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Binary_Distinguish_GRB_Datasetv1
Binary_Distinguish_GRB_Datasetv1.zip
dtk-25.04
gpuout
*.h5
*.log
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# Default ignored files
/shelf/
/workspace.xml
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ResNet-CBAM-README.md
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FROM image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.13.1-ubuntu20.04-dtk24.04.1-py3.10
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README.md 0 → 100755
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# https://github.com/zhangzhengde0225/binary_distinguish_GRB_by_DL
## 论文
Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data
- https://arxiv.org/abs/2303.00370
## 模型结构
卷积神经网络（CNN）架构的示意图如下。每个卷积块顶部和底部的数字分别表示卷积步幅（默认值为1）和卷积核的数量。图底部的数字描述了特征图的长度和宽度的变化。
<div align=left>
<img src="./assets/architectures.png"/>
</div>
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.13.1-ubuntu20.04-dtk24.04.1-py3.10
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install matplotlib -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
### Dockerfile（方法二）
docker build -t <IMAGE_NAME>:<TAG> .
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install matplotlib -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
## 数据集
```
Binary_Distinguish_GRB_Datasetv1/data/
└── dataset_256ms/
├── test_count_map_256ms.npy
├── test_info.npy
├── train_count_map_256ms.npy
├── train_info.npy
├── validate_count_map_256ms.npy
└── validate_info.npy
└── ......
```
## 训练
```bash
#jupter文件参考train_model.ipynb
cd /path/your_code_data/code
python train.py
```
注意：请根据您的需求修改choonse_time_bin、 choose_model="plain-CNN-、data_set_dir等参数
## 推理
```bash
#jupter文件参考test_model.ipynb
cd /path/your_code_data/code
python test.py
```
## result
### 精度
模型信息：
- Epoch :20
- choose_model :ResNet-CBAM
- choonse_time_bin :256 ms
硬件：
- DCU :K100 AI
- GPU :A800
| | accu | precision | recall | f1_score |
|:-------:|:------------:|:----------:|:--------:|:----------:|
| k100 ai | 93.718257%| 97.597977% |88.684664% | 92.9281% |
| A800 | 93.611334%| 98.514212% |87.593337% | 92.7334% |
### 性能
模型信息：
- Epoch :20
- choose_model :ResNet-CBAM
- choonse_time_bin :256 ms
硬件：
- DCU :K100 AI
- GPU :A800
| | train | test |
|:-------:|:----------:|:---------:|
| k100 ai | 140ms/step | 39ms/step |
| A800 | 66ms/step | 20ms/step |
## 应用场景
### 算法类别
`AI for science`
### 热点应用行业
`科研,教育`
## 源码仓库及问题反馈
无
## 参考资料
* https://github.com/zhangzhengde0225/binary_distinguish_GRB_by_DL
README_ori.md 0 → 100755
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# binary_distinguish_GRB_by_DL
Paper information：
```
Peng Zhang, Bing Li, RenZhou Gui, et al. 2023. Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data. Submitted to journal (The Astrophysical Journal Supplement Series).
```
The deep learning algorithms is applied to
distinguish gamma-ray bursts.
This directory contains dataset, deep learning algorithms, and candidates.
The samples in the dataset are count map consisting of light curves for each energy band, which extracted from the observation data of [Fermi/GBM](https://gammaray.nsstc.nasa.gov/gbm/).
The deep learning algorithms is convolutional neural network.
The candidates are the possible GRBs found from one year observations (20210701-20220701) of Fermi/GBM by applying the optimal model.
![210702A](./ref_file/example.png)
Top: count map
Middle: light curve of all energy band
Bottom: heat map of features
## requirements
numpy==1.15.4
kears==2.14
tensorflow==1.12.0
keras_contrib
```
how to install keras_contrib:
---------------
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install
---------------
or use keras source code (keras_contrib directory) directly
```
## Datasets
download link:
## Trained model
download link of all models:
best model (ResNet-CBAM@64ms): [h5 file](./trained_model/resnet-CBAM_64ms.h5)
## Candidates
candidate list: [csv](./candidates/candidate_list_20221221.csv)
image of candidate list: [rar](./candidates/img_of_candidate_list_20221221.rar)
example:
![210702A](./ref_file/candidate_210702A_2021-07-02T002344.png)
Left: mapping-curves of feature
Right: heat map of features
## train model
how to train model
see jupyter-notebook: [ipynb](./code/train_model.ipynb)
## Test model
how to test the already trained model
see jupyter-notebook: [ipynb](./code/test_model.ipynb)
## Citation
If you use any part of this code, please cite our paper:
Peng Zhang, Bing Li, RenZhou Gui, et al. 2023. Application of Deep Learning Methods for Distinguishing
Gamma-Ray Bursts from Fermi/GBM TTE Data. Submitted to journal (The Astrophysical Journal Supplement Series).
