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TMDSEVM572X: caffe-jacinto训练网络,网络结构不符合结构

Part Number: TMDSEVM572X

使用caffe-jacinto去训练一个网络时,定义网络的最后一层的卷积核为1*1,输出为1。训练时报错(附图),但是看官方例程的网络也有相似的结构,只是输出不一样,我是模仿官方结构的。请问问题出在哪儿?

训练报错:

官方相似结构(object detection的mobilenet结构),图中的conv3_1/sep和conv3_2/sep都是kernel为1*1,group都为1,但output channel和group是不匹配的。

本人网络最后部分:

layer {
  name: "fu1_1/dw"
  type: "Convolution"
  bottom: "conv7_3"
  top: "fu1_1/dw"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 64
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "fu1_1/dw/bn"
  type: "BatchNorm"
  bottom: "fu1_1/dw"
  top: "fu1_1/dw"
  batch_norm_param {
    scale_bias: true
  }
}
layer {
  name: "relu1_1/dw"
  type: "ReLU"
  bottom: "fu1_1/dw"
  top: "fu1_1/dw"
}
layer {
  name: "fu1_1/sep"
  type: "Convolution"
  bottom: "fu1_1/dw"
  top: "fu1_1/sep"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "fu1_1/sep/bn"
  type: "BatchNorm"
  bottom: "fu1_1/sep"
  top: "fu1_1/sep"
  batch_norm_param {
    scale_bias: true
  }
}
layer {
  name: "relu1_1/sep"
  type: "ReLU"
  bottom: "fu1_1/sep"
  top: "fu1_1/sep"
}
layer {
  name: "fu1_2/dw"
  type: "Convolution"
  bottom: "fu1_1/sep"
  top: "fu1_2/dw"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 64
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "fu1_2/dw/bn"
  type: "BatchNorm"
  bottom: "fu1_2/dw"
  top: "fu1_2/dw"
  batch_norm_param {
    scale_bias: true
  }
}
layer {
  name: "relu1_2/dw"
  type: "ReLU"
  bottom: "fu1_2/dw"
  top: "fu1_2/dw"
}
layer {
  name: "fu1_2/sep"
  type: "Convolution"
  bottom: "fu1_2/dw"
  top: "estdmap"
  convolution_param {
    num_output: 1
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}

本人网络最后部分结构: