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applications/高精度中文识别模型.md 0 → 100644
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# 高精度中文场景文本识别模型SVTR
## 1. 简介
PP-OCRv3是百度开源的超轻量级场景文本检测识别模型库，其中超轻量的场景中文识别模型SVTR_LCNet使用了SVTR算法结构。为了保证速度，SVTR_LCNet将SVTR模型的Local Blocks替换为LCNet，使用两层Global Blocks。在中文场景中，PP-OCRv3识别主要使用如下优化策略：
- GTC：Attention指导CTC训练策略；
- TextConAug：挖掘文字上下文信息的数据增广策略；
- TextRotNet：自监督的预训练模型；
- UDML：联合互学习策略；
- UIM：无标注数据挖掘方案。
其中 *UIM：无标注数据挖掘方案* 使用了高精度的SVTR中文模型进行无标注文件的刷库，该模型在PP-OCRv3识别的数据集上训练，精度对比如下表。
|中文识别算法|模型|UIM|精度|
| --- | --- | --- |--- |
|PP-OCRv3|SVTR_LCNet| w/o |78.4%|
|PP-OCRv3|SVTR_LCNet| w |79.4%|
|SVTR|SVTR-Tiny|-|82.5%|
aistudio项目链接: [高精度中文场景文本识别模型SVTR](https://aistudio.baidu.com/aistudio/projectdetail/4263032)
## 2. SVTR中文模型使用
### 环境准备
本任务基于Aistudio完成, 具体环境如下：
- 操作系统: Linux
- PaddlePaddle: 2.3
- PaddleOCR: dygraph
下载 PaddleOCR代码
```bash
git clone -b dygraph https://github.com/PaddlePaddle/PaddleOCR
```
安装依赖库
```bash
pip install -r PaddleOCR/requirements.txt -i https://mirror.baidu.com/pypi/simple
```
### 快速使用
获取SVTR中文模型文件，请扫码填写问卷，加入PaddleOCR官方交流群获取全部OCR垂类模型下载链接、《动手学OCR》电子书等全套OCR学习资料🎁
<div align="center">
<img src="https://ai-studio-static-online.cdn.bcebos.com/dd721099bd50478f9d5fb13d8dd00fad69c22d6848244fd3a1d3980d7fefc63e" width = "150" height = "150" />
</div>
```bash
# 解压模型文件
tar xf svtr_ch_high_accuracy.tar
```
预测中文文本，以下图为例：
![](../doc/imgs_words/ch/word_1.jpg)
预测命令：
```bash
# CPU预测
python tools/infer_rec.py -c configs/rec/rec_svtrnet_ch.yml -o Global.pretrained_model=./svtr_ch_high_accuracy/best_accuracy Global.infer_img=./doc/imgs_words/ch/word_1.jpg Global.use_gpu=False
# GPU预测
#python tools/infer_rec.py -c configs/rec/rec_svtrnet_ch.yml -o Global.pretrained_model=./svtr_ch_high_accuracy/best_accuracy Global.infer_img=./doc/imgs_words/ch/word_1.jpg Global.use_gpu=True
```
可以看到最后打印结果为
- result: 韩国小馆 0.9853458404541016
0.9853458404541016为预测置信度。
### 推理模型导出与预测
inference 模型（paddle.jit.save保存的模型） 一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。 训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，多用于恢复训练等。 与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合于实际系统集成。
运行识别模型转inference模型命令，如下：
```bash
python tools/export_model.py -c configs/rec/rec_svtrnet_ch.yml -o Global.pretrained_model=./svtr_ch_high_accuracy/best_accuracy Global.save_inference_dir=./inference/svtr_ch
```
转换成功后，在目录下有三个文件：
```shell
inference/svtr_ch/
├── 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
```
inference模型预测，命令如下：
```bash
# CPU预测
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_1.jpg" --rec_algorithm='SVTR' --rec_model_dir=./inference/svtr_ch/ --rec_image_shape='3, 32, 320' --rec_char_dict_path=ppocr/utils/ppocr_keys_v1.txt --use_gpu=False
# GPU预测
#python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_1.jpg" --rec_algorithm='SVTR' --rec_model_dir=./inference/svtr_ch/ --rec_image_shape='3, 32, 320' --rec_char_dict_path=ppocr/utils/ppocr_keys_v1.txt --use_gpu=True
```
**注意**
- 使用SVTR算法时，需要指定--rec_algorithm='SVTR'
- 如果使用自定义字典训练的模型，需要将--rec_char_dict_path=ppocr/utils/ppocr_keys_v1.txt修改为自定义的字典
- --rec_image_shape='3, 32, 320' 该参数不能去掉
configs/rec/rec_mtb_nrtr.yml
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...@@ -49,7 +49,7 @@ Architecture: ...@@ -49,7 +49,7 @@ Architecture:
Loss: Loss:
name: NRTRLoss name: CELoss
smoothing: True smoothing: True
PostProcess: PostProcess:
...@@ -68,8 +68,8 @@ Train: ...@@ -68,8 +68,8 @@ Train:
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- NRTRLabelEncode: # Class handling label - NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg: - GrayRecResizeImg:
image_shape: [100, 32] image_shape: [100, 32] # W H
resize_type: PIL # PIL or OpenCV resize_type: PIL # PIL or OpenCV
- KeepKeys: - KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
...@@ -82,14 +82,14 @@ Train: ...@@ -82,14 +82,14 @@ Train:
Eval: Eval:
dataset: dataset:
name: LMDBDataSet name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/evaluation/ data_dir: ./train_data/data_lmdb_release/validation/
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- NRTRLabelEncode: # Class handling label - NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg: - GrayRecResizeImg:
image_shape: [100, 32] image_shape: [100, 32] # W H
resize_type: PIL # PIL or OpenCV resize_type: PIL # PIL or OpenCV
- KeepKeys: - KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
...@@ -97,5 +97,5 @@ Eval: ...@@ -97,5 +97,5 @@ Eval:
shuffle: False shuffle: False
drop_last: False drop_last: False
batch_size_per_card: 256 batch_size_per_card: 256
num_workers: 1 num_workers: 4
use_shared_memory: False use_shared_memory: False
configs/rec/rec_r45_abinet.yml 0 → 100644
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Global:
use_gpu: True
epoch_num: 10
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/r45_abinet/
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_abinet.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.99
clip_norm: 20.0
lr:
name: Piecewise
decay_epochs: [6]
values: [0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0.
Architecture:
model_type: rec
algorithm: ABINet
in_channels: 3
Transform:
Backbone:
name: ResNet45
Head:
name: ABINetHead
use_lang: True
iter_size: 3
Loss:
name: CELoss
ignore_index: &ignore_index 100 # Must be greater than the number of character classes
PostProcess:
name: ABINetLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetRecAug:
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 96
drop_last: True
num_workers: 4
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4
use_shared_memory: False
configs/rec/rec_svtrnet.yml
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...@@ -26,7 +26,7 @@ Optimizer: ...@@ -26,7 +26,7 @@ Optimizer:
name: AdamW name: AdamW
beta1: 0.9 beta1: 0.9
beta2: 0.99 beta2: 0.99
epsilon: 0.00000008 epsilon: 8.e-8
weight_decay: 0.05 weight_decay: 0.05
no_weight_decay_name: norm pos_embed no_weight_decay_name: norm pos_embed
one_dim_param_no_weight_decay: true one_dim_param_no_weight_decay: true
...@@ -77,14 +77,13 @@ Metric: ...@@ -77,14 +77,13 @@ Metric:
Train: Train:
dataset: dataset:
name: LMDBDataSet name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/ data_dir: ./train_data/data_lmdb_release/training
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- CTCLabelEncode: # Class handling label - CTCLabelEncode: # Class handling label
- RecResizeImg: - SVTRRecResizeImg:
character_dict_path:
image_shape: [3, 64, 256] image_shape: [3, 64, 256]
padding: False padding: False
- KeepKeys: - KeepKeys:
...@@ -98,14 +97,13 @@ Train: ...@@ -98,14 +97,13 @@ Train:
Eval: Eval:
dataset: dataset:
name: LMDBDataSet name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/ data_dir: ./train_data/data_lmdb_release/validation
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- CTCLabelEncode: # Class handling label - CTCLabelEncode: # Class handling label
- RecResizeImg: - SVTRRecResizeImg:
character_dict_path:
image_shape: [3, 64, 256] image_shape: [3, 64, 256]
padding: False padding: False
- KeepKeys: - KeepKeys:
......
configs/rec/rec_svtrnet_ch.yml 0 → 100644
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Global:
use_gpu: true
epoch_num: 100
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/svtr_ch_all/
save_epoch_step: 10
eval_batch_step:
- 0
- 2000
cal_metric_during_train: true
pretrained_model: null
checkpoints: null
save_inference_dir: null
use_visualdl: false
infer_img: doc/imgs_words/ch/word_1.jpg
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
max_text_length: 25
infer_mode: false
use_space_char: true
save_res_path: ./output/rec/predicts_svtr_tiny_ch_all.txt
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.99
epsilon: 8.0e-08
weight_decay: 0.05
no_weight_decay_name: norm pos_embed
one_dim_param_no_weight_decay: true
lr:
name: Cosine
learning_rate: 0.0005
warmup_epoch: 2
Architecture:
model_type: rec
algorithm: SVTR
Transform: null
Backbone:
name: SVTRNet
img_size:
- 32
- 320
out_char_num: 40
out_channels: 96
patch_merging: Conv
embed_dim:
- 64
- 128
- 256
depth:
- 3
- 6
- 3
num_heads:
- 2
- 4
- 8
mixer:
- Local
- Local
- Local
- Local
- Local
- Local
- Global
- Global
- Global
- Global
- Global
- Global
local_mixer:
- - 7
- 11
- - 7
- 11
- - 7
- 11
last_stage: true
prenorm: false
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTCHead
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data
label_file_list:
- ./train_data/train_list.txt
ext_op_transform_idx: 1
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecConAug:
prob: 0.5
ext_data_num: 2
image_shape:
- 32
- 320
- 3
- RecAug: null
- CTCLabelEncode: null
- SVTRRecResizeImg:
image_shape:
- 3
- 32
- 320
padding: true
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: true
batch_size_per_card: 256
drop_last: true
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data
label_file_list:
- ./train_data/val_list.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- CTCLabelEncode: null
- SVTRRecResizeImg:
image_shape:
- 3
- 32
- 320
padding: true
- KeepKeys:
keep_keys:
- image
- label
- length
loader:
shuffle: false
drop_last: false
batch_size_per_card: 256
num_workers: 2
profiler_options: null
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Global:
use_gpu: True
epoch_num: 20
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/vitstr_none_ce/
save_epoch_step: 1
# evaluation is run every 2000 iterations after the 0th iteration#
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_vitstr.txt
Optimizer:
name: Adadelta
epsilon: 1.e-8
rho: 0.95
clip_norm: 5.0
lr:
learning_rate: 1.0
Architecture:
model_type: rec
algorithm: ViTSTR
in_channels: 1
Transform:
Backbone:
name: ViTSTR
scale: tiny
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTCHead
Loss:
name: CELoss
with_all: True
ignore_index: &ignore_index 0 # Must be zero or greater than the number of character classes
PostProcess:
name: ViTSTRLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/training/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- ViTSTRLabelEncode: # Class handling label
ignore_index: *ignore_index
- GrayRecResizeImg:
image_shape: [224, 224] # W H
resize_type: PIL # PIL or OpenCV
inter_type: 'Image.BICUBIC'
scale: false
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 8
Eval:
dataset:
name: LMDBDataSet
data_dir: ./train_data/data_lmdb_release/validation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- ViTSTRLabelEncode: # Class handling label
ignore_index: *ignore_index
- GrayRecResizeImg:
image_shape: [224, 224] # W H
resize_type: PIL # PIL or OpenCV
inter_type: 'Image.BICUBIC'
scale: false
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 2
doc/doc_ch/algorithm_overview.md
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...@@ -66,6 +66,8 @@ ...@@ -66,6 +66,8 @@
- [x] [SAR](./algorithm_rec_sar.md) - [x] [SAR](./algorithm_rec_sar.md)
- [x] [SEED](./algorithm_rec_seed.md) - [x] [SEED](./algorithm_rec_seed.md)
- [x] [SVTR](./algorithm_rec_svtr.md) - [x] [SVTR](./algorithm_rec_svtr.md)
- [x] [ViTSTR](./algorithm_rec_vitstr.md)
- [x] [ABINet](./algorithm_rec_abinet.md)
参考[DTRB](https://arxiv.org/abs/1904.01906)[3]文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： 参考[DTRB](https://arxiv.org/abs/1904.01906)[3]文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
...@@ -84,7 +86,8 @@ ...@@ -84,7 +86,8 @@
|SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) | |SAR|Resnet31| 87.20% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) | |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
<a name="2"></a> <a name="2"></a>
......
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# 场景文本识别算法-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：
> [ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf)
> Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang
> CVPR, 2021
<a name="model"></a>
`ABINet`使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
|模型|骨干网络|配置文件|Acc|下载链接|
| --- | --- | --- | --- | --- |
|ABINet|ResNet45|[rec_r45_abinet.yml](../../configs/rec/rec_r45_abinet.yml)|90.75%|[预训练、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`ABINet`识别模型时需要**更换配置文件**为`ABINet`的[配置文件](../../configs/rec/rec_r45_abinet.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_r45_abinet.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_r45_abinet.yml
```
<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_r45_abinet.yml -o Global.pretrained_model=./rec_r45_abinet_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_r45_abinet.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_r45_abinet_train/best_accuracy
# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_words_en/'。
```
<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_r45_abinet_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_r45_abinet.yml -o Global.pretrained_model=./rec_r45_abinet_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_abinet/
```
**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_r45_abinet/
├── 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_words_en/word_10.png' --rec_model_dir='./inference/rec_r45_abinet/' --rec_algorithm='ABINet' --rec_image_shape='3,32,128' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt'
# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。
```
![](../imgs_words_en/word_10.png)
执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
结果如下：
```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999995231628418)
```
**注意**：
- 训练上述模型采用的图像分辨率是[3，32，128]，需要通过参数`rec_image_shape`设置为您训练时的识别图像形状。
- 在推理时需要设置参数`rec_char_dict_path`指定字典，如果您修改了字典，请修改该参数为您的字典文件。
- 如果您修改了预处理方法，需修改`tools/infer/predict_rec.py`中ABINet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. MJSynth和SynthText两种数据集来自于[ABINet源repo](https://github.com/FangShancheng/ABINet) 。
2. 我们使用ABINet作者提供的预训练模型进行finetune训练。
## 引用
```bibtex
@article{Fang2021ABINet,
title = {ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
author = {Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang},
booktitle = {CVPR},
year = {2021},
url = {https://arxiv.org/abs/2103.06495},
pages = {7098-7107}
}
```
doc/doc_ch/algorithm_rec_nrtr.md
浏览文件 @ 9b8ffb6c
...@@ -12,6 +12,7 @@ ...@@ -12,6 +12,7 @@
- [4.3 Serving服务化部署](#4-3) - [4.3 Serving服务化部署](#4-3)
- [4.4 更多推理部署](#4-4) - [4.4 更多推理部署](#4-4)
- [5. FAQ](#5) - [5. FAQ](#5)
- [6. 发行公告](#6)
<a name="1"></a> <a name="1"></a>
## 1. 算法简介 ## 1. 算法简介
...@@ -110,7 +111,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' ...@@ -110,7 +111,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png'
执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下： 执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
结果如下： 结果如下：
```shell ```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9265879392623901) Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)
``` ```
**注意**： **注意**：
...@@ -140,12 +141,147 @@ Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9265879392623901) ...@@ -140,12 +141,147 @@ Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9265879392623901)
1. `NRTR`论文中使用Beam搜索进行解码字符，但是速度较慢，这里默认未使用Beam搜索，以贪婪搜索进行解码字符。 1. `NRTR`论文中使用Beam搜索进行解码字符，但是速度较慢，这里默认未使用Beam搜索，以贪婪搜索进行解码字符。
<a name="6"></a>
## 6. 发行公告
1. release/2.6更新NRTR代码结构，新版NRTR可加载旧版(release/2.5及之前)模型参数，使用下面示例代码将旧版模型参数转换为新版模型参数：
```python
params = paddle.load('path/' + '.pdparams') # 旧版本参数
state_dict = model.state_dict() # 新版模型参数
new_state_dict = {}
for k1, v1 in state_dict.items():
k = k1
if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')]
k = params[k_para.replace('qkv', 'conv2')]
v = params[k_para.replace('qkv', 'conv3')]
new_state_dict[k1] = np.concatenate([q, k, v], -1)
elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'encoder' in k and 'norm3' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')]
elif 'encoder' in k and 'norm1' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')]
k = params[k_para.replace('qkv', 'conv2')]
v = params[k_para.replace('qkv', 'conv3')]
new_state_dict[k1] = np.concatenate([q, k, v], -1)
elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3))
new_state_dict[k1] = q[:, :, 0, 0]
elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
q = params[k_para.replace('q', 'conv1')]
new_state_dict[k1] = q
elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
k = params[k_para.replace('kv', 'conv2')]
v = params[k_para.replace('kv', 'conv3')]
new_state_dict[k1] = np.concatenate([k, v], -1)
elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'norm' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'mlp' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('fc', 'conv')
k_para = k_para.replace('mlp.', '')
w = params[k_para].transpose((1, 0, 2, 3))
new_state_dict[k1] = w[:, :, 0, 0]
elif 'mlp' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('fc', 'conv')
k_para = k_para.replace('mlp.', '')
w = params[k_para]
new_state_dict[k1] = w
else:
new_state_dict[k1] = params[k1]
if list(new_state_dict[k1].shape) != list(v1.shape):
print(k1)
for k, v1 in state_dict.items():
if k not in new_state_dict.keys():
print(1, k)
elif list(new_state_dict[k].shape) != list(v1.shape):
print(2, k)
model.set_state_dict(new_state_dict)
paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams')
```
2. 新版相比与旧版，代码结构简洁，推理速度有所提高。
## 引用 ## 引用
```bibtex ```bibtex
@article{Sheng2019NRTR, @article{Sheng2019NRTR,
title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition}, title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
author = {Fenfen Sheng and Zhineng Chen andBo Xu}, author = {Fenfen Sheng and Zhineng Chen and Bo Xu},
booktitle = {ICDAR}, booktitle = {ICDAR},
year = {2019}, year = {2019},
url = {http://arxiv.org/abs/1806.00926}, url = {http://arxiv.org/abs/1806.00926},
......
