diff --git a/README.md b/README.md index 2c30edc..e665d64 100644 --- a/README.md +++ b/README.md @@ -1,71 +1,208 @@ +# Deeplearning 使用说明 -## Preliminary -1. 对于每一个类，将数据如下处理, 保存成xlsx或者xls文件 +## 1. 项目约定 -| | | | | -|-------|-------|-------|-------| -| arbitrary value | value | arbitrary value | vlaue | -| arbitrary value | value | arbitrary value | vlaue | +### 1.1 输入数据格式 +每一类数据建议保存为 `xlsx/xls`。读取时默认取偶数列（索引 1,3,5...）作为特征，奇数列内容可忽略。 -即偶数列为一次循环的数据，奇数列为任意值即可 +示意： -2. 配置conda环境 -> pass - - -## Quickly Start -1. 将项目文件夹编辑成**日期+项目名** -2. 编辑好label名称，label名称命名变成英文或者数字 ->例如：”PDMS“， ”1“ 等, , 如果你的每一个类，下面又多个子特征则可以建立一个文件夹，在创建神经网络类的时候将**isDir**参数改成True即可. ->详细如下图： ->2.1. 如果只有一类特征 -> ![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/2024-12-28-f7f922-image.png) ->2.2. 如果有多类特征 ->![image.png](https://qq-pic.oss-cn-nanjing.aliyuncs.com/img/2024-12-28-e6a2bf-image.png) -3. 将准备好的文件夹移动到**Static**文件夹中（没有就建立），如果没有 **Result** 建立一个**Result**文件夹用来存放结果 - -4. 读取数据： -```python - # 以MaterialDiv为例 - projet_name = '20241009MaterialDiv' - label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood'] - # 使用库 divSet 划分训练集和数据集 - data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx') -``` - -5. 创建神经网络类 -```python - model = Qmlp( - X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test, - hidden_layers = [128], - dropout_rate=0 - ) -``` - -6. 训练并获取数据 -```python - pca_2d, pca_3d = model.get_PCA() - - model.fit(300) - - cm = model.get_cm() - epoch_data = model.get_epoch_data() - - save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d) - save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d) - save_to_xlsx(project_name=projet_name, file_name="cm", data=cm ) - save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data) -``` -## Advanced -### loadData 处理数据工具的使用 -||参数类型|默认值|参数作用| +| 任意值 | 特征值 | 任意值 | 特征值 | |---|---|---|---| -|folder|str|必填项|指定数据存放在Static下的哪个文件夹| -|lableNames| list| 必填项| 指定每一个类的label名称, 既可以用来读取相应的文件，也可以用来给label排序| -|isDir| bool| True| 若是上文Quickly Strat章节2.1情况需要改成False，2.2情况则是True| -|fileClass| str| 'xlsx'| 数据文件的后缀| -> tips: 数据读取是按照一下情况读取的(2.1和2.2是Quickly Start章节的2.1和2.2简称)： -> 2.1情况的第一类数据读取的地址是 ./Static/folder/labelsNames[0].xlsx, 其他类同理 -> 2.2情况的第二类数据读取的地址是 ./Static/folder/labelsNames[0]/*.xlsx, 其他同理 -### Qmlp 模型使用 -> pass +| arbitrary value | value | arbitrary value | value | +| arbitrary value | value | arbitrary value | value | + +### 1.2 目录约定 +训练数据放在 `Static/`，输出结果放在 `Result/`。 + +推荐目录： + +```text +. +├─ Static/ +│ └─ 20241009MaterialDiv/ +└─ Result/ +``` + +## 2. Conda 环境迁移 + +环境文件在 `conda_env/`： + +- `conda_env/environment.portable.yml`：通用迁移（推荐） +- `conda_env/environment.lock.txt`：精确锁定（同系统/同架构优先） +- `conda_env/env.yml`：历史文件 + +### 2.1 创建环境 + +```bash +# 方式1（推荐）：通用创建 +conda env create -f conda_env/environment.portable.yml +conda activate Deeplearning + +# 方式2：精确复现 +conda create -n Deeplearning --file conda_env/environment.lock.txt +conda activate Deeplearning + +# 验证 +python -V +python -c "import torch; print(torch.