The latest TensorRT version(4.0.1.6) features support for the group (aka depthwise-separable) convolutions, which makes it possible to convert MobileNet-V2 into TRT execution plan without using plugin layers.
I found group convolutions that can’t be int8. before and after the group convolution, the model switched from int8(fp32) to fp32(int8).How can I improve speed of my model？
Adding reformat layer: conv2_2/linear reformatted input 0 (conv2_2/dwise/bn) from Float(1,56,3136,301056) to Int8(1,56,3136:4,75264)
Adding reformat layer: conv3_1/linear + block_3_1 reformatted input 0 (conv3_1/dwise/bn) from Float(1,56,3136,451584) to Int8(1,56,3136:4,112896)
Adding reformat layer: conv4_3/linear reformatted input 0 (conv4_3/dwise/bn) from Float(1,28,784,150528) to Int8(1,28,784:4,37632)
Adding reformat layer: conv4_4/linear + block_4_4 reformatted input 0 (conv4_4/dwise/bn) from Float(1,28,784,301056) to Int8(1,28,784:4,75264)
Adding reformat layer: conv4_5/linear + block_4_5 reformatted input 0 (conv4_5/dwise/bn) from Float(1,28,784,301056) to Int8(1,28,784:4,75264)
Adding reformat layer: conv4_6/linear + block_4_6 reformatted input 0 (conv4_6/dwise/bn) from Float(1,28,784,301056) to Int8(1,28,784:4,75264)
Adding reformat layer: conv4_7/linear reformatted input 0 (conv4_7/dwise/bn) from Float(1,14,196,75264) to Int8(1,14,196:4,18816)
Adding reformat layer: conv5_1/linear + block_5_1 reformatted input 0 (conv5_1/dwise/bn) from Float(1,14,196,112896) to Int8(1,14,196:4,28224)
Adding reformat layer: conv5_2/linear + block_5_2 reformatted input 0 (conv5_2/dwise/bn) from Float(1,14,196,112896) to Int8(1,14,196:4,28224)
Adding reformat layer: conv5_3/linear reformatted input 0 (conv5_3/dwise/bn) from Float(1,7,49,28224) to Int8(1,7,49:4,7056)
Adding reformat layer: conv6_1/dwise + relu6_1/dwise reformatted input 0 (conv6_1/expand/bn) from Int8(1,7,49:4,11760) to Float(1,7,49,47040)
Adding reformat layer: conv6_1/linear + block_6_1 reformatted input 0 (conv6_1/dwise/bn) from Float(1,7,49,47040) to Int8(1,7,49:4,11760)
Adding reformat layer: conv6_2/dwise + relu6_2/dwise reformatted input 0 (conv6_2/expand/bn) from Int8(1,7,49:4,11760) to Float(1,7,49,47040)
Adding reformat layer: conv6_2/linear + block_6_2 reformatted input 0 (conv6_2/dwise/bn) from Float(1,7,49,47040) to Int8(1,7,49:4,11760)
Adding reformat layer: conv6_3/dwise + relu6_3/dwise reformatted input 0 (conv6_3/expand/bn) from Int8(1,7,49:4,11760) to Float(1,7,49,47040)
Adding reformat layer: conv6_3/linear reformatted input 0 (conv6_3/dwise/bn) from Float(1,7,49,47040) to Int8(1,7,49:4,11760)
mobilenetv2.prototxt:
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1/bn"
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
eps: 1e-5
}
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1/bn"
top: "conv1/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1/bn"
top: "conv1/bn"
}
layer {
name: "conv2_1/expand"
type: "Convolution"
bottom: "conv1/bn"
top: "conv2_1/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/expand/bn"
type: "BatchNorm"
bottom: "conv2_1/expand"
top: "conv2_1/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_1/expand/scale"
type: "Scale"
bottom: "conv2_1/expand/bn"
top: "conv2_1/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu2_1/expand"
type: "ReLU"
bottom: "conv2_1/expand/bn"
top: "conv2_1/expand/bn"
}
layer {
name: "conv2_1/dwise"
type: "Convolution"
bottom: "conv2_1/expand/bn"
top: "conv2_1/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
group: 32
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv2_1/dwise/bn"
type: "BatchNorm"
bottom: "conv2_1/dwise"
top: "conv2_1/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_1/dwise/scale"
type: "Scale"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu2_1/dwise"
type: "ReLU"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/dwise/bn"
}
layer {
name: "conv2_1/linear"
type: "Convolution"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 16
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/linear/bn"
type: "BatchNorm"
bottom: "conv2_1/linear"
top: "conv2_1/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_1/linear/scale"
type: "Scale"
bottom: "conv2_1/linear/bn"
top: "conv2_1/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv2_2/expand"
type: "Convolution"
bottom: "conv2_1/linear/bn"
top: "conv2_2/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/expand/bn"
type: "BatchNorm"
bottom: "conv2_2/expand"