doc/doc_ch/algorithm_rec_svtr.md
浏览文件 @ 9b8ffb6c
...@@ -111,7 +111,6 @@ python3 tools/export_model.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny ...@@ -111,7 +111,6 @@ python3 tools/export_model.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny
**注意：** **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。 - 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。
- 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SVTR的`infer_shape`。
转换成功后，在目录下有三个文件： 转换成功后，在目录下有三个文件：
``` ```
......
doc/doc_ch/algorithm_rec_vitstr.md 0 → 100644
浏览文件 @ 9b8ffb6c
# 场景文本识别算法-ViTSTR
- [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：
> [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/abs/2105.08582)
> Rowel Atienza
> ICDAR, 2021
<a name="model"></a>
`ViTSTR`使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法复现效果如下：
|模型|骨干网络|配置文件|Acc|下载链接|
| --- | --- | --- | --- | --- |
|ViTSTR|ViTSTR|[rec_vitstr_none_ce.yml](../../configs/rec/rec_vitstr_none_ce.yml)|79.82%|[训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_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`ViTSTR`识别模型时需要**更换配置文件**为`ViTSTR`的[配置文件](../../configs/rec/rec_vitstr_none_ce.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_vitstr_none_ce.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_vitstr_none_ce.yml
```
<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_vitstr_none_ce.yml -o Global.pretrained_model=./rec_vitstr_none_ce_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_vitstr_none_ce.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy
# 预测文件夹下所有图像时，可修改infer_img为文件夹，如 Global.infer_img='./doc/imgs_words_en/'。
```
<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_vitstr_none_ce_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_vitstr_none_ce.yml -o Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy Global.save_inference_dir=./inference/rec_vitstr/
```
**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ViTSTR的`infer_shape`。
转换成功后，在目录下有三个文件：
```
/inference/rec_vitstr/
├── 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_words_en/word_10.png' --rec_model_dir='./inference/rec_vitstr/' --rec_algorithm='ViTSTR' --rec_image_shape='1,224,224' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt'
# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。
```
![](../imgs_words_en/word_10.png)
执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
结果如下：
```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9998350143432617)
```
**注意**：
- 训练上述模型采用的图像分辨率是[1，224，224]，需要通过参数`rec_image_shape`设置为您训练时的识别图像形状。
- 在推理时需要设置参数`rec_char_dict_path`指定字典，如果您修改了字典，请修改该参数为您的字典文件。
- 如果您修改了预处理方法，需修改`tools/infer/predict_rec.py`中ViTSTR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ViTSTR，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. 在`ViTSTR`论文中，使用在ImageNet1k上的预训练权重进行初始化训练，我们在训练未采用预训练权重，最终精度没有变化甚至有所提高。
2. 我们仅仅复现了`ViTSTR`中的tiny版本，如果需要使用small、base版本，可将[ViTSTR源repo](https://github.com/roatienza/deep-text-recognition-benchmark) 中的预训练权重转为Paddle权重使用。
## 引用
```bibtex
@article{Atienza2021ViTSTR,
title = {Vision Transformer for Fast and Efficient Scene Text Recognition},
author = {Rowel Atienza},
booktitle = {ICDAR},
year = {2021},
url = {https://arxiv.org/abs/2105.08582}
}
```
doc/doc_en/algorithm_overview_en.md
浏览文件 @ 9b8ffb6c
...@@ -65,6 +65,8 @@ Supported text recognition algorithms (Click the link to get the tutorial): ...@@ -65,6 +65,8 @@ Supported text recognition algorithms (Click the link to get the tutorial):
- [x] [SAR](./algorithm_rec_sar_en.md) - [x] [SAR](./algorithm_rec_sar_en.md)
- [x] [SEED](./algorithm_rec_seed_en.md) - [x] [SEED](./algorithm_rec_seed_en.md)
- [x] [SVTR](./algorithm_rec_svtr_en.md) - [x] [SVTR](./algorithm_rec_svtr_en.md)
- [x] [ViTSTR](./algorithm_rec_vitstr_en.md)
- [x] [ABINet](./algorithm_rec_abinet_en.md)
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
...@@ -83,7 +85,8 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r ...@@ -83,7 +85,8 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) | |SAR|Resnet31| 87.20% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
|SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) | |SEED|Aster_Resnet| 85.35% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
|SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) | |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce_en | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
|ABINet|Resnet45| 90.75% | rec_r45_abinet_en | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
<a name="2"></a> <a name="2"></a>
......
doc/doc_en/algorithm_rec_abinet_en.md 0 → 100644
浏览文件 @ 9b8ffb6c
# ABINet
- [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:
> [ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf)
> Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang
> CVPR, 2021
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|Model|Backbone|config|Acc|Download link|
| --- | --- | --- | --- | --- |
|ABINet|ResNet45|[rec_r45_abinet.yml](../../configs/rec/rec_r45_abinet.yml)|90.75%|[pretrained & trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)|
<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_r45_abinet.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_r45_abinet.yml
```
Evaluation:
```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r45_abinet.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
Prediction:
```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r45_abinet.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_r45_abinet_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 ABINet text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar)) ), you can use the following command to convert:
```
python3 tools/export_model.py -c configs/rec/rec_r45_abinet.yml -o Global.pretrained_model=./rec_r45_abinet_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_abinet
```
**Note:**
- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
- If you modified the input size during training, please modify the `infer_shape` corresponding to ABINet in the `tools/export_model.py` file.
After the conversion is successful, there are three files in the directory:
```
/inference/rec_r45_abinet/
├── inference.pdiparams
├── inference.pdiparams.info
└── inference.pdmodel
```
For ABINet text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_r45_abinet/' --rec_algorithm='ABINet' --rec_image_shape='3,32,128' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt'
```
![](../imgs_words_en/word_10.png)
After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
The result is as follows:
```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999995231628418)
```
<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
1. Note that the MJSynth and SynthText datasets come from [ABINet repo](https://github.com/FangShancheng/ABINet).
2. We use the pre-trained model provided by the ABINet authors for finetune training.
## Citation
```bibtex
@article{Fang2021ABINet,
title = {ABINet: Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
author = {Shancheng Fang and Hongtao Xie and Yuxin Wang and Zhendong Mao and Yongdong Zhang},
booktitle = {CVPR},
year = {2021},
url = {https://arxiv.org/abs/2103.06495},
pages = {7098-7107}
}
```
doc/doc_en/algorithm_rec_nrtr_en.md
浏览文件 @ 9b8ffb6c
...@@ -12,6 +12,7 @@ ...@@ -12,6 +12,7 @@
- [4.3 Serving](#4-3) - [4.3 Serving](#4-3)
- [4.4 More](#4-4) - [4.4 More](#4-4)
- [5. FAQ](#5) - [5. FAQ](#5)
- [6. Release Note](#6)
<a name="1"></a> <a name="1"></a>
## 1. Introduction ## 1. Introduction
...@@ -25,7 +26,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval ...@@ -25,7 +26,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
|Model|Backbone|config|Acc|Download link| |Model|Backbone|config|Acc|Download link|
| --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- |
|NRTR|MTB|[rec_mtb_nrtr.yml](../../configs/rec/rec_mtb_nrtr.yml)|84.21%|[train model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)| |NRTR|MTB|[rec_mtb_nrtr.yml](../../configs/rec/rec_mtb_nrtr.yml)|84.21%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar)|
<a name="2"></a> <a name="2"></a>
## 2. Environment ## 2. Environment
...@@ -98,7 +99,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' ...@@ -98,7 +99,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png'
After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
The result is as follows: The result is as follows:
```shell ```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9265879392623901) Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)
``` ```
<a name="4-2"></a> <a name="4-2"></a>
...@@ -121,12 +122,146 @@ Not supported ...@@ -121,12 +122,146 @@ Not supported
1. In the `NRTR` paper, Beam search is used to decode characters, but the speed is slow. Beam search is not used by default here, and greedy search is used to decode characters. 1. In the `NRTR` paper, Beam search is used to decode characters, but the speed is slow. Beam search is not used by default here, and greedy search is used to decode characters.
<a name="6"></a>
## 6. Release Note
1. The release/2.6 version updates the NRTR code structure. The new version of NRTR can load the model parameters of the old version (release/2.5 and before), and you may use the following code to convert the old version model parameters to the new version model parameters:
```python
params = paddle.load('path/' + '.pdparams') # the old version parameters
state_dict = model.state_dict() # the new version model parameters
new_state_dict = {}
for k1, v1 in state_dict.items():
k = k1
if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')]
k = params[k_para.replace('qkv', 'conv2')]
v = params[k_para.replace('qkv', 'conv3')]
new_state_dict[k1] = np.concatenate([q, k, v], -1)
elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'encoder' in k and 'norm3' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')]
elif 'encoder' in k and 'norm1' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
q = params[k_para.replace('qkv', 'conv1')]
k = params[k_para.replace('qkv', 'conv2')]
v = params[k_para.replace('qkv', 'conv3')]
new_state_dict[k1] = np.concatenate([q, k, v], -1)
elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3))
new_state_dict[k1] = q[:, :, 0, 0]
elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
q = params[k_para.replace('q', 'conv1')]
new_state_dict[k1] = q
elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3))
v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3))
new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1)
elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
k = params[k_para.replace('kv', 'conv2')]
v = params[k_para.replace('kv', 'conv3')]
new_state_dict[k1] = np.concatenate([k, v], -1)
elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('cross_attn', 'multihead_attn')
new_state_dict[k1] = params[k_para]
elif 'decoder' in k and 'norm' in k:
k_para = k[:13] + 'layers.' + k[13:]
new_state_dict[k1] = params[k_para]
elif 'mlp' in k and 'weight' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('fc', 'conv')
k_para = k_para.replace('mlp.', '')
w = params[k_para].transpose((1, 0, 2, 3))
new_state_dict[k1] = w[:, :, 0, 0]
elif 'mlp' in k and 'bias' in k:
k_para = k[:13] + 'layers.' + k[13:]
k_para = k_para.replace('fc', 'conv')
k_para = k_para.replace('mlp.', '')
w = params[k_para]
new_state_dict[k1] = w
else:
new_state_dict[k1] = params[k1]
if list(new_state_dict[k1].shape) != list(v1.shape):
print(k1)
for k, v1 in state_dict.items():
if k not in new_state_dict.keys():
print(1, k)
elif list(new_state_dict[k].shape) != list(v1.shape):
print(2, k)
model.set_state_dict(new_state_dict)
paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams')
```
2. The new version has a clean code structure and improved inference speed compared with the old version.
## Citation ## Citation
```bibtex ```bibtex
@article{Sheng2019NRTR, @article{Sheng2019NRTR,
title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition}, title = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
author = {Fenfen Sheng and Zhineng Chen andBo Xu}, author = {Fenfen Sheng and Zhineng Chen and Bo Xu},
booktitle = {ICDAR}, booktitle = {ICDAR},
year = {2019}, year = {2019},
url = {http://arxiv.org/abs/1806.00926}, url = {http://arxiv.org/abs/1806.00926},
......
doc/doc_en/algorithm_rec_svtr_en.md
浏览文件 @ 9b8ffb6c
...@@ -88,7 +88,6 @@ python3 tools/export_model.py -c configs/rec/rec_svtrnet.yml -o Global.pretraine ...@@ -88,7 +88,6 @@ python3 tools/export_model.py -c configs/rec/rec_svtrnet.yml -o Global.pretraine
**Note:** **Note:**
- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. - If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
- If you modified the input size during training, please modify the `infer_shape` corresponding to SVTR in the `tools/export_model.py` file.
After the conversion is successful, there are three files in the directory: After the conversion is successful, there are three files in the directory:
``` ```
......
doc/doc_en/algorithm_rec_vitstr_en.md 0 → 100644
浏览文件 @ 9b8ffb6c
# ViTSTR
- [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:
> [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/abs/2105.08582)
> Rowel Atienza
> ICDAR, 2021
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
|Model|Backbone|config|Acc|Download link|
| --- | --- | --- | --- | --- |
|ViTSTR|ViTSTR|[rec_vitstr_none_ce.yml](../../configs/rec/rec_vitstr_none_ce.yml)|79.82%|[trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar)|
<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_vitstr_none_ce.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_vitstr_none_ce.yml
```
Evaluation:
```
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
```
Prediction:
```
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_vitstr_none_ce_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 ViTSTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar)) ), you can use the following command to convert:
```
python3 tools/export_model.py -c configs/rec/rec_vitstr_none_ce.yml -o Global.pretrained_model=./rec_vitstr_none_ce_train/best_accuracy Global.save_inference_dir=./inference/rec_vitstr
```
**Note:**
- If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file.
- If you modified the input size during training, please modify the `infer_shape` corresponding to ViTSTR in the `tools/export_model.py` file.
After the conversion is successful, there are three files in the directory:
```
/inference/rec_vitstr/
├── inference.pdiparams
├── inference.pdiparams.info
└── inference.pdmodel
```
For ViTSTR text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_vitstr/' --rec_algorithm='ViTSTR' --rec_image_shape='1,224,224' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt'
```
![](../imgs_words_en/word_10.png)
After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
The result is as follows:
```shell
Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9998350143432617)
```
<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
1. In the `ViTSTR` paper, using pre-trained weights on ImageNet1k for initial training, we did not use pre-trained weights in training, and the final accuracy did not change or even improved.
