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PaddleOCR
提交 c25eec88 编写于 作者: z37757's avatar z37757
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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into rfl_branch

上级 06e734a5 059349ab
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PPOCRLabel/PPOCRLabel.py
浏览文件 @ c25eec88
......@@ -2716,6 +2716,9 @@ class MainWindow(QMainWindow):
self._update_shape_color(shape)
self.keyDialog.addLabelHistory(key_text)
# save changed shape
self.setDirty()
def undoShapeEdit(self):
self.canvas.restoreShape()
self.labelList.clear()
......
PPOCRLabel/README_ch.md
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......@@ -2,7 +2,7 @@
# PPOCRLabelv2
PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置PP-OCR模型对数据自动标注和重新识别。使用Python3和PyQT5编写，支持矩形框标注和四点标注模式，导出格式可直接用于PaddleOCR检测和识别模型的训练。
PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置PP-OCR模型对数据自动标注和重新识别。使用Python3和PyQT5编写，支持矩形框标注、表格标注、不规则文本标注、关键信息标注模式，导出格式可直接用于PaddleOCR检测和识别模型的训练。
| 常规标注 | 表格标注 |
| :-------------------------------------------------: | :--------------------------------------------: |
......
PPOCRLabel/libs/canvas.py
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......@@ -611,8 +611,8 @@ class Canvas(QWidget):
if self.drawing() and not self.prevPoint.isNull() and not self.outOfPixmap(self.prevPoint):
p.setPen(QColor(0, 0, 0))
p.drawLine(self.prevPoint.x(), 0, self.prevPoint.x(), self.pixmap.height())
p.drawLine(0, self.prevPoint.y(), self.pixmap.width(), self.prevPoint.y())
p.drawLine(int(self.prevPoint.x()), 0, int(self.prevPoint.x()), self.pixmap.height())
p.drawLine(0, int(self.prevPoint.y()), self.pixmap.width(), int(self.prevPoint.y()))
self.setAutoFillBackground(True)
if self.verified:
......
PPOCRLabel/resources/strings/strings-en.properties
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......@@ -113,4 +113,4 @@ keyDialogTip=Enter object label
keyChange=Change Box Key
TableRecognition=Table Recognition
cellreRecognition=Cell Re-Recognition
exportJSON=Export Excel Label(PubTabNet)
exportJSON=Export Table Label
PPOCRLabel/resources/strings/strings-zh-CN.properties
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......@@ -113,4 +113,4 @@ keyDialogTip=请输入类型名称
keyChange=更改Box关键字类别
TableRecognition=表格识别
cellreRecognition=单元格重识别
exportJSON=导出表格JSON标注
\ No newline at end of file
exportJSON=导出表格标注
\ No newline at end of file
doc/doc_ch/distributed_training.md
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......@@ -41,16 +41,30 @@ python3 -m paddle.distributed.launch \
## 性能效果测试
* 在2机8卡P40的机器上，基于26W公开识别数据集(LSVT, RCTW, MTWI)上进行训练，最终耗时如下。
* 在2机8卡P40的机器上进行模型训练，不同模型的精度、训练耗时、多机加速比情况如下所示。
| 模型 | 配置 | 精度 | 单机8卡耗时 | 2机8卡耗时 | 加速比 |
|------|-----|--------|--------|--------|-----|
| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 67.0% | 2.50d | 1.67d | **1.5** |
| 模型 | 配置 | 数据集 | 单机8卡耗时/精度 | 2机8卡耗时/精度 | 加速比 |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 26W中文数据集 | 2.50d/66.7% | 1.67d/67.0% | **1.5** |
* 在4机8卡V100的机器上，基于全量数据训练，最终耗时如下
* 在3机8卡V100的机器上进行模型训练，不同模型的精度、训练耗时、多机加速比情况如下所示。
| 模型 | 配置 | 数据集 | 单机8卡耗时/精度 | 3机8卡耗时/精度 | 加速比 |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| SLANet | [SLANet.yml](../../configs/table/SLANet.yml) | PubTabNet | 49.8h/76.2% | 19.75h/74.77% | **2.52** |
| 模型 | 配置 | 精度 | 单机8卡耗时 | 4机8卡耗时 | 加速比 |
|------|-----|--------|--------|--------|-----|
| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | 74.0% | 10d | 2.84d | **3.5** |
> 注意：这里3机8卡训练时，单卡batch size相比于单机8卡不变，学习率乘以2 (默认乘以3的话，精度仅有73.42%)
* 在4机8卡V100的机器上进行模型训练，不同模型的精度、训练耗时、多机加速比情况如下所示。
| 模型 | 配置 | 数据集 | 单机8卡耗时/精度 | 4机8卡耗时/精度 | 加速比 |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | PP-OCRv3_rec data | 10d/- | 2.84d/74.0% | **3.5** |
