Open yanzi6969 opened 6 years ago
JingliangGao
commented
6 years ago Luckly，My workmate and I found the reason. The .weights you have download is not right, by the way, which means the .weights you have download can not match the .prototxt . You can download the old yolov2's weight here——https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU
Serge3006
commented
6 years ago Hi, I have the same issue when I try to convert the weights using the prototxt provided in indoor/convert/ and the weights provided in the yolo v2 webpage https://pjreddie.com/darknet/yolov2/. When I run the script the following error shows up:
Traceback (most recent call last):
File "convert_weights_to_caffemodel.py", line 68, in
Can someone when help me!
@JingliangGao I've tried with your weights but still have the same error.
djiayong5
commented
6 years ago The yolo.protext is a wrong, please use the following text and instead of yolo.protext:
name: "YOLONET"
layer {
name: "data"
type: "Input"
top: "data"
top: "label"
input_param { shape: { dim: 1 dim: 3 dim: 416 dim: 416 }
shape: { dim: 1 dim: 1 dim: 30 dim: 5 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn1"
type: "BatchNorm"
bottom: "conv1"
top: "bn1"
}
layer {
name: "scale1"
type: "Scale"
bottom: "bn1"
top: "scale1"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "scale1"
top: "scale1"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "scale1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn2"
type: "BatchNorm"
bottom: "conv2"
top: "bn2"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale2"
type: "Scale"
bottom: "bn2"
top: "scale2"
scale_param {
bias_term: true
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "scale2"
top: "scale2"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "scale2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn3"
type: "BatchNorm"
bottom: "conv3"
top: "bn3"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale3"
type: "Scale"
bottom: "bn3"
top: "scale3"
scale_param {
bias_term: true
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "scale3"
top: "scale3"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv4"
type: "Convolution"
bottom: "scale3"
top: "conv4"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0 #??
stride: 1
bias_term: false
}
}
layer {
name: "bn4"
type: "BatchNorm"
bottom: "conv4"
top: "bn4"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale4"
type: "Scale"
bottom: "bn4"
top: "scale4"
scale_param {
bias_term: true
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "scale4"
top: "scale4"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv5"
type: "Convolution"
bottom: "scale4"
top: "conv5"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn5"
type: "BatchNorm"
bottom: "conv5"
top: "bn5"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale5"
type: "Scale"
bottom: "bn5"
top: "scale5"
scale_param {
bias_term: true
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "scale5"
top: "scale5"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool5"
type: "Pooling"
bottom: "scale5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv6"
type: "Convolution"
bottom: "pool5"
top: "conv6"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn6"
type: "BatchNorm"
bottom: "conv6"
top: "bn6"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale6"
type: "Scale"
bottom: "bn6"
top: "scale6"
scale_param {
bias_term: true
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "scale6"
top: "scale6"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv7"
type: "Convolution"
bottom: "scale6"
top: "conv7"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn7"
type: "BatchNorm"
bottom: "conv7"
top: "bn7"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale7"
type: "Scale"
bottom: "bn7"
top: "scale7"
scale_param {
bias_term: true
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "scale7"
top: "scale7"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv8"
type: "Convolution"
bottom: "scale7"
top: "conv8"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn8"
type: "BatchNorm"
bottom: "conv8"
top: "bn8"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale8"
type: "Scale"
bottom: "bn8"
top: "scale8"
scale_param {
bias_term: true
}
}
layer {
name: "relu8"
type: "ReLU"
bottom: "scale8"
top: "scale8"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool8"
type: "Pooling"
bottom: "scale8"
top: "pool8"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv9"
type: "Convolution"
bottom: "pool8"
top: "conv9"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn9"
type: "BatchNorm"
bottom: "conv9"
