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PaddleSeg

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未验证 提交 7126bb37 编写于 作者: L LutaoChu 提交者： GitHub
浏览文件
操作

add HRNet, polish README, add precision and recall metrics (#256) 

* modify label tool

* fix label tools bug

* add RemoteSensing

* optimize data clip and normalize method for remote sensing

* fix label problem using mobile devices

* add README.md

* polish README.md, optimize main.py and transforms.py, add transforms.md

* add demo dataset

* add create_dataset_list tool, requirements.txt

* add channel-by-channel normalize and clip, remove RemoteSensing import

* update vdl to 2.0

* polish README

* add tool to randomly split dataset and generate file list

* open eval_best_metric while traing, fix typo

* remove python2.7 ci

* add hrnet, polish README

* add metrics for unet like precision,recall,etc

* save more attrs in model.yaml, fix typo

* optimize predict_demo.py

* fix hrnet dice loss bug

* polish remote sensing README

* refactor model base class

* modify train_demo.py

* Update README.md

* Update requirements.txt
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contrib/HumanSeg/models/humanseg.py
浏览文件 @ 7126bb37
......@@ -102,7 +102,7 @@ class SegModel(object):
# 当前模型状态
self.status = 'Normal'
def _get_single_car_bs(self, batch_size):
def _get_single_card_bs(self, batch_size):
if batch_size % len(self.places) == 0:
return int(batch_size // len(self.places))
else:
......@@ -144,7 +144,7 @@ class SegModel(object):
capacity=64,
use_double_buffer=True,
iterable=True)
batch_size_each_gpu = self._get_single_car_bs(batch_size)
batch_size_each_gpu = self._get_single_card_bs(batch_size)
self.train_data_loader.set_sample_list_generator(
dataset.generator(batch_size=batch_size_each_gpu),
places=self.places)
......
contrib/HumanSeg/models/load_model.py
浏览文件 @ 7126bb37
......@@ -24,7 +24,7 @@ import models
def load_model(model_dir):
if not osp.exists(osp.join(model_dir, "model.yml")):
raise Exception("There's not model.yml in {}".format(model_dir))
raise Exception("There's no model.yml in {}".format(model_dir))
with open(osp.join(model_dir, "model.yml")) as f:
info = yaml.load(f.read(), Loader=yaml.Loader)
status = info['status']
......
contrib/README.md
浏览文件 @ 7126bb37
......@@ -3,6 +3,7 @@
提供基于PaddlSeg最新的分割特色模型:
- [人像分割](./HumanSeg)
- [遥感分割](./RemoteSensing)
- [人体解析](./ACE2P)
- [车道线分割](./LaneNet)
- [工业表盘分割](#工业表盘分割)
......@@ -12,6 +13,14 @@
HumanSeg系列全新升级，提供三个适用于不同场景，包含适用于移动端实时分割场景的模型`HumanSeg-lite`，提供了包含光流的后处理的优化，使人像分割在视频场景中更加顺畅，更多详情请参考[HumanSeg](./HumanSeg)
## 遥感分割 Remote Sensing Segmentation
PaddleSeg遥感影像分割涵盖图像预处理、数据增强、模型训练、预测流程。
针对遥感数据多通道、分布范围大、分布不均的特点，我们支持多通道训练预测，内置10+多通道预处理和数据增强的策略，可结合实际业务场景进行定制组合，提升模型泛化能力和鲁棒性。
内置U-Net, HRNet两种主流分割网络，可选择不同的损失函数如Dice Loss, BCE Loss等方式强化小目标和不均衡样本场景下的分割精度。更多详情请参考[RemoteSensing](./RemoteSensing)
以下是遥感云检测的示例效果：
![](./RemoteSensing/docs/imgs/rs.png)
## 人体解析 Human Parsing
......
contrib/RemoteSensing/README.md
浏览文件 @ 7126bb37
# 遥感分割（RemoteSensing）
# PaddleSeg遥感影像分割
遥感影像分割是图像分割领域中的重要应用场景，广泛应用于土地测绘、环境监测、城市建设等领域。遥感影像分割的目标多种多样，有诸如积雪、农作物、道路、建筑、水源等地物目标，也有例如云层的空中目标。
PaddleSeg提供了针对遥感专题的语义分割库RemoteSensing，涵盖图像预处理、数据增强、模型训练、预测流程，帮助用户利用深度学习技术解决遥感影像分割问题。
PaddleSeg遥感影像分割涵盖图像预处理、数据增强、模型训练、预测流程，帮助用户利用深度学习技术解决遥感影像分割问题。
## 特点
针对遥感数据多通道、分布范围大、分布不均的特点，我们支持多通道训练预测，内置一系列多通道预处理和数据增强的策略，可结合实际业务场景进行定制组合，提升模型泛化能力和鲁棒性。
- 针对遥感数据多通道、分布范围大、分布不均的特点，我们支持多通道训练预测，内置10+多通道预处理和数据增强的策略，可结合实际业务场景进行定制组合，提升模型泛化能力和鲁棒性。
**Note:** 所有命令需要在`PaddleSeg/contrib/RemoteSensing/`目录下执行。
- 内置U-Net, HRNet两种主流分割网络，可选择不同的损失函数如Dice Loss, BCE Loss等方式强化小目标和不均衡样本场景下的分割精度。
以下是遥感云检测的示例效果：
![](./docs/imgs/rs.png)
## 前置依赖
**Note:** 若没有特殊说明，以下所有命令需要在`PaddleSeg/contrib/RemoteSensing/`目录下执行。
- Paddle 1.7.1+
由于图像分割模型计算开销大，推荐在GPU版本的PaddlePaddle下使用。
PaddlePaddle的安装, 请按照[官网指引](https://paddlepaddle.org.cn/install/quick)安装合适自己的版本。
......@@ -18,7 +24,6 @@ PaddlePaddle的安装, 请按照[官网指引](https://paddlepaddle.org.cn/insta
- 其他依赖安装
通过以下命令安装python包依赖，请确保至少执行过一次以下命令：
```
cd RemoteSensing
pip install -r requirements.txt
```
......@@ -63,9 +68,9 @@ RemoteSensing # 根目录
```
其中，相应的文件名可根据需要自行定义。
遥感领域图像格式多种多样，不同传感器产生的数据格式可能不同。为方便数据加载，本分割库统一采用numpy存储格式`npy`作为原图格式，采用`png`无损压缩格式作为标注图片格式。
原图的前两维是图像的尺寸，第3维是图像的通道数。
标注图像为单通道图像，像素值即为对应的类别,像素标注类别需要从0开始递增，
遥感影像的格式多种多样，不同传感器产生的数据格式也可能不同。PaddleSeg以numpy.ndarray数据类型进行图像预处理。为统一接口并方便数据加载，我们采用numpy存储格式`npy`作为原图格式，采用`png`无损压缩格式作为标注图片格式。
原图的尺寸应为(h, w, channel)，其中h, w为图像的高和宽，channel为图像的通道数。
标注图像为单通道图像，像素值即为对应的类别,像素标注类别需要从0开始递增。
例如0，1，2，3表示有4种类别，标注类别最多为256类。其中可以指定特定的像素值用于表示该值的像素不参与训练和评估（默认为255）。
`train_list.txt`和`val_list.txt`文本以空格为分割符分为两列，第一列为图像文件相对于dataset的相对路径，第二列为标注图像文件相对于dataset的相对路径。如下所示：
......@@ -93,154 +98,38 @@ labelB
### 1. 准备数据集
为了快速体验，我们准备了一个小型demo数据集，已位于`RemoteSensing/dataset/demo/`目录下.
对于您自己的数据集，您需要按照上述的数据协议进行格式转换，可分别使用numpy和pil库保存遥感数据和标注图片。其中numpy api示例如下：
对于您自己的数据集，您需要按照上述的数据协议进行格式转换，可分别使用numpy和Pillow库保存遥感数据和标注图片。其中numpy API示例如下：
```python
import numpy as np
# 保存遥感数据
# 将遥感数据保存到以 .npy 为扩展名的文件中
# img类型：numpy.ndarray
np.save(save_path, img)
```
### 2. 训练代码开发
通过如下`train_demo.py`代码进行训练。
> 导入RemoteSensing api
```python
import transforms.transforms as T
from readers.reader import Reader
from models import UNet
```
> 定义训练和验证时的数据处理和增强流程, 在`train_transforms`中加入了`RandomVerticalFlip`,`RandomHorizontalFlip`等数据增强方式。
```python
train_transforms = T.Compose([
T.RandomVerticalFlip(0.5),
T.RandomHorizontalFlip(0.5),
T.ResizeStepScaling(0.5, 2.0, 0.25),
T.RandomPaddingCrop(256),
T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
])
eval_transforms = T.Compose([
T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
])
```
> 定义数据读取器
```python
import os
import os.path as osp
train_list = osp.join(data_dir, 'train.txt')
val_list = osp.join(data_dir, 'val.txt')
label_list = osp.join(data_dir, 'labels.txt')
train_reader = Reader(
data_dir=data_dir,
file_list=train_list,
label_list=label_list,
transforms=train_transforms,
num_workers=8,
buffer_size=16,
shuffle=True,
parallel_method='thread')
eval_reader = Reader(
data_dir=data_dir,
file_list=val_list,
label_list=label_list,
transforms=eval_transforms,
num_workers=8,
buffer_size=16,
shuffle=False,
parallel_method='thread')
```
> 模型构建
```python
model = UNet(
num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
```
> 模型训练，并开启边训边评估
```python
model.train(
num_epochs=num_epochs,
train_reader=train_reader,
train_batch_size=train_batch_size,
eval_reader=eval_reader,
save_interval_epochs=5,
log_interval_steps=10,
save_dir=save_dir,
pretrain_weights=None,
optimizer=None,
learning_rate=lr,
use_vdl=True
)
```
### 3. 模型训练
> 设置GPU卡号
### 2. 模型训练
#### （1） 设置GPU卡号
```shell script
export CUDA_VISIBLE_DEVICES=0
```
> 在RemoteSensing目录下运行`train_demo.py`即可开始训练。
#### （2） 以U-Net为例，在RemoteSensing目录下运行`train_demo.py`即可开始训练。
```shell script
python train_demo.py --data_dir dataset/demo/ --save_dir saved_model/unet/ --channel 3 --num_epochs 20
```
### 4. 模型预测代码开发
通过如下`predict_demo.py`代码进行预测。
> 导入RemoteSensing api
```python
from models import load_model
```
> 加载训练过程中最好的模型，设置预测结果保存路径。
```python
import os
import os.path as osp
model = load_model(osp.join(save_dir, 'best_model'))
