Assertion Error with Custom Trained Caffe
Hi,
I would like to use the OpenCV DNN framework with a custom trained Caffe network.
I am using the newest OpenCV Version and contrib libs, I compiled two weeks ago, I guess. I have already checked the dnn samples, and with the precompiled SqueezeDet and SqueezeNet examples everything works just fine. But if I load my network I get the following exception:
OpenCV(3.4.1-dev) Error: Unspecified error (Can't create layer "cluster" of type "Python") in getLayerInstance, file opencv-master/modules/dnn/src/dnn.cpp, line 388 terminate called after throwing an instance of 'cv::Exception'
what(): OpenCV(3.4.1-dev) opencv-master/modules/dnn/src/dnn.cpp:388: error: (-2) Unspecified error: Can't create layer "cluster" of type "Python" in function getLayerInstance
The network should be fine, I have tested it outside the OpenCV framework. The caffe version is 0.15.14, digits version is 5.1-dev.
I use OpenCV in C++ not in Python, could it be the error?
Thanks a lot for any answers!
Mary
EDIT:
Here is my prototxt the last layer of the prototxt, that I am using. I am assuming that the problem is, the last layer...
input: "data"
input_shape {
dim: 1
dim: 3
dim: 384
dim: 1248
}
probably causes the problem:
layer {
{
name: "deploy_transform"
"cluster"
type: "Power"
"Python"
bottom: "data"
"coverage"
bottom: "bboxes"
top: "transformed_data"
power_param {
shift: -127.0
}
}
layer {
name: "conv1/7x7_s2"
type: "Convolution"
bottom: "transformed_data"
top: "conv1/7x7_s2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv1/relu_7x7"
type: "ReLU"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2"
}
layer {
name: "pool1/3x3_s2"
type: "Pooling"
bottom: "conv1/7x7_s2"
top: "pool1/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "pool1/norm1"
type: "LRN"
bottom: "pool1/3x3_s2"
top: "pool1/norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2/3x3_reduce"
type: "Convolution"
bottom: "pool1/norm1"
top: "conv2/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2/relu_3x3_reduce"
type: "ReLU"
bottom: "conv2/3x3_reduce"
top: "conv2/3x3_reduce"
}
layer {
name: "conv2/3x3"
type: "Convolution"
bottom: "conv2/3x3_reduce"
top: "conv2/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2/relu_3x3"
type: "ReLU"
bottom: "conv2/3x3"
top: "conv2/3x3"
}
layer {
name: "conv2/norm2"
type: "LRN"
bottom: "conv2/3x3"
top: "conv2/norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2/3x3_s2"
type: "Pooling"
bottom: "conv2/norm2"
top: "pool2/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "inception_3a/1x1"
type: "Convolution"
bottom: "pool2/3x3_s2"
top: "inception_3a/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_1x1"
type: "ReLU"
bottom: "inception_3a/1x1"
top: "inception_3a/1x1"
}
layer {
name: "inception_3a/3x3_reduce"
type: "Convolution"
bottom: "pool2/3x3_s2"
top: "inception_3a/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_3a/3x3_reduce"
top: "inception_3a/3x3_reduce"
}
layer {
name: "inception_3a/3x3"
type: "Convolution"
bottom: "inception_3a/3x3_reduce"
top: "inception_3a/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_3x3"
type: "ReLU"
bottom: "inception_3a/3x3"
top: "inception_3a/3x3"
}
layer {
name: "inception_3a/5x5_reduce"
type: "Convolution"
bottom: "pool2/3x3_s2"
top: "inception_3a/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_3a/5x5_reduce"
top: "inception_3a/5x5_reduce"
}
layer {
name: "inception_3a/5x5"
type: "Convolution"
bottom: "inception_3a/5x5_reduce"
top: "inception_3a/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_5x5"
type: "ReLU"
bottom: "inception_3a/5x5"
top: "inception_3a/5x5"
}
layer {
name: "inception_3a/pool"
type: "Pooling"
bottom: "pool2/3x3_s2"
top: "inception_3a/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_3a/pool_proj"
