caffe resnet18 -> Rt model with Segmentation fault (core dumped)

hello, I met segmentation fault when I trans. to rt model with caffe model. caffe model is resnet18.can you give me some advise.

model link: https://pan.baidu.com/s/1R-lmV_46AsEmHATYSKP78Q

prototxt:

name: “resnet18”
input: “data”
input_dim: 1
input_dim: 3
input_dim: 256
input_dim: 128

layer {
name: “conv1”
type: “Convolution”
bottom: “data”
top: “conv_blob1”
convolution_param {
num_output: 64
bias_term: false
pad: 3
kernel_size: 7
group: 1
stride: 2
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm1”
type: “BatchNorm”
bottom: “conv_blob1”
top: “batch_norm_blob1”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale1”
type: “Scale”
bottom: “batch_norm_blob1”
top: “batch_norm_blob1”
scale_param {
bias_term: true
}
}
layer {
name: “max_pool1”
type: “Pooling”
bottom: “batch_norm_blob1”
top: “max_pool_blob1”
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 1
}
}
layer {
name: “conv2”
type: “Convolution”
bottom: “max_pool_blob1”
top: “conv_blob2”
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm2”
type: “BatchNorm”
bottom: “conv_blob2”
top: “batch_norm_blob2”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale2”
type: “Scale”
bottom: “batch_norm_blob2”
top: “batch_norm_blob2”
scale_param {
bias_term: true
}
}
layer {
name: “relu1”
type: “ReLU”
bottom: “batch_norm_blob2”
top: “relu_blob1”
}
layer {
name: “conv3”
type: “Convolution”
bottom: “relu_blob1”
top: “conv_blob3”
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm3”
type: “BatchNorm”
bottom: “conv_blob3”
top: “batch_norm_blob3”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale3”
type: “Scale”
bottom: “batch_norm_blob3”
top: “batch_norm_blob3”
scale_param {
bias_term: true
}
}
layer {
name: “add1”
type: “Eltwise”
bottom: “batch_norm_blob3”
bottom: “max_pool_blob1”
top: “add_blob1”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu2”
type: “ReLU”
bottom: “add_blob1”
top: “relu_blob2”
}
layer {
name: “conv4”
type: “Convolution”
bottom: “relu_blob2”
top: “conv_blob4”
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm4”
type: “BatchNorm”
bottom: “conv_blob4”
top: “batch_norm_blob4”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale4”
type: “Scale”
bottom: “batch_norm_blob4”
top: “batch_norm_blob4”
scale_param {
bias_term: true
}
}
layer {
name: “relu3”
type: “ReLU”
bottom: “batch_norm_blob4”
top: “relu_blob3”
}
layer {
name: “conv5”
type: “Convolution”
bottom: “relu_blob3”
top: “conv_blob5”
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm5”
type: “BatchNorm”
bottom: “conv_blob5”
top: “batch_norm_blob5”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale5”
type: “Scale”
bottom: “batch_norm_blob5”
top: “batch_norm_blob5”
scale_param {
bias_term: true
}
}
layer {
name: “add2”
type: “Eltwise”
bottom: “batch_norm_blob5”
bottom: “relu_blob2”
top: “add_blob2”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu4”
type: “ReLU”
bottom: “add_blob2”
top: “relu_blob4”
}
layer {
name: “conv6”
type: “Convolution”
bottom: “relu_blob4”
top: “conv_blob6”
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 2
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm6”
type: “BatchNorm”
bottom: “conv_blob6”
top: “batch_norm_blob6”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale6”
type: “Scale”
bottom: “batch_norm_blob6”
top: “batch_norm_blob6”
scale_param {
bias_term: true
}
}
layer {
name: “relu5”
type: “ReLU”
bottom: “batch_norm_blob6”
top: “relu_blob5”
}
layer {
name: “conv7”
type: “Convolution”
bottom: “relu_blob5”
top: “conv_blob7”
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm7”
type: “BatchNorm”
bottom: “conv_blob7”
top: “batch_norm_blob7”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale7”