## Citation
```bibtex
@article{Atienza2021ViTSTR,
title = {Vision Transformer for Fast and Efficient Scene Text Recognition},
author = {Rowel Atienza},
booktitle = {ICDAR},
year = {2021},
url = {https://arxiv.org/abs/2105.08582}
}
```
ppocr/data/imaug/__init__.py
浏览文件 @ 9b8ffb6c
...@@ -22,8 +22,10 @@ from .make_shrink_map import MakeShrinkMap ...@@ -22,8 +22,10 @@ from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, RandomCropImgMask from .random_crop_data import EastRandomCropData, RandomCropImgMask
from .make_pse_gt import MakePseGt from .make_pse_gt import MakePseGt
from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
SRNRecResizeImg, GrayRecResizeImg, SARRecResizeImg, PRENResizeImg, \
ABINetRecResizeImg, SVTRRecResizeImg, ABINetRecAug
from .ssl_img_aug import SSLRotateResize from .ssl_img_aug import SSLRotateResize
from .randaugment import RandAugment from .randaugment import RandAugment
from .copy_paste import CopyPaste from .copy_paste import CopyPaste
......
ppocr/data/imaug/abinet_aug.py 0 → 100644
浏览文件 @ 9b8ffb6c
# 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.
"""
This code is refer from:
https://github.com/FangShancheng/ABINet/blob/main/transforms.py
"""
import math
import numbers
import random
import cv2
import numpy as np
from paddle.vision.transforms import Compose, ColorJitter
def sample_asym(magnitude, size=None):
return np.random.beta(1, 4, size) * magnitude
def sample_sym(magnitude, size=None):
return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude
def sample_uniform(low, high, size=None):
return np.random.uniform(low, high, size=size)
def get_interpolation(type='random'):
if type == 'random':
choice = [
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA
]
interpolation = choice[random.randint(0, len(choice) - 1)]
elif type == 'nearest':
interpolation = cv2.INTER_NEAREST
elif type == 'linear':
interpolation = cv2.INTER_LINEAR
elif type == 'cubic':
interpolation = cv2.INTER_CUBIC
elif type == 'area':
interpolation = cv2.INTER_AREA
else:
raise TypeError(
'Interpolation types only nearest, linear, cubic, area are supported!'
)
return interpolation
class CVRandomRotation(object):
def __init__(self, degrees=15):
assert isinstance(degrees,
numbers.Number), "degree should be a single number."
assert degrees >= 0, "degree must be positive."
self.degrees = degrees
@staticmethod
def get_params(degrees):
return sample_sym(degrees)
def __call__(self, img):
angle = self.get_params(self.degrees)
src_h, src_w = img.shape[:2]
M = cv2.getRotationMatrix2D(
center=(src_w / 2, src_h / 2), angle=angle, scale=1.0)
abs_cos, abs_sin = abs(M[0, 0]), abs(M[0, 1])
dst_w = int(src_h * abs_sin + src_w * abs_cos)
dst_h = int(src_h * abs_cos + src_w * abs_sin)
M[0, 2] += (dst_w - src_w) / 2
M[1, 2] += (dst_h - src_h) / 2
flags = get_interpolation()
return cv2.warpAffine(
img,
M, (dst_w, dst_h),
flags=flags,
borderMode=cv2.BORDER_REPLICATE)
class CVRandomAffine(object):
def __init__(self, degrees, translate=None, scale=None, shear=None):
assert isinstance(degrees,
numbers.Number), "degree should be a single number."
assert degrees >= 0, "degree must be positive."
self.degrees = degrees
if translate is not None:
assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
"translate should be a list or tuple and it must be of length 2."
for t in translate:
if not (0.0 <= t <= 1.0):
raise ValueError(
"translation values should be between 0 and 1")
self.translate = translate
if scale is not None:
assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
"scale should be a list or tuple and it must be of length 2."
for s in scale:
if s <= 0:
raise ValueError("scale values should be positive")
self.scale = scale
if shear is not None:
if isinstance(shear, numbers.Number):
if shear < 0:
raise ValueError(
"If shear is a single number, it must be positive.")
self.shear = [shear]
else:
assert isinstance(shear, (tuple, list)) and (len(shear) == 2), \
"shear should be a list or tuple and it must be of length 2."
self.shear = shear
else:
self.shear = shear
def _get_inverse_affine_matrix(self, center, angle, translate, scale,
shear):
# https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
from numpy import sin, cos, tan
if isinstance(shear, numbers.Number):
shear = [shear, 0]
if not isinstance(shear, (tuple, list)) and len(shear) == 2:
raise ValueError(
"Shear should be a single value or a tuple/list containing " +
"two values. Got {}".format(shear))
rot = math.radians(angle)
sx, sy = [math.radians(s) for s in shear]
cx, cy = center
tx, ty = translate
# RSS without scaling
a = cos(rot - sy) / cos(sy)
b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
c = sin(rot - sy) / cos(sy)
d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
# Inverted rotation matrix with scale and shear
# det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
M = [d, -b, 0, -c, a, 0]
M = [x / scale for x in M]
# Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
# Apply center translation: C * RSS^-1 * C^-1 * T^-1
M[2] += cx
M[5] += cy
return M
@staticmethod
def get_params(degrees, translate, scale_ranges, shears, height):
angle = sample_sym(degrees)
if translate is not None:
max_dx = translate[0] * height
max_dy = translate[1] * height
translations = (np.round(sample_sym(max_dx)),
np.round(sample_sym(max_dy)))
else:
translations = (0, 0)
if scale_ranges is not None:
scale = sample_uniform(scale_ranges[0], scale_ranges[1])
else:
scale = 1.0
if shears is not None:
if len(shears) == 1:
shear = [sample_sym(shears[0]), 0.]
elif len(shears) == 2:
shear = [sample_sym(shears[0]), sample_sym(shears[1])]
else:
shear = 0.0
return angle, translations, scale, shear
def __call__(self, img):
src_h, src_w = img.shape[:2]
angle, translate, scale, shear = self.get_params(
self.degrees, self.translate, self.scale, self.shear, src_h)
M = self._get_inverse_affine_matrix((src_w / 2, src_h / 2), angle,
(0, 0), scale, shear)
M = np.array(M).reshape(2, 3)
startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1),
(0, src_h - 1)]
project = lambda x, y, a, b, c: int(a * x + b * y + c)
endpoints = [(project(x, y, *M[0]), project(x, y, *M[1]))
for x, y in startpoints]
rect = cv2.minAreaRect(np.array(endpoints))
bbox = cv2.boxPoints(rect).astype(dtype=np.int)
max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
dst_w = int(max_x - min_x)
dst_h = int(max_y - min_y)
M[0, 2] += (dst_w - src_w) / 2
M[1, 2] += (dst_h - src_h) / 2
# add translate
dst_w += int(abs(translate[0]))
dst_h += int(abs(translate[1]))
if translate[0] < 0: M[0, 2] += abs(translate[0])
if translate[1] < 0: M[1, 2] += abs(translate[1])
flags = get_interpolation()
return cv2.warpAffine(
img,
M, (dst_w, dst_h),
flags=flags,
borderMode=cv2.BORDER_REPLICATE)
class CVRandomPerspective(object):
def __init__(self, distortion=0.5):
self.distortion = distortion
def get_params(self, width, height, distortion):
offset_h = sample_asym(
distortion * height / 2, size=4).astype(dtype=np.int)
offset_w = sample_asym(
distortion * width / 2, size=4).astype(dtype=np.int)
topleft = (offset_w[0], offset_h[0])
topright = (width - 1 - offset_w[1], offset_h[1])
botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
botleft = (offset_w[3], height - 1 - offset_h[3])
startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1),
(0, height - 1)]
endpoints = [topleft, topright, botright, botleft]
return np.array(
startpoints, dtype=np.float32), np.array(
endpoints, dtype=np.float32)
def __call__(self, img):
height, width = img.shape[:2]
startpoints, endpoints = self.get_params(width, height, self.distortion)
M = cv2.getPerspectiveTransform(startpoints, endpoints)
# TODO: more robust way to crop image
rect = cv2.minAreaRect(endpoints)
bbox = cv2.boxPoints(rect).astype(dtype=np.int)
max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
min_x, min_y = max(min_x, 0), max(min_y, 0)
flags = get_interpolation()
img = cv2.warpPerspective(
img,
M, (max_x, max_y),
flags=flags,
borderMode=cv2.BORDER_REPLICATE)
img = img[min_y:, min_x:]
return img
class CVRescale(object):
def __init__(self, factor=4, base_size=(128, 512)):
""" Define image scales using gaussian pyramid and rescale image to target scale.
Args:
factor: the decayed factor from base size, factor=4 keeps target scale by default.
base_size: base size the build the bottom layer of pyramid
"""
if isinstance(factor, numbers.Number):
self.factor = round(sample_uniform(0, factor))
elif isinstance(factor, (tuple, list)) and len(factor) == 2:
self.factor = round(sample_uniform(factor[0], factor[1]))
else:
raise Exception('factor must be number or list with length 2')
# assert factor is valid
self.base_h, self.base_w = base_size[:2]
def __call__(self, img):
if self.factor == 0: return img
src_h, src_w = img.shape[:2]
cur_w, cur_h = self.base_w, self.base_h
scale_img = cv2.resize(
img, (cur_w, cur_h), interpolation=get_interpolation())
for _ in range(self.factor):
scale_img = cv2.pyrDown(scale_img)
scale_img = cv2.resize(
scale_img, (src_w, src_h), interpolation=get_interpolation())
return scale_img
class CVGaussianNoise(object):
def __init__(self, mean=0, var=20):
self.mean = mean
if isinstance(var, numbers.Number):
self.var = max(int(sample_asym(var)), 1)
elif isinstance(var, (tuple, list)) and len(var) == 2:
self.var = int(sample_uniform(var[0], var[1]))
else:
raise Exception('degree must be number or list with length 2')
def __call__(self, img):
noise = np.random.normal(self.mean, self.var**0.5, img.shape)
img = np.clip(img + noise, 0, 255).astype(np.uint8)
return img
class CVMotionBlur(object):
def __init__(self, degrees=12, angle=90):
if isinstance(degrees, numbers.Number):
self.degree = max(int(sample_asym(degrees)), 1)
elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
self.degree = int(sample_uniform(degrees[0], degrees[1]))
else:
raise Exception('degree must be number or list with length 2')
self.angle = sample_uniform(-angle, angle)
def __call__(self, img):
M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2),
self.angle, 1)
motion_blur_kernel = np.zeros((self.degree, self.degree))
motion_blur_kernel[self.degree // 2, :] = 1
motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M,
(self.degree, self.degree))
motion_blur_kernel = motion_blur_kernel / self.degree
img = cv2.filter2D(img, -1, motion_blur_kernel)
img = np.clip(img, 0, 255).astype(np.uint8)
return img
class CVGeometry(object):
def __init__(self,
degrees=15,
translate=(0.3, 0.3),
scale=(0.5, 2.),
shear=(45, 15),
distortion=0.5,
p=0.5):
self.p = p
type_p = random.random()
if type_p < 0.33:
self.transforms = CVRandomRotation(degrees=degrees)
elif type_p < 0.66:
self.transforms = CVRandomAffine(
degrees=degrees, translate=translate, scale=scale, shear=shear)
else:
self.transforms = CVRandomPerspective(distortion=distortion)
def __call__(self, img):
if random.random() < self.p:
return self.transforms(img)
else:
return img
class CVDeterioration(object):
def __init__(self, var, degrees, factor, p=0.5):
self.p = p
transforms = []
if var is not None:
transforms.append(CVGaussianNoise(var=var))
if degrees is not None:
transforms.append(CVMotionBlur(degrees=degrees))
if factor is not None:
transforms.append(CVRescale(factor=factor))
random.shuffle(transforms)
transforms = Compose(transforms)
self.transforms = transforms
def __call__(self, img):
if random.random() < self.p:
return self.transforms(img)
else:
return img
class CVColorJitter(object):
def __init__(self,
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.1,
p=0.5):
self.p = p
self.transforms = ColorJitter(
brightness=brightness,
contrast=contrast,
saturation=saturation,
hue=hue)
def __call__(self, img):
if random.random() < self.p: return self.transforms(img)
else: return img
ppocr/data/imaug/label_ops.py
浏览文件 @ 9b8ffb6c
...@@ -157,37 +157,6 @@ class BaseRecLabelEncode(object): ...@@ -157,37 +157,6 @@ class BaseRecLabelEncode(object):
return text_list return text_list
class NRTRLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
use_space_char=False,
**kwargs):
super(NRTRLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) >= self.max_text_len - 1:
return None
data['length'] = np.array(len(text))
text.insert(0, 2)
text.append(3)
text = text + [0] * (self.max_text_len - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
return dict_character
class CTCLabelEncode(BaseRecLabelEncode): class CTCLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """ """ Convert between text-label and text-index """
...@@ -1046,3 +1015,99 @@ class MultiLabelEncode(BaseRecLabelEncode): ...@@ -1046,3 +1015,99 @@ class MultiLabelEncode(BaseRecLabelEncode):
data_out['label_sar'] = sar['label'] data_out['label_sar'] = sar['label']
data_out['length'] = ctc['length'] data_out['length'] = ctc['length']
return data_out return data_out
class NRTRLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
use_space_char=False,
**kwargs):
super(NRTRLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) >= self.max_text_len - 1:
return None
data['length'] = np.array(len(text))
text.insert(0, 2)
text.append(3)
text = text + [0] * (self.max_text_len - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
return dict_character
class ViTSTRLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
use_space_char=False,
ignore_index=0,
**kwargs):
super(ViTSTRLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
self.ignore_index = ignore_index
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) >= self.max_text_len:
return None
data['length'] = np.array(len(text))
text.insert(0, self.ignore_index)
text.append(1)
text = text + [self.ignore_index] * (self.max_text_len + 2 - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = ['<s>', '</s>'] + dict_character
return dict_character
class ABINetLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
use_space_char=False,
ignore_index=100,
**kwargs):
super(ABINetLabelEncode, self).__init__(
max_text_length, character_dict_path, use_space_char)
self.ignore_index = ignore_index
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) >= self.max_text_len:
return None
data['length'] = np.array(len(text))
text.append(0)
text = text + [self.ignore_index] * (self.max_text_len + 1 - len(text))
data['label'] = np.array(text)
return data
def add_special_char(self, dict_character):
dict_character = ['</s>'] + dict_character
return dict_character
ppocr/data/imaug/operators.py
浏览文件 @ 9b8ffb6c
...@@ -67,39 +67,6 @@ class DecodeImage(object): ...@@ -67,39 +67,6 @@ class DecodeImage(object):
return data return data
class NRTRDecodeImage(object):
""" decode image """
def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
self.img_mode = img_mode
self.channel_first = channel_first
def __call__(self, data):
img = data['image']
if six.PY2:
assert type(img) is str and len(
img) > 0, "invalid input 'img' in DecodeImage"
else:
assert type(img) is bytes and len(
img) > 0, "invalid input 'img' in DecodeImage"
img = np.frombuffer(img, dtype='uint8')
img = cv2.imdecode(img, 1)
if img is None:
return None
if self.img_mode == 'GRAY':
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif self.img_mode == 'RGB':
assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
img = img[:, :, ::-1]
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if self.channel_first:
img = img.transpose((2, 0, 1))
data['image'] = img
return data
class NormalizeImage(object): class NormalizeImage(object):
""" normalize image such as substract mean, divide std """ normalize image such as substract mean, divide std
""" """
......
ppocr/data/imaug/rec_img_aug.py
浏览文件 @ 9b8ffb6c
...@@ -19,6 +19,8 @@ import random ...@@ -19,6 +19,8 @@ import random
import copy import copy
from PIL import Image from PIL import Image
from .text_image_aug import tia_perspective, tia_stretch, tia_distort from .text_image_aug import tia_perspective, tia_stretch, tia_distort
from .abinet_aug import CVGeometry, CVDeterioration, CVColorJitter
from paddle.vision.transforms import Compose
class RecAug(object): class RecAug(object):
...@@ -94,6 +96,36 @@ class BaseDataAugmentation(object): ...@@ -94,6 +96,36 @@ class BaseDataAugmentation(object):
return data return data
class ABINetRecAug(object):
def __init__(self,
geometry_p=0.5,
deterioration_p=0.25,
colorjitter_p=0.25,
**kwargs):
self.transforms = Compose([
CVGeometry(
degrees=45,
translate=(0.0, 0.0),
scale=(0.5, 2.),
shear=(45, 15),
distortion=0.5,
p=geometry_p), CVDeterioration(
var=20, degrees=6, factor=4, p=deterioration_p),
CVColorJitter(
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.1,
p=colorjitter_p)
])
def __call__(self, data):
img = data['image']
img = self.transforms(img)
data['image'] = img
return data
class RecConAug(object): class RecConAug(object):
def __init__(self, def __init__(self,
prob=0.5, prob=0.5,
...@@ -148,46 +180,6 @@ class ClsResizeImg(object): ...@@ -148,46 +180,6 @@ class ClsResizeImg(object):
return data return data
class NRTRRecResizeImg(object):
def __init__(self, image_shape, resize_type, padding=False, **kwargs):
self.image_shape = image_shape
self.resize_type = resize_type
self.padding = padding
def __call__(self, data):
img = data['image']
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
image_shape = self.image_shape
if self.padding:
imgC, imgH, imgW = image_shape
# todo: change to 0 and modified image shape
h = img.shape[0]
w = img.shape[1]
ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW:
resized_w = imgW
else:
resized_w = int(math.ceil(imgH * ratio))
resized_image = cv2.resize(img, (resized_w, imgH))
norm_img = np.expand_dims(resized_image, -1)
norm_img = norm_img.transpose((2, 0, 1))
resized_image = norm_img.astype(np.float32) / 128. - 1.
