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bmount/gist:7cbc28fb1294ab0e4572db091b24af6a

Created December 4, 2016 23:02
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gistfile1.txt
name: "PVANET-lite"
# https://raw.githubusercontent.com/sanghoon/pva-faster-rcnn/master/models/pvanet/lite/original.pt
################################################################################
## Input
################################################################################
layer {
name: 'input-data'
type: 'Python'
top: 'data'
top: 'im_info'
top: 'gt_boxes'
include { phase: TRAIN }
python_param {
module: 'roi_data_layer.layer'
layer: 'RoIDataLayer'
param_str: "'num_classes': 21"
}
}
layer {
name: "input-data"
type: "DummyData"
top: "data"
top: "im_info"
include { phase: TEST }
dummy_data_param {
shape { dim: 1 dim: 3 dim: 640 dim: 1056 }
shape { dim: 1 dim: 4 }
}
}
################################################################################
## Conv 1
################################################################################
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32
kernel_size: 4 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1"
top: "conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
################################################################################
## Conv 2
################################################################################
layer {
name: "conv2"
type: "Convolution"
bottom: "conv1"
top: "conv2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 48
kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "conv2/bn"
type: "BatchNorm"
bottom: "conv2"
top: "conv2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "conv2/scale"
type: "Scale"
bottom: "conv2"
top: "conv2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
################################################################################
## Conv 3
################################################################################
layer {
name: "conv3"
type: "Convolution"
bottom: "conv2"
top: "conv3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96
kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "conv3/bn"
type: "BatchNorm"
bottom: "conv3"
top: "conv3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "conv3/scale"
type: "Scale"
bottom: "conv3"
top: "conv3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
################################################################################
## Inception 3a
################################################################################
layer {
name: "inc3a/pool1"
type: "Pooling"
bottom: "conv3"
top: "inc3a/pool1"
pooling_param {
kernel_size: 3 stride: 2 pad: 0
pool: MAX
}
}
layer {
name: "inc3a/conv1"
type: "Convolution"
bottom: "inc3a/pool1"
top: "inc3a/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv1/bn"
type: "BatchNorm"
bottom: "inc3a/conv1"
top: "inc3a/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv1/scale"
type: "Scale"
bottom: "inc3a/conv1"
top: "inc3a/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu1"
type: "ReLU"
bottom: "inc3a/conv1"
top: "inc3a/conv1"
}
layer {
name: "inc3a/conv3_1"
type: "Convolution"
bottom: "conv3"
top: "inc3a/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv3_1/bn"
type: "BatchNorm"
bottom: "inc3a/conv3_1"
top: "inc3a/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv3_1/scale"
type: "Scale"
bottom: "inc3a/conv3_1"
top: "inc3a/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu3_1"
type: "ReLU"
bottom: "inc3a/conv3_1"
top: "inc3a/conv3_1"
}
layer {
name: "inc3a/conv3_2"
type: "Convolution"
bottom: "inc3a/conv3_1"
top: "inc3a/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 64 kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv3_2/bn"
type: "BatchNorm"
bottom: "inc3a/conv3_2"
top: "inc3a/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv3_2/scale"
type: "Scale"
bottom: "inc3a/conv3_2"
top: "inc3a/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu3_2"
type: "ReLU"
bottom: "inc3a/conv3_2"
top: "inc3a/conv3_2"
}
layer {
name: "inc3a/conv5_1"
type: "Convolution"
bottom: "conv3"
top: "inc3a/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv5_1/bn"
type: "BatchNorm"
bottom: "inc3a/conv5_1"
top: "inc3a/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv5_1/scale"
type: "Scale"
bottom: "inc3a/conv5_1"
top: "inc3a/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu5_1"
type: "ReLU"
bottom: "inc3a/conv5_1"
top: "inc3a/conv5_1"
}
layer {
name: "inc3a/conv5_2"
type: "Convolution"
bottom: "inc3a/conv5_1"
top: "inc3a/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv5_2/bn"
type: "BatchNorm"
bottom: "inc3a/conv5_2"
top: "inc3a/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv5_2/scale"
type: "Scale"
bottom: "inc3a/conv5_2"
top: "inc3a/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu5_2"
type: "ReLU"
bottom: "inc3a/conv5_2"
