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添加DBnet代码

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# nv的卡上可以直接运行
```
conda create --name dbnet python=3.6.9
conda activate dbnet
conda init bash
conda activate dbnet
pip install -r requirement.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple/
pip install natsort addict
bash multi_gpu_train.sh
```
# Real-time Scene Text Detection with Differentiable Binarization
**note**: some code is inherited from [MhLiao/DB](https://github.com/MhLiao/DB)
[中文解读](https://zhuanlan.zhihu.com/p/94677957)
![network](imgs/paper/db.jpg)
## update
2020-06-07: 添加灰度图训练，训练灰度图时需要在配置里移除`dataset.args.transforms.Normalize`
## Install Using Conda
```
conda env create -f environment.yml
git clone https://github.com/WenmuZhou/DBNet.pytorch.git
cd DBNet.pytorch/
```
or
## Install Manually
```bash
conda create -n dbnet python=3.6
conda activate dbnet
conda install ipython pip
# python dependencies
pip install -r requirement.txt
# install PyTorch with cuda-10.1
# Note that you can change the cudatoolkit version to the version you want.
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
# clone repo
git clone https://github.com/WenmuZhou/DBNet.pytorch.git
cd DBNet.pytorch/
```
## Requirements
* pytorch 1.4+
* torchvision 0.5+
* gcc 4.9+
## Download
TBD
## Data Preparation
Training data: prepare a text `train.txt` in the following format, use '\t' as a separator
```
./datasets/train/img/001.jpg ./datasets/train/gt/001.txt
```
Validation data: prepare a text `test.txt` in the following format, use '\t' as a separator
```
./datasets/test/img/001.jpg ./datasets/test/gt/001.txt
```
- Store images in the `img` folder
- Store groundtruth in the `gt` folder
The groundtruth can be `.txt` files, with the following format:
```
x1, y1, x2, y2, x3, y3, x4, y4, annotation
```
## Train
1. config the `dataset['train']['dataset'['data_path']'`,`dataset['validate']['dataset'['data_path']`in [config/icdar2015_resnet18_fpn_DBhead_polyLR.yaml](cconfig/icdar2015_resnet18_fpn_DBhead_polyLR.yaml)
* . single gpu train
```bash
bash singlel_gpu_train.sh
```
* . Multi-gpu training
```bash
bash multi_gpu_train.sh
```
## Test
[eval.py](tools/eval.py) is used to test model on test dataset
1. config `model_path` in [eval.sh](eval.sh)
2. use following script to test
```bash
bash eval.sh
```
## Predict
[predict.py](tools/predict.py) Can be used to inference on all images in a folder
1. config `model_path`,`input_folder`,`output_folder` in [predict.sh](predict.sh)
2. use following script to predict
```
bash predict.sh
```
You can change the `model_path` in the `predict.sh` file to your model location.
tips: if result is not good, you can change `thre` in [predict.sh](predict.sh)
The project is still under development.
