name: "pytorch"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 256
input_dim: 256

layer {
    name: "ConvNdBackward1"
    type: "Convolution"
    bottom: "data"
    top: "ConvNdBackward1"
    convolution_param {
        num_output: 16
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward2_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward1"
    top: "BatchNormBackward2"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward2_scale"
    type: "Scale"
    bottom: "BatchNormBackward2"
    top: "BatchNormBackward2"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward3"
    type: "ReLU"
    bottom: "BatchNormBackward2"
    top: "BatchNormBackward2"
}
layer {
    name: "ConvNdBackward4"
    type: "Convolution"
    bottom: "BatchNormBackward2"
    top: "ConvNdBackward4"
    convolution_param {
        num_output: 16
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward5_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward4"
    top: "BatchNormBackward5"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward5_scale"
    type: "Scale"
    bottom: "BatchNormBackward5"
    top: "BatchNormBackward5"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward6"
    type: "ReLU"
    bottom: "BatchNormBackward5"
    top: "BatchNormBackward5"
}
layer {
    name: "MaxPool2dBackward8"
    type: "Pooling"
    bottom: "BatchNormBackward5"
    top: "MaxPool2dBackward8"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward9"
    type: "Convolution"
    bottom: "MaxPool2dBackward8"
    top: "ConvNdBackward9"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward10_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward9"
    top: "BatchNormBackward10"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward10_scale"
    type: "Scale"
    bottom: "BatchNormBackward10"
    top: "BatchNormBackward10"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward11"
    type: "ReLU"
    bottom: "BatchNormBackward10"
    top: "BatchNormBackward10"
}
layer {
    name: "ConvNdBackward12"
    type: "Convolution"
    bottom: "BatchNormBackward10"
    top: "ConvNdBackward12"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward13_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward12"
    top: "BatchNormBackward13"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward13_scale"
    type: "Scale"
    bottom: "BatchNormBackward13"
    top: "BatchNormBackward13"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward14"
    type: "ReLU"
    bottom: "BatchNormBackward13"
    top: "BatchNormBackward13"
}
layer {
    name: "MaxPool2dBackward16"
    type: "Pooling"
    bottom: "BatchNormBackward13"
    top: "MaxPool2dBackward16"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward17"
    type: "Convolution"
    bottom: "MaxPool2dBackward16"
    top: "ConvNdBackward17"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward18_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward17"
    top: "BatchNormBackward18"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward18_scale"
    type: "Scale"
    bottom: "BatchNormBackward18"
    top: "BatchNormBackward18"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward19"
    type: "ReLU"
    bottom: "BatchNormBackward18"
    top: "BatchNormBackward18"
}
layer {
    name: "ConvNdBackward20"
    type: "Convolution"
    bottom: "BatchNormBackward18"
    top: "ConvNdBackward20"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward21_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward20"
    top: "BatchNormBackward21"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward21_scale"
    type: "Scale"
    bottom: "BatchNormBackward21"
    top: "BatchNormBackward21"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward22"
    type: "ReLU"
    bottom: "BatchNormBackward21"
    top: "BatchNormBackward21"
}
layer {
    name: "MaxPool2dBackward24"
    type: "Pooling"
    bottom: "BatchNormBackward21"
    top: "MaxPool2dBackward24"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward25"
    type: "Convolution"
    bottom: "MaxPool2dBackward24"
    top: "ConvNdBackward25"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward26_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward25"
    top: "BatchNormBackward26"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward26_scale"
    type: "Scale"
    bottom: "BatchNormBackward26"
    top: "BatchNormBackward26"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward27"
    type: "ReLU"
    bottom: "BatchNormBackward26"
    top: "BatchNormBackward26"
}
layer {
    name: "ConvNdBackward28"
    type: "Convolution"
    bottom: "BatchNormBackward26"
    top: "ConvNdBackward28"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward29_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward28"
    top: "BatchNormBackward29"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward29_scale"
    type: "Scale"
    bottom: "BatchNormBackward29"
    top: "BatchNormBackward29"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward30"
    type: "ReLU"
    bottom: "BatchNormBackward29"
    top: "BatchNormBackward29"
}
layer {
    name: "MaxPool2dBackward32"
    type: "Pooling"
    bottom: "BatchNormBackward29"
    top: "MaxPool2dBackward32"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward33"
    type: "Convolution"
    bottom: "MaxPool2dBackward32"