type: “Scale”
bottom: “batch_norm_blob7”
top: “batch_norm_blob7”
scale_param {
bias_term: true
}
}
layer {
name: “conv8”
type: “Convolution”
bottom: “relu_blob4”
top: “conv_blob8”
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
group: 1
stride: 2
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm8”
type: “BatchNorm”
bottom: “conv_blob8”
top: “batch_norm_blob8”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale8”
type: “Scale”
bottom: “batch_norm_blob8”
top: “batch_norm_blob8”
scale_param {
bias_term: true
}
}
layer {
name: “add3”
type: “Eltwise”
bottom: “batch_norm_blob7”
bottom: “batch_norm_blob8”
top: “add_blob3”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu6”
type: “ReLU”
bottom: “add_blob3”
top: “relu_blob6”
}
layer {
name: “conv9”
type: “Convolution”
bottom: “relu_blob6”
top: “conv_blob9”
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm9”
type: “BatchNorm”
bottom: “conv_blob9”
top: “batch_norm_blob9”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale9”
type: “Scale”
bottom: “batch_norm_blob9”
top: “batch_norm_blob9”
scale_param {
bias_term: true
}
}
layer {
name: “relu7”
type: “ReLU”
bottom: “batch_norm_blob9”
top: “relu_blob7”
}
layer {
name: “conv10”
type: “Convolution”
bottom: “relu_blob7”
top: “conv_blob10”
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm10”
type: “BatchNorm”
bottom: “conv_blob10”
top: “batch_norm_blob10”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale10”
type: “Scale”
bottom: “batch_norm_blob10”
top: “batch_norm_blob10”
scale_param {
bias_term: true
}
}
layer {
name: “add4”
type: “Eltwise”
bottom: “batch_norm_blob10”
bottom: “relu_blob6”
top: “add_blob4”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu8”
type: “ReLU”
bottom: “add_blob4”
top: “relu_blob8”
}
layer {
name: “conv11”
type: “Convolution”
bottom: “relu_blob8”
top: “conv_blob11”
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 2
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm11”
type: “BatchNorm”
bottom: “conv_blob11”
top: “batch_norm_blob11”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale11”
type: “Scale”
bottom: “batch_norm_blob11”
top: “batch_norm_blob11”
scale_param {
bias_term: true
}
}
layer {
name: “relu9”
type: “ReLU”
bottom: “batch_norm_blob11”
top: “relu_blob9”
}
layer {
name: “conv12”
type: “Convolution”
bottom: “relu_blob9”
top: “conv_blob12”
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm12”
type: “BatchNorm”
bottom: “conv_blob12”
top: “batch_norm_blob12”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale12”
type: “Scale”
bottom: “batch_norm_blob12”
top: “batch_norm_blob12”
scale_param {
bias_term: true
}
}
layer {
name: “conv13”
type: “Convolution”
bottom: “relu_blob8”
top: “conv_blob13”
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
group: 1
stride: 2
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm13”
type: “BatchNorm”
bottom: “conv_blob13”
top: “batch_norm_blob13”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale13”
type: “Scale”
bottom: “batch_norm_blob13”
top: “batch_norm_blob13”
scale_param {
bias_term: true
}
}
layer {
name: “add5”
type: “Eltwise”
bottom: “batch_norm_blob12”
bottom: “batch_norm_blob13”
top: “add_blob5”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu10”
type: “ReLU”
bottom: “add_blob5”
top: “relu_blob10”
}
layer {
name: “conv14”
type: “Convolution”
bottom: “relu_blob10”
top: “conv_blob14”
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm14”
type: “BatchNorm”
bottom: “conv_blob14”
top: “batch_norm_blob14”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale14”
type: “Scale”
bottom: “batch_norm_blob14”
top: “batch_norm_blob14”
scale_param {
bias_term: true
}
}
layer {
name: “relu11”
type: “ReLU”