<h2 id="Performance">Performance</h2>
### [ICDAR 2015](http://rrc.cvc.uab.es/?ch=4)
only train on ICDAR2015 dataset
| Method | image size (short size) |learning rate | Precision (%) | Recall (%) | F-measure (%) | FPS |
|:--------------------------:|:-------:|:--------:|:--------:|:------------:|:---------------:|:-----:|
| SynthText-Defrom-ResNet-18(paper) | 736 |0.007 | 86.8 | 78.4 | 82.3 | 48 |
| ImageNet-resnet18-FPN-DBHead |736 |1e-3| 87.03 | 75.06 | 80.6 | 43 |
| ImageNet-Defrom-Resnet18-FPN-DBHead |736 |1e-3| 88.61 | 73.84 | 80.56 | 36 |
| ImageNet-resnet50-FPN-DBHead |736 |1e-3| 88.06 | 77.14 | 82.24 | 27 |
| ImageNet-resnest50-FPN-DBHead |736 |1e-3| 88.18 | 76.27 | 81.78 | 27 |
### examples
TBD
### todo
- [x] mutil gpu training
### reference
1. https://arxiv.org/pdf/1911.08947.pdf
2. https://github.com/WenmuZhou/PANet.pytorch
3. https://github.com/MhLiao/DB
**If this repository helps you，please star it. Thanks.**
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README.md 0 → 100644
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# DBnet
## 模型介绍
DBNet的是将二值化操作插入到分割网络中进行联合优化，这样网络可以自适应的预测图像中每一个像素点的阈值（区别去传统方法的固定阈值），从而可完全区分前景和背景的像素。
二值化阈值由网络学习得到，彻底将二值化这一步骤加入到网络里一起训练，这样最终的输出图对于阈值就会具有非常强的鲁棒性，在简化了后处理的同时提高了文本检测的效果。
## 模型结构
DBNet 模型网络结构主要分为 3 个模块：
- 第一模块（1）：使用的是一个 FPN 结构，分为自底向上的卷积操作与自顶向下的上采样，以此来获取多尺度的特征。1 图下面部分是 3x3 的卷积操作，按照卷积公式分别获取原图大小比例的 `1/2、1/4、1/8、1/16、1/32` 的特征图；然后自顶向下进行上采样 x2，然后与自底向上生成的相同大小的特征图融合；融合之后再采用 3x3 的卷积消除上采样的混叠效应；最后对每层输出结果进行上采样，统一为 1/4 大小的特征图。
- 第二模块（2）：将 1/4 大小的特征图经过一系列卷积和转置卷积的机构获取概率图 **P** 和阈值图 **T**，可参考 FCN 网络结构，目的是生成与原图一样大小的特征图 P 和 T。
- 第三模块（3）：将特征图 P 和 T 经过 DB 方法（后续介绍）得到近似二值图。
## 数据集
在本测试中可以使用icdar2015数据集。
## DBnet训练
### 环境配置
提供[光源](https://www.sourcefind.cn/#/service-details)拉取的训练的docker镜像：
* 训练镜像：docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-22.10.1-py37-latest
* pip install -r requirements.txt
### 训练
将训练数据解压到datasets目录下。
训练命令：
./run.sh
## 性能和准确率数据
测试数据使用的是icdar2015数据集，使用的加速卡是DCU Z100L。
| 卡数 | 性能 | 精度 |
| :--: | :--------------: | :-----------------------------------: |
| 1 | 22.9 samples/sec | recall: 0.767070, precision: 0.894410 |
### 参考
https://github.com/WenmuZhou/DBNet.pytorch
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from .base_trainer import BaseTrainer
from .base_dataset import BaseDataSet
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base/base_dataset.py 0 → 100644
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# -*- coding: utf-8 -*-
# @Time : 2019/12/4 13:12
# @Author : zhoujun
import copy
from torch.utils.data import Dataset
from data_loader.modules import *
class BaseDataSet(Dataset):
def __init__(self, data_path: str, img_mode, pre_processes, filter_keys, ignore_tags, transform=None,
target_transform=None):
assert img_mode in ['RGB', 'BRG', 'GRAY']
self.ignore_tags = ignore_tags
self.data_list = self.load_data(data_path)
item_keys = ['img_path', 'img_name', 'text_polys', 'texts', 'ignore_tags']
for item in item_keys:
assert item in self.data_list[0], 'data_list from load_data must contains {}'.format(item_keys)
self.img_mode = img_mode
self.filter_keys = filter_keys
self.transform = transform
self.target_transform = target_transform
self._init_pre_processes(pre_processes)
def _init_pre_processes(self, pre_processes):
self.aug = []
if pre_processes is not None:
for aug in pre_processes:
if 'args' not in aug:
args = {}
else:
args = aug['args']
if isinstance(args, dict):
cls = eval(aug['type'])(**args)
else:
cls = eval(aug['type'])(args)
self.aug.append(cls)
def load_data(self, data_path: str) -> list:
"""
把数据加载为一个list：
:params data_path: 存储数据的文件夹或者文件
return a dict ,包含了，'img_path','img_name','text_polys','texts','ignore_tags'
"""
raise NotImplementedError
