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@berak
Created January 12, 2020 11:18
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flownet1
input: "img0"
input: "img1"
input_shape {
dim: 1
dim: 3
dim: 720
dim: 1280
}
input_shape {
dim: 1
dim: 3
dim: 720
dim: 1280
}
layer {
name: "Eltwise1"
type: "Eltwise"
bottom: "img0"
top: "img0s"
eltwise_param {
operation: SUM
coeff: 0.00392156862745098
}
}
layer {
name: "Eltwise2"
type: "Eltwise"
bottom: "img1"
top: "img1s"
eltwise_param {
operation: SUM
coeff: 0.00392156862745098
}
}
layer {
name: "img0s_aug"
type: "DataAugmentation"
bottom: "img0s"
top: "img0_nomean"
augmentation_param {
augment_during_test: true
recompute_mean: 1000
mean_per_pixel: false
}
}
layer {
name: "img1s_aug"
type: "DataAugmentation"
bottom: "img1s"
top: "img1_nomean"
augmentation_param {
augment_during_test: true
recompute_mean: 1000
mean_per_pixel: false
}
}
layer {
name: "Resample1"
type: "Resample"
bottom: "img0_nomean"
top: "img0_nomean_resize"
resample_param {
width: 1024
height: 768
type: LINEAR
antialias: true
}
}
layer {
name: "Resample2"
type: "Resample"
bottom: "img1_nomean"
top: "img1_nomean_resize"
resample_param {
width: 1024
height: 768
type: LINEAR
antialias: true
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "img0_nomean_resize"
bottom: "img1_nomean_resize"
top: "conv1a"
top: "conv1b"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU1"
type: "ReLU"
bottom: "conv1a"
top: "conv1a"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "ReLU2"
type: "ReLU"
bottom: "conv1b"
top: "conv1b"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "conv1a"
bottom: "conv1b"
top: "conv2a"
top: "conv2b"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU3"
type: "ReLU"
bottom: "conv2a"
top: "conv2a"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "ReLU4"
type: "ReLU"
bottom: "conv2b"
top: "conv2b"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "conv2a"
bottom: "conv2b"
top: "conv3a"
top: "conv3b"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU5"
type: "ReLU"
bottom: "conv3a"
top: "conv3a"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "ReLU6"
type: "ReLU"
bottom: "conv3b"
top: "conv3b"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "corr"
type: "Correlation"
bottom: "conv3a"
bottom: "conv3b"
top: "corr"
correlation_param {
pad: 20
kernel_size: 1
max_displacement: 20
stride_1: 1
stride_2: 2
}
}
layer {
name: "ReLU7"
type: "ReLU"
bottom: "corr"
top: "corr"
relu_param {
negative_slope: 0.1
}
}
layer {
name: "conv_redir"
type: "Convolution"
bottom: "conv3a"
top: "conv_redir"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU8"
type: "ReLU"
bottom: "conv_redir"
top: "conv_redir"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "Concat1"
type: "Concat"
bottom: "conv_redir"
bottom: "corr"
top: "blob16"
concat_param {
axis: 1
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "blob16"
top: "conv3_1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU9"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3_1"
top: "conv4"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU10"
type: "ReLU"
bottom: "conv4"
top: "conv4"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "conv4"
top: "conv4_1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU11"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4_1"
top: "conv5"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU12"
type: "ReLU"
bottom: "conv5"
top: "conv5"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "conv5"
top: "conv5_1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU13"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv6"
type: "Convolution"
bottom: "conv5_1"
top: "conv6"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU14"
type: "ReLU"
bottom: "conv6"
top: "conv6"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "conv6_1"
type: "Convolution"
bottom: "conv6"
top: "conv6_1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU15"
type: "ReLU"
bottom: "conv6_1"
top: "conv6_1"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "Convolution1"
type: "Convolution"
bottom: "conv6_1"
top: "predict_flow6"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "deconv5"
type: "Deconvolution"
bottom: "conv6_1"
top: "deconv5"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU16"
type: "ReLU"
bottom: "deconv5"
top: "deconv5"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "upsample_flow6to5"