* **注意**
* 在训练的GPU卡数过多时，精度会稍微有所损失（1%左右），此时可以尝试通过添加warmup或者适当增加迭代轮数来弥补精度损失。
doc/doc_ch/inference_ppocr.md
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......@@ -144,7 +144,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
**注意** `PP-OCRv3`的识别模型使用的输入shape为`3,48,320`, 如果使用其他识别模型，则需根据模型设置参数`--rec_image_shape`。此外，`PP-OCRv3`的识别模型默认使用的`rec_algorithm`为`SVTR_LCNet`，注意和原始`SVTR`的区别。
以超轻量中文OCR模型推理为例，在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。`use_mp`表示是否使用多进程。`total_process_num`表示在使用多进程时的进程数。可视化识别结果默认保存到 ./inference_results 文件夹里面。
以超轻量中文OCR模型推理为例，在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径，也支持PDF文件、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。`use_mp`表示是否使用多进程。`total_process_num`表示在使用多进程时的进程数。可视化识别结果默认保存到 ./inference_results 文件夹里面。
```shell
# 使用方向分类器
......@@ -153,8 +153,11 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --de
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false
# 使用多进程
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
# 使用PDF文件,可以通过使用`page_num`参数来控制推理前几页，默认为0，表示推理所有页
python3 tools/infer/predict_system.py --image_dir="./xxx.pdf" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true --page_num=2
```
执行命令后，识别结果图像如下：
![](../imgs_results/system_res_00018069_v3.jpg)
......
doc/doc_ch/quickstart.md
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......@@ -75,6 +75,11 @@ cd /path/to/ppocr_img
......
```
此外，paddleocr也支持输入pdf文件，并且可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
- 单独使用检测：设置`--rec`为`false`
```bash
......@@ -165,12 +170,14 @@ from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = './imgs/11.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -196,6 +203,50 @@ im_show.save('result.jpg')
<a name="3"></a>
如果输入是PDF文件，那么可以参考下面代码进行可视化
```python
from paddleocr import PaddleOCR, draw_ocr
# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换
# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`
ocr = PaddleOCR(use_angle_cls=True, lang="ch"， page_num=2) # need to run only once to download and load model into memory
img_path = './xxx.pdf'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(img_path) as pdf:
for pg in range(0, pdf.pageCount):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.getPixmap(matrix=mat, alpha=False)
# if width or height > 2000 pixels, don't enlarge the image
if pm.width > 2000 or pm.height > 2000:
pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
imgs.append(img)
for idx in range(len(result)):
res = result[idx]
image = imgs[idx]
boxes = [line[0] for line in res]
txts = [line[1][0] for line in res]
scores = [line[1][1] for line in res]
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result_page_{}.jpg'.format(idx))
```
## 3. 小结
通过本节内容，相信您已经熟练掌握PaddleOCR whl包的使用方法并获得了初步效果。
......
doc/doc_ch/whl.md
浏览文件 @ c25eec88
......@@ -33,12 +33,14 @@ from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -71,12 +73,14 @@ from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, cls=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -109,7 +113,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
result = ocr.ocr(img_path, det=False, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -127,12 +133,14 @@ from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, rec=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
......@@ -163,7 +171,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
result = ocr.ocr(img_path, det=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -181,7 +191,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
result = ocr.ocr(img_path, det=False, rec=False, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -212,6 +224,11 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
......