def apply_pre_processes(self, data):
for aug in self.aug:
data = aug(data)
return data
def __getitem__(self, index):
try:
data = copy.deepcopy(self.data_list[index])
im = cv2.imread(data['img_path'], 1 if self.img_mode != 'GRAY' else 0)
if self.img_mode == 'RGB':
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
data['img'] = im
data['shape'] = [im.shape[0], im.shape[1]]
data = self.apply_pre_processes(data)
if self.transform:
data['img'] = self.transform(data['img'])
data['text_polys'] = data['text_polys'].tolist()
if len(self.filter_keys):
data_dict = {}
for k, v in data.items():
if k not in self.filter_keys:
data_dict[k] = v
return data_dict
else:
return data
except:
return self.__getitem__(np.random.randint(self.__len__()))
def __len__(self):
return len(self.data_list)
base/base_trainer.py 0 → 100644
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# -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:50
# @Author : zhoujun
import os
import pathlib
import shutil
from pprint import pformat
import anyconfig
import torch
from utils import setup_logger
class BaseTrainer:
def __init__(self, config, model, criterion):
config['trainer']['output_dir'] = os.path.join(str(pathlib.Path(os.path.abspath(__name__)).parent),
config['trainer']['output_dir'])
config['name'] = config['name'] + '_' + model.name
self.save_dir = os.path.join(config['trainer']['output_dir'], config['name'])
self.checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
if config['trainer']['resume_checkpoint'] == '' and config['trainer']['finetune_checkpoint'] == '':
shutil.rmtree(self.save_dir, ignore_errors=True)
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
self.global_step = 0
self.start_epoch = 0
self.config = config
self.model = model
self.criterion = criterion
# logger and tensorboard
self.tensorboard_enable = self.config['trainer']['tensorboard']
self.epochs = self.config['trainer']['epochs']
self.log_iter = self.config['trainer']['log_iter']
if config['local_rank'] == 0:
anyconfig.dump(config, os.path.join(self.save_dir, 'config.yaml'))
self.logger = setup_logger(os.path.join(self.save_dir, 'train.log'))
self.logger_info(pformat(self.config))
# device
torch.manual_seed(self.config['trainer']['seed']) # 为CPU设置随机种子
if torch.cuda.device_count() > 0 and torch.cuda.is_available():
self.with_cuda = True
torch.backends.cudnn.benchmark = True
self.device = torch.device("cuda")
torch.cuda.manual_seed(self.config['trainer']['seed']) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(self.config['trainer']['seed']) # 为所有GPU设置随机种子
else:
self.with_cuda = False
self.device = torch.device("cpu")
self.logger_info('train with device {} and pytorch {}'.format(self.device, torch.__version__))
# metrics
self.metrics = {'recall': 0, 'precision': 0, 'hmean': 0, 'train_loss': float('inf'),'best_model_epoch':0}
self.optimizer = self._initialize('optimizer', torch.optim, model.parameters())
# resume or finetune
if self.config['trainer']['resume_checkpoint'] != '':
self._load_checkpoint(self.config['trainer']['resume_checkpoint'], resume=True)
elif self.config['trainer']['finetune_checkpoint'] != '':
self._load_checkpoint(self.config['trainer']['finetune_checkpoint'], resume=False)
if self.config['lr_scheduler']['type'] != 'WarmupPolyLR':
self.scheduler = self._initialize('lr_scheduler', torch.optim.lr_scheduler, self.optimizer)
self.model.to(self.device)
if self.tensorboard_enable and config['local_rank'] == 0:
from torch.utils.tensorboard import SummaryWriter
self.writer = SummaryWriter(self.save_dir)
try:
# add graph
in_channels = 3 if config['dataset']['train']['dataset']['args']['img_mode'] != 'GRAY' else 1
dummy_input = torch.zeros(1, in_channels, 640, 640).to(self.device)
