@@ -137,13 +139,9 @@ paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
The result is a list, each item contains a text box, text and recognition confidence
```text
-[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
-[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
-[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
-[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
-[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
-[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
-[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
+[[[67.0, 51.0], [327.0, 46.0], [327.0, 74.0], [68.0, 80.0]], ('PHOCAPITAL', 0.9944712519645691)]
+[[[72.0, 92.0], [453.0, 84.0], [454.0, 114.0], [73.0, 122.0]], ('107 State Street', 0.9744491577148438)]
+[[[69.0, 135.0], [501.0, 125.0], [501.0, 156.0], [70.0, 165.0]], ('Montpelier Vermont', 0.9357033967971802)]
......
```
@@ -234,10 +232,10 @@ im_show.save('result.jpg')
Output will be a list, each item contains bounding box, text and recognition confidence
```bash
-[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
-[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
-[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
-......
+[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
+ [[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
+ [[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
+ ......
```
Visualization of results
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index 2b53d8ef8fd71950e80049628570793dcd49c424..a6b255f9501a0c2d34c31162385bcf03ff578aa3 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -1,17 +1,25 @@
# Text Recognition
-- [1. Data Preparation](#1-data-preparation)
- - [1.1 DataSet Preparation](#11-dataset-preparation)
- - [1.2 Dictionary](#12-dictionary)
- - [1.4 Add Space Category](#14-add-space-category)
-- [2.Training](#2training)
- - [2.1 Data Augmentation](#21-data-augmentation)
- - [2.2 General Training](#22-general-training)
- - [2.3 Multi-language Training](#23-multi-language-training)
- - [2.4 Training with Knowledge Distillation](#24-training-with-knowledge-distillation)
-- [3. Evalution](#3-evalution)
-- [4. Prediction](#4-prediction)
-- [5. Convert to Inference Model](#5-convert-to-inference-model)
+- [1. Data Preparation](#DATA_PREPARATION)
+ * [1.1 Costom Dataset](#Costom_Dataset)
+ * [1.2 Dataset Download](#Dataset_download)
+ * [1.3 Dictionary](#Dictionary)
+ * [1.4 Add Space Category](#Add_space_category)
+ * [1.5 Data Augmentation](#Data_Augmentation)
+- [2. Training](#TRAINING)
+ * [2.1 Start Training](#21-start-training)
+ * [2.2 Load Trained Model and Continue Training](#22-load-trained-model-and-continue-training)
+ * [2.3 Training with New Backbone](#23-training-with-new-backbone)
+ * [2.4 Mixed Precision Training](#24-amp-training)
+ * [2.5 Distributed Training](#25-distributed-training)
+ * [2.6 Training with knowledge distillation](#kd)
+ * [2.7 Multi-language Training](#Multi_language)
+ * [2.8 Training on other platform(Windows/macOS/Linux DCU)](#28)
+- [3. Evaluation and Test](#3-evaluation-and-test)
+ * [3.1 Evaluation](#31-evaluation)
+ * [3.2 Test](#32-test)
+- [4. Inference](#4-inference)
+- [5. FAQ](#5-faq)
## 1. Data Preparation
@@ -72,11 +80,8 @@ If you need to customize dic file, please add character_dict_path field in confi
If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
-
-## 2.Training
-
-### 2.1 Data Augmentation
+### 1.5 Data Augmentation
PaddleOCR provides a variety of data augmentation methods. All the augmentation methods are enabled by default.
@@ -84,11 +89,14 @@ The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, rand
Each disturbance method is selected with a 40% probability during the training process. For specific code implementation, please refer to: [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
-
-### 2.2 General Training
+
+## 2.Training
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:
+
+### 2.1 Start Training
+
First download the pretrain model, you can download the trained model to finetune on the icdar2015 data:
```
@@ -204,8 +212,99 @@ Eval:
```
**Note that the configuration file for prediction/evaluation must be consistent with the training.**
+
+### 2.2 Load Trained Model and Continue Training
+
+If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
+
+For example:
+```shell
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./your/trained/model
+```
+
+**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrained_model`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrained_model` will be loaded.
+
+
+### 2.3 Training with New Backbone
+
+The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
+necks->heads).
+
+```bash
+├── architectures # Code for building network
+├── transforms # Image Transformation Module
+├── backbones # Feature extraction module
+├── necks # Feature enhancement module
+└── heads # Output module
+```
+
+If the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the `Backbone` part of the configuration yml file.