type: "Deconvolution"
bottom: "predict_flow6"
top: "upsampled_flow6_to_5"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat2"
type: "Concat"
bottom: "conv5_1"
bottom: "deconv5"
bottom: "upsampled_flow6_to_5"
top: "concat5"
}
layer {
name: "Convolution2"
type: "Convolution"
bottom: "concat5"
top: "predict_flow5"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "deconv4"
type: "Deconvolution"
bottom: "concat5"
top: "deconv4"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU17"
type: "ReLU"
bottom: "deconv4"
top: "deconv4"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "upsample_flow5to4"
type: "Deconvolution"
bottom: "predict_flow5"
top: "upsampled_flow5_to_4"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat3"
type: "Concat"
bottom: "conv4_1"
bottom: "deconv4"
bottom: "upsampled_flow5_to_4"
top: "concat4"
}
layer {
name: "Convolution3"
type: "Convolution"
bottom: "concat4"
top: "predict_flow4"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "deconv3"
type: "Deconvolution"
bottom: "concat4"
top: "deconv3"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU18"
type: "ReLU"
bottom: "deconv3"
top: "deconv3"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "upsample_flow4to3"
type: "Deconvolution"
bottom: "predict_flow4"
top: "upsampled_flow4_to_3"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat4"
type: "Concat"
bottom: "conv3_1"
bottom: "deconv3"
bottom: "upsampled_flow4_to_3"
top: "concat3"
}
layer {
name: "Convolution4"
type: "Convolution"
bottom: "concat3"
top: "predict_flow3"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "deconv2"
type: "Deconvolution"
bottom: "concat3"
top: "deconv2"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU19"
type: "ReLU"
bottom: "deconv2"
top: "deconv2"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "upsample_flow3to2"
type: "Deconvolution"
bottom: "predict_flow3"
top: "upsampled_flow3_to_2"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat5"
type: "Concat"
bottom: "conv2a"
bottom: "deconv2"
bottom: "upsampled_flow3_to_2"
top: "concat2"
}
layer {
name: "Convolution5"
type: "Convolution"
bottom: "concat2"
top: "predict_flow2"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Eltwise3"
type: "Eltwise"
bottom: "predict_flow2"
top: "blob41"
eltwise_param {
operation: SUM
coeff: 20.0
}
}
layer {
name: "Accum1"
type: "Accum"
bottom: "blob41"
bottom: "img0_nomean_resize"
top: "blob42"
accum_param {
have_reference: true
}
}
layer {
name: "FlowWarp1"
type: "FlowWarp"
bottom: "img1_nomean_resize"
bottom: "blob42"
top: "blob43"
}
layer {
name: "Eltwise4"
type: "Eltwise"
bottom: "img0_nomean_resize"
bottom: "blob43"
top: "blob44"
eltwise_param {
operation: SUM
coeff: 1.0
coeff: -1.0
}
}
layer {
name: "ChannelNorm1"
type: "ChannelNorm"
bottom: "blob44"
top: "blob45"
}
layer {
name: "Eltwise5"
type: "Eltwise"
bottom: "blob42"
top: "blob46"
eltwise_param {
operation: SUM
coeff: 0.05
}
}
layer {
name: "Concat6"
type: "Concat"
bottom: "img0_nomean_resize"
bottom: "img1_nomean_resize"
bottom: "blob43"
bottom: "blob46"
bottom: "blob45"
top: "blob47"
concat_param {
axis: 1
}
}
layer {
name: "net2_conv1"
type: "Convolution"
bottom: "blob47"
top: "blob48"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU20"
type: "ReLU"
bottom: "blob48"
top: "blob48"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv2"
type: "Convolution"
bottom: "blob48"
top: "blob49"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU21"
type: "ReLU"
bottom: "blob49"
top: "blob49"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv3"
type: "Convolution"
bottom: "blob49"
top: "blob50"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU22"
type: "ReLU"
bottom: "blob50"
top: "blob50"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv3_1"
type: "Convolution"
bottom: "blob50"
top: "blob51"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU23"
type: "ReLU"
bottom: "blob51"
top: "blob51"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv4"
type: "Convolution"
bottom: "blob51"
top: "blob52"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU24"
type: "ReLU"
bottom: "blob52"
top: "blob52"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv4_1"
type: "Convolution"
bottom: "blob52"
top: "blob53"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU25"
type: "ReLU"
bottom: "blob53"
top: "blob53"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv5"
type: "Convolution"
bottom: "blob53"