top: "inc3a/conv5_2"
}
layer {
name: "inc3a/conv5_3"
type: "Convolution"
bottom: "inc3a/conv5_2"
top: "inc3a/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3a/conv5_3/bn"
type: "BatchNorm"
bottom: "inc3a/conv5_3"
top: "inc3a/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3a/conv5_3/scale"
type: "Scale"
bottom: "inc3a/conv5_3"
top: "inc3a/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3a/relu5_3"
type: "ReLU"
bottom: "inc3a/conv5_3"
top: "inc3a/conv5_3"
}
layer {
name: "inc3a"
type: "Concat"
bottom: "inc3a/conv1"
bottom: "inc3a/conv3_2"
bottom: "inc3a/conv5_3"
top: "inc3a"
}
################################################################################
## Inception 3b
################################################################################
layer {
name: "inc3b/conv1"
type: "Convolution"
bottom: "inc3a"
top: "inc3b/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv1/bn"
type: "BatchNorm"
bottom: "inc3b/conv1"
top: "inc3b/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv1/scale"
type: "Scale"
bottom: "inc3b/conv1"
top: "inc3b/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu1"
type: "ReLU"
bottom: "inc3b/conv1"
top: "inc3b/conv1"
}
layer {
name: "inc3b/conv3_1"
type: "Convolution"
bottom: "inc3a"
top: "inc3b/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv3_1/bn"
type: "BatchNorm"
bottom: "inc3b/conv3_1"
top: "inc3b/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv3_1/scale"
type: "Scale"
bottom: "inc3b/conv3_1"
top: "inc3b/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu3_1"
type: "ReLU"
bottom: "inc3b/conv3_1"
top: "inc3b/conv3_1"
}
layer {
name: "inc3b/conv3_2"
type: "Convolution"
bottom: "inc3b/conv3_1"
top: "inc3b/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 64 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv3_2/bn"
type: "BatchNorm"
bottom: "inc3b/conv3_2"
top: "inc3b/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv3_2/scale"
type: "Scale"
bottom: "inc3b/conv3_2"
top: "inc3b/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu3_2"
type: "ReLU"
bottom: "inc3b/conv3_2"
top: "inc3b/conv3_2"
}
layer {
name: "inc3b/conv5_1"
type: "Convolution"
bottom: "inc3a"
top: "inc3b/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv5_1/bn"
type: "BatchNorm"
bottom: "inc3b/conv5_1"
top: "inc3b/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv5_1/scale"
type: "Scale"
bottom: "inc3b/conv5_1"
top: "inc3b/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu5_1"
type: "ReLU"
bottom: "inc3b/conv5_1"
top: "inc3b/conv5_1"
}
layer {
name: "inc3b/conv5_2"
type: "Convolution"
bottom: "inc3b/conv5_1"
top: "inc3b/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv5_2/bn"
type: "BatchNorm"
bottom: "inc3b/conv5_2"
top: "inc3b/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv5_2/scale"
type: "Scale"
bottom: "inc3b/conv5_2"
top: "inc3b/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu5_2"
type: "ReLU"
bottom: "inc3b/conv5_2"
top: "inc3b/conv5_2"
}
layer {
name: "inc3b/conv5_3"
type: "Convolution"
bottom: "inc3b/conv5_2"
top: "inc3b/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3b/conv5_3/bn"
type: "BatchNorm"
bottom: "inc3b/conv5_3"
top: "inc3b/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3b/conv5_3/scale"
type: "Scale"
bottom: "inc3b/conv5_3"
top: "inc3b/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3b/relu5_3"
type: "ReLU"
bottom: "inc3b/conv5_3"
top: "inc3b/conv5_3"
}
layer {
name: "inc3b"
type: "Concat"
bottom: "inc3b/conv1"
bottom: "inc3b/conv3_2"
bottom: "inc3b/conv5_3"
top: "inc3b"
}
################################################################################
## Inception 3c
################################################################################
layer {
name: "inc3c/conv1"
type: "Convolution"
bottom: "inc3b"
top: "inc3c/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv1/bn"
type: "BatchNorm"
bottom: "inc3c/conv1"
top: "inc3c/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv1/scale"
type: "Scale"
bottom: "inc3c/conv1"
top: "inc3c/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu1"
type: "ReLU"
bottom: "inc3c/conv1"
top: "inc3c/conv1"
}
layer {
name: "inc3c/conv3_1"
type: "Convolution"
bottom: "inc3b"
top: "inc3c/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv3_1/bn"
type: "BatchNorm"
bottom: "inc3c/conv3_1"
top: "inc3c/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv3_1/scale"
type: "Scale"
bottom: "inc3c/conv3_1"
top: "inc3c/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu3_1"
type: "ReLU"
bottom: "inc3c/conv3_1"
top: "inc3c/conv3_1"
}
layer {
name: "inc3c/conv3_2"
type: "Convolution"
bottom: "inc3c/conv3_1"
top: "inc3c/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 64 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv3_2/bn"
type: "BatchNorm"
bottom: "inc3c/conv3_2"
top: "inc3c/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv3_2/scale"
type: "Scale"
