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kmustriver/gist:2474ad4eb9fc811f1e5b69a0bf47eb9c

Created November 23, 2017 10:53
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  1. kmustriver created this gist Nov 23, 2017.
    565 changes: 565 additions & 0 deletions gistfile1.txt
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    name: "VGG_ILSVRC_16_layers"

    input: 'data'
    input_shape {
    dim: 1
    dim: 3
    dim: 600
    dim: 900
    }

    input: 'im_info'
    input_shape {
    dim: 1
    dim: 3
    }

    layer {
    name: "conv1_1"
    type: "Convolution"
    bottom: "data"
    top: "conv1_1"
    param {
    lr_mult: 0
    decay_mult: 0
    }
    param {
    lr_mult: 0
    decay_mult: 0
    }
    convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu1_1"
    type: "ReLU"
    bottom: "conv1_1"
    top: "conv1_1"
    }
    layer {
    name: "conv1_2"
    type: "Convolution"
    bottom: "conv1_1"
    top: "conv1_2"
    param {
    lr_mult: 0
    decay_mult: 0
    }
    param {
    lr_mult: 0
    decay_mult: 0
    }
    convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu1_2"
    type: "ReLU"
    bottom: "conv1_2"
    top: "conv1_2"
    }
    layer {
    name: "pool1"
    type: "Pooling"
    bottom: "conv1_2"
    top: "pool1"
    pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    }
    }
    layer {
    name: "conv2_1"
    type: "Convolution"
    bottom: "pool1"
    top: "conv2_1"
    param {
    lr_mult: 0
    decay_mult: 0
    }
    param {
    lr_mult: 0
    decay_mult: 0
    }
    convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu2_1"
    type: "ReLU"
    bottom: "conv2_1"
    top: "conv2_1"
    }
    layer {
    name: "conv2_2"
    type: "Convolution"
    bottom: "conv2_1"
    top: "conv2_2"
    param {
    lr_mult: 0
    decay_mult: 0
    }
    param {
    lr_mult: 0
    decay_mult: 0
    }
    convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu2_2"
    type: "ReLU"
    bottom: "conv2_2"
    top: "conv2_2"
    }
    layer {
    name: "pool2"
    type: "Pooling"
    bottom: "conv2_2"
    top: "pool2"
    pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    }
    }
    layer {
    name: "conv3_1"
    type: "Convolution"
    bottom: "pool2"
    top: "conv3_1"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu3_1"
    type: "ReLU"
    bottom: "conv3_1"
    top: "conv3_1"
    }
    layer {
    name: "conv3_2"
    type: "Convolution"
    bottom: "conv3_1"
    top: "conv3_2"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu3_2"
    type: "ReLU"
    bottom: "conv3_2"
    top: "conv3_2"
    }
    layer {
    name: "conv3_3"
    type: "Convolution"
    bottom: "conv3_2"
    top: "conv3_3"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu3_3"
    type: "ReLU"
    bottom: "conv3_3"
    top: "conv3_3"
    }
    layer {
    name: "pool3"
    type: "Pooling"
    bottom: "conv3_3"
    top: "pool3"
    pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    }
    }
    layer {
    name: "conv4_1"
    type: "Convolution"
    bottom: "pool3"
    top: "conv4_1"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu4_1"
    type: "ReLU"
    bottom: "conv4_1"
    top: "conv4_1"
    }
    layer {
    name: "conv4_2"
    type: "Convolution"
    bottom: "conv4_1"
    top: "conv4_2"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu4_2"
    type: "ReLU"
    bottom: "conv4_2"
    top: "conv4_2"
    }
    layer {
    name: "conv4_3"
    type: "Convolution"
    bottom: "conv4_2"
    top: "conv4_3"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu4_3"
    type: "ReLU"
    bottom: "conv4_3"
    top: "conv4_3"
    }
    layer {
    name: "pool4"
    type: "Pooling"
    bottom: "conv4_3"
    top: "pool4"
    pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    }
    }
    layer {
    name: "conv5_1"
    type: "Convolution"
    bottom: "pool4"
    top: "conv5_1"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu5_1"
    type: "ReLU"
    bottom: "conv5_1"
    top: "conv5_1"
    }
    layer {
    name: "conv5_2"
    type: "Convolution"
    bottom: "conv5_1"
    top: "conv5_2"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu5_2"
    type: "ReLU"
    bottom: "conv5_2"
    top: "conv5_2"
    }
    layer {
    name: "conv5_3"
    type: "Convolution"
    bottom: "conv5_2"
    top: "conv5_3"
    param {
    lr_mult: 1
    }
    param {
    lr_mult: 2
    }
    convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "relu5_3"
    type: "ReLU"
    bottom: "conv5_3"
    top: "conv5_3"
    }

