PyTorch-24h 05_模块化

一只大鸽子

发布于 2023-02-24 19:20:27

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发布于 2023-02-24 19:20:27

文章被收录于专栏：Python基础、进阶与实战Python基础、进阶与实战

05. PyTorch Going Modular

本节内容：如何编写/组织python脚本？

我们要把 notebook 04. PyTorch Custom Datasets 变成一系列的脚本，称为模块化(going_modular).

什么是模块化?

模块化涉及将笔记本代码（来自 Jupyter Notebook 或 Google Colab 笔记本）转换为一系列提供类似功能的不同 Python 脚本。

例如，我们可以将笔记本代码从一系列单元格转换为以下 Python 文件：

? data_setup.py：准备和下载数据.
? engine.py：包含不同的训练函数.
? model_builder.py or model.py： PyTorch model.
? train.py：训练目标 PyTorch 模型.
? utils.py：专用于有用的实用程序功能的文件.

注意： 上述文件的命名和布局将取决于您的用例和代码要求。

为什么要模块化？

笔记本(jupyter notebook)非常适合迭代探索和快速运行实验。但是，对于更大规模的项目，您可能会发现 Python 脚本更具可重复性且更易于运行。

notebooks vs Python scripts

	Pros	Cons
Notebooks	容易开始实验	很难进行版本控制
	容易分享 (e.g. a link to a Google Colab notebook)	难以仅使用特定部分
	非常直观	文本和图形可能会妨碍代码

	Pros	Cons
Python scripts	便于整合代码 (避免像在notebook里需要重复写相同代码)	实验不那么直观(要运行整个脚本而不能只运行一段)
	可以用git进行版本控制
	很多开源项目使用这种形式
	大型项目可以在云服务器上运行 (notebooks通常不行)

My workflow

作者的工作流程：通常用 Jupyter/Google Colab notebooks 进行简单的尝试。然后将其中有用的部分改写成脚本。

编写机器学习代码有许多可能的工作流程。有些人更喜欢从脚本开始，其他人（像我）更喜欢从笔记本开始，稍后再考虑脚本。

PyTorch in the wild

在您的旅行中，您会看到许多基于 PyTorch 的 ML 项目的代码存储库都有关于如何以 Python 脚本形式运行 PyTorch 代码的说明。

例如，您可能会被指示在终端/命令行中运行如下代码来训练模型：

python train.py --model MODEL_NAME --batch_size BATCH_SIZE --lr LEARNING_RATE --num_epochs NUM_EPOCHS

使用各种超参数设置在命令行上运行PyTorch train.py脚本。

本例中, train.py 是目标脚本, 包含训练模型的函数。 --model, --batch_size, --lr 和 --num_epochs 被称为参数标志。可以为它们设置不同的值。例如，假设我们想从 notebook 04 训练 TinyVGG 模型 10 个 epoch，批量大小为 32，学习率为 0.001:

python train.py --model tinyvgg --batch_size 32 --lr 0.001 --num_epochs 10

您可以在您的 train.py 脚本中设置任意数量的这些参数标志以满足您的需要。

用于训练最先进的计算机视觉模型的 PyTorch 博客文章使用了这种风格。

PyTorch command line training script recipe for training state-of-the-art computer vision models with 8 GPUs. Source: PyTorch blog.

What we're going to cover

本节的主要概念是：将有用的笔记本代码单元转换为可重用的 Python 文件。 这样做将节省我们一遍又一遍地编写相同的代码。

本节有两个笔记本：

1. 05. Going Modular: Part 1 (cell mode) ：这个笔记本是传统的Jupyter笔记本/Google Colab笔记本，是笔记本04的精简版.
2. 05. Going Modular: Part 2 (script mode) ：这个笔记本与第1个笔记本相同，但增加了将每个主要部分转换为Python脚本的功能，例如data_setup.py和train.py.

本文档中的文本集中于代码单元05. Going Modular: Part 2 (script mode)，带有“%%writefile…”在顶部。

Why two parts?