top: "blob54"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU26"
type: "ReLU"
bottom: "blob54"
top: "blob54"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv5_1"
type: "Convolution"
bottom: "blob54"
top: "blob55"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU27"
type: "ReLU"
bottom: "blob55"
top: "blob55"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv6"
type: "Convolution"
bottom: "blob55"
top: "blob56"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU28"
type: "ReLU"
bottom: "blob56"
top: "blob56"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_conv6_1"
type: "Convolution"
bottom: "blob56"
top: "blob57"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU29"
type: "ReLU"
bottom: "blob57"
top: "blob57"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_predict_conv6"
type: "Convolution"
bottom: "blob57"
top: "blob58"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net2_deconv5"
type: "Deconvolution"
bottom: "blob57"
top: "blob59"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU30"
type: "ReLU"
bottom: "blob59"
top: "blob59"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_net2_upsample_flow6to5"
type: "Deconvolution"
bottom: "blob58"
top: "blob60"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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bias_filler {
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}
layer {
name: "Concat7"
type: "Concat"
bottom: "blob55"
bottom: "blob59"
bottom: "blob60"
top: "blob61"
}
layer {
name: "net2_predict_conv5"
type: "Convolution"
bottom: "blob61"
top: "blob62"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net2_deconv4"
type: "Deconvolution"
bottom: "blob61"
top: "blob63"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "ReLU31"
type: "ReLU"
bottom: "blob63"
top: "blob63"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_net2_upsample_flow5to4"
type: "Deconvolution"
bottom: "blob62"
top: "blob64"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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bias_filler {
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}
layer {
name: "Concat8"
type: "Concat"
bottom: "blob53"
bottom: "blob63"
bottom: "blob64"
top: "blob65"
}
layer {
name: "net2_predict_conv4"
type: "Convolution"
bottom: "blob65"
top: "blob66"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net2_deconv3"
type: "Deconvolution"
bottom: "blob65"
top: "blob67"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU32"
type: "ReLU"
bottom: "blob67"
top: "blob67"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_net2_upsample_flow4to3"
type: "Deconvolution"
bottom: "blob66"
top: "blob68"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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}
bias_filler {
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}
engine: CUDNN
}
}
layer {
name: "Concat9"
type: "Concat"
bottom: "blob51"
bottom: "blob67"
bottom: "blob68"
top: "blob69"
}
layer {
name: "net2_predict_conv3"
type: "Convolution"
bottom: "blob69"
top: "blob70"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net2_deconv2"
type: "Deconvolution"
bottom: "blob69"
top: "blob71"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU33"
type: "ReLU"
bottom: "blob71"
top: "blob71"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net2_net2_upsample_flow3to2"
type: "Deconvolution"
bottom: "blob70"
top: "blob72"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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}
bias_filler {
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}
engine: CUDNN
}
}
layer {
name: "Concat10"
type: "Concat"
bottom: "blob49"
bottom: "blob71"
bottom: "blob72"
top: "blob73"
}
layer {
name: "net2_predict_conv2"
type: "Convolution"
bottom: "blob73"
top: "blob74"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Eltwise6"
type: "Eltwise"
bottom: "blob74"
top: "blob75"
eltwise_param {
operation: SUM
coeff: 20.0
}
}
layer {
name: "Accum2"
type: "Accum"
bottom: "blob75"
bottom: "img0_nomean_resize"
top: "blob76"
accum_param {
have_reference: true
}
}
layer {
name: "FlowWarp2"
type: "FlowWarp"
bottom: "img1_nomean_resize"
bottom: "blob76"
top: "blob77"
}
layer {
name: "Eltwise7"
type: "Eltwise"
bottom: "img0_nomean_resize"
bottom: "blob77"
top: "blob78"
eltwise_param {
operation: SUM
coeff: 1.0
coeff: -1.0
}
}
layer {
name: "ChannelNorm2"
type: "ChannelNorm"
bottom: "blob78"
