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VGG_ScasNet.prototxt
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name: "VGG_ScasNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 400
input_dim: 400
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 1
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 1
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 1
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
bottom: "conv4_3"
top: "pool4"
name: "pool4"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
pad: 1
stride: 1
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
convolution_param {
num_output: 512
pad: 2
kernel_size: 3
dilation: 2
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
convolution_param {
num_output: 512
pad: 2
kernel_size: 3
dilation: 2
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
convolution_param {
num_output: 512
pad: 2
kernel_size: 3
dilation: 2
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
###################### context1 dilation rate = 6 ########################
layer {
name: "fc6_1"
type: "Convolution"
bottom: "pool5"
top: "fc6_1"
convolution_param {
num_output: 1024
pad: 6
kernel_size: 3
dilation: 6
}
}
layer {
name: "relu6_1"
type: "ReLU"
bottom: "fc6_1"
top: "fc6_1"
}
layer {
name: "drop6_1"
type: "Dropout"
bottom: "fc6_1"
top: "fc6_1"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc6_1_reduce"
type: "Convolution"
bottom: "fc6_1"
top: "fc6_1_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_1_reduce/relu"
type: "ReLU"
bottom: "fc6_1_reduce"
top: "fc6_1_reduce"
}
###################### context2 dilation rate = 12 ########################
layer {
name: "fc6_2"
type: "Convolution"
bottom: "pool5"
top: "fc6_2"
convolution_param {
num_output: 1024
pad: 12
kernel_size: 3
dilation: 12
}
}
layer {
name: "relu6_2"
type: "ReLU"
bottom: "fc6_2"
top: "fc6_2"
}
layer {
name: "drop6_2"
type: "Dropout"
bottom: "fc6_2"
top: "fc6_2"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc6_2_reduce"
type: "Convolution"
bottom: "fc6_2"
top: "fc6_2_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_2_reduce/relu"
type: "ReLU"
bottom: "fc6_2_reduce"
top: "fc6_2_reduce"
}
###################### context3 dilation rate = 18 ########################
layer {
name: "fc6_3"
type: "Convolution"
bottom: "pool5"
top: "fc6_3"
convolution_param {
num_output: 1024
pad: 18
kernel_size: 3
dilation: 18
}
}
layer {
name: "relu6_3"
type: "ReLU"
bottom: "fc6_3"
top: "fc6_3"
}
layer {
name: "drop6_3"
type: "Dropout"
bottom: "fc6_3"
top: "fc6_3"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc6_3_reduce"
type: "Convolution"
bottom: "fc6_3"
top: "fc6_3_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_3_reduce/relu"
type: "ReLU"
bottom: "fc6_3_reduce"
top: "fc6_3_reduce"
}
###################### context4 dilation rate = 24 ########################
layer {
name: "fc6_4"
type: "Convolution"
bottom: "pool5"
top: "fc6_4"
convolution_param {
num_output: 1024
pad: 24
kernel_size: 3
dilation: 24
}
}
layer {
name: "relu6_4"
type: "ReLU"
bottom: "fc6_4"
top: "fc6_4"
}
layer {
name: "drop6_4"
type: "Dropout"
bottom: "fc6_4"
top: "fc6_4"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc6_4_reduce"
type: "Convolution"
bottom: "fc6_4"
top: "fc6_4_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_4_reduce/relu"
type: "ReLU"
bottom: "fc6_4_reduce"
top: "fc6_4_reduce"
}
######################## residual correction 1 ####################
layer {
name: "fc6_34_merge1"
type: "Eltwise"
bottom: "fc6_4_reduce"
bottom: "fc6_3_reduce"
top: "fc6_34_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_34_1x1_reduce"
type: "Convolution"
bottom: "fc6_34_merge1"
top: "fc6_34_1x1_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_34_1x1_reduce/relu"
type: "ReLU"
bottom: "fc6_34_1x1_reduce"
top: "fc6_34_1x1_reduce"
}
layer {
name: "fc6_34_3x3"
type: "Convolution"
bottom: "fc6_34_1x1_reduce"
top: "fc6_34_3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "fc6_34_3x3/relu"
type: "ReLU"
bottom: "fc6_34_3x3"
top: "fc6_34_3x3"
}
layer {
name: "fc6_34_1x1_increase"
type: "Convolution"
bottom: "fc6_34_3x3"
top: "fc6_34_1x1_increase"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_34_merge2"
type: "Eltwise"
bottom: "fc6_34_merge1"
bottom: "fc6_34_1x1_increase"
top: "fc6_34_merge2"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_34_merge2_reduce"
type: "Convolution"
bottom: "fc6_34_merge2"
top: "fc6_34_merge2_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_34_merge2_reduce/relu"
type: "ReLU"
bottom: "fc6_34_merge2_reduce"
top: "fc6_34_merge2_reduce"
}
######################## residual correction 2 ####################
layer {
name: "fc6_234_merge1"
type: "Eltwise"
bottom: "fc6_34_merge2_reduce"
bottom: "fc6_2_reduce"
top: "fc6_234_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_234_1x1_reduce"
type: "Convolution"
bottom: "fc6_234_merge1"
top: "fc6_234_1x1_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_234_1x1_reduce/relu"
type: "ReLU"
bottom: "fc6_234_1x1_reduce"
top: "fc6_234_1x1_reduce"
}
layer {
name: "fc6_234_3x3"
type: "Convolution"
bottom: "fc6_234_1x1_reduce"
top: "fc6_234_3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "fc6_234_3x3/relu"
