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name: "Joe-DesNet-Deepfashion" | |
layer { | |
name: "data" | |
type: "Python" | |
top: "data" | |
top: "texture_label" | |
top: "fabric_label" | |
top: "shape_label" | |
top: "part_label" | |
top: "style_label" | |
top: "class_label" | |
python_param { | |
module: "deepfashion_multitask_datalayers" | |
layer: "MultilabelDataLayerAsync_Multitask" | |
param_str: "{\'image_root\': \'/data0/xujiu/proj/deep-fashion-desnet/\', \'im_shape\': [224, 224], \'split\': \'clean_train\', \'batch_size\': 9}" | |
} | |
} | |
layer { | |
name: "conv1" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv1" | |
convolution_param { | |
num_output: 96 | |
bias_term: false | |
pad: 3 | |
kernel_size: 7 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv1/bn" | |
type: "BatchNorm" | |
bottom: "conv1" | |
top: "conv1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv1/scale" | |
type: "Scale" | |
bottom: "conv1/bn" | |
top: "conv1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu1" | |
type: "ReLU" | |
bottom: "conv1/bn" | |
top: "conv1/bn" | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1/bn" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "conv2_1/x1/bn" | |
type: "BatchNorm" | |
bottom: "pool1" | |
top: "conv2_1/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_1/x1/scale" | |
type: "Scale" | |
bottom: "conv2_1/x1/bn" | |
top: "conv2_1/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_1/x1" | |
type: "ReLU" | |
bottom: "conv2_1/x1/bn" | |
top: "conv2_1/x1/bn" | |
} | |
layer { | |
name: "conv2_1/x1" | |
type: "Convolution" | |
bottom: "conv2_1/x1/bn" | |
top: "conv2_1/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_1/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_1/x1" | |
top: "conv2_1/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_1/x2/scale" | |
type: "Scale" | |
bottom: "conv2_1/x2/bn" | |
top: "conv2_1/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_1/x2" | |
type: "ReLU" | |
bottom: "conv2_1/x2/bn" | |
top: "conv2_1/x2/bn" | |
} | |
layer { | |
name: "conv2_1/x2" | |
type: "Convolution" | |
bottom: "conv2_1/x2/bn" | |
top: "conv2_1/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_1" | |
type: "Concat" | |
bottom: "pool1" | |
bottom: "conv2_1/x2" | |
top: "concat_2_1" | |
} | |
layer { | |
name: "conv2_2/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_1" | |
top: "conv2_2/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_2/x1/scale" | |
type: "Scale" | |
bottom: "conv2_2/x1/bn" | |
top: "conv2_2/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_2/x1" | |
type: "ReLU" | |
bottom: "conv2_2/x1/bn" | |
top: "conv2_2/x1/bn" | |
} | |
layer { | |
name: "conv2_2/x1" | |
type: "Convolution" | |
bottom: "conv2_2/x1/bn" | |
top: "conv2_2/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_2/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_2/x1" | |
top: "conv2_2/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_2/x2/scale" | |
type: "Scale" | |
bottom: "conv2_2/x2/bn" | |
top: "conv2_2/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_2/x2" | |
type: "ReLU" | |
bottom: "conv2_2/x2/bn" | |
top: "conv2_2/x2/bn" | |
} | |
layer { | |
name: "conv2_2/x2" | |
type: "Convolution" | |
bottom: "conv2_2/x2/bn" | |
top: "conv2_2/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_2" | |
type: "Concat" | |
bottom: "concat_2_1" | |
bottom: "conv2_2/x2" | |
top: "concat_2_2" | |
} | |
layer { | |
name: "conv2_3/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_2" | |
top: "conv2_3/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_3/x1/scale" | |
type: "Scale" | |
bottom: "conv2_3/x1/bn" | |
top: "conv2_3/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_3/x1" | |
type: "ReLU" | |
bottom: "conv2_3/x1/bn" | |
top: "conv2_3/x1/bn" | |
} | |
layer { | |
name: "conv2_3/x1" | |
type: "Convolution" | |
bottom: "conv2_3/x1/bn" | |
top: "conv2_3/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_3/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_3/x1" | |
top: "conv2_3/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_3/x2/scale" | |
type: "Scale" | |
bottom: "conv2_3/x2/bn" | |
top: "conv2_3/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_3/x2" | |
type: "ReLU" | |
bottom: "conv2_3/x2/bn" | |
top: "conv2_3/x2/bn" | |
} | |
layer { | |
name: "conv2_3/x2" | |
type: "Convolution" | |
bottom: "conv2_3/x2/bn" | |
top: "conv2_3/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_3" | |
type: "Concat" | |
bottom: "concat_2_2" | |
bottom: "conv2_3/x2" | |
top: "concat_2_3" | |
} | |
layer { | |
name: "conv2_4/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_3" | |
top: "conv2_4/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_4/x1/scale" | |
type: "Scale" | |
bottom: "conv2_4/x1/bn" | |
top: "conv2_4/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_4/x1" | |
type: "ReLU" | |
bottom: "conv2_4/x1/bn" | |
top: "conv2_4/x1/bn" | |
} | |
layer { | |
name: "conv2_4/x1" | |
type: "Convolution" | |
bottom: "conv2_4/x1/bn" | |
top: "conv2_4/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_4/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_4/x1" | |
top: "conv2_4/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_4/x2/scale" | |
type: "Scale" | |
bottom: "conv2_4/x2/bn" | |
top: "conv2_4/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_4/x2" | |
type: "ReLU" | |
bottom: "conv2_4/x2/bn" | |
top: "conv2_4/x2/bn" | |
} | |
layer { | |
name: "conv2_4/x2" | |
type: "Convolution" | |
bottom: "conv2_4/x2/bn" | |
top: "conv2_4/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_4" | |
type: "Concat" | |
bottom: "concat_2_3" | |
bottom: "conv2_4/x2" | |
top: "concat_2_4" | |
} | |
layer { | |
name: "conv2_5/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_4" | |
top: "conv2_5/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_5/x1/scale" | |
type: "Scale" | |
bottom: "conv2_5/x1/bn" | |
top: "conv2_5/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_5/x1" | |
type: "ReLU" | |
bottom: "conv2_5/x1/bn" | |
top: "conv2_5/x1/bn" | |
} | |
layer { | |
name: "conv2_5/x1" | |
type: "Convolution" | |
bottom: "conv2_5/x1/bn" | |
top: "conv2_5/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_5/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_5/x1" | |
top: "conv2_5/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_5/x2/scale" | |
type: "Scale" | |
bottom: "conv2_5/x2/bn" | |
top: "conv2_5/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_5/x2" | |
type: "ReLU" | |
bottom: "conv2_5/x2/bn" | |
top: "conv2_5/x2/bn" | |
} | |
layer { | |
name: "conv2_5/x2" | |
type: "Convolution" | |
bottom: "conv2_5/x2/bn" | |
top: "conv2_5/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_5" | |
type: "Concat" | |
bottom: "concat_2_4" | |
bottom: "conv2_5/x2" | |
top: "concat_2_5" | |
} | |
layer { | |
name: "conv2_6/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_5" | |
top: "conv2_6/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_6/x1/scale" | |
type: "Scale" | |
bottom: "conv2_6/x1/bn" | |
top: "conv2_6/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_6/x1" | |
type: "ReLU" | |
bottom: "conv2_6/x1/bn" | |
top: "conv2_6/x1/bn" | |
} | |
layer { | |
name: "conv2_6/x1" | |
type: "Convolution" | |
bottom: "conv2_6/x1/bn" | |
top: "conv2_6/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv2_6/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv2_6/x1" | |
top: "conv2_6/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_6/x2/scale" | |
type: "Scale" | |
bottom: "conv2_6/x2/bn" | |
top: "conv2_6/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_6/x2" | |
type: "ReLU" | |
bottom: "conv2_6/x2/bn" | |
top: "conv2_6/x2/bn" | |
} | |
layer { | |
name: "conv2_6/x2" | |
type: "Convolution" | |
bottom: "conv2_6/x2/bn" | |
top: "conv2_6/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_2_6" | |
type: "Concat" | |
bottom: "concat_2_5" | |
bottom: "conv2_6/x2" | |
top: "concat_2_6" | |
} | |
layer { | |
name: "conv2_blk/bn" | |
type: "BatchNorm" | |
bottom: "concat_2_6" | |
top: "conv2_blk/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv2_blk/scale" | |
type: "Scale" | |
bottom: "conv2_blk/bn" | |
top: "conv2_blk/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu2_blk" | |
type: "ReLU" | |
bottom: "conv2_blk/bn" | |
top: "conv2_blk/bn" | |
} | |
layer { | |
name: "conv2_blk" | |
type: "Convolution" | |
bottom: "conv2_blk/bn" | |
top: "conv2_blk" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "conv2_blk" | |
top: "pool2" | |
pooling_param { | |
pool: AVE | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv3_1/x1/bn" | |
type: "BatchNorm" | |
bottom: "pool2" | |
top: "conv3_1/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_1/x1/scale" | |
type: "Scale" | |
bottom: "conv3_1/x1/bn" | |
top: "conv3_1/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_1/x1" | |
type: "ReLU" | |