```
此外，paddleocr也支持输入pdf文件，并且可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
* 检测+识别
```bash
......@@ -290,12 +307,14 @@ ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_m
use_angle_cls=True)
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -325,12 +344,14 @@ from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
download_with_progressbar(img_path, 'tmp.jpg')
image = Image.open('tmp.jpg').convert('RGB')
boxes = [line[0] for line in result]
......@@ -362,12 +383,14 @@ img_path = 'PaddleOCR/doc/imgs/11.jpg'
img = cv2.imread(img_path)
# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消
result = ocr.ocr(img, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -376,14 +399,65 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
## 5 PDF文件作为输入
- 命令行模式
## 5 参数说明
可以通过指定参数`page_num`来控制推理前面几页，默认为0，表示推理所有页。
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
- 代码使用
```python
from paddleocr import PaddleOCR, draw_ocr
# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换
# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`
ocr = PaddleOCR(use_angle_cls=True, lang="ch"， page_num=2) # need to run only once to download and load model into memory
img_path = './xxx.pdf'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# 显示结果
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(img_path) as pdf:
for pg in range(0, pdf.pageCount):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.getPixmap(matrix=mat, alpha=False)
# if width or height > 2000 pixels, don't enlarge the image
if pm.width > 2000 or pm.height > 2000:
pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
imgs.append(img)
for idx in range(len(result)):
res = result[idx]
image = imgs[idx]
boxes = [line[0] for line in res]
txts = [line[1][0] for line in res]
scores = [line[1][1] for line in res]
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result_page_{}.jpg'.format(idx))
```
## 6 参数说明
| 字段 | 说明 | 默认值 |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
| use_gpu | 是否使用GPU | TRUE |
| gpu_mem | 初始化占用的GPU内存大小 | 8000M |
| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 | |
| image_dir | 通过命令行调用时执行预测的图片或文件夹路径 |
| page_num | 当输入类型为pdf文件时有效，指定预测前面page_num页，默认预测所有页 | 0 |
| det_algorithm | 使用的检测算法类型 | DB |
| det_model_dir | 检测模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/det`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None |
| det_max_side_len | 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放 | 960 |
......
doc/doc_en/distributed_training_en.md
浏览文件 @ c25eec88
......@@ -40,17 +40,29 @@ python3 -m paddle.distributed.launch \
## Performance comparison
* On two 8-card P40 graphics cards, the final time consumption and speedup ratio for public recognition dataset (LSVT, RCTW, MTWI) containing 260k images are as follows.
* We conducted model training on 2x8 P40 GPUs. Accuracy, training time, and multi machine acceleration ratio of different models are shown below.
| Model | Configuration | Configuration | 8 GPU training time / Accuracy | 3x8 GPU training time / Accuracy | Acceleration ratio |
| Model | Config file | Recognition acc | single 8-card training time | two 8-card training time | Speedup ratio |
|------|-----|--------|--------|--------|-----|
| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 67.0% | 2.50d | 1.67d | **1.5** |
| Model | Configuration | Configuration | 8 GPU training time / Accuracy | 3x8 GPU training time / Accuracy | Acceleration ratio |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| CRNN | [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) | 260k Chinese dataset | 2.50d/66.7% | 1.67d/67.0% | **1.5** |
* On four 8-card V100 graphics cards, the final time consumption and speedup ratio for full data are as follows.
* We conducted model training on 3x8 V100 GPUs. Accuracy, training time, and multi machine acceleration ratio of different models are shown below.
| Model | Config file | Recognition acc | single 8-card training time | four 8-card training time | Speedup ratio |
|------|-----|--------|--------|--------|-----|
| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | 74.0% | 10d | 2.84d | **3.5** |
| Model | Configuration | Configuration | 8 GPU training time / Accuracy | 3x8 GPU training time / Accuracy | Acceleration ratio |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| SLANet | [SLANet.yml](../../configs/table/SLANet.yml) | PubTabNet | 49.8h/76.2% | 19.75h/74.77% | **2.52** |
> Note: when training with 3x8 GPUs, the single card batch size is unchanged compared with the 1x8 GPUs' training process, and the learning rate is multiplied by 2 (if it is multiplied by 3 by default, the accuracy is only 73.42%).
* We conducted model training on 4x8 V100 GPUs. Accuracy, training time, and multi machine acceleration ratio of different models are shown below.
| Model | Configuration | Configuration | 8 GPU training time / Accuracy | 4x8 GPU training time / Accuracy | Acceleration ratio |
|:------:|:-----:|:--------:|:--------:|:--------:|:-----:|
| SVTR | [ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) | PP-OCRv3_rec data | 10d/- | 2.84d/74.0% | **3.5** |
doc/doc_en/inference_ppocr_en.md
浏览文件 @ c25eec88
......@@ -144,16 +144,17 @@ After executing the command, the prediction results (classification angle and sc
**Note**: The input shape used by the recognition model of `PP-OCRv3` is `3, 48, 320`. If you use other recognition models, you need to set the parameter `--rec_image_shape` according to the model. In addition, the `rec_algorithm` used by the recognition model of `PP-OCRv3` is `SVTR_LCNet` by default. Note the difference from the original `SVTR`.