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
padding_im[:, :, 0:resized_w] = resized_image
data['image'] = padding_im
return data
if self.resize_type == 'PIL':
image_pil = Image.fromarray(np.uint8(img))
img = image_pil.resize(self.image_shape, Image.ANTIALIAS)
img = np.array(img)
if self.resize_type == 'OpenCV':
img = cv2.resize(img, self.image_shape)
norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1))
data['image'] = norm_img.astype(np.float32) / 128. - 1.
return data
class RecResizeImg(object): class RecResizeImg(object):
def __init__(self, def __init__(self,
image_shape, image_shape,
...@@ -268,6 +260,84 @@ class PRENResizeImg(object): ...@@ -268,6 +260,84 @@ class PRENResizeImg(object):
return data return data
class GrayRecResizeImg(object):
def __init__(self,
image_shape,
resize_type,
inter_type='Image.ANTIALIAS',
scale=True,
padding=False,
**kwargs):
self.image_shape = image_shape
self.resize_type = resize_type
self.padding = padding
self.inter_type = eval(inter_type)
self.scale = scale
def __call__(self, data):
img = data['image']
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
image_shape = self.image_shape
if self.padding:
imgC, imgH, imgW = image_shape
# todo: change to 0 and modified image shape
h = img.shape[0]
w = img.shape[1]
ratio = w / float(h)
if math.ceil(imgH * ratio) > imgW:
resized_w = imgW
else:
resized_w = int(math.ceil(imgH * ratio))
resized_image = cv2.resize(img, (resized_w, imgH))
norm_img = np.expand_dims(resized_image, -1)
norm_img = norm_img.transpose((2, 0, 1))
resized_image = norm_img.astype(np.float32) / 128. - 1.
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
padding_im[:, :, 0:resized_w] = resized_image
data['image'] = padding_im
return data
if self.resize_type == 'PIL':
image_pil = Image.fromarray(np.uint8(img))
img = image_pil.resize(self.image_shape, self.inter_type)
img = np.array(img)
if self.resize_type == 'OpenCV':
img = cv2.resize(img, self.image_shape)
norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1))
if self.scale:
data['image'] = norm_img.astype(np.float32) / 128. - 1.
else:
data['image'] = norm_img.astype(np.float32) / 255.
return data
class ABINetRecResizeImg(object):
def __init__(self, image_shape, **kwargs):
self.image_shape = image_shape
def __call__(self, data):
img = data['image']
norm_img, valid_ratio = resize_norm_img_abinet(img, self.image_shape)
data['image'] = norm_img
data['valid_ratio'] = valid_ratio
return data
class SVTRRecResizeImg(object):
def __init__(self, image_shape, padding=True, **kwargs):
self.image_shape = image_shape
self.padding = padding
def __call__(self, data):
img = data['image']
norm_img, valid_ratio = resize_norm_img(img, self.image_shape,
self.padding)
data['image'] = norm_img
data['valid_ratio'] = valid_ratio
return data
def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25): def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
imgC, imgH, imgW_min, imgW_max = image_shape imgC, imgH, imgW_min, imgW_max = image_shape
h = img.shape[0] h = img.shape[0]
...@@ -386,6 +456,26 @@ def resize_norm_img_srn(img, image_shape): ...@@ -386,6 +456,26 @@ def resize_norm_img_srn(img, image_shape):
return np.reshape(img_black, (c, row, col)).astype(np.float32) return np.reshape(img_black, (c, row, col)).astype(np.float32)
def resize_norm_img_abinet(img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_w = imgW
resized_image = resized_image.astype('float32')
resized_image = resized_image / 255.
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
resized_image = (
resized_image - mean[None, None, ...]) / std[None, None, ...]
resized_image = resized_image.transpose((2, 0, 1))
resized_image = resized_image.astype('float32')
valid_ratio = min(1.0, float(resized_w / imgW))
return resized_image, valid_ratio
def srn_other_inputs(image_shape, num_heads, max_text_length): def srn_other_inputs(image_shape, num_heads, max_text_length):
imgC, imgH, imgW = image_shape imgC, imgH, imgW = image_shape
......
ppocr/losses/__init__.py
浏览文件 @ 9b8ffb6c
...@@ -30,7 +30,7 @@ from .det_fce_loss import FCELoss ...@@ -30,7 +30,7 @@ from .det_fce_loss import FCELoss
from .rec_ctc_loss import CTCLoss from .rec_ctc_loss import CTCLoss
from .rec_att_loss import AttentionLoss from .rec_att_loss import AttentionLoss
from .rec_srn_loss import SRNLoss from .rec_srn_loss import SRNLoss
from .rec_nrtr_loss import NRTRLoss from .rec_ce_loss import CELoss
from .rec_sar_loss import SARLoss from .rec_sar_loss import SARLoss
from .rec_aster_loss import AsterLoss from .rec_aster_loss import AsterLoss
from .rec_pren_loss import PRENLoss from .rec_pren_loss import PRENLoss
...@@ -60,7 +60,7 @@ def build_loss(config): ...@@ -60,7 +60,7 @@ def build_loss(config):
support_dict = [ support_dict = [
'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss', 'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss',
'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
'NRTRLoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss', 'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss' 'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss'
] ]
config = copy.deepcopy(config) config = copy.deepcopy(config)
......
ppocr/losses/rec_ce_loss.py 0 → 100644
浏览文件 @ 9b8ffb6c
import paddle
from paddle import nn
import paddle.nn.functional as F
class CELoss(nn.Layer):
def __init__(self,
smoothing=False,
with_all=False,
ignore_index=-1,
**kwargs):
super(CELoss, self).__init__()
if ignore_index >= 0:
self.loss_func = nn.CrossEntropyLoss(
reduction='mean', ignore_index=ignore_index)
else:
self.loss_func = nn.CrossEntropyLoss(reduction='mean')
self.smoothing = smoothing
self.with_all = with_all
def forward(self, pred, batch):
if isinstance(pred, dict): # for ABINet
loss = {}
loss_sum = []
for name, logits in pred.items():
if isinstance(logits, list):
logit_num = len(logits)
all_tgt = paddle.concat([batch[1]] * logit_num, 0)
all_logits = paddle.concat(logits, 0)
flt_logtis = all_logits.reshape([-1, all_logits.shape[2]])
flt_tgt = all_tgt.reshape([-1])
else:
flt_logtis = logits.reshape([-1, logits.shape[2]])
flt_tgt = batch[1].reshape([-1])
loss[name + '_loss'] = self.loss_func(flt_logtis, flt_tgt)
loss_sum.append(loss[name + '_loss'])
loss['loss'] = sum(loss_sum)
return loss
else:
if self.with_all: # for ViTSTR
tgt = batch[1]
pred = pred.reshape([-1, pred.shape[2]])
tgt = tgt.reshape([-1])
loss = self.loss_func(pred, tgt)
return {'loss': loss}
else: # for NRTR
max_len = batch[2].max()
tgt = batch[1][:, 1:2 + max_len]
pred = pred.reshape([-1, pred.shape[2]])
tgt = tgt.reshape([-1])
if self.smoothing:
eps = 0.1
n_class = pred.shape[1]
one_hot = F.one_hot(tgt, pred.shape[1])
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (
n_class - 1)
log_prb = F.log_softmax(pred, axis=1)
non_pad_mask = paddle.not_equal(
tgt, paddle.zeros(
tgt.shape, dtype=tgt.dtype))
loss = -(one_hot * log_prb).sum(axis=1)
loss = loss.masked_select(non_pad_mask).mean()
else:
loss = self.loss_func(pred, tgt)
return {'loss': loss}
ppocr/losses/rec_nrtr_loss.py 已删除 100644 → 0
浏览文件 @ bcd56d00
import paddle
from paddle import nn
import paddle.nn.functional as F
class NRTRLoss(nn.Layer):
def __init__(self, smoothing=True, **kwargs):
super(NRTRLoss, self).__init__()
self.loss_func = nn.CrossEntropyLoss(reduction='mean', ignore_index=0)
self.smoothing = smoothing
def forward(self, pred, batch):
pred = pred.reshape([-1, pred.shape[2]])
max_len = batch[2].max()
tgt = batch[1][:, 1:2 + max_len]
tgt = tgt.reshape([-1])
if self.smoothing:
eps = 0.1
n_class = pred.shape[1]
one_hot = F.one_hot(tgt, pred.shape[1])
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = F.log_softmax(pred, axis=1)
non_pad_mask = paddle.not_equal(
tgt, paddle.zeros(
tgt.shape, dtype=tgt.dtype))
loss = -(one_hot * log_prb).sum(axis=1)
loss = loss.masked_select(non_pad_mask).mean()
else:
loss = self.loss_func(pred, tgt)
return {'loss': loss}
ppocr/modeling/backbones/__init__.py
浏览文件 @ 9b8ffb6c
...@@ -28,35 +28,37 @@ def build_backbone(config, model_type): ...@@ -28,35 +28,37 @@ def build_backbone(config, model_type):
from .rec_mv1_enhance import MobileNetV1Enhance from .rec_mv1_enhance import MobileNetV1Enhance
from .rec_nrtr_mtb import MTB from .rec_nrtr_mtb import MTB
from .rec_resnet_31 import ResNet31 from .rec_resnet_31 import ResNet31
from .rec_resnet_45 import ResNet45
from .rec_resnet_aster import ResNet_ASTER from .rec_resnet_aster import ResNet_ASTER
from .rec_micronet import MicroNet from .rec_micronet import MicroNet
from .rec_efficientb3_pren import EfficientNetb3_PREN from .rec_efficientb3_pren import EfficientNetb3_PREN
from .rec_svtrnet import SVTRNet from .rec_svtrnet import SVTRNet
from .rec_vitstr import ViTSTR
support_dict = [ support_dict = [
'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', 'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB',
"ResNet31", "ResNet_ASTER", 'MicroNet', 'EfficientNetb3_PREN', 'ResNet31', 'ResNet45', 'ResNet_ASTER', 'MicroNet',
'SVTRNet' 'EfficientNetb3_PREN', 'SVTRNet', 'ViTSTR'
] ]
elif model_type == "e2e": elif model_type == 'e2e':
from .e2e_resnet_vd_pg import ResNet from .e2e_resnet_vd_pg import ResNet
support_dict = ['ResNet'] support_dict = ['ResNet']
elif model_type == 'kie': elif model_type == 'kie':
from .kie_unet_sdmgr import Kie_backbone from .kie_unet_sdmgr import Kie_backbone
support_dict = ['Kie_backbone'] support_dict = ['Kie_backbone']
elif model_type == "table": elif model_type == 'table':
from .table_resnet_vd import ResNet from .table_resnet_vd import ResNet
from .table_mobilenet_v3 import MobileNetV3 from .table_mobilenet_v3 import MobileNetV3
support_dict = ["ResNet", "MobileNetV3"] support_dict = ['ResNet', 'MobileNetV3']
elif model_type == 'vqa': elif model_type == 'vqa':
from .vqa_layoutlm import LayoutLMForSer, LayoutLMv2ForSer, LayoutLMv2ForRe, LayoutXLMForSer, LayoutXLMForRe from .vqa_layoutlm import LayoutLMForSer, LayoutLMv2ForSer, LayoutLMv2ForRe, LayoutXLMForSer, LayoutXLMForRe
support_dict = [ support_dict = [
"LayoutLMForSer", "LayoutLMv2ForSer", 'LayoutLMv2ForRe', 'LayoutLMForSer', 'LayoutLMv2ForSer', 'LayoutLMv2ForRe',
"LayoutXLMForSer", 'LayoutXLMForRe' 'LayoutXLMForSer', 'LayoutXLMForRe'
] ]
else: else:
raise NotImplementedError raise NotImplementedError
module_name = config.pop("name") module_name = config.pop('name')
assert module_name in support_dict, Exception( assert module_name in support_dict, Exception(
"when model typs is {}, backbone only support {}".format(model_type, "when model typs is {}, backbone only support {}".format(model_type,
support_dict)) support_dict))
......
