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add bda

pull/6437/head
WenmuZhou 2022-05-30 16:44:50 +08:00
parent 8a5a98707c
commit feca63686d
5 changed files with 70 additions and 100 deletions
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3
configs/cls/ch_PP-OCRv3/ch_PP-OCRv3_rotnet.yml
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@ -63,8 +63,7 @@ Train:
- DecodeImage:
img_mode: BGR
channel_first: false
- RecAug:
use_tia: False
- BaseDataAugmentation:
- RandAugment:
- SSLRotateResize:
image_shape: [3, 48, 320]

3
configs/cls/cls_mv3.yml
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@ -60,8 +60,7 @@ Train:
img_mode: BGR
channel_first: False
- ClsLabelEncode: # Class handling label
- RecAug:
use_tia: False
- BaseDataAugmentation:
- RandAugment:
- ClsResizeImg:
image_shape: [3, 48, 192]

2
doc/doc_ch/FAQ.md
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@ -682,7 +682,7 @@ lr:
#### Q: 关于dygraph分支中，文本识别模型训练，要使用数据增强应该如何设置？
**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4.由于tia数据增强特殊性，默认不采用，可以通过添加use_tia设置，使tia数据增强生效。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
**A**：可以参考[配置文件](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)在Train['dataset']['transforms']添加RecAug字段，使数据增强生效。可以通过添加对aug_prob设置，表示每种数据增强采用的概率。aug_prob默认是0.4。详细设置可以参考[ISSUE 1744](https://github.com/PaddlePaddle/PaddleOCR/issues/1744)。
#### Q: 训练过程中，训练程序意外退出/挂起，应该如何解决？

2
ppocr/data/imaug/__init__.py
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@ -22,7 +22,7 @@ from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, RandomCropImgMask
from .make_pse_gt import MakePseGt
from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
from .rec_img_aug import BaseDataAugmentation, RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
from .ssl_img_aug import SSLRotateResize
from .randaugment import RandAugment

160
ppocr/data/imaug/rec_img_aug.py
View File

@ -22,13 +22,74 @@ from .text_image_aug import tia_perspective, tia_stretch, tia_distort
class RecAug(object):
def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
self.use_tia = use_tia
self.aug_prob = aug_prob
def __init__(self,
tia_prob=True,
crop_prob=0.4,
reverse_prob=0.4,
noise_prob=0.4,
jitter_prob=0.4,
blur_prob=0.4,
hsv_aug_prob=0.4,
**kwargs):
self.tia_prob = tia_prob
self.bda = BaseDataAugmentation(crop_prob, reverse_prob, noise_prob,
jitter_prob, blur_prob, hsv_aug_prob)
def __call__(self, data):
img = data['image']
img = warp(img, 10, self.use_tia, self.aug_prob)
h, w, _ = img.shape
# tia
if random.random() <= self.tia_prob:
if h >= 20 and w >= 20:
img = tia_distort(img, random.randint(3, 6))
img = tia_stretch(img, random.randint(3, 6))
img = tia_perspective(img)
# bda
data['image'] = img
data = self.bda(data)
return data
class BaseDataAugmentation(object):
def __init__(self,
crop_prob=0.4,
reverse_prob=0.4,
noise_prob=0.4,
jitter_prob=0.4,
blur_prob=0.4,
hsv_aug_prob=0.4,
**kwargs):
self.crop_prob = crop_prob
self.reverse_prob = reverse_prob
self.noise_prob = noise_prob
self.jitter_prob = jitter_prob
self.blur_prob = blur_prob
self.hsv_aug_prob = hsv_aug_prob
def __call__(self, data):
img = data['image']
h, w, _ = img.shape
if random.random() <= self.crop_prob and h >= 20 and w >= 20:
img = get_crop(img)
if random.random() <= self.blur_prob:
img = blur(img)
if random.random() <= self.hsv_aug_prob:
img = hsv_aug(img)
if random.random() <= self.jitter_prob:
img = jitter(img)
if random.random() <= self.noise_prob:
img = add_gasuss_noise(img)
if random.random() <= self.reverse_prob:
img = 255 - img
data['image'] = img
return data
@ -359,7 +420,7 @@ def flag():
return 1 if random.random() > 0.5000001 else -1
def cvtColor(img):
def hsv_aug(img):
"""
cvtColor
"""
@ -427,50 +488,6 @@ def get_crop(image):
return crop_img
class Config:
"""
Config
"""
def __init__(self, use_tia):
self.anglex = random.random() * 30
self.angley = random.random() * 15
self.anglez = random.random() * 10
self.fov = 42
self.r = 0
self.shearx = random.random() * 0.3
self.sheary = random.random() * 0.05
self.borderMode = cv2.BORDER_REPLICATE
self.use_tia = use_tia
def make(self, w, h, ang):
"""
make
"""
self.anglex = random.random() * 5 * flag()
self.angley = random.random() * 5 * flag()
self.anglez = -1 * random.random() * int(ang) * flag()
self.fov = 42
self.r = 0
self.shearx = 0
self.sheary = 0
self.borderMode = cv2.BORDER_REPLICATE
self.w = w
self.h = h
self.perspective = self.use_tia
self.stretch = self.use_tia
self.distort = self.use_tia
self.crop = True
self.affine = False
self.reverse = True
self.noise = True
self.jitter = True
self.blur = True
self.color = True
def rad(x):
"""
rad
@ -554,48 +571,3 @@ def get_warpAffine(config):
rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
return rz
def warp(img, ang, use_tia=True, prob=0.4):
"""
warp
"""
h, w, _ = img.shape
config = Config(use_tia=use_tia)
config.make(w, h, ang)
new_img = img
if config.distort:
img_height, img_width = img.shape[0:2]
if random.random() <= prob and img_height >= 20 and img_width >= 20:
new_img = tia_distort(new_img, random.randint(3, 6))
if config.stretch:
img_height, img_width = img.shape[0:2]
if random.random() <= prob and img_height >= 20 and img_width >= 20:
new_img = tia_stretch(new_img, random.randint(3, 6))
if config.perspective:
if random.random() <= prob:
new_img = tia_perspective(new_img)
if config.crop:
img_height, img_width = img.shape[0:2]
if random.random() <= prob and img_height >= 20 and img_width >= 20:
new_img = get_crop(new_img)
if config.blur:
if random.random() <= prob:
new_img = blur(new_img)
if config.color:
if random.random() <= prob:
new_img = cvtColor(new_img)
if config.jitter:
new_img = jitter(new_img)
if config.noise:
if random.random() <= prob:
new_img = add_gasuss_noise(new_img)
if config.reverse:
if random.random() <= prob:
new_img = 255 - new_img
return new_img