bottom: "inc3c/conv3_2"
top: "inc3c/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu3_2"
type: "ReLU"
bottom: "inc3c/conv3_2"
top: "inc3c/conv3_2"
}
layer {
name: "inc3c/conv5_1"
type: "Convolution"
bottom: "inc3b"
top: "inc3c/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv5_1/bn"
type: "BatchNorm"
bottom: "inc3c/conv5_1"
top: "inc3c/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv5_1/scale"
type: "Scale"
bottom: "inc3c/conv5_1"
top: "inc3c/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu5_1"
type: "ReLU"
bottom: "inc3c/conv5_1"
top: "inc3c/conv5_1"
}
layer {
name: "inc3c/conv5_2"
type: "Convolution"
bottom: "inc3c/conv5_1"
top: "inc3c/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv5_2/bn"
type: "BatchNorm"
bottom: "inc3c/conv5_2"
top: "inc3c/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv5_2/scale"
type: "Scale"
bottom: "inc3c/conv5_2"
top: "inc3c/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu5_2"
type: "ReLU"
bottom: "inc3c/conv5_2"
top: "inc3c/conv5_2"
}
layer {
name: "inc3c/conv5_3"
type: "Convolution"
bottom: "inc3c/conv5_2"
top: "inc3c/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3c/conv5_3/bn"
type: "BatchNorm"
bottom: "inc3c/conv5_3"
top: "inc3c/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3c/conv5_3/scale"
type: "Scale"
bottom: "inc3c/conv5_3"
top: "inc3c/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3c/relu5_3"
type: "ReLU"
bottom: "inc3c/conv5_3"
top: "inc3c/conv5_3"
}
layer {
name: "inc3c"
type: "Concat"
bottom: "inc3c/conv1"
bottom: "inc3c/conv3_2"
bottom: "inc3c/conv5_3"
top: "inc3c"
}
################################################################################
## Inception 3d
################################################################################
layer {
name: "inc3d/conv1"
type: "Convolution"
bottom: "inc3c"
top: "inc3d/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv1/bn"
type: "BatchNorm"
bottom: "inc3d/conv1"
top: "inc3d/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv1/scale"
type: "Scale"
bottom: "inc3d/conv1"
top: "inc3d/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu1"
type: "ReLU"
bottom: "inc3d/conv1"
top: "inc3d/conv1"
}
layer {
name: "inc3d/conv3_1"
type: "Convolution"
bottom: "inc3c"
top: "inc3d/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv3_1/bn"
type: "BatchNorm"
bottom: "inc3d/conv3_1"
top: "inc3d/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv3_1/scale"
type: "Scale"
bottom: "inc3d/conv3_1"
top: "inc3d/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu3_1"
type: "ReLU"
bottom: "inc3d/conv3_1"
top: "inc3d/conv3_1"
}
layer {
name: "inc3d/conv3_2"
type: "Convolution"
bottom: "inc3d/conv3_1"
top: "inc3d/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 64 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv3_2/bn"
type: "BatchNorm"
bottom: "inc3d/conv3_2"
top: "inc3d/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv3_2/scale"
type: "Scale"
bottom: "inc3d/conv3_2"
top: "inc3d/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu3_2"
type: "ReLU"
bottom: "inc3d/conv3_2"
top: "inc3d/conv3_2"
}
layer {
name: "inc3d/conv5_1"
type: "Convolution"
bottom: "inc3c"
top: "inc3d/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv5_1/bn"
type: "BatchNorm"
bottom: "inc3d/conv5_1"
top: "inc3d/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv5_1/scale"
type: "Scale"
bottom: "inc3d/conv5_1"
top: "inc3d/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu5_1"
type: "ReLU"
bottom: "inc3d/conv5_1"
top: "inc3d/conv5_1"
}
layer {
name: "inc3d/conv5_2"
type: "Convolution"
bottom: "inc3d/conv5_1"
top: "inc3d/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv5_2/bn"
type: "BatchNorm"
bottom: "inc3d/conv5_2"
top: "inc3d/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv5_2/scale"
type: "Scale"
bottom: "inc3d/conv5_2"
top: "inc3d/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu5_2"
type: "ReLU"
bottom: "inc3d/conv5_2"
top: "inc3d/conv5_2"
}
layer {
name: "inc3d/conv5_3"
type: "Convolution"
bottom: "inc3d/conv5_2"
top: "inc3d/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3d/conv5_3/bn"
type: "BatchNorm"
bottom: "inc3d/conv5_3"
top: "inc3d/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3d/conv5_3/scale"
type: "Scale"
bottom: "inc3d/conv5_3"
top: "inc3d/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3d/relu5_3"
type: "ReLU"
bottom: "inc3d/conv5_3"
top: "inc3d/conv5_3"
}
layer {
name: "inc3d"
type: "Concat"
bottom: "inc3d/conv1"
bottom: "inc3d/conv3_2"
bottom: "inc3d/conv5_3"
top: "inc3d"
}
################################################################################
## Inception 3e
################################################################################
layer {
name: "inc3e/conv1"
type: "Convolution"
bottom: "inc3d"
top: "inc3e/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv1/bn"
type: "BatchNorm"
bottom: "inc3e/conv1"