top: "blob79"
}
layer {
name: "Eltwise8"
type: "Eltwise"
bottom: "blob76"
top: "blob80"
eltwise_param {
operation: SUM
coeff: 0.05
}
}
layer {
name: "Concat11"
type: "Concat"
bottom: "img0_nomean_resize"
bottom: "img1_nomean_resize"
bottom: "blob77"
bottom: "blob80"
bottom: "blob79"
top: "blob81"
concat_param {
axis: 1
}
}
layer {
name: "net3_conv1"
type: "Convolution"
bottom: "blob81"
top: "blob82"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 7
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU34"
type: "ReLU"
bottom: "blob82"
top: "blob82"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv2"
type: "Convolution"
bottom: "blob82"
top: "blob83"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 5
stride: 2
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU35"
type: "ReLU"
bottom: "blob83"
top: "blob83"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv3"
type: "Convolution"
bottom: "blob83"
top: "blob84"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 5
stride: 2
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU36"
type: "ReLU"
bottom: "blob84"
top: "blob84"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv3_1"
type: "Convolution"
bottom: "blob84"
top: "blob85"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
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engine: CUDNN
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}
layer {
name: "ReLU37"
type: "ReLU"
bottom: "blob85"
top: "blob85"
relu_param {
negative_slope: 0.1
engine: CUDNN
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layer {
name: "net3_conv4"
type: "Convolution"
bottom: "blob85"
top: "blob86"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 2
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU38"
type: "ReLU"
bottom: "blob86"
top: "blob86"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv4_1"
type: "Convolution"
bottom: "blob86"
top: "blob87"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
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}
engine: CUDNN
}
}
layer {
name: "ReLU39"
type: "ReLU"
bottom: "blob87"
top: "blob87"
relu_param {
negative_slope: 0.1
engine: CUDNN
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layer {
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type: "Convolution"
bottom: "blob87"
top: "blob88"
param {
lr_mult: 0
decay_mult: 0
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param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 2
weight_filler {
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bias_filler {
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engine: CUDNN
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}
layer {
name: "ReLU40"
type: "ReLU"
bottom: "blob88"
top: "blob88"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv5_1"
type: "Convolution"
bottom: "blob88"
top: "blob89"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU41"
type: "ReLU"
bottom: "blob89"
top: "blob89"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv6"
type: "Convolution"
bottom: "blob89"
top: "blob90"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU42"
type: "ReLU"
bottom: "blob90"
top: "blob90"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_conv6_1"
type: "Convolution"
bottom: "blob90"
top: "blob91"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "ReLU43"
type: "ReLU"
bottom: "blob91"
top: "blob91"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_predict_conv6"
type: "Convolution"
bottom: "blob91"
top: "blob92"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "net3_deconv5"
type: "Deconvolution"
bottom: "blob91"
top: "blob93"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU44"
type: "ReLU"
bottom: "blob93"
top: "blob93"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_net3_upsample_flow6to5"
type: "Deconvolution"
bottom: "blob92"
top: "blob94"
param {
lr_mult: 0
decay_mult: 0
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param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "Concat12"
type: "Concat"
bottom: "blob89"
bottom: "blob93"
bottom: "blob94"
top: "blob95"
}
layer {
name: "net3_predict_conv5"
type: "Convolution"
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top: "blob96"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 3
stride: 1
weight_filler {
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bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "net3_deconv4"
type: "Deconvolution"
