# DeepHealth Project

This is a deep learning project based on PyTorch. This project adheres to specific code style and file structure conventions to ensure clarity, maintainability, and reproducibility.

## 1. Project Structure

To maintain a clean and modular project, we adopt the following file organization:

DeepHealth/
    |-tain.py
    |-models.py
    |-utils.py
    |-data/
    |-requirements.txt
    |-README.md


### File Descriptions

* **`train.py`**:
    * **Core training script**. It contains the control flow for the entire training process.
    * Responsible for initializing the model, optimizer, DataLoader, etc.
    * Executes the training and validation loops.
    * Handles saving and loading checkpoints, logging, and other related tasks.

* **`models.py`**:
    * **Model and Loss Function Definitions**. This file stores the architecture for all neural network models.
    * All subclasses of `torch.nn.Module` should be defined in this file.
    * Custom loss functions should also be implemented here.

* **`utils.py`**:
    * **Utility Functions Module**. It contains reusable helper functions for the project.
    * Primarily responsible for data I/O operations, data preprocessing, performance metric calculations, logger configuration, or other logic that doesn't belong in the core model or training framework.

* **`data/`**:
    * **Data Storage Directory**. Used to store the datasets required for the project.
    * `data/raw/` stores the original, unprocessed data.
    * `data/processed/` stores data after it has been preprocessed.

* **`requirements.txt`**:
    * **Project Dependencies**. Lists all the Python packages and their versions required to run this project.

* **`README.md`**:
    * **Project Documentation**. Provides a high-level overview of the project, setup instructions, and usage guidelines.

## 2. Core Framework

* **Deep Learning Framework**: `PyTorch`

## 3. Coding Style

This project uniformly adopts the **Google Python Style Guide**. All submitted code should adhere to this standard to ensure consistency and readability.

Key features include:
* Using `yapf` or `black` for automatic code formatting.
* Following detailed naming conventions (`module_name`, `package_name`, `ClassName`, `method_name`, `ExceptionName`, `function_name`, `GLOBAL_CONSTANT_NAME`).
* Using Google-style docstrings.

Please refer to the official documentation: [Google Python Style Guide](http://google.github.io/styleguide/pyguide.html)