top: "inc3e/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv1/scale"
type: "Scale"
bottom: "inc3e/conv1"
top: "inc3e/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu1"
type: "ReLU"
bottom: "inc3e/conv1"
top: "inc3e/conv1"
}
layer {
name: "inc3e/conv3_1"
type: "Convolution"
bottom: "inc3d"
top: "inc3e/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv3_1/bn"
type: "BatchNorm"
bottom: "inc3e/conv3_1"
top: "inc3e/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv3_1/scale"
type: "Scale"
bottom: "inc3e/conv3_1"
top: "inc3e/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu3_1"
type: "ReLU"
bottom: "inc3e/conv3_1"
top: "inc3e/conv3_1"
}
layer {
name: "inc3e/conv3_2"
type: "Convolution"
bottom: "inc3e/conv3_1"
top: "inc3e/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 64 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv3_2/bn"
type: "BatchNorm"
bottom: "inc3e/conv3_2"
top: "inc3e/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv3_2/scale"
type: "Scale"
bottom: "inc3e/conv3_2"
top: "inc3e/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu3_2"
type: "ReLU"
bottom: "inc3e/conv3_2"
top: "inc3e/conv3_2"
}
layer {
name: "inc3e/conv5_1"
type: "Convolution"
bottom: "inc3d"
top: "inc3e/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv5_1/bn"
type: "BatchNorm"
bottom: "inc3e/conv5_1"
top: "inc3e/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv5_1/scale"
type: "Scale"
bottom: "inc3e/conv5_1"
top: "inc3e/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu5_1"
type: "ReLU"
bottom: "inc3e/conv5_1"
top: "inc3e/conv5_1"
}
layer {
name: "inc3e/conv5_2"
type: "Convolution"
bottom: "inc3e/conv5_1"
top: "inc3e/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv5_2/bn"
type: "BatchNorm"
bottom: "inc3e/conv5_2"
top: "inc3e/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv5_2/scale"
type: "Scale"
bottom: "inc3e/conv5_2"
top: "inc3e/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu5_2"
type: "ReLU"
bottom: "inc3e/conv5_2"
top: "inc3e/conv5_2"
}
layer {
name: "inc3e/conv5_3"
type: "Convolution"
bottom: "inc3e/conv5_2"
top: "inc3e/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc3e/conv5_3/bn"
type: "BatchNorm"
bottom: "inc3e/conv5_3"
top: "inc3e/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc3e/conv5_3/scale"
type: "Scale"
bottom: "inc3e/conv5_3"
top: "inc3e/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc3e/relu5_3"
type: "ReLU"
bottom: "inc3e/conv5_3"
top: "inc3e/conv5_3"
}
layer {
name: "inc3e"
type: "Concat"
bottom: "inc3e/conv1"
bottom: "inc3e/conv3_2"
bottom: "inc3e/conv5_3"
top: "inc3e"
}
################################################################################
## Inception 4a
################################################################################
layer {
name: "inc4a/pool1"
type: "Pooling"
bottom: "inc3e"
top: "inc4a/pool1"
pooling_param {
kernel_size: 3 stride: 2 pad: 0
pool: MAX
}
}
layer {
name: "inc4a/conv1"
type: "Convolution"
bottom: "inc4a/pool1"
top: "inc4a/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv1/bn"
type: "BatchNorm"
bottom: "inc4a/conv1"
top: "inc4a/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv1/scale"
type: "Scale"
bottom: "inc4a/conv1"
top: "inc4a/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu1"
type: "ReLU"
bottom: "inc4a/conv1"
top: "inc4a/conv1"
}
layer {
name: "inc4a/conv3_1"
type: "Convolution"
bottom: "inc3e"
top: "inc4a/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv3_1/bn"
type: "BatchNorm"
bottom: "inc4a/conv3_1"
top: "inc4a/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv3_1/scale"
type: "Scale"
bottom: "inc4a/conv3_1"
top: "inc4a/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu3_1"
type: "ReLU"
bottom: "inc4a/conv3_1"
top: "inc4a/conv3_1"
}
layer {
name: "inc4a/conv3_2"
type: "Convolution"
bottom: "inc4a/conv3_1"
top: "inc4a/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv3_2/bn"
type: "BatchNorm"
bottom: "inc4a/conv3_2"
top: "inc4a/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv3_2/scale"
type: "Scale"
bottom: "inc4a/conv3_2"
top: "inc4a/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu3_2"
type: "ReLU"
bottom: "inc4a/conv3_2"
top: "inc4a/conv3_2"
}
layer {
name: "inc4a/conv5_1"
type: "Convolution"
bottom: "inc3e"
top: "inc4a/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv5_1/bn"
type: "BatchNorm"
bottom: "inc4a/conv5_1"
top: "inc4a/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv5_1/scale"
type: "Scale"
bottom: "inc4a/conv5_1"
top: "inc4a/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu5_1"
type: "ReLU"
bottom: "inc4a/conv5_1"
top: "inc4a/conv5_1"
}
layer {
name: "inc4a/conv5_2"
type: "Convolution"
bottom: "inc4a/conv5_1"
top: "inc4a/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv5_2/bn"
type: "BatchNorm"
bottom: "inc4a/conv5_2"
top: "inc4a/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv5_2/scale"