top: "conv2_2/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_2/expand/scale"
type: "Scale"
bottom: "conv2_2/expand/bn"
top: "conv2_2/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu2_2/expand"
type: "ReLU"
bottom: "conv2_2/expand/bn"
top: "conv2_2/expand/bn"
}
layer {
name: "conv2_2/dwise"
type: "Convolution"
bottom: "conv2_2/expand/bn"
top: "conv2_2/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 96
bias_term: false
pad: 1
kernel_size: 3
group: 96
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv2_2/dwise/bn"
type: "BatchNorm"
bottom: "conv2_2/dwise"
top: "conv2_2/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_2/dwise/scale"
type: "Scale"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu2_2/dwise"
type: "ReLU"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/dwise/bn"
}
layer {
name: "conv2_2/linear"
type: "Convolution"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 24
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/linear/bn"
type: "BatchNorm"
bottom: "conv2_2/linear"
top: "conv2_2/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv2_2/linear/scale"
type: "Scale"
bottom: "conv2_2/linear/bn"
top: "conv2_2/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1/expand"
type: "Convolution"
bottom: "conv2_2/linear/bn"
top: "conv3_1/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 144
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/expand/bn"
type: "BatchNorm"
bottom: "conv3_1/expand"
top: "conv3_1/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_1/expand/scale"
type: "Scale"
bottom: "conv3_1/expand/bn"
top: "conv3_1/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu3_1/expand"
type: "ReLU"
bottom: "conv3_1/expand/bn"
top: "conv3_1/expand/bn"
}
layer {
name: "conv3_1/dwise"
type: "Convolution"
bottom: "conv3_1/expand/bn"
top: "conv3_1/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 144
bias_term: false
pad: 1
kernel_size: 3
group: 144
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv3_1/dwise/bn"
type: "BatchNorm"
bottom: "conv3_1/dwise"
top: "conv3_1/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_1/dwise/scale"
type: "Scale"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu3_1/dwise"
type: "ReLU"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/dwise/bn"
}
layer {
name: "conv3_1/linear"
type: "Convolution"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 24
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/linear/bn"
type: "BatchNorm"
bottom: "conv3_1/linear"
top: "conv3_1/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_1/linear/scale"
type: "Scale"
bottom: "conv3_1/linear/bn"
top: "conv3_1/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_3_1"
type: "Eltwise"
bottom: "conv2_2/linear/bn"
bottom: "conv3_1/linear/bn"
top: "block_3_1"
}
layer {
name: "conv3_2/expand"
type: "Convolution"
bottom: "block_3_1"
top: "conv3_2/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 144
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/expand/bn"
type: "BatchNorm"
bottom: "conv3_2/expand"
top: "conv3_2/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_2/expand/scale"
type: "Scale"
bottom: "conv3_2/expand/bn"
top: "conv3_2/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu3_2/expand"
type: "ReLU"
bottom: "conv3_2/expand/bn"
top: "conv3_2/expand/bn"
}
layer {
name: "conv3_2/dwise"
type: "Convolution"
bottom: "conv3_2/expand/bn"
top: "conv3_2/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 144
bias_term: false
pad: 1
kernel_size: 3
group: 144
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv3_2/dwise/bn"
type: "BatchNorm"
bottom: "conv3_2/dwise"
top: "conv3_2/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_2/dwise/scale"
type: "Scale"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu3_2/dwise"
type: "ReLU"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/dwise/bn"
}
layer {
name: "conv3_2/linear"
type: "Convolution"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/linear/bn"
type: "BatchNorm"
bottom: "conv3_2/linear"
top: "conv3_2/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv3_2/linear/scale"
type: "Scale"
bottom: "conv3_2/linear/bn"
top: "conv3_2/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1/expand"
type: "Convolution"
bottom: "conv3_2/linear/bn"
top: "conv4_1/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/expand/bn"
type: "BatchNorm"
bottom: "conv4_1/expand"
top: "conv4_1/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_1/expand/scale"
type: "Scale"