bottom: "conv3_1/x1/bn" | |
top: "conv3_1/x1/bn" | |
} | |
layer { | |
name: "conv3_1/x1" | |
type: "Convolution" | |
bottom: "conv3_1/x1/bn" | |
top: "conv3_1/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_1/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_1/x1" | |
top: "conv3_1/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_1/x2/scale" | |
type: "Scale" | |
bottom: "conv3_1/x2/bn" | |
top: "conv3_1/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_1/x2" | |
type: "ReLU" | |
bottom: "conv3_1/x2/bn" | |
top: "conv3_1/x2/bn" | |
} | |
layer { | |
name: "conv3_1/x2" | |
type: "Convolution" | |
bottom: "conv3_1/x2/bn" | |
top: "conv3_1/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_1" | |
type: "Concat" | |
bottom: "pool2" | |
bottom: "conv3_1/x2" | |
top: "concat_3_1" | |
} | |
layer { | |
name: "conv3_2/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_1" | |
top: "conv3_2/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_2/x1/scale" | |
type: "Scale" | |
bottom: "conv3_2/x1/bn" | |
top: "conv3_2/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_2/x1" | |
type: "ReLU" | |
bottom: "conv3_2/x1/bn" | |
top: "conv3_2/x1/bn" | |
} | |
layer { | |
name: "conv3_2/x1" | |
type: "Convolution" | |
bottom: "conv3_2/x1/bn" | |
top: "conv3_2/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_2/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_2/x1" | |
top: "conv3_2/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_2/x2/scale" | |
type: "Scale" | |
bottom: "conv3_2/x2/bn" | |
top: "conv3_2/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_2/x2" | |
type: "ReLU" | |
bottom: "conv3_2/x2/bn" | |
top: "conv3_2/x2/bn" | |
} | |
layer { | |
name: "conv3_2/x2" | |
type: "Convolution" | |
bottom: "conv3_2/x2/bn" | |
top: "conv3_2/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_2" | |
type: "Concat" | |
bottom: "concat_3_1" | |
bottom: "conv3_2/x2" | |
top: "concat_3_2" | |
} | |
layer { | |
name: "conv3_3/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_2" | |
top: "conv3_3/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_3/x1/scale" | |
type: "Scale" | |
bottom: "conv3_3/x1/bn" | |
top: "conv3_3/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_3/x1" | |
type: "ReLU" | |
bottom: "conv3_3/x1/bn" | |
top: "conv3_3/x1/bn" | |
} | |
layer { | |
name: "conv3_3/x1" | |
type: "Convolution" | |
bottom: "conv3_3/x1/bn" | |
top: "conv3_3/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_3/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_3/x1" | |
top: "conv3_3/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_3/x2/scale" | |
type: "Scale" | |
bottom: "conv3_3/x2/bn" | |
top: "conv3_3/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_3/x2" | |
type: "ReLU" | |
bottom: "conv3_3/x2/bn" | |
top: "conv3_3/x2/bn" | |
} | |
layer { | |
name: "conv3_3/x2" | |
type: "Convolution" | |
bottom: "conv3_3/x2/bn" | |
top: "conv3_3/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_3" | |
type: "Concat" | |
bottom: "concat_3_2" | |
bottom: "conv3_3/x2" | |
top: "concat_3_3" | |
} | |
layer { | |
name: "conv3_4/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_3" | |
top: "conv3_4/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_4/x1/scale" | |
type: "Scale" | |
bottom: "conv3_4/x1/bn" | |
top: "conv3_4/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_4/x1" | |
type: "ReLU" | |
bottom: "conv3_4/x1/bn" | |
top: "conv3_4/x1/bn" | |
} | |
layer { | |
name: "conv3_4/x1" | |
type: "Convolution" | |
bottom: "conv3_4/x1/bn" | |
top: "conv3_4/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_4/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_4/x1" | |
top: "conv3_4/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_4/x2/scale" | |
type: "Scale" | |
bottom: "conv3_4/x2/bn" | |
top: "conv3_4/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_4/x2" | |
type: "ReLU" | |
bottom: "conv3_4/x2/bn" | |
top: "conv3_4/x2/bn" | |
} | |
layer { | |
name: "conv3_4/x2" | |
type: "Convolution" | |
bottom: "conv3_4/x2/bn" | |
top: "conv3_4/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_4" | |
type: "Concat" | |
bottom: "concat_3_3" | |
bottom: "conv3_4/x2" | |
top: "concat_3_4" | |
} | |
layer { | |
name: "conv3_5/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_4" | |
top: "conv3_5/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_5/x1/scale" | |
type: "Scale" | |
bottom: "conv3_5/x1/bn" | |
top: "conv3_5/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_5/x1" | |
type: "ReLU" | |
bottom: "conv3_5/x1/bn" | |
top: "conv3_5/x1/bn" | |
} | |
layer { | |
name: "conv3_5/x1" | |
type: "Convolution" | |
bottom: "conv3_5/x1/bn" | |
top: "conv3_5/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_5/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_5/x1" | |
top: "conv3_5/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_5/x2/scale" | |
type: "Scale" | |
bottom: "conv3_5/x2/bn" | |
top: "conv3_5/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_5/x2" | |
type: "ReLU" | |
bottom: "conv3_5/x2/bn" | |
top: "conv3_5/x2/bn" | |
} | |
layer { | |
name: "conv3_5/x2" | |
type: "Convolution" | |
bottom: "conv3_5/x2/bn" | |
top: "conv3_5/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_5" | |
type: "Concat" | |
bottom: "concat_3_4" | |
bottom: "conv3_5/x2" | |
top: "concat_3_5" | |
} | |
layer { | |
name: "conv3_6/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_5" | |
top: "conv3_6/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_6/x1/scale" | |
type: "Scale" | |
bottom: "conv3_6/x1/bn" | |
top: "conv3_6/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_6/x1" | |
type: "ReLU" | |
bottom: "conv3_6/x1/bn" | |
top: "conv3_6/x1/bn" | |
} | |
layer { | |
name: "conv3_6/x1" | |
type: "Convolution" | |
bottom: "conv3_6/x1/bn" | |
top: "conv3_6/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_6/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_6/x1" | |
top: "conv3_6/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_6/x2/scale" | |
type: "Scale" | |
bottom: "conv3_6/x2/bn" | |
top: "conv3_6/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_6/x2" | |
type: "ReLU" | |
bottom: "conv3_6/x2/bn" | |
top: "conv3_6/x2/bn" | |
} | |
layer { | |
name: "conv3_6/x2" | |
type: "Convolution" | |
bottom: "conv3_6/x2/bn" | |
top: "conv3_6/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_6" | |
type: "Concat" | |
bottom: "concat_3_5" | |
bottom: "conv3_6/x2" | |
top: "concat_3_6" | |
} | |
layer { | |
name: "conv3_7/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_6" | |
top: "conv3_7/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_7/x1/scale" | |
type: "Scale" | |
bottom: "conv3_7/x1/bn" | |
top: "conv3_7/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_7/x1" | |
type: "ReLU" | |
bottom: "conv3_7/x1/bn" | |
top: "conv3_7/x1/bn" | |
} | |
layer { | |
name: "conv3_7/x1" | |
type: "Convolution" | |
bottom: "conv3_7/x1/bn" | |
top: "conv3_7/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_7/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_7/x1" | |
top: "conv3_7/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_7/x2/scale" | |
type: "Scale" | |
bottom: "conv3_7/x2/bn" | |
top: "conv3_7/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_7/x2" | |
type: "ReLU" | |
bottom: "conv3_7/x2/bn" | |
top: "conv3_7/x2/bn" | |
} | |
layer { | |
name: "conv3_7/x2" | |
type: "Convolution" | |
bottom: "conv3_7/x2/bn" | |
top: "conv3_7/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_7" | |
type: "Concat" | |
bottom: "concat_3_6" | |
bottom: "conv3_7/x2" | |
top: "concat_3_7" | |
} | |
layer { | |
name: "conv3_8/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_7" | |
top: "conv3_8/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_8/x1/scale" | |
type: "Scale" | |
bottom: "conv3_8/x1/bn" | |
top: "conv3_8/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_8/x1" | |
type: "ReLU" | |
bottom: "conv3_8/x1/bn" | |
top: "conv3_8/x1/bn" | |
} | |
layer { | |
name: "conv3_8/x1" | |
type: "Convolution" | |
bottom: "conv3_8/x1/bn" | |
top: "conv3_8/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_8/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_8/x1" | |
top: "conv3_8/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_8/x2/scale" | |
type: "Scale" | |
bottom: "conv3_8/x2/bn" | |
top: "conv3_8/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_8/x2" | |
type: "ReLU" | |
bottom: "conv3_8/x2/bn" | |
top: "conv3_8/x2/bn" | |
} | |
layer { | |
name: "conv3_8/x2" | |
type: "Convolution" | |
bottom: "conv3_8/x2/bn" | |
top: "conv3_8/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_8" | |
type: "Concat" | |
bottom: "concat_3_7" | |
bottom: "conv3_8/x2" | |
top: "concat_3_8" | |
} | |
layer { | |
name: "conv3_9/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_8" | |
top: "conv3_9/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_9/x1/scale" | |
