ResNet10 SSD

Does deepstream support ResNet10 SSD Caffe models ? I have tried with SSD(nvdsinfer_custom_impl_ssd)
with no luck

name: "ResNet-10-SSD"
layer {
  name: "data"
  type: "MemoryData"
  top: "data"
  top: "label"
  transform_param {
    mean_value: 117.258
    mean_value: 121.735
    mean_value: 127.623
  }
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 300
    width: 300
  }
}
layer {
  name: "data_bn"
  type: "BatchNorm"
  bottom: "data"
  top: "data_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "data_scale"
  type: "Scale"
  bottom: "data_bn"
  top: "data_bn"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data_bn"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "msra"
      variance_norm: FAN_OUT
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv1_bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "conv1_scale"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv1_pool"
  type: "Pooling"
  bottom: "conv1"
  top: "conv1_pool"
  pooling_param {
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "layer_64_1_conv1"
  type: "Convolution"
  bottom: "conv1_pool"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_64_1_bn2"
  type: "BatchNorm"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_64_1_scale2"
  type: "Scale"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_1_relu2"
  type: "ReLU"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
}
layer {
  name: "layer_64_1_conv2"
  type: "Convolution"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_64_1_sum"
  type: "Eltwise"
  bottom: "layer_64_1_conv2"
  bottom: "conv1_pool"
  top: "layer_64_1_sum"
}
layer {
  name: "layer_128_1_bn1"
  type: "BatchNorm"
  bottom: "layer_64_1_sum"
  top: "layer_128_1_bn1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_128_1_scale1"
  type: "Scale"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_bn1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu1"
  type: "ReLU"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_bn1"
}
layer {
  name: "layer_128_1_conv1"
  type: "Convolution"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_128_1_bn2"
  type: "BatchNorm"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_128_1_scale2"
  type: "Scale"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu2"
  type: "ReLU"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
}
layer {
  name: "layer_128_1_conv2"
  type: "Convolution"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_128_1_conv_expand"
  type: "Convolution"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_conv_expand"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_128_1_sum"
  type: "Eltwise"
  bottom: "layer_128_1_conv2"
  bottom: "layer_128_1_conv_expand"
  top: "layer_128_1_sum"
}
layer {
  name: "layer_256_1_bn1"
  type: "BatchNorm"
  bottom: "layer_128_1_sum"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_256_1_scale1"
  type: "Scale"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu1"
  type: "ReLU"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
}
layer {
  name: "layer_256_1_conv1"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_256_1_bn2"
  type: "BatchNorm"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_256_1_scale2"
  type: "Scale"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu2"
  type: "ReLU"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
}
layer {
  name: "layer_256_1_conv2"
  type: "Convolution"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_256_1_conv_expand"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv_expand"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_256_1_sum"
  type: "Eltwise"
  bottom: "layer_256_1_conv2"
  bottom: "layer_256_1_conv_expand"
  top: "layer_256_1_sum"
}
layer {
  name: "layer_512_1_bn1"
  type: "BatchNorm"
  bottom: "layer_256_1_sum"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_512_1_scale1"
  type: "Scale"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu1"
  type: "ReLU"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
}
layer {
  name: "layer_512_1_conv1"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_512_1_bn2"
  type: "BatchNorm"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "layer_512_1_scale2"
  type: "Scale"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu2"
  type: "ReLU"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
}
layer {
  name: "layer_512_1_conv2"
  type: "Convolution"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_512_1_conv_expand"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv_expand"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "layer_512_1_sum"
  type: "Eltwise"
  bottom: "layer_512_1_conv2"
  bottom: "layer_512_1_conv_expand"
  top: "layer_512_1_sum"
}
layer {
  name: "last_bn"
  type: "BatchNorm"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "last_scale"
  type: "Scale"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "last_relu"
  type: "ReLU"
  bottom: "layer_512_1_sum"
  top: "fc7"
}
layer {
  name: "conv6_1_h"
  type: "Convolution"
  bottom: "fc7"
  top: "conv6_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_1_relu"
  type: "ReLU"
  bottom: "conv6_1_h"
  top: "conv6_1_h"
}
layer {
  name: "conv6_2_h"
  type: "Convolution"
  bottom: "conv6_1_h"
  top: "conv6_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_relu"
  type: "ReLU"
  bottom: "conv6_2_h"
  top: "conv6_2_h"
}
layer {
  name: "conv7_1_h"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv7_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_1_relu"
  type: "ReLU"
  bottom: "conv7_1_h"
  top: "conv7_1_h"
}
layer {
  name: "conv7_2_h"
  type: "Convolution"
  bottom: "conv7_1_h"
  top: "conv7_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_relu"
  type: "ReLU"
  bottom: "conv7_2_h"
  top: "conv7_2_h"
}
layer {
  name: "conv8_1_h"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv8_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_1_relu"
  type: "ReLU"
  bottom: "conv8_1_h"
  top: "conv8_1_h"
}
layer {
  name: "conv8_2_h"
  type: "Convolution"
  bottom: "conv8_1_h"
  top: "conv8_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_relu"
  type: "ReLU"
  bottom: "conv8_2_h"
  top: "conv8_2_h"
}
layer {
  name: "conv9_1_h"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv9_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_1_relu"
  type: "ReLU"
  bottom: "conv9_1_h"
  top: "conv9_1_h"
}
layer {
  name: "conv9_2_h"
  type: "Convolution"
  bottom: "conv9_1_h"
  top: "conv9_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_relu"
  type: "ReLU"
  bottom: "conv9_2_h"
  top: "conv9_2_h"
}
layer {
  name: "conv4_3_norm"
  type: "Normalize"
  bottom: "layer_256_1_bn1"
  top: "conv4_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 20
    }
    channel_shared: false
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_ftune"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_loc_ftune"
  top: "conv4_3_norm_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "conv4_3_norm_mbox_loc_perm_ftune"
  top: "conv4_3_norm_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_ftune"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_conf_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_conf_ftune"
  top: "conv4_3_norm_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "conv4_3_norm_mbox_conf_perm_ftune"
  top: "conv4_3_norm_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv4_3_norm"
  bottom: "data"
  top: "conv4_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 30
    max_size: 60
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 8
    offset: 0.5