因为学习某物的最好方式是对比它与先前的不同。如果您并排运行每个笔记本，您会发现它们有何不同。

并排运行第 05 节的两个笔记本。您会注意到脚本模式笔记本具有额外的代码单元，可将单元模式笔记本中的代码转换为 Python 脚本。

What we're working towards

在本节结束时，我们希望实现两个目标：

1. 能够通过在命令行中使用一行代码：python train.py训练我们在notebook 04（Food Vision Mini）中构建的模型，
2. 可复用的Python脚本的目录结构，如：

going_modular/
├── going_modular/
│   ├── data_setup.py
│   ├── engine.py
│   ├── model_builder.py
│   ├── train.py
│   └── utils.py
├── models/
│   ├── 05_going_modular_cell_mode_tinyvgg_model.pth
│   └── 05_going_modular_script_mode_tinyvgg_model.pth
└── data/
    └── pizza_steak_sushi/
        ├── train/
        │   ├── pizza/
        │   │   ├── image01.jpeg
        │   │   └── ...
        │   ├── steak/
        │   └── sushi/
        └── test/
            ├── pizza/
            ├── steak/
            └── sushi/

Things to note

? Docstrings：编写可重现和可理解的代码很重要。考虑到这一点，我们将放入脚本中的每个函数/类都是使用 Google 的 Python 文档字符串样式。
? 脚本顶部的导入 - 由于我们将要创建的所有 Python 脚本都可以单独视为一个小程序，因此所有脚本都需要在脚本开头导入它们的输入模块例子：

# Import modules required for train.py
import os
import torch
import data_setup, engine, model_builder, utils

from torchvision import transforms

Where can you get help?

所有材料都可以在github上找到 https://github.com/mrdbourke/pytorch-deep-learning

官方文档通常很有帮助： PyTorch documentation：https://pytorch.org/docs/stable/index.html PyTorch developer forums：https://discuss.pytorch.org/

0. Cell mode vs. script mode

单元模式笔记本，例如 05. Going Modular Part 1 (cell mode) 是一个正常运行的notebook，notebook中的每个单元格都是代码。

脚本模式笔记本，例如 05. Going Modular 第 2 部分（脚本模式）与单元模式笔记本非常相似，但是，许多代码单元可以变成 Python 脚本。

注意： 您不需要通过笔记本（notebook）创建 Python 脚本，您可以直接通过 IDE（集成开发人员环境）创建它们，例如 PyCharm、VS Code。将脚本模式笔记本作为本节的一部分只是为了演示从笔记本到 Python 脚本的一种方式。

1. Get data

获取数据的方法和notebook 04中的一样。

通过 requests 下载一个.zip 文件，然后unzip。

import os
import requests
import zipfile
from pathlib import Path

# Setup path to data folder
data_path = Path("data/")
image_path = data_path / "pizza_steak_sushi"

# If the image folder doesn't exist, download it and prepare it... 
if image_path.is_dir():
    print(f"{image_path} directory exists.")
else:
    print(f"Did not find {image_path} directory, creating one...")
    image_path.mkdir(parents=True, exist_ok=True)
    
# Download pizza, steak, sushi data
with open(data_path / "pizza_steak_sushi.zip", "wb") as f:
    request = requests.get("https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip")
    print("Downloading pizza, steak, sushi data...")
    f.write(request.content)

# Unzip pizza, steak, sushi data
with zipfile.ZipFile(data_path / "pizza_steak_sushi.zip", "r") as zip_ref:
    print("Unzipping pizza, steak, sushi data...") 
    zip_ref.extractall(image_path)

# Remove zip file
os.remove(data_path / "pizza_steak_sushi.zip")

解压后的数据如下：

data/
└── pizza_steak_sushi/
    ├── train/
    │   ├── pizza/
    │   │   ├── train_image01.jpeg
    │   │   ├── test_image02.jpeg
    │   │   └── ...
    │   ├── steak/
    │   │   └── ...
    │   └── sushi/
    │       └── ...
    └── test/
        ├── pizza/
        │   ├── test_image01.jpeg
        │   └── test_image02.jpeg
        ├── steak/
        └── sushi/

2. Create Datasets and DataLoaders (`data_setup.py`)

获得数据后，我们可以将其转换为 PyTorch 的Dataset和DataLoader一个用于训练数据，一个用于测试数据。

我们将有用的 Dataset 和 DataLoader 创建代码转换成一个名为 create_dataloaders() 的函数。

并通过%%writefile going_modular/data_setup.py写入文件。

%%writefile going_modular/data_setup.py
"""
Contains functionality for creating PyTorch DataLoaders for 
image classification data.
"""
import os

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

NUM_WORKERS = os.cpu_count()

def create_dataloaders(
    train_dir: str, 
    test_dir: str, 
    transform: transforms.Compose, 
    batch_size: int, 
    num_workers: int=NUM_WORKERS
):
  """Creates training and testing DataLoaders.