type: "Convolution"
bottom: "inception_3a/pool"
top: "inception_3a/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_pool_proj"
type: "ReLU"
bottom: "inception_3a/pool_proj"
top: "inception_3a/pool_proj"
}
layer {
name: "inception_3a/output"
type: "Concat"
bottom: "inception_3a/1x1"
bottom: "inception_3a/3x3"
bottom: "inception_3a/5x5"
bottom: "inception_3a/pool_proj"
top: "inception_3a/output"
}
layer {
name: "inception_3b/1x1"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_1x1"
type: "ReLU"
bottom: "inception_3b/1x1"
top: "inception_3b/1x1"
}
layer {
name: "inception_3b/3x3_reduce"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_3b/3x3_reduce"
top: "inception_3b/3x3_reduce"
}
layer {
name: "inception_3b/3x3"
type: "Convolution"
bottom: "inception_3b/3x3_reduce"
top: "inception_3b/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_3x3"
type: "ReLU"
bottom: "inception_3b/3x3"
top: "inception_3b/3x3"
}
layer {
name: "inception_3b/5x5_reduce"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_3b/5x5_reduce"
top: "inception_3b/5x5_reduce"
}
layer {
name: "inception_3b/5x5"
type: "Convolution"
bottom: "inception_3b/5x5_reduce"
top: "inception_3b/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 96
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_5x5"
type: "ReLU"
bottom: "inception_3b/5x5"
top: "inception_3b/5x5"
}
layer {
name: "inception_3b/pool"
type: "Pooling"
bottom: "inception_3a/output"
top: "inception_3b/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_3b/pool_proj"
type: "Convolution"
bottom: "inception_3b/pool"
top: "inception_3b/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_pool_proj"
type: "ReLU"
bottom: "inception_3b/pool_proj"
top: "inception_3b/pool_proj"
}
layer {
name: "inception_3b/output"
type: "Concat"
bottom: "inception_3b/1x1"
bottom: "inception_3b/3x3"
bottom: "inception_3b/5x5"
bottom: "inception_3b/pool_proj"
top: "inception_3b/output"
}
layer {
name: "pool3/3x3_s2"
type: "Pooling"
bottom: "inception_3b/output"
top: "pool3/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "inception_4a/1x1"
type: "Convolution"
bottom: "pool3/3x3_s2"
top: "inception_4a/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_1x1"
type: "ReLU"
bottom: "inception_4a/1x1"
top: "inception_4a/1x1"
}
layer {
name: "inception_4a/3x3_reduce"
type: "Convolution"
bottom: "pool3/3x3_s2"
top: "inception_4a/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_4a/3x3_reduce"
top: "inception_4a/3x3_reduce"
}
layer {
name: "inception_4a/3x3"
type: "Convolution"
bottom: "inception_4a/3x3_reduce"
top: "inception_4a/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 208
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_3x3"
type: "ReLU"
bottom: "inception_4a/3x3"
top: "inception_4a/3x3"
}
layer {
name: "inception_4a/5x5_reduce"
type: "Convolution"
bottom: "pool3/3x3_s2"
top: "inception_4a/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_4a/5x5_reduce"
top: "inception_4a/5x5_reduce"
}
layer {
name: "inception_4a/5x5"
type: "Convolution"
bottom: "inception_4a/5x5_reduce"
top: "inception_4a/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 48
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_5x5"
type: "ReLU"
bottom: "inception_4a/5x5"
top: "inception_4a/5x5"
}
layer {
name: "inception_4a/pool"
type: "Pooling"
bottom: "pool3/3x3_s2"
top: "inception_4a/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_4a/pool_proj"
type: "Convolution"
bottom: "inception_4a/pool"
top: "inception_4a/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4a/relu_pool_proj"
type: "ReLU"
bottom: "inception_4a/pool_proj"
top: "inception_4a/pool_proj"
}
layer {
name: "inception_4a/output"
type: "Concat"
bottom: "inception_4a/1x1"
bottom: "inception_4a/3x3"
bottom: "inception_4a/5x5"