  }
}
layer {
  name: "fc7_mbox_loc_ftune"
  type: "Convolution"
  bottom: "fc7"
  top: "fc7_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc7_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "fc7_mbox_loc_ftune"
  top: "fc7_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "fc7_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "fc7_mbox_loc_perm_ftune"
  top: "fc7_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "fc7_mbox_conf_ftune"
  type: "Convolution"
  bottom: "fc7"
  top: "fc7_mbox_conf_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc7_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "fc7_mbox_conf_ftune"
  top: "fc7_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "fc7_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "fc7_mbox_conf_perm_ftune"
  top: "fc7_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "fc7_mbox_priorbox"
  type: "PriorBox"
  bottom: "fc7"
  bottom: "data"
  top: "fc7_mbox_priorbox"
  prior_box_param {
    min_size: 60
    max_size: 111
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 16
    offset: 0.5
  }
}
layer {
  name: "conv6_2_mbox_loc_ftune"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv6_2_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "conv6_2_mbox_loc_ftune"
  top: "conv6_2_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_2_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "conv6_2_mbox_loc_perm_ftune"
  top: "conv6_2_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv6_2_mbox_conf_ftune"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv6_2_mbox_conf_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "conv6_2_mbox_conf_ftune"
  top: "conv6_2_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_2_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "conv6_2_mbox_conf_perm_ftune"
  top: "conv6_2_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv6_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv6_2_h"
  bottom: "data"
  top: "conv6_2_mbox_priorbox"
  prior_box_param {
    min_size: 111
    max_size: 162
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 32
    offset: 0.5
  }
}
layer {
  name: "conv7_2_mbox_loc_ftune"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv7_2_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "conv7_2_mbox_loc_ftune"
  top: "conv7_2_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv7_2_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "conv7_2_mbox_loc_perm_ftune"
  top: "conv7_2_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv7_2_mbox_conf_ftune"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv7_2_mbox_conf_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 12
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "conv7_2_mbox_conf_ftune"
  top: "conv7_2_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv7_2_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "conv7_2_mbox_conf_perm_ftune"
  top: "conv7_2_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv7_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv7_2_h"
  bottom: "data"
  top: "conv7_2_mbox_priorbox"
  prior_box_param {
    min_size: 162
    max_size: 213
    aspect_ratio: 2
    aspect_ratio: 3
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 64
    offset: 0.5
  }
}
layer {
  name: "conv8_2_mbox_loc_ftune"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv8_2_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "conv8_2_mbox_loc_ftune"
  top: "conv8_2_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv8_2_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "conv8_2_mbox_loc_perm_ftune"
  top: "conv8_2_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv8_2_mbox_conf_ftune"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv8_2_mbox_conf_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "conv8_2_mbox_conf_ftune"
  top: "conv8_2_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv8_2_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "conv8_2_mbox_conf_perm_ftune"
  top: "conv8_2_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv8_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv8_2_h"
  bottom: "data"
  top: "conv8_2_mbox_priorbox"
  prior_box_param {
    min_size: 213
    max_size: 264
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 100
    offset: 0.5
  }
}
layer {
  name: "conv9_2_mbox_loc_ftune"
  type: "Convolution"
  bottom: "conv9_2_h"
  top: "conv9_2_mbox_loc_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_mbox_loc_perm_ftune"
  type: "Permute"
  bottom: "conv9_2_mbox_loc_ftune"
  top: "conv9_2_mbox_loc_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv9_2_mbox_loc_flat_ftune"
  type: "Flatten"
  bottom: "conv9_2_mbox_loc_perm_ftune"
  top: "conv9_2_mbox_loc_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv9_2_mbox_conf_ftune_ftune"
  type: "Convolution"
  bottom: "conv9_2_h"
  top: "conv9_2_mbox_conf_ftune_ftune"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_mbox_conf_perm_ftune"
  type: "Permute"
  bottom: "conv9_2_mbox_conf_ftune_ftune"
  top: "conv9_2_mbox_conf_perm_ftune"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv9_2_mbox_conf_flat_ftune"
  type: "Flatten"
  bottom: "conv9_2_mbox_conf_perm_ftune"
  top: "conv9_2_mbox_conf_flat_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv9_2_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv9_2_h"
  bottom: "data"
  top: "conv9_2_mbox_priorbox"
  prior_box_param {
    min_size: 264
    max_size: 315
    aspect_ratio: 2
    flip: true
    clip: false
    variance: 0.1
    variance: 0.1
    variance: 0.2
    variance: 0.2
    step: 300
    offset: 0.5
  }
}
layer {
  name: "mbox_loc_ftune"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_loc_flat_ftune"
  bottom: "fc7_mbox_loc_flat_ftune"
  bottom: "conv6_2_mbox_loc_flat_ftune"
  bottom: "conv7_2_mbox_loc_flat_ftune"
  bottom: "conv8_2_mbox_loc_flat_ftune"
  bottom: "conv9_2_mbox_loc_flat_ftune"
  top: "mbox_loc_ftune"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_conf_ftune"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_conf_flat_ftune"
  bottom: "fc7_mbox_conf_flat_ftune"
  bottom: "conv6_2_mbox_conf_flat_ftune"
  bottom: "conv7_2_mbox_conf_flat_ftune"
  bottom: "conv8_2_mbox_conf_flat_ftune"
  bottom: "conv9_2_mbox_conf_flat_ftune"
  top: "mbox_conf_ftune"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_priorbox"
  type: "Concat"
  bottom: "conv4_3_norm_mbox_priorbox"
  bottom: "fc7_mbox_priorbox"
  bottom: "conv6_2_mbox_priorbox"
  bottom: "conv7_2_mbox_priorbox"
  bottom: "conv8_2_mbox_priorbox"
  bottom: "conv9_2_mbox_priorbox"
  top: "mbox_priorbox"
  concat_param {
    axis: 2
  }
}
layer {
  name: "mbox_conf_reshape_ftune"
  type: "Reshape"
  bottom: "mbox_conf_ftune"
  top: "mbox_conf_reshape_ftune"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 2
    }
  }
}
layer {
  name: "mbox_conf_softmax_ftune"
  type: "Softmax"
  bottom: "mbox_conf_reshape_ftune"
  top: "mbox_conf_softmax_ftune"
  softmax_param {
    axis: 2
  }
}
layer {
  name: "mbox_conf_flatten_ftune"
  type: "Flatten"
  bottom: "mbox_conf_softmax_ftune"
  top: "mbox_conf_flatten_ftune"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "detection_out"
  type: "DetectionOutput"
  bottom: "mbox_loc_ftune"
  bottom: "mbox_conf_flatten_ftune"
  bottom: "mbox_priorbox"
  top: "detection_out"
  detection_output_param {
    num_classes: 2
    share_location: true
    background_label_id: 0
    nms_param {
      nms_threshold: 0.45
      top_k: 400
    }
    code_type: CENTER_SIZE
    keep_top_k: 200
    confidence_threshold: 0.01
  }
}

