deepepochs 0.2.9

An easy-to-use tool for training Pytorch deep learning models

DeepEpochs

Pytorch深度学习模型训练工具。

安装

pip install deepepochs

使用

数据要求

• 训练集、验证集和测试集是torch.utils.data.Dataloader对象
• Dataloaer中每个mini-batch数据是一个tuple或list，其中最后一个是标签
  • 如果数据不包含标签，则请将最后一项置为None

指标计算

• 每个指标是一个函数
  • 它有两个参数，分别为模型的预测结果和标签
  • 返回值为当前mini-batch上的指标值

常规训练流程

from deepepochs import Trainer, CheckCallback, rename
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader, random_split
from torchmetrics import functional as MF

# datasets
data_dir = './dataset'
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_full = MNIST(data_dir, train=True, transform=transform, download=True)
train_ds, val_ds, _ = random_split(mnist_full, [5000, 5000, 50000])
test_ds = MNIST(data_dir, train=False, transform=transform, download=True)

# dataloaders
train_dl = DataLoader(train_ds, batch_size=32)
val_dl = DataLoader(val_ds, batch_size=32)
test_dl = DataLoader(test_ds, batch_size=32)

# pytorch model
channels, width, height = (1, 28, 28)
model = nn.Sequential(
    nn.Flatten(),
    nn.Linear(channels * width * height, 64),
    nn.ReLU(),
    nn.Dropout(0.1),
    nn.Linear(64, 64),
    nn.ReLU(),
    nn.Dropout(0.1),
    nn.Linear(64, 10)
)

def acc(preds, targets):
    return MF.accuracy(preds, targets, task='multiclass', num_classes=10)

@rename('')
def multi_metrics(preds, targets):
    return {
        'p': MF.precision(preds, targets, task='multiclass', num_classes=10),
        'r': MF.recall(preds, targets, task='multiclass', num_classes=10)
        }

checker = CheckCallback('loss', mode='min', patience=2)
opt = torch.optim.Adam(model.parameters(), lr=2e-4)
trainer = Trainer(model, F.cross_entropy, opt=opt, epochs=100, metrics=[acc, multi_metrics], callbacks=checker)

progress = trainer.fit(train_dl, val_dl)
test_rst = trainer.test(test_dl)

非常规训练流程

• 方法1:
  • 第1步：继承deepepochs.Callback类，定制满足需要的Callback
  • 第2步：使用deepepochs.Trainer训练模型，将定制的Callback对象作为Trainer的callbacks参数
• 方法2:
  • 第1步：继承deepepochs.TrainerBase类，定制满足需要的Trainer，实现train_step方法和evaluate_step方法
  • 第2步：调用定制Trainer训练模型。