HRNet-CLS-w32.prototxt

name: "HRNet-w48"
layer {
    name: "data"
    type: "ImageData"
    top: "data"
    top: "label"
    include {
        phase: TRAIN
    }
    transform_param {
        mirror: true
        crop_size: 224
        mean_value: 103.94
        mean_value: 116.78
        mean_value: 123.68
		scale:0.017
    }
    image_data_param {
        batch_size:64
        source: "/data/train.txt"
        root_folder: "/data"
    }
}
layer {
    bottom: "data"
    top: "conv1"
    name: "conv1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 2
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv1"
    top: "conv1_bn"
    name: "conv1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
	
}
layer {
    bottom: "conv1_bn"
    top: "conv1_bn"
    name: "conv1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "conv1_bn"
    top: "conv2"
    name: "conv2"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
        stride: 2
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv2"
    top: "conv2_bn"
    name: "conv2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "conv2_bn"
    top: "conv2_bn"
    name: "conv2_bn/relu"
    type: "ReLU"
}
#stage1 4 bottleneck
layer {
    bottom: "conv2_bn"
    top: "s1_branch0_r0_0"
    name: "s1_branch0_r0_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r0_0"
    top: "s1_branch0_r0_0_bn"
    name: "s1_branch0_r0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "conv2_bn"
    top: "s1_branch0_r0_0_0"
    name: "s1_branch0_r0_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r0_0_0"
    top: "s1_branch0_r0_0_0_bn"
    name: "s1_branch0_r0_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r0_0_0_bn"
    top: "s1_branch0_r0_0_0_bn"
    name: "s1_branch0_r0_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r0_0_0_bn"
    top: "s1_branch0_r0_1"
    name: "s1_branch0_r0_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r0_1"
    top: "s1_branch0_r0_1_bn"
    name: "s1_branch0_r0_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r0_1_bn"
    top: "s1_branch0_r0_1_bn"
    name: "s1_branch0_r0_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r0_1_bn"
    top: "s1_branch0_r0_2"
    name: "s1_branch0_r0_2"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r0_2"
    top: "s1_branch0_r0_2_bn"
    name: "s1_branch0_r0_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r0_0_bn"
    bottom: "s1_branch0_r0_2_bn"
    top: "s1_branch0_r0_e0"
    name: "s1_branch0_r0_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s1_branch0_r0_e0"
    top: "s1_branch0_r0_e0"
    name: "s1_branch0_r0_e0/relu"
    type: "ReLU"
}

layer {
    bottom: "s1_branch0_r0_e0"
    top: "s1_branch0_r1_0_0"
    name: "s1_branch0_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r1_0_0"
    top: "s1_branch0_r1_0_0_bn"
    name: "s1_branch0_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r1_0_0_bn"
    top: "s1_branch0_r1_0_0_bn"
    name: "s1_branch0_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r1_0_0_bn"
    top: "s1_branch0_r1_1"
    name: "s1_branch0_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r1_1"
    top: "s1_branch0_r1_1_bn"
    name: "s1_branch0_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r1_1_bn"
    top: "s1_branch0_r1_1_bn"
    name: "s1_branch0_r1_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r1_1_bn"
    top: "s1_branch0_r1_2"
    name: "s1_branch0_r1_2"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r1_2"
    top: "s1_branch0_r1_2_bn"
    name: "s1_branch0_r1_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r0_e0"
    bottom: "s1_branch0_r1_2_bn"
    top: "s1_branch0_r1_e0"
    name: "s1_branch0_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s1_branch0_r1_e0"
    top: "s1_branch0_r1_e0"
    name: "s1_branch0_r1_e0/relu"
    type: "ReLU"
}

layer {
    bottom: "s1_branch0_r1_e0"
    top: "s1_branch0_r2_0_0"
    name: "s1_branch0_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r2_0_0"
    top: "s1_branch0_r2_0_0_bn"
    name: "s1_branch0_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r2_0_0_bn"
    top: "s1_branch0_r2_0_0_bn"
    name: "s1_branch0_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r2_0_0_bn"
    top: "s1_branch0_r2_1"
    name: "s1_branch0_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r2_1"
    top: "s1_branch0_r2_1_bn"
    name: "s1_branch0_r2_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r2_1_bn"
    top: "s1_branch0_r2_1_bn"
    name: "s1_branch0_r2_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r2_1_bn"
    top: "s1_branch0_r2_2"
    name: "s1_branch0_r2_2"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r2_2"
    top: "s1_branch0_r2_2_bn"
    name: "s1_branch0_r2_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s1_branch0_r1_e0"
    bottom: "s1_branch0_r2_2_bn"
    top: "s1_branch0_r2_e0"
    name: "s1_branch0_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s1_branch0_r2_e0"
    top: "s1_branch0_r2_e0"
    name: "s1_branch0_r2_e0/relu"
    type: "ReLU"
}

layer {
    bottom: "s1_branch0_r2_e0"
    top: "s1_branch0_r3_0_0"
    name: "s1_branch0_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r3_0_0"
    top: "s1_branch0_r3_0_0_bn"
    name: "s1_branch0_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r3_0_0_bn"
    top: "s1_branch0_r3_0_0_bn"
    name: "s1_branch0_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r3_0_0_bn"
    top: "s1_branch0_r3_1"
    name: "s1_branch0_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r3_1"
    top: "s1_branch0_r3_1_bn"
    name: "s1_branch0_r3_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r3_1_bn"
    top: "s1_branch0_r3_1_bn"
    name: "s1_branch0_r3_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r3_1_bn"
    top: "s1_branch0_r3_2"
    name: "s1_branch0_r3_2"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_branch0_r3_2"
    top: "s1_branch0_r3_2_bn"
    name: "s1_branch0_r3_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s1_branch0_r2_e0"
    bottom: "s1_branch0_r3_2_bn"
    top: "s1_branch0_r3_e0"
    name: "s1_branch0_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s1_branch0_r3_e0"
    top: "s1_branch0_r3_e0"
    name: "s1_branch0_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_branch0_r3_e0"
    top: "s1_t1_branch0_0"
    name: "s1_t1_branch0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
        pad: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_t1_branch0_0"
    top: "s1_t1_branch0_0_bn"
    name: "s1_t1_branch0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_branch0_r3_e0"
    top: "s1_t1_branch1_0"
    name: "s1_t1_branch1_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