pred_dir = osp.join(save_dir, 'pred')
if not osp.exists(pred_dir):
os.mkdir(pred_dir)
```
> 使用模型对验证集进行测试，并保存预测结果。
```python
import numpy as np
from PIL import Image as Image
val_list = osp.join(data_dir, 'val.txt')
color_map = [0, 0, 0, 255, 255, 255]
with open(val_list) as f:
lines = f.readlines()
for line in lines:
img_path = line.split(' ')[0]
print('Predicting {}'.format(img_path))
img_path_ = osp.join(data_dir, img_path)
pred = model.predict(img_path_)
# 以伪彩色png图片保存预测结果
pred_name = osp.basename(img_path).rstrip('npy') + 'png'
pred_path = osp.join(pred_dir, pred_name)
pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
pred_mask.putpalette(color_map)
pred_mask.save(pred_path)
python train_demo.py --model_type unet --data_dir dataset/demo/ --save_dir saved_model/unet/ --channel 3 --num_epochs 20
```
### 5. 模型预测
> 设置GPU卡号
### 3. 模型预测
#### （1） 设置GPU卡号
```shell script
export CUDA_VISIBLE_DEVICES=0
```
> 在RemoteSensing目录下运行`predict_demo.py`即可开始训练。
#### （2） 以刚训练好的U-Net最优模型为例，在RemoteSensing目录下运行`predict_demo.py`即可开始训练。
```shell script
python predict_demo.py --data_dir dataset/demo/ --load_model_dir saved_model/unet/best_model/
python predict_demo.py --data_dir dataset/demo/ --file_list val.txt --load_model_dir saved_model/unet/best_model
```
## Api说明
## API说明
您可以使用`RemoteSensing`目录下提供的api构建自己的分割代码。
您可以使用`RemoteSensing`目录下提供的API构建自己的分割代码。
- [数据处理-transforms](docs/transforms.md)
contrib/RemoteSensing/models/__init__.py
浏览文件 @ 7126bb37
from .load_model import *
from .unet import *
from .hrnet import *
contrib/RemoteSensing/models/base.py
浏览文件 @ 7126bb37
......@@ -19,15 +19,16 @@ import numpy as np
import time
import math
import yaml
import tqdm
import cv2
import copy
import json
import utils.logging as logging
from collections import OrderedDict
from os import path as osp
from utils.pretrain_weights import get_pretrain_weights
from utils.utils import seconds_to_hms, get_environ_info
from utils.metrics import ConfusionMatrix
import transforms.transforms as T
import utils
import __init__
def dict2str(dict_input):
......@@ -41,12 +42,45 @@ def dict2str(dict_input):
return out.strip(', ')
class BaseAPI:
def __init__(self):
# 现有的CV模型都有这个属性，而这个属且也需要在eval时用到
self.num_classes = None
class BaseModel(object):
def __init__(self,
num_classes=2,
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255,
sync_bn=True):
self.init_params = locals()
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
"dice loss and bce loss is only applicable to binary classfication"
)
if class_weight is not None:
if isinstance(class_weight, list):
if len(class_weight) != num_classes:
raise ValueError(
"Length of class_weight should be equal to number of classes"
)
elif isinstance(class_weight, str):
if class_weight.lower() != 'dynamic':
raise ValueError(
"if class_weight is string, must be dynamic!")
else:
raise TypeError(
'Expect class_weight is a list or string but receive {}'.
format(type(class_weight)))
self.num_classes = num_classes
self.use_bce_loss = use_bce_loss
self.use_dice_loss = use_dice_loss
self.class_weight = class_weight
self.ignore_index = ignore_index
self.sync_bn = sync_bn
self.labels = None
if __init__.env_info['place'] == 'cpu':
self.env_info = get_environ_info()
if self.env_info['place'] == 'cpu':
self.places = fluid.cpu_places()
else:
self.places = fluid.cuda_places()
......@@ -60,10 +94,6 @@ class BaseAPI:
self.test_outputs = None
self.train_data_loader = None
self.eval_metrics = None
# 若模型是从inference model加载进来的，无法调用训练接口进行训练
self.trainable = True
# 是否使用多卡间同步BatchNorm均值和方差
self.sync_bn = False
# 当前模型状态
self.status = 'Normal'
......@@ -73,16 +103,20 @@ class BaseAPI:
else:
raise Exception("Please support correct batch_size, \
which can be divided by available cards({}) in {}".
format(__init__.env_info['num'],
__init__.env_info['place']))
format(self.env_info['num'],
self.env_info['place']))
def build_net(self, mode='train'):
"""应根据不同的情况进行构建"""
pass
def build_program(self):
# 构建训练网络
# build training network
self.train_inputs, self.train_outputs = self.build_net(mode='train')
self.train_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
# 构建预测网络
# build prediction network
self.test_prog = fluid.Program()
with fluid.program_guard(self.test_prog, startup_prog):
with fluid.unique_name.guard():
......@@ -90,15 +124,15 @@ class BaseAPI:
mode='test')
self.test_prog = self.test_prog.clone(for_test=True)
def arrange_transforms(self, transforms, mode='train'):
# 给transforms添加arrange操作
if transforms.transforms[-1].__class__.__name__.startswith('Arrange'):
transforms.transforms[-1] = T.ArrangeSegmenter(mode=mode)
def arrange_transform(self, transforms, mode='train'):
arrange_transform = T.ArrangeSegmenter
if type(transforms.transforms[-1]).__name__.startswith('Arrange'):
transforms.transforms[-1] = arrange_transform(mode=mode)
else:
transforms.transforms.append(T.ArrangeSegmenter(mode=mode))
transforms.transforms.append(arrange_transform(mode=mode))
def build_train_data_loader(self, reader, batch_size):
# 初始化data_loader
def build_train_data_loader(self, dataset, batch_size):
# init data_loader
if self.train_data_loader is None:
self.train_data_loader = fluid.io.DataLoader.from_generator(
feed_list=list(self.train_inputs.values()),
......@@ -106,72 +140,92 @@ class BaseAPI:
use_double_buffer=True,
iterable=True)
batch_size_each_gpu = self._get_single_card_bs(batch_size)
generator = reader.generator(
batch_size=batch_size_each_gpu, drop_last=True)
self.train_data_loader.set_sample_list_generator(
reader.generator(batch_size=batch_size_each_gpu),
dataset.generator(batch_size=batch_size_each_gpu),
places=self.places)
def net_initialize(self,
startup_prog=None,
pretrain_weights=None,
fuse_bn=False,
save_dir='.',
sensitivities_file=None,
eval_metric_loss=0.05):
if hasattr(self, 'backbone'):
backbone = self.backbone
else:
backbone = self.__class__.__name__
pretrain_weights = get_pretrain_weights(pretrain_weights, backbone,
save_dir)
resume_weights=None):
if startup_prog is None:
startup_prog = fluid.default_startup_program()
self.exe.run(startup_prog)
if pretrain_weights is not None:
if resume_weights is not None:
logging.info("Resume weights from {}".format(resume_weights))
if not osp.exists(resume_weights):
raise Exception("Path {} not exists.".format(resume_weights))
fluid.load(self.train_prog, osp.join(resume_weights, 'model'),
self.exe)
# Check is path ended by path spearator
if resume_weights[-1] == os.sep:
resume_weights = resume_weights[0:-1]
epoch_name = osp.basename(resume_weights)
# If resume weights is end of digit, restore epoch status
epoch = epoch_name.split('_')[-1]
if epoch.isdigit():
self.begin_epoch = int(epoch)
else:
raise ValueError("Resume model path is not valid!")
logging.info("Model checkpoint loaded successfully!")
elif pretrain_weights is not None:
logging.info(
"Load pretrain weights from {}.".format(pretrain_weights))
utils.utils.load_pretrain_weights(self.exe, self.train_prog,
pretrain_weights, fuse_bn)
# 进行裁剪
if sensitivities_file is not None:
from .slim.prune_config import get_sensitivities
sensitivities_file = get_sensitivities(sensitivities_file, self,