bottom: "conv4_1/expand/bn"
top: "conv4_1/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_1/expand"
type: "ReLU"
bottom: "conv4_1/expand/bn"
top: "conv4_1/expand/bn"
}
layer {
name: "conv4_1/dwise"
type: "Convolution"
bottom: "conv4_1/expand/bn"
top: "conv4_1/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_1/dwise/bn"
type: "BatchNorm"
bottom: "conv4_1/dwise"
top: "conv4_1/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_1/dwise/scale"
type: "Scale"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_1/dwise"
type: "ReLU"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/dwise/bn"
}
layer {
name: "conv4_1/linear"
type: "Convolution"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/linear/bn"
type: "BatchNorm"
bottom: "conv4_1/linear"
top: "conv4_1/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_1/linear/scale"
type: "Scale"
bottom: "conv4_1/linear/bn"
top: "conv4_1/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_4_1"
type: "Eltwise"
bottom: "conv3_2/linear/bn"
bottom: "conv4_1/linear/bn"
top: "block_4_1"
}
layer {
name: "conv4_2/expand"
type: "Convolution"
bottom: "block_4_1"
top: "conv4_2/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/expand/bn"
type: "BatchNorm"
bottom: "conv4_2/expand"
top: "conv4_2/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_2/expand/scale"
type: "Scale"
bottom: "conv4_2/expand/bn"
top: "conv4_2/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_2/expand"
type: "ReLU"
bottom: "conv4_2/expand/bn"
top: "conv4_2/expand/bn"
}
layer {
name: "conv4_2/dwise"
type: "Convolution"
bottom: "conv4_2/expand/bn"
top: "conv4_2/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_2/dwise/bn"
type: "BatchNorm"
bottom: "conv4_2/dwise"
top: "conv4_2/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_2/dwise/scale"
type: "Scale"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_2/dwise"
type: "ReLU"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/dwise/bn"
}
layer {
name: "conv4_2/linear"
type: "Convolution"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/linear/bn"
type: "BatchNorm"
bottom: "conv4_2/linear"
top: "conv4_2/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_2/linear/scale"
type: "Scale"
bottom: "conv4_2/linear/bn"
top: "conv4_2/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_4_2"
type: "Eltwise"
bottom: "block_4_1"
bottom: "conv4_2/linear/bn"
top: "block_4_2"
}
layer {
name: "conv4_3/expand"
type: "Convolution"
bottom: "block_4_2"
top: "conv4_3/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/expand/bn"
type: "BatchNorm"
bottom: "conv4_3/expand"
top: "conv4_3/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_3/expand/scale"
type: "Scale"
bottom: "conv4_3/expand/bn"
top: "conv4_3/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_3/expand"
type: "ReLU"
bottom: "conv4_3/expand/bn"
top: "conv4_3/expand/bn"
}
layer {
name: "conv4_3/dwise"
type: "Convolution"
bottom: "conv4_3/expand/bn"
top: "conv4_3/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_3/dwise/bn"
type: "BatchNorm"
bottom: "conv4_3/dwise"
top: "conv4_3/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_3/dwise/scale"
type: "Scale"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_3/dwise"
type: "ReLU"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/dwise/bn"
}
layer {
name: "conv4_3/linear"
type: "Convolution"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/linear/bn"
type: "BatchNorm"
bottom: "conv4_3/linear"
top: "conv4_3/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_3/linear/scale"
type: "Scale"
bottom: "conv4_3/linear/bn"
top: "conv4_3/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv4_4/expand"
type: "Convolution"
bottom: "conv4_3/linear/bn"
top: "conv4_4/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_4/expand/bn"
type: "BatchNorm"
bottom: "conv4_4/expand"
top: "conv4_4/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_4/expand/scale"
type: "Scale"
bottom: "conv4_4/expand/bn"
top: "conv4_4/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_4/expand"
type: "ReLU"
bottom: "conv4_4/expand/bn"
top: "conv4_4/expand/bn"
}
layer {
name: "conv4_4/dwise"
type: "Convolution"
bottom: "conv4_4/expand/bn"
top: "conv4_4/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_4/dwise/bn"
type: "BatchNorm"
bottom: "conv4_4/dwise"
top: "conv4_4/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_4/dwise/scale"
type: "Scale"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_4/dwise"