  Takes in a training directory and testing directory path and turns
  them into PyTorch Datasets and then into PyTorch DataLoaders.

  Args:
    train_dir: Path to training directory.
    test_dir: Path to testing directory.
    transform: torchvision transforms to perform on training and testing data.
    batch_size: Number of samples per batch in each of the DataLoaders.
    num_workers: An integer for number of workers per DataLoader.

  Returns:
    A tuple of (train_dataloader, test_dataloader, class_names).
    Where class_names is a list of the target classes.
    Example usage:
      train_dataloader, test_dataloader, class_names = \
        = create_dataloaders(train_dir=path/to/train_dir,
                             test_dir=path/to/test_dir,
                             transform=some_transform,
                             batch_size=32,
                             num_workers=4)
  """
  # Use ImageFolder to create dataset(s)
  train_data = datasets.ImageFolder(train_dir, transform=transform)
  test_data = datasets.ImageFolder(test_dir, transform=transform)

  # Get class names
  class_names = train_data.classes

  # Turn images into data loaders
  train_dataloader = DataLoader(
      train_data,
      batch_size=batch_size,
      shuffle=True,
      num_workers=num_workers,
      pin_memory=True,
  )
  test_dataloader = DataLoader(
      test_data,
      batch_size=batch_size,
      shuffle=True,
      num_workers=num_workers,
      pin_memory=True,
  )

  return train_dataloader, test_dataloader, class_names

我们现在可以用 data_setup.py获得DataLoader:

# Import data_setup.py
from going_modular import data_setup

# Create train/test dataloader and get class names as a list
train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(...)

3. Making a model (`model_builder.py`)

在过去的几个笔记本（notebook 03 和 notebook 04）中，我们已经构建了几次 TinyVGG 模型。

因此，将模型放入其文件中是有意义的，这样我们就可以一次又一次地重用它。

将我们的 TinyVGG() 模型变成脚本 %%writefile going_modular/model_builder.py:

%%writefile going_modular/model_builder.py
"""
Contains PyTorch model code to instantiate a TinyVGG model.
"""
import torch
from torch import nn 

class TinyVGG(nn.Module):
  """Creates the TinyVGG architecture.

  Replicates the TinyVGG architecture from the CNN explainer website in PyTorch.
  See the original architecture here: https://poloclub.github.io/cnn-explainer/
  
  Args:
    input_shape: An integer indicating number of input channels.
    hidden_units: An integer indicating number of hidden units between layers.
    output_shape: An integer indicating number of output units.
  """
  def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
      super().__init__()
      self.conv_block_1 = nn.Sequential(
          nn.Conv2d(in_channels=input_shape, 
                    out_channels=hidden_units, 
                    kernel_size=3, 
                    stride=1, 
                    padding=0),  
          nn.ReLU(),
          nn.Conv2d(in_channels=hidden_units, 
                    out_channels=hidden_units,
                    kernel_size=3,
                    stride=1,
                    padding=0),
          nn.ReLU(),
          nn.MaxPool2d(kernel_size=2,
                        stride=2)
      )
      self.conv_block_2 = nn.Sequential(
          nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=0),
          nn.ReLU(),
          nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=0),
          nn.ReLU(),
          nn.MaxPool2d(2)
      )
      self.classifier = nn.Sequential(
          nn.Flatten(),
          # Where did this in_features shape come from? 
          # It's because each layer of our network compresses and changes the shape of our inputs data.
          nn.Linear(in_features=hidden_units*13*13,
                    out_features=output_shape)
      )
    
  def forward(self, x: torch.Tensor):
      x = self.conv_block_1(x)
      x = self.conv_block_2(x)
      x = self.classifier(x)
      return x
      # return self.classifier(self.conv_block_2(self.conv_block_1(x))) # <- leverage the benefits of operator fusion