ppocr/modeling/backbones/rec_resnet_45.py 0 → 100644
浏览文件 @ 9b8ffb6c
# copyright (c) 2021 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.
"""
This code is refer from:
https://github.com/FangShancheng/ABINet/tree/main/modules
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import ParamAttr
from paddle.nn.initializer import KaimingNormal
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
import math
__all__ = ["ResNet45"]
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2D(
in_planes,
out_planes,
kernel_size=1,
stride=1,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
def conv3x3(in_channel, out_channel, stride=1):
return nn.Conv2D(
in_channel,
out_channel,
kernel_size=3,
stride=stride,
padding=1,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self, in_channels, channels, stride=1, downsample=None):
super().__init__()
self.conv1 = conv1x1(in_channels, channels)
self.bn1 = nn.BatchNorm2D(channels)
self.relu = nn.ReLU()
self.conv2 = conv3x3(channels, channels, stride)
self.bn2 = nn.BatchNorm2D(channels)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet45(nn.Layer):
def __init__(self, block=BasicBlock, layers=[3, 4, 6, 6, 3], in_channels=3):
self.inplanes = 32
super(ResNet45, self).__init__()
self.conv1 = nn.Conv2D(
3,
32,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
self.bn1 = nn.BatchNorm2D(32)
self.relu = nn.ReLU()
self.layer1 = self._make_layer(block, 32, layers[0], stride=2)
self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
self.layer5 = self._make_layer(block, 512, layers[4], stride=1)
self.out_channels = 512
# for m in self.modules():
# if isinstance(m, nn.Conv2D):
# n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
# downsample = True
downsample = nn.Sequential(
nn.Conv2D(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False),
nn.BatchNorm2D(planes * block.expansion), )
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
# print(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
# print(x)
x = self.layer4(x)
x = self.layer5(x)
return x
ppocr/modeling/backbones/rec_svtrnet.py
浏览文件 @ 9b8ffb6c
...@@ -147,7 +147,7 @@ class Attention(nn.Layer): ...@@ -147,7 +147,7 @@ class Attention(nn.Layer):
dim, dim,
num_heads=8, num_heads=8,
mixer='Global', mixer='Global',
HW=[8, 25], HW=None,
local_k=[7, 11], local_k=[7, 11],
qkv_bias=False, qkv_bias=False,
qk_scale=None, qk_scale=None,
...@@ -210,7 +210,7 @@ class Block(nn.Layer): ...@@ -210,7 +210,7 @@ class Block(nn.Layer):
num_heads, num_heads,
mixer='Global', mixer='Global',
local_mixer=[7, 11], local_mixer=[7, 11],
HW=[8, 25], HW=None,
mlp_ratio=4., mlp_ratio=4.,
qkv_bias=False, qkv_bias=False,
qk_scale=None, qk_scale=None,
...@@ -274,7 +274,9 @@ class PatchEmbed(nn.Layer): ...@@ -274,7 +274,9 @@ class PatchEmbed(nn.Layer):
img_size=[32, 100], img_size=[32, 100],
in_channels=3, in_channels=3,
embed_dim=768, embed_dim=768,
sub_num=2): sub_num=2,
patch_size=[4, 4],
mode='pope'):
super().__init__() super().__init__()
num_patches = (img_size[1] // (2 ** sub_num)) * \ num_patches = (img_size[1] // (2 ** sub_num)) * \
(img_size[0] // (2 ** sub_num)) (img_size[0] // (2 ** sub_num))
...@@ -282,6 +284,7 @@ class PatchEmbed(nn.Layer): ...@@ -282,6 +284,7 @@ class PatchEmbed(nn.Layer):
self.num_patches = num_patches self.num_patches = num_patches
self.embed_dim = embed_dim self.embed_dim = embed_dim
self.norm = None self.norm = None
if mode == 'pope':
if sub_num == 2: if sub_num == 2:
self.proj = nn.Sequential( self.proj = nn.Sequential(
ConvBNLayer( ConvBNLayer(
...@@ -326,6 +329,11 @@ class PatchEmbed(nn.Layer): ...@@ -326,6 +329,11 @@ class PatchEmbed(nn.Layer):
padding=1, padding=1,
act=nn.GELU, act=nn.GELU,
bias_attr=None)) bias_attr=None))
elif mode == 'linear':
self.proj = nn.Conv2D(
1, embed_dim, kernel_size=patch_size, stride=patch_size)
self.num_patches = img_size[0] // patch_size[0] * img_size[
1] // patch_size[1]
def forward(self, x): def forward(self, x):
B, C, H, W = x.shape B, C, H, W = x.shape
......
ppocr/modeling/backbones/rec_vitstr.py 0 → 100644
浏览文件 @ 9b8ffb6c
# copyright (c) 2021 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.
"""
This code is refer from:
https://github.com/roatienza/deep-text-recognition-benchmark/blob/master/modules/vitstr.py
"""
import numpy as np
import paddle
import paddle.nn as nn
from ppocr.modeling.backbones.rec_svtrnet import Block, PatchEmbed, zeros_, trunc_normal_, ones_
scale_dim_heads = {'tiny': [192, 3], 'small': [384, 6], 'base': [768, 12]}
class ViTSTR(nn.Layer):
def __init__(self,
img_size=[224, 224],
in_channels=1,
scale='tiny',
seqlen=27,
patch_size=[16, 16],
embed_dim=None,
depth=12,
num_heads=None,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_path_rate=0.,
drop_rate=0.,
attn_drop_rate=0.,
norm_layer='nn.LayerNorm',
act_layer='nn.GELU',
epsilon=1e-6,
out_channels=None,
**kwargs):
super().__init__()
self.seqlen = seqlen
embed_dim = embed_dim if embed_dim is not None else scale_dim_heads[
scale][0]
num_heads = num_heads if num_heads is not None else scale_dim_heads[
scale][1]
out_channels = out_channels if out_channels is not None else embed_dim
self.patch_embed = PatchEmbed(
img_size=img_size,
in_channels=in_channels,
embed_dim=embed_dim,
patch_size=patch_size,
mode='linear')
num_patches = self.patch_embed.num_patches
self.pos_embed = self.create_parameter(
shape=[1, num_patches + 1, embed_dim], default_initializer=zeros_)
self.add_parameter("pos_embed", self.pos_embed)
self.cls_token = self.create_parameter(
shape=[1, 1, embed_dim], default_initializer=zeros_)
self.add_parameter("cls_token", self.cls_token)
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = np.linspace(0, drop_path_rate, depth)
self.blocks = nn.LayerList([
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=eval(act_layer),
epsilon=epsilon,
prenorm=False) for i in range(depth)
])
self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
self.out_channels = out_channels
trunc_normal_(self.pos_embed)
trunc_normal_(self.cls_token)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
zeros_(m.bias)
ones_(m.weight)
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = paddle.tile(self.cls_token, repeat_times=[B, 1, 1])
x = paddle.concat((cls_tokens, x), axis=1)
x = x + self.pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = x[:, :self.seqlen]
return x.transpose([0, 2, 1]).unsqueeze(2)
ppocr/modeling/heads/__init__.py
浏览文件 @ 9b8ffb6c
...@@ -33,6 +33,7 @@ def build_head(config): ...@@ -33,6 +33,7 @@ def build_head(config):
from .rec_aster_head import AsterHead from .rec_aster_head import AsterHead
from .rec_pren_head import PRENHead from .rec_pren_head import PRENHead
from .rec_multi_head import MultiHead from .rec_multi_head import MultiHead
from .rec_abinet_head import ABINetHead
# cls head # cls head
from .cls_head import ClsHead from .cls_head import ClsHead
...@@ -46,7 +47,7 @@ def build_head(config): ...@@ -46,7 +47,7 @@ def build_head(config):
'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead',
'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer', 'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead', 'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead',
'MultiHead' 'MultiHead', 'ABINetHead'
] ]
#table head #table head
......
ppocr/modeling/heads/multiheadAttention.py 已删除 100755 → 0
浏览文件 @ bcd56d00
# copyright (c) 2021 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 paddle
from paddle import nn
import paddle.nn.functional as F
from paddle.nn import Linear
from paddle.nn.initializer import XavierUniform as xavier_uniform_
from paddle.nn.initializer import Constant as constant_
from paddle.nn.initializer import XavierNormal as xavier_normal_
zeros_ = constant_(value=0.)
ones_ = constant_(value=1.)
class MultiheadAttention(nn.Layer):
"""Allows the model to jointly attend to information
from different representation subspaces.
See reference: Attention Is All You Need
.. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
Args:
embed_dim: total dimension of the model
num_heads: parallel attention layers, or heads
"""
def __init__(self,
embed_dim,
num_heads,
dropout=0.,
bias=True,
add_bias_kv=False,
add_zero_attn=False):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim**-0.5
self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
self._reset_parameters()
self.conv1 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
self.conv2 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
self.conv3 = paddle.nn.Conv2D(
in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
def _reset_parameters(self):
xavier_uniform_(self.out_proj.weight)
def forward(self,
query,
key,
value,
key_padding_mask=None,
incremental_state=None,
attn_mask=None):
"""
Inputs of forward function
query: [target length, batch size, embed dim]
key: [sequence length, batch size, embed dim]
value: [sequence length, batch size, embed dim]
key_padding_mask: if True, mask padding based on batch size
incremental_state: if provided, previous time steps are cashed
need_weights: output attn_output_weights
static_kv: key and value are static
Outputs of forward function
attn_output: [target length, batch size, embed dim]
attn_output_weights: [batch size, target length, sequence length]
"""
q_shape = paddle.shape(query)
src_shape = paddle.shape(key)
q = self._in_proj_q(query)
k = self._in_proj_k(key)
v = self._in_proj_v(value)
q *= self.scaling
q = paddle.transpose(
paddle.reshape(
q, [q_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
k = paddle.transpose(
paddle.reshape(
k, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
v = paddle.transpose(
paddle.reshape(
v, [src_shape[0], q_shape[1], self.num_heads, self.head_dim]),
[1, 2, 0, 3])
if key_padding_mask is not None:
assert key_padding_mask.shape[0] == q_shape[1]
assert key_padding_mask.shape[1] == src_shape[0]
attn_output_weights = paddle.matmul(q,
paddle.transpose(k, [0, 1, 3, 2]))
if attn_mask is not None:
attn_mask = paddle.unsqueeze(paddle.unsqueeze(attn_mask, 0), 0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = paddle.reshape(
attn_output_weights,
[q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
key = paddle.unsqueeze(paddle.unsqueeze(key_padding_mask, 1), 2)
key = paddle.cast(key, 'float32')
y = paddle.full(
shape=paddle.shape(key), dtype='float32', fill_value='-inf')
y = paddle.where(key == 0., key, y)
attn_output_weights += y
attn_output_weights = F.softmax(
attn_output_weights.astype('float32'),
axis=-1,
dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16
else attn_output_weights.dtype)
attn_output_weights = F.dropout(
attn_output_weights, p=self.dropout, training=self.training)
attn_output = paddle.matmul(attn_output_weights, v)
attn_output = paddle.reshape(
paddle.transpose(attn_output, [2, 0, 1, 3]),
[q_shape[0], q_shape[1], self.embed_dim])
attn_output = self.out_proj(attn_output)
return attn_output
def _in_proj_q(self, query):
query = paddle.transpose(query, [1, 2, 0])
query = paddle.unsqueeze(query, axis=2)
res = self.conv1(query)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
def _in_proj_k(self, key):
key = paddle.transpose(key, [1, 2, 0])
key = paddle.unsqueeze(key, axis=2)
res = self.conv2(key)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
def _in_proj_v(self, value):
value = paddle.transpose(value, [1, 2, 0]) #(1, 2, 0)
value = paddle.unsqueeze(value, axis=2)
res = self.conv3(value)
res = paddle.squeeze(res, axis=2)
res = paddle.transpose(res, [2, 0, 1])
return res
ppocr/modeling/heads/rec_abinet_head.py 0 → 100644
浏览文件 @ 9b8ffb6c
# copyright (c) 2021 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.
"""
This code is refer from:
https://github.com/FangShancheng/ABINet/tree/main/modules
"""
import math
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle.nn import LayerList
from ppocr.modeling.heads.rec_nrtr_head import TransformerBlock, PositionalEncoding
class BCNLanguage(nn.Layer):
def __init__(self,
d_model=512,
nhead=8,
num_layers=4,
dim_feedforward=2048,
dropout=0.,
max_length=25,
detach=True,
num_classes=37):
super().__init__()
self.d_model = d_model
self.detach = detach
self.max_length = max_length + 1 # additional stop token
self.proj = nn.Linear(num_classes, d_model, bias_attr=False)
self.token_encoder = PositionalEncoding(
dropout=0.1, dim=d_model, max_len=self.max_length)
self.pos_encoder = PositionalEncoding(
dropout=0, dim=d_model, max_len=self.max_length)
self.decoder = nn.LayerList([
TransformerBlock(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
attention_dropout_rate=dropout,
residual_dropout_rate=dropout,
with_self_attn=False,
with_cross_attn=True) for i in range(num_layers)
])
self.cls = nn.Linear(d_model, num_classes)
def forward(self, tokens, lengths):
"""
Args:
tokens: (B, N, C) where N is length, B is batch size and C is classes number
lengths: (B,)
"""
if self.detach: tokens = tokens.detach()
embed = self.proj(tokens) # (B, N, C)
embed = self.token_encoder(embed) # (B, N, C)
padding_mask = _get_mask(lengths, self.max_length)
zeros = paddle.zeros_like(embed) # (B, N, C)
qeury = self.pos_encoder(zeros)
for decoder_layer in self.decoder:
qeury = decoder_layer(qeury, embed, cross_mask=padding_mask)
output = qeury # (B, N, C)
logits = self.cls(output) # (B, N, C)
return output, logits
def encoder_layer(in_c, out_c, k=3, s=2, p=1):
return nn.Sequential(
nn.Conv2D(in_c, out_c, k, s, p), nn.BatchNorm2D(out_c), nn.ReLU())
def decoder_layer(in_c,
out_c,
k=3,
s=1,
p=1,
mode='nearest',
scale_factor=None,
size=None):
align_corners = False if mode == 'nearest' else True
return nn.Sequential(
nn.Upsample(
size=size,
scale_factor=scale_factor,
mode=mode,
align_corners=align_corners),
nn.Conv2D(in_c, out_c, k, s, p),
nn.BatchNorm2D(out_c),
nn.ReLU())
class PositionAttention(nn.Layer):
def __init__(self,
max_length,
in_channels=512,
num_channels=64,
h=8,
w=32,
mode='nearest',
**kwargs):
super().__init__()
self.max_length = max_length
self.k_encoder = nn.Sequential(
encoder_layer(
in_channels, num_channels, s=(1, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)),
encoder_layer(
num_channels, num_channels, s=(2, 2)))
self.k_decoder = nn.Sequential(
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, num_channels, scale_factor=2, mode=mode),
decoder_layer(
num_channels, in_channels, size=(h, w), mode=mode))
self.pos_encoder = PositionalEncoding(
dropout=0, dim=in_channels, max_len=max_length)
self.project = nn.Linear(in_channels, in_channels)
def forward(self, x):
B, C, H, W = x.shape
k, v = x, x
# calculate key vector
features = []
for i in range(0, len(self.k_encoder)):
k = self.k_encoder[i](k)
features.append(k)
for i in range(0, len(self.k_decoder) - 1):
k = self.k_decoder[i](k)
# print(k.shape, features[len(self.k_decoder) - 2 - i].shape)
k = k + features[len(self.k_decoder) - 2 - i]
k = self.k_decoder[-1](k)
# calculate query vector
# TODO q=f(q,k)
zeros = paddle.zeros(
(B, self.max_length, C), dtype=x.dtype) # (T, N, C)
q = self.pos_encoder(zeros) # (B, N, C)
q = self.project(q) # (B, N, C)
# calculate attention
attn_scores = q @k.flatten(2) # (B, N, (H*W))
attn_scores = attn_scores / (C**0.5)
attn_scores = F.softmax(attn_scores, axis=-1)
v = v.flatten(2).transpose([0, 2, 1]) # (B, (H*W), C)
attn_vecs = attn_scores @v # (B, N, C)
return attn_vecs, attn_scores.reshape([0, self.max_length, H, W])
class ABINetHead(nn.Layer):
def __init__(self,
in_channels,
out_channels,
d_model=512,
nhead=8,
num_layers=3,
dim_feedforward=2048,
dropout=0.1,
max_length=25,
use_lang=False,
iter_size=1):
super().__init__()
self.max_length = max_length + 1
self.pos_encoder = PositionalEncoding(
dropout=0.1, dim=d_model, max_len=8 * 32)
self.encoder = nn.LayerList([
TransformerBlock(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
attention_dropout_rate=dropout,
residual_dropout_rate=dropout,
with_self_attn=True,
with_cross_attn=False) for i in range(num_layers)
])
self.decoder = PositionAttention(
max_length=max_length + 1, # additional stop token
mode='nearest', )
self.out_channels = out_channels
self.cls = nn.Linear(d_model, self.out_channels)
self.use_lang = use_lang
if use_lang:
self.iter_size = iter_size
self.language = BCNLanguage(
d_model=d_model,
nhead=nhead,
num_layers=4,
dim_feedforward=dim_feedforward,
dropout=dropout,
max_length=max_length,
num_classes=self.out_channels)
# alignment
self.w_att_align = nn.Linear(2 * d_model, d_model)
self.cls_align = nn.Linear(d_model, self.out_channels)
def forward(self, x, targets=None):
x = x.transpose([0, 2, 3, 1])
_, H, W, C = x.shape
feature = x.flatten(1, 2)
feature = self.pos_encoder(feature)
for encoder_layer in self.encoder:
feature = encoder_layer(feature)
feature = feature.reshape([0, H, W, C]).transpose([0, 3, 1, 2])
v_feature, attn_scores = self.decoder(
feature) # (B, N, C), (B, C, H, W)
vis_logits = self.cls(v_feature) # (B, N, C)
logits = vis_logits
vis_lengths = _get_length(vis_logits)
if self.use_lang:
align_logits = vis_logits
align_lengths = vis_lengths
all_l_res, all_a_res = [], []
for i in range(self.iter_size):
tokens = F.softmax(align_logits, axis=-1)
lengths = align_lengths
lengths = paddle.clip(
lengths, 2, self.max_length) # TODO:move to langauge model
l_feature, l_logits = self.language(tokens, lengths)
# alignment
all_l_res.append(l_logits)
fuse = paddle.concat((l_feature, v_feature), -1)
f_att = F.sigmoid(self.w_att_align(fuse))
output = f_att * v_feature + (1 - f_att) * l_feature
align_logits = self.cls_align(output) # (B, N, C)
align_lengths = _get_length(align_logits)
all_a_res.append(align_logits)
if self.training:
return {
'align': all_a_res,
'lang': all_l_res,
'vision': vis_logits
}
else:
logits = align_logits
if self.training:
return logits
else:
return F.softmax(logits, -1)
def _get_length(logit):
""" Greed decoder to obtain length from logit"""
out = (logit.argmax(-1) == 0)
abn = out.any(-1)
out_int = out.cast('int32')
out = (out_int.cumsum(-1) == 1) & out
out = out.cast('int32')
out = out.argmax(-1)
out = out + 1
out = paddle.where(abn, out, paddle.to_tensor(logit.shape[1]))
return out
def _get_mask(length, max_length):
"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
Unmasked positions are filled with float(0.0).
