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# NUnit
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# Benchmark Results
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BenchmarkDotNet.Artifacts/
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src/metric/__pycache__/
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healthchecksdb
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.ionide/
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src/models/backbone/__pycache__/
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|
||||
|
|
@ -0,0 +1,21 @@
|
|||
default:
|
||||
tags:
|
||||
- docker
|
||||
image:
|
||||
name: ufoym/deepo:all-jupyter
|
||||
entrypoint: [""]
|
||||
|
||||
before_script:
|
||||
- pip install -U .
|
||||
- pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
|
||||
- pip install -r requirements.txt
|
||||
|
||||
stages:
|
||||
- test
|
||||
|
||||
pytest:
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||||
stage: test
|
||||
script:
|
||||
- pip install -U .[dev]
|
||||
- pytest --cov=./
|
||||
coverage: '/^TOTAL.*\s+(\d+\%)$/'
|
||||
32
LICENSE
32
LICENSE
|
|
@ -1,17 +1,19 @@
|
|||
<copyright notice> By obtaining, using, and/or copying this software and/or
|
||||
its associated documentation, you agree that you have read, understood, and
|
||||
will comply with the following terms and conditions:
|
||||
Copyright (c) [2021] [The Supervised Layout Benchmark]
|
||||
|
||||
Permission to use, copy, modify, and distribute this software and its associated
|
||||
documentation for any purpose and without fee is hereby granted, provided
|
||||
that the above copyright notice appears in all copies, and that both that
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copyright notice and this permission notice appear in supporting documentation,
|
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and that the name of the copyright holder not be used in advertising or publicity
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pertaining to distribution of the software without specific, written permission.
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
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of this software and associated documentation files (the "Software"), to deal
|
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in the Software without restriction, including without limitation the rights
|
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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copies of the Software, and to permit persons to whom the Software is
|
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furnished to do so, subject to the following conditions:
|
||||
|
||||
THE COPYRIGHT HOLDER DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
|
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INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT
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SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
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DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM THE LOSS OF USE, DATA OR
|
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PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION,
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ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
|
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
|
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|
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
93
README.md
93
README.md
|
|
@ -1,20 +1,87 @@
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#### 从命令行创建一个新的仓库
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# supervised_layout_benchmark
|
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|
||||
```bash
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touch README.md
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git init
|
||||
git add README.md
|
||||
git commit -m "first commit"
|
||||
git remote add origin https://git.osredm.com/p57201394/supervised_layout_benchmark.git
|
||||
git push -u origin master
|
||||
## Introduction
|
||||
|
||||
This project aims to establish a deep neural network (DNN) surrogate modeling benchmark for the temperature field prediction of heat source layout (HSL-TFP) task, providing a set of representative DNN surrogates as baselines as well as the original code files for easy start and comparison.
|
||||
|
||||
## Running Requirements
|
||||
|
||||
- ### Software
|
||||
|
||||
- python：
|
||||
- cuda：
|
||||
- pytorch：
|
||||
|
||||
- ### Hardware
|
||||
|
||||
- A single GPU with at least 4GB.
|
||||
|
||||
|
||||
## Environment construction
|
||||
|
||||
- ``` pip install -r requirements.txt ```
|
||||
|
||||
## A quick start
|
||||
|
||||
The training, test and visualization can be accessed by running `main.py` file.
|
||||
|
||||
- The data is available at the server address: `\\192.168.2.1\mnt/share1/layout_data/v1.0/data/`（refer to [Readme for samples](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/samples/README.md)). Remember to modify variable `data_root` in the configuration file `config/config_complex_net.yml` to the right server address.
|
||||
|
||||
- Training
|
||||
|
||||
```python
|
||||
python main.py -m train
|
||||
```
|
||||
|
||||
#### 从命令行推送已经创建的仓库
|
||||
|
||||
```bash
|
||||
git remote add origin https://git.osredm.com/p57201394/supervised_layout_benchmark.git
|
||||
git push -u origin master
|
||||
or
|
||||
|
||||
```python
|
||||
python main.py --mode=train
|
||||
```
|
||||
|
||||
- Test
|
||||
|
||||
```python
|
||||
python main.py -m test --test_check_num=21
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```python
|
||||
python main.py --mode=test --test_check_num=21
|
||||
```
|
||||
|
||||
where variable `test_check_num` is the number of the saved model for test.
|
||||
|
||||
- Prediction visualization
|
||||
|
||||
```python
|
||||
python main.py -m plot --test_check_num=21
|
||||
```
|
||||
|
||||
or
|
||||
```python
|
||||
python main.py --mode=plot --test_check_num=21
|
||||
```
|
||||
|
||||
where variable `test_check_num` is the number of the saved model for plotting.
|
||||
|
||||
## Project architecture
|
||||
|
||||
- `config`: the configuration file
|
||||
- `notebook`: the test file for `notebook`
|
||||
- `outputs`: the output results by `test` and `plot` module. The test results is saved at `outputs/*.csv` and the plotting figures is saved at `outputs/predict_plot/`.
|
||||
- `src`: including surrogate model, training and testing files.
|
||||
- `test.py`: testing files.
|
||||
- `train.py`: training files.
|
||||
- `plot.py`: prediction visualization files.
|
||||
- `data`: data preprocessing and data loading files.
|
||||
- `metric`: evaluation metric file. (For details, see [Readme for metric](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/src/metric/README.md))
|
||||
- `models`: DNN surrogate models for the HSL-TFP task.
|
||||
- `utils`: useful tool function files.
|
||||
|
||||
## One tiny example
|
||||
|
||||
One tiny example for training and testing can be accessed based on the following instruction.
|
||||
* Some training and testing data are available at `samples/data`.
|
||||
* Based on the original configuration file, run `python main.py` directly for a quick experience of this tiny example.
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
# supervised_layout_benchmark
|
||||
|
||||
## 介绍
|
||||
|
||||
> 该项目主要用于实现卫星组件热布局不同深度代理模型训练、测试以及热布局预测作图.
|
||||
|
||||
## 环境要求
|
||||
|
||||
- ### 软件要求
|
||||
|
||||
- python：
|
||||
- cuda：
|
||||
- pytorch：
|
||||
|
||||
- ### 硬件要求
|
||||
|
||||
- 大约4GB显存的GPU
|
||||
|
||||
|
||||
## 构建环境
|
||||
|
||||
- ``` pip install -r requirements.txt ```
|
||||
|
||||
## 快速开始
|
||||
|
||||
> 运行训练、测试以及热布局作图统一通过main.py入口.
|
||||
|
||||
- 数据放在服务器`\\192.168.2.1\mnt/share1/layout_data/v1.0/data/`（详见[Readme](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/samples/README.md))，运行时请修改程序配置文件`config/config_complex_net.yml`中`data_root`输入变量为挂载服务器上数据地址.
|
||||
|
||||
- 训练和测试
|
||||
|
||||
```python
|
||||
python main.py -m train 或者 python main.py --mode=train
|
||||
```
|
||||
|
||||
- 测试
|
||||
|
||||
```python
|
||||
python main.py -m test --test_check_num=21 或者 python main.py --mode=test --test_check_num=21
|
||||
```
|
||||
|
||||
其中`test_check_num`是测试输入模型存储的编号.
|
||||
|
||||
- 热布局预测作图
|
||||
|
||||
```python
|
||||
python main.py -m plot --test_check_num=21 或者 python main.py --mode=plot --test_check_num=21
|
||||
```
|
||||
|
||||
其中`test_check_num`是作图输入模型存储的编号.
|
||||
|
||||
## 项目结构
|
||||
|
||||
- `benchmark`目录存放运行所需所有程序
|
||||
- `config`存放运行配置文件
|
||||
- `notebook`存放`notebook`测试文件
|
||||
- `outputs`用于存放`test`和`plot`作图输出结果，测试的输出结果保存在`outputs/*.csv`，`plot`结果保存在`outputs/predict_plot/`
|
||||
- `src`用于存放模型文件和测试训练文件
|
||||
- `test.py`测试程序
|
||||
- `train.py`训练程序
|
||||
- `plot.py`预测可视化程序
|
||||
- `data`文件夹存放数据预处理和读取程序
|
||||
- `metrics`文件夹存放热布局度量函数，详见[Readme](https://git.idrl.site/gongzhiqiang/supervised_layout_benchmark/blob/master/src/metric/README.md)
|
||||
- `models`热布局深度代理模型所用深度模型
|
||||
- `utils`工具类文件
|
||||
|
||||
## 其他
|
||||
|
||||
* 训练测试examples
|
||||
* 训练样本测试样本存放于`samples/data`中
|
||||
* 原始文件配置环境后，直接运行`python main.py`，即运行example
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
# config
|
||||
|
||||
# model
|
||||
## support SegNet_AlexNet, SegNet_VGG, SegNet_ResNet18, SegNet_ResNet34, SegNet_ResNet50, SegNet_ResNet101, SegNet_ResNet152
|
||||
## FPN_ResNet18, FPN_ResNet50, FPN_ResNet101, FPN_ResNet34, FPN_ResNet152
|
||||
## FCN_AlexNet, FCN_VGG, FCN_ResNet18, FCN_ResNet50, FCN_ResNet101, FCN_ResNet34, FCN_ResNet152
|
||||
## UNet_VGG
|
||||
model_name: FCN # choose from FPN, FCN, SegNet, UNet
|
||||
backbone: AlexNet # choose from AlexNet, VGG, ResNet18, ResNet50, ResNet101
|
||||
|
||||
# dataset path
|
||||
data_root: samples/data/
|
||||
boundary: one_point # choose from rm_wall, one_point, all_walls
|
||||
|
||||
# train/val set
|
||||
train_list: train/train_val.txt
|
||||
|
||||
# test set
|
||||
## choose the test set: test_0.txt, test_1.txt, test_2.txt, test_3.txt,test_4.txt,test_5.txt,test_6.txt
|
||||
test_list: test/test_0.txt
|
||||
|
||||
# metric for testing
|
||||
## choose from "mae_global", "mae_boundary", "mae_component",
|
||||
## "value_and_pos_error_of_maximum_temperature", "max_tem_spearmanr", "global_image_spearmanr"
|
||||
metric: mae_boundary
|
||||
|
||||
# dataset format: mat or h5
|
||||
data_format: mat
|
||||
batch_size: 2
|
||||
max_epochs: 50
|
||||
lr: 0.001
|
||||
|
||||
# number of gpus to use
|
||||
gpus: 1
|
||||
val_check_interval: 1.0
|
||||
|
||||
# num_workers in dataloader
|
||||
num_workers: 4
|
||||
|
||||
# preprocessing of data
|
||||
## input
|
||||
mean_layout: 0
|
||||
std_layout: 1000
|
||||
## output
|
||||
mean_heat: 298
|
||||
std_heat: 50
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
# data config for computation of metrics
|
||||
|
||||
## SIZE OF COMPONENTS
|
||||
units:
|
||||
- - 0.016
|
||||
- 0.012
|
||||
- - 0.012
|
||||
- 0.006
|
||||
- - 0.018
|
||||
- 0.009
|
||||
- - 0.018
|
||||
- 0.012
|
||||
- - 0.018
|
||||
- 0.018
|
||||
- - 0.012
|
||||
- 0.012
|
||||
- - 0.018
|
||||
- 0.006
|
||||
- - 0.009
|
||||
- 0.009
|
||||
- - 0.006
|
||||
- 0.024
|
||||
- - 0.006
|
||||
- 0.012
|
||||
- - 0.012
|
||||
- 0.024
|
||||
- - 0.024
|
||||
- 0.024
|
||||
|
||||
## POWERS OF THE COMPONENTS
|
||||
powers:
|
||||
- 4000
|
||||
- 16000
|
||||
- 6000
|
||||
- 8000
|
||||
- 10000
|
||||
- 14000
|
||||
- 16000
|
||||
- 20000
|
||||
- 8000
|
||||
- 16000
|
||||
- 10000
|
||||
- 20000
|
||||
|
||||
## LENGTH OF LAYOUT BOARD
|
||||
length: 0.1
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
FROM ufoym/deepo:pytorch
|
||||
LABEL maintainer="gongzhiqiang@alumni.sjtu.edu.cn"
|
||||
|
||||
WORKDIR /tmp
|
||||
COPY requirements.txt ./
|
||||
RUN pip install -r requirements.txt
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
This function denotes the main function to train/test/plot
|
||||
Usage:
|
||||
python main.py [FLAGS]
|
||||
|
||||
@author: gongzhiqiang
|
||||
@contact: gongzhiqiang@alumni.sjtu.edu.cn
|
||||
|
||||
@version: 1.0
|
||||
@file: main.py
|
||||
@time: 2020-12-22
|
||||
|
||||
"""
|
||||
from pathlib import Path
|
||||
import configargparse
|
||||
|
||||
from src.LayoutDeepRegression import Model
|
||||
from src import train, test, plot
|
||||
|
||||
|
||||
def main():
|
||||
# default configuration file
|
||||
config_path = Path(__file__).absolute().parent / "config/config.yml"
|
||||
parser = configargparse.ArgParser(default_config_files=[str(config_path)], description="Hyper-parameters.")