save_dir)
from .slim.prune import get_params_ratios, prune_program
prune_params_ratios = get_params_ratios(
sensitivities_file, eval_metric_loss=eval_metric_loss)
prune_program(self, prune_params_ratios)
self.status = 'Prune'
utils.load_pretrained_weights(self.exe, self.train_prog,
pretrain_weights)
def get_model_info(self):
# 存储相应的信息到yml文件
info = dict()
info['Model'] = self.__class__.__name__
info['_Attributes'] = {}
if 'self' in self.init_params:
del self.init_params['self']
if '__class__' in self.init_params:
del self.init_params['__class__']
info['_init_params'] = self.init_params
info['_Attributes'] = dict()
info['_Attributes']['num_classes'] = self.num_classes
info['_Attributes']['labels'] = self.labels
try:
primary_metric_key = list(self.eval_metrics.keys())[0]
primary_metric_value = float(self.eval_metrics[primary_metric_key])
info['_Attributes']['eval_metrics'] = {
primary_metric_key: primary_metric_value
}
info['_Attributes']['eval_metric'] = dict()
for k, v in self.eval_metrics.items():
if isinstance(v, np.ndarray):
if v.size > 1:
v = [float(i) for i in v]
else:
v = float(v)
info['_Attributes']['eval_metric'][k] = v
except:
pass
if hasattr(self, 'test_transforms'):
if self.test_transforms is not None:
info['Transforms'] = list()
info['test_transforms'] = list()
for op in self.test_transforms.transforms:
name = op.__class__.__name__
attr = op.__dict__
info['Transforms'].append({name: attr})
info['test_transforms'].append({name: attr})
if hasattr(self, 'train_transforms'):
if self.train_transforms is not None:
info['train_transforms'] = list()
for op in self.train_transforms.transforms:
name = op.__class__.__name__
attr = op.__dict__
info['train_transforms'].append({name: attr})
if hasattr(self, 'train_init'):
if 'self' in self.train_init:
del self.train_init['self']
if 'train_reader' in self.train_init:
del self.train_init['train_reader']
if 'eval_reader' in self.train_init:
del self.train_init['eval_reader']
if 'optimizer' in self.train_init:
del self.train_init['optimizer']
info['train_init'] = self.train_init
return info
def save_model(self, save_dir):
......@@ -179,76 +233,139 @@ class BaseAPI:
if osp.exists(save_dir):
os.remove(save_dir)
os.makedirs(save_dir)
fluid.save(self.train_prog, osp.join(save_dir, 'model'))
model_info = self.get_model_info()
if self.status == 'Normal':
fluid.save(self.train_prog, osp.join(save_dir, 'model'))
model_info['status'] = self.status
with open(
osp.join(save_dir, 'model.yml'), encoding='utf-8',
mode='w') as f:
yaml.dump(model_info, f)
# 评估结果保存
if hasattr(self, 'eval_details'):
with open(osp.join(save_dir, 'eval_details.json'), 'w') as f:
json.dump(self.eval_details, f)
if self.status == 'Prune':
# 保存裁剪的shape
shapes = {}
for block in self.train_prog.blocks:
for param in block.all_parameters():
pd_var = fluid.global_scope().find_var(param.name)
pd_param = pd_var.get_tensor()
shapes[param.name] = np.array(pd_param).shape
with open(
osp.join(save_dir, 'prune.yml'), encoding='utf-8',
mode='w') as f:
yaml.dump(shapes, f)
# 模型保存成功的标志
# The flag of model for saving successfully
open(osp.join(save_dir, '.success'), 'w').close()
logging.info("Model saved in {}.".format(save_dir))
def train_loop(self,
num_epochs,
train_reader,
train_batch_size,
eval_reader=None,
eval_best_metric=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
use_vdl=False):
def export_inference_model(self, save_dir):
test_input_names = [var.name for var in list(self.test_inputs.values())]
test_outputs = list(self.test_outputs.values())
fluid.io.save_inference_model(
dirname=save_dir,
executor=self.exe,
params_filename='__params__',
feeded_var_names=test_input_names,
target_vars=test_outputs,
main_program=self.test_prog)
model_info = self.get_model_info()
model_info['status'] = 'Infer'
# Save input and output descrition of model
model_info['_ModelInputsOutputs'] = dict()
model_info['_ModelInputsOutputs']['test_inputs'] = [
[k, v.name] for k, v in self.test_inputs.items()
]
model_info['_ModelInputsOutputs']['test_outputs'] = [
[k, v.name] for k, v in self.test_outputs.items()
]
with open(
osp.join(save_dir, 'model.yml'), encoding='utf-8',
mode='w') as f:
yaml.dump(model_info, f)
# The flag of model for saving successfully
open(osp.join(save_dir, '.success'), 'w').close()
logging.info("Model for inference deploy saved in {}.".format(save_dir))
def default_optimizer(self,
learning_rate,
num_epochs,
num_steps_each_epoch,
lr_decay_power=0.9,
regularization_coeff=4e-5):
decay_step = num_epochs * num_steps_each_epoch
lr_decay = fluid.layers.polynomial_decay(
learning_rate,
decay_step,
end_learning_rate=0,
power=lr_decay_power)
optimizer = fluid.optimizer.Momentum(
lr_decay,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(
regularization_coeff=regularization_coeff))
return optimizer
def train(self,
num_epochs,
train_reader,
train_batch_size=2,
eval_reader=None,
eval_best_metric=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights=None,
resume_weights=None,
optimizer=None,
learning_rate=0.01,
lr_decay_power=0.9,
regularization_coeff=4e-5,
use_vdl=False):
self.labels = train_reader.labels
self.train_transforms = train_reader.transforms
self.train_init = locals()
self.begin_epoch = 0
if optimizer is None:
num_steps_each_epoch = train_reader.num_samples // train_batch_size
optimizer = self.default_optimizer(
learning_rate=learning_rate,
num_epochs=num_epochs,
num_steps_each_epoch=num_steps_each_epoch,
lr_decay_power=lr_decay_power,
regularization_coeff=regularization_coeff)
self.optimizer = optimizer
self.build_program()
self.net_initialize(
startup_prog=fluid.default_startup_program(),
pretrain_weights=pretrain_weights,
resume_weights=resume_weights)
if self.begin_epoch >= num_epochs:
raise ValueError(
("begin epoch[{}] is larger than num_epochs[{}]").format(
self.begin_epoch, num_epochs))
if not osp.isdir(save_dir):
if osp.exists(save_dir):
os.remove(save_dir)
os.makedirs(save_dir)
if use_vdl:
from visualdl import LogWriter
vdl_logdir = osp.join(save_dir, 'vdl_log')
# 给transform添加arrange操作
self.arrange_transforms(
transforms=train_reader.transforms, mode='train')
# 构建train_data_loader
# add arrange op tor transforms
self.arrange_transform(transforms=train_reader.transforms, mode='train')
self.build_train_data_loader(
reader=train_reader, batch_size=train_batch_size)
dataset=train_reader, batch_size=train_batch_size)
if eval_reader is not None:
self.eval_transforms = eval_reader.transforms
self.test_transforms = copy.deepcopy(eval_reader.transforms)
# 获取实时变化的learning rate
lr = self.optimizer._learning_rate
lr.persistable = True
if isinstance(lr, fluid.framework.Variable):
self.train_outputs['lr'] = lr
# 在多卡上跑训练
# 多卡训练
if self.parallel_train_prog is None:
build_strategy = fluid.compiler.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
if __init__.env_info['place'] != 'cpu' and len(self.places) > 1:
if self.env_info['place'] != 'cpu' and len(self.places) > 1:
build_strategy.sync_batch_norm = self.sync_bn
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_iteration_per_drop_scope = 1
self.parallel_train_prog = fluid.CompiledProgram(