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, pdf file is also supported, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
```shell
# use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true
# not use use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false
# use multi-process
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv3_det_infer/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
# use PDF files, you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
python3 tools/infer/predict_system.py --image_dir="./xxx.pdf" --det_model_dir="./ch_PP-OCRv3_det_infer/" --cls_model_dir="./cls/" --rec_model_dir="./ch_PP-OCRv3_rec_infer/" --use_angle_cls=true --page_num=2
```
......
doc/doc_en/quickstart_en.md
浏览文件 @ c25eec88
......@@ -86,6 +86,12 @@ If you do not use the provided test image, you can replace the following `--imag
......
```
pdf file is also supported, you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
* Only detection: set `--rec` to `false`
```bash
......@@ -176,12 +182,15 @@ from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
img_path = './imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -206,6 +215,50 @@ Visualization of results
<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
</div>
If the input is a PDF file, you can refer to the following code for visualization
```python
from paddleocr import PaddleOCR, draw_ocr
# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
# to switch the language model in order.
ocr = PaddleOCR(use_angle_cls=True, lang="ch"， page_num=2) # need to run only once to download and load model into memory
img_path = './xxx.pdf'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(img_path) as pdf:
for pg in range(0, pdf.pageCount):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.getPixmap(matrix=mat, alpha=False)
# if width or height > 2000 pixels, don't enlarge the image
if pm.width > 2000 or pm.height > 2000:
pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
imgs.append(img)
for idx in range(len(result)):
res = result[idx]
image = imgs[idx]
boxes = [line[0] for line in res]
txts = [line[1][0] for line in res]
scores = [line[1][1] for line in res]
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result_page_{}.jpg'.format(idx))
```
<a name="3"></a>
......
doc/doc_en/whl_en.md
浏览文件 @ c25eec88
......@@ -25,12 +25,14 @@ from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -60,11 +62,14 @@ from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(lang='en') # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -94,7 +99,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
result = ocr.ocr(img_path, det=False, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -109,12 +116,14 @@ from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path,rec=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
......@@ -141,7 +150,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR(lang='en') # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
result = ocr.ocr(img_path, det=False, cls=False)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -156,7 +167,9 @@ from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to load model into memory
img_path = 'PaddleOCR/doc/imgs_words_en/word_10.png'
result = ocr.ocr(img_path, det=False, rec=False, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
```
......@@ -185,6 +198,11 @@ Output will be a list, each item contains bounding box, text and recognition con
......
```
pdf file is also supported, you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
* detection and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en
......@@ -253,11 +271,14 @@ from paddleocr import PaddleOCR,draw_ocr
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -283,11 +304,14 @@ from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# show result
from PIL import Image
result = result[0]
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
......@@ -312,12 +336,14 @@ img_path = 'PaddleOCR/doc/imgs/11.jpg'
img = cv2.imread(img_path)
# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), If your own training model supports grayscale images, you can uncomment this line
result = ocr.ocr(img_path, cls=True)
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# show result
from PIL import Image
result = result[0]
download_with_progressbar(img_path, 'tmp.jpg')
image = Image.open('tmp.jpg').convert('RGB')
boxes = [line[0] for line in result]
......@@ -327,15 +353,66 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
## 5 PDF file
- Use by command line
you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
```bash
paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
```
- Use by code
## 5 Parameter Description
```python
from paddleocr import PaddleOCR, draw_ocr
# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
# to switch the language model in order.
ocr = PaddleOCR(use_angle_cls=True, lang="ch"， page_num=2) # need to run only once to download and load model into memory
img_path = './xxx.pdf'
result = ocr.ocr(img_path, cls=True)
for idx in range(len(result)):
res = result[idx]
for line in res:
print(line)
# draw result
import fitz
from PIL import Image
import cv2
import numpy as np
imgs = []
with fitz.open(img_path) as pdf:
for pg in range(0, pdf.pageCount):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.getPixmap(matrix=mat, alpha=False)
# if width or height > 2000 pixels, don't enlarge the image
if pm.width > 2000 or pm.height > 2000:
pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
imgs.append(img)
for idx in range(len(result)):
res = result[idx]
image = imgs[idx]
boxes = [line[0] for line in res]
txts = [line[1][0] for line in res]
scores = [line[1][1] for line in res]
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result_page_{}.jpg'.format(idx))
```
## 6 Parameter Description
| Parameter | Description | Default value |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
| use_gpu | use GPU or not | TRUE |
| gpu_mem | GPU memory size used for initialization | 8000M |
| image_dir | The images path or folder path for predicting when used by the command line | |
| page_num | Valid when the input type is pdf file, specify to predict the previous page_num pages, all pages are predicted by default | 0 |
| det_algorithm | Type of detection algorithm selected | DB |
| det_model_dir | the text detection inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/det`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
| det_max_side_len | The maximum size of the long side of the image. When the long side exceeds this value, the long side will be resized to this size, and the short side will be scaled proportionally | 960 |
......