bottom: "inception_4a/pool_proj"
top: "inception_4a/output"
}
layer {
name: "inception_4b/1x1"
type: "Convolution"
bottom: "inception_4a/output"
top: "inception_4b/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_1x1"
type: "ReLU"
bottom: "inception_4b/1x1"
top: "inception_4b/1x1"
}
layer {
name: "inception_4b/3x3_reduce"
type: "Convolution"
bottom: "inception_4a/output"
top: "inception_4b/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_4b/3x3_reduce"
top: "inception_4b/3x3_reduce"
}
layer {
name: "inception_4b/3x3"
type: "Convolution"
bottom: "inception_4b/3x3_reduce"
top: "inception_4b/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 224
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_3x3"
type: "ReLU"
bottom: "inception_4b/3x3"
top: "inception_4b/3x3"
}
layer {
name: "inception_4b/5x5_reduce"
type: "Convolution"
bottom: "inception_4a/output"
top: "inception_4b/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_4b/5x5_reduce"
top: "inception_4b/5x5_reduce"
}
layer {
name: "inception_4b/5x5"
type: "Convolution"
bottom: "inception_4b/5x5_reduce"
top: "inception_4b/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_5x5"
type: "ReLU"
bottom: "inception_4b/5x5"
top: "inception_4b/5x5"
}
layer {
name: "inception_4b/pool"
type: "Pooling"
bottom: "inception_4a/output"
top: "inception_4b/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_4b/pool_proj"
type: "Convolution"
bottom: "inception_4b/pool"
top: "inception_4b/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4b/relu_pool_proj"
type: "ReLU"
bottom: "inception_4b/pool_proj"
top: "inception_4b/pool_proj"
}
layer {
name: "inception_4b/output"
type: "Concat"
bottom: "inception_4b/1x1"
bottom: "inception_4b/3x3"
bottom: "inception_4b/5x5"
bottom: "inception_4b/pool_proj"
top: "inception_4b/output"
}
layer {
name: "inception_4c/1x1"
type: "Convolution"
bottom: "inception_4b/output"
top: "inception_4c/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_1x1"
type: "ReLU"
bottom: "inception_4c/1x1"
top: "inception_4c/1x1"
}
layer {
name: "inception_4c/3x3_reduce"
type: "Convolution"
bottom: "inception_4b/output"
top: "inception_4c/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_4c/3x3_reduce"
top: "inception_4c/3x3_reduce"
}
layer {
name: "inception_4c/3x3"
type: "Convolution"
bottom: "inception_4c/3x3_reduce"
top: "inception_4c/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_3x3"
type: "ReLU"
bottom: "inception_4c/3x3"
top: "inception_4c/3x3"
}
layer {
name: "inception_4c/5x5_reduce"
type: "Convolution"
bottom: "inception_4b/output"
top: "inception_4c/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_4c/5x5_reduce"
top: "inception_4c/5x5_reduce"
}
layer {
name: "inception_4c/5x5"
type: "Convolution"
bottom: "inception_4c/5x5_reduce"
top: "inception_4c/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_5x5"
type: "ReLU"
bottom: "inception_4c/5x5"
top: "inception_4c/5x5"
}
layer {
name: "inception_4c/pool"
type: "Pooling"
bottom: "inception_4b/output"
top: "inception_4c/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_4c/pool_proj"
type: "Convolution"
bottom: "inception_4c/pool"
top: "inception_4c/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4c/relu_pool_proj"
type: "ReLU"
bottom: "inception_4c/pool_proj"
top: "inception_4c/pool_proj"
}
layer {
name: "inception_4c/output"
type: "Concat"
bottom: "inception_4c/1x1"
bottom: "inception_4c/3x3"
bottom: "inception_4c/5x5"
bottom: "inception_4c/pool_proj"
top: "inception_4c/output"
}
layer {
name: "inception_4d/1x1"
type: "Convolution"
bottom: "inception_4c/output"
top: "inception_4d/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_1x1"
type: "ReLU"
bottom: "inception_4d/1x1"
top: "inception_4d/1x1"
}
layer {
name: "inception_4d/3x3_reduce"
type: "Convolution"
