c++ - 尺寸不匹配 CNN LibTorch/PyTorch

Question

我在 LibTorch 中有一个 CNN 结构，但尺寸不正确。我的目标是输入 3 channel 64x64 图像并为 DGAN 输出逻辑回归浮点数。最后一层我设置为输入 channel 36，因为如果我移除该层，输出神经元的维度为 6x6，所以我猜这是全连接输入所需的维度。我想知道:

您通常如何检查 LibTorch 或 Pytorch 中的尺寸(即检查最后一个模块所需的大小，检查每层有多少可训练参数...)

这种情况下的错误是什么

   #include <torch/torch.h>
   #include "parameters.h"
   using namespace torch;

    class DCGANDiscriminatorImpl: public nn::Module {

    private:
        nn::Conv2d conv1, conv2, conv3, conv4;
        nn::BatchNorm2d batch_norm1, batch_norm2;
        nn::Linear fc1;

    public:
        DCGANDiscriminatorImpl()
            :conv1(nn::Conv2dOptions(3, 64, 4).stride(2).padding(1).bias(false)),

             conv2(nn::Conv2dOptions(64, 128, 4).stride(2).padding(1).bias(false)),

             batch_norm1(128),

             conv3(nn::Conv2dOptions(128, 256, 4).stride(2).padding(1).bias(false)),

             batch_norm2(256),

             conv4(nn::Conv2dOptions(256, 1, 3).stride(1).padding(0).bias(false)),

             fc1(6*6, 1)

        {
            register_module("conv1", conv1);
            register_module("conv2", conv2);
            register_module("conv3", conv3);
            register_module("conv4", conv4);
            register_module("batch_norm1", batch_norm1);
            register_module("batch_norm2", batch_norm2);
            register_module("fc1", fc1);

        }

        Tensor forward(torch::Tensor x)
        {
            x = leaky_relu(conv1(x), cte::NEGATIVE_SLOPE);
            x = leaky_relu(batch_norm1(conv2(x)), cte::NEGATIVE_SLOPE);
            x = leaky_relu(batch_norm2(conv3(x)), cte::NEGATIVE_SLOPE);
            x = sigmoid(fc1(x));
            return x;
        }

    };

    TORCH_MODULE(DCGANDiscriminator);

我得到的错误是:

libc++abi.dylib: terminating with uncaught exception of type std::runtime_error: size mismatch, m1: [131072 x 8], m2: [36 x 1] at ../aten/src/TH/generic/THTensorMath.cpp:136

Answer 1

我有几个问题，但最后这个架构奏效了。

using namespace torch;

class DCGANDiscriminatorImpl: public nn::Module {

private:
    nn::Conv2d conv1, conv2, conv3, conv4;
    nn::BatchNorm2d batch_norm1, batch_norm2;
    nn::Linear fc1;

public:
    DCGANDiscriminatorImpl()
        :conv1(nn::Conv2dOptions(3, 64, 4).stride(2).padding(1).bias(false)),

         conv2(nn::Conv2dOptions(64, 128, 4).stride(2).padding(1).bias(false)),

         batch_norm1(128),

         conv3(nn::Conv2dOptions(128, 256, 4).stride(2).padding(1).bias(false)),

         batch_norm2(256),

         conv4(nn::Conv2dOptions(256, 64, 3).stride(1).padding(0).bias(false)),

         fc1(6*6*64, 1)

    {
        register_module("conv1", conv1);
        register_module("conv2", conv2);
        register_module("conv3", conv3);
        register_module("conv4", conv4);
        register_module("batch_norm1", batch_norm1);
        register_module("batch_norm2", batch_norm2);
        register_module("fc1", fc1);

    }

    Tensor forward(torch::Tensor x)
    {
        x = leaky_relu(conv1(x), cte::NEGATIVE_SLOPE);
        x = leaky_relu(batch_norm1(conv2(x)), cte::NEGATIVE_SLOPE);
        x = leaky_relu(batch_norm2(conv3(x)), cte::NEGATIVE_SLOPE);
        x = leaky_relu(conv4(x), cte::NEGATIVE_SLOPE);
        x = x.view({x.size(0), -1});
        x = sigmoid(fc1(x));
        return x;
    }

};

TORCH_MODULE(DCGANDiscriminator);