self.writer.add_graph(self.model, dummy_input)
torch.cuda.empty_cache()
except:
import traceback
self.logger.error(traceback.format_exc())
self.logger.warn('add graph to tensorboard failed')
# 分布式训练
if torch.cuda.device_count() > 1:
local_rank = config['local_rank']
self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[local_rank], output_device=local_rank, broadcast_buffers=False,
find_unused_parameters=True)
# make inverse Normalize
self.UN_Normalize = False
for t in self.config['dataset']['train']['dataset']['args']['transforms']:
if t['type'] == 'Normalize':
self.normalize_mean = t['args']['mean']
self.normalize_std = t['args']['std']
self.UN_Normalize = True
def train(self):
"""
Full training logic
"""
for epoch in range(self.start_epoch + 1, self.epochs + 1):
if self.config['distributed']:
self.train_loader.sampler.set_epoch(epoch)
self.epoch_result = self._train_epoch(epoch)
if self.config['lr_scheduler']['type'] != 'WarmupPolyLR':
self.scheduler.step()
self._on_epoch_finish()
if self.config['local_rank'] == 0 and self.tensorboard_enable:
self.writer.close()
self._on_train_finish()
def _train_epoch(self, epoch):
"""
Training logic for an epoch
:param epoch: Current epoch number
"""
raise NotImplementedError
def _eval(self, epoch):
"""
eval logic for an epoch
:param epoch: Current epoch number
"""
raise NotImplementedError
def _on_epoch_finish(self):
raise NotImplementedError
def _on_train_finish(self):
raise NotImplementedError
def _save_checkpoint(self, epoch, file_name):
"""
Saving checkpoints
:param epoch: current epoch number
:param log: logging information of the epoch
:param save_best: if True, rename the saved checkpoint to 'model_best.pth.tar'
"""
state_dict = self.model.module.state_dict() if self.config['distributed'] else self.model.state_dict()
state = {
'epoch': epoch,
'global_step': self.global_step,
'state_dict': state_dict,
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
'config': self.config,
'metrics': self.metrics
}
filename = os.path.join(self.checkpoint_dir, file_name)
torch.save(state, filename)
def _load_checkpoint(self, checkpoint_path, resume):
"""
Resume from saved checkpoints
:param checkpoint_path: Checkpoint path to be resumed
"""
self.logger_info("Loading checkpoint: {} ...".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
self.model.load_state_dict(checkpoint['state_dict'], strict=resume)
if resume:
self.global_step = checkpoint['global_step']
self.start_epoch = checkpoint['epoch']
self.config['lr_scheduler']['args']['last_epoch'] = self.start_epoch
# self.scheduler.load_state_dict(checkpoint['scheduler'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
if 'metrics' in checkpoint:
self.metrics = checkpoint['metrics']
if self.with_cuda:
for state in self.optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(self.device)
self.logger_info("resume from checkpoint {} (epoch {})".format(checkpoint_path, self.start_epoch))
else:
self.logger_info("finetune from checkpoint {}".format(checkpoint_path))
def _initialize(self, name, module, *args, **kwargs):
module_name = self.config[name]['type']
module_args = self.config[name]['args']
assert all([k not in module_args for k in kwargs]), 'Overwriting kwargs given in config file is not allowed'
module_args.update(kwargs)
return getattr(module, module_name)(*args, **module_args)
def inverse_normalize(self, batch_img):
if self.UN_Normalize:
batch_img[:, 0, :, :] = batch_img[:, 0, :, :] * self.normalize_std[0] + self.normalize_mean[0]
batch_img[:, 1, :, :] = batch_img[:, 1, :, :] * self.normalize_std[1] + self.normalize_mean[1]
batch_img[:, 2, :, :] = batch_img[:, 2, :, :] * self.normalize_std[2] + self.normalize_mean[2]