type: "Scale" | |
bottom: "conv3_9/x1/bn" | |
top: "conv3_9/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_9/x1" | |
type: "ReLU" | |
bottom: "conv3_9/x1/bn" | |
top: "conv3_9/x1/bn" | |
} | |
layer { | |
name: "conv3_9/x1" | |
type: "Convolution" | |
bottom: "conv3_9/x1/bn" | |
top: "conv3_9/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_9/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_9/x1" | |
top: "conv3_9/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_9/x2/scale" | |
type: "Scale" | |
bottom: "conv3_9/x2/bn" | |
top: "conv3_9/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_9/x2" | |
type: "ReLU" | |
bottom: "conv3_9/x2/bn" | |
top: "conv3_9/x2/bn" | |
} | |
layer { | |
name: "conv3_9/x2" | |
type: "Convolution" | |
bottom: "conv3_9/x2/bn" | |
top: "conv3_9/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_9" | |
type: "Concat" | |
bottom: "concat_3_8" | |
bottom: "conv3_9/x2" | |
top: "concat_3_9" | |
} | |
layer { | |
name: "conv3_10/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_9" | |
top: "conv3_10/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_10/x1/scale" | |
type: "Scale" | |
bottom: "conv3_10/x1/bn" | |
top: "conv3_10/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_10/x1" | |
type: "ReLU" | |
bottom: "conv3_10/x1/bn" | |
top: "conv3_10/x1/bn" | |
} | |
layer { | |
name: "conv3_10/x1" | |
type: "Convolution" | |
bottom: "conv3_10/x1/bn" | |
top: "conv3_10/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_10/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_10/x1" | |
top: "conv3_10/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_10/x2/scale" | |
type: "Scale" | |
bottom: "conv3_10/x2/bn" | |
top: "conv3_10/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_10/x2" | |
type: "ReLU" | |
bottom: "conv3_10/x2/bn" | |
top: "conv3_10/x2/bn" | |
} | |
layer { | |
name: "conv3_10/x2" | |
type: "Convolution" | |
bottom: "conv3_10/x2/bn" | |
top: "conv3_10/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_10" | |
type: "Concat" | |
bottom: "concat_3_9" | |
bottom: "conv3_10/x2" | |
top: "concat_3_10" | |
} | |
layer { | |
name: "conv3_11/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_10" | |
top: "conv3_11/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_11/x1/scale" | |
type: "Scale" | |
bottom: "conv3_11/x1/bn" | |
top: "conv3_11/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_11/x1" | |
type: "ReLU" | |
bottom: "conv3_11/x1/bn" | |
top: "conv3_11/x1/bn" | |
} | |
layer { | |
name: "conv3_11/x1" | |
type: "Convolution" | |
bottom: "conv3_11/x1/bn" | |
top: "conv3_11/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_11/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_11/x1" | |
top: "conv3_11/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_11/x2/scale" | |
type: "Scale" | |
bottom: "conv3_11/x2/bn" | |
top: "conv3_11/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_11/x2" | |
type: "ReLU" | |
bottom: "conv3_11/x2/bn" | |
top: "conv3_11/x2/bn" | |
} | |
layer { | |
name: "conv3_11/x2" | |
type: "Convolution" | |
bottom: "conv3_11/x2/bn" | |
top: "conv3_11/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_11" | |
type: "Concat" | |
bottom: "concat_3_10" | |
bottom: "conv3_11/x2" | |
top: "concat_3_11" | |
} | |
layer { | |
name: "conv3_12/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_11" | |
top: "conv3_12/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_12/x1/scale" | |
type: "Scale" | |
bottom: "conv3_12/x1/bn" | |
top: "conv3_12/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_12/x1" | |
type: "ReLU" | |
bottom: "conv3_12/x1/bn" | |
top: "conv3_12/x1/bn" | |
} | |
layer { | |
name: "conv3_12/x1" | |
type: "Convolution" | |
bottom: "conv3_12/x1/bn" | |
top: "conv3_12/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv3_12/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv3_12/x1" | |
top: "conv3_12/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_12/x2/scale" | |
type: "Scale" | |
bottom: "conv3_12/x2/bn" | |
top: "conv3_12/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_12/x2" | |
type: "ReLU" | |
bottom: "conv3_12/x2/bn" | |
top: "conv3_12/x2/bn" | |
} | |
layer { | |
name: "conv3_12/x2" | |
type: "Convolution" | |
bottom: "conv3_12/x2/bn" | |
top: "conv3_12/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_3_12" | |
type: "Concat" | |
bottom: "concat_3_11" | |
bottom: "conv3_12/x2" | |
top: "concat_3_12" | |
} | |
layer { | |
name: "conv3_blk/bn" | |
type: "BatchNorm" | |
bottom: "concat_3_12" | |
top: "conv3_blk/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv3_blk/scale" | |
type: "Scale" | |
bottom: "conv3_blk/bn" | |
top: "conv3_blk/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu3_blk" | |
type: "ReLU" | |
bottom: "conv3_blk/bn" | |
top: "conv3_blk/bn" | |
} | |
layer { | |
name: "conv3_blk" | |
type: "Convolution" | |
bottom: "conv3_blk/bn" | |
top: "conv3_blk" | |
convolution_param { | |
num_output: 384 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "conv3_blk" | |
top: "pool3" | |
pooling_param { | |
pool: AVE | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv4_1/x1/bn" | |
type: "BatchNorm" | |
bottom: "pool3" | |
top: "conv4_1/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_1/x1/scale" | |
type: "Scale" | |
bottom: "conv4_1/x1/bn" | |
top: "conv4_1/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_1/x1" | |
type: "ReLU" | |
bottom: "conv4_1/x1/bn" | |
top: "conv4_1/x1/bn" | |
} | |
layer { | |
name: "conv4_1/x1" | |
type: "Convolution" | |
bottom: "conv4_1/x1/bn" | |
top: "conv4_1/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_1/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_1/x1" | |
top: "conv4_1/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_1/x2/scale" | |
type: "Scale" | |
bottom: "conv4_1/x2/bn" | |
top: "conv4_1/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_1/x2" | |
type: "ReLU" | |
bottom: "conv4_1/x2/bn" | |
top: "conv4_1/x2/bn" | |
} | |
layer { | |
name: "conv4_1/x2" | |
type: "Convolution" | |
bottom: "conv4_1/x2/bn" | |
top: "conv4_1/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_1" | |
type: "Concat" | |
bottom: "pool3" | |
bottom: "conv4_1/x2" | |
top: "concat_4_1" | |
} | |
layer { | |
name: "conv4_2/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_1" | |
top: "conv4_2/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_2/x1/scale" | |
type: "Scale" | |
bottom: "conv4_2/x1/bn" | |
top: "conv4_2/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_2/x1" | |
type: "ReLU" | |
bottom: "conv4_2/x1/bn" | |
top: "conv4_2/x1/bn" | |
} | |
layer { | |
name: "conv4_2/x1" | |
type: "Convolution" | |
bottom: "conv4_2/x1/bn" | |
top: "conv4_2/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_2/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_2/x1" | |
top: "conv4_2/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_2/x2/scale" | |
type: "Scale" | |
bottom: "conv4_2/x2/bn" | |
top: "conv4_2/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_2/x2" | |
type: "ReLU" | |
bottom: "conv4_2/x2/bn" | |
top: "conv4_2/x2/bn" | |
} | |
layer { | |
name: "conv4_2/x2" | |
type: "Convolution" | |
bottom: "conv4_2/x2/bn" | |
top: "conv4_2/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_2" | |
type: "Concat" | |
bottom: "concat_4_1" | |
bottom: "conv4_2/x2" | |
top: "concat_4_2" | |
} | |
layer { | |
name: "conv4_3/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_2" | |
top: "conv4_3/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_3/x1/scale" | |
type: "Scale" | |
bottom: "conv4_3/x1/bn" | |
top: "conv4_3/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_3/x1" | |
type: "ReLU" | |
bottom: "conv4_3/x1/bn" | |
top: "conv4_3/x1/bn" | |
} | |
layer { | |
name: "conv4_3/x1" | |
type: "Convolution" | |
bottom: "conv4_3/x1/bn" | |
top: "conv4_3/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_3/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_3/x1" | |
top: "conv4_3/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_3/x2/scale" | |
type: "Scale" | |
bottom: "conv4_3/x2/bn" | |
top: "conv4_3/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_3/x2" | |
type: "ReLU" | |
bottom: "conv4_3/x2/bn" | |
top: "conv4_3/x2/bn" | |
} | |
layer { | |
name: "conv4_3/x2" | |
type: "Convolution" | |
bottom: "conv4_3/x2/bn" | |
top: "conv4_3/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_3" | |
type: "Concat" | |
bottom: "concat_4_2" | |
bottom: "conv4_3/x2" | |
top: "concat_4_3" | |
} | |
layer { | |
name: "conv4_4/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_3" | |
top: "conv4_4/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_4/x1/scale" | |
type: "Scale" | |
bottom: "conv4_4/x1/bn" | |
top: "conv4_4/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_4/x1" | |
type: "ReLU" | |
bottom: "conv4_4/x1/bn" | |
top: "conv4_4/x1/bn" | |
} | |
layer { | |
