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.gitignore

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+Binary_Distinguish_GRB_Datasetv1.zip
+dtk-25.04
+gpuout
+*.h5
+*.log
+

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README.md

+# 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

+# 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

+# 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

+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

+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

+from __future__ import absolute_import
+
+from .squash import squash