    #========= RPN ============

    # prepare lstm inputs
    layer {
    name: "im2col"
    bottom: "conv5_3"
    top: "im2col"
    type: "Im2col"
    convolution_param {
    pad: 1
    kernel_size: 3
    }
    }
    layer {
    name: "im2col_transpose"
    top: "im2col_transpose"
    bottom: "im2col"
    type: "Transpose"
    transpose_param {
    dim: 3
    dim: 2
    dim: 0
    dim: 1
    }
    }
    layer {
    name: "lstm_input"
    type: "Reshape"
    bottom: "im2col_transpose"
    top: "lstm_input"
    reshape_param {
    shape { dim: -1 }
    axis: 1
    num_axes: 2
    }
    }

    layer {
    name: "lstm"
    type: "Lstm"
    bottom: "lstm_input"
    top: "lstm"
    lstm_param {
    num_output: 128
    weight_filler {
    type: "gaussian"
    std: 0.01
    }
    bias_filler {
    type: "constant"
    }
    clipping_threshold: 1
    }
    }


    # ===================== rlstm ===================
    layer {
    name: "lstm-reverse1"
    type: "Reverse"
    bottom: "lstm_input"
    top: "rlstm_input"
    reverse_param {
    axis: 0
    }
    }
    layer {
    name: "rlstm"
    type: "Lstm"
    bottom: "rlstm_input"
    top: "rlstm-output"
    lstm_param {
    num_output: 128
    }
    }
    layer {
    name: "lstm-reverse2"
    type: "Reverse"
    bottom: "rlstm-output"
    top: "rlstm"
    reverse_param {
    axis: 0
    }
    }


    # merge lstm and rlstm
    layer {
    name: "merge_lstm_rlstm"
    type: "Concat"
    bottom: "lstm"
    bottom: "rlstm"
    top: "merge_lstm_rlstm"
    concat_param {
    axis: 2
    }
    }
    layer {
    name: "lstm_output_reshape"
    type: "Reshape"
    bottom: "merge_lstm_rlstm"
    top: "lstm_output_reshape"
    reshape_param {
    shape { dim: -1 dim: 1 }
    axis: 1
    num_axes: 1
    }
    }
    # transpose size of output as (N, C, H, W)
    layer {
    name: "lstm_output"
    type: "Transpose"
    bottom: "lstm_output_reshape"
    top: "lstm_output"
    transpose_param {
    dim: 2
    dim: 3
    dim: 1
    dim: 0
    }
    }
    layer {
    name: "fc"
    bottom: "lstm_output"
    top: "fc"
    type: "Convolution"
    convolution_param {
    num_output: 512
    kernel_size: 1
    }
    }
    layer {
    name: "relu_fc"
    type: "ReLU"
    bottom: "fc"
    top: "fc"
    }
    layer {
    name: "rpn_cls_score"
    type: "Convolution"
    bottom: "fc"
    top: "rpn_cls_score"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
    num_output: 20
    kernel_size: 1 pad: 0 stride: 1
    }
    }
    layer {
    bottom: "rpn_cls_score"
    top: "rpn_cls_score_reshape"
    name: "rpn_cls_score_reshape"
    type: "Reshape"
    reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
    }
    layer {
    name: "rpn_bbox_pred"
    type: "Convolution"
    bottom: "fc"
    top: "rpn_bbox_pred"
    param { lr_mult: 1.0 }
    param { lr_mult: 2.0 }
    convolution_param {
    num_output: 20
    kernel_size: 1 pad: 0 stride: 1
    }
    }
    layer {
    name: "rpn_cls_prob"
    type: "Softmax"
    bottom: "rpn_cls_score_reshape"
    top: "rpn_cls_prob"
    }

    layer {
    name: 'rpn_cls_prob_reshape'
    type: 'Reshape'
    bottom: 'rpn_cls_prob'
    top: 'rpn_cls_prob_reshape'
    reshape_param { shape { dim: 0 dim: 20 dim: -1 dim: 0 } }
    }

    layer {
    name: 'proposal'
    type: 'Python'
    bottom: 'rpn_cls_prob_reshape'
    bottom: 'rpn_bbox_pred'
    bottom: 'im_info'
    top: 'rois'
    top: 'scores'
    python_param {
    module: 'layers.text_proposal_layer'
    layer: 'ProposalLayer'
    param_str: "'feat_stride': 16"
    }
    }