        pad: 1
		stride:2
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s1_t1_branch1_0"
    top: "s1_t1_branch1_0_bn"
    name: "s1_t1_branch1_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
#stage2 2branch 4 basic block
layer {
    bottom: "s1_t1_branch0_0_bn"
    top: "s2_branch0_r1_0_0"
    name: "s2_branch0_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r1_0_0"
    top: "s2_branch0_r1_0_0_bn"
    name: "s2_branch0_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch0_r1_0_0_bn"
    top: "s2_branch0_r1_0_0_bn"
    name: "s2_branch0_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r1_0_0_bn"
    top: "s2_branch0_r1_1"
    name: "s2_branch0_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r1_1"
    top: "s2_branch0_r1_1_bn"
    name: "s2_branch0_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_t1_branch0_0_bn"
    bottom: "s2_branch0_r1_1_bn"
    top: "s2_branch0_r1_e0"
    name: "s1_branch0_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch0_r1_e0"
    top: "s2_branch0_r1_e0"
    name: "s2_branch0_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r1_e0"
    top: "s2_branch0_r2_0_0"
    name: "s2_branch0_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r2_0_0"
    top: "s2_branch0_r2_0_0_bn"
    name: "s2_branch0_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch0_r2_0_0_bn"
    top: "s2_branch0_r2_0_0_bn"
    name: "s2_branch0_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r2_0_0_bn"
    top: "s2_branch0_r2_1"
    name: "s2_branch0_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r2_1"
    top: "s2_branch0_r2_1_bn"
    name: "s2_branch0_r2_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s2_branch0_r1_e0"
    bottom: "s2_branch0_r2_1_bn"
    top: "s2_branch0_r2_e0"
    name: "s1_branch0_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch0_r2_e0"
    top: "s2_branch0_r2_e0"
    name: "s2_branch0_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r2_e0"
    top: "s2_branch0_r3_0_0"
    name: "s2_branch0_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r3_0_0"
    top: "s2_branch0_r3_0_0_bn"
    name: "s2_branch0_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch0_r3_0_0_bn"
    top: "s2_branch0_r3_0_0_bn"
    name: "s2_branch0_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r3_0_0_bn"
    top: "s2_branch0_r3_1"
    name: "s2_branch0_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r3_1"
    top: "s2_branch0_r3_1_bn"
    name: "s2_branch0_r3_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch0_r2_e0"
    bottom: "s2_branch0_r3_1_bn"
    top: "s2_branch0_r3_e0"
    name: "s1_branch0_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch0_r3_e0"
    top: "s2_branch0_r3_e0"
    name: "s2_branch0_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r3_e0"
    top: "s2_branch0_r4_0_0"
    name: "s2_branch0_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r4_0_0"
    top: "s2_branch0_r4_0_0_bn"
    name: "s2_branch0_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch0_r4_0_0_bn"
    top: "s2_branch0_r4_0_0_bn"
    name: "s2_branch0_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r4_0_0_bn"
    top: "s2_branch0_r4_1"
    name: "s2_branch0_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch0_r4_1"
    top: "s2_branch0_r4_1_bn"
    name: "s2_branch0_r4_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s2_branch0_r3_e0"
    bottom: "s2_branch0_r4_1_bn"
    top: "s2_branch0_r4_e0"
    name: "s1_branch0_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch0_r4_e0"
    top: "s2_branch0_r4_e0"
    name: "s2_branch0_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s1_t1_branch1_0_bn"
    top: "s2_branch1_r1_0_0"
    name: "s2_branch1_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r1_0_0"
    top: "s2_branch1_r1_0_0_bn"
    name: "s2_branch1_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r1_0_0_bn"
    top: "s2_branch1_r1_0_0_bn"
    name: "s2_branch1_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r1_0_0_bn"
    top: "s2_branch1_r1_1"
    name: "s2_branch1_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r1_1"
    top: "s2_branch1_r1_1_bn"
    name: "s2_branch1_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s1_t1_branch1_0_bn"
    bottom: "s2_branch1_r1_1_bn"
    top: "s2_branch1_r1_e0"
    name: "s1_branch1_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch1_r1_e0"
    top: "s2_branch1_r1_e0"
    name: "s2_branch1_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r1_e0"
    top: "s2_branch1_r2_0_0"
    name: "s2_branch1_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r2_0_0"
    top: "s2_branch1_r2_0_0_bn"
    name: "s2_branch1_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r2_0_0_bn"
    top: "s2_branch1_r2_0_0_bn"
    name: "s2_branch1_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r2_0_0_bn"
    top: "s2_branch1_r2_1"
    name: "s2_branch1_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r2_1"
    top: "s2_branch1_r2_1_bn"
    name: "s2_branch1_r2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s2_branch1_r1_e0"
    bottom: "s2_branch1_r2_1_bn"
    top: "s2_branch1_r2_e0"
    name: "s1_branch1_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch1_r2_e0"
    top: "s2_branch1_r2_e0"
    name: "s2_branch1_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r2_e0"
    top: "s2_branch1_r3_0_0"
    name: "s2_branch1_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r3_0_0"
    top: "s2_branch1_r3_0_0_bn"
    name: "s2_branch1_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r3_0_0_bn"
    top: "s2_branch1_r3_0_0_bn"
    name: "s2_branch1_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r3_0_0_bn"
    top: "s2_branch1_r3_1"
    name: "s2_branch1_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r3_1"
    top: "s2_branch1_r3_1_bn"
    name: "s2_branch1_r3_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r2_e0"
    bottom: "s2_branch1_r3_1_bn"
    top: "s2_branch1_r3_e0"
    name: "s1_branch1_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch1_r3_e0"
    top: "s2_branch1_r3_e0"
    name: "s2_branch1_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r3_e0"
    top: "s2_branch1_r4_0_0"
    name: "s2_branch1_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r4_0_0"
    top: "s2_branch1_r4_0_0_bn"
    name: "s2_branch1_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r4_0_0_bn"
    top: "s2_branch1_r4_0_0_bn"
    name: "s2_branch1_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch1_r4_0_0_bn"
    top: "s2_branch1_r4_1"
    name: "s2_branch1_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_branch1_r4_1"