+
+However, if you want to use a new Backbone, an example of replacing the backbones is as follows:
+
+1. Create a new file under the [ppocr/modeling/backbones](../../ppocr/modeling/backbones) folder, such as my_backbone.py.
+2. Add code in the my_backbone.py file, the sample code is as follows:
+
+```python
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class MyBackbone(nn.Layer):
+ def __init__(self, *args, **kwargs):
+ super(MyBackbone, self).__init__()
+ # your init code
+ self.conv = nn.xxxx
+
+ def forward(self, inputs):
+ # your network forward
+ y = self.conv(inputs)
+ return y
+```
+
+3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](../../ppocr/modeling/backbones/__init__.py) file.
+
+After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:
+
+```yaml
+ Backbone:
+ name: MyBackbone
+ args1: args1
+```
+
+**NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
+
+
+### 2.4 Mixed Precision Training
+
+If you want to speed up your training further, you can use [Auto Mixed Precision Training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html), taking a single machine and a single gpu as an example, the commands are as follows:
+
+```shell
+python3 tools/train.py -c configs/rec/rec_icdar15_train.yml \
+ -o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train \
+ Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
+ ```
+
+
+### 2.5 Distributed Training
+
+During multi-machine multi-gpu training, use the `--ips` parameter to set the used machine IP address, and the `--gpus` parameter to set the used GPU ID:
+
+```bash
+python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_icdar15_train.yml \
+ -o Global.pretrained_model=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train
+```
+
+**Note:** When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. In addition, training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`.
+
+
+### 2.6 Training with Knowledge Distillation
+
+Knowledge distillation is supported in PaddleOCR for text recognition training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
+
-### 2.3 Multi-language Training
+### 2.7 Multi-language Training
Currently, the multi-language algorithms supported by PaddleOCR are:
@@ -261,25 +360,35 @@ Eval:
...
```
-
+
+### 2.8 Training on other platform(Windows/macOS/Linux DCU)
-### 2.4 Training with Knowledge Distillation
+- Windows GPU/CPU
+The Windows platform is slightly different from the Linux platform:
+Windows platform only supports `single gpu` training and inference, specify GPU for training `set CUDA_VISIBLE_DEVICES=0`
+On the Windows platform, DataLoader only supports single-process mode, so you need to set `num_workers` to 0;
-Knowledge distillation is supported in PaddleOCR for text recognition training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
+- macOS
+GPU mode is not supported, you need to set `use_gpu` to False in the configuration file, and the rest of the training evaluation prediction commands are exactly the same as Linux GPU.
-
+- Linux DCU
+Running on a DCU device requires setting the environment variable `export HIP_VISIBLE_DEVICES=0,1,2,3`, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.
-## 3. Evalution
+
+## 3. Evaluation and Test
-The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
+
+### 3.1 Evaluation
+
+The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
```
# GPU evaluation, Global.checkpoints is the weight to be tested
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
-
-## 4. Prediction
+
+### 3.2 Test
Using the model trained by paddleocr, you can quickly get prediction through the following script.
@@ -341,9 +450,14 @@ infer_img: doc/imgs_words/ch/word_1.jpg
result: ('韩国小馆', 0.997218)
```
-
+
+## 4. Inference
+
+The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
+
+The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
-## 5. Convert to Inference Model
+Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
The recognition model is converted to the inference model in the same way as the detection, as follows:
@@ -361,7 +475,7 @@ If you have a model trained on your own dataset with a different dictionary file
After the conversion is successful, there are three files in the model save directory:
```
-inference/det_db/
+inference/rec_crnn/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
@@ -374,3 +488,10 @@ inference/det_db/
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path"
```
+
+
+## 5. FAQ
+
+Q1: After the training model is transferred to the inference model, the prediction effect is inconsistent?
+
+**A**: There are many such problems, and the problems are mostly caused by inconsistent preprocessing and postprocessing parameters when the trained model predicts and the preprocessing and postprocessing parameters when the inference model predicts. You can compare whether there are differences in preprocessing, postprocessing, and prediction in the configuration files used for training.
diff --git a/doc/doc_en/whl_en.md b/doc/doc_en/whl_en.md
index 35b2b1798ad8b566ee87e921e23be84a5ecccf24..40a2e122d19679a59e7e65df29dd59781b4a2143 100644
--- a/doc/doc_en/whl_en.md
+++ b/doc/doc_en/whl_en.md
@@ -172,40 +172,42 @@ show help information
paddleocr -h
```
+**Note**: The whl package uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3,48,320`, so if you use the recognition function, you need to add the parameter `--rec_image_shape 3,48,320`, if you do not use the default `PP- OCRv3` model, you do not need to set this parameter.