现在我们只需要导入模块，不用每次都实现一遍了：

import torch
# Import model_builder.py
from going_modular import model_builder
device = "cuda" if torch.cuda.is_available() else "cpu"

# Instantiate an instance of the model from the "model_builder.py" script
torch.manual_seed(42)
model = model_builder.TinyVGG(input_shape=3,
                              hidden_units=10, 
                              output_shape=len(class_names)).to(device)

4. Creating `train_step()` and `test_step()` functions and `train()` to combine them

我们写过很多训练函数notebook 04:

1. train_step() - takes in a model, a DataLoader, a loss function and an optimizer and trains the model on the DataLoader.
2. test_step() - takes in a model, a DataLoader and a loss function and evaluates the model on the DataLoader.
3. train() - performs 1. and 2. together for a given number of epochs and returns a results dictionary.

由于这些将是我们模型训练的引擎，我们可以将它们全部放入名为 engine.py 的 Python 脚本中 %%writefile going_modular/engine.py:

%%writefile going_modular/engine.py
"""
Contains functions for training and testing a PyTorch model.
"""
import torch

from tqdm.auto import tqdm
from typing import Dict, List, Tuple

def train_step(model: torch.nn.Module, 
               dataloader: torch.utils.data.DataLoader, 
               loss_fn: torch.nn.Module, 
               optimizer: torch.optim.Optimizer,
               device: torch.device) -> Tuple[float, float]:
  """Trains a PyTorch model for a single epoch.

  Turns a target PyTorch model to training mode and then
  runs through all of the required training steps (forward
  pass, loss calculation, optimizer step).

  Args:
    model: A PyTorch model to be trained.
    dataloader: A DataLoader instance for the model to be trained on.
    loss_fn: A PyTorch loss function to minimize.
    optimizer: A PyTorch optimizer to help minimize the loss function.
    device: A target device to compute on (e.g. "cuda" or "cpu").

  Returns:
    A tuple of training loss and training accuracy metrics.
    In the form (train_loss, train_accuracy). For example:
    
    (0.1112, 0.8743)
  """
  # Put model in train mode
  model.train()
  
  # Setup train loss and train accuracy values
  train_loss, train_acc = 0, 0
  
  # Loop through data loader data batches
  for batch, (X, y) in enumerate(dataloader):
      # Send data to target device
      X, y = X.to(device), y.to(device)

      # 1. Forward pass
      y_pred = model(X)

      # 2. Calculate  and accumulate loss
      loss = loss_fn(y_pred, y)
      train_loss += loss.item() 

      # 3. Optimizer zero grad
      optimizer.zero_grad()

      # 4. Loss backward
      loss.backward()

      # 5. Optimizer step
      optimizer.step()

      # Calculate and accumulate accuracy metric across all batches
      y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)
      train_acc += (y_pred_class == y).sum().item()/len(y_pred)

  # Adjust metrics to get average loss and accuracy per batch 
  train_loss = train_loss / len(dataloader)
  train_acc = train_acc / len(dataloader)
  return train_loss, train_acc

def test_step(model: torch.nn.Module, 
              dataloader: torch.utils.data.DataLoader, 
              loss_fn: torch.nn.Module,
              device: torch.device) -> Tuple[float, float]:
  """Tests a PyTorch model for a single epoch.

  Turns a target PyTorch model to "eval" mode and then performs
  a forward pass on a testing dataset.

  Args:
    model: A PyTorch model to be tested.
    dataloader: A DataLoader instance for the model to be tested on.
    loss_fn: A PyTorch loss function to calculate loss on the test data.
    device: A target device to compute on (e.g. "cuda" or "cpu").