    top: "s2_branch1_r4_1_bn"
    name: "s2_branch1_r4_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s2_branch1_r3_e0"
    bottom: "s2_branch1_r4_1_bn"
    top: "s2_branch1_r4_e0"
    name: "s1_branch1_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_branch1_r4_e0"
    top: "s2_branch1_r4_e0"
    name: "s2_branch1_r4_e0/relu"
    type: "ReLU"
}
#transform2
layer {
    bottom: "s2_branch1_r4_e0"
    top: "s2_t1_branch0_0"
    name: "s2_t1_branch0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_t1_branch0_0"
    top: "s2_t1_branch0_0_bn"
    name: "s2_t1_branch0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_t1_branch0_0_bn"
    top: "s2_t1_branch0_0_bn_up2"
    name: "s2_t1_branch0_0_bn_up2"
    type: "Interp"
	interp_param{
		zoom_factor:2
		pad_beg:0
		pad_end:0
	}
}
layer {
    bottom: "s2_branch0_r4_e0"
    bottom: "s2_t1_branch0_0_bn_up2"
    top: "s2_t0_branch0_e0"
    name: "s2_t0_branch0_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_t0_branch0_e0"
    top: "s2_t0_branch0_e0"
    name: "s2_t0_branch0_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_branch0_r4_e0"
    top: "s2_t1_branch0_1"
    name: "s2_t1_branch0_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_t1_branch0_1"
    top: "s2_t1_branch0_1_bn"
    name: "s2_t1_branch0_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_branch1_r4_e0"
    bottom: "s2_t1_branch0_1_bn"
    top: "s2_t1_branch1_e0"
    name: "s2_t1_branch1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s2_t1_branch1_e0"
    top: "s2_t1_branch1_e0"
    name: "s2_t1_branch1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_t1_branch1_e0"
    top: "s2_t2_branch0_0"
    name: "s2_t2_branch0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s2_t2_branch0_0"
    top: "s2_t2_branch0_0_bn"
    name: "s2_t2_branch0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_t2_branch0_0_bn"
    top: "s2_t2_branch0_0_bn"
    name: "s2_t2_branch0_0_bn/relu"
    type: "ReLU"
}
#stage3
layer {
    bottom: "s2_t0_branch0_e0"
    top: "s3_branch0_r1_0_0"
    name: "s3_branch0_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r1_0_0"
    top: "s3_branch0_r1_0_0_bn"
    name: "s3_branch0_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r1_0_0_bn"
    top: "s3_branch0_r1_0_0_bn"
    name: "s3_branch0_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r1_0_0_bn"
    top: "s3_branch0_r1_1"
    name: "s3_branch0_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r1_1"
    top: "s3_branch0_r1_1_bn"
    name: "s3_branch0_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s2_t0_branch0_e0"
    bottom: "s3_branch0_r1_1_bn"
    top: "s3_branch0_r1_e0"
    name: "s1_branch0_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch0_r1_e0"
    top: "s3_branch0_r1_e0"
    name: "s3_branch0_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r1_e0"
    top: "s3_branch0_r2_0_0"
    name: "s3_branch0_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r2_0_0"
    top: "s3_branch0_r2_0_0_bn"
    name: "s3_branch0_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r2_0_0_bn"
    top: "s3_branch0_r2_0_0_bn"
    name: "s3_branch0_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r2_0_0_bn"
    top: "s3_branch0_r2_1"
    name: "s3_branch0_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r2_1"
    top: "s3_branch0_r2_1_bn"
    name: "s3_branch0_r2_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r1_e0"
    bottom: "s3_branch0_r2_1_bn"
    top: "s3_branch0_r2_e0"
    name: "s1_branch0_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch0_r2_e0"
    top: "s3_branch0_r2_e0"
    name: "s3_branch0_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r2_e0"
    top: "s3_branch0_r3_0_0"
    name: "s3_branch0_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r3_0_0"
    top: "s3_branch0_r3_0_0_bn"
    name: "s3_branch0_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r3_0_0_bn"
    top: "s3_branch0_r3_0_0_bn"
    name: "s3_branch0_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r3_0_0_bn"
    top: "s3_branch0_r3_1"
    name: "s3_branch0_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r3_1"
    top: "s3_branch0_r3_1_bn"
    name: "s3_branch0_r3_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s3_branch0_r2_e0"
    bottom: "s3_branch0_r3_1_bn"
    top: "s3_branch0_r3_e0"
    name: "s1_branch0_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch0_r3_e0"
    top: "s3_branch0_r3_e0"
    name: "s3_branch0_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r3_e0"
    top: "s3_branch0_r4_0_0"
    name: "s3_branch0_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r4_0_0"
    top: "s3_branch0_r4_0_0_bn"
    name: "s3_branch0_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r4_0_0_bn"
    top: "s3_branch0_r4_0_0_bn"
    name: "s3_branch0_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r4_0_0_bn"
    top: "s3_branch0_r4_1"
    name: "s3_branch0_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch0_r4_1"
    top: "s3_branch0_r4_1_bn"
    name: "s3_branch0_r4_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_r3_e0"
    bottom: "s3_branch0_r4_1_bn"
    top: "s3_branch0_r4_e0"
    name: "s1_branch0_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch0_r4_e0"
    top: "s3_branch0_r4_e0"
    name: "s3_branch0_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_t1_branch1_e0"
    top: "s3_branch1_r1_0_0"
    name: "s3_branch1_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r1_0_0"
    top: "s3_branch1_r1_0_0_bn"
    name: "s3_branch1_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch1_r1_0_0_bn"
    top: "s3_branch1_r1_0_0_bn"
    name: "s3_branch1_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r1_0_0_bn"
    top: "s3_branch1_r1_1"
    name: "s3_branch1_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r1_1"
    top: "s3_branch1_r1_1_bn"
    name: "s3_branch1_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s2_t1_branch1_e0"
    bottom: "s3_branch1_r1_1_bn"
    top: "s3_branch1_r1_e0"
    name: "s1_branch1_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch1_r1_e0"
    top: "s3_branch1_r1_e0"
    name: "s3_branch1_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r1_e0"
    top: "s3_branch1_r2_0_0"
    name: "s3_branch1_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r2_0_0"
    top: "s3_branch1_r2_0_0_bn"
    name: "s3_branch1_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch1_r2_0_0_bn"
    top: "s3_branch1_r2_0_0_bn"
    name: "s3_branch1_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r2_0_0_bn"
    top: "s3_branch1_r2_1"
    name: "s3_branch1_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r2_1"