bottom: "blob95"
top: "blob97"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
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convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "ReLU45"
type: "ReLU"
bottom: "blob97"
top: "blob97"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_net3_upsample_flow5to4"
type: "Deconvolution"
bottom: "blob96"
top: "blob98"
param {
lr_mult: 0
decay_mult: 0
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param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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kernel_size: 4
stride: 2
weight_filler {
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}
bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "Concat13"
type: "Concat"
bottom: "blob87"
bottom: "blob97"
bottom: "blob98"
top: "blob99"
}
layer {
name: "net3_predict_conv4"
type: "Convolution"
bottom: "blob99"
top: "blob100"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net3_deconv3"
type: "Deconvolution"
bottom: "blob99"
top: "blob101"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
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}
layer {
name: "ReLU46"
type: "ReLU"
bottom: "blob101"
top: "blob101"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_net3_upsample_flow4to3"
type: "Deconvolution"
bottom: "blob100"
top: "blob102"
param {
lr_mult: 0
decay_mult: 0
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param {
lr_mult: 0
decay_mult: 0
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convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat14"
type: "Concat"
bottom: "blob85"
bottom: "blob101"
bottom: "blob102"
top: "blob103"
}
layer {
name: "net3_predict_conv3"
type: "Convolution"
bottom: "blob103"
top: "blob104"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "net3_deconv2"
type: "Deconvolution"
bottom: "blob103"
top: "blob105"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU47"
type: "ReLU"
bottom: "blob105"
top: "blob105"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "net3_net3_upsample_flow3to2"
type: "Deconvolution"
bottom: "blob104"
top: "blob106"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat15"
type: "Concat"
bottom: "blob83"
bottom: "blob105"
bottom: "blob106"
top: "blob107"
}
layer {
name: "net3_predict_conv2"
type: "Convolution"
bottom: "blob107"
top: "blob108"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Eltwise9"
type: "Eltwise"
bottom: "blob108"
top: "blob109"
eltwise_param {
operation: SUM
coeff: 20.0
}
}
layer {
name: "Concat16"
type: "Concat"
bottom: "img0_nomean_resize"
bottom: "img1_nomean_resize"
top: "blob110"
concat_param {
axis: 1
}
}
layer {
name: "netsd_conv0"
type: "Convolution"
bottom: "blob110"
top: "blob111"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU48"
type: "ReLU"
bottom: "blob111"
top: "blob111"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv1"
type: "Convolution"
bottom: "blob111"
top: "blob112"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU49"
type: "ReLU"
bottom: "blob112"
top: "blob112"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv1_1"
type: "Convolution"
bottom: "blob112"
top: "blob113"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU50"
type: "ReLU"
bottom: "blob113"
top: "blob113"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv2"
type: "Convolution"
bottom: "blob113"
top: "blob114"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU51"
type: "ReLU"
bottom: "blob114"
top: "blob114"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv2_1"
type: "Convolution"
bottom: "blob114"
top: "blob115"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU52"
type: "ReLU"
bottom: "blob115"
top: "blob115"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv3"
type: "Convolution"
bottom: "blob115"
top: "blob116"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU53"
type: "ReLU"
bottom: "blob116"
top: "blob116"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv3_1"
type: "Convolution"
bottom: "blob116"
top: "blob117"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU54"
type: "ReLU"
bottom: "blob117"
top: "blob117"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv4"
type: "Convolution"
bottom: "blob117"
top: "blob118"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU55"
type: "ReLU"
bottom: "blob118"
top: "blob118"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv4_1"
type: "Convolution"
bottom: "blob118"