self.train_prog).with_data_parallel(
loss_name=self.train_outputs['loss'].name,
......@@ -259,16 +376,27 @@ class BaseAPI:
train_reader.num_samples / train_batch_size)
num_steps = 0
time_stat = list()
time_train_one_epoch = None
time_eval_one_epoch = None
total_num_steps_eval = 0
# eval times
total_eval_times = math.ceil(num_epochs / save_interval_epochs)
eval_batch_size = train_batch_size
if eval_reader is not None:
total_num_steps_eval = math.ceil(
eval_reader.num_samples / eval_batch_size)
if use_vdl:
# VisualDL component
from visualdl import LogWriter
vdl_logdir = osp.join(save_dir, 'vdl_log')
log_writer = LogWriter(vdl_logdir)
best_accuracy = -1.0
best_metric = -1.0
best_model_epoch = 1
for i in range(num_epochs):
for i in range(self.begin_epoch, num_epochs):
records = list()
step_start_time = time.time()
epoch_start_time = time.time()
for step, data in enumerate(self.train_data_loader()):
outputs = self.exe.run(
self.parallel_train_prog,
......@@ -277,22 +405,15 @@ class BaseAPI:
outputs_avg = np.mean(np.array(outputs), axis=1)
records.append(outputs_avg)
# 训练完成剩余时间预估
current_time = time.time()
step_cost_time = current_time - step_start_time
step_start_time = current_time
# time estimated to complete the training
currend_time = time.time()
step_cost_time = currend_time - step_start_time
step_start_time = currend_time
if len(time_stat) < 20:
time_stat.append(step_cost_time)
else:
time_stat[num_steps % 20] = step_cost_time
eta = ((num_epochs - i) * total_num_steps - step -
1) * np.mean(time_stat)
eta_h = math.floor(eta / 3600)
eta_m = math.floor((eta - eta_h * 3600) / 60)
eta_s = int(eta - eta_h * 3600 - eta_m * 60)
eta_str = "{}:{}:{}".format(eta_h, eta_m, eta_s)
# 每间隔log_interval_steps，输出loss信息
num_steps += 1
if num_steps % log_interval_steps == 0:
step_metrics = OrderedDict(
......@@ -301,38 +422,52 @@ class BaseAPI:
if use_vdl:
for k, v in step_metrics.items():
log_writer.add_scalar(
tag="Training: {}".format(k),
value=v,
step=num_steps)
step=num_steps,
tag='train/{}'.format(k),
value=v)
# 计算剩余时间
avg_step_time = np.mean(time_stat)
if time_train_one_epoch is not None:
eta = (num_epochs - i - 1) * time_train_one_epoch + (
total_num_steps - step - 1) * avg_step_time
else:
eta = ((num_epochs - i) * total_num_steps - step -
1) * avg_step_time
if time_eval_one_epoch is not None:
eval_eta = (total_eval_times - i // save_interval_epochs
) * time_eval_one_epoch
else:
eval_eta = (total_eval_times - i // save_interval_epochs
) * total_num_steps_eval * avg_step_time
eta_str = seconds_to_hms(eta + eval_eta)
logging.info(
"[TRAIN] Epoch={}/{}, Step={}/{}, {}, eta={}".format(
i + 1, num_epochs, step + 1, total_num_steps,
dict2str(step_metrics), eta_str))
"[TRAIN] Epoch={}/{}, Step={}/{}, {}, time_each_step={}s, eta={}"
.format(i + 1, num_epochs, step + 1, total_num_steps,
dict2str(step_metrics), round(avg_step_time, 2),
eta_str))
train_metrics = OrderedDict(
zip(list(self.train_outputs.keys()), np.mean(records, axis=0)))
logging.info('[TRAIN] Epoch {} finished, {} .'.format(
i + 1, dict2str(train_metrics)))
time_train_one_epoch = time.time() - epoch_start_time
# 每间隔save_interval_epochs, 在验证集上评估和对模型进行保存
eval_epoch_start_time = time.time()
if (i + 1) % save_interval_epochs == 0 or i == num_epochs - 1:
current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1))
if not osp.isdir(current_save_dir):
os.makedirs(current_save_dir)
if eval_reader is not None:
# 检测目前仅支持单卡评估，训练数据batch大小与显卡数量之商为验证数据batch大小。
eval_batch_size = train_batch_size
self.eval_metrics, self.eval_details = self.evaluate(
self.eval_metrics = self.evaluate(
eval_reader=eval_reader,
batch_size=eval_batch_size,
verbose=True,
epoch_id=i + 1,
return_details=True)
logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
i + 1, dict2str(self.eval_metrics)))
epoch_id=i + 1)
# 保存最优模型
current_metric = self.eval_metrics[eval_best_metric]
if current_metric > best_accuracy:
best_accuracy = current_metric
if current_metric > best_metric:
best_metric = current_metric
best_model_epoch = i + 1
best_model_dir = osp.join(save_dir, "best_model")
self.save_model(save_dir=best_model_dir)
......@@ -344,10 +479,131 @@ class BaseAPI:
if v.size > 1:
continue
log_writer.add_scalar(
tag="Evaluation: {}".format(k),
step=i + 1,
step=num_steps,
tag='evaluate/{}'.format(k),
value=v)
self.save_model(save_dir=current_save_dir)
logging.info(
'Current evaluated best model in eval_reader is epoch_{}, {}={}'
.format(best_model_epoch, eval_best_metric, best_accuracy))
time_eval_one_epoch = time.time() - eval_epoch_start_time
if eval_reader is not None:
logging.info(
'Current evaluated best model in validation dataset is epoch_{}, {}={}'
.format(best_model_epoch, eval_best_metric,
best_metric))
def evaluate(self, eval_reader, batch_size=1, epoch_id=None):
"""评估。
Args:
eval_reader (reader): 评估数据读取器。
batch_size (int): 评估时的batch大小。默认1。
epoch_id (int): 当前评估模型所在的训练轮数。
return_details (bool): 是否返回详细信息。默认False。
Returns:
dict: 当return_details为False时，返回dict。包含关键字：'miou'、'category_iou'、'macc'、
'category_acc'和'kappa'，分别表示平均iou、各类别iou、平均准确率、各类别准确率和kappa系数。
tuple (metrics, eval_details)：当return_details为True时，增加返回dict (eval_details)，
包含关键字：'confusion_matrix'，表示评估的混淆矩阵。
"""
self.arrange_transform(transforms=eval_reader.transforms, mode='train')
total_steps = math.ceil(eval_reader.num_samples * 1.0 / batch_size)
conf_mat = ConfusionMatrix(self.num_classes, streaming=True)
data_generator = eval_reader.generator(
batch_size=batch_size, drop_last=False)
if not hasattr(self, 'parallel_test_prog'):
self.parallel_test_prog = fluid.CompiledProgram(
self.test_prog).with_data_parallel(
share_vars_from=self.parallel_train_prog)
logging.info(
"Start to evaluating(total_samples={}, total_steps={})...".format(
eval_reader.num_samples, total_steps))
for step, data in tqdm.tqdm(
enumerate(data_generator()), total=total_steps):
images = np.array([d[0] for d in data])
images = images.astype(np.float32)
labels = np.array([d[1] for d in data])
num_samples = images.shape[0]
if num_samples < batch_size:
num_pad_samples = batch_size - num_samples
pad_images = np.tile(images[0:1], (num_pad_samples, 1, 1, 1))
images = np.concatenate([images, pad_images])
feed_data = {'image': images}
outputs = self.exe.run(
self.parallel_test_prog,
feed=feed_data,
fetch_list=list(self.test_outputs.values()),
return_numpy=True)
pred = outputs[0]
if num_samples < batch_size:
pred = pred[0:num_samples]
mask = labels != self.ignore_index
conf_mat.calculate(pred=pred, label=labels, ignore=mask)
_, iou = conf_mat.mean_iou()
logging.debug("[EVAL] Epoch={}, Step={}/{}, iou={}".format(
epoch_id, step + 1, total_steps, iou))
category_iou, miou = conf_mat.mean_iou()
category_acc, macc = conf_mat.accuracy()
precision, recall = conf_mat.precision_recall()
metrics = OrderedDict(
zip([
'miou', 'category_iou', 'macc', 'category_acc', 'kappa',
'precision', 'recall'
], [
miou, category_iou, macc, category_acc,
conf_mat.kappa(), precision, recall
]))
logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
epoch_id, dict2str(metrics)))
return metrics
def predict(self, im_file, transforms=None):