    top: "s3_branch1_r2_1_bn"
    name: "s3_branch1_r2_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch1_r2_1_bn"
    top: "s3_branch1_r2_1_bn"
    name: "s3_branch1_r2_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r1_e0"
    bottom: "s3_branch1_r2_1_bn"
    top: "s3_branch1_r2_e0"
    name: "s1_branch1_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch1_r2_e0"
    top: "s3_branch1_r2_e0"
    name: "s3_branch1_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r2_e0"
    top: "s3_branch1_r3_0_0"
    name: "s3_branch1_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r3_0_0"
    top: "s3_branch1_r3_0_0_bn"
    name: "s3_branch1_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch1_r3_0_0_bn"
    top: "s3_branch1_r3_0_0_bn"
    name: "s3_branch1_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r3_0_0_bn"
    top: "s3_branch1_r3_1"
    name: "s3_branch1_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r3_1"
    top: "s3_branch1_r3_1_bn"
    name: "s3_branch1_r3_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s3_branch1_r2_e0"
    bottom: "s3_branch1_r3_1_bn"
    top: "s3_branch1_r3_e0"
    name: "s1_branch1_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch1_r3_e0"
    top: "s3_branch1_r3_e0"
    name: "s3_branch1_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r3_e0"
    top: "s3_branch1_r4_0_0"
    name: "s3_branch1_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r4_0_0"
    top: "s3_branch1_r4_0_0_bn"
    name: "s3_branch1_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch1_r4_0_0_bn"
    top: "s3_branch1_r4_0_0_bn"
    name: "s3_branch1_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_r4_0_0_bn"
    top: "s3_branch1_r4_1"
    name: "s3_branch1_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch1_r4_1"
    top: "s3_branch1_r4_1_bn"
    name: "s3_branch1_r4_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s3_branch1_r3_e0"
    bottom: "s3_branch1_r4_1_bn"
    top: "s3_branch1_r4_e0"
    name: "s1_branch1_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch1_r4_e0"
    top: "s3_branch1_r4_e0"
    name: "s3_branch1_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s2_t2_branch0_0_bn"
    top: "s3_branch2_r1_0_0"
    name: "s3_branch2_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r1_0_0"
    top: "s3_branch2_r1_0_0_bn"
    name: "s3_branch2_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch2_r1_0_0_bn"
    top: "s3_branch2_r1_0_0_bn"
    name: "s3_branch2_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r1_0_0_bn"
    top: "s3_branch2_r1_1"
    name: "s3_branch2_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r1_1"
    top: "s3_branch2_r1_1_bn"
    name: "s3_branch2_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s2_t2_branch0_0_bn"
    bottom: "s3_branch2_r1_1_bn"
    top: "s3_branch2_r1_e0"
    name: "s1_branch2_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch2_r1_e0"
    top: "s3_branch2_r1_e0"
    name: "s3_branch2_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r1_e0"
    top: "s3_branch2_r2_0_0"
    name: "s3_branch2_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r2_0_0"
    top: "s3_branch2_r2_0_0_bn"
    name: "s3_branch2_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch2_r2_0_0_bn"
    top: "s3_branch2_r2_0_0_bn"
    name: "s3_branch2_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r2_0_0_bn"
    top: "s3_branch2_r2_1"
    name: "s3_branch2_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r2_1"
    top: "s3_branch2_r2_1_bn"
    name: "s3_branch2_r2_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}

layer {
    bottom: "s3_branch2_r1_e0"
    bottom: "s3_branch2_r2_1_bn"
    top: "s3_branch2_r2_e0"
    name: "s1_branch2_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch2_r2_e0"
    top: "s3_branch2_r2_e0"
    name: "s3_branch2_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r2_e0"
    top: "s3_branch2_r3_0_0"
    name: "s3_branch2_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r3_0_0"
    top: "s3_branch2_r3_0_0_bn"
    name: "s3_branch2_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch2_r3_0_0_bn"
    top: "s3_branch2_r3_0_0_bn"
    name: "s3_branch2_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r3_0_0_bn"
    top: "s3_branch2_r3_1"
    name: "s3_branch2_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r3_1"
    top: "s3_branch2_r3_1_bn"
    name: "s3_branch2_r3_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch2_r3_1_bn"
    top: "s3_branch2_r3_1_bn"
    name: "s3_branch2_r3_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r2_e0"
    bottom: "s3_branch2_r3_1_bn"
    top: "s3_branch2_r3_e0"
    name: "s1_branch2_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch2_r3_e0"
    top: "s3_branch2_r3_e0"
    name: "s3_branch2_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r3_e0"
    top: "s3_branch2_r4_0_0"
    name: "s3_branch2_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r4_0_0"
    top: "s3_branch2_r4_0_0_bn"
    name: "s3_branch2_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch2_r4_0_0_bn"
    top: "s3_branch2_r4_0_0_bn"
    name: "s3_branch2_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r4_0_0_bn"
    top: "s3_branch2_r4_1"
    name: "s3_branch2_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_branch2_r4_1"
    top: "s3_branch2_r4_1_bn"
    name: "s3_branch2_r4_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch2_r4_1_bn"
    top: "s3_branch2_r4_1_bn"
    name: "s3_branch2_r4_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch2_r3_e0"
    bottom: "s3_branch2_r4_1_bn"
    top: "s3_branch2_r4_e0"
    name: "s1_branch2_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch2_r4_e0"
    top: "s3_branch2_r4_e0"
    name: "s3_branch2_r4_e0/relu"
    type: "ReLU"
}
#transform3
layer {
    bottom: "s3_branch1_r4_e0"
    top: "s3_t0_branch0_1"
    name: "s3_t0_branch0_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t0_branch0_1"
    top: "s3_t0_branch0_1_bn"
    name: "s3_t0_branch0_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_t0_branch0_1_bn"
    top: "s3_t0_branch0_1_bn_up2"
    name: "s3_t0_branch0_1_bn_up2"
    type: "Interp"
    interp_param {
        zoom_factor: 2
		pad_beg:0
		pad_end:0
    }
}
layer {
    bottom: "s3_branch2_r4_e0"
    top: "s3_t0_branch0_2"
    name: "s3_t0_branch0_2"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t0_branch0_2"
    top: "s3_t0_branch0_2_bn"
    name: "s3_t0_branch0_2_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_t0_branch0_2_bn"
    top: "s3_t0_branch0_2_bn_up4"
    name: "s3_t0_branch0_2_bn_up4"