name: "conv4_4/x1" | |
type: "Convolution" | |
bottom: "conv4_4/x1/bn" | |
top: "conv4_4/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_4/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_4/x1" | |
top: "conv4_4/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_4/x2/scale" | |
type: "Scale" | |
bottom: "conv4_4/x2/bn" | |
top: "conv4_4/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_4/x2" | |
type: "ReLU" | |
bottom: "conv4_4/x2/bn" | |
top: "conv4_4/x2/bn" | |
} | |
layer { | |
name: "conv4_4/x2" | |
type: "Convolution" | |
bottom: "conv4_4/x2/bn" | |
top: "conv4_4/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_4" | |
type: "Concat" | |
bottom: "concat_4_3" | |
bottom: "conv4_4/x2" | |
top: "concat_4_4" | |
} | |
layer { | |
name: "conv4_5/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_4" | |
top: "conv4_5/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_5/x1/scale" | |
type: "Scale" | |
bottom: "conv4_5/x1/bn" | |
top: "conv4_5/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_5/x1" | |
type: "ReLU" | |
bottom: "conv4_5/x1/bn" | |
top: "conv4_5/x1/bn" | |
} | |
layer { | |
name: "conv4_5/x1" | |
type: "Convolution" | |
bottom: "conv4_5/x1/bn" | |
top: "conv4_5/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_5/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_5/x1" | |
top: "conv4_5/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_5/x2/scale" | |
type: "Scale" | |
bottom: "conv4_5/x2/bn" | |
top: "conv4_5/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_5/x2" | |
type: "ReLU" | |
bottom: "conv4_5/x2/bn" | |
top: "conv4_5/x2/bn" | |
} | |
layer { | |
name: "conv4_5/x2" | |
type: "Convolution" | |
bottom: "conv4_5/x2/bn" | |
top: "conv4_5/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_5" | |
type: "Concat" | |
bottom: "concat_4_4" | |
bottom: "conv4_5/x2" | |
top: "concat_4_5" | |
} | |
layer { | |
name: "conv4_6/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_5" | |
top: "conv4_6/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_6/x1/scale" | |
type: "Scale" | |
bottom: "conv4_6/x1/bn" | |
top: "conv4_6/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_6/x1" | |
type: "ReLU" | |
bottom: "conv4_6/x1/bn" | |
top: "conv4_6/x1/bn" | |
} | |
layer { | |
name: "conv4_6/x1" | |
type: "Convolution" | |
bottom: "conv4_6/x1/bn" | |
top: "conv4_6/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_6/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_6/x1" | |
top: "conv4_6/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_6/x2/scale" | |
type: "Scale" | |
bottom: "conv4_6/x2/bn" | |
top: "conv4_6/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_6/x2" | |
type: "ReLU" | |
bottom: "conv4_6/x2/bn" | |
top: "conv4_6/x2/bn" | |
} | |
layer { | |
name: "conv4_6/x2" | |
type: "Convolution" | |
bottom: "conv4_6/x2/bn" | |
top: "conv4_6/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_6" | |
type: "Concat" | |
bottom: "concat_4_5" | |
bottom: "conv4_6/x2" | |
top: "concat_4_6" | |
} | |
layer { | |
name: "conv4_7/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_6" | |
top: "conv4_7/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_7/x1/scale" | |
type: "Scale" | |
bottom: "conv4_7/x1/bn" | |
top: "conv4_7/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_7/x1" | |
type: "ReLU" | |
bottom: "conv4_7/x1/bn" | |
top: "conv4_7/x1/bn" | |
} | |
layer { | |
name: "conv4_7/x1" | |
type: "Convolution" | |
bottom: "conv4_7/x1/bn" | |
top: "conv4_7/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_7/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_7/x1" | |
top: "conv4_7/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_7/x2/scale" | |
type: "Scale" | |
bottom: "conv4_7/x2/bn" | |
top: "conv4_7/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_7/x2" | |
type: "ReLU" | |
bottom: "conv4_7/x2/bn" | |
top: "conv4_7/x2/bn" | |
} | |
layer { | |
name: "conv4_7/x2" | |
type: "Convolution" | |
bottom: "conv4_7/x2/bn" | |
top: "conv4_7/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_7" | |
type: "Concat" | |
bottom: "concat_4_6" | |
bottom: "conv4_7/x2" | |
top: "concat_4_7" | |
} | |
layer { | |
name: "conv4_8/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_7" | |
top: "conv4_8/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_8/x1/scale" | |
type: "Scale" | |
bottom: "conv4_8/x1/bn" | |
top: "conv4_8/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_8/x1" | |
type: "ReLU" | |
bottom: "conv4_8/x1/bn" | |
top: "conv4_8/x1/bn" | |
} | |
layer { | |
name: "conv4_8/x1" | |
type: "Convolution" | |
bottom: "conv4_8/x1/bn" | |
top: "conv4_8/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_8/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_8/x1" | |
top: "conv4_8/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_8/x2/scale" | |
type: "Scale" | |
bottom: "conv4_8/x2/bn" | |
top: "conv4_8/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_8/x2" | |
type: "ReLU" | |
bottom: "conv4_8/x2/bn" | |
top: "conv4_8/x2/bn" | |
} | |
layer { | |
name: "conv4_8/x2" | |
type: "Convolution" | |
bottom: "conv4_8/x2/bn" | |
top: "conv4_8/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_8" | |
type: "Concat" | |
bottom: "concat_4_7" | |
bottom: "conv4_8/x2" | |
top: "concat_4_8" | |
} | |
layer { | |
name: "conv4_9/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_8" | |
top: "conv4_9/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_9/x1/scale" | |
type: "Scale" | |
bottom: "conv4_9/x1/bn" | |
top: "conv4_9/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_9/x1" | |
type: "ReLU" | |
bottom: "conv4_9/x1/bn" | |
top: "conv4_9/x1/bn" | |
} | |
layer { | |
name: "conv4_9/x1" | |
type: "Convolution" | |
bottom: "conv4_9/x1/bn" | |
top: "conv4_9/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_9/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_9/x1" | |
top: "conv4_9/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_9/x2/scale" | |
type: "Scale" | |
bottom: "conv4_9/x2/bn" | |
top: "conv4_9/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_9/x2" | |
type: "ReLU" | |
bottom: "conv4_9/x2/bn" | |
top: "conv4_9/x2/bn" | |
} | |
layer { | |
name: "conv4_9/x2" | |
type: "Convolution" | |
bottom: "conv4_9/x2/bn" | |
top: "conv4_9/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_9" | |
type: "Concat" | |
bottom: "concat_4_8" | |
bottom: "conv4_9/x2" | |
top: "concat_4_9" | |
} | |
layer { | |
name: "conv4_10/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_9" | |
top: "conv4_10/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_10/x1/scale" | |
type: "Scale" | |
bottom: "conv4_10/x1/bn" | |
top: "conv4_10/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_10/x1" | |
type: "ReLU" | |
bottom: "conv4_10/x1/bn" | |
top: "conv4_10/x1/bn" | |
} | |
layer { | |
name: "conv4_10/x1" | |
type: "Convolution" | |
bottom: "conv4_10/x1/bn" | |
top: "conv4_10/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_10/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_10/x1" | |
top: "conv4_10/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_10/x2/scale" | |
type: "Scale" | |
bottom: "conv4_10/x2/bn" | |
top: "conv4_10/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_10/x2" | |
type: "ReLU" | |
bottom: "conv4_10/x2/bn" | |
top: "conv4_10/x2/bn" | |
} | |
layer { | |
name: "conv4_10/x2" | |
type: "Convolution" | |
bottom: "conv4_10/x2/bn" | |
top: "conv4_10/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_10" | |
type: "Concat" | |
bottom: "concat_4_9" | |
bottom: "conv4_10/x2" | |
top: "concat_4_10" | |
} | |
layer { | |
name: "conv4_11/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_10" | |
top: "conv4_11/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_11/x1/scale" | |
type: "Scale" | |
bottom: "conv4_11/x1/bn" | |
top: "conv4_11/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_11/x1" | |
type: "ReLU" | |
bottom: "conv4_11/x1/bn" | |
top: "conv4_11/x1/bn" | |
} | |
layer { | |
name: "conv4_11/x1" | |
type: "Convolution" | |
bottom: "conv4_11/x1/bn" | |
top: "conv4_11/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_11/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_11/x1" | |
top: "conv4_11/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_11/x2/scale" | |
type: "Scale" | |
bottom: "conv4_11/x2/bn" | |
top: "conv4_11/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_11/x2" | |
type: "ReLU" | |
bottom: "conv4_11/x2/bn" | |
top: "conv4_11/x2/bn" | |
} | |
layer { | |
name: "conv4_11/x2" | |
type: "Convolution" | |
bottom: "conv4_11/x2/bn" | |
top: "conv4_11/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_11" | |
type: "Concat" | |
bottom: "concat_4_10" | |
bottom: "conv4_11/x2" | |
top: "concat_4_11" | |
} | |
layer { | |
name: "conv4_12/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_11" | |
top: "conv4_12/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_12/x1/scale" | |
type: "Scale" | |
bottom: "conv4_12/x1/bn" | |
top: "conv4_12/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_12/x1" | |
type: "ReLU" | |
bottom: "conv4_12/x1/bn" | |
top: "conv4_12/x1/bn" | |
} | |
layer { | |
name: "conv4_12/x1" | |
type: "Convolution" | |
bottom: "conv4_12/x1/bn" | |
top: "conv4_12/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_12/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_12/x1" | |
top: "conv4_12/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_12/x2/scale" | |
type: "Scale" | |
bottom: "conv4_12/x2/bn" | |
top: "conv4_12/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_12/x2" | |
type: "ReLU" | |
bottom: "conv4_12/x2/bn" | |
top: "conv4_12/x2/bn" | |
} | |
layer { | |
name: "conv4_12/x2" | |
type: "Convolution" | |
bottom: "conv4_12/x2/bn" | |
top: "conv4_12/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_12" | |
type: "Concat" | |
bottom: "concat_4_11" | |
bottom: "conv4_12/x2" | |
top: "concat_4_12" | |
} | |
layer { | |
name: "conv4_13/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_12" | |
top: "conv4_13/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_13/x1/scale" | |
type: "Scale" | |
bottom: "conv4_13/x1/bn" | |
top: "conv4_13/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_13/x1" | |
type: "ReLU" | |
bottom: "conv4_13/x1/bn" | |
top: "conv4_13/x1/bn" | |
} | |
layer { | |
name: "conv4_13/x1" | |
type: "Convolution" | |
bottom: "conv4_13/x1/bn" | |
top: "conv4_13/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_13/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_13/x1" | |
top: "conv4_13/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_13/x2/scale" | |
type: "Scale" | |
bottom: "conv4_13/x2/bn" | |
top: "conv4_13/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_13/x2" | |
type: "ReLU" | |
bottom: "conv4_13/x2/bn" | |
top: "conv4_13/x2/bn" | |
} | |
layer { | |
name: "conv4_13/x2" | |
type: "Convolution" | |
bottom: "conv4_13/x2/bn" | |
top: "conv4_13/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_13" | |
type: "Concat" | |
bottom: "concat_4_12" | |
bottom: "conv4_13/x2" | |
top: "concat_4_13" | |
} | |
layer { | |
name: "conv4_14/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_13" | |
top: "conv4_14/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_14/x1/scale" | |
type: "Scale" | |
bottom: "conv4_14/x1/bn" | |
top: "conv4_14/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_14/x1" | |
type: "ReLU" | |
bottom: "conv4_14/x1/bn" | |
top: "conv4_14/x1/bn" | |
} | |
layer { | |
name: "conv4_14/x1" | |
type: "Convolution" | |
bottom: "conv4_14/x1/bn" | |
top: "conv4_14/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_14/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_14/x1" | |
top: "conv4_14/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_14/x2/scale" | |
type: "Scale" | |
bottom: "conv4_14/x2/bn" | |
top: "conv4_14/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_14/x2" | |
type: "ReLU" | |
bottom: "conv4_14/x2/bn" | |
top: "conv4_14/x2/bn" | |
} | |
layer { | |
name: "conv4_14/x2" | |
type: "Convolution" | |
bottom: "conv4_14/x2/bn" | |
top: "conv4_14/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_14" | |
type: "Concat" | |
bottom: "concat_4_13" | |
bottom: "conv4_14/x2" | |
top: "concat_4_14" | |
} | |
layer { | |
name: "conv4_15/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_14" | |
top: "conv4_15/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_15/x1/scale" | |
type: "Scale" | |
bottom: "conv4_15/x1/bn" | |
top: "conv4_15/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_15/x1" | |
type: "ReLU" | |
bottom: "conv4_15/x1/bn" | |
top: "conv4_15/x1/bn" | |
} | |
layer { | |
name: "conv4_15/x1" | |
type: "Convolution" | |
bottom: "conv4_15/x1/bn" | |
top: "conv4_15/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_15/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_15/x1" | |
top: "conv4_15/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_15/x2/scale" | |
type: "Scale" | |
bottom: "conv4_15/x2/bn" | |
top: "conv4_15/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_15/x2" | |
type: "ReLU" | |
bottom: "conv4_15/x2/bn" | |
top: "conv4_15/x2/bn" | |
} | |
layer { | |
name: "conv4_15/x2" | |
type: "Convolution" | |
bottom: "conv4_15/x2/bn" | |
top: "conv4_15/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_15" | |
type: "Concat" | |
bottom: "concat_4_14" | |
bottom: "conv4_15/x2" | |
top: "concat_4_15" | |
} | |
layer { | |
name: "conv4_16/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_15" | |
top: "conv4_16/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_16/x1/scale" | |
type: "Scale" | |
bottom: "conv4_16/x1/bn" | |
top: "conv4_16/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_16/x1" | |
type: "ReLU" | |
bottom: "conv4_16/x1/bn" | |
top: "conv4_16/x1/bn" | |
} | |
layer { | |
name: "conv4_16/x1" | |
type: "Convolution" | |
bottom: "conv4_16/x1/bn" | |
top: "conv4_16/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_16/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_16/x1" | |
top: "conv4_16/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_16/x2/scale" | |
type: "Scale" | |
bottom: "conv4_16/x2/bn" | |
top: "conv4_16/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_16/x2" | |
type: "ReLU" | |
bottom: "conv4_16/x2/bn" | |
top: "conv4_16/x2/bn" | |
} | |
layer { | |
name: "conv4_16/x2" | |
type: "Convolution" | |
bottom: "conv4_16/x2/bn" | |
top: "conv4_16/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_16" | |
type: "Concat" | |
bottom: "concat_4_15" | |
bottom: "conv4_16/x2" | |
top: "concat_4_16" | |
} | |
layer { | |
name: "conv4_17/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_16" | |
top: "conv4_17/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_17/x1/scale" | |
type: "Scale" | |
bottom: "conv4_17/x1/bn" | |
top: "conv4_17/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_17/x1" | |
type: "ReLU" | |
bottom: "conv4_17/x1/bn" | |
top: "conv4_17/x1/bn" | |
} | |
layer { | |
name: "conv4_17/x1" | |
type: "Convolution" | |
bottom: "conv4_17/x1/bn" | |
top: "conv4_17/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_17/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_17/x1" | |
top: "conv4_17/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_17/x2/scale" | |
type: "Scale" | |
bottom: "conv4_17/x2/bn" | |
top: "conv4_17/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_17/x2" | |
type: "ReLU" | |
bottom: "conv4_17/x2/bn" | |
top: "conv4_17/x2/bn" | |
} | |
layer { | |
name: "conv4_17/x2" | |
type: "Convolution" | |
bottom: "conv4_17/x2/bn" | |
top: "conv4_17/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_17" | |
type: "Concat" | |
bottom: "concat_4_16" | |
bottom: "conv4_17/x2" | |
top: "concat_4_17" | |
} | |
layer { | |
name: "conv4_18/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_17" | |
top: "conv4_18/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_18/x1/scale" | |
type: "Scale" | |
bottom: "conv4_18/x1/bn" | |
top: "conv4_18/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_18/x1" | |
type: "ReLU" | |
bottom: "conv4_18/x1/bn" | |
top: "conv4_18/x1/bn" | |
} | |
layer { | |
name: "conv4_18/x1" | |
type: "Convolution" | |
bottom: "conv4_18/x1/bn" | |
top: "conv4_18/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_18/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_18/x1" | |
top: "conv4_18/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_18/x2/scale" | |
type: "Scale" | |
bottom: "conv4_18/x2/bn" | |
top: "conv4_18/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_18/x2" | |
type: "ReLU" | |
bottom: "conv4_18/x2/bn" | |
top: "conv4_18/x2/bn" | |
} | |
layer { | |
name: "conv4_18/x2" | |
type: "Convolution" | |
bottom: "conv4_18/x2/bn" | |
top: "conv4_18/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_18" | |
type: "Concat" | |
bottom: "concat_4_17" | |
bottom: "conv4_18/x2" | |
top: "concat_4_18" | |
} | |
layer { | |
name: "conv4_19/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_18" | |
top: "conv4_19/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_19/x1/scale" | |
type: "Scale" | |
bottom: "conv4_19/x1/bn" | |
top: "conv4_19/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_19/x1" | |
type: "ReLU" | |
bottom: "conv4_19/x1/bn" | |
top: "conv4_19/x1/bn" | |
} | |
layer { | |
name: "conv4_19/x1" | |
type: "Convolution" | |
bottom: "conv4_19/x1/bn" | |
top: "conv4_19/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_19/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_19/x1" | |
top: "conv4_19/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_19/x2/scale" | |
type: "Scale" | |
bottom: "conv4_19/x2/bn" | |
top: "conv4_19/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_19/x2" | |
type: "ReLU" | |
bottom: "conv4_19/x2/bn" | |
top: "conv4_19/x2/bn" | |
} | |
layer { | |
name: "conv4_19/x2" | |
type: "Convolution" | |