|
||||
|
||||
# configuration file
|
||||
parser.add_argument("--config", is_config_file=True, default=False, help="config file path")
|
||||
|
||||
# mode
|
||||
parser.add_argument("-m", "--mode", type=str, default="train", help="model: train or test or plot")
|
||||
|
||||
# args for training
|
||||
parser.add_argument("--gpus", type=int, default=0, help="how many gpus")
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--max_epochs", default=20, type=int)
|
||||
parser.add_argument("--lr", default="0.01", type=float)
|
||||
parser.add_argument("--resume_from_checkpoint", type=str, help="resume from checkpoint")
|
||||
parser.add_argument("--num_workers", default=2, type=int, help="num_workers in DataLoader")
|
||||
parser.add_argument("--seed", type=int, default=1, help="seed")
|
||||
parser.add_argument("--use_16bit", type=bool, default=False, help="use 16bit precision")
|
||||
parser.add_argument("--profiler", action="store_true", help="use profiler")
|
||||
|
||||
# args for validation
|
||||
parser.add_argument("--val_check_interval", type=float, default=1,
|
||||
help="how often within one training epoch to check the validation set")
|
||||
|
||||
# args for testing
|
||||
parser.add_argument("--test_check_num", default='0', type=str, help="checkpoint for test")
|
||||
parser.add_argument("--test_args", action="store_true", help="print args")
|
||||
|
||||
# args from Model
|
||||
parser = Model.add_model_specific_args(parser)
|
||||
hparams = parser.parse_args()
|
||||
|
||||
# running
|
||||
assert hparams.mode in ["train", "test", "plot"]
|
||||
if hparams.test_args:
|
||||
print(hparams)
|
||||
else:
|
||||
getattr(eval(hparams.mode), "main")(hparams)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
tqdm==4.42.1
|
||||
scipy==1.4.1
|
||||
pytest==5.3.5
|
||||
numpy==1.18.1
|
||||
matplotlib==3.1.3
|
||||
ConfigArgParse==1.2.3
|
||||
pytorch_lightning==1.1.2
|
||||
PyYAML==5.3.1
|
||||
scikit_learn==0.23.2
|
||||
torch>=1.5.0
|
||||
torchvision==0.8.1
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
# Datasets for benchmark
|
||||
|
||||
## 介绍
|
||||
|
||||
> 该数据库用于支持热布局温度场预测任务，数据地址：/192.168.2.1/mnt/share1/layout_data/v1.0/data/
|
||||
>
|
||||
> samples中提供数据库的样例
|
||||
|
||||
## 数据库结构
|
||||
|
||||
> 数据库提供三种不同边界：小孔散热、单边散热和四周全散热
|
||||
|
||||
- `data`中存放不同边界数据库
|
||||
- `one_point`小孔散热边界
|
||||
- `train`存放训练数据
|
||||
- `train`训练样本存放文件夹
|
||||
- `train_val.txt`用于网络训练的数据list
|
||||
- `test`存放测试数据
|
||||
- `test`测试样本存放文件夹
|
||||
- `test_*.txt`用于测试的数据list，其中`test_0.txt`、`test_1.txt`、`test_2.txt`、`test_3.txt`、`test_4.txt`、`test_5.txt`、`test_6.txt`分别存放了不同方式采样得到的测试样本
|
||||
- `rm_wall`单边散热边界
|
||||
- `train`
|
||||
- `train`
|
||||
- `train_val.txt`
|
||||
- `test`
|
||||
- `test`
|
||||
- `test_*.txt`
|
||||
- `all_walls`四周全散热边界
|
||||
- `train`
|
||||
- `train`
|
||||
- `train_val.txt`
|
||||
- `test`
|
||||
- `test`
|
||||
- `test_*.txt`
|
||||
|
||||
## 组件介绍
|
||||
|
||||
> 布局区域是`0.1m*0.1m`方形区域，共有12个大小功率不同组件
|
||||
|
||||
* 组件大小、功率
|
||||
|
||||
| 组件 | 长(m) | 宽(m) | 功率($W/m^2$) |
|
||||
| :--: | :---: | :---: | :-----------: |
|
||||
| 1 | 0.016 | 0.012 | 4000 |
|
||||
| 2 | 0.012 | 0.006 | 16000 |
|
||||
| 3 | 0.018 | 0.009 | 6000 |
|
||||
| 4 | 0.018 | 0.012 | 8000 |
|
||||
| 5 | 0.018 | 0.018 | 10000 |
|
||||
| 6 | 0.012 | 0.012 | 14000 |
|
||||
| 7 | 0.018 | 0.006 | 16000 |
|
||||
| 8 | 0.009 | 0.009 | 20000 |
|
||||
| 9 | 0.006 | 0.024 | 8000 |
|
||||
| 10 | 0.006 | 0.012 | 16000 |
|
||||
| 11 | 0.012 | 0.024 | 10000 |
|
||||
| 12 | 0.024 | 0.024 | 20000 |
|
||||
|
||||
* 组件布局示例
|
||||
|
||||
|  |  |
|
||||
| :-----------------------------------------------------: | :-----------------------------------------------------: |
|
||||
| Example 1 | Example 2 |
|
||||
|
||||
|
||||
|
||||
## 数据库详情
|
||||
|
||||
* train包含2000组sequence采样方式生成的训练样本 ，示例如下
|
||||
|
||||
|  |  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| heat layout | one point | rm_wall | all_walls |
|
||||
|
||||
|
||||
|
||||
* test包含不同方式获得的测试样本40000组
|
||||
|
||||
* `test_0.txt`通过sequence采样方式生成的10000组测试样本 ，示例如下
|
||||
|
||||
|  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| Example 1 | Example 2 | Example 3 |
|
||||
|
||||
|
||||
|
||||
* `test_1.txt`通过gibbs方式采样生成的10000组测试样本 ，示例如下
|
||||
|
||||
|  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| Example 1 | Example 2 | Example 3 |
|
||||
|
||||
|
||||
|
||||
* `test_2.txt`功率相同或相近组件相邻构成的特殊组件布局样本，共有4类情况，每类情况1000组测试样本 ，示例如下
|
||||
|
||||
|  |  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
|  |  |  |  |
|
||||
| 8和12号组件 | 2和7和10号组件 | 5和11号组件 | 4和9号组件 |
|
||||
|
||||
|
||||
|
||||
* `test_3.txt`组件布局密集在上半部，1/5区域，2/5区域，3/5区域，4/5区域，或下半部的测试样本，各1000组 ，示例如下
|
||||
|
||||
|  |  |  |  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| 上半部 | 1/5区域 | 2/5区域 | 3/5区域 | 4/5区域 | 下半部 |
|
||||
|
||||
* `test_4.txt`组件布局密集在左半部，1/5区域，2/5区域，3/5区域，4/5区域，或右半部的测试样本，各1000组 ，示例如下
|
||||
|
||||
|  |  |  |  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| 左半部 | 1/5区域 | 2/5区域 | 3/5区域 | 4/5区域 | 右半部 |
|
||||
|
||||
* `test_5.txt`组件布局在内部较小方形区域测试样本，共考虑100x100区域，120x120区域，140x140区域3种情况，各1000组测试样本 ，示例如下
|
||||
|
||||
|  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
|  |  |  |
|
||||
| 100x100 | 120x120 | 140x140 |
|
||||
|
||||
|
||||
|
||||
* `test_6.txt`最大功率布局在角落中的特殊样本，共1000组测试样本，示例如下
|
||||
|
||||
|  |  |  |  |
|
||||
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|
||||
| 右下角 | 左上角 | 左下角 | 左下角 |
|
||||
|
||||
|
||||
|
||||
## 其他
|
||||
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|
|
@ -0,0 +1,8 @@
|
|||
aw_test0_1.mat
|
||||
aw_test0_2.mat
|
||||
aw_test0_3.mat
|
||||
aw_test0_4.mat
|
||||
aw_test0_5.mat
|
||||
aw_test0_6.mat
|
||||
aw_test0_7.mat
|
||||
aw_test0_8.mat
|
||||
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|
|
@ -0,0 +1,10 @@
|
|||
aw_train_1.mat
|
||||
aw_train_2.mat
|
||||
aw_train_3.mat
|
||||
aw_train_4.mat
|
||||
aw_train_5.mat
|
||||
aw_train_6.mat
|
||||
aw_train_7.mat
|
||||
aw_train_8.mat
|
||||
aw_train_9.mat
|
||||
aw_train_10.mat
|
||||
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|
|
@ -0,0 +1,8 @@
|
|||
op_test0_1.mat
|
||||
op_test0_2.mat
|
||||
op_test0_3.mat
|
||||
op_test0_4.mat
|
||||
op_test0_5.mat
|
||||
op_test0_6.mat
|
||||
op_test0_7.mat
|
||||
op_test0_8.mat
|
||||
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|
|
@ -0,0 +1,10 @@
|
|||
op_train_1.mat
|
||||
op_train_2.mat
|
||||
op_train_3.mat
|
||||
op_train_4.mat
|
||||
op_train_5.mat
|
||||
op_train_6.mat
|
||||
op_train_7.mat
|
||||
op_train_8.mat
|
||||
op_train_9.mat
|
||||
op_train_10.mat
|
||||
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|
|
@ -0,0 +1,8 @@
|
|||
rw_test0_1.mat
|
||||
rw_test0_2.mat
|
||||
rw_test0_3.mat
|
||||
rw_test0_4.mat
|
||||
rw_test0_5.mat
|
||||
rw_test0_6.mat
|
||||
rw_test0_7.mat
|
||||
rw_test0_8.mat
|
||||
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|
|
@ -0,0 +1,10 @@
|
|||
rw_train_1.mat
|
||||
rw_train_2.mat
|
||||
rw_train_3.mat
|
||||
rw_train_4.mat
|
||||
rw_train_5.mat
|
||||
rw_train_6.mat
|
||||
rw_train_7.mat
|
||||
rw_train_8.mat
|
||||
rw_train_9.mat
|
||||
rw_train_10.mat
|
||||
|
|
@ -0,0 +1,200 @@
|
|||
# encoding: utf-8
|
||||
import math
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.data import DataLoader, random_split
|
||||
import torchvision
|
||||
from torch.optim.lr_scheduler import ExponentialLR
|
||||
from pytorch_lightning import LightningModule
|
||||
|
||||
from src.data.layout import LayoutDataset
|
||||
import src.utils.np_transforms as transforms
|
||||
import src.models as models
|
||||
from src.metric.metrics import Metric
|
||||
|
||||
|
||||
class Model(LightningModule):
|
||||
|
||||
def __init__(self, hparams):
|
||||
super().__init__()
|
||||
self.hparams = hparams
|
||||
self._build_model()
|
||||
self.criterion = nn.L1Loss()
|
||||
self.train_dataset = None
|
||||
self.val_dataset = None
|
||||
self.test_dataset = None
|
||||
|
||||
def _build_model(self):
|
||||
model_list = ["SegNet_AlexNet", "SegNet_VGG", "SegNet_ResNet18", "SegNet_ResNet50",
|
||||
"SegNet_ResNet101", "SegNet_ResNet34", "SegNet_ResNet152",
|
||||
"FPN_ResNet18", "FPN_ResNet50", "FPN_ResNet101", "FPN_ResNet34", "FPN_ResNet152",
|
||||
"FCN_AlexNet", "FCN_VGG", "FCN_ResNet18", "FCN_ResNet50", "FCN_ResNet101",
|
||||
"FCN_ResNet34", "FCN_ResNet152",
|
||||
"UNet_VGG"]
|
||||
layout_model = self.hparams.model_name + '_' + self.hparams.backbone
|
||||
assert layout_model in model_list
|
||||
self.model = getattr(models, layout_model)(in_channels=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.model(x)
|
||||
x = torch.sigmoid(x)
|
||||
return x
|
||||
|
||||
def __dataloader(self, dataset, shuffle=False):
|
||||
loader = DataLoader(
|
||||
dataset=dataset,
|
||||
shuffle=shuffle,
|
||||
batch_size=self.hparams.batch_size,
|
||||
num_workers=self.hparams.num_workers,
|
||||
)
|
||||
return loader
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = torch.optim.Adam(self.parameters(),
|
||||
lr=self.hparams.lr)
|
||||
scheduler = ExponentialLR(optimizer, gamma=0.99)
|
||||
return [optimizer], [scheduler]
|
||||
|
||||
def prepare_data(self):
|
||||
"""Prepare dataset
|
||||
"""
|
||||
size: int = self.hparams.input_size
|
||||
transform_layout = transforms.Compose(
|
||||
[
|
||||
transforms.Resize(size=(size, size)),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(
|
||||
torch.tensor([self.hparams.mean_layout]),
|
||||
torch.tensor([self.hparams.std_layout]),
|
||||
),
|
||||
]
|
||||
)
|
||||
transform_heat = transforms.Compose(
|
||||
[
|
||||
transforms.Resize(size=(size, size)),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(
|
||||
torch.tensor([self.hparams.mean_heat]),
|
||||
torch.tensor([self.hparams.std_heat]),
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
# here only support format "mat"
|
||||
assert self.hparams.data_format == "mat"
|
||||
trainval_dataset = LayoutDataset(
|
||||
self.hparams.data_root,
|
||||
self.hparams.boundary,
|
||||
list_path=self.hparams.train_list,
|
||||
train=True,
|
||||
transform=transform_layout,
|
||||
target_transform=transform_heat,
|
||||
)
|
||||
test_dataset = LayoutDataset(
|
||||
self.hparams.data_root,
|
||||
self.hparams.boundary,
|
||||
list_path=self.hparams.test_list,
|
||||
train=False,
|
||||
transform=transform_layout,
|
||||
target_transform=transform_heat,
|
||||
)
|
||||
|
||||
# split train/val set
|
||||
train_length, val_length = int(len(trainval_dataset) * 0.8), int(len(trainval_dataset) * 0.2)
|
||||
train_dataset, val_dataset = torch.utils.data.random_split(trainval_dataset,
|
||||
[train_length, val_length])
|
||||
|
||||
print(
|
||||
f"Prepared dataset, train:{int(len(train_dataset))},\
|
||||
val:{int(len(val_dataset))}, test:{len(test_dataset)}"
|
||||
)
|
||||
|
||||
# assign to use in dataloaders
|
||||
self.train_dataset = self.__dataloader(train_dataset, shuffle=True)
|
||||
self.val_dataset = self.__dataloader(val_dataset, shuffle=False)
|
||||
self.test_dataset = self.__dataloader(test_dataset, shuffle=False)
|
||||
|
||||
def train_dataloader(self):
|
||||
return self.train_dataset
|
||||
|
||||
def val_dataloader(self):
|
||||
return self.val_dataset
|
||||
|
||||
def test_dataloader(self):
|
||||
return self.test_dataset
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
layout, heat = batch
|
||||
heat_pred = self(layout)
|
||||
loss = self.criterion(heat, heat_pred)
|
||||
self.log("train/training_mae", loss * self.hparams.std_heat)
|
||||
|
||||
if batch_idx == 0:
|
||||
grid = torchvision.utils.make_grid(
|
||||
heat_pred[:4, ...], normalize=True
|
||||
)
|
||||
self.logger.experiment.add_image(
|
||||
"train_pred_heat_field", grid, self.global_step
|
||||
)
|
||||
if self.global_step == 0:
|
||||
grid = torchvision.utils.make_grid(
|
||||
heat[:4, ...], normalize=True
|
||||
)
|
||||
self.logger.experiment.add_image(
|
||||
"train_heat_field", grid, self.global_step
|
||||
)
|
||||
|
||||
return {"loss": loss}
|
||||
|
||||
def validation_step(self, batch, batch_idx):
|
||||
layout, heat = batch
|
||||
heat_pred = self(layout)
|
||||
loss = self.criterion(heat, heat_pred)
|
||||
return {"val_loss": loss}
|
||||
|
||||
def validation_epoch_end(self, outputs):
|
||||
val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean()
|
||||
self.log("val/val_mae", val_loss_mean.item() * self.hparams.std_heat)
|
||||
|
||||
def test_step(self, batch, batch_idx):
|
||||
layout, heat = batch
|
||||
heat_pred = self(layout)
|
||||
|
||||
data_config = Path(__file__).absolute().parent.parent / "config/data.yml"
|
||||
layout_metric = Metric(heat_pred, heat, boundary=self.hparams.boundary,
|
||||
layout=layout, data_config=data_config, hparams=self.hparams)
|
||||
assert self.hparams.metric in layout_metric.metrics
|
||||
loss = getattr(layout_metric, self.hparams.metric)()
|
||||
return {"test_loss": loss}
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
test_loss_mean = torch.stack([x["test_loss"] for x in outputs]).mean()
|
||||
self.log("test_loss (" + self.hparams.metric +")", test_loss_mean.item())
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parser): # pragma: no-cover
|
||||
"""Parameters you define here will be available to your model through `self.hparams`.
|
||||
"""
|
||||
# dataset args
|
||||
parser.add_argument("--data_root", type=str, required=True, help="path of dataset")
|
||||
parser.add_argument("--train_list", type=str, required=True, help="path of train dataset list")
|
||||
parser.add_argument("--train_size", default=0.8, type=float, help="train_size in train_test_split")
|
||||
parser.add_argument("--test_list", type=str, required=True, help="path of test dataset list")
|
||||
parser.add_argument("--boundary", type=str, default="rm_wall", help="boundary condition")
|
||||
parser.add_argument("--data_format", type=str, default="mat", choices=["mat", "h5"], help="dataset format")
|
||||
|
||||
# Normalization params
|
||||
parser.add_argument("--mean_layout", default=0, type=float)
|
||||
parser.add_argument("--std_layout", default=1, type=float)
|
||||
parser.add_argument("--mean_heat", default=0, type=float)
|
||||
parser.add_argument("--std_heat", default=1, type=float)
|
||||
|
||||
# Model params (opt)
|
||||
parser.add_argument("--input_size", default=200, type=int)
|
||||
parser.add_argument("--model_name", type=str, default='SegNet', help="the name of chosen model")
|
||||
parser.add_argument("--backbone", type=str, default='ResNet18', help="the used backbone in the regression model")
|
||||
parser.add_argument("--metric", type=str, default='mae_global',
|
||||
help="the used metric for evaluation of testing")
|
||||
return parser
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""Layout dataset
|
||||
"""
|
||||
import os
|
||||
from .loadresponse import LoadResponse, mat_loader
|
||||
|
||||
|
||||
class LayoutDataset(LoadResponse):
|
||||
"""Layout dataset (mutiple files) generated by 'layout-generator'.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root,
|
||||
sub_dir,
|
||||
list_path=None,
|
||||
train=True,
|
||||
transform=None,
|
||||
target_transform=None,
|
||||
load_name="F",
|
||||
resp_name="u",
|
||||
):
|
||||
subdir = os.path.join("train", "train") \
|
||||
if train else os.path.join("test", "test")
|
||||
|
||||
# find the path of the list of train/test samples
|
||||
list_path = os.path.join(root, sub_dir, list_path)
|
||||
|
||||
# find the root path of the samples
|
||||
root = os.path.join(root, sub_dir, subdir)
|
||||
|
||||
super().__init__(
|
||||
root,
|
||||
mat_loader,
|
||||
list_path,
|
||||
load_name=load_name,
|
||||
resp_name=resp_name,
|
||||
extensions="mat",
|
||||
transform=transform,
|
||||
target_transform=target_transform,
|
||||
)
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""Load Response Dataset.