top: "blob119"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU56"
type: "ReLU"
bottom: "blob119"
top: "blob119"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv5"
type: "Convolution"
bottom: "blob119"
top: "blob120"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
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pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU57"
type: "ReLU"
bottom: "blob120"
top: "blob120"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv5_1"
type: "Convolution"
bottom: "blob120"
top: "blob121"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU58"
type: "ReLU"
bottom: "blob121"
top: "blob121"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv6"
type: "Convolution"
bottom: "blob121"
top: "blob122"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU59"
type: "ReLU"
bottom: "blob122"
top: "blob122"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_conv6_1"
type: "Convolution"
bottom: "blob122"
top: "blob123"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU60"
type: "ReLU"
bottom: "blob123"
top: "blob123"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_Convolution1"
type: "Convolution"
bottom: "blob123"
top: "blob124"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_deconv5"
type: "Deconvolution"
bottom: "blob123"
top: "blob125"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU61"
type: "ReLU"
bottom: "blob125"
top: "blob125"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_upsample_flow6to5"
type: "Deconvolution"
bottom: "blob124"
top: "blob126"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat17"
type: "Concat"
bottom: "blob121"
bottom: "blob125"
bottom: "blob126"
top: "blob127"
}
layer {
name: "netsd_interconv5"
type: "Convolution"
bottom: "blob127"
top: "blob128"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_Convolution2"
type: "Convolution"
bottom: "blob128"
top: "blob129"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_deconv4"
type: "Deconvolution"
bottom: "blob127"
top: "blob130"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU62"
type: "ReLU"
bottom: "blob130"
top: "blob130"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_upsample_flow5to4"
type: "Deconvolution"
bottom: "blob129"
top: "blob131"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat18"
type: "Concat"
bottom: "blob119"
bottom: "blob130"
bottom: "blob131"
top: "blob132"
}
layer {
name: "netsd_interconv4"
type: "Convolution"
bottom: "blob132"
top: "blob133"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_Convolution3"
type: "Convolution"
bottom: "blob133"
top: "blob134"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_deconv3"
type: "Deconvolution"
bottom: "blob132"
top: "blob135"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU63"
type: "ReLU"
bottom: "blob135"
top: "blob135"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_upsample_flow4to3"
type: "Deconvolution"
bottom: "blob134"
top: "blob136"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat19"
type: "Concat"
bottom: "blob117"
bottom: "blob135"
bottom: "blob136"
top: "blob137"
}
layer {
name: "netsd_interconv3"
type: "Convolution"
bottom: "blob137"
top: "blob138"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_Convolution4"
type: "Convolution"
bottom: "blob138"
top: "blob139"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_deconv2"
type: "Deconvolution"
bottom: "blob137"
top: "blob140"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU64"
type: "ReLU"
bottom: "blob140"
top: "blob140"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "netsd_upsample_flow3to2"
type: "Deconvolution"
bottom: "blob139"
top: "blob141"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat20"
type: "Concat"
bottom: "blob115"
bottom: "blob140"
bottom: "blob141"
top: "blob142"
}
layer {
name: "netsd_interconv2"
type: "Convolution"
bottom: "blob142"
top: "blob143"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "netsd_Convolution5"
type: "Convolution"
bottom: "blob143"
top: "blob144"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Eltwise10"
type: "Eltwise"
bottom: "blob144"
top: "blob145"
eltwise_param {
operation: SUM
coeff: 0.05
}
}
layer {
name: "Resample3"
type: "Resample"
bottom: "blob145"
bottom: "img0_nomean_resize"
top: "blob146"
resample_param {
type: NEAREST
antialias: false
factor: 1.0
}
}
layer {
name: "Resample4"
type: "Resample"
bottom: "blob109"
bottom: "img0_nomean_resize"