  Returns:
    A tuple of testing loss and testing accuracy metrics.
    In the form (test_loss, test_accuracy). For example:
    
    (0.0223, 0.8985)
  """
  # Put model in eval mode
  model.eval() 
  
  # Setup test loss and test accuracy values
  test_loss, test_acc = 0, 0
  
  # Turn on inference context manager
  with torch.inference_mode():
      # Loop through DataLoader batches
      for batch, (X, y) in enumerate(dataloader):
          # Send data to target device
          X, y = X.to(device), y.to(device)
  
          # 1. Forward pass
          test_pred_logits = model(X)

          # 2. Calculate and accumulate loss
          loss = loss_fn(test_pred_logits, y)
          test_loss += loss.item()
          
          # Calculate and accumulate accuracy
          test_pred_labels = test_pred_logits.argmax(dim=1)
          test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))
          
  # Adjust metrics to get average loss and accuracy per batch 
  test_loss = test_loss / len(dataloader)
  test_acc = test_acc / len(dataloader)
  return test_loss, test_acc

def train(model: torch.nn.Module, 
          train_dataloader: torch.utils.data.DataLoader, 
          test_dataloader: torch.utils.data.DataLoader, 
          optimizer: torch.optim.Optimizer,
          loss_fn: torch.nn.Module,
          epochs: int,
          device: torch.device) -> Dict[str, List]:
  """Trains and tests a PyTorch model.

  Passes a target PyTorch models through train_step() and test_step()
  functions for a number of epochs, training and testing the model
  in the same epoch loop.

  Calculates, prints and stores evaluation metrics throughout.

  Args:
    model: A PyTorch model to be trained and tested.
    train_dataloader: A DataLoader instance for the model to be trained on.
    test_dataloader: A DataLoader instance for the model to be tested on.
    optimizer: A PyTorch optimizer to help minimize the loss function.
    loss_fn: A PyTorch loss function to calculate loss on both datasets.
    epochs: An integer indicating how many epochs to train for.
    device: A target device to compute on (e.g. "cuda" or "cpu").

  Returns:
    A dictionary of training and testing loss as well as training and
    testing accuracy metrics. Each metric has a value in a list for 
    each epoch.
    In the form: {train_loss: [...],
                  train_acc: [...],
                  test_loss: [...],
                  test_acc: [...]} 
    For example if training for epochs=2: 
                 {train_loss: [2.0616, 1.0537],
                  train_acc: [0.3945, 0.3945],
                  test_loss: [1.2641, 1.5706],
                  test_acc: [0.3400, 0.2973]} 
  """
  # Create empty results dictionary
  results = {"train_loss": [],
      "train_acc": [],
      "test_loss": [],
      "test_acc": []
  }
  
  # Loop through training and testing steps for a number of epochs
  for epoch in tqdm(range(epochs)):
      train_loss, train_acc = train_step(model=model,
                                          dataloader=train_dataloader,
                                          loss_fn=loss_fn,
                                          optimizer=optimizer,
                                          device=device)
      test_loss, test_acc = test_step(model=model,
          dataloader=test_dataloader,
          loss_fn=loss_fn,
          device=device)
      
      # Print out what's happening
      print(
          f"Epoch: {epoch+1} | "
          f"train_loss: {train_loss:.4f} | "
          f"train_acc: {train_acc:.4f} | "
          f"test_loss: {test_loss:.4f} | "
          f"test_acc: {test_acc:.4f}"
      )

      # Update results dictionary
      results["train_loss"].append(train_loss)
      results["train_acc"].append(train_acc)
      results["test_loss"].append(test_loss)
      results["test_acc"].append(test_acc)

  # Return the filled results at the end of the epochs
  return results

现在只需要从 engine.py 导入函数：

# Import engine.py
from going_modular import engine

# Use train() by calling it from engine.py
engine.train(...)

5. Creating a function to save the model (`utils.py`)

通常，您会希望在训练时或训练后保存模型。

由于我们在以前的笔记本中已经编写了几次保存模型的代码，因此将其转换为函数并将其保存到文件是有意义的。

将辅助函数存储在一个名为“utils.py”（实用程序utilities的缩写）的文件中是一种常见的做法。

让我们将功能 save_model() 写入文件 utils.py ： %%writefile going_modular/utils.py:

%%writefile going_modular/utils.py
"""
Contains various utility functions for PyTorch model training and saving.
"""
import torch
from pathlib import Path

def save_model(model: torch.nn.Module,
               target_dir: str,
               model_name: str):
  """Saves a PyTorch model to a target directory.

  Args:
    model: A target PyTorch model to save.
    target_dir: A directory for saving the model to.
    model_name: A filename for the saved model. Should include
      either ".pth" or ".pt" as the file extension.
  