paddleocr.py
浏览文件 @ c25eec88
......@@ -47,7 +47,7 @@ __all__ = [
]
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.6.0.1'
VERSION = '2.6.0.2'
SUPPORT_REC_MODEL = ['CRNN', 'SVTR_LCNet']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
......@@ -428,8 +428,8 @@ def check_img(img):
download_with_progressbar(img, 'tmp.jpg')
img = 'tmp.jpg'
image_file = img
img, flag, _ = check_and_read(image_file)
if not flag:
img, flag_gif, flag_pdf = check_and_read(image_file)
if not flag_gif and not flag_pdf:
with open(image_file, 'rb') as f:
img = img_decode(f.read())
if img is None:
......@@ -500,6 +500,7 @@ class PaddleOCR(predict_system.TextSystem):
logger.debug(params)
# init det_model and rec_model
super().__init__(params)
self.page_num = params.page_num
def ocr(self, img, det=True, rec=True, cls=True):
"""
......@@ -520,24 +521,43 @@ class PaddleOCR(predict_system.TextSystem):
)
img = check_img(img)
# for infer pdf file
if isinstance(img, list):
if self.page_num > len(img) or self.page_num == 0:
self.page_num = len(img)
imgs = img[:self.page_num]
else:
imgs = [img]
if det and rec:
ocr_res = []
for idx, img in enumerate(imgs):
dt_boxes, rec_res, _ = self.__call__(img, cls)
return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
tmp_res = [[box.tolist(), res]
for box, res in zip(dt_boxes, rec_res)]
ocr_res.append(tmp_res)
return ocr_res
elif det and not rec:
ocr_res = []
for idx, img in enumerate(imgs):
dt_boxes, elapse = self.text_detector(img)
if dt_boxes is None:
return None
return [box.tolist() for box in dt_boxes]
tmp_res = [box.tolist() for box in dt_boxes]
ocr_res.append(tmp_res)
return ocr_res
else:
ocr_res = []
cls_res = []
for idx, img in enumerate(imgs):
if not isinstance(img, list):
img = [img]
if self.use_angle_cls and cls:
img, cls_res, elapse = self.text_classifier(img)
img, cls_res_tmp, elapse = self.text_classifier(img)
if not rec:
return cls_res
cls_res.append(cls_res_tmp)
rec_res, elapse = self.text_recognizer(img)
return rec_res
ocr_res.append(rec_res)
if not rec:
return cls_res
return ocr_res
class PPStructure(StructureSystem):
......@@ -633,7 +653,9 @@ def main():
rec=args.rec,
cls=args.use_angle_cls)
if result is not None:
for line in result:
for idx in range(len(result)):
res = result[idx]
for line in res:
logger.info(line)
elif args.type == 'structure':
img, flag_gif, flag_pdf = check_and_read(img_path)
......
ppocr/modeling/backbones/rec_efficientb3_pren.py
浏览文件 @ c25eec88
......@@ -21,124 +21,165 @@ from __future__ import division
from __future__ import print_function
import math
from collections import namedtuple
import re
import collections
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ['EfficientNetb3']
GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'num_classes',
'width_coefficient', 'depth_coefficient', 'depth_divisor', 'min_depth',
'drop_connect_rate', 'image_size'
])
class EffB3Params:
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'stride', 'se_ratio'
])
class BlockDecoder:
@staticmethod
def get_global_params():
"""
The fllowing are efficientnetb3's arch superparams, but to fit for scene
text recognition task, the resolution(image_size) here is changed
from 300 to 64.