type: "Scale"
bottom: "inc4a/conv5_2"
top: "inc4a/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu5_2"
type: "ReLU"
bottom: "inc4a/conv5_2"
top: "inc4a/conv5_2"
}
layer {
name: "inc4a/conv5_3"
type: "Convolution"
bottom: "inc4a/conv5_2"
top: "inc4a/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 stride: 2 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4a/conv5_3/bn"
type: "BatchNorm"
bottom: "inc4a/conv5_3"
top: "inc4a/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4a/conv5_3/scale"
type: "Scale"
bottom: "inc4a/conv5_3"
top: "inc4a/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4a/relu5_3"
type: "ReLU"
bottom: "inc4a/conv5_3"
top: "inc4a/conv5_3"
}
layer {
name: "inc4a"
type: "Concat"
bottom: "inc4a/conv1"
bottom: "inc4a/conv3_2"
bottom: "inc4a/conv5_3"
top: "inc4a"
}
################################################################################
## Inception 4b
################################################################################
layer {
name: "inc4b/conv1"
type: "Convolution"
bottom: "inc4a"
top: "inc4b/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv1/bn"
type: "BatchNorm"
bottom: "inc4b/conv1"
top: "inc4b/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv1/scale"
type: "Scale"
bottom: "inc4b/conv1"
top: "inc4b/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu1"
type: "ReLU"
bottom: "inc4b/conv1"
top: "inc4b/conv1"
}
layer {
name: "inc4b/conv3_1"
type: "Convolution"
bottom: "inc4a"
top: "inc4b/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv3_1/bn"
type: "BatchNorm"
bottom: "inc4b/conv3_1"
top: "inc4b/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv3_1/scale"
type: "Scale"
bottom: "inc4b/conv3_1"
top: "inc4b/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu3_1"
type: "ReLU"
bottom: "inc4b/conv3_1"
top: "inc4b/conv3_1"
}
layer {
name: "inc4b/conv3_2"
type: "Convolution"
bottom: "inc4b/conv3_1"
top: "inc4b/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv3_2/bn"
type: "BatchNorm"
bottom: "inc4b/conv3_2"
top: "inc4b/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv3_2/scale"
type: "Scale"
bottom: "inc4b/conv3_2"
top: "inc4b/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu3_2"
type: "ReLU"
bottom: "inc4b/conv3_2"
top: "inc4b/conv3_2"
}
layer {
name: "inc4b/conv5_1"
type: "Convolution"
bottom: "inc4a"
top: "inc4b/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv5_1/bn"
type: "BatchNorm"
bottom: "inc4b/conv5_1"
top: "inc4b/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv5_1/scale"
type: "Scale"
bottom: "inc4b/conv5_1"
top: "inc4b/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu5_1"
type: "ReLU"
bottom: "inc4b/conv5_1"
top: "inc4b/conv5_1"
}
layer {
name: "inc4b/conv5_2"
type: "Convolution"
bottom: "inc4b/conv5_1"
top: "inc4b/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv5_2/bn"
type: "BatchNorm"
bottom: "inc4b/conv5_2"
top: "inc4b/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv5_2/scale"
type: "Scale"
bottom: "inc4b/conv5_2"
top: "inc4b/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu5_2"
type: "ReLU"
bottom: "inc4b/conv5_2"
top: "inc4b/conv5_2"
}
layer {
name: "inc4b/conv5_3"
type: "Convolution"
bottom: "inc4b/conv5_2"
top: "inc4b/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4b/conv5_3/bn"
type: "BatchNorm"
bottom: "inc4b/conv5_3"
top: "inc4b/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4b/conv5_3/scale"
type: "Scale"
bottom: "inc4b/conv5_3"
top: "inc4b/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4b/relu5_3"
type: "ReLU"
bottom: "inc4b/conv5_3"
top: "inc4b/conv5_3"
}
layer {
name: "inc4b"
type: "Concat"
bottom: "inc4b/conv1"
bottom: "inc4b/conv3_2"
bottom: "inc4b/conv5_3"
top: "inc4b"
}
################################################################################
## Inception 4c
################################################################################
layer {
name: "inc4c/conv1"
type: "Convolution"
bottom: "inc4b"
top: "inc4c/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv1/bn"
type: "BatchNorm"
bottom: "inc4c/conv1"
top: "inc4c/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv1/scale"
type: "Scale"
bottom: "inc4c/conv1"
top: "inc4c/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu1"
type: "ReLU"
bottom: "inc4c/conv1"
top: "inc4c/conv1"
}
layer {
name: "inc4c/conv3_1"
type: "Convolution"
bottom: "inc4b"
top: "inc4c/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv3_1/bn"
type: "BatchNorm"
bottom: "inc4c/conv3_1"
top: "inc4c/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv3_1/scale"
type: "Scale"
bottom: "inc4c/conv3_1"
top: "inc4c/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu3_1"
type: "ReLU"
bottom: "inc4c/conv3_1"
top: "inc4c/conv3_1"
}
layer {
name: "inc4c/conv3_2"
type: "Convolution"
bottom: "inc4c/conv3_1"
top: "inc4c/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv3_2/bn"
type: "BatchNorm"