type: "ReLU"
bottom: "fc6_234_3x3"
top: "fc6_234_3x3"
}
layer {
name: "fc6_234_1x1_increase"
type: "Convolution"
bottom: "fc6_234_3x3"
top: "fc6_234_1x1_increase"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_234_merge2"
type: "Eltwise"
bottom: "fc6_234_merge1"
bottom: "fc6_234_1x1_increase"
top: "fc6_234_merge2"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_234_merge2_reduce"
type: "Convolution"
bottom: "fc6_234_merge2"
top: "fc6_234_merge2_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_234_merge2_reduce/relu"
type: "ReLU"
bottom: "fc6_234_merge2_reduce"
top: "fc6_234_merge2_reduce"
}
######################## residual correction 3 ####################
layer {
name: "fc6_1234_merge1"
type: "Eltwise"
bottom: "fc6_234_merge2_reduce"
bottom: "fc6_1_reduce"
top: "fc6_1234_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_1234_1x1_reduce"
type: "Convolution"
bottom: "fc6_1234_merge1"
top: "fc6_1234_1x1_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_1234_1x1_reduce/relu"
type: "ReLU"
bottom: "fc6_1234_1x1_reduce"
top: "fc6_1234_1x1_reduce"
}
layer {
name: "fc6_1234_3x3"
type: "Convolution"
bottom: "fc6_1234_1x1_reduce"
top: "fc6_1234_3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "fc6_1234_3x3/relu"
type: "ReLU"
bottom: "fc6_1234_3x3"
top: "fc6_1234_3x3"
}
layer {
name: "fc6_1234_1x1_increase"
type: "Convolution"
bottom: "fc6_1234_3x3"
top: "fc6_1234_1x1_increase"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_1234_merge2"
type: "Eltwise"
bottom: "fc6_1234_merge1"
bottom: "fc6_1234_1x1_increase"
top: "fc6_1234_merge2"
eltwise_param {
operation: SUM
}
}
layer {
name: "fc6_1234_merge2_reduce"
type: "Convolution"
bottom: "fc6_1234_merge2"
top: "fc6_1234_merge2_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "fc6_1234_merge2_reduce/relu"
type: "ReLU"
bottom: "fc6_1234_merge2_reduce"
top: "fc6_1234_merge2_reduce"
}
######################## residual correction 4 ####################
layer {
name: "conv5_3_reduce"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_reduce/relu"
type: "ReLU"
bottom: "conv5_3_reduce"
top: "conv5_3_reduce"
}
layer {
name: "conv5_3_merge1"
type: "Eltwise"
bottom: "fc6_1234_merge2_reduce"
bottom: "conv5_3_reduce"
top: "conv5_3_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv5_3_1x1_reduce"
type: "Convolution"
bottom: "conv5_3_merge1"
top: "conv5_3_1x1_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_1x1_reduce"
}
layer {
name: "conv5_3_3x3"
type: "Convolution"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv5_3_3x3/relu"
type: "ReLU"
bottom: "conv5_3_3x3"
top: "conv5_3_3x3"
}
layer {
name: "conv5_3_1x1_increase"
type: "Convolution"
bottom: "conv5_3_3x3"
top: "conv5_3_1x1_increase"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_merge2"
type: "Eltwise"
bottom: "conv5_3_merge1"
bottom: "conv5_3_1x1_increase"
top: "conv5_3_merge2"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv5_3_merge2_reduce"
type: "Convolution"
bottom: "conv5_3_merge2"
top: "conv5_3_merge2_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_merge2_reduce/relu"
type: "ReLU"
bottom: "conv5_3_merge2_reduce"
top: "conv5_3_merge2_reduce"
}
######################## residual correction 5 ####################
layer {
name: "conv4_3_reduce"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_3_reduce"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_reduce/relu"
type: "ReLU"
bottom: "conv4_3_reduce"
top: "conv4_3_reduce"
}
layer {
name: "conv4_3_merge1"
type: "Eltwise"
bottom: "conv5_3_merge2_reduce"
bottom: "conv4_3_reduce"
top: "conv4_3_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv4_3_1x1_reduce"
type: "Convolution"
bottom: "conv4_3_merge1"
top: "conv4_3_1x1_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_1x1_reduce"
}
layer {
name: "conv4_3_3x3"
type: "Convolution"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_3_3x3/relu"
type: "ReLU"
bottom: "conv4_3_3x3"
top: "conv4_3_3x3"
}
layer {
name: "conv4_3_1x1_increase"
type: "Convolution"
bottom: "conv4_3_3x3"
top: "conv4_3_1x1_increase"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_merge2"
type: "Eltwise"
bottom: "conv4_3_merge1"
bottom: "conv4_3_1x1_increase"
top: "conv4_3_merge2"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv4_3_merge2_reduce"
type: "Convolution"
bottom: "conv4_3_merge2"
top: "conv4_3_merge2_reduce"
convolution_param {
num_output: 256
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_merge2_reduce/relu"
type: "ReLU"
bottom: "conv4_3_merge2_reduce"
top: "conv4_3_merge2_reduce"
}
layer {
name: "conv4_3_merge2_interp"
type: "Interp"
bottom: "conv4_3_merge2_reduce"
top: "conv4_3_merge2_interp"
interp_param {
height: 101
width: 101
}
}######################## residual correction 6 ####################
layer {
name: "conv3_3_reduce"
type: "Convolution"
bottom: "conv3_3"
top: "conv3_3_reduce"
convolution_param {
num_output: 256
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_3_reduce/relu"
type: "ReLU"
bottom: "conv3_3_reduce"
top: "conv3_3_reduce"
}
layer {
name: "conv3_3_merge1"
type: "Eltwise"
bottom: "conv4_3_merge2_interp"
bottom: "conv3_3_reduce"
top: "conv3_3_merge1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv3_3_1x1_reduce"
type: "Convolution"