bottom: "conv4_19/x2/bn" | |
top: "conv4_19/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_19" | |
type: "Concat" | |
bottom: "concat_4_18" | |
bottom: "conv4_19/x2" | |
top: "concat_4_19" | |
} | |
layer { | |
name: "conv4_20/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_19" | |
top: "conv4_20/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_20/x1/scale" | |
type: "Scale" | |
bottom: "conv4_20/x1/bn" | |
top: "conv4_20/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_20/x1" | |
type: "ReLU" | |
bottom: "conv4_20/x1/bn" | |
top: "conv4_20/x1/bn" | |
} | |
layer { | |
name: "conv4_20/x1" | |
type: "Convolution" | |
bottom: "conv4_20/x1/bn" | |
top: "conv4_20/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_20/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_20/x1" | |
top: "conv4_20/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_20/x2/scale" | |
type: "Scale" | |
bottom: "conv4_20/x2/bn" | |
top: "conv4_20/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_20/x2" | |
type: "ReLU" | |
bottom: "conv4_20/x2/bn" | |
top: "conv4_20/x2/bn" | |
} | |
layer { | |
name: "conv4_20/x2" | |
type: "Convolution" | |
bottom: "conv4_20/x2/bn" | |
top: "conv4_20/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_20" | |
type: "Concat" | |
bottom: "concat_4_19" | |
bottom: "conv4_20/x2" | |
top: "concat_4_20" | |
} | |
layer { | |
name: "conv4_21/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_20" | |
top: "conv4_21/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_21/x1/scale" | |
type: "Scale" | |
bottom: "conv4_21/x1/bn" | |
top: "conv4_21/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_21/x1" | |
type: "ReLU" | |
bottom: "conv4_21/x1/bn" | |
top: "conv4_21/x1/bn" | |
} | |
layer { | |
name: "conv4_21/x1" | |
type: "Convolution" | |
bottom: "conv4_21/x1/bn" | |
top: "conv4_21/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_21/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_21/x1" | |
top: "conv4_21/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_21/x2/scale" | |
type: "Scale" | |
bottom: "conv4_21/x2/bn" | |
top: "conv4_21/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_21/x2" | |
type: "ReLU" | |
bottom: "conv4_21/x2/bn" | |
top: "conv4_21/x2/bn" | |
} | |
layer { | |
name: "conv4_21/x2" | |
type: "Convolution" | |
bottom: "conv4_21/x2/bn" | |
top: "conv4_21/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_21" | |
type: "Concat" | |
bottom: "concat_4_20" | |
bottom: "conv4_21/x2" | |
top: "concat_4_21" | |
} | |
layer { | |
name: "conv4_22/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_21" | |
top: "conv4_22/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_22/x1/scale" | |
type: "Scale" | |
bottom: "conv4_22/x1/bn" | |
top: "conv4_22/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_22/x1" | |
type: "ReLU" | |
bottom: "conv4_22/x1/bn" | |
top: "conv4_22/x1/bn" | |
} | |
layer { | |
name: "conv4_22/x1" | |
type: "Convolution" | |
bottom: "conv4_22/x1/bn" | |
top: "conv4_22/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_22/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_22/x1" | |
top: "conv4_22/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_22/x2/scale" | |
type: "Scale" | |
bottom: "conv4_22/x2/bn" | |
top: "conv4_22/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_22/x2" | |
type: "ReLU" | |
bottom: "conv4_22/x2/bn" | |
top: "conv4_22/x2/bn" | |
} | |
layer { | |
name: "conv4_22/x2" | |
type: "Convolution" | |
bottom: "conv4_22/x2/bn" | |
top: "conv4_22/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_22" | |
type: "Concat" | |
bottom: "concat_4_21" | |
bottom: "conv4_22/x2" | |
top: "concat_4_22" | |
} | |
layer { | |
name: "conv4_23/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_22" | |
top: "conv4_23/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_23/x1/scale" | |
type: "Scale" | |
bottom: "conv4_23/x1/bn" | |
top: "conv4_23/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_23/x1" | |
type: "ReLU" | |
bottom: "conv4_23/x1/bn" | |
top: "conv4_23/x1/bn" | |
} | |
layer { | |
name: "conv4_23/x1" | |
type: "Convolution" | |
bottom: "conv4_23/x1/bn" | |
top: "conv4_23/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_23/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_23/x1" | |
top: "conv4_23/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_23/x2/scale" | |
type: "Scale" | |
bottom: "conv4_23/x2/bn" | |
top: "conv4_23/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_23/x2" | |
type: "ReLU" | |
bottom: "conv4_23/x2/bn" | |
top: "conv4_23/x2/bn" | |
} | |
layer { | |
name: "conv4_23/x2" | |
type: "Convolution" | |
bottom: "conv4_23/x2/bn" | |
top: "conv4_23/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_23" | |
type: "Concat" | |
bottom: "concat_4_22" | |
bottom: "conv4_23/x2" | |
top: "concat_4_23" | |
} | |
layer { | |
name: "conv4_24/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_23" | |
top: "conv4_24/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_24/x1/scale" | |
type: "Scale" | |
bottom: "conv4_24/x1/bn" | |
top: "conv4_24/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_24/x1" | |
type: "ReLU" | |
bottom: "conv4_24/x1/bn" | |
top: "conv4_24/x1/bn" | |
} | |
layer { | |
name: "conv4_24/x1" | |
type: "Convolution" | |
bottom: "conv4_24/x1/bn" | |
top: "conv4_24/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_24/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_24/x1" | |
top: "conv4_24/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_24/x2/scale" | |
type: "Scale" | |
bottom: "conv4_24/x2/bn" | |
top: "conv4_24/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_24/x2" | |
type: "ReLU" | |
bottom: "conv4_24/x2/bn" | |
top: "conv4_24/x2/bn" | |
} | |
layer { | |
name: "conv4_24/x2" | |
type: "Convolution" | |
bottom: "conv4_24/x2/bn" | |
top: "conv4_24/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_24" | |
type: "Concat" | |
bottom: "concat_4_23" | |
bottom: "conv4_24/x2" | |
top: "concat_4_24" | |
} | |
layer { | |
name: "conv4_25/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_24" | |
top: "conv4_25/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_25/x1/scale" | |
type: "Scale" | |
bottom: "conv4_25/x1/bn" | |
top: "conv4_25/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_25/x1" | |
type: "ReLU" | |
bottom: "conv4_25/x1/bn" | |
top: "conv4_25/x1/bn" | |
} | |
layer { | |
name: "conv4_25/x1" | |
type: "Convolution" | |
bottom: "conv4_25/x1/bn" | |
top: "conv4_25/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_25/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_25/x1" | |
top: "conv4_25/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_25/x2/scale" | |
type: "Scale" | |
bottom: "conv4_25/x2/bn" | |
top: "conv4_25/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_25/x2" | |
type: "ReLU" | |
bottom: "conv4_25/x2/bn" | |
top: "conv4_25/x2/bn" | |
} | |
layer { | |
name: "conv4_25/x2" | |
type: "Convolution" | |
bottom: "conv4_25/x2/bn" | |
top: "conv4_25/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_25" | |
type: "Concat" | |
bottom: "concat_4_24" | |
bottom: "conv4_25/x2" | |
top: "concat_4_25" | |
} | |
layer { | |
name: "conv4_26/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_25" | |
top: "conv4_26/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_26/x1/scale" | |
type: "Scale" | |
bottom: "conv4_26/x1/bn" | |
top: "conv4_26/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_26/x1" | |
type: "ReLU" | |
bottom: "conv4_26/x1/bn" | |
top: "conv4_26/x1/bn" | |
} | |
layer { | |
name: "conv4_26/x1" | |
type: "Convolution" | |
bottom: "conv4_26/x1/bn" | |
top: "conv4_26/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_26/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_26/x1" | |
top: "conv4_26/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_26/x2/scale" | |
type: "Scale" | |
bottom: "conv4_26/x2/bn" | |
top: "conv4_26/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_26/x2" | |
type: "ReLU" | |
bottom: "conv4_26/x2/bn" | |
top: "conv4_26/x2/bn" | |
} | |
layer { | |
name: "conv4_26/x2" | |
type: "Convolution" | |
bottom: "conv4_26/x2/bn" | |
top: "conv4_26/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_26" | |
type: "Concat" | |
bottom: "concat_4_25" | |
bottom: "conv4_26/x2" | |
top: "concat_4_26" | |
} | |
layer { | |
name: "conv4_27/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_26" | |
top: "conv4_27/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_27/x1/scale" | |
type: "Scale" | |
bottom: "conv4_27/x1/bn" | |
top: "conv4_27/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_27/x1" | |
type: "ReLU" | |
bottom: "conv4_27/x1/bn" | |
top: "conv4_27/x1/bn" | |
} | |
layer { | |
name: "conv4_27/x1" | |
type: "Convolution" | |
bottom: "conv4_27/x1/bn" | |
top: "conv4_27/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_27/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_27/x1" | |