    type: "Interp"
    interp_param {
        zoom_factor: 4
		pad_beg:0
		pad_end:0
    }
}
layer {
    bottom: "s3_branch0_r4_e0"
    bottom: "s3_t0_branch0_1_bn_up2"
    bottom: "s3_t0_branch0_2_bn_up4"
    top: "s3_branch0_e0"
    name: "s3_branch0_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch0_e0"
    top: "s3_branch0_e0"
    name: "s3_branch0_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r4_e0"
    top: "s3_t1_branch1_0"
    name: "s3_t1_branch1_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t1_branch1_0"
    top: "s3_t1_branch1_0_bn"
    name: "s3_t1_branch1_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_branch2_r4_e0"
    top: "s3_t1_branch1_2"
    name: "s3_t1_branch1_2"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t1_branch1_2"
    top: "s3_t1_branch1_2_bn"
    name: "s3_t1_branch1_2_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_t1_branch1_2_bn"
    top: "s3_t1_branch1_2_bn_up2"
    name: "s3_t1_branch1_2_bn_up2"
    type: "Interp"
    interp_param {
        zoom_factor: 2
		pad_beg:0
		pad_end:0
    }
}
layer {
    bottom: "s3_t1_branch1_0_bn"
    bottom: "s3_branch1_r4_e0"
    bottom: "s3_t1_branch1_2_bn_up2"
    top: "s3_branch1_e0"
    name: "s3_branch1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch1_e0"
    top: "s3_branch1_e0"
    name: "s3_branch1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch0_r4_e0"
    top: "s3_t2_branch2_0"
    name: "s3_t2_branch2_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t2_branch2_0"
    top: "s3_t2_branch2_0_bn"
    name: "s3_t2_branch2_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_t2_branch2_0_bn"
    top: "s3_t2_branch2_0_1"
    name: "s3_t2_branch2_0_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t2_branch2_0_1"
    top: "s3_t2_branch2_0_1_bn"
    name: "s3_t2_branch2_0_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_branch1_r4_e0"
    top: "s3_t2_branch2_1"
    name: "s3_t2_branch2_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t2_branch2_1"
    top: "s3_t2_branch2_1_bn"
    name: "s3_t2_branch2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}

layer {
    bottom: "s3_t2_branch2_0_1_bn"
    bottom: "s3_t2_branch2_1_bn"
    bottom: "s3_branch2_r4_e0"
    top: "s3_branch3_e0"
    name: "s3_branch3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s3_branch3_e0"
    top: "s3_branch3_e0"
    name: "s3_branch3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch3_e0"
    top: "s3_t3_branch3_0"
    name: "s3_t3_branch3_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s3_t3_branch3_0"
    top: "s3_t3_branch3_0_bn"
    name: "s3_t3_branch3_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_t3_branch3_0_bn"
    top: "s3_t3_branch3_0_bn"
    name: "s3_t3_branch3_0_bn/relu"
    type: "ReLU"
}
#stage4
layer {
    bottom: "s3_branch0_e0"
    top: "s4_branch0_r1_0_0"
    name: "s4_branch0_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r1_0_0"
    top: "s4_branch0_r1_0_0_bn"
    name: "s4_branch0_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch0_r1_0_0_bn"
    top: "s4_branch0_r1_0_0_bn"
    name: "s4_branch0_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r1_0_0_bn"
    top: "s4_branch0_r1_1"
    name: "s4_branch0_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r1_1"
    top: "s4_branch0_r1_1_bn"
    name: "s4_branch0_r1_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s3_branch0_e0"
    bottom: "s4_branch0_r1_1_bn"
    top: "s4_branch0_r1_e0"
    name: "s1_branch2_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch0_r1_e0"
    top: "s4_branch0_r1_e0"
    name: "s4_branch0_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r1_e0"
    top: "s4_branch0_r2_0_0"
    name: "s4_branch0_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r2_0_0"
    top: "s4_branch0_r2_0_0_bn"
    name: "s4_branch0_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r2_0_0_bn"
    top: "s4_branch0_r2_0_0_bn"
    name: "s4_branch0_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r2_0_0_bn"
    top: "s4_branch0_r2_1"
    name: "s4_branch0_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r2_1"
    top: "s4_branch0_r2_1_bn"
    name: "s4_branch0_r2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r1_e0"
    bottom: "s4_branch0_r2_1_bn"
    top: "s4_branch0_r2_e0"
    name: "s1_branch2_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch0_r2_e0"
    top: "s4_branch0_r2_e0"
    name: "s4_branch0_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r2_e0"
    top: "s4_branch0_r3_0_0"
    name: "s4_branch0_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r3_0_0"
    top: "s4_branch0_r3_0_0_bn"
    name: "s4_branch0_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r3_0_0_bn"
    top: "s4_branch0_r3_0_0_bn"
    name: "s4_branch0_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r3_0_0_bn"
    top: "s4_branch0_r3_1"
    name: "s4_branch0_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r3_1"
    top: "s4_branch0_r3_1_bn"
    name: "s4_branch0_r3_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r2_e0"
    bottom: "s4_branch0_r3_1_bn"
    top: "s4_branch0_r3_e0"
    name: "s1_branch2_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch0_r3_e0"
    top: "s4_branch0_r3_e0"
    name: "s4_branch0_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r3_e0"
    top: "s4_branch0_r4_0_0"
    name: "s4_branch0_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_0_0"
    top: "s4_branch0_r4_0_0_bn"
    name: "s4_branch0_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r4_0_0_bn"
    top: "s4_branch0_r4_0_0_bn"
    name: "s4_branch0_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r4_0_0_bn"
    top: "s4_branch0_r4_1"
    name: "s4_branch0_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 32
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_1"
    top: "s4_branch0_r4_1_bn"
    name: "s4_branch0_r4_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch0_r3_e0"
    bottom: "s4_branch0_r4_1_bn"
    top: "s4_branch0_r4_e0"
    name: "s1_branch2_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch0_r4_e0"
    top: "s4_branch0_r4_e0"
    name: "s4_branch0_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch1_e0"
    top: "s4_branch1_r1_0_0"
    name: "s4_branch1_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r1_0_0"
    top: "s4_branch1_r1_0_0_bn"
    name: "s4_branch1_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r1_0_0_bn"
    top: "s4_branch1_r1_0_0_bn"
    name: "s4_branch1_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r1_0_0_bn"
    top: "s4_branch1_r1_1"
    name: "s4_branch1_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r1_1"