+
* detection classification and recognition
```bash
-paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true --lang en
+paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true --lang en --rec_image_shape 3,48,320
```
Output will be a list, each item contains bounding box, text and recognition confidence
```bash
-[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
-[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
-[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
+[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
+[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
+[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
......
```
* detection and recognition
```bash
-paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en
+paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en --rec_image_shape 3,48,320
```
Output will be a list, each item contains bounding box, text and recognition confidence
```bash
-[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
-[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
-[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
+[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
+[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
+[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
......
```
* classification and recognition
```bash
-paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en
+paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en --rec_image_shape 3,48,320
```
Output will be a list, each item contains text and recognition confidence
```bash
-['PAIN', 0.990372]
+['PAIN', 0.9934559464454651]
```
* only detection
@@ -215,20 +217,20 @@ paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --rec false
Output will be a list, each item only contains bounding box
```bash
-[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
-[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
-[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
+[[397.0, 802.0], [1092.0, 802.0], [1092.0, 841.0], [397.0, 841.0]]
+[[397.0, 750.0], [1211.0, 750.0], [1211.0, 789.0], [397.0, 789.0]]
+[[397.0, 702.0], [1209.0, 698.0], [1209.0, 734.0], [397.0, 738.0]]
......
```
* only recognition
```bash
-paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en
+paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en --rec_image_shape 3,48,320
```
Output will be a list, each item contains text and recognition confidence
```bash
-['PAIN', 0.990372]
+['PAIN', 0.9934559464454651]
```
* only classification
@@ -366,5 +368,4 @@ im_show.save('result.jpg')
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
| show_log | Whether to print log| FALSE |
| type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr |
-| ocr_version | OCR Model version number, the current model support list is as follows: PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv2 |
-| structure_version | table structure Model version number, the current model support list is as follows: STRUCTURE support english table structure model | STRUCTURE |
+| ocr_version | OCR Model version number, the current model support list is as follows: PP-OCRv3 support Chinese and English detection and recognition model and direction classifier model, PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv3 |
diff --git a/doc/imgs_results/system_res_00018069_v3.jpg b/doc/imgs_results/system_res_00018069_v3.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..51808ca556b52239cad3602cd5602f4d5d0ab7ce
Binary files /dev/null and b/doc/imgs_results/system_res_00018069_v3.jpg differ
diff --git a/paddleocr.py b/paddleocr.py
index cb2c34f69f68d289b317d4737bd23385c77c3d95..417350839ac4d1e512c7396831f89ab4b2d6c724 100644
--- a/paddleocr.py
+++ b/paddleocr.py
@@ -47,16 +47,46 @@ __all__ = [
]
SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.5'
+VERSION = '2.5.0.1'
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
-DEFAULT_OCR_MODEL_VERSION = 'PP-OCR'
-SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2']
-DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE'
-SUPPORT_STRUCTURE_MODEL_VERSION = ['STRUCTURE']
+DEFAULT_OCR_MODEL_VERSION = 'PP-OCRv3'
+SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2', 'PP-OCRv3']
+DEFAULT_STRUCTURE_MODEL_VERSION = 'PP-STRUCTURE'
+SUPPORT_STRUCTURE_MODEL_VERSION = ['PP-STRUCTURE']
MODEL_URLS = {
'OCR': {
+ 'PP-OCRv3': {
+ 'det': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar',
+ },
+ 'en': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar',
+ },
+ },
+ 'rec': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar',
+ 'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
+ },
+ 'en': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar',
+ 'dict_path': './ppocr/utils/en_dict.txt'
+ },
+ },
+ 'cls': {
+ 'ch': {
+ 'url':
+ 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
+ }
+ },
+ },
'PP-OCRv2': {
'det': {
'ch': {
@@ -72,7 +102,7 @@ MODEL_URLS = {
}
}
},
- DEFAULT_OCR_MODEL_VERSION: {
+ 'PP-OCR': {
'det': {
'ch': {
'url':
@@ -173,7 +203,7 @@ MODEL_URLS = {
}
},
'STRUCTURE': {
- DEFAULT_STRUCTURE_MODEL_VERSION: {
+ 'PP-STRUCTURE': {
'table': {
'en': {
'url':
@@ -198,16 +228,17 @@ def parse_args(mMain=True):
"--ocr_version",
type=str,
choices=SUPPORT_OCR_MODEL_VERSION,
- default='PP-OCRv2',
+ default='PP-OCRv3',
help='OCR Model version, the current model support list is as follows: '
- '1. PP-OCRv2 Support Chinese detection and recognition model. '
- '2. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
+ '1. PP-OCRv3 Support Chinese and English detection and recognition model, and direction classifier model'
+ '2. PP-OCRv2 Support Chinese detection and recognition model. '
+ '3. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
)
parser.add_argument(
"--structure_version",
type=str,
choices=SUPPORT_STRUCTURE_MODEL_VERSION,
- default='STRUCTURE',
+ default='PP-STRUCTURE',
help='Model version, the current model support list is as follows:'
' 1. STRUCTURE Support en table structure model.')