type: "ReLU"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/dwise/bn"
}
layer {
name: "conv4_4/linear"
type: "Convolution"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_4/linear/bn"
type: "BatchNorm"
bottom: "conv4_4/linear"
top: "conv4_4/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_4/linear/scale"
type: "Scale"
bottom: "conv4_4/linear/bn"
top: "conv4_4/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_4_4"
type: "Eltwise"
bottom: "conv4_3/linear/bn"
bottom: "conv4_4/linear/bn"
top: "block_4_4"
}
layer {
name: "conv4_5/expand"
type: "Convolution"
bottom: "block_4_4"
top: "conv4_5/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_5/expand/bn"
type: "BatchNorm"
bottom: "conv4_5/expand"
top: "conv4_5/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_5/expand/scale"
type: "Scale"
bottom: "conv4_5/expand/bn"
top: "conv4_5/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_5/expand"
type: "ReLU"
bottom: "conv4_5/expand/bn"
top: "conv4_5/expand/bn"
}
layer {
name: "conv4_5/dwise"
type: "Convolution"
bottom: "conv4_5/expand/bn"
top: "conv4_5/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_5/dwise/bn"
type: "BatchNorm"
bottom: "conv4_5/dwise"
top: "conv4_5/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_5/dwise/scale"
type: "Scale"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_5/dwise"
type: "ReLU"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/dwise/bn"
}
layer {
name: "conv4_5/linear"
type: "Convolution"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_5/linear/bn"
type: "BatchNorm"
bottom: "conv4_5/linear"
top: "conv4_5/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_5/linear/scale"
type: "Scale"
bottom: "conv4_5/linear/bn"
top: "conv4_5/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_4_5"
type: "Eltwise"
bottom: "block_4_4"
bottom: "conv4_5/linear/bn"
top: "block_4_5"
}
layer {
name: "conv4_6/expand"
type: "Convolution"
bottom: "block_4_5"
top: "conv4_6/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_6/expand/bn"
type: "BatchNorm"
bottom: "conv4_6/expand"
top: "conv4_6/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_6/expand/scale"
type: "Scale"
bottom: "conv4_6/expand/bn"
top: "conv4_6/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_6/expand"
type: "ReLU"
bottom: "conv4_6/expand/bn"
top: "conv4_6/expand/bn"
}
layer {
name: "conv4_6/dwise"
type: "Convolution"
bottom: "conv4_6/expand/bn"
top: "conv4_6/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_6/dwise/bn"
type: "BatchNorm"
bottom: "conv4_6/dwise"
top: "conv4_6/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_6/dwise/scale"
type: "Scale"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_6/dwise"
type: "ReLU"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/dwise/bn"
}
layer {
name: "conv4_6/linear"
type: "Convolution"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_6/linear/bn"
type: "BatchNorm"
bottom: "conv4_6/linear"
top: "conv4_6/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_6/linear/scale"
type: "Scale"
bottom: "conv4_6/linear/bn"
top: "conv4_6/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_4_6"
type: "Eltwise"
bottom: "block_4_5"
bottom: "conv4_6/linear/bn"
top: "block_4_6"
}
layer {
name: "conv4_7/expand"
type: "Convolution"
bottom: "block_4_6"
top: "conv4_7/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_7/expand/bn"
type: "BatchNorm"
bottom: "conv4_7/expand"
top: "conv4_7/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_7/expand/scale"
type: "Scale"
bottom: "conv4_7/expand/bn"
top: "conv4_7/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_7/expand"
type: "ReLU"
bottom: "conv4_7/expand/bn"
top: "conv4_7/expand/bn"
}
layer {
name: "conv4_7/dwise"
type: "Convolution"
bottom: "conv4_7/expand/bn"
top: "conv4_7/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_7/dwise/bn"
type: "BatchNorm"
bottom: "conv4_7/dwise"
top: "conv4_7/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_7/dwise/scale"
type: "Scale"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu4_7/dwise"
type: "ReLU"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/dwise/bn"
}
layer {
name: "conv4_7/linear"
type: "Convolution"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_7/linear/bn"
type: "BatchNorm"
bottom: "conv4_7/linear"
top: "conv4_7/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv4_7/linear/scale"
type: "Scale"
bottom: "conv4_7/linear/bn"
top: "conv4_7/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1/expand"