    top: "s4_branch1_r1_1_bn"
    name: "s4_branch1_r1_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch1_e0"
    bottom: "s4_branch1_r1_1_bn"
    top: "s4_branch1_r1_e0"
    name: "s1_branch2_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch1_r1_e0"
    top: "s4_branch1_r1_e0"
    name: "s4_branch1_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r1_e0"
    top: "s4_branch1_r2_0_0"
    name: "s4_branch1_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r2_0_0"
    top: "s4_branch1_r2_0_0_bn"
    name: "s4_branch1_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r2_0_0_bn"
    top: "s4_branch1_r2_0_0_bn"
    name: "s4_branch1_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r2_0_0_bn"
    top: "s4_branch1_r2_1"
    name: "s4_branch1_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r2_1"
    top: "s4_branch1_r2_1_bn"
    name: "s4_branch1_r2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r1_e0"
    bottom: "s4_branch1_r2_1_bn"
    top: "s4_branch1_r2_e0"
    name: "s1_branch2_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch1_r2_e0"
    top: "s4_branch1_r2_e0"
    name: "s4_branch1_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r2_e0"
    top: "s4_branch1_r3_0_0"
    name: "s4_branch1_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r3_0_0"
    top: "s4_branch1_r3_0_0_bn"
    name: "s4_branch1_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r3_0_0_bn"
    top: "s4_branch1_r3_0_0_bn"
    name: "s4_branch1_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r3_0_0_bn"
    top: "s4_branch1_r3_1"
    name: "s4_branch1_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r3_1"
    top: "s4_branch1_r3_1_bn"
    name: "s4_branch1_r3_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r2_e0"
    bottom: "s4_branch1_r3_1_bn"
    top: "s4_branch1_r3_e0"
    name: "s1_branch2_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch1_r3_e0"
    top: "s4_branch1_r3_e0"
    name: "s4_branch1_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r3_e0"
    top: "s4_branch1_r4_0_0"
    name: "s4_branch1_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_0_0"
    top: "s4_branch1_r4_0_0_bn"
    name: "s4_branch1_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r4_0_0_bn"
    top: "s4_branch1_r4_0_0_bn"
    name: "s4_branch1_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r4_0_0_bn"
    top: "s4_branch1_r4_1"
    name: "s4_branch1_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_1"
    top: "s4_branch1_r4_1_bn"
    name: "s4_branch1_r4_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch1_r3_e0"
    bottom: "s4_branch1_r4_1_bn"
    top: "s4_branch1_r4_e0"
    name: "s1_branch2_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch1_r4_e0"
    top: "s4_branch1_r4_e0"
    name: "s4_branch1_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_branch3_e0"
    top: "s4_branch2_r1_0_0"
    name: "s4_branch2_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r1_0_0"
    top: "s4_branch2_r1_0_0_bn"
    name: "s4_branch2_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r1_0_0_bn"
    top: "s4_branch2_r1_0_0_bn"
    name: "s4_branch2_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r1_0_0_bn"
    top: "s4_branch2_r1_1"
    name: "s4_branch2_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r1_1"
    top: "s4_branch2_r1_1_bn"
    name: "s4_branch2_r1_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_branch3_e0"
    bottom: "s4_branch2_r1_1_bn"
    top: "s4_branch2_r1_e0"
    name: "s1_branch2_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch2_r1_e0"
    top: "s4_branch2_r1_e0"
    name: "s4_branch2_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r1_e0"
    top: "s4_branch2_r2_0_0"
    name: "s4_branch2_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r2_0_0"
    top: "s4_branch2_r2_0_0_bn"
    name: "s4_branch2_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r2_0_0_bn"
    top: "s4_branch2_r2_0_0_bn"
    name: "s4_branch2_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r2_0_0_bn"
    top: "s4_branch2_r2_1"
    name: "s4_branch2_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r2_1"
    top: "s4_branch2_r2_1_bn"
    name: "s4_branch2_r2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r1_e0"
    bottom: "s4_branch2_r2_1_bn"
    top: "s4_branch2_r2_e0"
    name: "s1_branch2_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch2_r2_e0"
    top: "s4_branch2_r2_e0"
    name: "s4_branch2_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r2_e0"
    top: "s4_branch2_r3_0_0"
    name: "s4_branch2_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r3_0_0"
    top: "s4_branch2_r3_0_0_bn"
    name: "s4_branch2_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r3_0_0_bn"
    top: "s4_branch2_r3_0_0_bn"
    name: "s4_branch2_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r3_0_0_bn"
    top: "s4_branch2_r3_1"
    name: "s4_branch2_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r3_1"
    top: "s4_branch2_r3_1_bn"
    name: "s4_branch2_r3_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r2_e0"
    bottom: "s4_branch2_r3_1_bn"
    top: "s4_branch2_r3_e0"
    name: "s1_branch2_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch2_r3_e0"
    top: "s4_branch2_r3_e0"
    name: "s4_branch2_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r3_e0"
    top: "s4_branch2_r4_0_0"
    name: "s4_branch2_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_0_0"
    top: "s4_branch2_r4_0_0_bn"
    name: "s4_branch2_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r4_0_0_bn"
    top: "s4_branch2_r4_0_0_bn"
    name: "s4_branch2_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r4_0_0_bn"
    top: "s4_branch2_r4_1"
    name: "s4_branch2_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_1"
    top: "s4_branch2_r4_1_bn"
    name: "s4_branch2_r4_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch2_r3_e0"
    bottom: "s4_branch2_r4_1_bn"
    top: "s4_branch2_r4_e0"
    name: "s1_branch2_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch2_r4_e0"
    top: "s4_branch2_r4_e0"
    name: "s4_branch2_r4_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s3_t3_branch3_0_bn"
    top: "s4_branch3_r1_0_0"
    name: "s4_branch3_r1_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r1_0_0"
    top: "s4_branch3_r1_0_0_bn"
    name: "s4_branch3_r1_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r1_0_0_bn"
    top: "s4_branch3_r1_0_0_bn"
    name: "s4_branch3_r1_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r1_0_0_bn"
    top: "s4_branch3_r1_1"
    name: "s4_branch3_r1_1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r1_1"