def logger_info(self, s):
if self.config['local_rank'] == 0:
self.logger.info(s)
config/.ipynb_checkpoints/icdar2015-checkpoint.yaml 0 → 100644
View file @ 97243508
name: DBNet
dataset:
train:
dataset:
type: ICDAR2015Dataset # 数据集类型
args:
data_path: # 一个存放 img_path \t gt_path的文件
- ''
pre_processes: # 数据的预处理过程，包含augment和标签制作
- type: IaaAugment # 使用imgaug进行变换
args:
- {'type':Fliplr, 'args':{'p':0.5}}
- {'type': Affine, 'args':{'rotate':[-10,10]}}
- {'type':Resize,'args':{'size':[0.5,3]}}
- type: EastRandomCropData
args:
size: [640,640]
max_tries: 50
keep_ratio: true
- type: MakeBorderMap
args:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- type: MakeShrinkMap
args:
shrink_ratio: 0.4
min_text_size: 8
transforms: # 对图片进行的变换方式
- type: ToTensor
args: {}
- type: Normalize
args:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
img_mode: RGB
filter_keys: [img_path,img_name,text_polys,texts,ignore_tags,shape] # 返回数据之前，从数据字典里删除的key
ignore_tags: ['*', '###']
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 0
collate_fn: ''
validate:
dataset:
type: ICDAR2015Dataset
args:
data_path:
- ''
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
transforms:
- type: ToTensor
args: {}
- type: Normalize
args:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
img_mode: RGB
filter_keys: []
ignore_tags: ['*', '###']
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 0
collate_fn: ICDARCollectFN
\ No newline at end of file
config/.ipynb_checkpoints/icdar2015_dcn_resnet18_FPN_DBhead_polyLR-checkpoint.yaml 0 → 100644
View file @ 97243508
name: DBNet
base: ['config/icdar2015.yaml']
arch:
type: Model
backbone:
type: deformable_resnet18
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
amsgrad: true
lr_scheduler:
type: WarmupPolyLR
args:
warmup_epoch: 3
trainer:
seed: 2
epochs: 50
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output
tensorboard: true
dataset:
train:
dataset:
args:
data_path:
- ./datasets/train.txt
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: true
num_workers: 6
collate_fn: ''
validate:
dataset:
args:
data_path:
- ./datasets/test.txt
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 6
collate_fn: ICDARCollectFN
\ No newline at end of file
config/SynthText.yaml 0 → 100644
View file @ 97243508
name: DBNet
dataset:
train:
dataset:
type: SynthTextDataset # 数据集类型
args:
data_path: ''# SynthTextDataset 根目录
pre_processes: # 数据的预处理过程，包含augment和标签制作
- type: IaaAugment # 使用imgaug进行变换
args:
- {'type':Fliplr, 'args':{'p':0.5}}
- {'type': Affine, 'args':{'rotate':[-10,10]}}
- {'type':Resize,'args':{'size':[0.5,3]}}
- type: EastRandomCropData
args:
size: [640,640]
max_tries: 50
keep_ratio: true
- type: MakeBorderMap
args:
shrink_ratio: 0.4
- type: MakeShrinkMap
args:
shrink_ratio: 0.4
min_text_size: 8
transforms: # 对图片进行的变换方式
- type: ToTensor
args: {}
- type: Normalize
args:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
img_mode: RGB
filter_keys: ['img_path','img_name','text_polys','texts','ignore_tags','shape'] # 返回数据之前，从数据字典里删除的key
ignore_tags: ['*', '###']
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 0
collate_fn: ''
\ No newline at end of file
config/SynthText_resnet18_FPN_DBhead_polyLR.yaml 0 → 100644
View file @ 97243508
name: DBNet
base: ['config/SynthText.yaml']
arch:
type: Model
backbone:
type: resnet18
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
amsgrad: true
lr_scheduler:
type: WarmupPolyLR
args:
warmup_epoch: 3
trainer:
seed: 2
epochs: 1200
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output
tensorboard: true
dataset:
train:
dataset:
args:
data_path: ./datasets/SynthText
img_mode: RGB
loader:
batch_size: 2
shuffle: true
pin_memory: true