type: "Convolution"
bottom: "conv4_7/linear/bn"
top: "conv5_1/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/expand/bn"
type: "BatchNorm"
bottom: "conv5_1/expand"
top: "conv5_1/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_1/expand/scale"
type: "Scale"
bottom: "conv5_1/expand/bn"
top: "conv5_1/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_1/expand"
type: "ReLU"
bottom: "conv5_1/expand/bn"
top: "conv5_1/expand/bn"
}
layer {
name: "conv5_1/dwise"
type: "Convolution"
bottom: "conv5_1/expand/bn"
top: "conv5_1/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_1/dwise/bn"
type: "BatchNorm"
bottom: "conv5_1/dwise"
top: "conv5_1/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_1/dwise/scale"
type: "Scale"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_1/dwise"
type: "ReLU"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/dwise/bn"
}
layer {
name: "conv5_1/linear"
type: "Convolution"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/linear/bn"
type: "BatchNorm"
bottom: "conv5_1/linear"
top: "conv5_1/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_1/linear/scale"
type: "Scale"
bottom: "conv5_1/linear/bn"
top: "conv5_1/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_5_1"
type: "Eltwise"
bottom: "conv4_7/linear/bn"
bottom: "conv5_1/linear/bn"
top: "block_5_1"
}
layer {
name: "conv5_2/expand"
type: "Convolution"
bottom: "block_5_1"
top: "conv5_2/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/expand/bn"
type: "BatchNorm"
bottom: "conv5_2/expand"
top: "conv5_2/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_2/expand/scale"
type: "Scale"
bottom: "conv5_2/expand/bn"
top: "conv5_2/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_2/expand"
type: "ReLU"
bottom: "conv5_2/expand/bn"
top: "conv5_2/expand/bn"
}
layer {
name: "conv5_2/dwise"
type: "Convolution"
bottom: "conv5_2/expand/bn"
top: "conv5_2/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_2/dwise/bn"
type: "BatchNorm"
bottom: "conv5_2/dwise"
top: "conv5_2/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_2/dwise/scale"
type: "Scale"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_2/dwise"
type: "ReLU"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/dwise/bn"
}
layer {
name: "conv5_2/linear"
type: "Convolution"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/linear/bn"
type: "BatchNorm"
bottom: "conv5_2/linear"
top: "conv5_2/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_2/linear/scale"
type: "Scale"
bottom: "conv5_2/linear/bn"
top: "conv5_2/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_5_2"
type: "Eltwise"
bottom: "block_5_1"
bottom: "conv5_2/linear/bn"
top: "block_5_2"
}
layer {
name: "conv5_3/expand"
type: "Convolution"
bottom: "block_5_2"
top: "conv5_3/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/expand/bn"
type: "BatchNorm"
bottom: "conv5_3/expand"
top: "conv5_3/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_3/expand/scale"
type: "Scale"
bottom: "conv5_3/expand/bn"
top: "conv5_3/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_3/expand"
type: "ReLU"
bottom: "conv5_3/expand/bn"
top: "conv5_3/expand/bn"
}
layer {
name: "conv5_3/dwise"
type: "Convolution"
bottom: "conv5_3/expand/bn"
top: "conv5_3/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_3/dwise/bn"
type: "BatchNorm"
bottom: "conv5_3/dwise"
top: "conv5_3/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_3/dwise/scale"
type: "Scale"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu5_3/dwise"
type: "ReLU"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/dwise/bn"
}
layer {
name: "conv5_3/linear"
type: "Convolution"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/linear/bn"
type: "BatchNorm"
bottom: "conv5_3/linear"
top: "conv5_3/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv5_3/linear/scale"
type: "Scale"
bottom: "conv5_3/linear/bn"
top: "conv5_3/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv6_1/expand"
type: "Convolution"
bottom: "conv5_3/linear/bn"
top: "conv6_1/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_1/expand/bn"
type: "BatchNorm"
bottom: "conv6_1/expand"
top: "conv6_1/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_1/expand/scale"
type: "Scale"
bottom: "conv6_1/expand/bn"
top: "conv6_1/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_1/expand"
type: "ReLU"
bottom: "conv6_1/expand/bn"
top: "conv6_1/expand/bn"
}
layer {
name: "conv6_1/dwise"
type: "Convolution"
bottom: "conv6_1/expand/bn"