Hi,
In theory, TRT models should work fine. Do you train ResNet10 SSD Caffe models by yourself? Or download it from somewhere?

Our team trained them, we used it with tensorRT directly on a different inference platform

The models did not work with deepstream though

net-scale-factor=0.0039215697906911373
model-file=/opt/nvidia/deepstream/deepstream-4.0/samples/models/resnet10_ssd_******v1/model_iter_50000.caffemodel
proto-file=/opt/nvidia/deepstream/deepstream-4.0/samples/models/resnet10_ssd_******v1v1/deploy.prototxt
model-engine-file=/opt/nvidia/deepstream/deepstream-4.0/samples/models/resnet10_ssd_******v1v1/model_iter_50000.engine
labelfile-path=/opt/nvidia/deepstream/deepstream-4.0/samples/models/resnet10_ssd_******v1v1/corresp.txt

batch-size=30
process-mode=1
model-color-format=0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=3
interval=0
gie-unique-id=1
output-blob-names=detection_out
#parse-bbox-func-name=NvDsInferParseCustomSSDUff
#custom-lib-path=/opt/nvidia/deepstream/deepstream-4.0/sources/deepstream_4.x_apps/nvdsinfer_customparser_ssd_uff/libnvds_infercustomparser_ssd_uff.so
#enable-dbscan=1

Do I need to use the SSD plugin if yes which one?

nvdsinfer_custom_impl_ssd
or
https://github.com/NVIDIA-AI-IOT/deepstream_4.x_apps
(This TF example, but I assume i does not make any difference )

Hi, please find the TRT supported caffe ops in https://docs.nvidia.com/deeplearning/sdk/tensorrt-support-matrix/index.html#supported-ops, if the layer in your network is not in this list, it should be implemented by TRT plugin.
BTW, TRT itself has already implemented many plugins - https://github.com/NVIDIA/TensorRT/tree/master/plugin , you can use them if your network includes the plugin.

1 Like

Thanks this is solved