合并FC层和反卷积层

Question

我正在尝试将反卷积层与完全连接的层合并以用于处理卷积层，但当我在合并Deconv层与FC层之后进行重塑时，我的批处理大小发生了变化，这是我所做的过程：

......

    layer {
      name: "fc4"
      type: "InnerProduct"
      bottom: "fc3"
      top: "fc4"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      inner_product_param {
        num_output: 21904
        weight_filler {
          type: "gaussian"
          std: 0.01
        }
        bias_filler {
          type: "constant"
          value: 0
        }
      }
    }
    layer {
      name: "deconv"
      type: "Deconvolution"
      bottom: "data"
      top: "deconv"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 8
        weight_filler {
          type: "gaussian"
          std: 0.01
        }
        bias_filler {
          type: "constant"
          value: 0
        }
      }
    }
    layer {
      type: "Flatten"
      bottom: "deconv"
      top: "dec_flatten"
      name: "dec_flatten"
    }
    layer {
      name: "concat"
      bottom: "fc4"
      bottom: "dec_flatten"
      top: "concat"
      type: "Concat"
      concat_param {
        axis: 1
      }
    }
    layer {
      name: "reshape"
      type: "Reshape"
      bottom: "concat"
      top: "output"
      reshape_param {
        shape {
         dim: -1
         dim: 1
         dim: 148
         dim: 148
        }
      }
    }
    layer {
      name: "conv1"
      type: "Convolution"
      bottom: "output"
      top: "conv1"
      param {
        lr_mult: 1
        decay_mult: 1
      }
      param {
        lr_mult: 2
        decay_mult: 0
      }
      convolution_param {
        num_output: 16
        kernel_size: 5
        stride: 1
        weight_filler {
          type: "gaussian"
          std: 0.01
        }
        bias_filler {
          type: "constant"
          value: 0
        }
      }
    }
    ....

在终端I上，输出如下：

0428 11:45:41.201318 52048 net.cpp:454] fc4 <- fc3
I0428 11:45:41.201325 52048 net.cpp:411] fc4 -> fc4
I0428 11:45:41.320688 52048 net.cpp:150] Setting up fc4
I0428 11:45:41.320735 52048 net.cpp:157] Top shape: 1 21904 (21904)
I0428 11:45:41.320740 52048 net.cpp:165] Memory required for data: 114240
I0428 11:45:41.320752 52048 layer_factory.hpp:76] Creating layer deconv
I0428 11:45:41.320770 52048 net.cpp:106] Creating Layer deconv
I0428 11:45:41.320776 52048 net.cpp:454] deconv <- data_data_0_split_1
I0428 11:45:41.320786 52048 net.cpp:411] deconv -> deconv
I0428 11:45:41.322069 52048 net.cpp:150] Setting up deconv
I0428 11:45:41.322095 52048 net.cpp:157] Top shape: 1 256 37 37 (350464)
I0428 11:45:41.322100 52048 net.cpp:165] Memory required for data: 1516096
I0428 11:45:41.322110 52048 layer_factory.hpp:76] Creating layer dec_flatten
I0428 11:45:41.322119 52048 net.cpp:106] Creating Layer dec_flatten
I0428 11:45:41.322124 52048 net.cpp:454] dec_flatten <- deconv
I0428 11:45:41.322130 52048 net.cpp:411] dec_flatten -> dec_flatten
I0428 11:45:41.322156 52048 net.cpp:150] Setting up dec_flatten
I0428 11:45:41.322163 52048 net.cpp:157] Top shape: 1 350464 (350464)
I0428 11:45:41.322167 52048 net.cpp:165] Memory required for data: 2917952
I0428 11:45:41.322171 52048 layer_factory.hpp:76] Creating layer concat
I0428 11:45:41.322180 52048 net.cpp:106] Creating Layer concat
I0428 11:45:41.322183 52048 net.cpp:454] concat <- fc4
I0428 11:45:41.322188 52048 net.cpp:454] concat <- dec_flatten
I0428 11:45:41.322194 52048 net.cpp:411] concat -> concat
I0428 11:45:41.322216 52048 net.cpp:150] Setting up concat
I0428 11:45:41.322223 52048 net.cpp:157] Top shape: 1 372368 (372368)
I0428 11:45:41.322227 52048 net.cpp:165] Memory required for data: 4407424
I0428 11:45:41.322232 52048 layer_factory.hpp:76] Creating layer reshape
I0428 11:45:41.322242 52048 net.cpp:106] Creating Layer reshape
I0428 11:45:41.322247 52048 net.cpp:454] reshape <- concat
I0428 11:45:41.322252 52048 net.cpp:411] reshape -> output
I0428 11:45:41.322283 52048 net.cpp:150] Setting up reshape
I0428 11:45:41.322295 52048 net.cpp:157] Top shape: 17 1 148 148 (372368)
I0428 11:45:41.322311 52048 net.cpp:165] Memory required for data: 5896896
I0428 11:45:41.322315 52048 layer_factory.hpp:76] Creating layer conv1
I0428 11:45:41.322325 52048 net.cpp:106] Creating Layer conv1
I0428 11:45:41.322330 52048 net.cpp:454] conv1 <- output
I0428 11:45:41.322337 52048 net.cpp:411] conv1 -> conv1
I0428 11:45:41.323410 52048 net.cpp:150] Setting up conv1
I0428 11:45:41.323438 52048 net.cpp:157] Top shape: 17 16 144 144 (5640192)

当我重塑我的批次大小改变时，他们知道如何重塑而不改变批次大小吗？

谢谢

Answer 1

concat斑点的形状是1x372368。

现在，您正在尝试使用prototxt重塑此blob数据：

  reshape_param {
    shape {
     dim: -1
     dim: 1
     dim: 148
     dim: 148
    }

由于您已经固定了h=148、w=148、c=1的大小，因此n的值由372368/(148*148*1) = 17计算

如果您不想更改批次计数，则必须更改h、w或c的值，以便h*w*c*n = 372368。

编辑1:您的编辑的答案:现在，您的concat层的每个图像的维度为207936，您正在尝试使用1024维度标签来计算损失。这不起作用，因为两个底部斑点的尺寸是不同的。您可以尝试使用num_output: 1024将inner product层连接到concat层的输出。然后，该inner product层的输出可以与标签一起用于损失层。