top: "conv6_1/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_1/dwise/bn"
type: "BatchNorm"
bottom: "conv6_1/dwise"
top: "conv6_1/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_1/dwise/scale"
type: "Scale"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_1/dwise"
type: "ReLU"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/dwise/bn"
}
layer {
name: "conv6_1/linear"
type: "Convolution"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_1/linear/bn"
type: "BatchNorm"
bottom: "conv6_1/linear"
top: "conv6_1/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_1/linear/scale"
type: "Scale"
bottom: "conv6_1/linear/bn"
top: "conv6_1/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_6_1"
type: "Eltwise"
bottom: "conv5_3/linear/bn"
bottom: "conv6_1/linear/bn"
top: "block_6_1"
}
layer {
name: "conv6_2/expand"
type: "Convolution"
bottom: "block_6_1"
top: "conv6_2/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_2/expand/bn"
type: "BatchNorm"
bottom: "conv6_2/expand"
top: "conv6_2/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_2/expand/scale"
type: "Scale"
bottom: "conv6_2/expand/bn"
top: "conv6_2/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_2/expand"
type: "ReLU"
bottom: "conv6_2/expand/bn"
top: "conv6_2/expand/bn"
}
layer {
name: "conv6_2/dwise"
type: "Convolution"
bottom: "conv6_2/expand/bn"
top: "conv6_2/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_2/dwise/bn"
type: "BatchNorm"
bottom: "conv6_2/dwise"
top: "conv6_2/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_2/dwise/scale"
type: "Scale"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_2/dwise"
type: "ReLU"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/dwise/bn"
}
layer {
name: "conv6_2/linear"
type: "Convolution"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_2/linear/bn"
type: "BatchNorm"
bottom: "conv6_2/linear"
top: "conv6_2/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_2/linear/scale"
type: "Scale"
bottom: "conv6_2/linear/bn"
top: "conv6_2/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "block_6_2"
type: "Eltwise"
bottom: "block_6_1"
bottom: "conv6_2/linear/bn"
top: "block_6_2"
}
layer {
name: "conv6_3/expand"
type: "Convolution"
bottom: "block_6_2"
top: "conv6_3/expand"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_3/expand/bn"
type: "BatchNorm"
bottom: "conv6_3/expand"
top: "conv6_3/expand/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_3/expand/scale"
type: "Scale"
bottom: "conv6_3/expand/bn"
top: "conv6_3/expand/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_3/expand"
type: "ReLU"
bottom: "conv6_3/expand/bn"
top: "conv6_3/expand/bn"
}
layer {
name: "conv6_3/dwise"
type: "Convolution"
bottom: "conv6_3/expand/bn"
top: "conv6_3/dwise"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_3/dwise/bn"
type: "BatchNorm"
bottom: "conv6_3/dwise"
top: "conv6_3/dwise/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_3/dwise/scale"
type: "Scale"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/dwise/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_3/dwise"
type: "ReLU"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/dwise/bn"
}
layer {
name: "conv6_3/linear"
type: "Convolution"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/linear"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 320
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_3/linear/bn"
type: "BatchNorm"
bottom: "conv6_3/linear"
top: "conv6_3/linear/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_3/linear/scale"
type: "Scale"
bottom: "conv6_3/linear/bn"
top: "conv6_3/linear/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv6_4"
type: "Convolution"
bottom: "conv6_3/linear/bn"
top: "conv6_4"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 1280
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_4/bn"
type: "BatchNorm"
bottom: "conv6_4"
top: "conv6_4/bn"
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
eps: 1e-5
}
}
layer {
name: "conv6_4/scale"
type: "Scale"
bottom: "conv6_4/bn"
top: "conv6_4/bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
scale_param {
bias_term: true
}
}
layer {
name: "relu6_4"
type: "ReLU"
bottom: "conv6_4/bn"
top: "conv6_4/bn"
}
layer {
name: "pool6"
type: "Pooling"
bottom: "conv6_4/bn"
top: "pool6"
pooling_param {
pool: AVE
global_pooling: true
}
}
layer {
name: "fc7"
type: "Convolution"
bottom: "pool6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 1000
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "fc7"
top: "prob"
}