  Example usage:
    save_model(model=model_0,
               target_dir="models",
               model_name="05_going_modular_tingvgg_model.pth")
  """
  # Create target directory
  target_dir_path = Path(target_dir)
  target_dir_path.mkdir(parents=True,
                        exist_ok=True)
  
  # Create model save path
  assert model_name.endswith(".pth") or model_name.endswith(".pt"), "model_name should end with '.pt' or '.pth'"
  model_save_path = target_dir_path / model_name

  # Save the model state_dict()
  print(f"[INFO] Saving model to: {model_save_path}")
  torch.save(obj=model.state_dict(),
             f=model_save_path)

想要保存模型时只需要导入 save_model() ：

# Import utils.py
from going_modular import utils

# Save a model to file
save_model(model=...
           target_dir=...,
           model_name=...)

6. Train, evaluate and save the model (`train.py`)

如前所述，您经常会遇到 PyTorch 存储库，它们将所有功能组合在一个 train.py 文件中。

该文件本质上是说“使用任何可用数据训练模型”。

在我们的train.py文件中，我们将结合我们创建的其他 Python 脚本的所有功能，并使用它来训练模型。

通过这种方式，我们可以在命令行上使用一行代码来训练 PyTorch 模型：

python train.py

为了创建 train.py ，我们将实现如下步骤：

1. 导入各种依赖项，即torch、os、torchvision.transforms以及going_modular目录、data_setup、engine、model_builder、utils中的所有脚本。

? 注意： 由于 train.py 将在 going_modular 目录中，我们可以通过 import ... 导入其他模块，而不是 from going_modular import ...。

1. 设置各种超参数，例如批量大小、时期数、学习率和隐藏单元数（这些可以在未来通过 argparse 设置）。
2. 设置训练和测试目录。
3. 设置与设备无关的代码。
4. 创建必要的数据转换。
5. 使用 data_setup.py 创建 DataLoader。
6. 使用 model_builder.py 创建模型。
7. 设置损失函数和优化器。
8. 使用engine.py 训练模型。
9. 使用 utils.py 保存模型。

我们可以使用 %%writefile going_modular/train.py 行从笔记本单元创建文件：

%%writefile going_modular/train.py
"""
Trains a PyTorch image classification model using device-agnostic code.
"""

import os
import torch
import data_setup, engine, model_builder, utils

from torchvision import transforms

# Setup hyperparameters
NUM_EPOCHS = 5
BATCH_SIZE = 32
HIDDEN_UNITS = 10
LEARNING_RATE = 0.001

# Setup directories
train_dir = "data/pizza_steak_sushi/train"
test_dir = "data/pizza_steak_sushi/test"

# Setup target device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Create transforms
data_transform = transforms.Compose([
  transforms.Resize((64, 64)),
  transforms.ToTensor()
])

# Create DataLoaders with help from data_setup.py
train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(
    train_dir=train_dir,
    test_dir=test_dir,
    transform=data_transform,
    batch_size=BATCH_SIZE
)

# Create model with help from model_builder.py
model = model_builder.TinyVGG(
    input_shape=3,
    hidden_units=HIDDEN_UNITS,
    output_shape=len(class_names)
).to(device)

# Set loss and optimizer
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),
                             lr=LEARNING_RATE)

# Start training with help from engine.py
engine.train(model=model,
             train_dataloader=train_dataloader,
             test_dataloader=test_dataloader,
             loss_fn=loss_fn,
             optimizer=optimizer,
             epochs=NUM_EPOCHS,
             device=device)

# Save the model with help from utils.py
utils.save_model(model=model,
                 target_dir="models",
                 model_name="05_going_modular_script_mode_tinyvgg_model.pth")

我们可用下面语句训练模型：

python train.py

这样做将利用我们创建的所有其他代码脚本。

如果我们愿意，我们可以调整我们的 train.py 文件以使用 Python 的 argparse 模块的参数标志输入，这将允许我们提供不同的超参数设置，如前所述：

参考argparse模块

python train.py --model MODEL_NAME --batch_size BATCH_SIZE --lr LEARNING_RATE --num_epochs NUM_EPOCHS

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原始发表：2023-02-07，如有侵权请联系?cloudcommunity@tencent.com 删除

开源

python

pytorch

jupyter notebook

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