"""
length = length.unsqueeze(-1)
B = paddle.shape(length)[0]
grid = paddle.arange(0, max_length).unsqueeze(0).tile([B, 1])
zero_mask = paddle.zeros([B, max_length], dtype='float32')
inf_mask = paddle.full([B, max_length], '-inf', dtype='float32')
diag_mask = paddle.diag(
paddle.full(
[max_length], '-inf', dtype=paddle.float32),
offset=0,
name=None)
mask = paddle.where(grid >= length, inf_mask, zero_mask)
mask = mask.unsqueeze(1) + diag_mask
return mask.unsqueeze(1)
ppocr/modeling/heads/rec_nrtr_head.py
浏览文件 @ 9b8ffb6c
...@@ -14,20 +14,15 @@ ...@@ -14,20 +14,15 @@
import math import math
import paddle import paddle
import copy
from paddle import nn from paddle import nn
import paddle.nn.functional as F import paddle.nn.functional as F
from paddle.nn import LayerList from paddle.nn import LayerList
from paddle.nn.initializer import XavierNormal as xavier_uniform_ # from paddle.nn.initializer import XavierNormal as xavier_uniform_
from paddle.nn import Dropout, Linear, LayerNorm, Conv2D from paddle.nn import Dropout, Linear, LayerNorm
import numpy as np import numpy as np
from ppocr.modeling.heads.multiheadAttention import MultiheadAttention from ppocr.modeling.backbones.rec_svtrnet import Mlp, zeros_, ones_
from paddle.nn.initializer import Constant as constant_
from paddle.nn.initializer import XavierNormal as xavier_normal_ from paddle.nn.initializer import XavierNormal as xavier_normal_
zeros_ = constant_(value=0.)
ones_ = constant_(value=1.)
class Transformer(nn.Layer): class Transformer(nn.Layer):
"""A transformer model. User is able to modify the attributes as needed. The architechture """A transformer model. User is able to modify the attributes as needed. The architechture
...@@ -45,7 +40,6 @@ class Transformer(nn.Layer): ...@@ -45,7 +40,6 @@ class Transformer(nn.Layer):
dropout: the dropout value (default=0.1). dropout: the dropout value (default=0.1).
custom_encoder: custom encoder (default=None). custom_encoder: custom encoder (default=None).
custom_decoder: custom decoder (default=None). custom_decoder: custom decoder (default=None).
""" """
def __init__(self, def __init__(self,
...@@ -54,45 +48,49 @@ class Transformer(nn.Layer): ...@@ -54,45 +48,49 @@ class Transformer(nn.Layer):
num_encoder_layers=6, num_encoder_layers=6,
beam_size=0, beam_size=0,
num_decoder_layers=6, num_decoder_layers=6,
max_len=25,
dim_feedforward=1024, dim_feedforward=1024,
attention_dropout_rate=0.0, attention_dropout_rate=0.0,
residual_dropout_rate=0.1, residual_dropout_rate=0.1,
custom_encoder=None,
custom_decoder=None,
in_channels=0, in_channels=0,
out_channels=0, out_channels=0,
scale_embedding=True): scale_embedding=True):
super(Transformer, self).__init__() super(Transformer, self).__init__()
self.out_channels = out_channels + 1 self.out_channels = out_channels + 1
self.max_len = max_len
self.embedding = Embeddings( self.embedding = Embeddings(
d_model=d_model, d_model=d_model,
vocab=self.out_channels, vocab=self.out_channels,
padding_idx=0, padding_idx=0,
scale_embedding=scale_embedding) scale_embedding=scale_embedding)
self.positional_encoding = PositionalEncoding( self.positional_encoding = PositionalEncoding(
dropout=residual_dropout_rate, dropout=residual_dropout_rate, dim=d_model)
dim=d_model, )
if custom_encoder is not None:
self.encoder = custom_encoder
else:
if num_encoder_layers > 0: if num_encoder_layers > 0:
encoder_layer = TransformerEncoderLayer( self.encoder = nn.LayerList([
d_model, nhead, dim_feedforward, attention_dropout_rate, TransformerBlock(
residual_dropout_rate) d_model,
self.encoder = TransformerEncoder(encoder_layer, nhead,
num_encoder_layers) dim_feedforward,
attention_dropout_rate,
residual_dropout_rate,
with_self_attn=True,
with_cross_attn=False) for i in range(num_encoder_layers)
])
else: else:
self.encoder = None self.encoder = None
if custom_decoder is not None: self.decoder = nn.LayerList([
self.decoder = custom_decoder TransformerBlock(
else: d_model,
decoder_layer = TransformerDecoderLayer( nhead,
d_model, nhead, dim_feedforward, attention_dropout_rate, dim_feedforward,
residual_dropout_rate) attention_dropout_rate,
self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers) residual_dropout_rate,
with_self_attn=True,
with_cross_attn=True) for i in range(num_decoder_layers)
])
self._reset_parameters()
self.beam_size = beam_size self.beam_size = beam_size
self.d_model = d_model self.d_model = d_model
self.nhead = nhead self.nhead = nhead
...@@ -105,7 +103,7 @@ class Transformer(nn.Layer): ...@@ -105,7 +103,7 @@ class Transformer(nn.Layer):
def _init_weights(self, m): def _init_weights(self, m):
if isinstance(m, nn.Conv2D): if isinstance(m, nn.Linear):
xavier_normal_(m.weight) xavier_normal_(m.weight)
if m.bias is not None: if m.bias is not None:
zeros_(m.bias) zeros_(m.bias)
...@@ -113,24 +111,20 @@ class Transformer(nn.Layer): ...@@ -113,24 +111,20 @@ class Transformer(nn.Layer):
def forward_train(self, src, tgt): def forward_train(self, src, tgt):
tgt = tgt[:, :-1] tgt = tgt[:, :-1]
tgt_key_padding_mask = self.generate_padding_mask(tgt) tgt = self.embedding(tgt)
tgt = self.embedding(tgt).transpose([1, 0, 2])
tgt = self.positional_encoding(tgt) tgt = self.positional_encoding(tgt)
tgt_mask = self.generate_square_subsequent_mask(tgt.shape[0]) tgt_mask = self.generate_square_subsequent_mask(tgt.shape[1])
if self.encoder is not None: if self.encoder is not None:
src = self.positional_encoding(src.transpose([1, 0, 2])) src = self.positional_encoding(src)
memory = self.encoder(src) for encoder_layer in self.encoder:
src = encoder_layer(src)
memory = src # B N C
else: else:
memory = src.squeeze(2).transpose([2, 0, 1]) memory = src # B N C
output = self.decoder( for decoder_layer in self.decoder:
tgt, tgt = decoder_layer(tgt, memory, self_mask=tgt_mask)
memory, output = tgt
tgt_mask=tgt_mask,
memory_mask=None,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=None)
output = output.transpose([1, 0, 2])
logit = self.tgt_word_prj(output) logit = self.tgt_word_prj(output)
return logit return logit
...@@ -140,8 +134,8 @@ class Transformer(nn.Layer): ...@@ -140,8 +134,8 @@ class Transformer(nn.Layer):
src: the sequence to the encoder (required). src: the sequence to the encoder (required).
tgt: the sequence to the decoder (required). tgt: the sequence to the decoder (required).
Shape: Shape:
- src: :math:`(S, N, E)`. - src: :math:`(B, sN, C)`.
- tgt: :math:`(T, N, E)`. - tgt: :math:`(B, tN, C)`.
Examples: Examples:
>>> output = transformer_model(src, tgt) >>> output = transformer_model(src, tgt)
""" """
...@@ -157,36 +151,35 @@ class Transformer(nn.Layer): ...@@ -157,36 +151,35 @@ class Transformer(nn.Layer):
return self.forward_test(src) return self.forward_test(src)
def forward_test(self, src): def forward_test(self, src):
bs = paddle.shape(src)[0] bs = paddle.shape(src)[0]
if self.encoder is not None: if self.encoder is not None:
src = self.positional_encoding(paddle.transpose(src, [1, 0, 2])) src = self.positional_encoding(src)
memory = self.encoder(src) for encoder_layer in self.encoder:
src = encoder_layer(src)
memory = src # B N C
else: else:
memory = paddle.transpose(paddle.squeeze(src, 2), [2, 0, 1]) memory = src
dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64) dec_seq = paddle.full((bs, 1), 2, dtype=paddle.int64)
dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32) dec_prob = paddle.full((bs, 1), 1., dtype=paddle.float32)
for len_dec_seq in range(1, 25): for len_dec_seq in range(1, self.max_len):
dec_seq_embed = paddle.transpose(self.embedding(dec_seq), [1, 0, 2]) dec_seq_embed = self.embedding(dec_seq)
dec_seq_embed = self.positional_encoding(dec_seq_embed) dec_seq_embed = self.positional_encoding(dec_seq_embed)
tgt_mask = self.generate_square_subsequent_mask( tgt_mask = self.generate_square_subsequent_mask(
paddle.shape(dec_seq_embed)[0]) paddle.shape(dec_seq_embed)[1])
output = self.decoder( tgt = dec_seq_embed
dec_seq_embed, for decoder_layer in self.decoder:
memory, tgt = decoder_layer(tgt, memory, self_mask=tgt_mask)
tgt_mask=tgt_mask, dec_output = tgt
memory_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None)
dec_output = paddle.transpose(output, [1, 0, 2])
dec_output = dec_output[:, -1, :] dec_output = dec_output[:, -1, :]
word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=-1)
preds_idx = paddle.argmax(word_prob, axis=1) preds_idx = paddle.argmax(word_prob, axis=-1)
if paddle.equal_all( if paddle.equal_all(
preds_idx, preds_idx,
paddle.full( paddle.full(
paddle.shape(preds_idx), 3, dtype='int64')): paddle.shape(preds_idx), 3, dtype='int64')):
break break
preds_prob = paddle.max(word_prob, axis=1) preds_prob = paddle.max(word_prob, axis=-1)
dec_seq = paddle.concat( dec_seq = paddle.concat(
[dec_seq, paddle.reshape(preds_idx, [-1, 1])], axis=1) [dec_seq, paddle.reshape(preds_idx, [-1, 1])], axis=1)
dec_prob = paddle.concat( dec_prob = paddle.concat(
...@@ -194,10 +187,10 @@ class Transformer(nn.Layer): ...@@ -194,10 +187,10 @@ class Transformer(nn.Layer):
return [dec_seq, dec_prob] return [dec_seq, dec_prob]
def forward_beam(self, images): def forward_beam(self, images):
''' Translation work in one batch ''' """ Translation work in one batch """
def get_inst_idx_to_tensor_position_map(inst_idx_list): def get_inst_idx_to_tensor_position_map(inst_idx_list):
''' Indicate the position of an instance in a tensor. ''' """ Indicate the position of an instance in a tensor. """
return { return {
inst_idx: tensor_position inst_idx: tensor_position
for tensor_position, inst_idx in enumerate(inst_idx_list) for tensor_position, inst_idx in enumerate(inst_idx_list)
...@@ -205,7 +198,7 @@ class Transformer(nn.Layer): ...@@ -205,7 +198,7 @@ class Transformer(nn.Layer):
def collect_active_part(beamed_tensor, curr_active_inst_idx, def collect_active_part(beamed_tensor, curr_active_inst_idx,
n_prev_active_inst, n_bm): n_prev_active_inst, n_bm):
''' Collect tensor parts associated to active instances. ''' """ Collect tensor parts associated to active instances. """
beamed_tensor_shape = paddle.shape(beamed_tensor) beamed_tensor_shape = paddle.shape(beamed_tensor)
n_curr_active_inst = len(curr_active_inst_idx) n_curr_active_inst = len(curr_active_inst_idx)
...@@ -237,9 +230,8 @@ class Transformer(nn.Layer): ...@@ -237,9 +230,8 @@ class Transformer(nn.Layer):
return active_src_enc, active_inst_idx_to_position_map return active_src_enc, active_inst_idx_to_position_map
def beam_decode_step(inst_dec_beams, len_dec_seq, enc_output, def beam_decode_step(inst_dec_beams, len_dec_seq, enc_output,
inst_idx_to_position_map, n_bm, inst_idx_to_position_map, n_bm):
memory_key_padding_mask): """ Decode and update beam status, and then return active beam idx """
''' Decode and update beam status, and then return active beam idx '''
def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq): def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
dec_partial_seq = [ dec_partial_seq = [
...@@ -249,19 +241,15 @@ class Transformer(nn.Layer): ...@@ -249,19 +241,15 @@ class Transformer(nn.Layer):
dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq]) dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq])
return dec_partial_seq return dec_partial_seq
def predict_word(dec_seq, enc_output, n_active_inst, n_bm, def predict_word(dec_seq, enc_output, n_active_inst, n_bm):
memory_key_padding_mask): dec_seq = self.embedding(dec_seq)
dec_seq = paddle.transpose(self.embedding(dec_seq), [1, 0, 2])
dec_seq = self.positional_encoding(dec_seq) dec_seq = self.positional_encoding(dec_seq)
tgt_mask = self.generate_square_subsequent_mask( tgt_mask = self.generate_square_subsequent_mask(
paddle.shape(dec_seq)[0]) paddle.shape(dec_seq)[1])
dec_output = self.decoder( tgt = dec_seq
dec_seq, for decoder_layer in self.decoder:
enc_output, tgt = decoder_layer(tgt, enc_output, self_mask=tgt_mask)
tgt_mask=tgt_mask, dec_output = tgt
tgt_key_padding_mask=None,
memory_key_padding_mask=memory_key_padding_mask, )
dec_output = paddle.transpose(dec_output, [1, 0, 2])
dec_output = dec_output[:, dec_output = dec_output[:,
-1, :] # Pick the last step: (bh * bm) * d_h -1, :] # Pick the last step: (bh * bm) * d_h
word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1) word_prob = F.softmax(self.tgt_word_prj(dec_output), axis=1)
...@@ -281,8 +269,7 @@ class Transformer(nn.Layer): ...@@ -281,8 +269,7 @@ class Transformer(nn.Layer):
n_active_inst = len(inst_idx_to_position_map) n_active_inst = len(inst_idx_to_position_map)
dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq) dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm, word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm)
None)
# Update the beam with predicted word prob information and collect incomplete instances # Update the beam with predicted word prob information and collect incomplete instances
active_inst_idx_list = collect_active_inst_idx_list( active_inst_idx_list = collect_active_inst_idx_list(
inst_dec_beams, word_prob, inst_idx_to_position_map) inst_dec_beams, word_prob, inst_idx_to_position_map)
...@@ -303,10 +290,10 @@ class Transformer(nn.Layer): ...@@ -303,10 +290,10 @@ class Transformer(nn.Layer):
with paddle.no_grad(): with paddle.no_grad():
#-- Encode #-- Encode
if self.encoder is not None: if self.encoder is not None:
src = self.positional_encoding(images.transpose([1, 0, 2])) src = self.positional_encoding(images)
src_enc = self.encoder(src) src_enc = self.encoder(src)
else: else:
src_enc = images.squeeze(2).transpose([0, 2, 1]) src_enc = images
n_bm = self.beam_size n_bm = self.beam_size
src_shape = paddle.shape(src_enc) src_shape = paddle.shape(src_enc)
...@@ -317,11 +304,11 @@ class Transformer(nn.Layer): ...@@ -317,11 +304,11 @@ class Transformer(nn.Layer):
inst_idx_to_position_map = get_inst_idx_to_tensor_position_map( inst_idx_to_position_map = get_inst_idx_to_tensor_position_map(
active_inst_idx_list) active_inst_idx_list)
# Decode # Decode
for len_dec_seq in range(1, 25): for len_dec_seq in range(1, self.max_len):
src_enc_copy = src_enc.clone() src_enc_copy = src_enc.clone()
active_inst_idx_list = beam_decode_step( active_inst_idx_list = beam_decode_step(
inst_dec_beams, len_dec_seq, src_enc_copy, inst_dec_beams, len_dec_seq, src_enc_copy,
inst_idx_to_position_map, n_bm, None) inst_idx_to_position_map, n_bm)
if not active_inst_idx_list: if not active_inst_idx_list:
break # all instances have finished their path to <EOS> break # all instances have finished their path to <EOS>
src_enc, inst_idx_to_position_map = collate_active_info( src_enc, inst_idx_to_position_map = collate_active_info(
...@@ -354,259 +341,122 @@ class Transformer(nn.Layer): ...@@ -354,259 +341,122 @@ class Transformer(nn.Layer):
shape=[sz, sz], dtype='float32', fill_value='-inf'), shape=[sz, sz], dtype='float32', fill_value='-inf'),
diagonal=1) diagonal=1)
mask = mask + mask_inf mask = mask + mask_inf
return mask return mask.unsqueeze([0, 1])
def generate_padding_mask(self, x):
padding_mask = paddle.equal(x, paddle.to_tensor(0, dtype=x.dtype))
return padding_mask
def _reset_parameters(self):
"""Initiate parameters in the transformer model."""
