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caffe----->NCS stick
I try to check MTCNN's o_12net ,
because o_12net have two output layer : conv4_2 and prob layers, and both their output is important,
so, we need two output layer,
but it seems that SDK2.05 only support 1 output layer,
so I decide to use concat layer to combine conv4_2 (output shape = 1,4,1,1) and prob (output shape=1,2,1,1) layer
but the output'dimension behavior incorrect,
mvNCCheck 12net.prototxt -w 12net.caffemodel -s 12 -on out
its output shapesize is 4,
mvNCCheck 12net.prototxt -w 12net.caffemodel -s 12 -on out -ec
it's output shapesize is 2,
if there are something wrong with concat layer in caffe?
- Tags:
- Caffe
- Kernel_size
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or if there have any way to get two layer's output
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ok now I can make sure that concat layer has bugs in ncsdk2
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@zufeifei Yes there is some bugs with the NCSDK's concat layer at the moment. Right now it doesn't support concat being the last layer. If you use a dummy reshape layer as seen in https://ncsforum.movidius.com/discussion/comment/2831/#Comment_2831 you can bypass this.
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@zufeifei Also, the NCSDK only supports 1 output at the moment.
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@Tome_at_Intel
Oh!
It is a great help,
I would try it immediately!
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@Tome_at_Intel
In caffe , a reshape layer follows the concat layer can bypass the problem,
but in tensorflow, a reshape layer seems could not help to bypass the concat layer
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@Tome_at_Intel
Hi,
I just finish test onet.prototxt,
I find if the concat layer has three bottom,
although there is a reshape layer followed,
an "IndexError: list index out of range" would happen,
if there any tricks to do with this problem?
,here is the prototxt, hope it can help you to deal with the problem:
,,
name: "48Net"
input: "data"
input_shape{
dim: 1
dim: 3
dim: 48
dim: 48
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 32
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: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
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: "pool2"
type: "Pooling"
bottom: "prelu2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 64
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu3"
type: "PReLU"
bottom: "conv3"
top: "prelu3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "prelu3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "pool3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
convolution_param {
num_output: 128
kernel_size: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu4"
type: "PReLU"
bottom: "conv4"
top: "prelu4"
}
layer {
name: "conv5"
type: "InnerProduct"
bottom: "prelu4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 3
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prelu5"
type: "PReLU"
bottom: "conv5"
top: "prelu5"
}
layer {
name: "conv6_1"
type: "InnerProduct"
bottom: "prelu5"
top: "conv6_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2"
type: "InnerProduct"
bottom: "prelu5"
top: "conv6_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 4
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_3"
type: "InnerProduct"
bottom: "prelu5"
top: "conv6_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
#kernel_size: 1
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "prob1"
type: "Softmax"
bottom: "conv6_1"
top: "prob1"
}
layer{
name:"concat1_out"
type:"Concat"
bottom:"conv6_3"
bottom:"conv6_2"
bottom:"prob1"
top:"concat1_out"
concat_param{
axis:1
}
}
layer {
name: "output1"
type: "Reshape"
bottom: "concat1_out"
top: "output1"
reshape_param {
shape {
dim: 1
dim: 16
}
}
}
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@zufeifei Looks like you need to adjust the final reshape layer to:
layer {
name: "output1"
type: "Reshape"
bottom: "concat1_out"
top: "output1"
reshape_param {
shape {
dim: 1
dim: 1
dim: 16
}
}
}
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@Tome_at_Intel
I apoligized for troubling you again,
I wonder if there any trick to bypass the concat layer which concats (1,2,7,3) and (1,4,7,3)
name: "PNet"
input: "data"
input_shape{
dim: 1
dim: 3
dim: 23
dim: 15
}
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: "concat1"
type: "Concat"
bottom: "prob1"
bottom: "conv4_2"
top: "concat1"
}
layer {
name: "output"
type: "Reshape"
bottom: "concat1"
top: "output"
reshape_param{
shape{
dim:1
dim:6
dim:7
dim:3
}
}
}
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@zufeifei There isn't a way I can think of to bypass it if you need both the output from the conv4_2 layer and the prob1 layer. Since NCSDK does not support multiple output, using concat with a reshape is the only way I know of how to do this. Is there a specific reason why you want to bypass the concat layer?
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@Tome_at_Intel
It's one part of a face detect work, the Pnet returns ROI and the Possibility of the ROI, so I need to get both the prob1 layer(scores) and conv4_2 layer's output(ROI)
I will try to use two graphs to get Prob1 and conv4_2 layer's output seperately.
Thank you for your patient.
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@zufeifei What progress have you made with MTCNN? It will be grateful.
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