ResNet-18 Caffemodel and prototxt
20763 단어 caffecaffemodelgithubCNNResNet-18
Prototxt:
name: "ResNet-18"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
data_param {
source: "../ilsvrc2012/ilsvrc2012_shrt_256_train"
batch_size: 100
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
data_param {
source: "../ilsvrc2012/ilsvrc2012_shrt_256_val"
batch_size: 50
backend: LMDB
}
}
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "bn_conv1"
type: "BatchNorm"
}
layer {
bottom: "conv1"
top: "conv1"
name: "scale_conv1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_relu"
type: "ReLU"
}
layer {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
kernel_size: 3
stride: 2
pool: MAX
}
}
layer {
bottom: "pool1"
top: "res2a_branch1"
name: "res2a_branch1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "bn2a_branch1"
type: "BatchNorm"
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "scale2a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "pool1"
top: "res2a_branch2a"
name: "res2a_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "bn2a_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "scale2a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "res2a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2b"
name: "res2a_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "bn2a_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "scale2a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2a_branch1"
bottom: "res2a_branch2b"
top: "res2a"
name: "res2a"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res2a"
top: "res2a"
name: "res2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a"
top: "res2b_branch2a"
name: "res2b_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "bn2b_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "scale2b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "res2b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2b"
name: "res2b_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "bn2b_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "scale2b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2a"
bottom: "res2b_branch2b"
top: "res2b"
name: "res2b"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res2b"
top: "res2b"
name: "res2b_relu"
type: "ReLU"
}
layer {
bottom: "res2b"
top: "res3a_branch1"
name: "res3a_branch1"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "bn3a_branch1"
type: "BatchNorm"
}
layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "scale3a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2b"
top: "res3a_branch2a"
name: "res3a_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "bn3a_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "scale3a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "res3a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2b"
name: "res3a_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "bn3a_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "scale3a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3a_branch1"
bottom: "res3a_branch2b"
top: "res3a"
name: "res3a"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res3a"
top: "res3a"
name: "res3a_relu"
type: "ReLU"
}
layer {
bottom: "res3a"
top: "res3b_branch2a"
name: "res3b_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "bn3b_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "scale3b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "res3b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2b"
name: "res3b_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "bn3b_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "scale3b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3a"
bottom: "res3b_branch2b"
top: "res3b"
name: "res3b"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res3b"
top: "res3b"
name: "res3b_relu"
type: "ReLU"
}
layer {
bottom: "res3b"
top: "res4a_branch1"
name: "res4a_branch1"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "bn4a_branch1"
type: "BatchNorm"
}
layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "scale4a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3b"
top: "res4a_branch2a"
name: "res4a_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "bn4a_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "scale4a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "res4a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2b"
name: "res4a_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "bn4a_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "scale4a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4a_branch1"
bottom: "res4a_branch2b"
top: "res4a"
name: "res4a"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res4a"
top: "res4a"
name: "res4a_relu"
type: "ReLU"
}
layer {
bottom: "res4a"
top: "res4b_branch2a"
name: "res4b_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "bn4b_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "scale4b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "res4b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2b"
name: "res4b_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "bn4b_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "scale4b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4a"
bottom: "res4b_branch2b"
top: "res4b"
name: "res4b"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res4b"
top: "res4b"
name: "res4b_relu"
type: "ReLU"
}
layer {
bottom: "res4b"
top: "res5a_branch1"
name: "res5a_branch1"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "bn5a_branch1"
type: "BatchNorm"
}
layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "scale5a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4b"
top: "res5a_branch2a"
name: "res5a_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 2
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "bn5a_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "scale5a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "res5a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2b"
name: "res5a_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "bn5a_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "scale5a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res5a_branch1"
bottom: "res5a_branch2b"
top: "res5a"
name: "res5a"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res5a"
top: "res5a"
name: "res5a_relu"
type: "ReLU"
}
layer {
bottom: "res5a"
top: "res5b_branch2a"
name: "res5b_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "bn5b_branch2a"
type: "BatchNorm"
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "scale5b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "res5b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2b"
name: "res5b_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1
stride: 1
weight_filler {
type: "msra"
}
bias_term: false
}
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "bn5b_branch2b"
type: "BatchNorm"
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "scale5b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res5a"
bottom: "res5b_branch2b"
top: "res5b"
name: "res5b"
type: "Eltwise"
eltwise_param {
operation: SUM
}
}
layer {
bottom: "res5b"
top: "res5b"
name: "res5b_relu"
type: "ReLU"
}
layer {
bottom: "res5b"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
kernel_size: 7
stride: 1
pool: AVE
}
}
layer {
bottom: "pool5"
top: "fc1000"
name: "fc1000"
type: "InnerProduct"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 1
}
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "fc1000"
bottom: "label"
name: "loss"
type: "SoftmaxWithLoss"
top: "loss"
}
layer {
bottom: "fc1000"
bottom: "label"
top: "acc/top-1"
name: "acc/top-1"
type: "Accuracy"
include {
phase: TEST
}
}
layer {
bottom: "fc1000"
bottom: "label"
top: "acc/top-5"
name: "acc/top-5"
type: "Accuracy"
include {
phase: TEST
}
accuracy_param {
top_k: 5
}
}
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error: too few arguments in function call최근에 caffe가 오류를 보고했습니다. 제 cudnn 버전(cuDNN:ver.7.0.5)입니다. 그리고 caffe에서 cudnn 버전이 너무 낮아서 오류가 발생했습니다. 해결 방법: 장: 수정: 전재:https:/...
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