top: "blob147"
resample_param {
type: NEAREST
antialias: false
factor: 1.0
}
}
layer {
name: "ChannelNorm3"
type: "ChannelNorm"
bottom: "blob146"
top: "blob148"
}
layer {
name: "ChannelNorm4"
type: "ChannelNorm"
bottom: "blob147"
top: "blob149"
}
layer {
name: "FlowWarp3"
type: "FlowWarp"
bottom: "img1_nomean_resize"
bottom: "blob146"
top: "blob150"
}
layer {
name: "Eltwise11"
type: "Eltwise"
bottom: "img0_nomean_resize"
bottom: "blob150"
top: "blob151"
eltwise_param {
operation: SUM
coeff: 1.0
coeff: -1.0
}
}
layer {
name: "ChannelNorm5"
type: "ChannelNorm"
bottom: "blob151"
top: "blob152"
}
layer {
name: "FlowWarp4"
type: "FlowWarp"
bottom: "img1_nomean_resize"
bottom: "blob147"
top: "blob153"
}
layer {
name: "Eltwise12"
type: "Eltwise"
bottom: "img0_nomean_resize"
bottom: "blob153"
top: "blob154"
eltwise_param {
operation: SUM
coeff: 1.0
coeff: -1.0
}
}
layer {
name: "ChannelNorm6"
type: "ChannelNorm"
bottom: "blob154"
top: "blob155"
}
layer {
name: "Concat21"
type: "Concat"
bottom: "img0_nomean_resize"
bottom: "blob146"
bottom: "blob147"
bottom: "blob148"
bottom: "blob149"
bottom: "blob152"
bottom: "blob155"
top: "blob156"
concat_param {
axis: 1
}
}
layer {
name: "fuse_conv0"
type: "Convolution"
bottom: "blob156"
top: "blob157"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU65"
type: "ReLU"
bottom: "blob157"
top: "blob157"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_conv1"
type: "Convolution"
bottom: "blob157"
top: "blob158"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU66"
type: "ReLU"
bottom: "blob158"
top: "blob158"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_conv1_1"
type: "Convolution"
bottom: "blob158"
top: "blob159"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU67"
type: "ReLU"
bottom: "blob159"
top: "blob159"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_conv2"
type: "Convolution"
bottom: "blob159"
top: "blob160"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU68"
type: "ReLU"
bottom: "blob160"
top: "blob160"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_conv2_1"
type: "Convolution"
bottom: "blob160"
top: "blob161"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU69"
type: "ReLU"
bottom: "blob161"
top: "blob161"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse__Convolution5"
type: "Convolution"
bottom: "blob161"
top: "blob162"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "fuse_deconv1"
type: "Deconvolution"
bottom: "blob161"
top: "blob163"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU70"
type: "ReLU"
bottom: "blob163"
top: "blob163"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_upsample_flow2to1"
type: "Deconvolution"
bottom: "blob162"
top: "blob164"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat22"
type: "Concat"
bottom: "blob159"
bottom: "blob163"
bottom: "blob164"
top: "blob165"
}
layer {
name: "fuse_interconv1"
type: "Convolution"
bottom: "blob165"
top: "blob166"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "fuse__Convolution6"
type: "Convolution"
bottom: "blob166"
top: "blob167"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "fuse_deconv0"
type: "Deconvolution"
bottom: "blob165"
top: "blob168"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "ReLU71"
type: "ReLU"
bottom: "blob168"
top: "blob168"
relu_param {
negative_slope: 0.1
engine: CUDNN
}
}
layer {
name: "fuse_upsample_flow1to0"
type: "Deconvolution"
bottom: "blob167"
top: "blob169"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 4
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Concat23"
type: "Concat"
bottom: "blob157"
bottom: "blob168"
bottom: "blob169"
top: "blob170"
}
layer {
name: "fuse_interconv0"
type: "Convolution"
bottom: "blob170"
top: "blob171"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "fuse__Convolution7"
type: "Convolution"
bottom: "blob171"
top: "blob172"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
engine: CUDNN
}
}
layer {
name: "Resample5"
type: "Resample"
bottom: "blob172"
top: "predict_flow_resize"
resample_param {
width: 1024
height: 720
type: LINEAR
antialias: true
}
}
layer {
name: "scale_conv1"
type: "Convolution"
bottom: "predict_flow_resize"
top: "predict_flow_final"
convolution_param {
num_output: 2
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "diagonal"
diag_val: 1
diag_val: 0.9375
}
bias_filler {
type: "constant"
}
}
}
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