|
||||
"""
|
||||
import os
|
||||
|
||||
import scipy.io as sio
|
||||
import numpy as np
|
||||
from torchvision.datasets import VisionDataset
|
||||
|
||||
|
||||
class LoadResponse(VisionDataset):
|
||||
"""Some Information about LoadResponse dataset"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root,
|
||||
loader,
|
||||
list_path,
|
||||
load_name="F",
|
||||
resp_name="u",
|
||||
extensions=None,
|
||||
transform=None,
|
||||
target_transform=None,
|
||||
is_valid_file=None,
|
||||
):
|
||||
super().__init__(
|
||||
root, transform=transform, target_transform=target_transform
|
||||
)
|
||||
self.list_path = list_path
|
||||
self.loader = loader
|
||||
self.load_name = load_name
|
||||
self.resp_name = resp_name
|
||||
self.extensions = extensions
|
||||
self.sample_files = make_dataset_list(root, list_path, extensions, is_valid_file)
|
||||
|
||||
def __getitem__(self, index):
|
||||
path = self.sample_files[index]
|
||||
load, resp = self.loader(path, self.load_name, self.resp_name)
|
||||
|
||||
if self.transform is not None:
|
||||
load = self.transform(load)
|
||||
if self.target_transform is not None:
|
||||
resp = self.target_transform(resp)
|
||||
return load, resp
|
||||
|
||||
def __len__(self):
|
||||
return len(self.sample_files)
|
||||
|
||||
|
||||
def make_dataset(root_dir, extensions=None, is_valid_file=None):
|
||||
"""make_dataset() from torchvision.
|
||||
"""
|
||||
files = []
|
||||
root_dir = os.path.expanduser(root_dir)
|
||||
if not ((extensions is None) ^ (is_valid_file is None)):
|
||||
raise ValueError(
|
||||
"Both extensions and is_valid_file \
|
||||
cannot be None or not None at the same time"
|
||||
)
|
||||
if extensions is not None:
|
||||
is_valid_file = lambda x: has_allowed_extension(x, extensions)
|
||||
|
||||
assert os.path.isdir(root_dir), root_dir
|
||||
for root, _, fns in sorted(os.walk(root_dir, followlinks=True)):
|
||||
for fn in sorted(fns):
|
||||
path = os.path.join(root, fn)
|
||||
if is_valid_file(path):
|
||||
files.append(path)
|
||||
return files
|
||||
|
||||
|
||||
def make_dataset_list(root_dir, list_path, extensions=None, is_valid_file=None):
|
||||
"""make_dataset() from torchvision.
|
||||
"""
|
||||
files = []
|
||||
root_dir = os.path.expanduser(root_dir)
|
||||
if not ((extensions is None) ^ (is_valid_file is None)):
|
||||
raise ValueError(
|
||||
"Both extensions and is_valid_file \
|
||||
cannot be None or not None at the same time"
|
||||
)
|
||||
if extensions is not None:
|
||||
is_valid_file = lambda x: has_allowed_extension(x, extensions)
|
||||
|
||||
assert os.path.isdir(root_dir), root_dir
|
||||
with open(list_path, 'r') as rf:
|
||||
for line in rf.readlines():
|
||||
data_path = line.strip()
|
||||
path = os.path.join(root_dir, data_path)
|
||||
if is_valid_file(path):
|
||||
files.append(path)
|
||||
return files
|
||||
|
||||
|
||||
def has_allowed_extension(filename, extensions):
|
||||
return filename.lower().endswith(extensions)
|
||||
|
||||
|
||||
def mat_loader(path, load_name, resp_name=None):
|
||||
mats = sio.loadmat(path)
|
||||
load = mats.get(load_name)
|
||||
resp = mats.get(resp_name) if resp_name is not None else None
|
||||
return load, resp
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
total_num = 50000
|
||||
with open('train'+str(total_num)+'.txt', 'w') as wf:
|
||||
for idx in range(int(total_num*0.8)):
|
||||
wf.write('Example'+str(idx)+'.mat'+'\n')
|
||||
with open('val'+str(total_num)+'.txt', 'w') as wf:
|
||||
for idx in range(int(total_num*0.8), total_num):
|
||||
wf.write('Example'+str(idx)+'.mat'+'\n')
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
# Metrics for benchmark
|
||||
|
||||
## 介绍
|
||||
|
||||
> 本项目根据不同的需求构造了不同的metric准则，评价模型训练的好坏。
|
||||
|
||||
## Metrics准则
|
||||
|
||||
> 根据不同的需求，构造了pixel-level metrics，image-level metrics和batch-level metrics
|
||||
|
||||
* Pixel-level metrics
|
||||
* `value_and_pos_error_of_maximum_temperature`: 最高温的预测误差和最高温发生位置的预测误差
|
||||
* 可选参数`output_type`：`value`和`position`，默认`value`，其中`value`输出最高温预测误差，`position`输出最高温位置预测误差。
|
||||
|
||||
* Image-level metrics
|
||||
* `mae_global`: 全局温度平均预测误差
|
||||
* `mae_boundary`: 边界处温度平均预测误差
|
||||
* 可选参数`output_type`：`Dirichlet`和`Neumann`，默认`Dirichlet`，其中`Dirichlet`输出`Dirichlet`边界处温度平均预测误差，`Neumann`输出`Neumann`边界处温度平均预测误差。
|
||||
* `mae_component`: 最大的组件处温度平均预测误差
|
||||
* `global_image_spearmanr`: 预测温度场和真实温度场的Spearman相关系数
|
||||
|
||||
- Batch-level metrics
|
||||
- `max_tem_spearmanr`: 不同样本的预测最高温排序和真实最高温排序的Spearman相关系数，衡量代理模型对不同布局对应的最高温进行正确排序的能力
|
||||
|
||||
## 其他
|
||||
|
|
@ -0,0 +1,464 @@
|
|||
# encoding: utf-8
|
||||
import copy
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
from scipy.stats import spearmanr
|
||||
|
||||
|
||||
class Metric:
|
||||
|
||||
def __init__(self, input, target, boundary=None,
|
||||
layout=None, data_config=None, hparams=None):
|
||||
"""
|
||||
Args:
|
||||
input: (batch size x 1 x N x N or N x N) the predicted temperature field
|
||||
target: (batch size x 1 x N x N or N x N) the real temperature field
|
||||
boundary: 'all_walls' - all the dirichlet BCs
|
||||
: 'rm_wall' - the neumann BCs for three sides and the dirichlet BCs for one side
|
||||
: 'one_point' - all the neumann BCs except one tiny heatsink with dirichlet BC
|
||||
layout: Input layout
|
||||
data_config: Dataset parameter
|
||||
hparams: Model parameter
|
||||
"""
|
||||
self.data_config = data_config
|
||||
self.layout = layout
|
||||
self.boundary = boundary
|
||||
self.input = input
|
||||
self.target = target
|
||||
self.hparams = hparams
|
||||
self.data = None
|
||||
self.data_info()
|
||||
self.metrics = self.all_metrics()
|
||||
|
||||
def all_metrics(self):
|
||||
self.metrics = ["mae_global", "mae_boundary", "mae_component",
|
||||
"value_and_pos_error_of_maximum_temperature", "max_tem_spearmanr",
|
||||
"global_image_spearmanr"]
|
||||
return self.metrics
|
||||
|
||||
def data_info(self):
|
||||
data_yaml = open(self.data_config, 'r', encoding='gbk')
|
||||
self.data = yaml.load(data_yaml, yaml.FullLoader)
|
||||
self.L = self.data['length']
|
||||
self.power = np.array(self.data['powers']) / self.hparams.std_layout
|
||||
self.comp_size = np.array(self.data['units'])
|
||||
self.comp_pixel_size = np.round(self.comp_size / self.L * 200).astype(int)
|
||||
|
||||
# -------------------tool functions--------------------------#
|
||||
def identify_same_power(self, power):
|
||||
org_power = np.array(power)
|
||||
power1 = np.array(list(set(power))) # 对元素去重
|
||||
indx1 = []
|
||||
indx2 = []
|
||||
indx3 = []
|
||||
for i in range(len(power1)):
|
||||
ind = np.where(org_power == power1[i])[0]
|
||||
if len(ind) == 1: # 一个组件一个功率
|
||||
indx1 = indx1 + list(ind)
|
||||
elif len(ind) == 2: # 两个组件一个功率
|
||||
indx2 = indx2 + list(ind)
|
||||
elif len(ind) == 3: # 三个组件一个功率
|
||||
indx3 = indx3 + list(ind)
|
||||
else:
|
||||
print('There are four components with the same intensity!')
|
||||
return (indx1, indx2, indx3)
|
||||
|
||||
def identify_component_boundary(self, layout, power, boundary):
|
||||
"""
|
||||
find the pixel locations of components
|
||||
|
||||
Args:
|
||||
layout: pixel-level representation
|
||||
power: the component dissapation power
|
||||
boundary: 'all_walls' - all the dirichlet BCs
|
||||
: 'rm_wall' - the neumann BCs for three sides and the dirichlet BCs for one side
|
||||
: 'one_point' - all the neumann BCs except one tiny heatsink with dirichlet BC
|
||||
When boundary is 'one_point', the input layout should be transposed and then read in an inverse-row order.
|
||||
Returns:
|
||||
location: -> Tensor: N * 4, pixel coordinates
|
||||
"""
|
||||
if boundary == 'one_point':
|
||||
temp = layout.cpu().numpy().T[::-1].copy()
|
||||
layout = torch.from_numpy(temp)
|
||||
|
||||
comp_num = len(power)
|
||||
location = torch.zeros(comp_num, 4)
|
||||
|
||||
(indx1, indx2, indx3) = self.identify_same_power(power)
|
||||
|
||||
for i in range(comp_num):
|
||||
[index_x, index_y] = torch.where(layout == power[i])
|
||||
if i in indx1:
|
||||
xmin, xmax = torch.min(index_x).item(), torch.max(index_x).item()
|
||||
ymin, ymax = torch.min(index_y).item(), torch.max(index_y).item()
|
||||
location[i, :] = torch.tensor([xmin, xmax, ymin, ymax])
|
||||
if i in indx2: # [3, 8, 4, 10, 7, 11] # 4 和 9 号组件，P=8, 5和11，P=10, 8和12，P=12
|
||||
flag1 = 0
|
||||
layout_flag = torch.zeros_like(layout)
|
||||
layout_flag[index_x, index_y] = 1
|
||||
|
||||
for j in range(int(len(indx2)/2)):
|
||||
temp = indx2[(2*j): (2*j + 2)]
|
||||
if i in temp:
|
||||
comp_index = temp
|
||||
|
||||
comp_coord = self.find_comp_coordinate(layout_flag, self.comp_pixel_size, comp_index)
|
||||
if comp_coord is None:
|
||||
pass
|
||||
else:
|
||||
location[comp_index[0], :] = torch.tensor(comp_coord[0])
|
||||
location[comp_index[1], :] = torch.tensor(comp_coord[1])
|
||||
flag1 = 1
|
||||
if flag1 == 0:
|
||||
print("Something wrong! Cannot locate the component #", i)
|
||||
if i in indx3: # [1, 6, 9]
|
||||
if i == 1:
|
||||
flag2 = 0 # to indicate whether locate the components
|
||||
layout_flag = torch.zeros_like(layout)
|
||||
layout_flag[index_x, index_y] = 1
|
||||
xmin1, ymin1 = self.find_left_top_point(index_x, index_y)
|
||||
xmax1 = xmin1 + self.comp_pixel_size[i, 0] - 1
|
||||
ymax1 = ymin1 + self.comp_pixel_size[i, 1] - 1
|
||||
layout_flag[xmin1: (xmax1 + 1), ymin1: (ymax1 + 1)] = 0
|
||||
for j in range(int(len(indx3)/3)):
|
||||
temp = indx3[(3*j): (3*j + 3)]
|
||||
if i in temp:
|
||||
comp_index = temp
|
||||
comp_index.remove(i)
|
||||
comp_coord = self.find_comp_coordinate(layout_flag, self.comp_pixel_size, comp_index)
|
||||
if comp_coord is None:
|
||||
pass
|
||||
else:
|
||||
location[i, :] = torch.tensor([xmin1, xmax1, ymin1, ymax1])
|
||||
location[comp_index[0], :] = torch.tensor(comp_coord[0])
|
||||
location[comp_index[1], :] = torch.tensor(comp_coord[1])
|
||||
flag2 += 1
|
||||
if i == 9 and flag2 == 0:
|
||||
print("Something wrong! Cannot locate components # 2, 7, 10")
|
||||
return location
|
||||
|
||||
def find_left_top_point(self, index_x, index_y):
|
||||
x_min = torch.min(index_x).item()
|
||||
indx_min = torch.where(index_x == torch.min(index_x))[0]
|
||||
temp = index_y[indx_min]
|
||||
y_min = torch.min(temp).item()
|
||||
return (x_min, y_min)
|
||||
|
||||
def find_comp_coordinate(self, layout, comp_pixel_size, comp_index):
|
||||
layout_flag = copy.deepcopy(layout)
|
||||
|
||||
indx, indy = torch.where(layout_flag == 1)
|
||||
x_min1, y_min1 = self.find_left_top_point(indx, indy)
|
||||
x_max1 = x_min1 + comp_pixel_size[comp_index[0], 0] - 1
|
||||
y_max1 = y_min1 + comp_pixel_size[comp_index[0], 1] - 1
|
||||
layout_flag[x_min1: x_max1 + 1, y_min1: y_max1 + 1] = 0
|
||||
|
||||
indx, indy = torch.where(layout_flag == 1)
|
||||
x_min2, y_min2 = self.find_left_top_point(indx, indy)
|
||||
x_max2 = x_min2 + comp_pixel_size[comp_index[1], 0] - 1
|
||||
y_max2 = y_min2 + comp_pixel_size[comp_index[1], 1] - 1
|
||||
layout_flag[x_min2: x_max2 + 1, y_min2: y_max2 + 1] = 0
|
||||
if torch.sum(layout_flag) == 0:
|
||||
return ([x_min1, x_max1, y_min1, y_max1], [x_min2, x_max2, y_min2, y_max2])
|
||||
else:
|
||||
layout_flag = copy.deepcopy(layout)
|
||||
x_max1 = x_min1 + comp_pixel_size[comp_index[1], 0] - 1
|
||||
y_max1 = y_min1 + comp_pixel_size[comp_index[1], 1] - 1
|
||||
layout_flag[x_min1: x_max1 + 1, y_min1: y_max1 + 1] = 0
|
||||
indx, indy = torch.where(layout_flag == 1)
|
||||
x_min2, y_min2 = self.find_left_top_point(indx, indy)
|
||||
x_max2 = x_min2 + comp_pixel_size[comp_index[0], 0] - 1
|
||||
y_max2 = y_min2 + comp_pixel_size[comp_index[0], 1] - 1
|
||||
layout_flag[x_min2: x_max2 + 1, y_min2: y_max2 + 1] = 0
|
||||
if torch.sum(layout_flag) == 0:
|
||||
return ([x_min2, x_max2, y_min2, y_max2], [x_min1, x_max1, y_min1, y_max1])
|
||||
else:
|
||||
return None
|
||||
# -------------------tool functions--------------------------#
|
||||
|
||||
# --------------metric functions from here-------------------#
|
||||
def mae_global(self):
|
||||
"""
|
||||
calculate the global temperature prediction mean absolute error between input and target.
|
||||
|
||||
Returns:
|
||||
mae: the mean absolute error of the whole field for a batch of samples
|
||||
"""
|
||||
return F.l1_loss(self.input, self.target, reduction='mean') * self.hparams.std_heat
|
||||
|
||||
def mae_boundary(self, output_type='Dirichlet', reduction='mean'):
|
||||
"""
|
||||
calculate the temperature perdiction mean abosolute error of the boundary of the domain.
|
||||
|
||||
The input and target are tensors.
