1
SegNet에 업 샘플 레이어가 있고, 이미지가 480 * 360이며, 이미지 (565 * 584)를 사용할 때 다음 오류가 발생합니다.SegNet의 업 샘플 레이어를 이해하는 방법
여기I0929 03:58:06.238135 22750 net.cpp:368] upsample4 -> pool4_D
I0929 03:58:06.238142 22750 net.cpp:120] Setting up upsample4
F0929 03:58:06.238164 22750 upsample_layer.cpp:63] Check failed: bottom[0]->height() == bottom[1]->height() (38 vs. 37)
은 정의입니다 : 내가 upsample_w 및 upsample_h 변경해야한다고 생각
layer {
name: "upsample4"
type: "Upsample"
bottom: "conv5_1_D"
top: "pool4_D"
bottom: "pool4_mask"
upsample_param {
scale: 2
upsample_w: 60
upsample_h: 45
}
}
,하지만 난 어떤 몸 정확한 value.Can을 알고 나에게 scaleupsample_wupsample_h과 크기 사이의 관계를 말하지 않는다 이미지 또는 방법 그것을 계산하십시오.
네트의 전체 정의는 : 당신은 upsample_w 및 upsample_h을 변경해야합니다
name: "VGG_ILSVRC_16_layer"
layer {
name: "data"
type: "DenseImageData"
top: "data"
top: "label"
dense_image_data_param {
source: "/home/zhaimo/SegNet/CamVid/mytrain.txt" # Change this to the absolute path to your data file
batch_size: 4 # Change this number to a batch size that will fit on your GPU
shuffle: true
}
}
layer {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 64
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv1_1"
top: "conv1_1"
name: "conv1_1_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: "ReLU"
}
layer {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 64
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv1_2"
top: "conv1_2"
name: "conv1_2_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: "ReLU"
}
layer {
bottom: "conv1_2"
top: "pool1"
top: "pool1_mask"
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 128
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv2_1"
top: "conv2_1"
name: "conv2_1_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: "ReLU"
}
layer {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 128
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv2_2"
top: "conv2_2"
name: "conv2_2_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: "ReLU"
}
layer {
bottom: "conv2_2"
top: "pool2"
top: "pool2_mask"
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv3_1"
top: "conv3_1"
name: "conv3_1_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: "ReLU"
}
layer {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv3_2"
top: "conv3_2"
name: "conv3_2_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: "ReLU"
}
layer {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv3_3"
top: "conv3_3"
name: "conv3_3_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: "ReLU"
}
layer {
bottom: "conv3_3"
top: "pool3"
top: "pool3_mask"
name: "pool3"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4_1"
top: "conv4_1"
name: "conv4_1_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: "ReLU"
}
layer {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4_2"
top: "conv4_2"
name: "conv4_2_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: "ReLU"
}
layer {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4_3"
top: "conv4_3"
name: "conv4_3_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: "ReLU"
}
layer {
bottom: "conv4_3"
top: "pool4"
top: "pool4_mask"
name: "pool4"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv5_1"
top: "conv5_1"
name: "conv5_1_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: "ReLU"
}
layer {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv5_2"
top: "conv5_2"
name: "conv5_2_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: "ReLU"
}
layer {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: "Convolution"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv5_3"
top: "conv5_3"
name: "conv5_3_bn"
type: "BN"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: "ReLU"
}
layer {
bottom: "conv5_3"
top: "pool5"
top: "pool5_mask"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "upsample5"
type: "Upsample"
bottom: "pool5"
top: "pool5_D"
bottom: "pool5_mask"
upsample_param {
scale: 2
upsample_w: 30
upsample_h: 23
}
}
....(The rest is omitted)
'conv5_1_D'의 '모양'이 'pool4_mask'의 '모양'과 다른 **이므로 '높이'가 다릅니다. – Shai