"""
GlobalParams = namedtuple('GlobalParams', [
'drop_connect_rate', 'width_coefficient', 'depth_coefficient',
'depth_divisor', 'image_size'
])
global_params = GlobalParams(
drop_connect_rate=0.3,
width_coefficient=1.2,
depth_coefficient=1.4,
depth_divisor=8,
image_size=64)
return global_params
def _decode_block_string(block_string):
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
assert (('s' in options and len(options['s']) == 1) or
(len(options['s']) == 2 and options['s'][0] == options['s'][1]))
return BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
@staticmethod
def get_block_params():
BlockParams = namedtuple('BlockParams', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'se_ratio', 'stride'
])
block_params = [
BlockParams(3, 1, 32, 16, 1, True, 0.25, 1),
BlockParams(3, 2, 16, 24, 6, True, 0.25, 2),
BlockParams(5, 2, 24, 40, 6, True, 0.25, 2),
BlockParams(3, 3, 40, 80, 6, True, 0.25, 2),
BlockParams(5, 3, 80, 112, 6, True, 0.25, 1),
BlockParams(5, 4, 112, 192, 6, True, 0.25, 2),
BlockParams(3, 1, 192, 320, 6, True, 0.25, 1)
def decode(string_list):
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2,
image_size=None,
num_classes=1000):
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
return block_params
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
num_classes=num_classes,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
image_size=image_size, )
return blocks_args, global_params
class EffUtils:
@staticmethod
def round_filters(filters, global_params):
"""Calculate and round number of filters based on depth multiplier."""
""" Calculate and round number of filters based on depth multiplier. """
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
new_filters = int(filters + divisor / 2) // divisor * divisor
min_depth = min_depth or divisor
new_filters = max(min_depth,
int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters:
new_filters += divisor
return int(new_filters)
@staticmethod
def round_repeats(repeats, global_params):
"""Round number of filters based on depth multiplier."""
""" Round number of filters based on depth multiplier. """
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
class ConvBlock(nn.Layer):
def __init__(self, block_params):
super(ConvBlock, self).__init__()
self.block_args = block_params
self.has_se = (self.block_args.se_ratio is not None) and \
(0 < self.block_args.se_ratio <= 1)
self.id_skip = block_params.id_skip
class MbConvBlock(nn.Layer):
def __init__(self, block_args):
super(MbConvBlock, self).__init__()
self._block_args = block_args
self.has_se = (self._block_args.se_ratio is not None) and \
(0 < self._block_args.se_ratio <= 1)
self.id_skip = block_args.id_skip
# expansion phase
self.input_filters = self.block_args.input_filters
output_filters = \
self.block_args.input_filters * self.block_args.expand_ratio
if self.block_args.expand_ratio != 1:
self.expand_conv = nn.Conv2D(
self.input_filters, output_filters, 1, bias_attr=False)
self.bn0 = nn.BatchNorm(output_filters)
self.inp = self._block_args.input_filters
oup = self._block_args.input_filters * self._block_args.expand_ratio
if self._block_args.expand_ratio != 1:
self._expand_conv = nn.Conv2D(self.inp, oup, 1, bias_attr=False)
self._bn0 = nn.BatchNorm(oup)
# depthwise conv phase
k = self.block_args.kernel_size
s = self.block_args.stride
self.depthwise_conv = nn.Conv2D(
output_filters,
output_filters,
groups=output_filters,
k = self._block_args.kernel_size
s = self._block_args.stride
if isinstance(s, list):
s = s[0]
self._depthwise_conv = nn.Conv2D(
oup,
oup,
groups=oup,
kernel_size=k,
stride=s,
padding='same',
bias_attr=False)
self.bn1 = nn.BatchNorm(output_filters)
self._bn1 = nn.BatchNorm(oup)
# squeeze and excitation layer, if desired
if self.has_se:
num_squeezed_channels = max(1,
int(self.block_args.input_filters *
self.block_args.se_ratio))
self.se_reduce = nn.Conv2D(output_filters, num_squeezed_channels, 1)
self.se_expand = nn.Conv2D(num_squeezed_channels, output_filters, 1)
# output phase
self.final_oup = self.block_args.output_filters
self.project_conv = nn.Conv2D(
output_filters, self.final_oup, 1, bias_attr=False)
self.bn2 = nn.BatchNorm(self.final_oup)
self.swish = nn.Swish()
def drop_connect(self, inputs, p, training):
int(self._block_args.input_filters *
self._block_args.se_ratio))
self._se_reduce = nn.Conv2D(oup, num_squeezed_channels, 1)
self._se_expand = nn.Conv2D(num_squeezed_channels, oup, 1)