|
||||
|
||||
Args:
|
||||
output_type: 'Dirichlet' for outputing the error of Dirichlet boundary
|
||||
'Neumann' for outputing the error of Neumann boundary
|
||||
Returns:
|
||||
mae: (dirichlet, neumann) -> tuple: the specific (mean for batch > 1) mae in the boundary
|
||||
"""
|
||||
if self.input.dim() == 2:
|
||||
[nx, ny] = self.input.shape
|
||||
batch = 1
|
||||
std_input = self.input.unsqueeze(0).unsqueeze(0).cpu()
|
||||
std_target = self.target.unsqueeze(0).unsqueeze(0).cpu()
|
||||
elif self.input.dim() == 4:
|
||||
[batch, channel, nx, ny] = self.input.shape
|
||||
std_input = self.input.cpu()
|
||||
std_target = self.target.cpu()
|
||||
if channel != 1:
|
||||
raise ValueError('Please input tensors with channel = 1.')
|
||||
else:
|
||||
raise ValueError("Please input four-dim or two-dim tensors with (batch * 1 *) N * N.")
|
||||
|
||||
num_boundaryelement = 2*nx + 2*ny - 4 # 边界元素总数
|
||||
# 初始化边界总 mask
|
||||
mask = torch.zeros([nx, ny])
|
||||
mask[..., 0, :] = 1
|
||||
mask[..., -1, :] = 1
|
||||
mask[..., :, 0] = 1
|
||||
mask[..., :, -1] = 1
|
||||
if self.boundary == 'all_walls':
|
||||
num_dBC = num_boundaryelement
|
||||
num_nBC = 0
|
||||
dBC_mask = mask
|
||||
nBC_mask = mask - dBC_mask
|
||||
else:
|
||||
[index_x, index_y] = torch.where(self.target[0, 0, :, :] == torch.min(self.target[0, 0, :, :]))
|
||||
dBC_mask = torch.zeros_like(mask)
|
||||
num_dBC = torch.max(torch.tensor([index_x[-1] - index_x[0] + 1, (index_y[-1] - index_y[0] + 1)])).item()
|
||||
num_nBC = num_boundaryelement - num_dBC
|
||||
dBC_mask[index_x, index_y] = 1
|
||||
nBC_mask = mask - dBC_mask
|
||||
dBC_mask.unsqueeze_(0).unsqueeze_(0)
|
||||
nBC_mask.unsqueeze_(0).unsqueeze_(0)
|
||||
|
||||
dBC_input = std_input * dBC_mask
|
||||
dBC_target = std_target * dBC_mask
|
||||
nBC_input = std_input * nBC_mask
|
||||
nBC_target = std_target * nBC_mask
|
||||
|
||||
dirichletBC_mae = torch.sum(torch.abs(dBC_input - dBC_target), (1, 2, 3)) / num_dBC
|
||||
neumannBC_mae = (torch.sum(torch.abs(nBC_input - nBC_target), (1, 2, 3)) / num_nBC if num_nBC else torch.zeros([batch]))
|
||||
if reduction == 'mean':
|
||||
dir_mae = torch.mean(dirichletBC_mae)
|
||||
neu_mae = torch.mean(neumannBC_mae)
|
||||
elif reduction == 'max':
|
||||
dir_mae = torch.max(dirichletBC_mae)
|
||||
neu_mae = torch.max(neumannBC_mae)
|
||||
else:
|
||||
raise ValueError("Please input reduction with 'mean' or 'max'.")
|
||||
if output_type == 'Dirichlet':
|
||||
return dir_mae * self.hparams.std_heat
|
||||
elif output_type == 'Neumann':
|
||||
return neu_mae * self.hparams.std_heat
|
||||
else:
|
||||
raise ValueError("Please input the right boundary type ('Dirichlet' or 'Neumann').")
|
||||
|
||||
def mae_component(self, xs=None, ys=None):
|
||||
"""
|
||||
calculate the prediction mean absolute error of component-covering area
|
||||
|
||||
Args:
|
||||
xs: meshgrid, N * N, when mesh = 'nonuniform', it is needed.
|
||||
ys: meshgrid, N * N, when mesh = 'nonuniform', it is needed.
|
||||
Returns:
|
||||
comp_mae: -> list: with N elements
|
||||
Note:
|
||||
xs and ys have been generated and added automatically and specifically.
|
||||
"""
|
||||
if self.input.dim() != self.layout.dim():
|
||||
raise ValueError("Please input 'layout' with the same size as 'input' tensors.")
|
||||
|
||||
if self.input.dim() == 2:
|
||||
[nx, ny] = self.input.shape
|
||||
batch = 1
|
||||
std_input = self.input.unsqueeze(0).unsqueeze(0).cpu()
|
||||
std_target = self.target.unsqueeze(0).unsqueeze(0).cpu()
|
||||
std_layout = self.layout.unsqueeze(0).unsqueeze(0).cpu()
|
||||
elif self.input.dim() == 4:
|
||||
[batch, channel, nx, ny] = self.input.shape
|
||||
std_input = self.input.cpu()
|
||||
std_target = self.target.cpu()
|
||||
std_layout = self.layout
|
||||
if channel != 1:
|
||||
raise ValueError('Please input tensors with channel = 1.')
|
||||
else:
|
||||
raise ValueError("Please input four-dim or two-dim tensors with (batch * 1 *) N * N.")
|
||||
|
||||
domain_length = self.L
|
||||
|
||||
mesh = 'uniform'
|
||||
if self.boundary == 'one_point':
|
||||
mesh = 'nonuniform'
|
||||
comp_mae_max_batch = torch.zeros(batch)
|
||||
for k in range(batch):
|
||||
single_input = std_input[k, 0, :, :]
|
||||
single_target = std_target[k, 0, :, :]
|
||||
single_layout = std_layout[k, 0, :, :]
|
||||
|
||||
location = self.identify_component_boundary(single_layout, self.power, self.boundary)
|
||||
comp_num = len(self.power)
|
||||
comp_mae = []
|
||||
comp_mask = torch.zeros([comp_num, nx, ny])
|
||||
comp_mae = torch.zeros([comp_num])
|
||||
for i in range(comp_num):
|
||||
[xmin, xmax, ymin, ymax] = location[i, :].numpy().astype(int)
|
||||
mask = torch.zeros(nx, ny)
|
||||
if mesh == 'uniform':
|
||||
mask[xmin:(xmax + 1), ymin:(ymax + 1)] = 1
|
||||
num_element = (xmax - xmin + 1) * (ymax - ymin + 1)
|
||||
else:
|
||||
if xs is None or ys is None:
|
||||
xs = torch.linspace(0, domain_length, steps=200) # 生成200个均匀排列的数
|
||||
ys = torch.linspace(0, domain_length, steps=200)
|
||||
# 对应有限差分计算过程中的网格自适应加密函数
|
||||
xs = 4 / ((xs[-1] - xs[0])**2) * ((xs - (xs[-1] + xs[0]) / 2)**3) + (xs[0] + xs[-1]) / 2
|
||||
ys = ys**2 / (ys[0] + ys[-1]) + ys[0] * ys[-1] / (ys[0] + ys[-1])
|
||||
xs, ys = torch.meshgrid(xs, ys)
|
||||
x_min = xmin * domain_length / nx
|
||||
x_max = (xmax + 1) * domain_length / nx
|
||||
y_min = ymin * domain_length / ny
|
||||
y_max = (ymax + 1) * domain_length / ny
|
||||
ind = (xs >= x_min) & (xs <= x_max) & (ys >= y_min) & (ys <= y_max)
|
||||
mask[ind] = 1
|
||||
num_element = torch.sum(mask).item()
|
||||
comp_mask[i, :, :] = mask
|
||||
|
||||
comp_input = single_input * mask
|
||||
comp_target = single_target * mask
|
||||
|
||||
mae = torch.sum(torch.abs(comp_input - comp_target)) / num_element
|
||||
comp_mae[i] = mae
|
||||
comp_mae_max = torch.max(comp_mae)
|
||||
comp_mae_max_batch[k] = comp_mae_max
|
||||
return torch.mean(comp_mae_max_batch) * self.hparams.std_heat
|
||||
|
||||
def value_and_pos_error_of_maximum_temperature(self, output_type='value'):
|
||||
"""
|
||||
calculate the absolute error of the maximum temperature between input and target
|
||||
|
||||
Args:
|
||||
output_type: 'value' for outputing the value error of maximum temperature
|
||||
'position' for outputing the position error of maximum temperature
|
||||
Returns:
|
||||
error_max_tem: batch : the error of the maximum temperature between input and target
|
||||
error_max_tem_pos: batch : the element error of the position of the maximum temperature
|
||||
"""
|
||||
if self.input.dim() == 2:
|
||||
[nx, ny] = self.input.shape
|
||||
batch = 1
|
||||
std_input = self.input.unsqueeze(0)
|
||||
std_target = self.target.unsqueeze(0)
|
||||
elif self.input.dim() == 4:
|
||||
[batch, channel, nx, ny] = self.input.shape
|
||||
std_input = self.input.squeeze(1)
|
||||
std_target = self.target.squeeze(1)
|
||||
if channel != 1:
|
||||
raise ValueError('Please input tensors with channel = 1.')
|
||||
else:
|
||||
raise ValueError("Please input four-dim or two-dim tensors with (batch * 1 *) N * N.")
|
||||
|
||||
[input_max_tem, input_ind] = torch.max(std_input.reshape(batch, -1), 1)
|
||||
[target_max_tem, target_ind] = torch.max(std_target.reshape(batch, -1), 1)
|
||||
# 计算最高温的误差
|
||||
error_max_temp = torch.abs(input_max_tem - target_max_tem)
|
||||
# 找出最高温对应位置
|
||||
input_max_tem_pos = torch.zeros(batch, 2)
|
||||
target_max_tem_pos = torch.zeros(batch, 2)
|
||||
for i in range(batch):
|
||||
ind1 = input_ind[i].item()
|
||||
ind2 = target_ind[i].item()
|
||||
flag = ind1 % ny
|
||||
ind1_x = ((ind1 // ny) if flag > 0 else (ind1 // ny - 1))
|
||||
ind1_y = ((flag - 1) if flag > 0 else (ny - 1))
|
||||
flag = ind2 % ny
|
||||
ind2_x = ((ind2 // ny) if flag > 0 else (ind2 // ny - 1))
|
||||
ind2_y = ((flag - 1) if flag > 0 else (ny - 1))
|
||||
input_max_tem_pos[i, :] = torch.Tensor([ind1_x, ind1_y])
|
||||
target_max_tem_pos[i, :] = torch.Tensor([ind2_x, ind2_y])
|
||||
diff_pos = input_max_tem_pos - target_max_tem_pos
|
||||
error_max_temp_pos = torch.sum(diff_pos * diff_pos, dim=1).sqrt_()
|
||||
if output_type == 'value':
|
||||
return torch.mean(error_max_temp) * self.hparams.std_heat
|
||||
elif output_type == 'position':
|
||||
return torch.mean(error_max_temp_pos)
|
||||
else:
|
||||
return ValueError("Please input the right output type ('value' or 'position').")
|
||||
|
||||
def max_tem_spearmanr(self):
|
||||
"""
|
||||
calculate the indicator (spearmanr) of the maximum temperature between input and target
|
||||
|
||||
Returns:
|
||||
rho: [-1, 1]
|
||||
p_value: the smaller the better. (ideal: p_value < 0.05)
|
||||
"""
|
||||
if self.input.dim() == 2:
|
||||
[nx, ny] = self.input.shape
|
||||
batch = 1
|
||||
std_input = self.input.unsqueeze(0)
|
||||
std_target = self.target.unsqueeze(0)
|
||||
elif self.input.dim() == 4:
|
||||
[batch, channel, nx, ny] = self.input.shape
|
||||
std_input = self.input.squeeze(1)
|
||||
std_target = self.target.squeeze(1)
|
||||
if channel != 1:
|
||||
raise ValueError('Please input tensors with channel = 1.')
|
||||
else:
|
||||
raise ValueError("Please input four-dim or two-dim tensors with (batch * 1 *) N * N.")
|
||||
|
||||
if batch == 1:
|
||||
raise ValueError('please provide a batch of samples (batch > 1).')
|
||||
input_max_tem = torch.max(std_input.reshape(batch, -1), 1)[0].data.cpu().numpy()
|
||||
target_max_tem = torch.max(std_target.reshape(batch, -1), 1)[0].data.cpu().numpy()
|
||||
rho, p_value = spearmanr(target_max_tem, input_max_tem)
|
||||
return torch.tensor(rho)
|
||||
|
||||
def global_image_spearmanr(self):
|
||||
"""
|
||||
calculate the indicator (spearmanr) correlation coefficient between input and target
|
||||
|
||||
Returns:
|
||||
rho: [-1, 1]
|
||||
p_value: the smaller the better. (ideal: p_value < 0.05)
|
||||
"""
|
||||
if self.input.dim() == 2:
|
||||
[nx, ny] = self.input.shape
|
||||
batch = 1
|
||||
std_input = self.input.unsqueeze(0)
|
||||
std_target = self.target.unsqueeze(0)
|
||||
elif self.input.dim() == 4:
|
||||
[batch, channel, nx, ny] = self.input.shape
|
||||
std_input = self.input.squeeze(1)
|
||||
std_target = self.target.squeeze(1)
|
||||
if channel != 1:
|
||||
raise ValueError('Please input tensors with channel = 1.')
|
||||
else:
|
||||
raise ValueError("Please input four-dim or two-dim tensors with (batch * 1 *) N * N.")
