86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
from pathlib import Path
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import sys
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import typing
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import torch
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import torchinfo
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import ignite.engine
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import ignite.metrics
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from ignite.engine import Engine, Events
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from ignite.handlers.tqdm_logger import ProgressBar
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from dataset import MnistDataLoaders
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from model import Cnn
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import settings
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sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
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import gpu_utils
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class Trainer:
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"""核心训练器"""
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device: torch.device
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data_source: MnistDataLoaders
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model: Cnn
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trainer: Engine
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evaluator: Engine
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pbar: ProgressBar
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def __init__(self):
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# 创建训练设备，模型和数据加载器。
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self.device = gpu_utils.get_gpu_device()
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self.model = Cnn().to(self.device)
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self.data_source = MnistDataLoaders(batch_size=settings.N_BATCH_SIZE)
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# 展示模型结构。批次为指定批次数量，通道只有一个灰度通道，大小28x28。
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torchinfo.summary(self.model, (settings.N_BATCH_SIZE, 1, 28, 28))
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# 优化器和损失函数
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optimizer = torch.optim.Adam(self.model.parameters(), eps=1e-7)
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criterion = torch.nn.CrossEntropyLoss()
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# 创建训练器
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self.trainer = ignite.engine.create_supervised_trainer(
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self.model, optimizer, criterion, self.device)
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# 将训练器关联到进度条
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self.pbar = ProgressBar(persist=True)
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self.pbar.attach(self.trainer, output_transform=lambda loss: {"loss": loss})
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# 创建测试的评估器的评估量
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evaluator_metrics = {
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# 这个Accuracy要的是logits，而不是possibilities，
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# 所以依然是不需要softmax处理后的结果。
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"accuracy": ignite.metrics.Accuracy(device=self.device),
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"loss": ignite.metrics.Loss(criterion, device=self.device)
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}
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# 创建测试评估器
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self.evaluator = ignite.engine.create_supervised_evaluator(
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self.model, metrics=evaluator_metrics, device=self.device)
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def train_model(self):
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# 训练模型
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self.trainer.run(self.data_source.train_loader, max_epochs=settings.N_EPOCH)
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def save_model(self):
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# 确保保存模型的文件夹存在。
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settings.SAVED_MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
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# 仅保存模型参数
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torch.save(self.model.state_dict(), settings.SAVED_MODEL_PATH)
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print(f'Model was saved into: {settings.SAVED_MODEL_PATH}')
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def test_model(self):
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# 测试模型并输出结果
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self.evaluator.run(self.data_source.test_loader)
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metrics = self.evaluator.state.metrics
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print(f"Accuracy: {metrics['accuracy']:.4f} Loss: {metrics['loss']:.4f}")
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def main():
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trainer = Trainer()
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trainer.train_model()
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trainer.save_model()
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trainer.test_model()
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if __name__ == "__main__":
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gpu_utils.print_gpu_availability()
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main()
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