bottom: "inception_4c/output"
top: "inception_4d/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 144
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_4d/3x3_reduce"
top: "inception_4d/3x3_reduce"
}
layer {
name: "inception_4d/3x3"
type: "Convolution"
bottom: "inception_4d/3x3_reduce"
top: "inception_4d/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 288
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_3x3"
type: "ReLU"
bottom: "inception_4d/3x3"
top: "inception_4d/3x3"
}
layer {
name: "inception_4d/5x5_reduce"
type: "Convolution"
bottom: "inception_4c/output"
top: "inception_4d/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_4d/5x5_reduce"
top: "inception_4d/5x5_reduce"
}
layer {
name: "inception_4d/5x5"
type: "Convolution"
bottom: "inception_4d/5x5_reduce"
top: "inception_4d/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_5x5"
type: "ReLU"
bottom: "inception_4d/5x5"
top: "inception_4d/5x5"
}
layer {
name: "inception_4d/pool"
type: "Pooling"
bottom: "inception_4c/output"
top: "inception_4d/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_4d/pool_proj"
type: "Convolution"
bottom: "inception_4d/pool"
top: "inception_4d/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4d/relu_pool_proj"
type: "ReLU"
bottom: "inception_4d/pool_proj"
top: "inception_4d/pool_proj"
}
layer {
name: "inception_4d/output"
type: "Concat"
bottom: "inception_4d/1x1"
bottom: "inception_4d/3x3"
bottom: "inception_4d/5x5"
bottom: "inception_4d/pool_proj"
top: "inception_4d/output"
}
layer {
name: "inception_4e/1x1"
type: "Convolution"
bottom: "inception_4d/output"
top: "inception_4e/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_1x1"
type: "ReLU"
bottom: "inception_4e/1x1"
top: "inception_4e/1x1"
}
layer {
name: "inception_4e/3x3_reduce"
type: "Convolution"
bottom: "inception_4d/output"
top: "inception_4e/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_4e/3x3_reduce"
top: "inception_4e/3x3_reduce"
}
layer {
name: "inception_4e/3x3"
type: "Convolution"
bottom: "inception_4e/3x3_reduce"
top: "inception_4e/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_3x3"
type: "ReLU"
bottom: "inception_4e/3x3"
top: "inception_4e/3x3"
}
layer {
name: "inception_4e/5x5_reduce"
type: "Convolution"
bottom: "inception_4d/output"
top: "inception_4e/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_4e/5x5_reduce"
top: "inception_4e/5x5_reduce"
}
layer {
name: "inception_4e/5x5"
type: "Convolution"
bottom: "inception_4e/5x5_reduce"
top: "inception_4e/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_5x5"
type: "ReLU"
bottom: "inception_4e/5x5"
top: "inception_4e/5x5"
}
layer {
name: "inception_4e/pool"
type: "Pooling"
bottom: "inception_4d/output"
top: "inception_4e/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_4e/pool_proj"
type: "Convolution"
bottom: "inception_4e/pool"
top: "inception_4e/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_4e/relu_pool_proj"
type: "ReLU"
bottom: "inception_4e/pool_proj"
top: "inception_4e/pool_proj"
}
layer {
name: "inception_4e/output"
type: "Concat"
bottom: "inception_4e/1x1"
bottom: "inception_4e/3x3"
bottom: "inception_4e/5x5"
bottom: "inception_4e/pool_proj"
top: "inception_4e/output"
}
layer {
name: "inception_5a/1x1"
type: "Convolution"
bottom: "inception_4e/output"
top: "inception_5a/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_1x1"
type: "ReLU"
bottom: "inception_5a/1x1"
top: "inception_5a/1x1"
}
layer {
name: "inception_5a/3x3_reduce"
type: "Convolution"
bottom: "inception_4e/output"
top: "inception_5a/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_5a/3x3_reduce"
top: "inception_5a/3x3_reduce"
}
layer {
name: "inception_5a/3x3"
type: "Convolution"
bottom: "inception_5a/3x3_reduce"
top: "inception_5a/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_3x3"
