189 lines
6.4 KiB
Python
189 lines
6.4 KiB
Python
from pathlib import Path
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import sys
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import typing
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import numpy
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import torch
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from torch.utils.data import DataLoader, Dataset
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from torchvision.transforms import v2 as tvtrans
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import matplotlib.pyplot as plt
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import torch.nn.functional as F
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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 CNN(torch.nn.Module):
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"""卷积神经网络模型"""
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def __init__(self):
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super(CNN, self).__init__()
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self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=(3, 3))
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self.pool1 = torch.nn.MaxPool2d(kernel_size=(2, 2))
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self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=(3, 3))
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self.pool2 = torch.nn.MaxPool2d(kernel_size=(2, 2))
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self.conv3 = torch.nn.Conv2d(64, 64, kernel_size=(3, 3))
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self.flatten = torch.nn.Flatten()
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# 28x28过第一轮卷积后变为26x26，过第一轮池化后变为13x13
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# 过第二轮卷积后变为11x11，过第二轮池化后变为5x5
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# 过第三轮卷积后变为3x3。
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# 最后一轮卷积核个数为64。
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self.fc1 = torch.nn.Linear(64 * 3 * 3, 64)
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self.fc2 = torch.nn.Linear(64, 10)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = self.pool1(x)
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x = F.relu(self.conv2(x))
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x = self.pool2(x)
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x = F.relu(self.conv3(x))
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x = self.flatten(x)
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return F.softmax(x, dim=1)
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class MnistDataset(Dataset):
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"""用于加载Mnist的自定义数据集"""
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shape: int
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transform: tvtrans.Transform
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images_data: numpy.ndarray
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labels_data: torch.Tensor
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def __init__(self, images: numpy.ndarray, labels: numpy.ndarray, transform: tvtrans.Transform):
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images_len: int = images.size(0)
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labels_len: int = labels.size(0)
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assert (images_len == labels_len)
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self.shape = images_len
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self.images_data = images
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self.labels_data = torch.from_numpy(labels)
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self.transform = transform
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def __getitem__(self, index):
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return self.transform(self.images_data[index]), self.labels_data[index]
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def __len__(self):
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return self.shape
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class DataSource:
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"""用于读取MNIST数据的数据读取器"""
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train_data: DataLoader
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test_data: DataLoader
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def __init__(self, batch_size: int):
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datasets_path = Path(__file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
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datasets = numpy.load(datasets_path)
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# 所有图片均为黑底白字
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# 6万张训练图片：60000x28x28。标签只有第一维。
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train_images = datasets['x_train']
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train_labels = datasets['y_train']
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# 1万张测试图片：10000x28x28。标签只有第一维。
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test_images = datasets['x_test']
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test_labels = datasets['y_test']
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# 定义数据转换器
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trans = tvtrans.Compose([
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# 从uint8转换为float32并自动归一化到0-1区间
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# tvtrans.ToTensor(),
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tvtrans.ToImage(),
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tvtrans.ToDtype(torch.float32, scale=True),
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# 为了符合后面图像的输入颜色通道条件，要在最后挤出一个新的维度
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#tvtrans.Lambda(lambda x: x.unsqueeze(-1))
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])
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# 创建数据集
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train_dataset = MnistDataset(train_images,
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train_labels,
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transform=trans)
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test_dataset = MnistDataset(test_images, test_labels, transform=trans)
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# 赋值到自身
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self.train_data = DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=False)
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self.test_data = DataLoader(dataset=test_dataset,
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batch_size=batch_size,
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shuffle=False)
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class Trainer:
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N_EPOCH: typing.ClassVar[int] = 5
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N_BATCH_SIZE: typing.ClassVar[int] = 1000
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device: torch.device
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data_source: DataSource
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cnn: 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 = DataSource(Trainer.N_BATCH_SIZE)
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self.cnn = CNN().to(self.device)
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def train(self):
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optimizer = torch.optim.Adam(self.cnn.parameters())
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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.cnn.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_data):
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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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optimizer.zero_grad()
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prediction: torch.Tensor = self.cnn(gpu_images)
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loss: torch.Tensor = loss_func(prediction, gpu_labels)
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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.cnn.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.cnn.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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for batch_images, batch_labels in self.data_source.test_data:
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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.cnn(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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if __name__ == "__main__":
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gpu_utils.print_gpu_availability()
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main()
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