top: "bn9"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale9"
type: "Scale"
bottom: "bn9"
top: "scale9"
scale_param {
bias_term: true
}
}
layer {
name: "relu9"
type: "ReLU"
bottom: "scale9"
top: "scale9"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv10"
type: "Convolution"
bottom: "scale9"
top: "conv10"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn10"
type: "BatchNorm"
bottom: "conv10"
top: "bn10"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale10"
type: "Scale"
bottom: "bn10"
top: "scale10"
scale_param {
bias_term: true
}
}
layer {
name: "relu10"
type: "ReLU"
bottom: "scale10"
top: "scale10"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv11"
type: "Convolution"
bottom: "scale10"
top: "conv11"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn11"
type: "BatchNorm"
bottom: "conv11"
top: "bn11"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale11"
type: "Scale"
bottom: "bn11"
top: "scale11"
scale_param {
bias_term: true
}
}
layer {
name: "relu11"
type: "ReLU"
bottom: "scale11"
top: "scale11"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv12"
type: "Convolution"
bottom: "scale11"
top: "conv12"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn12"
type: "BatchNorm"
bottom: "conv12"
top: "bn12"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale12"
type: "Scale"
bottom: "bn12"
top: "scale12"
scale_param {
bias_term: true
}
}
layer {
name: "relu12"
type: "ReLU"
bottom: "scale12"
top: "scale12"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv13"
type: "Convolution"
bottom: "scale12"
top: "conv13"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn13"
type: "BatchNorm"
bottom: "conv13"
top: "bn13"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale13"
type: "Scale"
bottom: "bn13"
top: "scale13"
scale_param {
bias_term: true
}
}
layer {
name: "relu13"
type: "ReLU"
bottom: "scale13"
top: "scale13"
relu_param{
negative_slope: 0.1
}
}
layer {
name: "pool13"
type: "Pooling"
bottom: "scale13"
top: "pool13"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer{
name: "conv14"
type: "Convolution"
bottom: "pool13"
top: "conv14"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn14"
type: "BatchNorm"
bottom: "conv14"
top: "bn14"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale14"
type: "Scale"
bottom: "bn14"
top: "scale14"
scale_param {
bias_term: true
}
}
layer {
name: "relu14"
type: "ReLU"
bottom: "scale14"
top: "scale14"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv15"
type: "Convolution"
bottom: "scale14"
top: "conv15"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn15"
type: "BatchNorm"
bottom: "conv15"
top: "bn15"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale15"
type: "Scale"
bottom: "bn15"
top: "scale15"
scale_param {
bias_term: true
}
}
layer {
name: "relu15"
type: "ReLU"
bottom: "scale15"
top: "scale15"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv16"
type: "Convolution"
bottom: "scale15"
top: "conv16"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn16"
type: "BatchNorm"
bottom: "conv16"
top: "bn16"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale16"
type: "Scale"
bottom: "bn16"
top: "scale16"
scale_param {
bias_term: true
}
}
layer {
name: "relu16"
type: "ReLU"
bottom: "scale16"
top: "scale16"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv17"
type: "Convolution"
bottom: "scale16"
top: "conv17"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
name: "bn17"
type: "BatchNorm"
bottom: "conv17"
top: "bn17"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale17"
type: "Scale"
bottom: "bn17"
top: "scale17"
scale_param {
bias_term: true
}
}
layer {
name: "relu17"
type: "ReLU"
bottom: "scale17"
top: "scale17"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv18"
type: "Convolution"
bottom: "scale17"
top: "conv18"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn18"
type: "BatchNorm"
bottom: "conv18"
top: "bn18"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale18"
type: "Scale"
bottom: "bn18"
top: "scale18"
scale_param {
bias_term: true
}
}
layer {
name: "relu18"
type: "ReLU"
bottom: "scale18"
top: "scale18"
relu_param{
negative_slope: 0.1
}
}
layer{
name: "conv19"
type: "Convolution"
bottom: "scale18"
top: "conv19"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn19"
type: "BatchNorm"
bottom: "conv19"
top: "bn19"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale19"
type: "Scale"
bottom: "bn19"
top: "scale19"
scale_param {
bias_term: true
}
}
layer {
name: "relu19"
type: "ReLU"
bottom: "scale19"
top: "scale19"
relu_param{
negative_slope: 0.1
}
}
