can not open test_feat_conv34atrous_v2.prototxt

[MisayaZ]

i do not know what is the content of test_feat_conv34atrous_v2.prototxt which is a binary type and I do not know how to generate it.

[zhangliliang]

"test_feat_conv34atrous_v2.prototxt" is a text file which could be opened and edited used vim or gedit in Ubuntu system.

It would be like below.

name: "VGG_ILSVRC_16"

input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224

input: "rois"
input_dim: 1 # to be changed on-the-fly to num ROIs
input_dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing
input_dim: 1
input_dim: 1

layer {
    bottom: "data"
    top: "conv1_1"
    name: "conv1_1"
    type: "Convolution"
    param {
        lr_mult: 0.0
    }
    param {
        lr_mult: 0.0
    }
    convolution_param {
        num_output: 64
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv1_1"
    top: "conv1_1"
    name: "relu1_1"
    type: "ReLU"
}

layer {
    bottom: "conv1_1"
    top: "conv1_2"
    name: "conv1_2"
    param {
        lr_mult: 0.0
    }
    param {
        lr_mult: 0.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 64
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv1_2"
    top: "conv1_2"
    name: "relu1_2"
    type: "ReLU"
}

layer {
    bottom: "conv1_2"
    top: "pool1"
    name: "pool1"
    type: "Pooling"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}

layer {
    bottom: "pool1"
    top: "conv2_1"
    name: "conv2_1"
    param {
        lr_mult: 0.0
    }
    param {
        lr_mult: 0.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 128
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv2_1"
    top: "conv2_1"
    name: "relu2_1"
    type: "ReLU"
}

layer {
    bottom: "conv2_1"
    top: "conv2_2"
    name: "conv2_2"
    param {
        lr_mult: 0.0
    }
    param {
        lr_mult: 0.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 128
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv2_2"
    top: "conv2_2"
    name: "relu2_2"
    type: "ReLU"
}

layer {
    bottom: "conv2_2"
    top: "pool2"
    name: "pool2"
    type: "Pooling"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}

layer {
    bottom: "pool2"
    top: "conv3_1"
    name: "conv3_1"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv3_1"
    top: "conv3_1"
    name: "relu3_1"
    type: "ReLU"
}

layer {
    bottom: "conv3_1"
    top: "conv3_2"
    name: "conv3_2"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv3_2"
    top: "conv3_2"
    name: "relu3_2"
    type: "ReLU"
}

layer {
    bottom: "conv3_2"
    top: "conv3_3"
    name: "conv3_3"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
    }
}

layer {
    bottom: "conv3_3"
    top: "conv3_3"
    name: "relu3_3"
    type: "ReLU"
}

layer {
    bottom: "conv3_3"
    top: "pool3"
    name: "pool3"
    type: "Pooling"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 1
    }
}

layer {
    bottom: "pool3"
    top: "conv4_1"
    name: "conv4_1"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 512
        pad: 2
        kernel_size: 3
        filter_stride: 2
    }
}

layer {
    bottom: "conv4_1"
    top: "conv4_1"
    name: "relu4_1"
    type: "ReLU"
}

layer {
    bottom: "conv4_1"
    top: "conv4_2"
    name: "conv4_2"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 512
        pad: 2
        kernel_size: 3
        filter_stride: 2
    }
}

layer {
    bottom: "conv4_2"
    top: "conv4_2"
    name: "relu4_2"
    type: "ReLU"
}

layer {
    bottom: "conv4_2"
    top: "conv4_3"
    name: "conv4_3"
    param {
        lr_mult: 1.0
    }
    param {
        lr_mult: 2.0
    }
    type: "Convolution"
    convolution_param {
        num_output: 512
        pad: 2
        kernel_size: 3
        filter_stride: 2
    }
}

layer {
    bottom: "conv4_3"
    top: "conv4_3"
    name: "relu4_3"
    type: "ReLU"
}


# ------------- roi layer --------

layer {
    bottom: "conv3_3"
    bottom: "rois"
    top: "roi_pool3"
    name: "roi_pool3"
    type: "ROIPooling"
    roi_pooling_param {
        pooled_w: 7
        pooled_h: 7
        spatial_scale: 0.25  # (1/4)
    }
}

layer {
    bottom: "conv4_3"
    bottom: "rois"
    top: "roi_pool4_3"
    name: "roi_pool4_3"
    type: "ROIPooling"
    roi_pooling_param {
        pooled_w: 7
        pooled_h: 7
        spatial_scale: 0.25  # (1/4)
    }
}

layer {
    bottom: "roi_pool3"
    top: "roi_pool3_flat"
    name: "roi_pool3_flat"
    type: "Flatten"
}

layer {
    bottom: "roi_pool4_3"
    top: "roi_pool4_3_flat"
    name: "roi_pool4_3_flat"
    type: "Flatten"
}

layer {
    bottom: "roi_pool3_flat"
    bottom: "roi_pool4_3_flat"
    top: "concat_feat"
    name: "concat_feat"
    type: "Concat"
}