# output phase and some util class
self.final_oup = self._block_args.output_filters
self._project_conv = nn.Conv2D(oup, self.final_oup, 1, bias_attr=False)
self._bn2 = nn.BatchNorm(self.final_oup)
self._swish = nn.Swish()
def _drop_connect(self, inputs, p, training):
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
random_tensor = keep_prob
......@@ -151,22 +192,23 @@ class ConvBlock(nn.Layer):
def forward(self, inputs, drop_connect_rate=None):
# expansion and depthwise conv
x = inputs
if self.block_args.expand_ratio != 1:
x = self.swish(self.bn0(self.expand_conv(inputs)))
x = self.swish(self.bn1(self.depthwise_conv(x)))
if self._block_args.expand_ratio != 1:
x = self._swish(self._bn0(self._expand_conv(inputs)))
x = self._swish(self._bn1(self._depthwise_conv(x)))
# squeeze and excitation
if self.has_se:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self.se_expand(self.swish(self.se_reduce(x_squeezed)))
x_squeezed = self._se_expand(
self._swish(self._se_reduce(x_squeezed)))
x = F.sigmoid(x_squeezed) * x
x = self.bn2(self.project_conv(x))
x = self._bn2(self._project_conv(x))
# skip conntection and drop connect
if self.id_skip and self.block_args.stride == 1 and \
self.input_filters == self.final_oup:
if self.id_skip and self._block_args.stride == 1 and \
self.inp == self.final_oup:
if drop_connect_rate:
x = self.drop_connect(
x = self._drop_connect(
x, p=drop_connect_rate, training=self.training)
x = x + inputs
return x
......@@ -175,54 +217,63 @@ class ConvBlock(nn.Layer):
class EfficientNetb3_PREN(nn.Layer):
def __init__(self, in_channels):
super(EfficientNetb3_PREN, self).__init__()
self.blocks_params = EffB3Params.get_block_params()
self.global_params = EffB3Params.get_global_params()
"""
the fllowing are efficientnetb3's superparams,
they means efficientnetb3 network's width, depth, resolution and
dropout respectively, to fit for text recognition task, the resolution
here is changed from 300 to 64.
"""
w, d, s, p = 1.2, 1.4, 64, 0.3
self._blocks_args, self._global_params = efficientnet(
width_coefficient=w,
depth_coefficient=d,
dropout_rate=p,
image_size=s)
self.out_channels = []
# stem
stem_channels = EffUtils.round_filters(32, self.global_params)
self.conv_stem = nn.Conv2D(
in_channels, stem_channels, 3, 2, padding='same', bias_attr=False)
self.bn0 = nn.BatchNorm(stem_channels)
out_channels = EffUtils.round_filters(32, self._global_params)
self._conv_stem = nn.Conv2D(
in_channels, out_channels, 3, 2, padding='same', bias_attr=False)
self._bn0 = nn.BatchNorm(out_channels)
self.blocks = []
# build blocks
self._blocks = []
# to extract three feature maps for fpn based on efficientnetb3 backbone
self.concerned_block_idxes = [7, 17, 25]
concerned_idx = 0
for i, block_params in enumerate(self.blocks_params):
block_params = block_params._replace(
input_filters=EffUtils.round_filters(block_params.input_filters,
self.global_params),
output_filters=EffUtils.round_filters(
block_params.output_filters, self.global_params),
num_repeat=EffUtils.round_repeats(block_params.num_repeat,
self.global_params))
self.blocks.append(
self.add_sublayer("{}-0".format(i), ConvBlock(block_params)))
concerned_idx += 1
if concerned_idx in self.concerned_block_idxes:
self.out_channels.append(block_params.output_filters)
if block_params.num_repeat > 1:
block_params = block_params._replace(
input_filters=block_params.output_filters, stride=1)
for j in range(block_params.num_repeat - 1):
self.blocks.append(
self.add_sublayer('{}-{}'.format(i, j + 1),
ConvBlock(block_params)))
concerned_idx += 1
if concerned_idx in self.concerned_block_idxes:
self.out_channels.append(block_params.output_filters)
self.swish = nn.Swish()
self._concerned_block_idxes = [7, 17, 25]
_concerned_idx = 0
for i, block_args in enumerate(self._blocks_args):
block_args = block_args._replace(
input_filters=EffUtils.round_filters(block_args.input_filters,
self._global_params),
output_filters=EffUtils.round_filters(block_args.output_filters,
self._global_params),
num_repeat=EffUtils.round_repeats(block_args.num_repeat,
self._global_params))
self._blocks.append(
self.add_sublayer(f"{i}-0", MbConvBlock(block_args)))
_concerned_idx += 1
if _concerned_idx in self._concerned_block_idxes:
self.out_channels.append(block_args.output_filters)
if block_args.num_repeat > 1:
block_args = block_args._replace(
input_filters=block_args.output_filters, stride=1)
for j in range(block_args.num_repeat - 1):
self._blocks.append(