bottom: "inc4c/conv3_2"
top: "inc4c/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv3_2/scale"
type: "Scale"
bottom: "inc4c/conv3_2"
top: "inc4c/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu3_2"
type: "ReLU"
bottom: "inc4c/conv3_2"
top: "inc4c/conv3_2"
}
layer {
name: "inc4c/conv5_1"
type: "Convolution"
bottom: "inc4b"
top: "inc4c/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv5_1/bn"
type: "BatchNorm"
bottom: "inc4c/conv5_1"
top: "inc4c/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv5_1/scale"
type: "Scale"
bottom: "inc4c/conv5_1"
top: "inc4c/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu5_1"
type: "ReLU"
bottom: "inc4c/conv5_1"
top: "inc4c/conv5_1"
}
layer {
name: "inc4c/conv5_2"
type: "Convolution"
bottom: "inc4c/conv5_1"
top: "inc4c/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv5_2/bn"
type: "BatchNorm"
bottom: "inc4c/conv5_2"
top: "inc4c/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv5_2/scale"
type: "Scale"
bottom: "inc4c/conv5_2"
top: "inc4c/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu5_2"
type: "ReLU"
bottom: "inc4c/conv5_2"
top: "inc4c/conv5_2"
}
layer {
name: "inc4c/conv5_3"
type: "Convolution"
bottom: "inc4c/conv5_2"
top: "inc4c/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4c/conv5_3/bn"
type: "BatchNorm"
bottom: "inc4c/conv5_3"
top: "inc4c/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4c/conv5_3/scale"
type: "Scale"
bottom: "inc4c/conv5_3"
top: "inc4c/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4c/relu5_3"
type: "ReLU"
bottom: "inc4c/conv5_3"
top: "inc4c/conv5_3"
}
layer {
name: "inc4c"
type: "Concat"
bottom: "inc4c/conv1"
bottom: "inc4c/conv3_2"
bottom: "inc4c/conv5_3"
top: "inc4c"
}
################################################################################
## Inception 4d
################################################################################
layer {
name: "inc4d/conv1"
type: "Convolution"
bottom: "inc4c"
top: "inc4d/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv1/bn"
type: "BatchNorm"
bottom: "inc4d/conv1"
top: "inc4d/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv1/scale"
type: "Scale"
bottom: "inc4d/conv1"
top: "inc4d/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu1"
type: "ReLU"
bottom: "inc4d/conv1"
top: "inc4d/conv1"
}
layer {
name: "inc4d/conv3_1"
type: "Convolution"
bottom: "inc4c"
top: "inc4d/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv3_1/bn"
type: "BatchNorm"
bottom: "inc4d/conv3_1"
top: "inc4d/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv3_1/scale"
type: "Scale"
bottom: "inc4d/conv3_1"
top: "inc4d/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu3_1"
type: "ReLU"
bottom: "inc4d/conv3_1"
top: "inc4d/conv3_1"
}
layer {
name: "inc4d/conv3_2"
type: "Convolution"
bottom: "inc4d/conv3_1"
top: "inc4d/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv3_2/bn"
type: "BatchNorm"
bottom: "inc4d/conv3_2"
top: "inc4d/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv3_2/scale"
type: "Scale"
bottom: "inc4d/conv3_2"
top: "inc4d/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu3_2"
type: "ReLU"
bottom: "inc4d/conv3_2"
top: "inc4d/conv3_2"
}
layer {
name: "inc4d/conv5_1"
type: "Convolution"
bottom: "inc4c"
top: "inc4d/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv5_1/bn"
type: "BatchNorm"
bottom: "inc4d/conv5_1"
top: "inc4d/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv5_1/scale"
type: "Scale"
bottom: "inc4d/conv5_1"
top: "inc4d/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu5_1"
type: "ReLU"
bottom: "inc4d/conv5_1"
top: "inc4d/conv5_1"
}
layer {
name: "inc4d/conv5_2"
type: "Convolution"
bottom: "inc4d/conv5_1"
top: "inc4d/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv5_2/bn"
type: "BatchNorm"
bottom: "inc4d/conv5_2"
top: "inc4d/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv5_2/scale"
type: "Scale"
bottom: "inc4d/conv5_2"
top: "inc4d/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu5_2"
type: "ReLU"
bottom: "inc4d/conv5_2"
top: "inc4d/conv5_2"
}
layer {
name: "inc4d/conv5_3"
type: "Convolution"
bottom: "inc4d/conv5_2"
top: "inc4d/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4d/conv5_3/bn"
type: "BatchNorm"
bottom: "inc4d/conv5_3"
top: "inc4d/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4d/conv5_3/scale"
type: "Scale"
bottom: "inc4d/conv5_3"
top: "inc4d/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4d/relu5_3"
type: "ReLU"
bottom: "inc4d/conv5_3"
top: "inc4d/conv5_3"
}
layer {
name: "inc4d"
type: "Concat"
bottom: "inc4d/conv1"
bottom: "inc4d/conv3_2"
bottom: "inc4d/conv5_3"
top: "inc4d"
}
################################################################################
## Inception 4e
################################################################################
layer {
name: "inc4e/conv1"
type: "Convolution"
bottom: "inc4d"
top: "inc4e/conv1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv1/bn"
