets[:, 1 : max_len + 2]
+        loc_targets_mask = loc_targets_mask[:, 1 : max_len + 2]
 
         loc_loss = (
             F.smooth_l1_loss(

ppocr/modeling/backbones/__init__.py

@@ -25,6 +25,7 @@ def build_backbone(config, model_type):
         from .rec_lcnetv3 import PPLCNetV3
         from .rec_hgnet import PPHGNet_small
         from .rec_vit import ViT
+        from .det_pp_lcnet_v2 import PPLCNetV2_base
         from .rec_repvit import RepSVTR_det
 
         support_dict = [
@@ -35,6 +36,7 @@ def build_backbone(config, model_type):
             "PPLCNet",
             "PPLCNetV3",
             "PPHGNet_small",
+            "PPLCNetV2_base",
             "RepSVTR_det",
         ]
         if model_type == "table":