    top: "ConvNdBackward33"
    convolution_param {
        num_output: 256
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward34_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward33"
    top: "BatchNormBackward34"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward34_scale"
    type: "Scale"
    bottom: "BatchNormBackward34"
    top: "BatchNormBackward34"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward35"
    type: "ReLU"
    bottom: "BatchNormBackward34"
    top: "BatchNormBackward34"
}
layer {
    name: "ConvNdBackward36"
    type: "Convolution"
    bottom: "BatchNormBackward34"
    top: "ConvNdBackward36"
    convolution_param {
        num_output: 256
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward37_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward36"
    top: "BatchNormBackward37"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward37_scale"
    type: "Scale"
    bottom: "BatchNormBackward37"
    top: "BatchNormBackward37"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward38"
    type: "ReLU"
    bottom: "BatchNormBackward37"
    top: "BatchNormBackward37"
}
layer {
    name: "ConvNdBackward39"
    type: "Deconvolution"
    bottom: "BatchNormBackward37"
    top: "ConvNdBackward39"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 2
        kernel_w: 2
        stride: 2
        dilation: 1
    }
}
layer {
    name: "ConcatBackward40"
    type: "Concat"
    bottom: "BatchNormBackward29"
    bottom: "ConvNdBackward39"
    top: "ConcatBackward40"
    concat_param {
        axis: 1
    }
}
layer {
    name: "ConvNdBackward41"
    type: "Convolution"
    bottom: "ConcatBackward40"
    top: "ConvNdBackward41"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward42"
    type: "ReLU"
    bottom: "ConvNdBackward41"
    top: "ConvNdBackward41"
}
layer {
    name: "ConvNdBackward43"
    type: "Convolution"
    bottom: "ConvNdBackward41"
    top: "ConvNdBackward43"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward44"
    type: "ReLU"
    bottom: "ConvNdBackward43"
    top: "ConvNdBackward43"
}
layer {
    name: "ConvNdBackward45"
    type: "Deconvolution"
    bottom: "ConvNdBackward43"
    top: "ConvNdBackward45"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 2
        kernel_w: 2
        stride: 2
        dilation: 1
    }
}
layer {
    name: "ConcatBackward46"
    type: "Concat"
    bottom: "BatchNormBackward21"
    bottom: "ConvNdBackward45"
    top: "ConcatBackward46"
    concat_param {
        axis: 1
    }
}
layer {
    name: "ConvNdBackward47"
    type: "Convolution"
    bottom: "ConcatBackward46"
    top: "ConvNdBackward47"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward48"
    type: "ReLU"
    bottom: "ConvNdBackward47"
    top: "ConvNdBackward47"
}
layer {
    name: "ConvNdBackward49"
    type: "Convolution"
    bottom: "ConvNdBackward47"
    top: "ConvNdBackward49"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward50"
    type: "ReLU"
    bottom: "ConvNdBackward49"
    top: "ConvNdBackward49"
}
layer {
    name: "ConvNdBackward51"
    type: "Deconvolution"
    bottom: "ConvNdBackward49"
    top: "ConvNdBackward51"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 2
        kernel_w: 2
        stride: 2
        dilation: 1
    }
}
layer {
    name: "ConcatBackward52"
    type: "Concat"
    bottom: "BatchNormBackward13"
    bottom: "ConvNdBackward51"
    top: "ConcatBackward52"
    concat_param {
        axis: 1
    }
}
layer {
    name: "ConvNdBackward53"
    type: "Convolution"
    bottom: "ConcatBackward52"
    top: "ConvNdBackward53"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward54"
    type: "ReLU"
    bottom: "ConvNdBackward53"
    top: "ConvNdBackward53"
}
layer {
    name: "ConvNdBackward55"
    type: "Convolution"
    bottom: "ConvNdBackward53"
    top: "ConvNdBackward55"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward56"
    type: "ReLU"
    bottom: "ConvNdBackward55"
    top: "ConvNdBackward55"
}
layer {
    name: "ConvNdBackward57"
    type: "Deconvolution"
    bottom: "ConvNdBackward55"
    top: "ConvNdBackward57"
    convolution_param {
        num_output: 16
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 2
        kernel_w: 2
        stride: 2
        dilation: 1
    }
}
layer {
    name: "ConcatBackward58"
    type: "Concat"
    bottom: "BatchNormBackward5"
    bottom: "ConvNdBackward57"
    top: "ConcatBackward58"
    concat_param {
        axis: 1
    }
}
layer {
    name: "ConvNdBackward59"
    type: "Convolution"
    bottom: "ConcatBackward58"
    top: "ConvNdBackward59"
    convolution_param {
        num_output: 16
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward60"
    type: "ReLU"
    bottom: "ConvNdBackward59"
    top: "ConvNdBackward59"
}
layer {
    name: "ConvNdBackward61"
    type: "Convolution"
    bottom: "ConvNdBackward59"
    top: "ConvNdBackward61"
    convolution_param {
        num_output: 16
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "ThresholdBackward62"
    type: "ReLU"
    bottom: "ConvNdBackward61"
    top: "ConvNdBackward61"
}
layer {
    name: "ConvNdBackward63"
    type: "Convolution"
    bottom: "ConvNdBackward61"
    top: "ConvNdBackward63"
    convolution_param {
        num_output: 17
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 1
        kernel_w: 1
        stride: 1
        dilation: 1
    }
}