type: "BatchNorm"
bottom: "inc4e/conv1"
top: "inc4e/conv1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv1/scale"
type: "Scale"
bottom: "inc4e/conv1"
top: "inc4e/conv1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu1"
type: "ReLU"
bottom: "inc4e/conv1"
top: "inc4e/conv1"
}
layer {
name: "inc4e/conv3_1"
type: "Convolution"
bottom: "inc4d"
top: "inc4e/conv3_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv3_1/bn"
type: "BatchNorm"
bottom: "inc4e/conv3_1"
top: "inc4e/conv3_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv3_1/scale"
type: "Scale"
bottom: "inc4e/conv3_1"
top: "inc4e/conv3_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu3_1"
type: "ReLU"
bottom: "inc4e/conv3_1"
top: "inc4e/conv3_1"
}
layer {
name: "inc4e/conv3_2"
type: "Convolution"
bottom: "inc4e/conv3_1"
top: "inc4e/conv3_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 96 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv3_2/bn"
type: "BatchNorm"
bottom: "inc4e/conv3_2"
top: "inc4e/conv3_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv3_2/scale"
type: "Scale"
bottom: "inc4e/conv3_2"
top: "inc4e/conv3_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu3_2"
type: "ReLU"
bottom: "inc4e/conv3_2"
top: "inc4e/conv3_2"
}
layer {
name: "inc4e/conv5_1"
type: "Convolution"
bottom: "inc4d"
top: "inc4e/conv5_1"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 16 kernel_size: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv5_1/bn"
type: "BatchNorm"
bottom: "inc4e/conv5_1"
top: "inc4e/conv5_1"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv5_1/scale"
type: "Scale"
bottom: "inc4e/conv5_1"
top: "inc4e/conv5_1"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu5_1"
type: "ReLU"
bottom: "inc4e/conv5_1"
top: "inc4e/conv5_1"
}
layer {
name: "inc4e/conv5_2"
type: "Convolution"
bottom: "inc4e/conv5_1"
top: "inc4e/conv5_2"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv5_2/bn"
type: "BatchNorm"
bottom: "inc4e/conv5_2"
top: "inc4e/conv5_2"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv5_2/scale"
type: "Scale"
bottom: "inc4e/conv5_2"
top: "inc4e/conv5_2"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu5_2"
type: "ReLU"
bottom: "inc4e/conv5_2"
top: "inc4e/conv5_2"
}
layer {
name: "inc4e/conv5_3"
type: "Convolution"
bottom: "inc4e/conv5_2"
top: "inc4e/conv5_3"
param { lr_mult: 0.1 decay_mult: 0.1 }
param { lr_mult: 0.2 decay_mult: 0 }
convolution_param {
num_output: 32 kernel_size: 3 pad: 1
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "inc4e/conv5_3/bn"
type: "BatchNorm"
bottom: "inc4e/conv5_3"
top: "inc4e/conv5_3"
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
param { lr_mult: 0 decay_mult: 0 }
batch_norm_param { use_global_stats: true }
}
layer {
name: "inc4e/conv5_3/scale"
type: "Scale"
bottom: "inc4e/conv5_3"
top: "inc4e/conv5_3"
param { lr_mult: 0.1 decay_mult: 0 }
param { lr_mult: 0.1 decay_mult: 0 }
scale_param { bias_term: true }
}
layer {
name: "inc4e/relu5_3"
type: "ReLU"
bottom: "inc4e/conv5_3"
top: "inc4e/conv5_3"
}
layer {
name: "inc4e"
type: "Concat"
bottom: "inc4e/conv1"
bottom: "inc4e/conv3_2"
bottom: "inc4e/conv5_3"
top: "inc4e"
}
################################################################################
## hyper feature
################################################################################
layer {
name: "downsample"
type: "Pooling"
bottom: "conv3"
top: "downsample"
pooling_param {
kernel_size: 3 stride: 2 pad: 0
pool: MAX
}
}
layer {
name: "upsample"
type: "Deconvolution"
bottom: "inc4e"
top: "upsample"
param { lr_mult: 0 decay_mult: 0 }
convolution_param {
num_output: 256
kernel_size: 4 stride: 2 pad: 1
group: 256
weight_filler: { type: "bilinear" }
bias_term: false
}
}
layer {
name: "concat"
type: "Concat"
bottom: "downsample"
bottom: "inc3e"
bottom: "upsample"
top: "concat"
concat_param { axis: 1 }
}
layer {
name: "convf"
type: "Convolution"
bottom: "concat"
top: "convf"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256
kernel_size: 1 stride: 1 pad: 0
weight_filler { type: "xavier" std: 0.1 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "reluf"
type: "ReLU"
bottom: "convf"
top: "convf"
}
################################################################################
## RPN
################################################################################
### RPN ###
layer {
name: "rpn_conv1"
type: "Convolution"
bottom: "convf"
top: "rpn_conv1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256
kernel_size: 1 stride: 1 pad: 0
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_relu1"
type: "ReLU"
bottom: "rpn_conv1"
top: "rpn_conv1"
}
layer {
name: "rpn_cls_score"
type: "Convolution"
bottom: "rpn_conv1"
top: "rpn_cls_score"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 50
kernel_size: 1 stride: 1 pad: 0
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_bbox_pred"
type: "Convolution"