    top: "s4_branch3_r1_1_bn"
    name: "s4_branch3_r1_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s3_t3_branch3_0_bn"
    bottom: "s4_branch3_r1_1_bn"
    top: "s4_branch3_r1_e0"
    name: "s1_branch3_r1_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch3_r1_e0"
    top: "s4_branch3_r1_e0"
    name: "s4_branch3_r1_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r1_e0"
    top: "s4_branch3_r2_0_0"
    name: "s4_branch3_r2_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r2_0_0"
    top: "s4_branch3_r2_0_0_bn"
    name: "s4_branch3_r2_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r2_0_0_bn"
    top: "s4_branch3_r2_0_0_bn"
    name: "s4_branch3_r2_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r2_0_0_bn"
    top: "s4_branch3_r2_1"
    name: "s4_branch3_r2_1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r2_1"
    top: "s4_branch3_r2_1_bn"
    name: "s4_branch3_r2_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r1_e0"
    bottom: "s4_branch3_r2_1_bn"
    top: "s4_branch3_r2_e0"
    name: "s1_branch3_r2_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch3_r2_e0"
    top: "s4_branch3_r2_e0"
    name: "s4_branch3_r2_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r2_e0"
    top: "s4_branch3_r3_0_0"
    name: "s4_branch3_r3_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r3_0_0"
    top: "s4_branch3_r3_0_0_bn"
    name: "s4_branch3_r3_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r3_0_0_bn"
    top: "s4_branch3_r3_0_0_bn"
    name: "s4_branch3_r3_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r3_0_0_bn"
    top: "s4_branch3_r3_1"
    name: "s4_branch3_r3_1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r3_1"
    top: "s4_branch3_r3_1_bn"
    name: "s4_branch3_r3_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r2_e0"
    bottom: "s4_branch3_r3_1_bn"
    top: "s4_branch3_r3_e0"
    name: "s1_branch3_r3_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch3_r3_e0"
    top: "s4_branch3_r3_e0"
    name: "s4_branch3_r3_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r3_e0"
    top: "s4_branch3_r4_0_0"
    name: "s4_branch3_r4_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_0_0"
    top: "s4_branch3_r4_0_0_bn"
    name: "s4_branch3_r4_0_0_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r4_0_0_bn"
    top: "s4_branch3_r4_0_0_bn"
    name: "s4_branch3_r4_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r4_0_0_bn"
    top: "s4_branch3_r4_1"
    name: "s4_branch3_r4_1"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_1"
    top: "s4_branch3_r4_1_bn"
    name: "s4_branch3_r4_1_bn"
    type: "CuDNNBatchNorm"
	scale_filler{
		type:"constant"
		value:1
	}
	bias_filler{
		type:"constant"
		value:0
	}
	frozen:false
}
layer {
    bottom: "s4_branch3_r3_e0"
    bottom: "s4_branch3_r4_1_bn"
    top: "s4_branch3_r4_e0"
    name: "s1_branch3_r4_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch3_r4_e0"
    top: "s4_branch3_r4_e0"
    name: "s4_branch3_r4_e0/relu"
    type: "ReLU"
}
#############################################################################
#################change channel to 128,256,512,1024########################
layer {
    bottom: "s4_branch0_r4_e0"
    top: "s4_branch0_r4_e0_128_0"
    name: "s4_branch0_r4_e0_128_0"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_e0_128_0"
    top: "s4_branch0_r4_e0_128_0_bn"
    name: "s4_branch0_r4_e0_128_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch0_r4_e0"
    top: "s4_branch0_r4_e0_128_0_0"
    name: "s4_branch0_r4_e0_128_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_e0_128_0_0"
    top: "s4_branch0_r4_e0_128_0_0_bn"
    name: "s4_branch0_r4_e0_128_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch0_r4_e0_128_0_0_bn"
    top: "s4_branch0_r4_e0_128_0_0_bn"
    name: "s4_branch0_r4_e0_128_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r4_e0_128_0_0_bn"
    top: "s4_branch0_r4_e0_128_1"
    name: "s4_branch0_r4_e0_128_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_e0_128_1"
    top: "s4_branch0_r4_e0_128_1_bn"
    name: "s4_branch0_r4_e0_128_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch0_r4_e0_128_1_bn"
    top: "s4_branch0_r4_e0_128_1_bn"
    name: "s4_branch0_r4_e0_128_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch0_r4_e0_128_1_bn"
    top: "s4_branch0_r4_e0_128_2"
    name: "s4_branch0_r4_e0_128_2"
    type: "Convolution"
    convolution_param {
        num_output: 128
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch0_r4_e0_128_2"
    top: "s4_branch0_r4_e0_128_2_bn"
    name: "s4_branch0_r4_e0_128_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch0_r4_e0_128_0_bn"
    bottom: "s4_branch0_r4_e0_128_2_bn"
    top: "s4_branch0_r4_e0_128_e0"
    name: "s4_branch0_r4_e0_128_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch0_r4_e0_128_e0"
    top: "s4_branch0_r4_e0_128_e0"
    name: "s4_branch0_r4_e0_128_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r4_e0"
    top: "s4_branch1_r4_e0_256_0"
    name: "s4_branch1_r4_e0_256_0"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_e0_256_0"
    top: "s4_branch1_r4_e0_256_0_bn"
    name: "s4_branch1_r4_e0_256_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch1_r4_e0"
    top: "s4_branch1_r4_e0_256_0_0"
    name: "s4_branch1_r4_e0_256_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_e0_256_0_0"
    top: "s4_branch1_r4_e0_256_0_0_bn"
    name: "s4_branch1_r4_e0_256_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch1_r4_e0_256_0_0_bn"
    top: "s4_branch1_r4_e0_256_0_0_bn"
    name: "s4_branch1_r4_e0_256_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r4_e0_256_0_0_bn"
    top: "s4_branch1_r4_e0_256_1"
    name: "s4_branch1_r4_e0_256_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_e0_256_1"
    top: "s4_branch1_r4_e0_256_1_bn"
    name: "s4_branch1_r4_e0_256_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch1_r4_e0_256_1_bn"
    top: "s4_branch1_r4_e0_256_1_bn"
    name: "s4_branch1_r4_e0_256_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch1_r4_e0_256_1_bn"
    top: "s4_branch1_r4_e0_256_2"
    name: "s4_branch1_r4_e0_256_2"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch1_r4_e0_256_2"
    top: "s4_branch1_r4_e0_256_2_bn"
    name: "s4_branch1_r4_e0_256_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch1_r4_e0_256_0_bn"
    bottom: "s4_branch1_r4_e0_256_2_bn"
    top: "s4_branch1_r4_e0_256_e0"
    name: "s4_branch1_r4_e0_256_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch1_r4_e0_256_e0"
    top: "s4_branch1_r4_e0_256_e0"