__version__)" +``` + +### 2.2 同名环境已存在时 + +```bash +# 方式A：保留旧环境，改名创建 +conda env create -f conda_env/environment.portable.yml -n Deeplearning_v2 +conda activate Deeplearning_v2 + +# 或者（lock 方式） +conda create -n Deeplearning_v2 --file conda_env/environment.lock.txt +conda activate Deeplearning_v2 +``` + +```bash +# 方式B：删除旧环境后重建（谨慎） +conda env remove -n Deeplearning +conda env create -f conda_env/environment.portable.yml +conda activate Deeplearning +``` + +### 2.3 重新导出环境 + +```bash +conda env export -n Deeplearning --no-builds > conda_env/environment.portable.yml +conda list -n Deeplearning --explicit > conda_env/environment.lock.txt +``` + +## 3. 快速开始 + +### 3.1 准备数据 +1. 将数据目录命名为 `日期+项目名`，例如 `20241009MaterialDiv`。 +2. 准备 `label_names`（建议英文或数字）。 +3. 将数据目录放入 `Static/`。 + +### 3.2 数据目录模板 + +单文件模式（`isDir=False`）： + +```text +Static/ + 20241009MaterialDiv/ + Acrlic.xlsx + Ecoflex.xlsx + PDMS.xlsx + PLA.xlsx + Wood.xlsx +``` + +多子特征模式（`isDir=True`）： + +```text +Static/ + 20241009MaterialDiv/ + Acrlic/ + sample_01.xlsx + sample_02.xlsx + Ecoflex/ + sample_01.xlsx + sample_02.xlsx + PDMS/ + sample_01.xlsx + sample_02.xlsx + PLA/ + sample_01.xlsx + sample_02.xlsx + Wood/ + sample_01.xlsx + sample_02.xlsx +``` + +命名规则（重要）： + +- `label_names` 中每一项必须与文件名（`isDir=False`）或子文件夹名（`isDir=True`）完全一致（区分大小写）。 +- `label_names` 顺序就是标签编码顺序，训练结果和混淆矩阵按该顺序展示。 + +示例： + +```python +label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood'] +``` + +对应关系： + +```text +Acrlic <-> Acrlic.xlsx 或 Acrlic/ +Ecoflex <-> Ecoflex.xlsx 或 Ecoflex/ +PDMS <-> PDMS.xlsx 或 PDMS/ +PLA <-> PLA.xlsx 或 PLA/ +Wood <-> Wood.xlsx 或 Wood/ +``` + +### 3.3 训练示例 + +```python +from Qtorch.Models.Qmlp import Qmlp +from Qfunctions.divSet import divSet +from Qfunctions.loaData import load_data +from Qfunctions.saveToxlsx import save_to_xlsx + +projet_name = '20241009MaterialDiv' +label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood'] + +# 读取数据 +data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx') + +# 划分训练/测试集 +X_train, X_test, y_train, y_test, encoder = divSet( + data=data, + labels=label_names, + test_size=0.3 +) + +# 构建模型 +model = Qmlp( + X_train=X_train, + X_test=X_test, + y_train=y_train, + y_test=y_test, + hidden_layers=[128], + dropout_rate=0 +) + +# 训练与导出结果 +pca_2d, pca_3d = model.get_PCA() +model.fit(300) + +cm = model.get_cm() +cmn = model.get_cmn() +epoch_data = model.get_epoch_data() + +save_to_xlsx(project_name=projet_name, file_name='pca_2d', data=pca_2d) +save_to_xlsx(project_name=projet_name, file_name='pca_3d', data=pca_3d) +save_to_xlsx(project_name=projet_name, file_name='cm', data=cm) +save_to_xlsx(project_name=projet_name, file_name='cmn', data=cmn) +save_to_xlsx(project_name=projet_name, file_name='acc_and_loss', data=epoch_data) +``` + +## 4. load_data 参数说明 + +| 参数 | 类型 | 默认值 | 说明 | +|---|---|---|---| +| folder | str | 必填 | `Static/` 下的数据目录名 | +| labelNames | list | 必填 | 类别名称列表，用于读取和排序标签 | +| isDir | bool | True | `False` 对应单文件模式，`True` 对应多子特征模式 | +| fileClass | str | xlsx | 数据文件后缀 | + +读取路径规则： + +- 单文件模式：`./Static/folder/labelNames[i].xlsx` +- 多子特征模式：`./Static/folder/labelNames[i]/*.xlsx` + +## 5. 