This is the caffe code for 1 branch of pyramid pooling of the LargeFOV model available at
http://liangchiehchen.com/projects/DeepLabv2_vgg.html 13
### hole = 6
layer {
  name: "fc6_1"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    pad: 6
    kernel_size: 3
    dilation: 6
  }
}
layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "fc6_1"
  top: "fc6_1"
}
layer {
  name: "drop6_1"
  type: "Dropout"
  bottom: "fc6_1"
  top: "fc6_1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7_1"
  type: "Convolution"
  bottom: "fc6_1"
  top: "fc7_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    kernel_size: 1
  }
}
layer {
  name: "relu7_1"
  type: "ReLU"
  bottom: "fc7_1"
  top: "fc7_1"
}
layer {
  name: "drop7_1"
  type: "Dropout"
  bottom: "fc7_1"
  top: "fc7_1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_${EXP}_1"
  type: "Convolution"
  bottom: "fc7_1"
  top: "fc8_${EXP}_1"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: ${NUM_LABELS}
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

You can see that learning rate and weight decay for bias is different from the learning rate and weight decay for the weights. Moreover, the bias is removed from fc8 etc.
If you are using the above code in caffe, you will get different result as compared to pytorch code, unless you make the same changes in the pytorch code.