    name: "s4_branch1_r4_e0_256_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r4_e0"
    top: "s4_branch2_r4_e0_512_0"
    name: "s4_branch2_r4_e0_512_0"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_e0_512_0"
    top: "s4_branch2_r4_e0_512_0_bn"
    name: "s4_branch2_r4_e0_512_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch2_r4_e0"
    top: "s4_branch2_r4_e0_512_0_0"
    name: "s4_branch2_r4_e0_512_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_e0_512_0_0"
    top: "s4_branch2_r4_e0_512_0_0_bn"
    name: "s4_branch2_r4_e0_512_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch2_r4_e0_512_0_0_bn"
    top: "s4_branch2_r4_e0_512_0_0_bn"
    name: "s4_branch2_r4_e0_512_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r4_e0_512_0_0_bn"
    top: "s4_branch2_r4_e0_512_1"
    name: "s4_branch2_r4_e0_512_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_e0_512_1"
    top: "s4_branch2_r4_e0_512_1_bn"
    name: "s4_branch2_r4_e0_512_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch2_r4_e0_512_1_bn"
    top: "s4_branch2_r4_e0_512_1_bn"
    name: "s4_branch2_r4_e0_512_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch2_r4_e0_512_1_bn"
    top: "s4_branch2_r4_e0_512_2"
    name: "s4_branch2_r4_e0_512_2"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch2_r4_e0_512_2"
    top: "s4_branch2_r4_e0_512_2_bn"
    name: "s4_branch2_r4_e0_512_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch2_r4_e0_512_0_bn"
    bottom: "s4_branch2_r4_e0_512_2_bn"
    top: "s4_branch2_r4_e0_512_e0"
    name: "s4_branch2_r4_e0_512_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch2_r4_e0_512_e0"
    top: "s4_branch2_r4_e0_512_e0"
    name: "s4_branch2_r4_e0_512_e0/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r4_e0"
    top: "s4_branch3_r4_e0_1024_0"
    name: "s4_branch3_r4_e0_1024_0"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_e0_1024_0"
    top: "s4_branch3_r4_e0_1024_0_bn"
    name: "s4_branch3_r4_e0_1024_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch3_r4_e0"
    top: "s4_branch3_r4_e0_1024_0_0"
    name: "s4_branch3_r4_e0_1024_0_0"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_e0_1024_0_0"
    top: "s4_branch3_r4_e0_1024_0_0_bn"
    name: "s4_branch3_r4_e0_1024_0_0_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch3_r4_e0_1024_0_0_bn"
    top: "s4_branch3_r4_e0_1024_0_0_bn"
    name: "s4_branch3_r4_e0_1024_0_0_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r4_e0_1024_0_0_bn"
    top: "s4_branch3_r4_e0_1024_1"
    name: "s4_branch3_r4_e0_1024_1"
    type: "Convolution"
    convolution_param {
        num_output: 64
        kernel_size: 3
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_e0_1024_1"
    top: "s4_branch3_r4_e0_1024_1_bn"
    name: "s4_branch3_r4_e0_1024_1_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch3_r4_e0_1024_1_bn"
    top: "s4_branch3_r4_e0_1024_1_bn"
    name: "s4_branch3_r4_e0_1024_1_bn/relu"
    type: "ReLU"
}
layer {
    bottom: "s4_branch3_r4_e0_1024_1_bn"
    top: "s4_branch3_r4_e0_1024_2"
    name: "s4_branch3_r4_e0_1024_2"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "s4_branch3_r4_e0_1024_2"
    top: "s4_branch3_r4_e0_1024_2_bn"
    name: "s4_branch3_r4_e0_1024_2_bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch3_r4_e0_1024_0_bn"
    bottom: "s4_branch3_r4_e0_1024_2_bn"
    top: "s4_branch3_r4_e0_1024_e0"
    name: "s4_branch3_r4_e0_1024_e0"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "s4_branch3_r4_e0_1024_e0"
    top: "s4_branch3_r4_e0_1024_e0"
    name: "s4_branch3_r4_e0_1024_e0/relu"
    type: "ReLU"
}
############################################cls-branch#####################################
layer {
    bottom: "s4_branch0_r4_e0_128_e0"
    top: "conv5"
    name: "conv5"
    type: "Convolution"
    convolution_param {
        num_output: 256
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv5"
    top: "conv5/bn"
    name: "conv5/bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch1_r4_e0_256_e0"
    bottom: "conv5/bn"
    top: "elt_1"
    name: "elt_1"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "elt_1"
    top: "elt_1"
    name: "elt_1/relu"
    type: "ReLU"
}
layer {
    bottom: "elt_1"
    top: "conv6"
    name: "conv6"
    type: "Convolution"
    convolution_param {
        num_output: 512
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv6"
    top: "conv6/bn"
    name: "conv6/bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch2_r4_e0_512_e0"
    bottom: "conv6/bn"
    top: "elt_2"
    name: "elt_2"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "elt_2"
    top: "elt_2"
    name: "elt_2/relu"
    type: "ReLU"
}
layer {
    bottom: "elt_2"
    top: "conv7"
    name: "conv7"
    type: "Convolution"
    convolution_param {
        num_output: 1024
        kernel_size: 3
		stride:2
		pad:1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv7"
    top: "conv7/bn"
    name: "conv7/bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "s4_branch3_r4_e0_1024_e0"
    bottom: "conv7/bn"
    top: "elt_3"
    name: "elt_3"
    type: "Eltwise"
    eltwise_param {
        operation: SUM
    }
}
layer {
    bottom: "elt_3"
    top: "elt_3"
    name: "elt_3/relu"
    type: "ReLU"
}
layer {
    bottom: "elt_3"
    top: "conv8"
    name: "conv8"
    type: "Convolution"
    convolution_param {
        num_output: 2048
        kernel_size: 1
        weight_filler {
            type: "msra"
        }
        bias_term: false
    }
}
layer {
    bottom: "conv8"
    top: "conv8/bn"
    name: "conv8/bn"
    type: "CuDNNBatchNorm"
	cudnn_batch_norm_param{
		scale_filler{
			type:"constant"
			value:1
		}
		bias_filler{
			type:"constant"
			value:0
		}
	frozen:false
	}
}
layer {
    bottom: "conv8/bn"
    top: "conv8/bn"
    name: "conv8/bn/relu"
    type: "ReLU"
}
layer {
    bottom: "conv8/bn"
    top: "pool5"
    name: "pool5"
    type: "Pooling"
    pooling_param {
        kernel_size: 7
        stride: 1
        pool: AVE
    }
}

layer {
    bottom: "pool5"
    top: "fc1000"
    name: "fc1000"
    type: "InnerProduct"
    param {
        lr_mult: 1
        decay_mult: 1
    }
    param {
        lr_mult: 2
        decay_mult: 1
    }
    inner_product_param {
        num_output: 1000
        weight_filler {
            type: "xavier"
        }
        bias_filler {
            type: "constant"
            value: 0
        }
    }
}

layer {
    bottom: "fc1000"
    bottom: "label"
    name: "loss"
    type: "SoftmaxWithLoss"
    top: "loss"
}
layer {
    bottom: "fc1000"
    bottom: "label"
    top: "acc/top-1"
    name: "acc/top-1"
    type: "Accuracy"
    accuracy_param {
        use_log:false
    }
}