ResNet-CBAM-README.md 0 → 100755
View file @ 8de66223
# https://github.com/zhangzhengde0225/binary_distinguish_GRB_by_DL
## 论文
Application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data
- https://arxiv.org/abs/2303.00370
## 模型结构
卷积神经网络（CNN）架构的示意图如下。每个卷积块顶部和底部的数字分别表示卷积步幅（默认值为1）和卷积核的数量。图底部的数字描述了特征图的长度和宽度的变化。
<div align=left>
<img src="./assets/architectures.png"/>
</div>
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/tensorflow:2.13.1-ubuntu20.04-dtk24.04.1-py3.10
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install matplotlib -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
### Dockerfile（方法二）
docker build -t <IMAGE_NAME>:<TAG> .
docker run --shm-size 50g --network=host --name=binary_distinguish_GRB_by_DL --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install matplotlib -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
## 数据集
```
Binary_Distinguish_GRB_Datasetv1/data/
└── dataset_256ms/
├── test_count_map_256ms.npy
├── test_info.npy
├── train_count_map_256ms.npy
├── train_info.npy
├── validate_count_map_256ms.npy
└── validate_info.npy
└── ......
```
## 训练
```bash
#jupter文件参考train_model.ipynb
cd /path/your_code_data/code
python train.py
```
注意：请根据您的需求修改choonse_time_bin、 choose_model="plain-CNN-、data_set_dir等参数
## 推理
```bash
#jupter文件参考test_model.ipynb
cd /path/your_code_data/code
python test.py
```
## result
### 精度
模型信息：
- Epoch :20
- choose_model :ResNet-CBAM
- choonse_time_bin :256 ms
硬件：
- DCU :K100 AI
- GPU :A800
| | accu | precision | recall | f1_score |
|:-------:|:------------:|:----------:|:--------:|:----------:|
| k100 ai | 93.718257%| 97.597977% |88.684664% | 92.9281% |
| A800 | 93.611334%| 98.514212% |87.593337% | 92.7334% |
### 性能
模型信息：
- Epoch :20
- trainBatchSize=32
- choose_model :ResNet-CBAM
- choonse_time_bin :256 ms
硬件：
- 单卡 DCU :K100 AI
| | epoch | iter | samples |
|:-------:|:----------:|:----------:|:----------------:|
| k100 ai | 26 s/epoch | 93 ms/step | 344 samples/s |
## 应用场景
### 算法类别
`AI for science`
### 热点应用行业
`科研,教育`
## 源码仓库及问题反馈
无
## 参考资料
* https://github.com/zhangzhengde0225/binary_distinguish_GRB_by_DL
code/data_utils.py 0 → 100755
View file @ 8de66223
import numpy as np
from keras.utils import to_categorical
def normalize_item(item):
_range = np.max(item) - np.min(item)
return (item - np.min(item)) / _range
def normalize_data_array(data_array):
result = []
for data_item in data_array:
result.append(normalize_item(data_item))
return np.array(result)
def get_train_val_data(dataset_dir, time_bin):
def get_y_data(info_array):
class_name_array = info_array[:, 2]
temp_y_pre = np.ones(class_name_array.size)
temp_y_pre[class_name_array == "normal"] = 0
return temp_y_pre
if time_bin not in ["64ms", "128ms", "256ms"]:
raise Exception("The time_bin should in [64ms,128ms,256ms]")
train_x = np.load(f"{dataset_dir}train_count_map_{time_bin}.npy")
train_info = np.load(f"{dataset_dir}train_info.npy")
train_y = get_y_data(train_info)
val_x = np.load(f"{dataset_dir}validate_count_map_{time_bin}.npy")
val_info = np.load(f"{dataset_dir}validate_info.npy")
val_y = get_y_data(val_info)
train_x = train_x[:, :, :, np.newaxis]
val_x = val_x[:, :, :, np.newaxis]
train_x = normalize_data_array(train_x)
val_x = normalize_data_array(val_x)
class_num = 2
train_y = to_categorical(train_y, class_num)
val_y = to_categorical(val_y, class_num)
return (train_x, train_y, train_info), (val_x, val_y, val_info)
def get_test_data(dataset_dir, time_bin):
def get_y_data(info_array):
class_name_array = info_array[:, 2]
temp_y_pre = np.ones(class_name_array.size)
temp_y_pre[class_name_array == "normal"] = 0
return temp_y_pre
if time_bin not in ["64ms", "128ms", "256ms"]:
raise Exception("The time_bin should in [64ms,128ms,256ms]")
test_x = np.load(f"{dataset_dir}test_count_map_{time_bin}.npy")
test_info = np.load(f"{dataset_dir}test_info.npy")
test_y = get_y_data(test_info)
test_x = test_x[:, :, :, np.newaxis]
test_x = normalize_data_array(test_x)
class_num = 2
test_y = to_categorical(test_y, class_num)
return (test_x, test_y, test_info)
code/keras_contrib/__init__.py 0 → 100755
View file @ 8de66223
from __future__ import absolute_import
from . import backend
from . import datasets
from . import layers
from . import preprocessing
from . import utils
from . import wrappers
from . import callbacks
from . import constraints
from . import initializers
from . import metrics
from . import losses
from . import optimizers
from . import regularizers
__version__ = '0.0.2'
code/keras_contrib/activations/__init__.py 0 → 100755
View file @ 8de66223
from __future__ import absolute_import
from .squash import squash
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