1
セグネットにアップサンプル層があり、それ自身の画像が480×360であることに気付きました。画像（565 * 584）を使用したい場合、次のエラーを受け取ります。セグネットのアップサンプル層の理解方法
ここI0929 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)
は定義です：私はupsample_wとupsample_h変更すべきだと思う
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
}
}
が、私はすべてのボディ正確なvalue.Canを知っている私にscaleupsample_wupsample_hと大きさの関係を教えていませんイメージや方法それを計算する。
ネットの定義全体：
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)
エラーは、 'conv5_1_D'の' shape'が 'pool4_mask'の' shape'より** different **です：異なる 'height'を持っているからです。 – Shai