self.add_sublayer(f'{i}-{j+1}', MbConvBlock(block_args)))
_concerned_idx += 1
if _concerned_idx in self._concerned_block_idxes:
self.out_channels.append(block_args.output_filters)
self._swish = nn.Swish()
def forward(self, inputs):
outs = []
x = self.swish(self.bn0(self.conv_stem(inputs)))
for idx, block in enumerate(self.blocks):
drop_connect_rate = self.global_params.drop_connect_rate
x = self._swish(self._bn0(self._conv_stem(inputs)))
for idx, block in enumerate(self._blocks):
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self.blocks)
drop_connect_rate *= float(idx) / len(self._blocks)
x = block(x, drop_connect_rate=drop_connect_rate)
if idx in self.concerned_block_idxes:
if idx in self._concerned_block_idxes:
outs.append(x)
return outs
ppocr/modeling/heads/table_att_head.py
浏览文件 @ c25eec88
......@@ -82,7 +82,8 @@ class TableAttentionHead(nn.Layer):
batch_size = fea.shape[0]
hidden = paddle.zeros((batch_size, self.hidden_size))
output_hiddens = paddle.zeros((batch_size, self.max_text_length + 1, self.hidden_size))
output_hiddens = paddle.zeros(
(batch_size, self.max_text_length + 1, self.hidden_size))
if self.training and targets is not None:
structure = targets[0]
for i in range(self.max_text_length + 1):
......@@ -91,17 +92,11 @@ class TableAttentionHead(nn.Layer):
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens[:, i, :] = outputs
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
output = paddle.concat(output_hiddens, axis=1)
structure_probs = self.structure_generator(output)
if self.loc_type == 1:
loc_preds = self.loc_generator(output)
loc_preds = F.sigmoid(loc_preds)
else:
structure_probs = self.structure_generator(output_hiddens)
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
else:
......@@ -118,17 +113,15 @@ class TableAttentionHead(nn.Layer):
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens[:, i, :] = outputs
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
structure_probs_step = self.structure_generator(outputs)
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
output = output_hiddens
structure_probs = self.structure_generator(output)
structure_probs = self.structure_generator(output_hiddens)
structure_probs = F.softmax(structure_probs)
loc_fea = fea.transpose([0, 2, 1])
loc_fea = self.loc_fea_trans(loc_fea)
loc_fea = loc_fea.transpose([0, 2, 1])
loc_concat = paddle.concat([output, loc_fea], axis=2)
loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
loc_preds = self.loc_generator(loc_concat)
loc_preds = F.sigmoid(loc_preds)
return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
......@@ -203,8 +196,10 @@ class SLAHead(nn.Layer):
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
hidden = paddle.zeros((batch_size, self.hidden_size))
structure_preds = paddle.zeros((batch_size, self.max_text_length + 1, self.num_embeddings))
loc_preds = paddle.zeros((batch_size, self.max_text_length + 1, self.loc_reg_num))
structure_preds = paddle.zeros(
(batch_size, self.max_text_length + 1, self.num_embeddings))
loc_preds = paddle.zeros(
(batch_size, self.max_text_length + 1, self.loc_reg_num))
structure_preds.stop_gradient = True
loc_preds.stop_gradient = True
if self.training and targets is not None:
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ c25eec88
......@@ -650,6 +650,7 @@ class PRENLabelDecode(BaseRecLabelDecode):
return result_list
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
......
tools/export_model.py
浏览文件 @ c25eec88
......@@ -77,7 +77,7 @@ def export_single_model(model,
elif arch_config["algorithm"] == "PREN":
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 64, 512], dtype="float32"),
shape=[None, 3, 64, 256], dtype="float32"),
]
model = to_static(model, input_spec=other_shape)
elif arch_config["model_type"] == "sr":
......
tools/infer/predict_rec.py
浏览文件 @ c25eec88
......@@ -106,6 +106,8 @@ class TextRecognizer(object):
"character_dict_path": None,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "PREN":
postprocess_params = {'name': 'PRENLabelDecode'}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger)
......@@ -400,7 +402,7 @@ class TextRecognizer(object):
self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "VisionLAN":
elif self.rec_algorithm in ["VisionLAN", "PREN"]:
norm_img = self.resize_norm_img_vl(img_list[indices[ino]],
self.rec_image_shape)
norm_img = norm_img[np.newaxis, :]
......
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