|
||||
|
||||
spear_batch = torch.zeros(batch)
|
||||
for i in range(batch):
|
||||
single_input = std_input[i, :, :].reshape(-1).data.cpu().numpy()
|
||||
single_target = std_target[i, :, :].reshape(-1).data.cpu().numpy()
|
||||
rho, p_value = spearmanr(single_input, single_target)
|
||||
spear_batch[i] = rho
|
||||
return torch.mean(spear_batch)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
data_config = Path(__file__).absolute().parent.parent.parent / "config/data.yml"
|
||||
data_yaml = open(data_config, 'r', encoding='gbk')
|
||||
data = yaml.load(data_yaml, Loader=yaml.FullLoader)
|
||||
L = data['length']
|
||||
power = data['powers']
|
||||
comp_size = data['units']
|
||||
|
||||
print(np.array(L))
|
||||
print(np.array(power))
|
||||
print(np.array(comp_size))
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
from .unet import *
|
||||
from .fcn import *
|
||||
from .segnet import *
|
||||
from .fpn import *
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
from .alexnet import *
|
||||
from .resnet import *
|
||||
from .vgg import *
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
Alexnet backbone
|
||||
|
||||
"""
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.model_zoo as model_zoo
|
||||
|
||||
|
||||
__all__ = ["AlexNet"]
|
||||
|
||||
|
||||
class AlexNet(nn.Module):
|
||||
def __init__(self, in_channels=1, bn=False):
|
||||
super(AlexNet, self).__init__()
|
||||
self.features3 = nn.Sequential(
|
||||
# kernel(11, 11) -> kernel(7, 7)
|
||||
nn.Conv2d(in_channels=in_channels, out_channels=64,
|
||||
kernel_size=7, stride=4, padding=3),
|
||||
nn.BatchNorm2d(64) if bn else nn.GroupNorm(32, 64),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
# padding=0 -> padding=1
|
||||
self.features4 = nn.Sequential(
|
||||
nn.Conv2d(in_channels=64, out_channels=192, kernel_size=5, padding=2),
|
||||
nn.BatchNorm2d(192) if bn else nn.GroupNorm(32, 192),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.features5 = nn.Sequential(
|
||||
nn.Conv2d(in_channels=192, out_channels=384, kernel_size=3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.features3(x)
|
||||
x, indices3 = self.maxpool(x)
|
||||
x = self.features4(x)
|
||||
x, indices4 = self.maxpool(x)
|
||||
x = self.features5(x)
|
||||
x, indices5 = self.maxpool(x)
|
||||
return x
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = torch.zeros(8, 1, 200, 200)
|
||||
net = Alexnet()
|
||||
print(net)
|
||||
y = net(x)
|
||||
print()
|
||||
|
|
@ -0,0 +1,233 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
ResNet backbone
|
||||
|
||||
"""
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.model_zoo as model_zoo
|
||||
|
||||
|
||||
__all__ = ["ResNet", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152"]
|
||||
|
||||
|
||||
model_urls = {
|
||||
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
|
||||
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
|
||||
"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
|
||||
"resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
|
||||
"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
|
||||
}
|
||||
|
||||
|
||||
def conv3x3(in_planes, out_planes, stride=1):
|
||||
"""3x3 convolution with padding"""
|
||||
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||
|
||||
|
||||
class BasicBlock(nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv1 = conv3x3(inplanes, planes, stride)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.conv2 = conv3x3(planes, planes)
|
||||
self.bn2 = nn.BatchNorm2d(planes)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(x)
|
||||
|
||||
out += residual
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||
super(Bottleneck, self).__init__()
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.conv2 = nn.Conv2d(planes,
|
||||
planes,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=1,
|
||||
bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(planes)
|
||||
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(planes * 4)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
if self.downsample is not None:
|
||||
residual = self.downsample(x)
|
||||
|
||||
out += residual
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet(nn.Module):
|
||||
def __init__(self, block, layers, in_channels=1):
|
||||
self.inplanes = 64
|
||||
super(ResNet, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(64)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
self.layer1 = self._make_layer(block, 64, layers[0])
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / n))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.weight.data.fill_(1)
|
||||
m.bias.data.zero_()
|
||||
|
||||
def _make_layer(self, block, planes, blocks, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.inplanes,
|
||||
planes * block.expansion,
|
||||
kernel_size=1,
|
||||
stride=stride,
|
||||
bias=False),
|
||||
nn.BatchNorm2d(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.inplanes, planes, stride, downsample))
|
||||
self.inplanes = planes * block.expansion
|
||||
for i in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def _load_pretrained_model(self, model_url):
|
||||
pretrain_dict = model_zoo.load_url(model_url)
|
||||
model_dict = {}
|
||||
state_dict = self.state_dict()
|
||||
for k, v in pretrain_dict.items():
|
||||
if k in state_dict:
|
||||
model_dict[k] = v
|
||||
state_dict.update(model_dict)
|
||||
self.load_state_dict(state_dict)
|
||||
|
||||
def forward(self, input):
|
||||
x = self.conv1(input)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
c1 = self.maxpool(x)
|
||||
c2 = self.layer1(c1)
|
||||
c3 = self.layer2(c2)
|
||||
c4 = self.layer3(c3)
|
||||
c5 = self.layer4(c4)
|
||||
return c1, c2, c3, c4, c5
|
||||
|
||||
|
||||
def resnet18(pretrained=False, in_channels=1, **kwargs):
|
||||
"""Constructs a ResNet-18 model.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
model = ResNet(BasicBlock, [2, 2, 2, 2], in_channels=in_channels, **kwargs)
|
||||
if pretrained:
|
||||
model._load_pretrained_model(model_urls['resnet18'])
|
||||
return model
|
||||
|
||||
|
||||
def resnet34(pretrained=False, in_channels=1, **kwargs):
|
||||
"""Constructs a ResNet-34 model.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
model = ResNet(BasicBlock, [3, 4, 6, 3], in_channels=in_channels, **kwargs)
|
||||
if pretrained:
|
||||
model._load_pretrained_model(model_urls['resnet34'])
|
||||
return model
|
||||
|
||||
|
||||
def resnet50(pretrained=False, in_channels=1, **kwargs):
|
||||
"""Constructs a ResNet-50 model.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
model = ResNet(Bottleneck, [3, 4, 6, 3], in_channels=in_channels, **kwargs)
|
||||
if pretrained:
|
||||
model._load_pretrained_model(model_urls["resnet50"])
|
||||
return model
|
||||
|
||||
|
||||
def resnet101(pretrained=False, in_channels=1, **kwargs):
|
||||
"""Constructs a ResNet-101 model.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
model = ResNet(Bottleneck, [3, 4, 23, 3], in_channels=in_channels, **kwargs)
|
||||
if pretrained:
|
||||
model._load_pretrained_model(model_urls["resnet101"])
|
||||
return model
|
||||
|
||||
|
||||
def resnet152(pretrained=False, in_channels=1, **kwargs):
|
||||
"""Constructs a ResNet-152 model.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
"""
|
||||
model = ResNet(Bottleneck, [3, 8, 36, 3], in_channels=in_channels, **kwargs)
|
||||
if pretrained:
|
||||
model._load_pretrained_model(model_urls["resnet152"])
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = torch.zeros(8, 1, 640, 640)
|
||||
net = resnet50()
|
||||
print(net)
|
||||
y = net(x)
|
||||
print()
|
||||
|
|
@ -0,0 +1,196 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
VGG backbone
|
||||
|
||||
"""
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Union, List, Dict, Any, cast
|
||||
|
||||
from src.utils.vgg_utils import load_state_dict_from_url
|
||||
|
||||
|
||||
__all__ = [
|
||||
"VGG", "vgg11", "vgg11_bn", "vgg13", "vgg13_bn", "vgg16", "vgg16_bn",
|
||||
"vgg19_bn", "vgg19",
|
||||
]
|
||||
|
||||
|
||||
model_urls = {
|
||||
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
|
||||
'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
|
||||
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
|
||||
'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
|
||||
'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
|
||||
'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
|
||||
'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
|
||||
'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
|
||||
}
|
||||
|
||||
|
||||
class VGG(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
features: nn.Module,
|
||||
num_classes: int = 1000,
|
||||
init_weights: bool = True
|
||||
) -> None:
|
||||
super(VGG, self).__init__()
|
||||
self.features = features
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
|
||||
self.classifier = nn.Sequential(
|
||||
nn.Linear(512 * 7 * 7, 4096),
|
||||
nn.ReLU(True),
|
||||
nn.Dropout(),
|
||||
nn.Linear(4096, 4096),
|
||||
nn.ReLU(True),
|
||||
nn.Dropout(),
|
||||
nn.Linear(4096, num_classes),
|
||||
)
|
||||
if init_weights:
|
||||
self._initialize_weights()
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.features(x)
|
||||
x = self.avgpool(x)
|
||||
x = torch.flatten(x, 1)
|
||||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
def _initialize_weights(self) -> None:
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.normal_(m.weight, 0, 0.01)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
|
||||
def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential:
|
||||
layers: List[nn.Module] = []
|
||||
in_channels = 3
|
||||
for v in cfg:
|
||||
if v == 'M':
|
||||
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
|
||||
else:
|
||||
v = cast(int, v)
|
||||
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
|
||||
if batch_norm:
|
||||
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
|
||||
else:
|
||||
layers += [conv2d, nn.ReLU(inplace=True)]
|
||||
in_channels = v
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
cfgs: Dict[str, List[Union[str, int]]] = {
|
||||
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
||||
'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
||||
'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
|
||||
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
|
||||
}
|
||||
|
||||
|
||||
def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG:
|
||||
if pretrained:
|
||||
kwargs['init_weights'] = False
|
||||
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
|
||||
if pretrained:
|
||||
state_dict = load_state_dict_from_url(model_urls[arch],
|
||||
progress=progress)
|
||||
model.load_state_dict(state_dict)
|
||||
return model
|
||||
|
||||
|
||||
def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 11-layer model (configuration "A") from
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 11-layer model (configuration "A") with batch normalization
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 13-layer model (configuration "B")
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 13-layer model (configuration "B") with batch normalization
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 16-layer model (configuration "D")
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 16-layer model (configuration "D") with batch normalization
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 19-layer model (configuration "E")
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
|
||||
r"""VGG 19-layer model (configuration 'E') with batch normalization
|
||||
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)
|
||||
|
|
@ -0,0 +1,217 @@
|
|||
# encoding: utf-8
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from .backbone import *
|
||||
|
||||
|
||||
__all__ = [
|
||||
"FCN_VGG", "FCN_AlexNet", "FCN_ResNet18", "FCN_ResNet34",
|
||||
"FCN_ResNet50", "FCN_ResNet101", "FCN_ResNet152",
|
||||
]
|
||||
|
||||
|
||||
class Conv3x3GNReLU(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, out_channels, upsample=False):
|
||||
super().__init__()
|
||||
self.upsample = upsample
|
||||
self.block = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False),
|
||||
nn.GroupNorm(32, out_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
def forward(self, x, size):
|
||||
if self.upsample:
|
||||
x = F.interpolate(x, size=size, mode="bilinear", align_corners=True)
|
||||
x = self.block(x)
|
||||
return x
|
||||
|
||||
|
||||
class FCN_VGG(nn.Module):
|
||||
|
||||
def __init__(self, inter_channels=256, in_channels=1, bn=False):
|
||||
super(FCN_VGG, self).__init__()
|
||||
vgg = vgg16()
|
||||
features, classifier = list(vgg.features.children()), list(vgg.classifier.children())
|
||||
|
||||
if in_channels != 3:
|
||||
features[0] = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1)
|
||||
for f in features:
|
||||
if 'MaxPool' in f.__class__.__name__:
|
||||
f.ceil_mode = True
|
||||
elif 'ReLU' in f.__class__.__name__:
|
||||
f.inplace = True
|
||||
|
||||
features_temp = []
|
||||
if not bn:
|
||||
for i in range(len(features)):
|
||||
features_temp.append(features[i])
|
||||
if isinstance(features[i], nn.Conv2d):
|
||||
features_temp.append(nn.GroupNorm(32, features[i].out_channels))
|
||||
|
||||
self.features3 = nn.Sequential(*features[:17])
|
||||
self.features4 = nn.Sequential(*features[17: 24])
|
||||
self.features5 = nn.Sequential(*features[24:])
|
||||
|
||||
self.score_pool3 = nn.Conv2d(256, inter_channels, kernel_size=1)
|
||||
self.score_pool4 = nn.Conv2d(512, inter_channels, kernel_size=1)
|
||||
|
||||
fc6 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
|
||||
fc7 = nn.Conv2d(512, 512, kernel_size=1)
|
||||
score_fr = nn.Conv2d(512, inter_channels, kernel_size=1)
|
||||
|
||||
self.score_fr = nn.Sequential(
|
||||
fc6, nn.ReLU(inplace=True), fc7, nn.ReLU(inplace=True), score_fr
|
||||
)
|
||||
self.upscore2 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.upscore_pool4 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.final_conv = nn.Conv2d(inter_channels, 1, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
pool3 = self.features3(x)
|
||||
pool4 = self.features4(pool3)
|
||||
pool5 = self.features5(pool4)
|
||||
|
||||
score_fr = self.score_fr(pool5)
|
||||
upscore2 = self.upscore2(score_fr, pool4.size()[-2:])
|
||||
|
||||
score_pool4 = self.score_pool4(pool4)
|
||||
upscore_pool4 = self.upscore_pool4(score_pool4 + upscore2, pool3.size()[-2:])
|
||||
|
||||
score_pool3 = self.score_pool3(pool3)
|
||||
upscore8 = F.interpolate(self.final_conv(score_pool3 + upscore_pool4), x.size()[-2:], mode='bilinear', align_corners=True)
|
||||
return upscore8
|
||||
|
||||
|
||||
class FCN_AlexNet(nn.Module):
|
||||
|
||||
def __init__(self, inter_channels=256, in_channels=1):
|
||||
super(FCN_AlexNet, self).__init__()
|
||||
self.alexnet = AlexNet(in_channels=in_channels)
|
||||
|
||||
self.score_pool3 = nn.Conv2d(64, inter_channels, kernel_size=1)
|
||||
self.score_pool4 = nn.Conv2d(192, inter_channels, kernel_size=1)
|
||||
|
||||
fc6 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
|
||||
fc7 = nn.Conv2d(512, 512, kernel_size=1)
|
||||
score_fr = nn.Conv2d(512, inter_channels, kernel_size=1)
|
||||
|
||||
self.score_fr = nn.Sequential(
|
||||
fc6, nn.ReLU(inplace=True), fc7, nn.ReLU(inplace=True), score_fr
|
||||
)
|
||||
self.upscore2 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.upscore_pool4 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.final_conv = nn.Conv2d(inter_channels, 1, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
pool3 = self.alexnet.features3(x)
|
||||
pool4 = self.alexnet.features4(pool3)
|
||||
pool5 = self.alexnet.features5(pool4)
|
||||
|
||||