"""预测。
Args:
img_file(str|np.ndarray): 预测图像。
transforms(transforms.transforms): 数据预处理操作。
Returns:
dict: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，
像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)
"""
if isinstance(im_file, str):
if not osp.exists(im_file):
raise ValueError(
'The Image file does not exist: {}'.format(im_file))
if transforms is None and not hasattr(self, 'test_transforms'):
raise Exception("transforms need to be defined, now is None.")
if transforms is not None:
self.arrange_transform(transforms=transforms, mode='test')
im, im_info = transforms(im_file)
else:
self.arrange_transform(transforms=self.test_transforms, mode='test')
im, im_info = self.test_transforms(im_file)
im = im.astype(np.float32)
im = np.expand_dims(im, axis=0)
result = self.exe.run(
self.test_prog,
feed={'image': im},
fetch_list=list(self.test_outputs.values()))
pred = result[0]
logit = result[1]
logit = np.squeeze(logit)
logit = np.transpose(logit, (1, 2, 0))
pred = np.squeeze(pred).astype('uint8')
keys = list(im_info.keys())
for k in keys[::-1]:
if k == 'shape_before_resize':
h, w = im_info[k][0], im_info[k][1]
pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
logit = cv2.resize(logit, (w, h), cv2.INTER_LINEAR)
elif k == 'shape_before_padding':
h, w = im_info[k][0], im_info[k][1]
pred = pred[0:h, 0:w]
logit = logit[0:h, 0:w, :]
return {'label_map': pred, 'score_map': logit}
contrib/RemoteSensing/models/hrnet.py 0 → 100644
浏览文件 @ 7126bb37
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
import paddle.fluid as fluid
import os
from os import path as osp
import numpy as np
from collections import OrderedDict
import copy
import math
import time
import tqdm
import cv2
import yaml
import utils
import utils.logging as logging
from utils.utils import seconds_to_hms, get_environ_info
from utils.metrics import ConfusionMatrix
import nets
import transforms.transforms as T
from .base import BaseModel
def dict2str(dict_input):
out = ''
for k, v in dict_input.items():
try:
v = round(float(v), 6)
except:
pass
out = out + '{}={}, '.format(k, v)
return out.strip(', ')
class HRNet(BaseModel):
def __init__(self,
num_classes=2,
input_channel=3,
stage1_num_modules=1,
stage1_num_blocks=[4],
stage1_num_channels=[64],
stage2_num_modules=1,
stage2_num_blocks=[4, 4],
stage2_num_channels=[18, 36],
stage3_num_modules=4,
stage3_num_blocks=[4, 4, 4],
stage3_num_channels=[18, 36, 72],
stage4_num_modules=3,
stage4_num_blocks=[4, 4, 4, 4],
stage4_num_channels=[18, 36, 72, 144],
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255,
sync_bn=True):
super().__init__(
num_classes=num_classes,
use_bce_loss=use_bce_loss,
use_dice_loss=use_dice_loss,
class_weight=class_weight,
ignore_index=ignore_index,
sync_bn=sync_bn)
self.init_params = locals()
self.input_channel = input_channel
self.stage1_num_modules = stage1_num_modules
self.stage1_num_blocks = stage1_num_blocks
self.stage1_num_channels = stage1_num_channels
self.stage2_num_modules = stage2_num_modules
self.stage2_num_blocks = stage2_num_blocks
self.stage2_num_channels = stage2_num_channels
self.stage3_num_modules = stage3_num_modules
self.stage3_num_blocks = stage3_num_blocks
self.stage3_num_channels = stage3_num_channels
self.stage4_num_modules = stage4_num_modules
self.stage4_num_blocks = stage4_num_blocks
self.stage4_num_channels = stage4_num_channels
def build_net(self, mode='train'):
"""应根据不同的情况进行构建"""
model = nets.HRNet(
self.num_classes,
self.input_channel,
mode=mode,
stage1_num_modules=self.stage1_num_modules,
stage1_num_blocks=self.stage1_num_blocks,
stage1_num_channels=self.stage1_num_channels,
stage2_num_modules=self.stage2_num_modules,
stage2_num_blocks=self.stage2_num_blocks,
stage2_num_channels=self.stage2_num_channels,
stage3_num_modules=self.stage3_num_modules,
stage3_num_blocks=self.stage3_num_blocks,
stage3_num_channels=self.stage3_num_channels,
stage4_num_modules=self.stage4_num_modules,
stage4_num_blocks=self.stage4_num_blocks,
stage4_num_channels=self.stage4_num_channels,
use_bce_loss=self.use_bce_loss,
use_dice_loss=self.use_dice_loss,
class_weight=self.class_weight,
ignore_index=self.ignore_index)
inputs = model.generate_inputs()
model_out = model.build_net(inputs)
outputs = OrderedDict()
if mode == 'train':
self.optimizer.minimize(model_out)
outputs['loss'] = model_out
else:
outputs['pred'] = model_out[0]
outputs['logit'] = model_out[1]
return inputs, outputs
def train(self,
num_epochs,
train_reader,
train_batch_size=2,
eval_reader=None,
eval_best_metric='kappa',
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights=None,
resume_weights=None,
optimizer=None,
learning_rate=0.01,
lr_decay_power=0.9,
regularization_coeff=5e-4,
use_vdl=False):
super().train(
num_epochs=num_epochs,
train_reader=train_reader,
train_batch_size=train_batch_size,
eval_reader=eval_reader,
eval_best_metric=eval_best_metric,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
pretrain_weights=pretrain_weights,
resume_weights=resume_weights,
optimizer=optimizer,
learning_rate=learning_rate,
lr_decay_power=lr_decay_power,
regularization_coeff=regularization_coeff,
use_vdl=use_vdl)
contrib/RemoteSensing/models/load_model.py
浏览文件 @ 7126bb37
......@@ -25,7 +25,7 @@ import models
def load_model(model_dir):
if not osp.exists(osp.join(model_dir, "model.yml")):
raise Exception("There's not model.yml in {}".format(model_dir))
raise Exception("There's no model.yml in {}".format(model_dir))
with open(osp.join(model_dir, "model.yml")) as f:
info = yaml.load(f.read(), Loader=yaml.Loader)
status = info['status']
......@@ -35,8 +35,7 @@ def load_model(model_dir):
info['Model']))
model = getattr(models, info['Model'])(**info['_init_params'])
if status == "Normal" or \
status == "Prune":
if status == "Normal":
startup_prog = fluid.Program()
model.test_prog = fluid.Program()
with fluid.program_guard(model.test_prog, startup_prog):
......@@ -45,17 +44,12 @@ def load_model(model_dir):
mode='test')
model.test_prog = model.test_prog.clone(for_test=True)
model.exe.run(startup_prog)
if status == "Prune":
from .slim.prune import update_program
model.test_prog = update_program(model.test_prog, model_dir,
model.places[0])
import pickle
with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
load_dict = pickle.load(f)
fluid.io.set_program_state(model.test_prog, load_dict)
elif status == "Infer" or \
status == "Quant":
elif status == "Infer":
[prog, input_names, outputs] = fluid.io.load_inference_model(
model_dir, model.exe, params_filename='__params__')
model.test_prog = prog
......@@ -67,8 +61,8 @@ def load_model(model_dir):
for i, out in enumerate(outputs):
var_desc = test_outputs_info[i]
model.test_outputs[var_desc[0]] = out
if 'Transforms' in info:
model.test_transforms = build_transforms(info['Transforms'])
if 'test_transforms' in info:
model.test_transforms = build_transforms(info['test_transforms'])
model.eval_transforms = copy.deepcopy(model.test_transforms)
if '_Attributes' in info:
......
contrib/RemoteSensing/models/unet.py
浏览文件 @ 7126bb37
......@@ -13,19 +13,18 @@
#limitations under the License.
from __future__ import absolute_import