常见问题 + +### 5.1 找不到文件 +优先检查： + +- `label_names` 与文件/文件夹是否同名 +- `isDir` 是否与目录结构匹配 +- 文件后缀是否与 `fileClass` 一致 diff --git a/env.yml b/conda_env/env.yml similarity index 100% rename from env.yml rename to conda_env/env.yml diff --git a/conda_env/environment.lock.txt b/conda_env/environment.lock.txt new file mode 100644 index 0000000..a86e66b --- /dev/null +++ b/conda_env/environment.lock.txt @@ -0,0 +1,163 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: linux-64 +# created-by: conda 26.1.1 +@EXPLICIT +https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda +https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.11.26-h06a4308_0.conda +https://conda.anaconda.org/nvidia/linux-64/cuda-cudart-12.4.127-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/cuda-cupti-12.4.127-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/cuda-nvrtc-12.4.127-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/cuda-nvtx-12.4.127-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/cuda-opencl-12.4.127-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda +https://conda.anaconda.org/nvidia/linux-64/libcublas-12.4.2.65-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libcufft-11.2.0.44-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libcufile-1.9.1.3-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libcurand-10.3.5.147-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libcusolver-11.6.0.99-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libcusparse-12.3.0.142-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/libgfortran5-11.2.0-h1234567_1.conda +https://conda.anaconda.org/nvidia/linux-64/libnpp-12.2.5.2-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libnvfatbin-12.4.127-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libnvjitlink-12.4.99-0.tar.bz2 +https://conda.anaconda.org/nvidia/linux-64/libnvjpeg-12.3.1.89-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda +https://repo.anaconda.com/pkgs/main/noarch/pybind11-abi-5-hd3eb1b0_0.conda +https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2 +https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda +https://conda.anaconda.org/nvidia/linux-64/cuda-libraries-12.4.0-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-11.2.0-h00389a5_1.conda +https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda +https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda +https://conda.anaconda.org/nvidia/linux-64/cuda-runtime-12.4.0-0.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda +https://conda.anaconda.org/pytorch/linux-64/pytorch-cuda-12.4-hc786d27_6.