for p in self.parameters(): class MultiheadAttention(nn.Layer):
if p.dim() > 1: """Allows the model to jointly attend to information
xavier_uniform_(p) from different representation subspaces.
See reference: Attention Is All You Need
.. math::
\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
class TransformerEncoder(nn.Layer):
"""TransformerEncoder is a stack of N encoder layers
Args: Args:
encoder_layer: an instance of the TransformerEncoderLayer() class (required). embed_dim: total dimension of the model
num_layers: the number of sub-encoder-layers in the encoder (required). num_heads: parallel attention layers, or heads
norm: the layer normalization component (optional).
"""
def __init__(self, encoder_layer, num_layers):
super(TransformerEncoder, self).__init__()
self.layers = _get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
def forward(self, src):
"""Pass the input through the endocder layers in turn.
Args:
src: the sequnce to the encoder (required).
mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
""" """
output = src
for i in range(self.num_layers):
output = self.layers[i](output,
src_mask=None,
src_key_padding_mask=None)
return output def __init__(self, embed_dim, num_heads, dropout=0., self_attn=False):
super(MultiheadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
# self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scale = self.head_dim**-0.5
self.self_attn = self_attn
if self_attn:
self.qkv = nn.Linear(embed_dim, embed_dim * 3)
else:
self.q = nn.Linear(embed_dim, embed_dim)
self.kv = nn.Linear(embed_dim, embed_dim * 2)
self.attn_drop = nn.Dropout(dropout)
self.out_proj = nn.Linear(embed_dim, embed_dim)
def forward(self, query, key=None, attn_mask=None):
class TransformerDecoder(nn.Layer): qN = query.shape[1]
"""TransformerDecoder is a stack of N decoder layers
Args: if self.self_attn:
decoder_layer: an instance of the TransformerDecoderLayer() class (required). qkv = self.qkv(query).reshape(
num_layers: the number of sub-decoder-layers in the decoder (required). (0, qN, 3, self.num_heads, self.head_dim)).transpose(
norm: the layer normalization component (optional). (2, 0, 3, 1, 4))
q, k, v = qkv[0], qkv[1], qkv[2]
else:
kN = key.shape[1]
q = self.q(query).reshape(
[0, qN, self.num_heads, self.head_dim]).transpose([0, 2, 1, 3])
kv = self.kv(key).reshape(
(0, kN, 2, self.num_heads, self.head_dim)).transpose(
(2, 0, 3, 1, 4))
k, v = kv[0], kv[1]
""" attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
def __init__(self, decoder_layer, num_layers): if attn_mask is not None:
super(TransformerDecoder, self).__init__() attn += attn_mask
self.layers = _get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
def forward(self, attn = F.softmax(attn, axis=-1)
tgt, attn = self.attn_drop(attn)
memory,
tgt_mask=None,
memory_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None):
"""Pass the inputs (and mask) through the decoder layer in turn.
Args: x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape(
tgt: the sequence to the decoder (required). (0, qN, self.embed_dim))
memory: the sequnce from the last layer of the encoder (required). x = self.out_proj(x)
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
"""
output = tgt
for i in range(self.num_layers):
output = self.layers[i](
output,
memory,
tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
return output
class TransformerEncoderLayer(nn.Layer):
"""TransformerEncoderLayer is made up of self-attn and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args: return x
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
class TransformerBlock(nn.Layer):
def __init__(self, def __init__(self,
d_model, d_model,
nhead, nhead,
dim_feedforward=2048, dim_feedforward=2048,
attention_dropout_rate=0.0, attention_dropout_rate=0.0,
residual_dropout_rate=0.1): residual_dropout_rate=0.1,
super(TransformerEncoderLayer, self).__init__() with_self_attn=True,
with_cross_attn=False,
epsilon=1e-5):
super(TransformerBlock, self).__init__()
self.with_self_attn = with_self_attn
if with_self_attn:
self.self_attn = MultiheadAttention( self.self_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.conv1 = Conv2D(
in_channels=d_model,
out_channels=dim_feedforward,
kernel_size=(1, 1))
self.conv2 = Conv2D(
in_channels=dim_feedforward,
out_channels=d_model,
kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate)
self.dropout2 = Dropout(residual_dropout_rate)
def forward(self, src, src_mask=None, src_key_padding_mask=None):
"""Pass the input through the endocder layer.
Args:
src: the sequnce to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
"""
src2 = self.self_attn(
src,
src,
src,
attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src)
src = paddle.transpose(src, [1, 2, 0])
src = paddle.unsqueeze(src, 2)
src2 = self.conv2(F.relu(self.conv1(src)))
src2 = paddle.squeeze(src2, 2)
src2 = paddle.transpose(src2, [2, 0, 1])
src = paddle.squeeze(src, 2)
src = paddle.transpose(src, [2, 0, 1])
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
class TransformerDecoderLayer(nn.Layer):
"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
This standard decoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
"""
def __init__(self,
d_model, d_model,
nhead, nhead,
dim_feedforward=2048, dropout=attention_dropout_rate,
attention_dropout_rate=0.0, self_attn=with_self_attn)
residual_dropout_rate=0.1): self.norm1 = LayerNorm(d_model, epsilon=epsilon)
super(TransformerDecoderLayer, self).__init__()
self.self_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.multihead_attn = MultiheadAttention(
d_model, nhead, dropout=attention_dropout_rate)
self.conv1 = Conv2D(
in_channels=d_model,
out_channels=dim_feedforward,
kernel_size=(1, 1))
self.conv2 = Conv2D(
in_channels=dim_feedforward,
out_channels=d_model,
kernel_size=(1, 1))
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout1 = Dropout(residual_dropout_rate) self.dropout1 = Dropout(residual_dropout_rate)
self.with_cross_attn = with_cross_attn
if with_cross_attn:
self.cross_attn = MultiheadAttention( #for self_attn of encoder or cross_attn of decoder
d_model,
nhead,
dropout=attention_dropout_rate)
self.norm2 = LayerNorm(d_model, epsilon=epsilon)
self.dropout2 = Dropout(residual_dropout_rate) self.dropout2 = Dropout(residual_dropout_rate)
self.dropout3 = Dropout(residual_dropout_rate)
def forward(self, self.mlp = Mlp(in_features=d_model,
tgt, hidden_features=dim_feedforward,
memory, act_layer=nn.ReLU,
tgt_mask=None, drop=residual_dropout_rate)
memory_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None):
"""Pass the inputs (and mask) through the decoder layer.
Args: self.norm3 = LayerNorm(d_model, epsilon=epsilon)
tgt: the sequence to the decoder layer (required).
memory: the sequnce from the last layer of the encoder (required).
tgt_mask: the mask for the tgt sequence (optional).
memory_mask: the mask for the memory sequence (optional).
tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
memory_key_padding_mask: the mask for the memory keys per batch (optional).
""" self.dropout3 = Dropout(residual_dropout_rate)
tgt2 = self.self_attn(
tgt,
tgt,
tgt,
attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(
tgt,
memory,
memory,
attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
# default
tgt = paddle.transpose(tgt, [1, 2, 0])
tgt = paddle.unsqueeze(tgt, 2)
tgt2 = self.conv2(F.relu(self.conv1(tgt)))
tgt2 = paddle.squeeze(tgt2, 2)
tgt2 = paddle.transpose(tgt2, [2, 0, 1])
tgt = paddle.squeeze(tgt, 2)
tgt = paddle.transpose(tgt, [2, 0, 1])
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
def forward(self, tgt, memory=None, self_mask=None, cross_mask=None):
if self.with_self_attn:
tgt1 = self.self_attn(tgt, attn_mask=self_mask)
tgt = self.norm1(tgt + self.dropout1(tgt1))
def _get_clones(module, N): if self.with_cross_attn:
return LayerList([copy.deepcopy(module) for i in range(N)]) tgt2 = self.cross_attn(tgt, key=memory, attn_mask=cross_mask)
tgt = self.norm2(tgt + self.dropout2(tgt2))
tgt = self.norm3(tgt + self.dropout3(self.mlp(tgt)))
return tgt
class PositionalEncoding(nn.Layer): class PositionalEncoding(nn.Layer):
...@@ -651,8 +501,9 @@ class PositionalEncoding(nn.Layer): ...@@ -651,8 +501,9 @@ class PositionalEncoding(nn.Layer):
Examples: Examples:
>>> output = pos_encoder(x) >>> output = pos_encoder(x)
""" """
x = x.transpose([1, 0, 2])
x = x + self.pe[:paddle.shape(x)[0], :] x = x + self.pe[:paddle.shape(x)[0], :]
return self.dropout(x) return self.dropout(x).transpose([1, 0, 2])
class PositionalEncoding_2d(nn.Layer): class PositionalEncoding_2d(nn.Layer):
...@@ -725,7 +576,7 @@ class PositionalEncoding_2d(nn.Layer): ...@@ -725,7 +576,7 @@ class PositionalEncoding_2d(nn.Layer):
class Embeddings(nn.Layer): class Embeddings(nn.Layer):
def __init__(self, d_model, vocab, padding_idx, scale_embedding): def __init__(self, d_model, vocab, padding_idx=None, scale_embedding=True):
super(Embeddings, self).__init__() super(Embeddings, self).__init__()
self.embedding = nn.Embedding(vocab, d_model, padding_idx=padding_idx) self.embedding = nn.Embedding(vocab, d_model, padding_idx=padding_idx)
w0 = np.random.normal(0.0, d_model**-0.5, w0 = np.random.normal(0.0, d_model**-0.5,
...@@ -742,7 +593,7 @@ class Embeddings(nn.Layer): ...@@ -742,7 +593,7 @@ class Embeddings(nn.Layer):
class Beam(): class Beam():
''' Beam search ''' """ Beam search """
def __init__(self, size, device=False): def __init__(self, size, device=False):
......
ppocr/postprocess/__init__.py
浏览文件 @ 9b8ffb6c
...@@ -27,7 +27,7 @@ from .sast_postprocess import SASTPostProcess ...@@ -27,7 +27,7 @@ from .sast_postprocess import SASTPostProcess
from .fce_postprocess import FCEPostProcess from .fce_postprocess import FCEPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \
DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \ DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \
SEEDLabelDecode, PRENLabelDecode SEEDLabelDecode, PRENLabelDecode, ViTSTRLabelDecode, ABINetLabelDecode
from .cls_postprocess import ClsPostProcess from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess from .pg_postprocess import PGPostProcess
from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess
...@@ -42,7 +42,7 @@ def build_post_process(config, global_config=None): ...@@ -42,7 +42,7 @@ def build_post_process(config, global_config=None):
'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode', 'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess', 'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess',
'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode', 'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode',
'DistillationSARLabelDecode' 'DistillationSARLabelDecode', 'ViTSTRLabelDecode', 'ABINetLabelDecode'
] ]
if config['name'] == 'PSEPostProcess': if config['name'] == 'PSEPostProcess':
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ 9b8ffb6c
...@@ -140,70 +140,6 @@ class DistillationCTCLabelDecode(CTCLabelDecode): ...@@ -140,70 +140,6 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
return output return output
class NRTRLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if len(preds) == 2:
preds_id = preds[0]
preds_prob = preds[1]
if isinstance(preds_id, paddle.Tensor):
preds_id = preds_id.numpy()
if isinstance(preds_prob, paddle.Tensor):
preds_prob = preds_prob.numpy()
if preds_id[0][0] == 2:
preds_idx = preds_id[:, 1:]
preds_prob = preds_prob[:, 1:]
else:
preds_idx = preds_id
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:, 1:])
else:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:, 1:])
return text, label
def add_special_char(self, dict_character):
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
return dict_character
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
if text_index[batch_idx][idx] == 3: # end
break
try:
char_list.append(self.character[int(text_index[batch_idx][
idx])])
except:
continue
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
conf_list.append(1)
text = ''.join(char_list)
result_list.append((text.lower(), np.mean(conf_list).tolist()))
return result_list
class AttnLabelDecode(BaseRecLabelDecode): class AttnLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """ """ Convert between text-label and text-index """
...@@ -752,3 +688,122 @@ class PRENLabelDecode(BaseRecLabelDecode): ...@@ -752,3 +688,122 @@ class PRENLabelDecode(BaseRecLabelDecode):
return text return text
label = self.decode(label) label = self.decode(label)
return text, label return text, label
class NRTRLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if len(preds) == 2:
preds_id = preds[0]
preds_prob = preds[1]
if isinstance(preds_id, paddle.Tensor):
preds_id = preds_id.numpy()
if isinstance(preds_prob, paddle.Tensor):
preds_prob = preds_prob.numpy()
if preds_id[0][0] == 2:
preds_idx = preds_id[:, 1:]
preds_prob = preds_prob[:, 1:]
else:
preds_idx = preds_id
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:, 1:])
else:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:, 1:])
return text, label
def add_special_char(self, dict_character):
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
return dict_character
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
try:
char_idx = self.character[int(text_index[batch_idx][idx])]
except:
continue
if char_idx == '</s>': # end
break
char_list.append(char_idx)
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
conf_list.append(1)
text = ''.join(char_list)
result_list.append((text.lower(), np.mean(conf_list).tolist()))
return result_list
class ViTSTRLabelDecode(NRTRLabelDecode):
""" Convert between text-label and text-index """
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(ViTSTRLabelDecode, self).__init__(character_dict_path,
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds[:, 1:].numpy()
else:
preds = preds[:, 1:]
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:, 1:])
return text, label
def add_special_char(self, dict_character):
dict_character = ['<s>', '</s>'] + dict_character
return dict_character
class ABINetLabelDecode(NRTRLabelDecode):
""" Convert between text-label and text-index """
def __init__(self, character_dict_path=None, use_space_char=False,
**kwargs):
super(ABINetLabelDecode, self).__init__(character_dict_path,
use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, dict):
preds = preds['align'][-1].numpy()
elif isinstance(preds, paddle.Tensor):
preds = preds.numpy()
else:
preds = preds
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label)
return text, label
def add_special_char(self, dict_character):
dict_character = ['</s>'] + dict_character
return dict_character
test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml
浏览文件 @ 9b8ffb6c
...@@ -49,7 +49,7 @@ Architecture: ...@@ -49,7 +49,7 @@ Architecture:
Loss: Loss:
name: NRTRLoss name: CELoss
smoothing: True smoothing: True
PostProcess: PostProcess:
...@@ -69,7 +69,7 @@ Train: ...@@ -69,7 +69,7 @@ Train:
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- NRTRLabelEncode: # Class handling label - NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg: - GrayRecResizeImg:
image_shape: [100, 32] image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV resize_type: PIL # PIL or OpenCV
- KeepKeys: - KeepKeys:
...@@ -90,7 +90,7 @@ Eval: ...@@ -90,7 +90,7 @@ Eval:
img_mode: BGR img_mode: BGR
channel_first: False channel_first: False
- NRTRLabelEncode: # Class handling label - NRTRLabelEncode: # Class handling label
- NRTRRecResizeImg: - GrayRecResizeImg:
image_shape: [100, 32] image_shape: [100, 32]
resize_type: PIL # PIL or OpenCV resize_type: PIL # PIL or OpenCV
- KeepKeys: - KeepKeys:
...@@ -99,5 +99,5 @@ Eval: ...@@ -99,5 +99,5 @@ Eval:
shuffle: False shuffle: False
drop_last: False drop_last: False
batch_size_per_card: 256 batch_size_per_card: 256
num_workers: 1 num_workers: 4
use_shared_memory: False use_shared_memory: False
test_tipc/configs/rec_r45_abinet/rec_r45_abinet.yml 0 → 100644
浏览文件 @ 9b8ffb6c
Global:
use_gpu: True
epoch_num: 10
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/r45_abinet/
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_abinet.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.99
clip_norm: 20.0
lr:
name: Piecewise
decay_epochs: [6]
values: [0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0.