import os.path as osp
import numpy as np
import math
import cv2
import paddle.fluid as fluid
import utils.logging as logging
from collections import OrderedDict
from .base import BaseAPI
from .base import BaseModel
from utils.metrics import ConfusionMatrix
import nets
class UNet(BaseAPI):
class UNet(BaseModel):
"""实现UNet网络的构建并进行训练、评估、预测和模型导出。
Args:
......@@ -55,9 +54,16 @@ class UNet(BaseAPI):
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
ignore_index=255,
sync_bn=True):
super().__init__(
num_classes=num_classes,
use_bce_loss=use_bce_loss,
use_dice_loss=use_dice_loss,
class_weight=class_weight,
ignore_index=ignore_index,
sync_bn=sync_bn)
self.init_params = locals()
super(UNet, self).__init__()
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
......@@ -115,24 +121,6 @@ class UNet(BaseAPI):
outputs['logit'] = model_out[1]
return inputs, outputs
def default_optimizer(self,
learning_rate,
num_epochs,
num_steps_each_epoch,
lr_decay_power=0.9):
decay_step = num_epochs * num_steps_each_epoch
lr_decay = fluid.layers.polynomial_decay(
learning_rate,
decay_step,
end_learning_rate=0,
power=lr_decay_power)
optimizer = fluid.optimizer.Momentum(
lr_decay,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(
regularization_coeff=4e-05))
return optimizer
def train(self,
num_epochs,
train_reader,
......@@ -142,13 +130,13 @@ class UNet(BaseAPI):
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights='COCO',
pretrain_weights=None,
resume_weights=None,
optimizer=None,
learning_rate=0.01,
lr_decay_power=0.9,
use_vdl=False,
sensitivities_file=None,
eval_metric_loss=0.05):
regularization_coeff=5e-4,
use_vdl=False):
"""训练。
Args:
......@@ -160,46 +148,17 @@ class UNet(BaseAPI):
save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。
log_interval_steps (int): 训练日志输出间隔（单位：迭代次数）。默认为2。
save_dir (str): 模型保存路径。默认'output'。
pretrain_weights (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'COCO'，
则自动下载在COCO图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认为'COCO'。
pretrain_weights (str): 若指定为路径时，则加载路径下预训练模型；若为None，则不使用预训练模型。
optimizer (paddle.fluid.optimizer): 优化器。当改参数为None时，使用默认的优化器：使用
fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。
learning_rate (float): 默认优化器的初始学习率。默认0.01。
lr_decay_power (float): 默认优化器学习率多项式衰减系数。默认0.9。
use_vdl (bool): 是否使用VisualDL进行可视化。默认False。
sensitivities_file (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，
则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。
eval_metric_loss (float): 可容忍的精度损失。默认为0.05。
Raises:
ValueError: 模型从inference model进行加载。
"""
if not self.trainable:
raise ValueError(
"Model is not trainable since it was loaded from a inference model."
)
self.labels = train_reader.labels
if optimizer is None:
num_steps_each_epoch = train_reader.num_samples // train_batch_size
optimizer = self.default_optimizer(
learning_rate=learning_rate,
num_epochs=num_epochs,
num_steps_each_epoch=num_steps_each_epoch,
lr_decay_power=lr_decay_power)
self.optimizer = optimizer
# 构建训练、验证、预测网络
self.build_program()
# 初始化网络权重
self.net_initialize(
startup_prog=fluid.default_startup_program(),
pretrain_weights=pretrain_weights,
save_dir=save_dir,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
# 训练
self.train_loop(
super().train(
num_epochs=num_epochs,
train_reader=train_reader,
train_batch_size=train_batch_size,
......@@ -208,6 +167,12 @@ class UNet(BaseAPI):
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
pretrain_weights=pretrain_weights,
resume_weights=resume_weights,
optimizer=optimizer,
learning_rate=learning_rate,
lr_decay_power=lr_decay_power,
regularization_coeff=regularization_coeff,
use_vdl=use_vdl)
def evaluate(self,
......@@ -231,7 +196,7 @@ class UNet(BaseAPI):
tuple (metrics, eval_details)：当return_details为True时，增加返回dict (eval_details)，
包含关键字：'confusion_matrix'，表示评估的混淆矩阵。
"""
self.arrange_transforms(transforms=eval_reader.transforms, mode='eval')
self.arrange_transform(transforms=eval_reader.transforms, mode='eval')
total_steps = math.ceil(eval_reader.num_samples * 1.0 / batch_size)
conf_mat = ConfusionMatrix(self.num_classes, streaming=True)
data_generator = eval_reader.generator(
......@@ -272,11 +237,16 @@ class UNet(BaseAPI):
category_iou, miou = conf_mat.mean_iou()
category_acc, macc = conf_mat.accuracy()
precision, recall = conf_mat.precision_recall()
metrics = OrderedDict(
zip(['miou', 'category_iou', 'macc', 'category_acc', 'kappa'],
[miou, category_iou, macc, category_acc,
conf_mat.kappa()]))
zip([
'miou', 'category_iou', 'macc', 'category_acc', 'kappa',
'precision', 'recall'
], [
miou, category_iou, macc, category_acc,
conf_mat.kappa(), precision, recall
]))
if return_details:
eval_details = {
'confusion_matrix': conf_mat.confusion_matrix.tolist()
......@@ -296,11 +266,10 @@ class UNet(BaseAPI):
if transforms is None and not hasattr(self, 'test_transforms'):
raise Exception("transforms need to be defined, now is None.")
if transforms is not None:
self.arrange_transforms(transforms=transforms, mode='test')
self.arrange_transform(transforms=transforms, mode='test')
im, im_info = transforms(im_file)
else:
self.arrange_transforms(
transforms=self.test_transforms, mode='test')
self.arrange_transform(transforms=self.test_transforms, mode='test')
im, im_info = self.test_transforms(im_file)
im = im.astype(np.float32)
im = np.expand_dims(im, axis=0)
......@@ -319,4 +288,4 @@ class UNet(BaseAPI):
h, w = im_info[k][0], im_info[k][1]
pred = pred[0:h, 0:w]
return pred
return {'label_map': pred}
contrib/RemoteSensing/nets/__init__.py
浏览文件 @ 7126bb37
from .unet import UNet
from .hrnet import HRNet
contrib/RemoteSensing/nets/hrnet.py 0 → 100644
浏览文件 @ 7126bb37
# coding: utf8
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import OrderedDict
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
from .loss import softmax_with_loss
from .loss import dice_loss
from .loss import bce_loss
from .libs import sigmoid_to_softmax
class HRNet(object):
def __init__(self,
num_classes,
input_channel=3,
mode='train',
stage1_num_modules=1,
stage1_num_blocks=[4],
stage1_num_channels=[64],
stage2_num_modules=1,
stage2_num_blocks=[4, 4],
stage2_num_channels=[18, 36],
stage3_num_modules=4,
stage3_num_blocks=[4, 4, 4],
stage3_num_channels=[18, 36, 72],
stage4_num_modules=3,
stage4_num_blocks=[4, 4, 4, 4],
stage4_num_channels=[18, 36, 72, 144],
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
"dice loss and bce loss is only applicable to binary classfication"
)
if class_weight is not None:
if isinstance(class_weight, list):
if len(class_weight) != num_classes:
raise ValueError(
"Length of class_weight should be equal to number of classes"
)
elif isinstance(class_weight, str):
if class_weight.lower() != 'dynamic':
raise ValueError(
"if class_weight is string, must be dynamic!")
else:
raise TypeError(
'Expect class_weight is a list or string but receive {}'.
format(type(class_weight)))
self.num_classes = num_classes
self.input_channel = input_channel
self.mode = mode
self.use_bce_loss = use_bce_loss
self.use_dice_loss = use_dice_loss
self.class_weight = class_weight
self.ignore_index = ignore_index
self.stage1_num_modules = stage1_num_modules