score_fr = self.score_fr(pool5)
|
||||
upscore2 = self.upscore2(score_fr, pool4.size()[-2:])
|
||||
|
||||
score_pool4 = self.score_pool4(pool4)
|
||||
upscore_pool4 = self.upscore_pool4(score_pool4 + upscore2, pool3.size()[-2:])
|
||||
|
||||
score_pool3 = self.score_pool3(pool3)
|
||||
upscore8 = F.interpolate(self.final_conv(score_pool3 + upscore_pool4), x.size()[-2:],
|
||||
mode='bilinear', align_corners=True)
|
||||
return upscore8
|
||||
|
||||
|
||||
class FCN_ResNet(nn.Module):
|
||||
|
||||
def __init__(self, backbone, inter_channels=256):
|
||||
super(FCN_ResNet, self).__init__()
|
||||
self.backbone = backbone
|
||||
|
||||
self.score_pool3 = nn.Conv2d(backbone.layer2[0].downsample[1].num_features,
|
||||
inter_channels, kernel_size=1)
|
||||
self.score_pool4 = nn.Conv2d(backbone.layer3[0].downsample[1].num_features,
|
||||
inter_channels, kernel_size=1)
|
||||
|
||||
fc6 = nn.Conv2d(backbone.layer4[0].downsample[1].num_features,
|
||||
512, kernel_size=3, padding=1)
|
||||
fc7 = nn.Conv2d(512, 512, kernel_size=1)
|
||||
score_fr = nn.Conv2d(512, inter_channels, kernel_size=1)
|
||||
self.score_fr = nn.Sequential(
|
||||
fc6, nn.ReLU(inplace=True), fc7, nn.ReLU(inplace=True), score_fr
|
||||
)
|
||||
self.upscore2 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.upscore_pool4 = Conv3x3GNReLU(inter_channels, inter_channels, upsample=True)
|
||||
self.final_conv = nn.Conv2d(inter_channels, 1, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
_, _, pool3, pool4, pool5 = self.backbone(x)
|
||||
|
||||
score_fr = self.score_fr(pool5)
|
||||
upscore2 = self.upscore2(score_fr, pool4.size()[-2:])
|
||||
|
||||
score_pool4 = self.score_pool4(pool4)
|
||||
upscore_pool4 = self.upscore_pool4(score_pool4 + upscore2, pool3.size()[-2:])
|
||||
|
||||
score_pool3 = self.score_pool3(pool3)
|
||||
upscore8 = F.interpolate(self.final_conv(score_pool3 + upscore_pool4), x.size()[-2:], mode='bilinear', align_corners=True)
|
||||
return upscore8
|
||||
|
||||
|
||||
def FCN_ResNet18(in_channels=1, **kwargs):
|
||||
"""
|
||||
Constructs FCN based on ResNet18 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet18(in_channels=in_channels)
|
||||
model = FCN_ResNet(backbone_net, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FCN_ResNet34(in_channels=1, **kwargs):
|
||||
"""
|
||||
Constructs FCN based on ResNet18 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet34(in_channels=in_channels)
|
||||
model = FCN_ResNet(backbone_net, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FCN_ResNet50(in_channels=1, **kwargs):
|
||||
"""
|
||||
Constructs FCN based on ResNet50 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet50(in_channels=in_channels)
|
||||
model = FCN_ResNet(backbone_net, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FCN_ResNet101(in_channels=1, **kwargs):
|
||||
"""
|
||||
Constructs FCN based on ResNet101 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet101(in_channels=in_channels)
|
||||
model = FCN_ResNet(backbone_net, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FCN_ResNet152(in_channels=1, **kwargs):
|
||||
"""
|
||||
Constructs FCN based on ResNet18 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet152(in_channels=in_channels)
|
||||
model = FCN_ResNet(backbone_net, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = FCN_AlexNet(in_channels=1, inter_channels=128)
|
||||
x = torch.randn(1, 1, 200, 200)
|
||||
with torch.no_grad():
|
||||
y = model(x)
|
||||
print(y.shape)
|
||||
|
|
@ -0,0 +1,170 @@
|
|||
# encoding: utf-8
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from src.utils.model_init import weights_init
|
||||
from .backbone import *
|
||||
|
||||
|
||||
__all__ = ["FPN_ResNet18", "FPN_ResNet34", "FPN_ResNet50", "FPN_ResNet101", "FPN_ResNet152"]
|
||||
|
||||
|
||||
class Conv3x3GNReLU(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, upsample=False):
|
||||
super().__init__()
|
||||
self.upsample = upsample
|
||||
self.block = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False),
|
||||
nn.GroupNorm(32, out_channels),
|
||||
# nn.BatchNorm2d(out_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
def forward(self, x, size):
|
||||
x = self.block(x)
|
||||
if self.upsample:
|
||||
x = F.interpolate(x, size=size, mode="bilinear", align_corners=True)
|
||||
return x
|
||||
|
||||
|
||||
class FPNBlock(nn.Module):
|
||||
def __init__(self, pyramid_channels, skip_channels):
|
||||
super().__init__()
|
||||
self.skip_conv = nn.Conv2d(skip_channels, pyramid_channels, kernel_size=1)
|
||||
|
||||
def forward(self, x):
|
||||
x, skip = x
|
||||
x = F.interpolate(x, size=skip.size()[-2:], mode="bilinear", align_corners=True)
|
||||
skip = self.skip_conv(skip)
|
||||
|
||||
x = x + skip
|
||||
return x
|
||||
|
||||
|
||||
class SegmentationBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, n_upsamples=0):
|
||||
super().__init__()
|
||||
|
||||
self.blocks = [Conv3x3GNReLU(in_channels, out_channels, upsample=bool(n_upsamples))]
|
||||
|
||||
if n_upsamples > 1:
|
||||
for _ in range(1, n_upsamples):
|
||||
self.blocks.append(Conv3x3GNReLU(out_channels, out_channels, upsample=True))
|
||||
|
||||
self.blocks_name = []
|
||||
for i, block in enumerate(self.blocks):
|
||||
self.add_module("Block_{}".format(i), block)
|
||||
self.blocks_name.append("Block_{}".format(i))
|
||||
|
||||
def forward(self, x, sizes=[]):
|
||||
for i, block_name in enumerate(self.blocks_name):
|
||||
x = getattr(self, block_name)(x, sizes[i])
|
||||
return x
|
||||
|
||||
|
||||
class FPN_ResNet(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
backbone,
|
||||
encoder_channels,
|
||||
pyramid_channels=256,
|
||||
segmentation_channels=128,
|
||||
final_upsampling=4,
|
||||
final_channels=1,
|
||||
dropout=0.2,
|
||||
):
|
||||
super().__init__()
|
||||
self.backbone = backbone
|
||||
self.backbone.apply(weights_init)
|
||||
self.final_upsampling = final_upsampling
|
||||
self.conv1 = nn.Conv2d(encoder_channels[0],
|
||||
pyramid_channels,
|
||||
kernel_size=(1, 1))
|
||||
|
||||
self.p4 = FPNBlock(pyramid_channels, encoder_channels[1])
|
||||
self.p3 = FPNBlock(pyramid_channels, encoder_channels[2])
|
||||
self.p2 = FPNBlock(pyramid_channels, encoder_channels[3])
|
||||
|
||||
self.s5 = SegmentationBlock(pyramid_channels,
|
||||
segmentation_channels,
|
||||
n_upsamples=3)
|
||||
self.s4 = SegmentationBlock(pyramid_channels,
|
||||
segmentation_channels,
|
||||
n_upsamples=2)
|
||||
self.s3 = SegmentationBlock(pyramid_channels,
|
||||
segmentation_channels,
|
||||
n_upsamples=1)
|
||||
self.s2 = SegmentationBlock(pyramid_channels,
|
||||
segmentation_channels,
|
||||
n_upsamples=0)
|
||||
|
||||
self.dropout = nn.Dropout2d(p=dropout, inplace=True)
|
||||
self.final_conv = nn.Conv2d(segmentation_channels,
|
||||
final_channels,
|
||||
kernel_size=1,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.backbone(x)
|
||||
|
||||
_, c2, c3, c4, c5 = x
|
||||
|
||||
p5 = self.conv1(c5)
|
||||
p4 = self.p4([p5, c4])
|
||||
p3 = self.p3([p4, c3])
|
||||
p2 = self.p2([p3, c2])
|
||||
|
||||
s5 = self.s5(p5, sizes=[c4.size()[-2:], c3.size()[-2:], c2.size()[-2:]])
|
||||
s4 = self.s4(p4, sizes=[c3.size()[-2:], c2.size()[-2:]])
|
||||
s3 = self.s3(p3, sizes=[c2.size()[-2:]])
|
||||
s2 = self.s2(p2, sizes=[c2.size()[-2:]])
|
||||
|
||||
# x = torch.cat([s5, s4, s3, s2], dim=1)
|
||||
x = s5 + s4 + s3 + s2
|
||||
|
||||
x = self.dropout(x)
|
||||
x = self.final_conv(x)
|
||||
|
||||
if self.final_upsampling is not None and self.final_upsampling > 1:
|
||||
x = F.interpolate(x, scale_factor=self.final_upsampling, mode="bilinear", align_corners=True)
|
||||
return x
|
||||
|
||||
|
||||
def FPN_ResNet18(in_channels=1, **kwargs):
|
||||
"""FPN with ResNet18 as backbone
|
||||
"""
|
||||
backbone = resnet18(in_channels=in_channels)
|
||||
model = FPN_ResNet(backbone, encoder_channels=[512, 256, 128, 64], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FPN_ResNet34(in_channels=1, **kwargs):
|
||||
"""FPN with ResNet18 as backbone
|
||||
"""
|
||||
backbone = resnet34(in_channels=in_channels)
|
||||
model = FPN_ResNet(backbone, encoder_channels=[512, 256, 128, 64], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FPN_ResNet50(in_channels=1, **kwargs):
|
||||
"""FPN with ResNet50 as backbone
|
||||
"""
|
||||
backbone = resnet50(in_channels=in_channels)
|
||||
model = FPN_ResNet(backbone, encoder_channels=[2048, 1024, 512, 256], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FPN_ResNet101(in_channels=1, **kwargs):
|
||||
"""FPN with ResNet101 as backbone
|
||||
"""
|
||||
backbone = resnet101(in_channels=in_channels)
|
||||
model = FPN_ResNet(backbone, encoder_channels=[2048, 1024, 512, 256], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def FPN_ResNet152(in_channels=1, **kwargs):
|
||||
"""FPN with ResNet101 as backbone
|
||||
"""
|
||||
backbone = resnet152(in_channels=in_channels)
|
||||
model = FPN_ResNet(backbone, encoder_channels=[2048, 1024, 512, 256], **kwargs)
|
||||
return model
|
||||
|
|
@ -0,0 +1,550 @@
|
|||
# encoding: utf-8
|
||||
from math import ceil
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .backbone import *
|
||||
|
||||
|
||||
__all__ = ["SegNet_VGG", "SegNet_VGG_GN", "SegNet_AlexNet", "SegNet_ResNet18",
|
||||
"SegNet_ResNet50", "SegNet_ResNet101", "SegNet_ResNet34", "SegNet_ResNet152"]
|
||||
|
||||
|
||||
# required class for decoder of SegNet_ResNet
|
||||
class DecoderBottleneck(nn.Module):
|
||||
|
||||
def __init__(self, in_channels):
|
||||
super(DecoderBottleneck, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, in_channels // 4,
|
||||
kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(in_channels // 4)
|
||||
self.conv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4,
|
||||
kernel_size=2, stride=2, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(in_channels // 4)
|
||||
self.conv3 = nn.Conv2d(in_channels // 4, in_channels // 2, 1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(in_channels // 2)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.ConvTranspose2d(in_channels, in_channels // 2,
|
||||
kernel_size=2, stride=2, bias=False),
|
||||
nn.BatchNorm2d(in_channels // 2))
|
||||
|
||||
def forward(self, x):
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
identity = self.downsample(x)
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
# required class for decoder of SegNet_ResNet
|
||||
class LastBottleneck(nn.Module):
|
||||
|
||||
def __init__(self, in_channels):
|
||||
super(LastBottleneck, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, in_channels // 4,
|
||||
kernel_size=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(in_channels // 4)
|
||||
self.conv2 = nn.Conv2d(in_channels // 4, in_channels // 4,
|
||||
kernel_size=3, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(in_channels // 4)
|
||||
self.conv3 = nn.Conv2d(in_channels // 4, in_channels // 4, 1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(in_channels // 4)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_channels, in_channels // 4, kernel_size=1, bias=False),
|
||||
nn.BatchNorm2d(in_channels // 4))
|
||||
|
||||
def forward(self, x):
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv3(out)
|
||||
out = self.bn3(out)
|
||||
|
||||
identity = self.downsample(x)
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
# required class for decoder of SegNet_ResNet
|
||||
class DecoderBasicBlock(nn.Module):
|
||||
|
||||
def __init__(self, in_channels):
|
||||
super(DecoderBasicBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, in_channels // 2,
|
||||
kernel_size=3, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(in_channels // 2)
|
||||
self.conv2 = nn.ConvTranspose2d(in_channels // 2, in_channels // 2,
|
||||
kernel_size=2, stride=2, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(in_channels // 2)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.ConvTranspose2d(in_channels, in_channels // 2,
|
||||
kernel_size=2, stride=2, bias=False),
|
||||
nn.BatchNorm2d(in_channels // 2))
|
||||
|
||||
def forward(self, x):
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
identity = self.downsample(x)
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
class LastBasicBlock(nn.Module):
|
||||
|
||||
def __init__(self, in_channels):
|
||||
super(LastBasicBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, in_channels,
|
||||
kernel_size=3, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(in_channels)
|
||||
self.conv2 = nn.Conv2d(in_channels, in_channels,
|
||||
kernel_size=3, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(in_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.downsample = nn.Sequential(
|
||||
nn.Conv2d(in_channels, in_channels, kernel_size=1, bias=False),
|
||||
nn.BatchNorm2d(in_channels))
|
||||
|
||||
def forward(self, x):
|
||||
out = self.conv1(x)
|
||||
out = self.bn1(out)
|
||||
out = self.relu(out)
|
||||
out = self.conv2(out)
|
||||
out = self.bn2(out)
|
||||
|
||||
identity = self.downsample(x)
|
||||
out += identity
|
||||
out = self.relu(out)
|
||||
return out
|
||||
|
||||
|
||||
class SegNet_VGG(nn.Module):
|
||||
|
||||
def __init__(self, out_channels=1, in_channels=1, pretrained=False):
|
||||
super(SegNet_VGG, self).__init__()
|
||||
vgg_bn = vgg16_bn(pretrained=pretrained)
|
||||
encoder = list(vgg_bn.features.children())
|
||||
|
||||
# Adjust the input size
|
||||
if in_channels != 3:
|
||||
encoder[0] = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
# Encoder, VGG without any maxpooling
|
||||
self.stage1_encoder = nn.Sequential(*encoder[:6])
|
||||
self.stage2_encoder = nn.Sequential(*encoder[7:13])
|
||||
self.stage3_encoder = nn.Sequential(*encoder[14:23])
|
||||
self.stage4_encoder = nn.Sequential(*encoder[24:33])
|
||||
self.stage5_encoder = nn.Sequential(*encoder[34:-1])
|
||||
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
|
||||
|
||||
# Decoder, same as the encoder but reversed, maxpool will not be used
|
||||
decoder = encoder
|
||||
decoder = [i for i in list(reversed(decoder)) if not isinstance(i, nn.MaxPool2d)]
|
||||
# Replace the last conv layer
|
||||
decoder[-1] = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
|
||||
# When reversing, we also reversed conv->batchN->relu, correct it
|
||||
decoder = [item for i in range(0, len(decoder), 3)
|
||||
for item in decoder[i:i + 3][::-1]]
|
||||
# Replace some conv layers & batchN after them
|
||||
for i, module in enumerate(decoder):
|
||||
if isinstance(module, nn.Conv2d):
|
||||
if module.in_channels != module.out_channels:
|
||||
decoder[i + 1] = nn.BatchNorm2d(module.in_channels)
|
||||
decoder[i] = nn.Conv2d(module.out_channels, module.in_channels,
|
||||
kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.stage1_decoder = nn.Sequential(*decoder[0:9])
|
||||
self.stage2_decoder = nn.Sequential(*decoder[9:18])
|
||||
self.stage3_decoder = nn.Sequential(*decoder[18:27])
|
||||
self.stage4_decoder = nn.Sequential(*decoder[27:33])
|
||||
self.stage5_decoder = nn.Sequential(*decoder[33:],
|
||||
nn.Conv2d(64, out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
)
|
||||
self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
|
||||
|
||||
self._initialize_weights(self.stage1_decoder, self.stage2_decoder, self.stage3_decoder,
|
||||
self.stage4_decoder, self.stage5_decoder)
|
||||
|
||||
def _initialize_weights(self, *stages):
|
||||
for modules in stages:
|
||||
for module in modules.modules():
|
||||
if isinstance(module, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.BatchNorm2d):
|
||||
module.weight.data.fill_(1)
|
||||
module.bias.data.zero_()
|
||||
|
||||
def forward(self, x):
|
||||
# Encoder
|
||||
x = self.stage1_encoder(x)
|
||||
x1_size = x.size()
|
||||