top: "conv4_27/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_27/x2/scale" | |
type: "Scale" | |
bottom: "conv4_27/x2/bn" | |
top: "conv4_27/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_27/x2" | |
type: "ReLU" | |
bottom: "conv4_27/x2/bn" | |
top: "conv4_27/x2/bn" | |
} | |
layer { | |
name: "conv4_27/x2" | |
type: "Convolution" | |
bottom: "conv4_27/x2/bn" | |
top: "conv4_27/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_27" | |
type: "Concat" | |
bottom: "concat_4_26" | |
bottom: "conv4_27/x2" | |
top: "concat_4_27" | |
} | |
layer { | |
name: "conv4_28/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_27" | |
top: "conv4_28/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_28/x1/scale" | |
type: "Scale" | |
bottom: "conv4_28/x1/bn" | |
top: "conv4_28/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_28/x1" | |
type: "ReLU" | |
bottom: "conv4_28/x1/bn" | |
top: "conv4_28/x1/bn" | |
} | |
layer { | |
name: "conv4_28/x1" | |
type: "Convolution" | |
bottom: "conv4_28/x1/bn" | |
top: "conv4_28/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_28/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_28/x1" | |
top: "conv4_28/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_28/x2/scale" | |
type: "Scale" | |
bottom: "conv4_28/x2/bn" | |
top: "conv4_28/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_28/x2" | |
type: "ReLU" | |
bottom: "conv4_28/x2/bn" | |
top: "conv4_28/x2/bn" | |
} | |
layer { | |
name: "conv4_28/x2" | |
type: "Convolution" | |
bottom: "conv4_28/x2/bn" | |
top: "conv4_28/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_28" | |
type: "Concat" | |
bottom: "concat_4_27" | |
bottom: "conv4_28/x2" | |
top: "concat_4_28" | |
} | |
layer { | |
name: "conv4_29/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_28" | |
top: "conv4_29/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_29/x1/scale" | |
type: "Scale" | |
bottom: "conv4_29/x1/bn" | |
top: "conv4_29/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_29/x1" | |
type: "ReLU" | |
bottom: "conv4_29/x1/bn" | |
top: "conv4_29/x1/bn" | |
} | |
layer { | |
name: "conv4_29/x1" | |
type: "Convolution" | |
bottom: "conv4_29/x1/bn" | |
top: "conv4_29/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_29/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_29/x1" | |
top: "conv4_29/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_29/x2/scale" | |
type: "Scale" | |
bottom: "conv4_29/x2/bn" | |
top: "conv4_29/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_29/x2" | |
type: "ReLU" | |
bottom: "conv4_29/x2/bn" | |
top: "conv4_29/x2/bn" | |
} | |
layer { | |
name: "conv4_29/x2" | |
type: "Convolution" | |
bottom: "conv4_29/x2/bn" | |
top: "conv4_29/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_29" | |
type: "Concat" | |
bottom: "concat_4_28" | |
bottom: "conv4_29/x2" | |
top: "concat_4_29" | |
} | |
layer { | |
name: "conv4_30/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_29" | |
top: "conv4_30/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_30/x1/scale" | |
type: "Scale" | |
bottom: "conv4_30/x1/bn" | |
top: "conv4_30/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_30/x1" | |
type: "ReLU" | |
bottom: "conv4_30/x1/bn" | |
top: "conv4_30/x1/bn" | |
} | |
layer { | |
name: "conv4_30/x1" | |
type: "Convolution" | |
bottom: "conv4_30/x1/bn" | |
top: "conv4_30/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_30/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_30/x1" | |
top: "conv4_30/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_30/x2/scale" | |
type: "Scale" | |
bottom: "conv4_30/x2/bn" | |
top: "conv4_30/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_30/x2" | |
type: "ReLU" | |
bottom: "conv4_30/x2/bn" | |
top: "conv4_30/x2/bn" | |
} | |
layer { | |
name: "conv4_30/x2" | |
type: "Convolution" | |
bottom: "conv4_30/x2/bn" | |
top: "conv4_30/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_30" | |
type: "Concat" | |
bottom: "concat_4_29" | |
bottom: "conv4_30/x2" | |
top: "concat_4_30" | |
} | |
layer { | |
name: "conv4_31/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_30" | |
top: "conv4_31/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_31/x1/scale" | |
type: "Scale" | |
bottom: "conv4_31/x1/bn" | |
top: "conv4_31/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_31/x1" | |
type: "ReLU" | |
bottom: "conv4_31/x1/bn" | |
top: "conv4_31/x1/bn" | |
} | |
layer { | |
name: "conv4_31/x1" | |
type: "Convolution" | |
bottom: "conv4_31/x1/bn" | |
top: "conv4_31/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_31/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_31/x1" | |
top: "conv4_31/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_31/x2/scale" | |
type: "Scale" | |
bottom: "conv4_31/x2/bn" | |
top: "conv4_31/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_31/x2" | |
type: "ReLU" | |
bottom: "conv4_31/x2/bn" | |
top: "conv4_31/x2/bn" | |
} | |
layer { | |
name: "conv4_31/x2" | |
type: "Convolution" | |
bottom: "conv4_31/x2/bn" | |
top: "conv4_31/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_31" | |
type: "Concat" | |
bottom: "concat_4_30" | |
bottom: "conv4_31/x2" | |
top: "concat_4_31" | |
} | |
layer { | |
name: "conv4_32/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_31" | |
top: "conv4_32/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_32/x1/scale" | |
type: "Scale" | |
bottom: "conv4_32/x1/bn" | |
top: "conv4_32/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_32/x1" | |
type: "ReLU" | |
bottom: "conv4_32/x1/bn" | |
top: "conv4_32/x1/bn" | |
} | |
layer { | |
name: "conv4_32/x1" | |
type: "Convolution" | |
bottom: "conv4_32/x1/bn" | |
top: "conv4_32/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_32/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_32/x1" | |
top: "conv4_32/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_32/x2/scale" | |
type: "Scale" | |
bottom: "conv4_32/x2/bn" | |
top: "conv4_32/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_32/x2" | |
type: "ReLU" | |
bottom: "conv4_32/x2/bn" | |
top: "conv4_32/x2/bn" | |
} | |
layer { | |
name: "conv4_32/x2" | |
type: "Convolution" | |
bottom: "conv4_32/x2/bn" | |
top: "conv4_32/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_32" | |
type: "Concat" | |
bottom: "concat_4_31" | |
bottom: "conv4_32/x2" | |
top: "concat_4_32" | |
} | |
layer { | |
name: "conv4_33/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_32" | |
top: "conv4_33/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_33/x1/scale" | |
type: "Scale" | |
bottom: "conv4_33/x1/bn" | |
top: "conv4_33/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_33/x1" | |
type: "ReLU" | |
bottom: "conv4_33/x1/bn" | |
top: "conv4_33/x1/bn" | |
} | |
layer { | |
name: "conv4_33/x1" | |
type: "Convolution" | |
bottom: "conv4_33/x1/bn" | |
top: "conv4_33/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_33/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_33/x1" | |
top: "conv4_33/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_33/x2/scale" | |
type: "Scale" | |
bottom: "conv4_33/x2/bn" | |
top: "conv4_33/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_33/x2" | |
type: "ReLU" | |
bottom: "conv4_33/x2/bn" | |
top: "conv4_33/x2/bn" | |
} | |
layer { | |
name: "conv4_33/x2" | |
type: "Convolution" | |
bottom: "conv4_33/x2/bn" | |
top: "conv4_33/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_33" | |
type: "Concat" | |
bottom: "concat_4_32" | |
bottom: "conv4_33/x2" | |
top: "concat_4_33" | |
} | |
layer { | |
name: "conv4_34/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_33" | |
top: "conv4_34/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_34/x1/scale" | |
type: "Scale" | |
bottom: "conv4_34/x1/bn" | |
top: "conv4_34/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_34/x1" | |
type: "ReLU" | |
bottom: "conv4_34/x1/bn" | |
top: "conv4_34/x1/bn" | |
} | |
layer { | |
name: "conv4_34/x1" | |
type: "Convolution" | |
bottom: "conv4_34/x1/bn" | |
top: "conv4_34/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_34/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_34/x1" | |
top: "conv4_34/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_34/x2/scale" | |
type: "Scale" | |
bottom: "conv4_34/x2/bn" | |
top: "conv4_34/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_34/x2" | |
type: "ReLU" | |
bottom: "conv4_34/x2/bn" | |
top: "conv4_34/x2/bn" | |
} | |
layer { | |
name: "conv4_34/x2" | |
type: "Convolution" | |
bottom: "conv4_34/x2/bn" | |
top: "conv4_34/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_34" | |
type: "Concat" | |
bottom: "concat_4_33" | |
bottom: "conv4_34/x2" | |
top: "concat_4_34" | |
} | |
layer { | |
name: "conv4_35/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_34" | |
top: "conv4_35/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_35/x1/scale" | |