type: "ReLU"
bottom: "inception_5a/3x3"
top: "inception_5a/3x3"
}
layer {
name: "inception_5a/5x5_reduce"
type: "Convolution"
bottom: "inception_4e/output"
top: "inception_5a/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_5a/5x5_reduce"
top: "inception_5a/5x5_reduce"
}
layer {
name: "inception_5a/5x5"
type: "Convolution"
bottom: "inception_5a/5x5_reduce"
top: "inception_5a/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_5x5"
type: "ReLU"
bottom: "inception_5a/5x5"
top: "inception_5a/5x5"
}
layer {
name: "inception_5a/pool"
type: "Pooling"
bottom: "inception_4e/output"
top: "inception_5a/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_5a/pool_proj"
type: "Convolution"
bottom: "inception_5a/pool"
top: "inception_5a/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5a/relu_pool_proj"
type: "ReLU"
bottom: "inception_5a/pool_proj"
top: "inception_5a/pool_proj"
}
layer {
name: "inception_5a/output"
type: "Concat"
bottom: "inception_5a/1x1"
bottom: "inception_5a/3x3"
bottom: "inception_5a/5x5"
bottom: "inception_5a/pool_proj"
top: "inception_5a/output"
}
layer {
name: "inception_5b/1x1"
type: "Convolution"
bottom: "inception_5a/output"
top: "inception_5b/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 384
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_1x1"
type: "ReLU"
bottom: "inception_5b/1x1"
top: "inception_5b/1x1"
}
layer {
name: "inception_5b/3x3_reduce"
type: "Convolution"
bottom: "inception_5a/output"
top: "inception_5b/3x3_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_5b/3x3_reduce"
top: "inception_5b/3x3_reduce"
}
layer {
name: "inception_5b/3x3"
type: "Convolution"
bottom: "inception_5b/3x3_reduce"
top: "inception_5b/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_3x3"
type: "ReLU"
bottom: "inception_5b/3x3"
top: "inception_5b/3x3"
}
layer {
name: "inception_5b/5x5_reduce"
type: "Convolution"
bottom: "inception_5a/output"
top: "inception_5b/5x5_reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_5b/5x5_reduce"
top: "inception_5b/5x5_reduce"
}
layer {
name: "inception_5b/5x5"
type: "Convolution"
bottom: "inception_5b/5x5_reduce"
top: "inception_5b/5x5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_5x5"
type: "ReLU"
bottom: "inception_5b/5x5"
top: "inception_5b/5x5"
}
layer {
name: "inception_5b/pool"
type: "Pooling"
bottom: "inception_5a/output"
top: "inception_5b/pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "inception_5b/pool_proj"
type: "Convolution"
bottom: "inception_5b/pool"
top: "inception_5b/pool_proj"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_5b/relu_pool_proj"
type: "ReLU"
bottom: "inception_5b/pool_proj"
top: "inception_5b/pool_proj"
}
layer {
name: "inception_5b/output"
type: "Concat"
bottom: "inception_5b/1x1"
bottom: "inception_5b/3x3"
bottom: "inception_5b/5x5"
bottom: "inception_5b/pool_proj"
top: "inception_5b/output"
}
layer {
name: "pool5/drop_s1"
type: "Dropout"
bottom: "inception_5b/output"
top: "pool5/drop_s1"
dropout_param {
dropout_ratio: 0.4
}
}
layer {
name: "cvg/classifier"
type: "Convolution"
bottom: "pool5/drop_s1"
top: "cvg/classifier"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 1
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "coverage/sig"
type: "Sigmoid"
bottom: "cvg/classifier"
top: "coverage"
}
layer {
name: "bbox/regressor"
type: "Convolution"
bottom: "pool5/drop_s1"
top: "bboxes"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 4
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "cluster"
type: "Python"
bottom: "coverage"
bottom: "bboxes"
top: "bbox-list"
"bbox-list"
python_param {
{
module: "caffe.layers.detectnet.clustering"
"caffe.layers.detectnet.clustering"
layer: "ClusterDetections"
"ClusterDetections"
param_str: "1248, 352, 16, 0.6, 3, 0.02, 22, 1"
1"
}
}
}