x, indices1 = self.pool(x)
|
||||
|
||||
x = self.stage2_encoder(x)
|
||||
x2_size = x.size()
|
||||
x, indices2 = self.pool(x)
|
||||
|
||||
x = self.stage3_encoder(x)
|
||||
x3_size = x.size()
|
||||
x, indices3 = self.pool(x)
|
||||
|
||||
x = self.stage4_encoder(x)
|
||||
x4_size = x.size()
|
||||
x, indices4 = self.pool(x)
|
||||
|
||||
x = self.stage5_encoder(x)
|
||||
x5_size = x.size()
|
||||
x, indices5 = self.pool(x)
|
||||
|
||||
# Decoder
|
||||
x = self.unpool(x, indices=indices5, output_size=x5_size)
|
||||
x = self.stage1_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices4, output_size=x4_size)
|
||||
x = self.stage2_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices3, output_size=x3_size)
|
||||
x = self.stage3_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices2, output_size=x2_size)
|
||||
x = self.stage4_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices1, output_size=x1_size)
|
||||
x = self.stage5_decoder(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SegNet_VGG_GN(nn.Module):
|
||||
|
||||
def __init__(self, out_channels=1, in_channels=3, pretrained=False):
|
||||
super(SegNet_VGG_GN, self).__init__()
|
||||
vgg_bn = vgg16_bn(pretrained=pretrained)
|
||||
encoder = list(vgg_bn.features.children())
|
||||
|
||||
# Adjust the input size
|
||||
if in_channels != 3:
|
||||
encoder[0] = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
#
|
||||
for i in range(len(encoder)):
|
||||
if isinstance(encoder[i], nn.BatchNorm2d):
|
||||
encoder[i] = nn.GroupNorm(32, encoder[i].num_features)
|
||||
|
||||
# Encoder, VGG without any maxpooling
|
||||
self.stage1_encoder = nn.Sequential(*encoder[:6])
|
||||
self.stage2_encoder = nn.Sequential(*encoder[7:13])
|
||||
self.stage3_encoder = nn.Sequential(*encoder[14:23])
|
||||
self.stage4_encoder = nn.Sequential(*encoder[24:33])
|
||||
self.stage5_encoder = nn.Sequential(*encoder[34:-1])
|
||||
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
|
||||
|
||||
# Decoder, same as the encoder but reversed, maxpool will not be used
|
||||
decoder = encoder
|
||||
decoder = [i for i in list(reversed(decoder)) if not isinstance(i, nn.MaxPool2d)]
|
||||
# Replace the last conv layer
|
||||
decoder[-1] = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
|
||||
# When reversing, we also reversed conv->batchN->relu, correct it
|
||||
decoder = [item for i in range(0, len(decoder), 3)
|
||||
for item in decoder[i:i + 3][::-1]]
|
||||
# Replace some conv layers & batchN after them
|
||||
for i, module in enumerate(decoder):
|
||||
if isinstance(module, nn.Conv2d):
|
||||
if module.in_channels != module.out_channels:
|
||||
decoder[i + 1] = nn.GroupNorm(32, module.in_channels)
|
||||
decoder[i] = nn.Conv2d(module.out_channels, module.in_channels,
|
||||
kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.stage1_decoder = nn.Sequential(*decoder[0:9])
|
||||
self.stage2_decoder = nn.Sequential(*decoder[9:18])
|
||||
self.stage3_decoder = nn.Sequential(*decoder[18:27])
|
||||
self.stage4_decoder = nn.Sequential(*decoder[27:33])
|
||||
self.stage5_decoder = nn.Sequential(*decoder[33:], nn.Conv2d(64,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1))
|
||||
self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
|
||||
|
||||
self._initialize_weights(self.stage1_decoder, self.stage2_decoder, self.stage3_decoder,
|
||||
self.stage4_decoder, self.stage5_decoder)
|
||||
|
||||
def _initialize_weights(self, *stages):
|
||||
for modules in stages:
|
||||
for module in modules.modules():
|
||||
if isinstance(module, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.BatchNorm2d):
|
||||
module.weight.data.fill_(1)
|
||||
module.bias.data.zero_()
|
||||
|
||||
def forward(self, x):
|
||||
# Encoder
|
||||
x = self.stage1_encoder(x)
|
||||
x1_size = x.size()
|
||||
x, indices1 = self.pool(x)
|
||||
|
||||
x = self.stage2_encoder(x)
|
||||
x2_size = x.size()
|
||||
x, indices2 = self.pool(x)
|
||||
|
||||
x = self.stage3_encoder(x)
|
||||
x3_size = x.size()
|
||||
x, indices3 = self.pool(x)
|
||||
|
||||
x = self.stage4_encoder(x)
|
||||
x4_size = x.size()
|
||||
x, indices4 = self.pool(x)
|
||||
|
||||
x = self.stage5_encoder(x)
|
||||
x5_size = x.size()
|
||||
x, indices5 = self.pool(x)
|
||||
|
||||
# Decoder
|
||||
x = self.unpool(x, indices=indices5, output_size=x5_size)
|
||||
x = self.stage1_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices4, output_size=x4_size)
|
||||
x = self.stage2_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices3, output_size=x3_size)
|
||||
x = self.stage3_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices2, output_size=x2_size)
|
||||
x = self.stage4_decoder(x)
|
||||
|
||||
x = self.unpool(x, indices=indices1, output_size=x1_size)
|
||||
x = self.stage5_decoder(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SegNet_AlexNet(nn.Module):
|
||||
|
||||
def __init__(self, out_channels=1, in_channels=1, bn=False):
|
||||
super(SegNet_AlexNet, self).__init__()
|
||||
self.stage3_encoder = nn.Sequential(
|
||||
# kernel(11, 11) -> kernel(7, 7)
|
||||
nn.Conv2d(in_channels, 64, kernel_size=7, stride=4, padding=3),
|
||||
nn.BatchNorm2d(64) if bn else nn.GroupNorm(32, 64),
|
||||
nn.ReLU(inplace=True),
|
||||
# padding=0 -> padding=1
|
||||
)
|
||||
self.stage4_encoder = nn.Sequential(
|
||||
nn.Conv2d(64, 192, kernel_size=5, padding=2),
|
||||
nn.BatchNorm2d(192) if bn else nn.GroupNorm(32, 192),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.stage5_encoder = nn.Sequential(
|
||||
nn.Conv2d(192, 384, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(384) if bn else nn.GroupNorm(32, 384),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(384, 256, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(256) if bn else nn.GroupNorm(32, 256),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(256, 256, kernel_size=3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=False, return_indices=True)
|
||||
self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
|
||||
self.stage5_decoder = nn.Sequential(
|
||||
nn.Conv2d(256, 256, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(256) if bn else nn.GroupNorm(32, 256),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(256, 384, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(384) if bn else nn.GroupNorm(32, 384),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(384, 192, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(192) if bn else nn.GroupNorm(32, 192),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.stage4_decoder = nn.Sequential(
|
||||
nn.Conv2d(192, 64, kernel_size=5, padding=2),
|
||||
nn.BatchNorm2d(64) if bn else nn.GroupNorm(32, 64),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.stage3_decoder = nn.Sequential(
|
||||
nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2, bias=False),
|
||||
nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2, bias=False),
|
||||
nn.Conv2d(64, out_channels, kernel_size=7, stride=1, padding=3),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x3 = self.stage3_encoder(x)
|
||||
x3_size = x3.size()
|
||||
x3, indices3 = self.maxpool(x3)
|
||||
x4 = self.stage4_encoder(x3)
|
||||
x4_size = x4.size()
|
||||
x4, indices4 = self.maxpool(x4)
|
||||
x5 = self.stage5_encoder(x4)
|
||||
x5_size = x5.size()
|
||||
x5, indices5 = self.maxpool(x5)
|
||||
|
||||
out = self.unpool(x5, indices=indices5, output_size=x5_size)
|
||||
out = self.stage5_decoder(out)
|
||||
out = self.unpool(out, indices=indices4, output_size=x4_size)
|
||||
out = self.stage4_decoder(out)
|
||||
out = self.unpool(out, indices=indices3, output_size=x3_size)
|
||||
out = self.stage3_decoder(out)
|
||||
return out
|
||||
|
||||
|
||||
class SegNet_ResNet(nn.Module):
|
||||
|
||||
def __init__(self, backbone, out_channels=1, is_bottleneck=False, in_channels=1):
|
||||
super(SegNet_ResNet, self).__init__()
|
||||
resnet_backbone = backbone
|
||||
encoder = list(resnet_backbone.children())
|
||||
if in_channels != 3:
|
||||
encoder[0] = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1)
|
||||
encoder[3].return_indices = True
|
||||
|
||||
# Encoder
|
||||
self.first_conv = nn.Sequential(*encoder[:4])
|
||||
resnet_blocks = list(resnet_backbone.children())[4:]
|
||||
self.encoder = nn.Sequential(*resnet_blocks)
|
||||
|
||||
# Decoder
|
||||
resnet_r_blocks = list(resnet_backbone.children())[4:][::-1]
|
||||
decoder = []
|
||||
if is_bottleneck:
|
||||
channels = (2048, 1024, 512)
|
||||
else:
|
||||
channels = (512, 256, 128)
|
||||
for i, block in enumerate(resnet_r_blocks[:-1]):
|
||||
new_block = list(block.children())[::-1][:-1]
|
||||
decoder.append(nn.Sequential(*new_block,
|
||||
DecoderBottleneck(channels[i])
|
||||
if is_bottleneck else DecoderBasicBlock(channels[i])))
|
||||
new_block = list(resnet_r_blocks[-1].children())[::-1][:-1]
|
||||
decoder.append(nn.Sequential(*new_block,
|
||||
LastBottleneck(256)
|
||||
if is_bottleneck else LastBasicBlock(64)))
|
||||
|
||||
self.decoder = nn.Sequential(*decoder)
|
||||
self.last_conv = nn.Sequential(
|
||||
nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2, bias=False),
|
||||
nn.Conv2d(64, out_channels, kernel_size=3, stride=1, padding=1)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
inputsize = x.size()
|
||||
|
||||
# Encoder
|
||||
x, indices = self.first_conv(x)
|
||||
x = self.encoder(x)
|
||||
|
||||
# Decoder
|
||||
x = self.decoder(x)
|
||||
h_diff = ceil((x.size()[2] - indices.size()[2]) / 2)
|
||||
w_diff = ceil((x.size()[3] - indices.size()[3]) / 2)
|
||||
if indices.size()[2] % 2 == 1:
|
||||
x = x[:, :, h_diff:x.size()[2] - (h_diff - 1),
|
||||
w_diff: x.size()[3] - (w_diff - 1)]
|
||||
else:
|
||||
x = x[:, :, h_diff:x.size()[2] - h_diff, w_diff: x.size()[3] - w_diff]
|
||||
|
||||
x = F.max_unpool2d(x, indices, kernel_size=2, stride=2)
|
||||
x = self.last_conv(x)
|
||||
|
||||
if inputsize != x.size():
|
||||
h_diff = (x.size()[2] - inputsize[2]) // 2
|
||||
w_diff = (x.size()[3] - inputsize[3]) // 2
|
||||
x = x[:, :, h_diff:x.size()[2] - h_diff, w_diff: x.size()[3] - w_diff]
|
||||
if h_diff % 2 != 0: x = x[:, :, :-1, :]
|
||||
if w_diff % 2 != 0: x = x[:, :, :, :-1]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def SegNet_ResNet18(in_channels=1, out_channels=1, **kwargs):
|
||||
"""
|
||||
Construct SegNet based on ResNet18 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet18()
|
||||
model = SegNet_ResNet(backbone_net, out_channels=out_channels, is_bottleneck=False,
|
||||
in_channels=in_channels, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def SegNet_ResNet34(in_channels=1, out_channels=1, **kwargs):
|
||||
"""
|
||||
Construct SegNet based on ResNet18 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet34()
|
||||
model = SegNet_ResNet(backbone_net, out_channels=out_channels, is_bottleneck=False,
|
||||
in_channels=in_channels, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def SegNet_ResNet50(in_channels=1, out_channels=1, **kwargs):
|
||||
"""
|
||||
Construct SegNet based on ResNet50 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet50()
|
||||
model = SegNet_ResNet(backbone_net, out_channels=out_channels, is_bottleneck=True,
|
||||
in_channels=in_channels, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def SegNet_ResNet101(in_channels=1, out_channels=1, **kwargs):
|
||||
"""
|
||||
Construct SegNet based on ResNet101 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet101()
|
||||
model = SegNet_ResNet(backbone_net, out_channels=out_channels, is_bottleneck=True,
|
||||
in_channels=in_channels, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def SegNet_ResNet152(in_channels=1, out_channels=1, **kwargs):
|
||||
"""
|
||||
Construct SegNet based on ResNet101 model.
|
||||
|
||||
"""
|
||||
backbone_net = resnet101()
|
||||
model = SegNet_ResNet(backbone_net, out_channels=out_channels, is_bottleneck=True,
|
||||
in_channels=in_channels, **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = SegNet_AlexNet(in_channels=1, out_channels=1)
|
||||
print(model)
|
||||
x = torch.randn(1, 1, 200, 200)
|
||||
with torch.no_grad():
|
||||
y = model(x)
|
||||
print(y.shape)
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
# encoding: utf-8
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from src.utils.unet_initialize import initialize_weights
|
||||
|
||||
|
||||
__all__ = ["UNet_VGG"]
|
||||
|
||||
|
||||
class _EncoderBlock(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, out_channels, dropout=False, polling=True, bn=False):
|
||||
super(_EncoderBlock, self).__init__()
|
||||
layers = [
|
||||
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(out_channels) if bn else nn.GroupNorm(32, out_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(out_channels) if bn else nn.GroupNorm(32, out_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
]
|
||||
if dropout:
|
||||
layers.append(nn.Dropout())
|
||||
self.encode = nn.Sequential(*layers)
|
||||
self.pool = None
|
||||
if polling:
|
||||
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
|
||||
|
||||
def forward(self, x):
|
||||
if self.pool is not None:
|
||||
x = self.pool(x)
|
||||
return self.encode(x)
|
||||
|
||||
|
||||
class _DecoderBlock(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, middle_channels, out_channels, bn=False):
|
||||
super(_DecoderBlock, self).__init__()
|
||||
self.decode = nn.Sequential(
|
||||
nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(middle_channels) if bn else nn.GroupNorm(32, middle_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(middle_channels, middle_channels, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(middle_channels) if bn else nn.GroupNorm(32, middle_channels),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=2, stride=2),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.decode(x)
|
||||
|
||||
|
||||
class UNet_VGG(nn.Module):
|
||||
|
||||
def __init__(self, out_channels=1, in_channels=1, bn=False):
|
||||
super(UNet_VGG, self).__init__()
|
||||
self.enc1 = _EncoderBlock(in_channels, 64, polling=False, bn=bn)
|
||||
self.enc2 = _EncoderBlock(64, 128, bn=bn)
|
||||
self.enc3 = _EncoderBlock(128, 256, bn=bn)
|
||||
self.enc4 = _EncoderBlock(256, 512, bn=bn)
|
||||
self.polling = nn.MaxPool2d(kernel_size=2, stride=2)
|
||||
self.center = _DecoderBlock(512, 1024, 512, bn=bn)
|
||||
self.dec4 = _DecoderBlock(1024, 512, 256, bn=bn)
|
||||
self.dec3 = _DecoderBlock(512, 256, 128, bn=bn)
|
||||
self.dec2 = _DecoderBlock(256, 128, 64, bn=bn)
|
||||
self.dec1 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(64) if bn else nn.GroupNorm(32, 64),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(64, 64, kernel_size=3, padding=1),
|
||||
nn.BatchNorm2d(64) if bn else nn.GroupNorm(32, 64),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.final = nn.Conv2d(64, out_channels, kernel_size=1)
|
||||
initialize_weights(self)
|
||||
|
||||
def forward(self, x):
|
||||
enc1 = self.enc1(x)
|
||||
enc2 = self.enc2(enc1)
|
||||
enc3 = self.enc3(enc2)
|
||||
enc4 = self.enc4(enc3)
|
||||
center = self.center(self.polling(enc4))
|
||||
dec4 = self.dec4(torch.cat([F.interpolate(center, enc4.size()[-2:], mode='bilinear',
|
||||
align_corners=True), enc4], 1))
|
||||
dec3 = self.dec3(torch.cat([dec4, enc3], 1))
|
||||
dec2 = self.dec2(torch.cat([dec3, enc2], 1))
|
||||
dec1 = self.dec1(torch.cat([dec2, enc1], 1))
|
||||
final = self.final(dec1)
|
||||
return final
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = UNet(in_channels=1, out_channels=1)
|
||||
print(model)
|
||||
x = torch.randn(1, 1, 200, 200)
|
||||
with torch.no_grad():
|
||||
y = model(x)
|
||||
print(y.shape)
|
||||
|
|
@ -0,0 +1,146 @@
|
|||
"""
|
||||
Runs a model on a single node across multiple gpus.