self.stage1_num_blocks = stage1_num_blocks
self.stage1_num_channels = stage1_num_channels
self.stage2_num_modules = stage2_num_modules
self.stage2_num_blocks = stage2_num_blocks
self.stage2_num_channels = stage2_num_channels
self.stage3_num_modules = stage3_num_modules
self.stage3_num_blocks = stage3_num_blocks
self.stage3_num_channels = stage3_num_channels
self.stage4_num_modules = stage4_num_modules
self.stage4_num_blocks = stage4_num_blocks
self.stage4_num_channels = stage4_num_channels
def build_net(self, inputs):
if self.use_dice_loss or self.use_bce_loss:
self.num_classes = 1
image = inputs['image']
logit = self._high_resolution_net(image, self.num_classes)
if self.num_classes == 1:
out = sigmoid_to_softmax(logit)
out = fluid.layers.transpose(out, [0, 2, 3, 1])
else:
out = fluid.layers.transpose(logit, [0, 2, 3, 1])
pred = fluid.layers.argmax(out, axis=3)
pred = fluid.layers.unsqueeze(pred, axes=[3])
if self.mode == 'train':
label = inputs['label']
mask = label != self.ignore_index
return self._get_loss(logit, label, mask)
else:
if self.num_classes == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = fluid.layers.softmax(logit, axis=1)
return pred, logit
return logit
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32',
shape=[None, self.input_channel, None, None],
name='image')
if self.mode == 'train':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
elif self.mode == 'eval':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
return inputs
def _get_loss(self, logit, label, mask):
avg_loss = 0
if not (self.use_dice_loss or self.use_bce_loss):
avg_loss += softmax_with_loss(
logit,
label,
mask,
num_classes=self.num_classes,
weight=self.class_weight,
ignore_index=self.ignore_index)
else:
if self.use_dice_loss:
avg_loss += dice_loss(logit, label, mask)
if self.use_bce_loss:
avg_loss += bce_loss(
logit, label, mask, ignore_index=self.ignore_index)
return avg_loss
def _conv_bn_layer(self,
input,
filter_size,
num_filters,
stride=1,
padding=1,
num_groups=1,
if_act=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=num_groups,
act=None,
param_attr=ParamAttr(initializer=MSRA(), name=name + '_weights'),
bias_attr=False)
bn_name = name + '_bn'
bn = fluid.layers.batch_norm(
input=conv,
param_attr=ParamAttr(
name=bn_name + "_scale",
initializer=fluid.initializer.Constant(1.0)),
bias_attr=ParamAttr(
name=bn_name + "_offset",
initializer=fluid.initializer.Constant(0.0)),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
bn = fluid.layers.relu(bn)
return bn
def _basic_block(self,
input,
num_filters,
stride=1,
downsample=False,
name=None):
residual = input
conv = self._conv_bn_layer(
input=input,
filter_size=3,
num_filters=num_filters,
stride=stride,
name=name + '_conv1')
conv = self._conv_bn_layer(
input=conv,
filter_size=3,
num_filters=num_filters,
if_act=False,
name=name + '_conv2')
if downsample:
residual = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
if_act=False,
name=name + '_downsample')
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
def _bottleneck_block(self,
input,
num_filters,
stride=1,
downsample=False,
name=None):
residual = input
conv = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
name=name + '_conv1')
conv = self._conv_bn_layer(
input=conv,
filter_size=3,
num_filters=num_filters,
stride=stride,
name=name + '_conv2')
conv = self._conv_bn_layer(
input=conv,
filter_size=1,
num_filters=num_filters * 4,
if_act=False,
name=name + '_conv3')
if downsample:
residual = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters * 4,
if_act=False,
name=name + '_downsample')
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
def _fuse_layers(self, x, channels, multi_scale_output=True, name=None):
out = []
for i in range(len(channels) if multi_scale_output else 1):
residual = x[i]
shape = fluid.layers.shape(residual)[-2:]
for j in range(len(channels)):
if j > i:
y = self._conv_bn_layer(
x[j],
filter_size=1,
num_filters=channels[i],
if_act=False,
name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
y = fluid.layers.resize_bilinear(input=y, out_shape=shape)
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
elif j < i:
y = x[j]
for k in range(i - j):
if k == i - j - 1:
y = self._conv_bn_layer(
y,
filter_size=3,
num_filters=channels[i],
stride=2,
if_act=False,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1))
else:
y = self._conv_bn_layer(
y,
filter_size=3,
num_filters=channels[j],
stride=2,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1))
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
residual = fluid.layers.relu(residual)
out.append(residual)
return out
def _branches(self, x, block_num, channels, name=None):
out = []
for i in range(len(channels)):
residual = x[i]
for j in range(block_num[i]):
residual = self._basic_block(
residual,
channels[i],
name=name + '_branch_layer_' + str(i + 1) + '_' +
str(j + 1))
out.append(residual)
return out
def _high_resolution_module(self,
x,
blocks,
channels,
multi_scale_output=True,
name=None):
residual = self._branches(x, blocks, channels, name=name)
out = self._fuse_layers(
residual,
channels,
multi_scale_output=multi_scale_output,
name=name)
return out
def _transition_layer(self, x, in_channels, out_channels, name=None):
num_in = len(in_channels)
num_out = len(out_channels)
out = []
for i in range(num_out):
if i < num_in:
if in_channels[i] != out_channels[i]:
residual = self._conv_bn_layer(
x[i],
filter_size=3,
num_filters=out_channels[i],
name=name + '_layer_' + str(i + 1))
out.append(residual)
else:
out.append(x[i])
else:
residual = self._conv_bn_layer(
x[-1],
filter_size=3,
num_filters=out_channels[i],
stride=2,
name=name + '_layer_' + str(i + 1))
out.append(residual)
return out
def _stage(self,
x,
num_modules,
num_blocks,
num_channels,
multi_scale_output=True,
name=None):
out = x
for i in range(num_modules):
if i == num_modules - 1 and multi_scale_output == False:
out = self._high_resolution_module(
out,
num_blocks,
num_channels,
multi_scale_output=False,
name=name + '_' + str(i + 1))
else:
out = self._high_resolution_module(
out, num_blocks, num_channels, name=name + '_' + str(i + 1))
return out
def _layer1(self, input, num_modules, num_blocks, num_channels, name=None):
# num_modules 默认为1,是否增加处理，官网实现为[1]，是否对齐。
conv = input
for i in range(num_blocks[0]):
conv = self._bottleneck_block(
conv,
num_filters=num_channels[0],
downsample=True if i == 0 else False,
name=name + '_' + str(i + 1))
return conv
def _high_resolution_net(self, input, num_classes):
x = self._conv_bn_layer(
input=input,
filter_size=3,
num_filters=self.stage1_num_channels[0],
stride=2,
if_act=True,
name='layer1_1')
x = self._conv_bn_layer(
input=x,
filter_size=3,
num_filters=self.stage1_num_channels[0],
stride=2,
if_act=True,
name='layer1_2')
la1 = self._layer1(
x,
self.stage1_num_modules,
self.stage1_num_blocks,
self.stage1_num_channels,
name='layer2')
tr1 = self._transition_layer([la1],
self.stage1_num_channels,
self.stage2_num_channels,
name='tr1')
st2 = self._stage(
tr1,
self.stage2_num_modules,
self.stage2_num_blocks,
self.stage2_num_channels,
name='st2')