diff --git a/ppocr/modeling/architectures/base_model.py b/ppocr/modeling/architectures/base_model.py
index f5b29f94057d5b1f1fbec27686d5f1d679b15479..c6b50d4886daa9bfd2f863c1d8fd6dbc3d1e42c0 100644
--- a/ppocr/modeling/architectures/base_model.py
+++ b/ppocr/modeling/architectures/base_model.py
@@ -92,6 +92,9 @@ class BaseModel(nn.Layer):
else:
y["head_out"] = x
if self.return_all_feats:
- return y
+ if self.training:
+ return y
+ else:
+ return {"head_out": y["head_out"]}
else:
return x
diff --git a/ppocr/utils/utility.py b/ppocr/utils/utility.py
index dc2a6e7405e4d01ddffabf4a23c98bcf3ea3c205..48a84cfdf91555523da9ff882ee463d0e3d2d9b7 100755
--- a/ppocr/utils/utility.py
+++ b/ppocr/utils/utility.py
@@ -49,18 +49,23 @@ def get_check_global_params(mode):
return check_params
+def _check_image_file(path):
+ img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+ return any([path.lower().endswith(e) for e in img_end])
+
+
def get_image_file_list(img_file):
imgs_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
- img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
- if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
+ img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
+ if os.path.isfile(img_file) and _check_image_file(file_path):
imgs_lists.append(img_file)
elif os.path.isdir(img_file):
for single_file in os.listdir(img_file):
file_path = os.path.join(img_file, single_file)
- if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
+ if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))
diff --git a/ppstructure/docs/quickstart.md b/ppstructure/docs/quickstart.md
index 6610035d1442f988ac69763724ce78f6db35ae20..31e59416247b4f0e6b6d82fb13e0d3841a113a5f 100644
--- a/ppstructure/docs/quickstart.md
+++ b/ppstructure/docs/quickstart.md
@@ -194,5 +194,6 @@ dict 里各个字段说明如下
| layout | 前向中是否执行版面分析 | True |
| table | 前向中是否执行表格识别 | True |
| ocr | 对于版面分析中的非表格区域，是否执行ocr。当layout为False时会被自动设置为False | True |
+| structure_version | 表格结构化模型版本，可选 PP-STRUCTURE。PP-STRUCTURE支持表格结构化模型 | PP-STRUCTURE |
大部分参数和PaddleOCR whl包保持一致，见 [whl包文档](../../doc/doc_ch/whl.md)
diff --git a/ppstructure/docs/quickstart_en.md b/ppstructure/docs/quickstart_en.md
index 853436ff07e665fb140a749e8ccbde4392ea5c13..1f78b43ea3334648a37a37745737a6a26e27ece3 100644
--- a/ppstructure/docs/quickstart_en.md
+++ b/ppstructure/docs/quickstart_en.md
@@ -194,5 +194,5 @@ Please refer to: [Documentation Visual Q&A](../vqa/README.md) .
| layout | Whether to perform layout analysis in forward | True |
| table | Whether to perform table recognition in forward | True |
| ocr | Whether to perform ocr for non-table areas in layout analysis. When layout is False, it will be automatically set to False | True |
-
+| structure_version | table structure Model version number, the current model support list is as follows: PP-STRUCTURE support english table structure model | PP-STRUCTURE |
Most of the parameters are consistent with the PaddleOCR whl package, see [whl package documentation](../../doc/doc_en/whl.md)
+ 
+
+ 
+
-
+