layer{ name: "conv20" type: "Convolution" bottom: "scale19" top: "conv20" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false }
}
layer {
name: "bn20"
type: "BatchNorm"
bottom: "conv20"
top: "bn20"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale20"
type: "Scale"
bottom: "bn20"
top: "scale20"
scale_param {
bias_term: true
}
}
layer {
name: "relu20"
type: "ReLU"
bottom: "scale20"
top: "scale20"
relu_param {
negative_slope: 0.1
}
}
layer { name: "concat1" type: "Concat" bottom: "scale13" top: "concat1" }
layer{ name: "conv21" type: "Convolution" bottom: "concat1" top: "conv21" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false }
}
layer {
name: "bn21"
type: "BatchNorm"
bottom: "conv21"
top: "bn21"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale21"
type: "Scale"
bottom: "bn21"
top: "scale21"
scale_param {
bias_term: true
}
}
layer {
name: "relu21"
type: "ReLU"
bottom: "scale21"
top: "scale21"
relu_param {
negative_slope: 0.1
}
}
layer { name: "reorg1" type: "Reorg" bottom: "scale21" top: "reorg1" reorg_param { stride: 2 } }
layer { name: "concat2" type: "Concat" bottom: "reorg1" bottom: "scale20" top: "concat2" }
layer{
name: "conv22"
type: "Convolution"
bottom: "concat2"
top: "conv22"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
name: "bn22"
type: "BatchNorm"
bottom: "conv22"
top: "bn22"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "scale22"
type: "Scale"
bottom: "bn22"
top: "scale22"
scale_param {
bias_term: true
}
}
layer {
name: "relu22"
type: "ReLU"
bottom: "scale22"
top: "scale22"
relu_param{
negative_slope: 0.1
}
}
layer { name: "conv23" type: "Convolution" bottom: "scale22" top: "conv23" convolution_param { num_output: 425 kernel_size: 1 pad: 0 stride: 1 } } layer { name: "region1" type: "RegionLoss" bottom: "conv23" bottom: "label" top: "region1" region_loss_param { side: 13 num_class: 80 coords: 4 num: 5 } }
SimonPrPr
commented
6 years ago @djiayong5 Could you please tell me which weights is attached to the prototxt you gave? Is the weights from https://pjreddie.com/darknet/yolov2/ ?
Serge3006
commented
6 years ago The prototxt file I have use is the following:
PD: I have used the YOLO v2 608x608 coco weights provided in the preddie page. https://pjreddie.com/darknet/yolov2/
name: "YOLONET" input: "data" input_shape { dim: 1 dim: 3 dim: 608 dim: 608 }
layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 32 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn1" type: "BatchNorm" bottom: "conv1" top: "bn1" } layer { name: "scale1" type: "Scale" bottom: "bn1" top: "scale1" scale_param { bias_term: true } } layer { name: "relu1" type: "ReLU" bottom: "scale1" top: "scale1" relu_param{ negative_slope: 0.1 } } layer { name: "pool1" type: "Pooling" bottom: "scale1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer{ name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn2" type: "BatchNorm" bottom: "conv2" top: "bn2" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale2" type: "Scale" bottom: "bn2" top: "scale2" scale_param { bias_term: true } } layer { name: "relu2" type: "ReLU" bottom: "scale2" top: "scale2" relu_param{ negative_slope: 0.1 } } layer { name: "pool2" type: "Pooling" bottom: "scale2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layer{ name: "conv3" type: "Convolution" bottom: "pool2" top: "conv3" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn3" type: "BatchNorm" bottom: "conv3" top: "bn3" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale3" type: "Scale" bottom: "bn3" top: "scale3" scale_param { bias_term: true } } layer { name: "relu3" type: "ReLU" bottom: "scale3" top: "scale3" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv4" type: "Convolution" bottom: "scale3" top: "conv4" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn4" type: "BatchNorm" bottom: "conv4" top: "bn4" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale4" type: "Scale" bottom: "bn4" top: "scale4" scale_param { bias_term: true } } layer { name: "relu4" type: "ReLU" bottom: "scale4" top: "scale4" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv5" type: "Convolution" bottom: "scale4" top: "conv5" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn5" type: "BatchNorm" bottom: "conv5" top: "bn5" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale5" type: "Scale" bottom: "bn5" top: "scale5" scale_param { bias_term: true } } layer { name: "relu5" type: "ReLU" bottom: "scale5" top: "scale5" relu_param{ negative_slope: 0.1 } } layer { name: "pool5" type: "Pooling" bottom: "scale5" top: "pool5" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layer{ name: "conv6" type: "Convolution" bottom: "pool5" top: "conv6" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn6" type: "BatchNorm" bottom: "conv6" top: "bn6" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale6" type: "Scale" bottom: "bn6" top: "scale6" scale_param { bias_term: true } } layer { name: "relu6" type: "ReLU" bottom: "scale6" top: "scale6" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv7" type: "Convolution" bottom: "scale6" top: "conv7" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn7" type: "BatchNorm" bottom: "conv7" top: "bn7" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale7" type: "Scale" bottom: "bn7" top: "scale7" scale_param { bias_term: true } } layer { name: "relu7" type: "ReLU" bottom: "scale7" top: "scale7" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv8" type: "Convolution" bottom: "scale7" top: "conv8" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn8" type: "BatchNorm" bottom: "conv8" top: "bn8" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale8" type: "Scale" bottom: "bn8" top: "scale8" scale_param { bias_term: true } } layer { name: "relu8" type: "ReLU" bottom: "scale8" top: "scale8" relu_param{ negative_slope: 0.1 } } layer { name: "pool8" type: "Pooling" bottom: "scale8" top: "pool8" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layer{ name: "conv9" type: "Convolution" bottom: "pool8" top: "conv9" convolution_param { num_output: 512 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn9" type: "BatchNorm" bottom: "conv9" top: "bn9" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale9" type: "Scale" bottom: "bn9" top: "scale9" scale_param { bias_term: true } } layer { name: "relu9" type: "ReLU" bottom: "scale9" top: "scale9" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv10" type: "Convolution" bottom: "scale9" top: "conv10" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn10" type: "BatchNorm" bottom: "conv10" top: "bn10" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale10" type: "Scale" bottom: "bn10" top: "scale10" scale_param { bias_term: true } } layer { name: "relu10" type: "ReLU" bottom: "scale10" top: "scale10" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv11" type: "Convolution" bottom: "scale10" top: "conv11" convolution_param { num_output: 512 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn11" type: "BatchNorm" bottom: "conv11" top: "bn11" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale11" type: "Scale" bottom: "bn11" top: "scale11" scale_param { bias_term: true } } layer { name: "relu11" type: "ReLU" bottom: "scale11" top: "scale11" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv12" type: "Convolution" bottom: "scale11" top: "conv12" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn12" type: "BatchNorm" bottom: "conv12" top: "bn12" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale12" type: "Scale" bottom: "bn12" top: "scale12" scale_param { bias_term: true } } layer { name: "relu12" type: "ReLU" bottom: "scale12" top: "scale12" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv13" type: "Convolution" bottom: "scale12" top: "conv13" convolution_param { num_output: 512 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn13" type: "BatchNorm" bottom: "conv13" top: "bn13" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale13" type: "Scale" bottom: "bn13" top: "scale13" scale_param { bias_term: true } } layer { name: "relu13" type: "ReLU" bottom: "scale13" top: "scale13" relu_param{ negative_slope: 0.1 } } layer { name: "pool13" type: "Pooling" bottom: "scale13" top: "pool13" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }
layer{ name: "conv14" type: "Convolution" bottom: "pool13" top: "conv14" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn14" type: "BatchNorm" bottom: "conv14" top: "bn14" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale14" type: "Scale" bottom: "bn14" top: "scale14" scale_param { bias_term: true } } layer { name: "relu14" type: "ReLU" bottom: "scale14" top: "scale14" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv15" type: "Convolution" bottom: "scale14" top: "conv15" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn15" type: "BatchNorm" bottom: "conv15" top: "bn15" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale15" type: "Scale" bottom: "bn15" top: "scale15" scale_param { bias_term: true } } layer { name: "relu15" type: "ReLU" bottom: "scale15" top: "scale15" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv16" type: "Convolution" bottom: "scale15" top: "conv16" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn16" type: "BatchNorm" bottom: "conv16" top: "bn16" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale16" type: "Scale" bottom: "bn16" top: "scale16" scale_param { bias_term: true } } layer { name: "relu16" type: "ReLU" bottom: "scale16" top: "scale16" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv17" type: "Convolution" bottom: "scale16" top: "conv17" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } } layer { name: "bn17" type: "BatchNorm" bottom: "conv17" top: "bn17" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale17" type: "Scale" bottom: "bn17" top: "scale17" scale_param { bias_term: true } } layer { name: "relu17" type: "ReLU" bottom: "scale17" top: "scale17" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv18" type: "Convolution" bottom: "scale17" top: "conv18" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn18" type: "BatchNorm" bottom: "conv18" top: "bn18" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale18" type: "Scale" bottom: "bn18" top: "scale18" scale_param { bias_term: true } } layer { name: "relu18" type: "ReLU" bottom: "scale18" top: "scale18" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv19" type: "Convolution" bottom: "scale18" top: "conv19" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn19" type: "BatchNorm" bottom: "conv19" top: "bn19" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale19" type: "Scale" bottom: "bn19" top: "scale19" scale_param { bias_term: true } } layer { name: "relu19" type: "ReLU" bottom: "scale19" top: "scale19" relu_param{ negative_slope: 0.1 } }
layer{ name: "conv20" type: "Convolution" bottom: "scale19" top: "conv20" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false }
} layer { name: "bn20" type: "BatchNorm" bottom: "conv20" top: "bn20" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale20" type: "Scale" bottom: "bn20" top: "scale20" scale_param { bias_term: true } } layer { name: "relu20" type: "ReLU" bottom: "scale20" top: "scale20" relu_param { negative_slope: 0.1 } }
layer { name: "concat1" type: "Concat" bottom: "scale13" top: "concat1" }
layer { name: "conv21" type: "Convolution" bottom: "concat1" top: "conv21" convolution_param { num_output: 64 kernel_size: 1 stride: 1 pad: 0 bias_term: false } } layer { name: "bn21" type: "BatchNorm" bottom: "conv21" top: "bn21" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } }
layer { name: "scale21" type: "Scale" bottom: "bn21" top: "scale21" scale_param { bias_term: true } } layer { name: "relu21" type: "ReLU" bottom: "scale21" top: "scale21" relu_param{ negative_slope: 0.1 } }
layer { name: "reorg1" type: "Reorg" bottom: "scale21" top: "reorg1" reorg_param { stride: 2 } }
layer { name: "concat2" type: "Concat" bottom: "reorg1" bottom: "scale20" top: "concat2" }
layer{ name: "conv22" type: "Convolution" bottom: "concat2" top: "conv22" convolution_param { num_output: 1024 kernel_size: 3 pad: 1 stride: 1 bias_term: false } } layer { name: "bn22" type: "BatchNorm" bottom: "conv22" top: "bn22" param { lr_mult: 0 } param { lr_mult: 0 } param { lr_mult: 0 } } layer { name: "scale22" type: "Scale" bottom: "bn22" top: "scale22" scale_param { bias_term: true } } layer { name: "relu22" type: "ReLU" bottom: "scale22" top: "scale22" relu_param{ negative_slope: 0.1 } }
layer { name: "conv23" type: "Convolution" bottom: "scale22" top: "conv23" convolution_param { num_output: 425 kernel_size: 1 stride: 1 pad: 0 } }
mozpp
commented
6 years ago yolo官网的cfg和这里的prototxt不同。 从yolo-voc.cfg文件可以看到，第25层为route层，逆向9层拿到第16层26 26 512的输出，并由第26层的reorg层把26 26 512 变形为13 13 2048，再有第27层的route层连接24层和26层的输出，堆叠为13 13 3072，由最后一个卷积核为3 * 3的卷积层进行跨通道的信息融合并把通道降维为1024。
Thanks for your share.I meet a problem when I try to convert the weights to caffemodel. python convert_weights_to_caffemodel.py yolo.prototxt yolo.weights yolo.caffemodel the yolo.prototxt is provided by you,and the yolo.caffemodel(yolov2) is downloaded from https://github.com/AlexeyAB/darknet ,when I run the program,I got the error: conv20(conv) bn20(batchnorm) scale20(scale) conv21(conv) Traceback (most recent call last): File "convert_weights_to_caffemodel.py", line 70, in
net.params[pr][0].data[...] = np.reshape(netWeights[count:count + weightSize], dims)
File "/home/zcy/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 232, in reshape
return _wrapfunc(a, 'reshape', newshape, order=order)
File "/home/zcy/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 57, in _wrapfunc
return getattr(obj, method)(*args, **kwds)
ValueError: cannot reshape array of size 12265129 into shape (1024,3072,3,3)
I dont know why this happened?