Pytorch CNN 错误：预期输入batch_size (4) 与目标batch_size (64) 匹配

自十一月以来我一直在自学这一点，任何对此的帮助都将非常感激，谢谢您的关注，因为我似乎在兜圈子。我正在尝试使用与 Mnist 数据集一起使用的 Pytorch CNN 示例。现在我正在尝试修改CNN以进行面部关键点识别。我使用的 Kaggle 数据集 (CSV) 包含 7048 个训练图像和关键点（每张脸 15 个关键点）和 1783 个测试图像。我分割训练数据集并将图像转换为 jpeg，为关键点（形状 15、2）制作单独的文件。我已经制作了数据集和数据加载器，可以迭代并显示图像并绘制关键点。当我运行 CNN 时，我收到此错误。

> Net(
  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (conv2_drop): Dropout2d(p=0.5)
  (fc1): Linear(in_features=589824, out_features=100, bias=True)
  (fc2): Linear(in_features=100, out_features=30, bias=True)
)
Data and target shape:  torch.Size([64, 96, 96])   torch.Size([64, 15, 2])
Data and target shape:  torch.Size([64, 1, 96, 96])   torch.Size([64, 15, 2])

Traceback (most recent call last):
  File "/home/keith/PycharmProjects/FacialLandMarks/WorkOut.py", line 416, in <module>
    main()
  File "/home/keith/PycharmProjects/FacialLandMarks/WorkOut.py", line 412, in main
    train(args, model, device, train_loader, optimizer, epoch)
  File "/home/keith/PycharmProjects/FacialLandMarks/WorkOut.py", line 324, in train
    loss = F.nll_loss(output, target)
  File "/home/keith/Desktop/PycharmProjects/fkp/FacialLandMarks/lib/python3.6/site-packages/torch/nn/functional.py", line 1788, in nll_loss
    .format(input.size(0), target.size(0)))
ValueError: Expected input batch_size (4) to match target batch_size (64).

Process finished with exit code 1

这是我读过的一些链接，我无法找出问题所在 但可能会帮助别人。

https://github.com/pytorch/pytorch/issues/11762 如何修改此 PyTorch 卷积神经网络以接受 64 x 64 图像并正确输出预测？pytorch-卷积神经网络-接受-a-64-x-64-imPytorch 验证模型错误：预期输入batch_size (3) 与目标batch_size (4) 匹配模型错误预期输入批量大小 3 与目标 ba 匹配

这是我的代码：

    class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=(2, 2))
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=(2, 2))
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(64 * 96 * 96, 100)
        self.fc2 = nn.Linear(100, 30)  # 30 is x and y key points

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 64 * 96 * 96)
        # x = x.view(x.size(0), -1)
        # x = x.view(x.size()[0], 30, -1)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, batch in enumerate(train_loader):
        data = batch['image']
        target = batch['key_points']
        print('Data and target shape: ', data.shape, ' ', target.shape)
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        data = data.unsqueeze(1).float()

        print('Data and target shape: ', data.shape, ' ', target.shape)

        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


# def test(args, model, device, test_loader):
#     model.eval()
#     test_loss = 0
#     correct = 0
#     with torch.no_grad():
#         for data, target in test_loader:
#             data, target = data.to(device), target.to(device)
#             output = model(data)
#             test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
#             pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
#             correct += pred.eq(target.view_as(pred)).sum().item()
#
#     test_loss /= len(test_loader.dataset)
#     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
#         test_loss, correct, len(test_loader.dataset),
#         100. * correct / len(test_loader.dataset)))



def main():
    # Training settings
    parser = argparse.ArgumentParser(description='Project')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',   # ========  epoch
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
    train_data_set = FaceKeyPointDataSet(csv_file='faces/Kep_points_and_id.csv',
                                         root_dir='faces/',
                                         transform=transforms.Compose([
                                             # Rescale(96),
                                             ToTensor()
                                         ]))

    train_loader = DataLoader(train_data_set, batch_size=args.batch_size,
                              shuffle=True)

    print('Number of samples: ', len(train_data_set))
    print('Number of train_loader: ', len(train_loader))

    model = Net().to(device)
    print(model)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        # test(args, model, device, test_loader)

if __name__ == '__main__':
    main()

要了解出了什么问题，您可以在前进的每一步之后打印形状：

# Input data
torch.Size([64, 1, 96, 96])
x = F.relu(F.max_pool2d(self.conv1(x), 2))
torch.Size([64, 32, 48, 48])
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
torch.Size([64, 64, 24, 24])
x = x.view(-1, 64 * 96 * 96)
torch.Size([4, 589824])
x = F.relu(self.fc1(x))
torch.Size([4, 100])
x = F.dropout(x, training=self.training)
torch.Size([4, 100])
x = self.fc2(x)
torch.Size([4, 30])
return F.log_softmax(x, dim=1)    
torch.Size([4, 30])

• Your maxpool2d层减少了特征图的高度和宽度。
• “观点”应该是x = x.view(-1, 64 * 24 * 24)
• 第一个线性层的大小：self.fc1 = nn.Linear(64 * 24 * 24, 100)

这会给你的output = model(data)最终形状为torch.Size([64, 30])

但这段代码在计算负对数似然损失时仍然会面临一个问题：

输入预计包含每个班级的分数。输入必须 是大小为（小批量，C）的 2D 张量。该标准期望一个类 索引（0 到 C-1）作为大小为 1D 张量的每个值的目标 小批量

其中类索引只是标签：

代表一个类的值。例如：

0 - 0 级、1 - 1 级、

由于最后一个 nn 层输出超过 30 个类别的 softmax，我假设这是您想要分类的输出类别， 所以目标的转换：

target = target.view(64, -1) # gives 64X30 ie, 30 values per channel
loss = F.nll_loss(x, torch.max(t, 1)[1]) # takes max amongst the 30 values as class label

这是当目标是超过 30 个类别的概率分布时，如果不是，可以在此之前进行 soft-max。因此，30 个值中的最大值将代表最高概率 - 因此该类正是您的输出所代表的，因此您可以计算两个值之间的 nll。 。