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda +https://conda.anaconda.org/nvidia/linux-64/cudatoolkit-11.5.1-hcf5317a_9.tar.bz2 +https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda +https://repo.anaconda.com/pkgs/main/linux-64/gmp-6.2.1-h295c915_3.conda 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cuda-nvtx=12.4.127 + - cuda-opencl=12.4.127 + - cuda-runtime=12.4.0 + - cudatoolkit=11.5.1 + - cycler=0.11.0 + - cyrus-sasl=2.1.28 + - dbus=1.13.18 + - debugpy=1.6.7 + - et_xmlfile=1.1.0 + - expat=2.6.2 + - ffmpeg=4.3 + - filelock=3.13.1 + - fontconfig=2.14.1 + - fonttools=4.51.0 + - freetype=2.12.1 + - glib=2.78.4 + - glib-tools=2.78.4 + - gmp=6.2.1 + - gnutls=3.6.15 + - gst-plugins-base=1.14.1 + - gstreamer=1.14.1 + - icu=73.1 + - idna=3.7 + - intel-openmp=2023.1.0 + - jinja2=3.1.4 + - joblib=1.4.2 + - jpeg=9e + - kiwisolver=1.4.4 + - krb5=1.20.1 + - lame=3.100 + - lcms2=2.12 + - ld_impl_linux-64=2.38 + - lerc=3.0 + - libbrotlicommon=1.0.9 + - libbrotlidec=1.0.9 + - libbrotlienc=1.0.9 + - libclang=14.0.6 + - libclang13=14.0.6 + - libcublas=12.4.2.65 + - libcufft=11.2.0.44 + - libcufile=1.9.1.3 + - libcups=2.4.2 + - libcurand=10.3.5.147 + - libcusolver=11.6.0.99 + - libcusparse=12.3.0.142 + - libdeflate=1.17 + - libedit=3.1.20230828 + - libffi=3.4.4 + - libgcc-ng=11.2.0 + - libgfortran-ng=11.2.0 + - libgfortran5=11.2.0 + - libglib=2.78.4 + - libgomp=11.2.0 + - libiconv=1.16 + - libidn2=2.3.4 + - libjpeg-turbo=2.0.0 + - libllvm14=14.0.6 + - libnpp=12.2.5.2 + - libnvfatbin=12.4.127 + - libnvjitlink=12.4.99 + - libnvjpeg=12.3.1.89 + - libpng=1.6.39 + - libpq=12.17 + - libstdcxx-ng=11.2.0 + - libtasn1=4.19.0 + - libtiff=4.5.1 + - libunistring=0.9.10 + - libuuid=1.41.5 + - libwebp-base=1.3.2 + - libxcb=1.15 + - libxkbcommon=1.0.1 + - libxml2=2.13.1 + - llvm-openmp=14.0.6 + - lz4-c=1.9.4 + - markupsafe=2.1.3 + - matplotlib=3.8.4 + - matplotlib-base=3.8.4 + - mkl=2023.1.0 + - mkl-service=2.4.0 + - mkl_fft=1.3.8 + - mkl_random=1.2.4 + - mpmath=1.3.0 + - mysql=5.7.24 + - ncurses=6.4 + - nettle=3.7.3 + - networkx=3.3 + - numexpr=2.8.7 + - numpy=1.26.4 + - numpy-base=1.26.4 + - openh264=2.1.1 + - openjpeg=2.5.2 + - openpyxl=3.1.5 + - openssl=3.0.15 + - packaging=24.1 + - pandas=2.2.2 + - pcre2=10.42 + - pillow=10.4.0 + - pip=24.2 + - ply=3.11 + - pybind11-abi=5 + - pyopengl=3.1.1a1 + - pyparsing=3.0.9 + - pyqt=5.15.10 + - pyqt5-sip=12.13.0 + - pysocks=1.7.1 + - python=3.12.4 + - python-dateutil=2.9.0post0 + - python-tzdata=2023.3 + - pytorch=2.4.0 + - pytorch-cuda=12.4 + - pytorch-mutex=1.0 + - pytz=2024.1 + - pyyaml=6.0.1 + - qt-main=5.15.2 + - readline=8.2 + - requests=2.32.3 + - scikit-learn=1.5.1 + - scipy=1.13.1 + - seaborn=0.13.2 + - setuptools=72.1.0 + - sip=6.7.12 + - six=1.16.0 + - sqlite=3.45.3 + - sympy=1.12 + - tbb=2021.8.0 + - threadpoolctl=3.5.0 + - tk=8.6.14 + - torchaudio=2.4.0 + - torchtriton=3.0.0 + - torchvision=0.19.0 + - tornado=6.4.1 + - tqdm=4.66.5 + - typing_extensions=4.11.0 + - tzdata=2024a + - unicodedata2=15.1.0 + - urllib3=2.2.2 + - wheel=0.43.0 + - xlrd=2.0.1 + - xz=5.4.6 + - yaml=0.2.5 + - zlib=1.2.13 + - zstd=1.5.5 + - pip: + - autopep8==2.3.1 + - basedpyright==1.16.0 + - black==24.8.0 + - click==8.1.7 + - fsspec==2024.6.1 + - graphviz==0.20.3 + - greenlet==3.0.3 + - msgpack==1.0.8 + - mypy-extensions==1.0.0 + - nodejs-wheel-binaries==20.16.0 + - pathspec==0.12.1 + - platformdirs==4.2.2 + - pycodestyle==2.12.1 + - pynvim==0.5.0