tr2 = self._transition_layer(
st2, self.stage2_num_channels, self.stage3_num_channels, name='tr2')
st3 = self._stage(
tr2,
self.stage3_num_modules,
self.stage3_num_blocks,
self.stage3_num_channels,
name='st3')
tr3 = self._transition_layer(
st3, self.stage3_num_channels, self.stage4_num_channels, name='tr3')
st4 = self._stage(
tr3,
self.stage4_num_modules,
self.stage4_num_blocks,
self.stage4_num_channels,
name='st4')
# upsample
shape = fluid.layers.shape(st4[0])[-2:]
st4[1] = fluid.layers.resize_bilinear(st4[1], out_shape=shape)
st4[2] = fluid.layers.resize_bilinear(st4[2], out_shape=shape)
st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=shape)
out = fluid.layers.concat(st4, axis=1)
last_channels = sum(self.stage4_num_channels)
out = self._conv_bn_layer(
input=out,
filter_size=1,
num_filters=last_channels,
stride=1,
if_act=True,
name='conv-2')
out = fluid.layers.conv2d(
input=out,
num_filters=num_classes,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(initializer=MSRA(), name='conv-1_weights'),
bias_attr=False)
input_shape = fluid.layers.shape(input)[-2:]
out = fluid.layers.resize_bilinear(out, input_shape)
return out
contrib/RemoteSensing/predict_demo.py
浏览文件 @ 7126bb37
import os
import os.path as osp
import sys
import numpy as np
from PIL import Image as Image
import argparse
......@@ -8,46 +9,81 @@ from models import load_model
def parse_args():
parser = argparse.ArgumentParser(description='RemoteSensing predict')
parser.add_argument(
'--single_img',
dest='single_img',
help='single image path to predict',
default=None,
type=str)
parser.add_argument(
'--data_dir',
dest='data_dir',
help='dataset directory',
default=None,
type=str)
parser.add_argument(
'--file_list',
dest='file_list',
help='file name of predict file list',
default=None,
type=str)
parser.add_argument(
'--load_model_dir',
dest='load_model_dir',
help='model load directory',
default=None,
type=str)
parser.add_argument(
'--save_img_dir',
dest='save_img_dir',
help='save directory name of predict results',
default='predict_results',
type=str)
if len(sys.argv) < 2:
parser.print_help()
sys.exit(1)
return parser.parse_args()
args = parse_args()
data_dir = args.data_dir
file_list = args.file_list
single_img = args.single_img
load_model_dir = args.load_model_dir
save_img_dir = args.save_img_dir
if not osp.exists(save_img_dir):
os.makedirs(save_img_dir)
# predict
model = load_model(load_model_dir)
pred_dir = osp.join(load_model_dir, 'predict')
if not osp.exists(pred_dir):
os.mkdir(pred_dir)
val_list = osp.join(data_dir, 'val.txt')
color_map = [0, 0, 0, 255, 255, 255]
with open(val_list) as f:
lines = f.readlines()
for line in lines:
img_path = line.split(' ')[0]
print('Predicting {}'.format(img_path))
img_path_ = osp.join(data_dir, img_path)
pred = model.predict(img_path_)
# 以伪彩色png图片保存预测结果
pred_name = osp.basename(img_path).rstrip('npy') + 'png'
pred_path = osp.join(pred_dir, pred_name)
pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
pred_mask.putpalette(color_map)
pred_mask.save(pred_path)
color_map = [0, 0, 0, 0, 255, 0]
if single_img is not None:
pred = model.predict(single_img)
# 以伪彩色png图片保存预测结果
pred_name = osp.basename(single_img).rstrip('npy') + 'png'
pred_path = osp.join(save_img_dir, pred_name)
pred_mask = Image.fromarray(pred['label_map'].astype(np.uint8), mode='P')
pred_mask.putpalette(color_map)
pred_mask.save(pred_path)
elif (file_list is not None) and (data_dir is not None):
with open(osp.join(data_dir, file_list)) as f:
lines = f.readlines()
for line in lines:
img_path = line.split(' ')[0]
print('Predicting {}'.format(img_path))
img_path_ = osp.join(data_dir, img_path)
pred = model.predict(img_path_)
# 以伪彩色png图片保存预测结果
pred_name = osp.basename(img_path).rstrip('npy') + 'png'
pred_path = osp.join(save_img_dir, pred_name)
pred_mask = Image.fromarray(
pred['label_map'].astype(np.uint8), mode='P')
pred_mask.putpalette(color_map)
pred_mask.save(pred_path)
else:
raise Exception(
'You should either set the parameter single_img, or set the parameters data_dir, file_list.'
)
contrib/RemoteSensing/train_demo.py
浏览文件 @ 7126bb37
......@@ -2,11 +2,17 @@ import os.path as osp
import argparse
import transforms.transforms as T
from readers.reader import Reader
from models import UNet
from models import UNet, HRNet
def parse_args():
parser = argparse.ArgumentParser(description='RemoteSensing training')
parser.add_argument(
'--model_type',
dest='model_type',
help="Model type for traing, which is one of ('unet', 'hrnet')",
type=str,
default='hrnet')
parser.add_argument(
'--data_dir',
dest='data_dir',
......@@ -43,7 +49,6 @@ def parse_args():
args = parse_args()
data_dir = args.data_dir
save_dir = args.save_dir
channel = args.channel
......@@ -52,17 +57,9 @@ train_batch_size = args.train_batch_size
lr = args.lr
# 定义训练和验证时的transforms
train_transforms = T.Compose([
T.RandomVerticalFlip(0.5),
T.RandomHorizontalFlip(0.5),
T.ResizeStepScaling(0.5, 2.0, 0.25),
T.RandomPaddingCrop(256),
T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
])
train_transforms = T.Compose([T.RandomHorizontalFlip(0.5), T.Normalize()])
eval_transforms = T.Compose([
T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
])
eval_transforms = T.Compose([T.Normalize()])
train_list = osp.join(data_dir, 'train.txt')
val_list = osp.join(data_dir, 'val.txt')
......@@ -74,23 +71,30 @@ train_reader = Reader(
file_list=train_list,
label_list=label_list,
transforms=train_transforms,
num_workers=8,
buffer_size=16,
shuffle=True,
parallel_method='thread')
shuffle=True)
eval_reader = Reader(
data_dir=data_dir,
file_list=val_list,
label_list=label_list,
transforms=eval_transforms,
num_workers=8,
buffer_size=16,
shuffle=False,
parallel_method='thread')
transforms=eval_transforms)
model = UNet(
num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
if args.model_type == 'unet':
model = UNet(
num_classes=2,
input_channel=channel,
use_bce_loss=True,
use_dice_loss=True)
elif args.model_type == 'hrnet':
model = HRNet(
num_classes=2,
input_channel=channel,
use_bce_loss=True,
use_dice_loss=True)
else:
raise ValueError(
"--model_type: {} is set wrong, it shold be one of ('unet', "
"'hrnet')".format(args.model_type))
model.train(
num_epochs=num_epochs,
......@@ -100,7 +104,5 @@ model.train(
save_interval_epochs=5,
log_interval_steps=10,
save_dir=save_dir,
pretrain_weights=None,
optimizer=None,
learning_rate=lr,
use_vdl=True)
contrib/RemoteSensing/utils/metrics.py
浏览文件 @ 7126bb37
......@@ -143,3 +143,14 @@ class ConfusionMatrix(object):
kappa = (po - pe) / (1 - pe)
return kappa
def precision_recall(self):
'''
precision, recall of foreground(value=1) for 2 categories
'''
TP = self.confusion_matrix[1, 1]
FN = self.confusion_matrix[1, 0]
FP = self.confusion_matrix[0, 1]
recall = TP / (TP + FN)
precision = TP / (TP + FP)
return precision, recall
contrib/RemoteSensing/utils/utils.py
浏览文件 @ 7126bb37
......@@ -12,13 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import time
import os
import os.path as osp
import numpy as np
import six
import yaml
import math
from . import logging
......
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