190 lines
7.1 KiB
Python
190 lines
7.1 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.metrics import Accuracy, Loss
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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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# device: torch.device
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# data_source: MnistDataLoaders
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# model: Cnn
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# def __init__(self):
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# self.device = gpu_utils.get_gpu_device()
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# self.data_source = MnistDataLoaders(Trainer.N_BATCH_SIZE)
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# self.model = Cnn().to(self.device)
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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, (Trainer.N_BATCH_SIZE, 1, 28, 28))
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# def train(self):
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# optimizer = torch.optim.Adam(self.model.parameters(), eps=1e-7)
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# # optimizer = torch.optim.AdamW(
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# # self.model.parameters(),
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# # lr=0.001, # 两者默认学习率都是 0.001
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# # betas=(0.9, 0.999), # 两者默认值相同
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# # eps=1e-07, # 【关键】匹配 TensorFlow 的默认 epsilon
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# # weight_decay=0.0, # 两者默认都是 0
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# # amsgrad=False # 两者默认都是 False
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# # )
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# loss_func = torch.nn.CrossEntropyLoss()
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# for epoch in range(Trainer.N_EPOCH):
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# self.model.train()
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# batch_images: torch.Tensor
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# batch_labels: torch.Tensor
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# for batch_index, (batch_images, batch_labels) in enumerate(self.data_source.train_loader):
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# gpu_images = batch_images.to(self.device)
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# gpu_labels = batch_labels.to(self.device)
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# prediction: torch.Tensor = self.model(gpu_images)
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# loss: torch.Tensor = loss_func(prediction, gpu_labels)
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# optimizer.zero_grad()
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# loss.backward()
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# optimizer.step()
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# if batch_index % 100 == 0:
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# literal_loss = loss.item()
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# print(f'Epoch: {epoch+1}, Batch: {batch_index}, Loss: {literal_loss:.4f}')
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# def save(self):
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# file_dir_path = Path(__file__).resolve().parent.parent / 'models'
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# file_dir_path.mkdir(parents=True, exist_ok=True)
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# file_path = file_dir_path / 'cnn.pth'
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# torch.save(self.model.state_dict(), file_path)
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# print(f'模型已保存至：{file_path}')
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# def test(self):
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# self.model.eval()
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# correct_sum = 0
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# total_sum = 0
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# with torch.no_grad():
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# batch_images: torch.Tensor
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# batch_labels: torch.Tensor
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# for batch_images, batch_labels in self.data_source.test_loader:
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# gpu_images = batch_images.to(self.device)
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# gpu_labels = batch_labels.to(self.device)
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# possibilities: torch.Tensor = self.model(gpu_images)
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# # 输出出来是10个数字各自的可能性，所以要选取最高可能性的那个对比
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# # 在dim=1上找最大的那个，就选那个。dim=0是批次所以不管他。
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# _, prediction = possibilities.max(1)
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# # 返回标签的个数作为这一批的总个数
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# total_sum += gpu_labels.size(0)
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# correct_sum += prediction.eq(gpu_labels).sum()
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# test_acc = 100. * correct_sum / total_sum
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# print(f"准确率: {test_acc:.4f}%，共测试了{total_sum}张图片")
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# def main():
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# trainer = Trainer()
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# trainer.train()
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# trainer.save()
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# trainer.test()
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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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trainer_accuracy: Accuracy
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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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# 输出转换为这种形式，因为后面的测量需要用其中一些参数。
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# 默认的输出只输出loss。
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output_transform=lambda x, y, y_pred, loss: {
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'loss': loss.item(),
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'y': y,
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'y_pred': y_pred
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}
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)
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# 设置训练器测量数据
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self.trainer_accuracy = Accuracy(
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device=self.device,
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# 转换为Accuracy需要的形式。
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output_transform=lambda o: (o['y_pred'], o['y'])
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)
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self.trainer_accuracy.attach(self.trainer, 'accuracy')
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# YYC MARK: 这里要手动reset一下，不然第一次运行没有accuracy
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self.trainer_accuracy.reset()
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# 每次epoch前重置accuracy
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self.trainer.add_event_handler(
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Events.EPOCH_STARTED,
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lambda: self.trainer_accuracy.reset()
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)
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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,
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metric_names=['accuracy'],
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output_transform=lambda o: {"loss": o['loss']})
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# 训练完毕后保存模型
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self.trainer.add_event_handler(
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Events.COMPLETED,
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lambda: self.save_model()
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)
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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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"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 run(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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# 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"Test Done. Accuracy: {metrics['accuracy']:.4f} Loss: {metrics['loss']:.4f}")
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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 main():
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trainer = Trainer()
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trainer.run()
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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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