|
||||
"""
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
import scipy.io as sio
|
||||
import matplotlib.pyplot as plt
|
||||
import configargparse
|
||||
|
||||
from src.LayoutDeepRegression import Model
|
||||
|
||||
|
||||
def main(hparams):
|
||||
model = Model(hparams).cuda()
|
||||
|
||||
print(hparams)
|
||||
print()
|
||||
|
||||
# Model loading
|
||||
model_path = os.path.join(f'lightning_logs/version_' +
|
||||
hparams.test_check_num, 'checkpoints/')
|
||||
ckpt = list(Path(model_path).glob("*.ckpt"))[0]
|
||||
print(ckpt)
|
||||
|
||||
model = model.load_from_checkpoint(str(ckpt))
|
||||
|
||||
model.eval()
|
||||
model.cuda()
|
||||
mae_test = []
|
||||
|
||||
# Testing Set
|
||||
root = hparams.data_root
|
||||
boundary = hparams.boundary
|
||||
test_list = hparams.test_list
|
||||
file_path = os.path.join(root, boundary, test_list)
|
||||
root_dir = os.path.join(root, boundary, 'test', 'test')
|
||||
|
||||
with open(file_path, 'r') as fp:
|
||||
for line in fp.readlines():
|
||||
# Data Reading
|
||||
data_path = line.strip()
|
||||
path = os.path.join(root_dir, data_path)
|
||||
data = sio.loadmat(path)
|
||||
u_true, layout = data["u"], data["F"]
|
||||
|
||||
# Plot Layout and Real Temperature Field
|
||||
fig = plt.figure(figsize=(10.5, 3))
|
||||
|
||||
grid_x = np.linspace(0, 0.1, num=200)
|
||||
grid_y = np.linspace(0, 0.1, num=200)
|
||||
X, Y = np.meshgrid(grid_x, grid_y)
|
||||
|
||||
plt.subplot(131)
|
||||
plt.title('Heat Source Layout')
|
||||
im = plt.pcolormesh(X, Y, layout)
|
||||
plt.colorbar(im)
|
||||
fig.tight_layout(w_pad=3.0)
|
||||
|
||||
layout = torch.Tensor(layout / 1000.0).unsqueeze(0).unsqueeze(0).cuda()
|
||||
print(layout.size())
|
||||
heat = torch.Tensor((u_true - 298) / 50.0).unsqueeze(0).unsqueeze(0).cuda()
|
||||
with torch.no_grad():
|
||||
heat_pre = model(layout)
|
||||
mae = F.l1_loss(heat, heat_pre) * 50
|
||||
print('MAE:', mae)
|
||||
mae_test.append(mae.item())
|
||||
heat_pre = heat_pre.squeeze(0).squeeze(0).cpu().numpy() * 50.0 + 298
|
||||
hmax = max(np.max(heat_pre), np.max(u_true))
|
||||
hmin = min(np.min(heat_pre), np.min(u_true))
|
||||
|
||||
plt.subplot(132)
|
||||
plt.title('Real Temperature Field')
|
||||
if "xs" and "ys" in data.keys():
|
||||
xs, ys = data["xs"], data["ys"]
|
||||
im = plt.pcolormesh(xs, ys, u_true, vmin=hmin, vmax=hmax)
|
||||
plt.axis('equal')
|
||||
else:
|
||||
im = plt.pcolormesh(X, Y, u_true, vmin=hmin, vmax=hmax)
|
||||
plt.colorbar(im)
|
||||
|
||||
plt.subplot(133)
|
||||
plt.title('Predicted Temperature Field')
|
||||
if "xs" and "ys" in data.keys():
|
||||
xs, ys = data["xs"], data["ys"]
|
||||
im = plt.pcolormesh(xs, ys, heat_pre, vmin=hmin, vmax=hmax)
|
||||
plt.axis('equal')
|
||||
else:
|
||||
im = plt.pcolormesh(X, Y, heat_pre, vmin=hmin, vmax=hmax)
|
||||
plt.colorbar(im)
|
||||
|
||||
save_name = os.path.join('outputs/predict_plot', os.path.splitext(os.path.basename(path))[0]+'.jpg')
|
||||
fig.savefig(save_name, dpi=300)
|
||||
plt.close()
|
||||
|
||||
mae_test = np.array(mae_test)
|
||||
print(mae_test.mean())
|
||||
np.savetxt('outputs/mae_test.csv', mae_test, fmt='%f', delimiter=',')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# ------------------------
|
||||
# TRAINING ARGUMENTS
|
||||
# ------------------------
|
||||
# these are project-wide arguments
|
||||
# default configuration file
|
||||
config_path = Path(__file__).absolute().parent / "config/config.yml"
|
||||
parser = configargparse.ArgParser(default_config_files=[str(config_path)], description="Hyper-parameters.")
|
||||
|
||||
# configuration file
|
||||
parser.add_argument("--config", is_config_file=True, default=False, help="config file path")
|
||||
|
||||
# mode
|
||||
parser.add_argument("-m", "--mode", type=str, default="train", help="model: train or test or plot")
|
||||
|
||||
# args for training
|
||||
parser.add_argument("--gpus", type=int, default=0, help="how many gpus")
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--max_epochs", default=20, type=int)
|
||||
parser.add_argument("--lr", default="0.01", type=float)
|
||||
parser.add_argument("--resume_from_checkpoint", type=str, help="resume from checkpoint")
|
||||
parser.add_argument("--num_workers", default=2, type=int, help="num_workers in DataLoader")
|
||||
parser.add_argument("--seed", type=int, default=1, help="seed")
|
||||
parser.add_argument("--use_16bit", type=bool, default=False, help="use 16bit precision")
|
||||
parser.add_argument("--profiler", action="store_true", help="use profiler")
|
||||
|
||||
# args for validation
|
||||
parser.add_argument("--val_check_interval", type=float, default=1,
|
||||
help="how often within one training epoch to check the validation set")
|
||||
|
||||
# args for testing
|
||||
parser.add_argument("--test_check_num", default='0', type=str, help="checkpoint for test")
|
||||
parser.add_argument("--test_args", action="store_true", help="print args")
|
||||
|
||||
parser = Model.add_model_specific_args(parser)
|
||||
hparams = parser.parse_args()
|
||||
|
||||
# test args in cli
|
||||
if hparams.test_args:
|
||||
print(hparams)
|
||||
else:
|
||||
main(hparams)
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
"""
|
||||
Runs a model on a single node across multiple gpus.
|
||||
"""
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from torch.backends import cudnn
|
||||
import configargparse
|
||||
import numpy as np
|
||||
import pytorch_lightning as pl
|
||||
|
||||
from src.LayoutDeepRegression import Model
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
"""
|
||||
seed = hparams.seed
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
cudnn.deterministic = True
|
||||
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
model = Model(hparams)
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = pl.Trainer(
|
||||
gpus=hparams.gpus,
|
||||
precision=16 if hparams.use_16bit else 32,
|
||||
# limit_test_batches=0.05
|
||||
)
|
||||
|
||||
model_path = os.path.join(f'lightning_logs/version_' +
|
||||
hparams.test_check_num, 'checkpoints/')
|
||||
model_path = list(Path(model_path).glob("*.ckpt"))[0]
|
||||
test_model = model.load_from_checkpoint(checkpoint_path=model_path, hparams=hparams)
|
||||
|
||||
# ------------------------
|
||||
# 3 START PREDICTING
|
||||
# ------------------------
|
||||
print(hparams)
|
||||
print()
|
||||
|
||||
trainer.test(model=test_model)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# ------------------------
|
||||
# TESTING ARGUMENTS
|
||||
# ------------------------
|
||||
# these are project-wide arguments
|
||||
config_path = Path(__file__).absolute().parent / "config/config.yml"
|
||||
parser = configargparse.ArgParser(default_config_files=[str(config_path)], description="Hyper-parameters.")
|
||||
parser.add_argument("--config", is_config_file=True, default=False, help="config file path")
|
||||
|
||||
# args
|
||||
parser.add_argument("--save_check_num", default=0, type=int, help="checkpoint for test")
|
||||
parser.add_argument("--max_epochs", default=20, type=int)
|
||||
parser.add_argument("--max_iters", default=40000, type=int)
|
||||
parser.add_argument("--resume_from_checkpoint", type=str, help="resume from checkpoint")
|
||||
parser.add_argument("--seed", type=int, default=1, help="seed")
|
||||
parser.add_argument("--gpus", type=int, default=0, help="how many gpus")
|
||||
parser.add_argument("--use_16bit", type=bool, default=False, help="use 16bit precision")
|
||||
parser.add_argument("--val_check_interval", type=float, default=1,
|
||||
help="how often within one training epoch to check the validation set")
|
||||
parser.add_argument("--profiler", action="store_true", help="use profiler")
|
||||
parser.add_argument("--test_args", action="store_true", help="print args")
|
||||
|
||||
parser = Model.add_model_specific_args(parser)
|
||||
hparams = parser.parse_args()
|
||||
|
||||
# test args in cli
|
||||
if hparams.test_args:
|
||||
print(hparams)
|
||||
else:
|
||||
main(hparams)
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
"""
|
||||
Runs a model on a single node across multiple gpus.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from torch.backends import cudnn
|
||||
import configargparse
|
||||
import numpy as np
|
||||
import pytorch_lightning as pl
|
||||
|
||||
from src.LayoutDeepRegression import Model
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
"""
|
||||
seed = hparams.seed
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
cudnn.deterministic = True
|
||||
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
model = Model(hparams)
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = pl.Trainer(
|
||||
max_epochs=hparams.max_epochs,
|
||||
gpus=hparams.gpus,
|
||||
precision=16 if hparams.use_16bit else 32,
|
||||
val_check_interval=hparams.val_check_interval,
|
||||
resume_from_checkpoint=hparams.resume_from_checkpoint,
|
||||
profiler=hparams.profiler,
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 3 START TRAINING
|
||||
# ------------------------
|
||||
print(hparams)
|
||||
print()
|
||||
trainer.fit(model)
|
||||
|
||||
trainer.test()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# ------------------------
|
||||
# TRAINING ARGUMENTS
|
||||
# ------------------------
|
||||
# these are project-wide arguments
|
||||
config_path = Path(__file__).absolute().parent.parent / "config/config.yml"
|
||||
parser = configargparse.ArgParser(default_config_files=[str(config_path)], description="Hyper-parameters.")
|
||||
parser.add_argument("--config", is_config_file=True, default=False, help="config file path")
|
||||
|
||||
# args
|
||||
parser.add_argument("--max_epochs", default=20, type=int)
|
||||
parser.add_argument("--max_iters", default=None, type=int)
|
||||
parser.add_argument("--resume_from_checkpoint", type=str, help="resume from checkpoint")
|
||||
parser.add_argument("--seed", type=int, default=1, help="seed")
|
||||
parser.add_argument("--gpus", type=int, default=0, help="how many gpus")
|
||||
parser.add_argument("--use_16bit", type=bool, default=False, help="use 16bit precision")
|
||||
parser.add_argument("--val_check_interval", type=float, default=1,
|
||||
help="how often within one training epoch to check the validation set")
|
||||
parser.add_argument("--profiler", action="store_true", help="use profiler")
|
||||
parser.add_argument("--test_args", action="store_true", help="print args")
|
||||
|
||||
parser = Model.add_model_specific_args(parser)
|
||||
hparams = parser.parse_args()
|
||||
|
||||
# test args in cli
|
||||
if hparams.test_args:
|
||||
print(hparams)
|
||||
else:
|
||||
main(hparams)
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
import torch
|
||||
|
||||
|
||||
def weights_init(m):
|
||||
"""
|
||||
模型的权重初始化函数，由模型调用，如CRNN model
|
||||
:param m: 待初始化的模型 nn.Module
|
||||
:return:
|
||||
"""
|
||||
class_name = m.__class__.__name__
|
||||
if class_name.find("Conv") != -1:
|
||||
torch.nn.init.kaiming_normal_(m.weight,
|
||||
mode="fan_out",
|
||||
nonlinearity="relu") # 初始化卷积层权重
|
||||
# torch.nn.init.xavier_normal_(m.weight)
|
||||
elif (class_name.find("BatchNorm") != -1
|
||||
and class_name.find("WithFixedBatchNorm") == -1
|
||||
): # batch norm层不能用kaiming_normal初始化
|
||||
torch.nn.init.constant_(m.weight, 1)
|
||||
torch.nn.init.constant_(m.bias, 0)
|
||||
# m.weight.data.normal_(1.0, 0.02)
|
||||
# m.bias.data.fill_(0)
|
||||
elif class_name.find("Linear") != -1:
|
||||
torch.nn.init.xavier_normal_(m.weight.data)
|
||||
if m.bias is not None:
|
||||
m.bias.data.fill_(0)
|
||||
elif class_name.find("LSTM") != -1 or class_name.find("LSTMCell") != -1:
|
||||
for name, param in m.named_parameters():
|
||||
if "weight_ih" in name:
|
||||
torch.nn.init.xavier_uniform_(param.data)
|
||||
elif "weight_hh" in name:
|
||||
torch.nn.init.orthogonal_(param.data)
|
||||
elif "bias" in name:
|
||||
param.data.fill_(0)
|
||||
|
||||
|
||||
def weights_init_without_kaiming(m):
|
||||
"""
|
||||
模型的权重初始化函数，由模型调用，如CRNN model
|
||||
:param m: 待初始化的模型 nn.Module
|
||||
:return:
|
||||
"""
|
||||
class_name = m.__class__.__name__
|
||||
if class_name.find("Conv") != -1:
|
||||
torch.nn.init.xavier_normal_(m.weight)
|
||||
# torch.nn.init.normal_(m.weight) # 初始化卷积层权重
|
||||
elif (class_name.find("BatchNorm") != -1
|
||||
and class_name.find("WithFixedBatchNorm") == -1
|
||||
): # batch norm层不能用kaiming_normal初始化
|
||||
torch.nn.init.constant_(m.weight, 1)
|
||||
torch.nn.init.constant_(m.bias, 0)
|
||||
# m.weight.data.normal_(1.0, 0.02)
|
||||
# m.bias.data.fill_(0)
|
||||
elif class_name.find("Linear") != -1:
|
||||
torch.nn.init.xavier_normal_(m.weight.data)
|
||||
if m.bias is not None:
|
||||
m.bias.data.fill_(0)
|
||||
elif class_name.find("LSTM") != -1 or class_name.find("LSTMCell") != -1:
|
||||
for name, param in m.named_parameters():
|
||||
if "weight_ih" in name:
|
||||
torch.nn.init.xavier_uniform_(param.data)
|
||||
elif "weight_hh" in name:
|
||||
torch.nn.init.orthogonal_(param.data)
|
||||
elif "bias" in name:
|
||||
param.data.fill_(0)
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
"""
|
||||
Desc : Transforms.
|
||||
"""
|
||||
# File : np_transforms.py
|
||||
# Time : 2020/04/06 17:24:54
|
||||
# Author : Zweien
|
||||
# Contact : 278954153@qq.com
|
||||
|
||||
import torch
|
||||
from torchvision import transforms
|
||||
from torch.nn.functional import interpolate
|
||||
|
||||
|
||||
class ToTensor:
|
||||
"""Transform np.array to torch.tensor
|
||||
Args:
|
||||
add_dim (bool, optional): add first dim. Defaults to True.
|
||||
type_ (torch.dtype, optional): dtype of the tensor. Defaults to tensor.torch.float32.
|
||||
Returns:
|
||||
torch.tensor: tensor
|
||||
"""
|
||||
|
||||
def __init__(self, add_dim=True, type_=torch.float32):
|
||||
|
||||
self.add_dim = add_dim
|
||||
self.type = type_
|
||||
|
||||
def __call__(self, x):
|
||||
if self.add_dim:
|
||||
return torch.tensor(x, dtype=self.type).unsqueeze(0)
|
||||
return torch.tensor(x, dtype=self.type)
|
||||
|
||||
|
||||
class Resize:
|
||||
|
||||
def __init__(self, size):
|
||||
self.size = size
|
||||
|
||||
def __call__(self, x):
|
||||
x_tensor = torch.tensor(x)
|
||||
x_dim = x_tensor.dim()
|
||||
for _ in range(4 - x_dim):
|
||||
x_tensor = x_tensor.unsqueeze(0)
|
||||
x_resize = interpolate(x_tensor, size=self.size)
|
||||
for _ in range(4-x_dim):
|
||||
x_resize = x_resize.squeeze(0)
|
||||
return x_resize.numpy()
|
||||
|
||||
|
||||
class Lambda(transforms.Lambda):
|
||||
pass
|
||||
|
||||
|
||||
class Compose(transforms.Compose):
|
||||
pass
|
||||
|
||||
|
||||
class Normalize(transforms.Normalize):
|
||||
pass
|
||||
|
|
@ -0,0 +1,29 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
def get_upsampling_weight(in_channels, out_channels, kernel_size):
|
||||
factor = (kernel_size + 1) // 2
|
||||
if kernel_size % 2 == 1:
|
||||
center = factor - 1
|
||||
else:
|
||||
center = factor - 0.5
|
||||
og = np.ogrid[:kernel_size, :kernel_size]
|
||||
filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
|
||||
weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size), dtype=np.float64)
|
||||
weight[list(range(in_channels)), list(range(out_channels)), :, :] = filt
|
||||
return torch.from_numpy(weight).float()
|
||||
|
||||
|
||||
def initialize_weights(*models):
|
||||
for model in models:
|
||||
for module in model.modules():
|
||||
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
|
||||
nn.init.kaiming_normal_(module.weight)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.BatchNorm2d):
|
||||
module.weight.data.fill_(1)
|
||||
module.bias.data.zero_()
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
# -*- encoding: utf-8 -*-
|
||||
try:
|
||||
from torch.hub import load_state_dict_from_url
|
||||
except ImportError:
|
||||
from torch.utils.model_zoo import load_url as load_state_dict_from_url
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
# content of test_sample.py
|
||||
def inc(x):
|
||||
return x + 1
|
||||
|
||||
|
||||
def test_answer():
|
||||
assert inc(3) == 4
|
||||
Loading…
Reference in New Issue