Architecture:
model_type: rec
algorithm: ABINet
in_channels: 3
Transform:
Backbone:
name: ResNet45
Head:
name: ABINetHead
use_lang: True
iter_size: 3
Loss:
name: CELoss
ignore_index: &ignore_index 100 # Must be greater than the number of character classes
PostProcess:
name: ABINetLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data/
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetRecAug:
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 96
drop_last: True
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- ABINetLabelEncode: # Class handling label
ignore_index: *ignore_index
- ABINetRecResizeImg:
image_shape: [3, 32, 128]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4
use_shared_memory: False
test_tipc/configs/rec_r45_abinet/train_infer_python.txt 0 → 100644
浏览文件 @ 9b8ffb6c
===========================train_params===========================
model_name:rec_abinet
python:python3.7
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=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/rec_r45_abinet/rec_r45_abinet.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_r45_abinet/rec_r45_abinet.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_r45_abinet/rec_r45_abinet.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
train_model:./inference/rec_r45_abinet_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_r45_abinet/rec_r45_abinet.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,128" --rec_algorithm="ABINet"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1|6
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,32,128]}]
test_tipc/configs/rec_svtrnet/rec_svtrnet.yml 0 → 100644
浏览文件 @ 9b8ffb6c
Global:
use_gpu: True
epoch_num: 20
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/svtr/
save_epoch_step: 1
# evaluation is run every 2000 iterations after the 0th iteration
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path:
character_type: en
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_svtr_tiny.txt
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.99
epsilon: 8.e-8
weight_decay: 0.05
no_weight_decay_name: norm pos_embed
one_dim_param_no_weight_decay: true
lr:
name: Cosine
learning_rate: 0.0005
warmup_epoch: 2
Architecture:
model_type: rec
algorithm: SVTR
Transform:
name: STN_ON
tps_inputsize: [32, 64]
tps_outputsize: [32, 100]
num_control_points: 20
tps_margins: [0.05,0.05]
stn_activation: none
Backbone:
name: SVTRNet
img_size: [32, 100]
out_char_num: 25
out_channels: 192
patch_merging: 'Conv'
embed_dim: [64, 128, 256]
depth: [3, 6, 3]
num_heads: [2, 4, 8]
mixer: ['Local','Local','Local','Local','Local','Local','Global','Global','Global','Global','Global','Global']
local_mixer: [[7, 11], [7, 11], [7, 11]]
last_stage: True
prenorm: false
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTCHead
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data/
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- SVTRRecResizeImg:
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 512
drop_last: True
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- SVTRRecResizeImg:
image_shape: [3, 64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 2
test_tipc/configs/rec_svtrnet/train_infer_python.txt 0 → 100644
浏览文件 @ 9b8ffb6c
===========================train_params===========================
model_name:rec_svtrnet
python:python3.7
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=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/rec_svtrnet/rec_svtrnet.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_svtrnet/rec_svtrnet.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_svtrnet/rec_svtrnet.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
train_model:./inference/rec_svtrnet_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_svtrnet/rec_svtrnet.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,64,256" --rec_algorithm="SVTR"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1|6
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,64,256]}]
test_tipc/configs/rec_vitstr_none_ce/rec_vitstr_none_ce.yml 0 → 100644
浏览文件 @ 9b8ffb6c
Global:
use_gpu: True
epoch_num: 20
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/vitstr_none_ce/
save_epoch_step: 1
# evaluation is run every 2000 iterations after the 0th iteration#
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/EN_symbol_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: False
save_res_path: ./output/rec/predicts_vitstr.txt
Optimizer:
name: Adadelta
epsilon: 1.e-8
rho: 0.95
clip_norm: 5.0
lr:
learning_rate: 1.0
Architecture:
model_type: rec
algorithm: ViTSTR
in_channels: 1
Transform:
Backbone:
name: ViTSTR
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTCHead
Loss:
name: CELoss
smoothing: False
with_all: True
ignore_index: &ignore_index 0 # Must be zero or greater than the number of character classes
PostProcess:
name: ViTSTRLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data/
label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- ViTSTRLabelEncode: # Class handling label
ignore_index: *ignore_index
- GrayRecResizeImg:
image_shape: [224, 224] # W H
resize_type: PIL # PIL or OpenCV
inter_type: 'Image.BICUBIC'
scale: false
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ic15_data
label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- ViTSTRLabelEncode: # Class handling label
ignore_index: *ignore_index
- GrayRecResizeImg:
image_shape: [224, 224] # W H
resize_type: PIL # PIL or OpenCV
inter_type: 'Image.BICUBIC'
scale: false
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 2
test_tipc/configs/rec_vitstr_none_ce/train_infer_python.txt 0 → 100644
浏览文件 @ 9b8ffb6c
===========================train_params===========================
model_name:rec_vitstr
python:python3.7
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=300
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=64
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c test_tipc/configs/rec_vitstr_none_ce/rec_vitstr_none_ce.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_vitstr_none_ce/rec_vitstr_none_ce.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c test_tipc/configs/rec_vitstr_none_ce/rec_vitstr_none_ce.yml -o
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
train_model:./inference/rec_vitstr_none_ce_train/best_accuracy
infer_export:tools/export_model.py -c test_tipc/configs/rec_vitstr_none_ce/rec_vitstr_none_ce.yml -o
infer_quant:False
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/EN_symbol_dict.txt --rec_image_shape="1,224,224" --rec_algorithm="ViTSTR"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1|6
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
--save_log_path:./test/output/
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[1,224,224]}]
tools/export_model.py
浏览文件 @ 9b8ffb6c
...@@ -31,7 +31,12 @@ from ppocr.utils.logging import get_logger ...@@ -31,7 +31,12 @@ from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser from tools.program import load_config, merge_config, ArgsParser
def export_single_model(model, arch_config, save_path, logger, quanter=None): def export_single_model(model,
arch_config,
save_path,
logger,
input_shape=None,
quanter=None):
if arch_config["algorithm"] == "SRN": if arch_config["algorithm"] == "SRN":
max_text_length = arch_config["Head"]["max_text_length"] max_text_length = arch_config["Head"]["max_text_length"]
other_shape = [ other_shape = [
...@@ -64,7 +69,7 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None): ...@@ -64,7 +69,7 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None):
else: else:
other_shape = [ other_shape = [
paddle.static.InputSpec( paddle.static.InputSpec(
shape=[None, 3, 64, 256], dtype="float32"), shape=[None] + input_shape, dtype="float32"),
] ]
model = to_static(model, input_spec=other_shape) model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "PREN": elif arch_config["algorithm"] == "PREN":
...@@ -73,6 +78,25 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None): ...@@ -73,6 +78,25 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None):
shape=[None, 3, 64, 512], dtype="float32"), shape=[None, 3, 64, 512], dtype="float32"),
] ]
model = to_static(model, input_spec=other_shape) model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "ViTSTR":
other_shape = [
paddle.static.InputSpec(
shape=[None, 1, 224, 224], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "ABINet":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 32, 128], dtype="float32"),
]
# print([None, 3, 32, 128])
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "NRTR":
other_shape = [
paddle.static.InputSpec(
shape=[None, 1, 32, 100], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
else: else:
infer_shape = [3, -1, -1] infer_shape = [3, -1, -1]
if arch_config["model_type"] == "rec": if arch_config["model_type"] == "rec":
...@@ -84,8 +108,6 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None): ...@@ -84,8 +108,6 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None):
"When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training" "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training"
) )
infer_shape[-1] = 100 infer_shape[-1] = 100
if arch_config["algorithm"] == "NRTR":
infer_shape = [1, 32, 100]
elif arch_config["model_type"] == "table": elif arch_config["model_type"] == "table":
infer_shape = [3, 488, 488] infer_shape = [3, 488, 488]
model = to_static( model = to_static(
...@@ -157,6 +179,13 @@ def main(): ...@@ -157,6 +179,13 @@ def main():
arch_config = config["Architecture"] arch_config = config["Architecture"]
if arch_config["algorithm"] == "SVTR" and arch_config["Head"][
"name"] != 'MultiHead':
input_shape = config["Eval"]["dataset"]["transforms"][-2][
'SVTRRecResizeImg']['image_shape']
else:
input_shape = None
if arch_config["algorithm"] in ["Distillation", ]: # distillation model if arch_config["algorithm"] in ["Distillation", ]: # distillation model
archs = list(arch_config["Models"].values()) archs = list(arch_config["Models"].values())
for idx, name in enumerate(model.model_name_list): for idx, name in enumerate(model.model_name_list):
...@@ -165,7 +194,8 @@ def main(): ...@@ -165,7 +194,8 @@ def main():
sub_model_save_path, logger) sub_model_save_path, logger)
else: else:
save_path = os.path.join(save_path, "inference") save_path = os.path.join(save_path, "inference")
export_single_model(model, arch_config, save_path, logger) export_single_model(
model, arch_config, save_path, logger, input_shape=input_shape)
if __name__ == "__main__": if __name__ == "__main__":
......
tools/infer/predict_rec.py
浏览文件 @ 9b8ffb6c
...@@ -69,6 +69,18 @@ class TextRecognizer(object): ...@@ -69,6 +69,18 @@ class TextRecognizer(object):
"character_dict_path": args.rec_char_dict_path, "character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char "use_space_char": args.use_space_char
} }
elif self.rec_algorithm == 'ViTSTR':
postprocess_params = {
'name': 'ViTSTRLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'ABINet':
postprocess_params = {
'name': 'ABINetLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params) self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \ self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger) utility.create_predictor(args, 'rec', logger)
...@@ -96,15 +108,22 @@ class TextRecognizer(object): ...@@ -96,15 +108,22 @@ class TextRecognizer(object):
def resize_norm_img(self, img, max_wh_ratio): def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape imgC, imgH, imgW = self.rec_image_shape
if self.rec_algorithm == 'NRTR': if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR':
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# return padding_im # return padding_im
image_pil = Image.fromarray(np.uint8(img)) image_pil = Image.fromarray(np.uint8(img))
img = image_pil.resize([100, 32], Image.ANTIALIAS) if self.rec_algorithm == 'ViTSTR':
img = image_pil.resize([imgW, imgH], Image.BICUBIC)
else:
img = image_pil.resize([imgW, imgH], Image.ANTIALIAS)
img = np.array(img) img = np.array(img)
norm_img = np.expand_dims(img, -1) norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1)) norm_img = norm_img.transpose((2, 0, 1))
return norm_img.astype(np.float32) / 128. - 1. if self.rec_algorithm == 'ViTSTR':
norm_img = norm_img.astype(np.float32) / 255.
else:
norm_img = norm_img.astype(np.float32) / 128. - 1.
return norm_img
assert imgC == img.shape[2] assert imgC == img.shape[2]
imgW = int((imgH * max_wh_ratio)) imgW = int((imgH * max_wh_ratio))
...@@ -132,17 +151,6 @@ class TextRecognizer(object): ...@@ -132,17 +151,6 @@ class TextRecognizer(object):
padding_im[:, :, 0:resized_w] = resized_image padding_im[:, :, 0:resized_w] = resized_image
return padding_im return padding_im
def resize_norm_img_svtr(self, img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
return resized_image
def resize_norm_img_srn(self, img, image_shape): def resize_norm_img_srn(self, img, image_shape):
imgC, imgH, imgW = image_shape imgC, imgH, imgW = image_shape
...@@ -250,6 +258,35 @@ class TextRecognizer(object): ...@@ -250,6 +258,35 @@ class TextRecognizer(object):
return padding_im, resize_shape, pad_shape, valid_ratio return padding_im, resize_shape, pad_shape, valid_ratio
def resize_norm_img_svtr(self, img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
return resized_image
def resize_norm_img_abinet(self, img, image_shape):
imgC, imgH, imgW = image_shape
resized_image = cv2.resize(
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
resized_image = resized_image.astype('float32')
resized_image = resized_image / 255.
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
resized_image = (
resized_image - mean[None, None, ...]) / std[None, None, ...]
resized_image = resized_image.transpose((2, 0, 1))
resized_image = resized_image.astype('float32')
return resized_image
def __call__(self, img_list): def __call__(self, img_list):
img_num = len(img_list) img_num = len(img_list)
# Calculate the aspect ratio of all text bars # Calculate the aspect ratio of all text bars
...@@ -300,6 +337,11 @@ class TextRecognizer(object): ...@@ -300,6 +337,11 @@ class TextRecognizer(object):
self.rec_image_shape) self.rec_image_shape)
norm_img = norm_img[np.newaxis, :] norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img) norm_img_batch.append(norm_img)
elif self.rec_algorithm == "ABINet":
norm_img = self.resize_norm_img_abinet(
img_list[indices[ino]], self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
else: else:
norm_img = self.resize_norm_img(img_list[indices[ino]], norm_img = self.resize_norm_img(img_list[indices[ino]],
max_wh_ratio) max_wh_ratio)
......
tools/program.py
浏览文件 @ 9b8ffb6c
...@@ -576,7 +576,8 @@ def preprocess(is_train=False): ...@@ -576,7 +576,8 @@ def preprocess(is_train=False):
assert alg in [ assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR' 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR',
'ViTSTR', 'ABINet'
] ]
if use_xpu: if use_xpu:
......
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