bottom: “batch_norm_blob14”
top: “relu_blob11”
}
layer {
name: “conv15”
type: “Convolution”
bottom: “relu_blob11”
top: “conv_blob15”
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm15”
type: “BatchNorm”
bottom: “conv_blob15”
top: “batch_norm_blob15”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale15”
type: “Scale”
bottom: “batch_norm_blob15”
top: “batch_norm_blob15”
scale_param {
bias_term: true
}
}
layer {
name: “add6”
type: “Eltwise”
bottom: “batch_norm_blob15”
bottom: “relu_blob10”
top: “add_blob6”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu12”
type: “ReLU”
bottom: “add_blob6”
top: “relu_blob12”
}
layer {
name: “conv16”
type: “Convolution”
bottom: “relu_blob12”
top: “conv_blob16”
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm16”
type: “BatchNorm”
bottom: “conv_blob16”
top: “batch_norm_blob16”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale16”
type: “Scale”
bottom: “batch_norm_blob16”
top: “batch_norm_blob16”
scale_param {
bias_term: true
}
}
layer {
name: “relu13”
type: “ReLU”
bottom: “batch_norm_blob16”
top: “relu_blob13”
}
layer {
name: “conv17”
type: “Convolution”
bottom: “relu_blob13”
top: “conv_blob17”
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm17”
type: “BatchNorm”
bottom: “conv_blob17”
top: “batch_norm_blob17”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale17”
type: “Scale”
bottom: “batch_norm_blob17”
top: “batch_norm_blob17”
scale_param {
bias_term: true
}
}
layer {
name: “conv18”
type: “Convolution”
bottom: “relu_blob12”
top: “conv_blob18”
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm18”
type: “BatchNorm”
bottom: “conv_blob18”
top: “batch_norm_blob18”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale18”
type: “Scale”
bottom: “batch_norm_blob18”
top: “batch_norm_blob18”
scale_param {
bias_term: true
}
}
layer {
name: “add7”
type: “Eltwise”
bottom: “batch_norm_blob17”
bottom: “batch_norm_blob18”
top: “add_blob7”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu14”
type: “ReLU”
bottom: “add_blob7”
top: “relu_blob14”
}
layer {
name: “conv19”
type: “Convolution”
bottom: “relu_blob14”
top: “conv_blob19”
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm19”
type: “BatchNorm”
bottom: “conv_blob19”
top: “batch_norm_blob19”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale19”
type: “Scale”
bottom: “batch_norm_blob19”
top: “batch_norm_blob19”
scale_param {
bias_term: true
}
}
layer {
name: “relu15”
type: “ReLU”
bottom: “batch_norm_blob19”
top: “relu_blob15”
}
layer {
name: “conv20”
type: “Convolution”
bottom: “relu_blob15”
top: “conv_blob20”
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: “xavier”
}
dilation: 1
}
}
layer {
name: “batch_norm20”
type: “BatchNorm”
bottom: “conv_blob20”
top: “batch_norm_blob20”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale20”
type: “Scale”
bottom: “batch_norm_blob20”
top: “batch_norm_blob20”
scale_param {
bias_term: true
}
}
layer {
name: “add8”
type: “Eltwise”
bottom: “batch_norm_blob20”
bottom: “relu_blob14”
top: “add_blob8”
eltwise_param {
operation: SUM
}
}
layer {
name: “relu16”
type: “ReLU”
bottom: “add_blob8”
top: “relu_blob16”
}
layer {
name: “ave_pool1”
type: “Pooling”
bottom: “relu_blob16”
top: “ave_pool_blob1”
pooling_param {
pool: AVE
global_pooling: true
#kernel_size: 17
#stride: 17
}
}
layer {
name: “view1”
type: “Reshape”
bottom: “ave_pool_blob1”
top: “view_blob1”
reshape_param {
shape {
dim: 0
dim: -1
dim: 1
dim: 1
}
}
}
layer {
name: “batch_norm21”
type: “BatchNorm”
bottom: “view_blob1”
top: “batch_norm_blob21”
batch_norm_param {
use_global_stats: true
eps: 9.999999747378752e-06
}
}
layer {
name: “bn_scale21”
type: “Scale”
bottom: “batch_norm_blob21”
top: “batch_norm_blob21”
scale_param {
bias_term: true
}
}