I have been working on implementing Caffe MTCNN on the ncs.
After compiling a PNet prototxt with models and running a sample application, I figured that the values are vastly incorrect from what I should be getting from a Caffe application running the same model.
Given the PNet weight definition (det1.caffemodel acquired from https://github.com/DuinoDu/mtcnn/tree/master/model ), all the values should be in the range of [-1, 1], however the output acquired via the NCS ranges from [-65344, 62912].
There is no correlation to be found between the outputs from NCS and Caffe app.
Is there a problem with the Caffe softmax layer on NCS?
Please help. Thanks.
name: "PNet"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 328
dim: 148
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 10
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU1"
type: "PReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU2"
type: "PReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "conv3"
type: "Convolution"
bottom: "conv2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "PReLU3"
type: "PReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4-1"
type: "Convolution"
bottom: "conv3"
top: "conv4-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 2
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4-2"
type: "Convolution"
bottom: "conv3"
top: "conv4-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv4-1"
top: "prob1"
}
layer {
name: "concat"
type: "Concat"
bottom: "conv4-2"
bottom: "prob1"
top: "concat"
}
layer {
name: "output"
type: "Flatten"
bottom: "concat"
top: "output"
}
链接已复制
5 回复数
@Tome_at_Intel Did you find the problem I mentioned above?
Regarding to PNet:
name: "PNet"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 328
dim: 148
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 10
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu1"
type: "PReLU"
bottom: "conv1"
top: "prelu1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "prelu1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu2"
type: "PReLU"
bottom: "conv2"
top: "prelu2"
}
layer {
name: "conv3"
type: "Convolution"
bottom: "prelu2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "prelu3"
}
layer {
name: "conv4-1"
type: "Convolution"
bottom: "prelu3"
top: "conv4-1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 2
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4-2"
type: "Convolution"
bottom: "prelu3"
top: "conv4-2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv4-1"
top: "prob1"
}
layer {
name: "concat"
type: "Concat"
bottom: "conv4-2"
bottom: "prob1"
top: "concat"
}
layer {
name: "output"
type: "Flatten"
bottom: "concat"
top: "output"
}
I have modified several places which are related to PRelu. These two prototxt can get same results in Caffe while different results in Movidius