bottom: "rpn_conv1"
top: "rpn_bbox_pred"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 100
kernel_size: 1 stride: 1 pad: 0
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
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-data'
type: 'Python'
bottom: 'rpn_cls_score'
bottom: 'gt_boxes'
bottom: 'im_info'
bottom: 'data'
top: 'rpn_labels'
top: 'rpn_bbox_targets'
top: 'rpn_bbox_inside_weights'
top: 'rpn_bbox_outside_weights'
include { phase: TRAIN }
python_param {
module: 'rpn.anchor_target_layer'
layer: 'AnchorTargetLayer'
param_str: "{'feat_stride': 16, 'ratios': [0.5, 0.667, 1, 1.5, 2], 'scales': [3, 6, 9, 16, 32]}"
}
}
layer {
name: "rpn_loss_cls"
type: "SoftmaxWithLoss"
bottom: "rpn_cls_score_reshape"
bottom: "rpn_labels"
propagate_down: 1
propagate_down: 0
top: "rpn_loss_cls"
include { phase: TRAIN }
loss_weight: 1
loss_param { ignore_label: -1 normalize: true }
}
layer {
name: "rpn_loss_bbox"
type: "SmoothL1Loss"
bottom: "rpn_bbox_pred"
bottom: "rpn_bbox_targets"
bottom: "rpn_bbox_inside_weights"
bottom: "rpn_bbox_outside_weights"
top: "rpn_loss_bbox"
include { phase: TRAIN }
loss_weight: 1
smooth_l1_loss_param { sigma: 3.0 }
}
################################################################################
## Proposal
################################################################################
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: 50 dim: -1 dim: 0 } }
}
layer {
name: 'proposal'
type: 'Python'
bottom: 'rpn_cls_prob_reshape'
bottom: 'rpn_bbox_pred'
bottom: 'im_info'
bottom: 'gt_boxes'
top: 'rois'
top: 'labels'
top: 'bbox_targets'
top: 'bbox_inside_weights'
top: 'bbox_outside_weights'
include { phase: TRAIN }
python_param {
module: 'rpn.proposal_layer'
layer: 'ProposalLayer2'
param_str: "{'feat_stride': 16, 'num_classes': 21, 'ratios': [0.5, 0.667, 1, 1.5, 2], 'scales': [3, 6, 9, 16, 32]}"
}
}
layer {
name: 'proposal'
type: 'Proposal'
bottom: 'rpn_cls_prob_reshape'
bottom: 'rpn_bbox_pred'
bottom: 'im_info'
top: 'rois'
top: 'scores'
include { phase: TEST }
proposal_param {
ratio: 0.5 ratio: 0.667 ratio: 1.0 ratio: 1.5 ratio: 2.0
scale: 3 scale: 6 scale: 9 scale: 16 scale: 32
base_size: 16
feat_stride: 16
pre_nms_topn: 6000
post_nms_topn: 200
nms_thresh: 0.7
min_size: 16
}
}
################################################################################
## RCNN
################################################################################
layer {
name: "roi_pool_conv5"
type: "ROIPooling"
bottom: "convf"
bottom: "rois"
top: "roi_pool_conv5"
roi_pooling_param {
pooled_w: 6 pooled_h: 6
spatial_scale: 0.0625 # 1/16
}
}
layer {
name: "fc6_L"
type: "InnerProduct"
bottom: "roi_pool_conv5"
top: "fc6_L"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 512
weight_filler { type: "xavier" std: 0.005 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "fc6_U"
type: "InnerProduct"
bottom: "fc6_L"
top: "fc6_U"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 4096
weight_filler { type: "xavier" std: 0.005 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6_U"
top: "fc6_U"
}
################################################################################
## fc 7
################################################################################
layer {
name: "fc7_L"
type: "InnerProduct"
bottom: "fc6_U"
top: "fc7_L"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 128
weight_filler { type: "xavier" std: 0.005 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "fc7_U"
type: "InnerProduct"
bottom: "fc7_L"
top: "fc7_U"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 4096
weight_filler { type: "xavier" std: 0.005 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7_U"
top: "fc7_U"
}
################################################################################
## output
################################################################################
layer {
name: "cls_score"
type: "InnerProduct"
bottom: "fc7_U"
top: "cls_score"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 21
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "bbox_pred"
type: "InnerProduct"
bottom: "fc7_U"
top: "bbox_pred"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 84
weight_filler { type: "gaussian" std: 0.001 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "loss_cls"
type: "SoftmaxWithLoss"
bottom: "cls_score"
bottom: "labels"
propagate_down: 1
propagate_down: 0
top: "loss_cls"
include { phase: TRAIN }
loss_weight: 1
loss_param { ignore_label: -1 normalize: true }
}
layer {
name: "loss_bbox"
type: "SmoothL1Loss"
bottom: "bbox_pred"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
bottom: "bbox_outside_weights"
top: "loss_bbox"
include { phase: TRAIN }
loss_weight: 1
}
layer {
name: "cls_prob"
type: "Softmax"
bottom: "cls_score"
top: "cls_prob"
include { phase: TEST }
loss_param {
ignore_label: -1
normalize: true
}
}
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