num_workers: 6
collate_fn: ''
\ No newline at end of file
config/icdar2015.yaml 0 → 100644
View file @ 97243508
name: DBNet
dataset:
train:
dataset:
type: ICDAR2015Dataset # 数据集类型
args:
data_path: # 一个存放 img_path \t gt_path的文件
- ''
pre_processes: # 数据的预处理过程，包含augment和标签制作
- type: IaaAugment # 使用imgaug进行变换
args:
- {'type':Fliplr, 'args':{'p':0.5}}
- {'type': Affine, 'args':{'rotate':[-10,10]}}
- {'type':Resize,'args':{'size':[0.5,3]}}
- type: EastRandomCropData
args:
size: [640,640]
max_tries: 50
keep_ratio: true
- type: MakeBorderMap
args:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- type: MakeShrinkMap
args:
shrink_ratio: 0.4
min_text_size: 8
transforms: # 对图片进行的变换方式
- type: ToTensor
args: {}
- type: Normalize
args:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
img_mode: RGB
filter_keys: [img_path,img_name,text_polys,texts,ignore_tags,shape] # 返回数据之前，从数据字典里删除的key
ignore_tags: ['*', '###']
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 0
collate_fn: ''
validate:
dataset:
type: ICDAR2015Dataset
args:
data_path:
- ''
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
transforms:
- type: ToTensor
args: {}
- type: Normalize
args:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
img_mode: RGB
filter_keys: []
ignore_tags: ['*', '###']
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 0
collate_fn: ICDARCollectFN
\ No newline at end of file
config/icdar2015_dcn_resnet18_FPN_DBhead_polyLR.yaml 0 → 100644
View file @ 97243508
name: DBNet
base: ['config/icdar2015.yaml']
arch:
type: Model
backbone:
type: deformable_resnet18
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
amsgrad: true
lr_scheduler:
type: WarmupPolyLR
args:
warmup_epoch: 3
trainer:
seed: 2
epochs: 50
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output
tensorboard: true
dataset:
train:
dataset:
args:
data_path:
- ./datasets/train.txt
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: true
num_workers: 6
collate_fn: ''
validate:
dataset:
args:
data_path:
- ./datasets/test.txt
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 6
collate_fn: ICDARCollectFN
\ No newline at end of file
config/icdar2015_resnet18_FPN_DBhead_polyLR.yaml 0 → 100644
View file @ 97243508
name: DBNet
base: ['config/icdar2015.yaml']
arch:
type: Model
backbone:
type: resnet18
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
amsgrad: true
lr_scheduler:
type: WarmupPolyLR
args:
warmup_epoch: 3
trainer:
seed: 2
epochs: 600
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output
tensorboard: true
dataset:
train:
dataset:
args:
data_path:
- ./datasets/train.txt
img_mode: RGB
loader:
batch_size: 32
shuffle: true
pin_memory: true
num_workers: 8
collate_fn: ''
validate:
dataset:
args:
data_path:
- ./datasets/test.txt
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 8
collate_fn: ICDARCollectFN
config/icdar2015_resnet18_FPN_DBhead_polyLR_finetune.yaml 0 → 100644
View file @ 97243508
name: DBNet
base: ['config/icdar2015.yaml']
arch:
type: Model
backbone:
type: resnet18
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
args:
lr: 0.001
weight_decay: 0
amsgrad: true
lr_scheduler:
type: StepLR
args:
step_size: 10
gama: 0.8
trainer:
seed: 2
epochs: 500
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output
tensorboard: true
dataset:
train:
dataset:
args:
data_path:
- ./datasets/train.txt
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: true
num_workers: 6
collate_fn: ''
validate:
dataset:
args:
data_path:
- ./datasets/test.txt
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
img_mode: RGB
loader:
batch_size: 1
shuffle: true
pin_memory: false
num_workers: 6
collate_fn: ICDARCollectFN
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