type: "Scale" | |
bottom: "conv4_35/x1/bn" | |
top: "conv4_35/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_35/x1" | |
type: "ReLU" | |
bottom: "conv4_35/x1/bn" | |
top: "conv4_35/x1/bn" | |
} | |
layer { | |
name: "conv4_35/x1" | |
type: "Convolution" | |
bottom: "conv4_35/x1/bn" | |
top: "conv4_35/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_35/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_35/x1" | |
top: "conv4_35/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_35/x2/scale" | |
type: "Scale" | |
bottom: "conv4_35/x2/bn" | |
top: "conv4_35/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_35/x2" | |
type: "ReLU" | |
bottom: "conv4_35/x2/bn" | |
top: "conv4_35/x2/bn" | |
} | |
layer { | |
name: "conv4_35/x2" | |
type: "Convolution" | |
bottom: "conv4_35/x2/bn" | |
top: "conv4_35/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_35" | |
type: "Concat" | |
bottom: "concat_4_34" | |
bottom: "conv4_35/x2" | |
top: "concat_4_35" | |
} | |
layer { | |
name: "conv4_36/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_35" | |
top: "conv4_36/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_36/x1/scale" | |
type: "Scale" | |
bottom: "conv4_36/x1/bn" | |
top: "conv4_36/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_36/x1" | |
type: "ReLU" | |
bottom: "conv4_36/x1/bn" | |
top: "conv4_36/x1/bn" | |
} | |
layer { | |
name: "conv4_36/x1" | |
type: "Convolution" | |
bottom: "conv4_36/x1/bn" | |
top: "conv4_36/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv4_36/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv4_36/x1" | |
top: "conv4_36/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_36/x2/scale" | |
type: "Scale" | |
bottom: "conv4_36/x2/bn" | |
top: "conv4_36/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_36/x2" | |
type: "ReLU" | |
bottom: "conv4_36/x2/bn" | |
top: "conv4_36/x2/bn" | |
} | |
layer { | |
name: "conv4_36/x2" | |
type: "Convolution" | |
bottom: "conv4_36/x2/bn" | |
top: "conv4_36/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_4_36" | |
type: "Concat" | |
bottom: "concat_4_35" | |
bottom: "conv4_36/x2" | |
top: "concat_4_36" | |
} | |
layer { | |
name: "conv4_blk/bn" | |
type: "BatchNorm" | |
bottom: "concat_4_36" | |
top: "conv4_blk/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv4_blk/scale" | |
type: "Scale" | |
bottom: "conv4_blk/bn" | |
top: "conv4_blk/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu4_blk" | |
type: "ReLU" | |
bottom: "conv4_blk/bn" | |
top: "conv4_blk/bn" | |
} | |
layer { | |
name: "conv4_blk" | |
type: "Convolution" | |
bottom: "conv4_blk/bn" | |
top: "conv4_blk" | |
convolution_param { | |
num_output: 1056 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "pool4" | |
type: "Pooling" | |
bottom: "conv4_blk" | |
top: "pool4" | |
pooling_param { | |
pool: AVE | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv5_1/x1/bn" | |
type: "BatchNorm" | |
bottom: "pool4" | |
top: "conv5_1/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_1/x1/scale" | |
type: "Scale" | |
bottom: "conv5_1/x1/bn" | |
top: "conv5_1/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_1/x1" | |
type: "ReLU" | |
bottom: "conv5_1/x1/bn" | |
top: "conv5_1/x1/bn" | |
} | |
layer { | |
name: "conv5_1/x1" | |
type: "Convolution" | |
bottom: "conv5_1/x1/bn" | |
top: "conv5_1/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_1/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_1/x1" | |
top: "conv5_1/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_1/x2/scale" | |
type: "Scale" | |
bottom: "conv5_1/x2/bn" | |
top: "conv5_1/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_1/x2" | |
type: "ReLU" | |
bottom: "conv5_1/x2/bn" | |
top: "conv5_1/x2/bn" | |
} | |
layer { | |
name: "conv5_1/x2" | |
type: "Convolution" | |
bottom: "conv5_1/x2/bn" | |
top: "conv5_1/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_1" | |
type: "Concat" | |
bottom: "pool4" | |
bottom: "conv5_1/x2" | |
top: "concat_5_1" | |
} | |
layer { | |
name: "conv5_2/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_1" | |
top: "conv5_2/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_2/x1/scale" | |
type: "Scale" | |
bottom: "conv5_2/x1/bn" | |
top: "conv5_2/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_2/x1" | |
type: "ReLU" | |
bottom: "conv5_2/x1/bn" | |
top: "conv5_2/x1/bn" | |
} | |
layer { | |
name: "conv5_2/x1" | |
type: "Convolution" | |
bottom: "conv5_2/x1/bn" | |
top: "conv5_2/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_2/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_2/x1" | |
top: "conv5_2/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_2/x2/scale" | |
type: "Scale" | |
bottom: "conv5_2/x2/bn" | |
top: "conv5_2/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_2/x2" | |
type: "ReLU" | |
bottom: "conv5_2/x2/bn" | |
top: "conv5_2/x2/bn" | |
} | |
layer { | |
name: "conv5_2/x2" | |
type: "Convolution" | |
bottom: "conv5_2/x2/bn" | |
top: "conv5_2/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_2" | |
type: "Concat" | |
bottom: "concat_5_1" | |
bottom: "conv5_2/x2" | |
top: "concat_5_2" | |
} | |
layer { | |
name: "conv5_3/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_2" | |
top: "conv5_3/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_3/x1/scale" | |
type: "Scale" | |
bottom: "conv5_3/x1/bn" | |
top: "conv5_3/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_3/x1" | |
type: "ReLU" | |
bottom: "conv5_3/x1/bn" | |
top: "conv5_3/x1/bn" | |
} | |
layer { | |
name: "conv5_3/x1" | |
type: "Convolution" | |
bottom: "conv5_3/x1/bn" | |
top: "conv5_3/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_3/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_3/x1" | |
top: "conv5_3/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_3/x2/scale" | |
type: "Scale" | |
bottom: "conv5_3/x2/bn" | |
top: "conv5_3/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_3/x2" | |
type: "ReLU" | |
bottom: "conv5_3/x2/bn" | |
top: "conv5_3/x2/bn" | |
} | |
layer { | |
name: "conv5_3/x2" | |
type: "Convolution" | |
bottom: "conv5_3/x2/bn" | |
top: "conv5_3/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_3" | |
type: "Concat" | |
bottom: "concat_5_2" | |
bottom: "conv5_3/x2" | |
top: "concat_5_3" | |
} | |
layer { | |
name: "conv5_4/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_3" | |
top: "conv5_4/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_4/x1/scale" | |
type: "Scale" | |
bottom: "conv5_4/x1/bn" | |
top: "conv5_4/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_4/x1" | |
type: "ReLU" | |
bottom: "conv5_4/x1/bn" | |
top: "conv5_4/x1/bn" | |
} | |
layer { | |
name: "conv5_4/x1" | |
type: "Convolution" | |
bottom: "conv5_4/x1/bn" | |
top: "conv5_4/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_4/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_4/x1" | |
top: "conv5_4/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_4/x2/scale" | |
type: "Scale" | |
bottom: "conv5_4/x2/bn" | |
top: "conv5_4/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_4/x2" | |
type: "ReLU" | |
bottom: "conv5_4/x2/bn" | |
top: "conv5_4/x2/bn" | |
} | |
layer { | |
name: "conv5_4/x2" | |
type: "Convolution" | |
bottom: "conv5_4/x2/bn" | |
top: "conv5_4/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_4" | |
type: "Concat" | |
bottom: "concat_5_3" | |
bottom: "conv5_4/x2" | |
top: "concat_5_4" | |
} | |
layer { | |
name: "conv5_5/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_4" | |
top: "conv5_5/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_5/x1/scale" | |
type: "Scale" | |
bottom: "conv5_5/x1/bn" | |
top: "conv5_5/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_5/x1" | |
type: "ReLU" | |
bottom: "conv5_5/x1/bn" | |
top: "conv5_5/x1/bn" | |
} | |
layer { | |
name: "conv5_5/x1" | |
type: "Convolution" | |
bottom: "conv5_5/x1/bn" | |
top: "conv5_5/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_5/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_5/x1" | |
top: "conv5_5/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_5/x2/scale" | |
type: "Scale" | |
bottom: "conv5_5/x2/bn" | |
top: "conv5_5/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_5/x2" | |
type: "ReLU" | |
bottom: "conv5_5/x2/bn" | |
top: "conv5_5/x2/bn" | |
} | |
layer { | |
name: "conv5_5/x2" | |
type: "Convolution" | |
bottom: "conv5_5/x2/bn" | |
top: "conv5_5/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_5" | |
type: "Concat" | |
bottom: "concat_5_4" | |
bottom: "conv5_5/x2" | |
top: "concat_5_5" | |
} | |
layer { | |
name: "conv5_6/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_5" | |
top: "conv5_6/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_6/x1/scale" | |
type: "Scale" | |
bottom: "conv5_6/x1/bn" | |
top: "conv5_6/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_6/x1" | |
type: "ReLU" | |
bottom: "conv5_6/x1/bn" | |
top: "conv5_6/x1/bn" | |
} | |
layer { | |
name: "conv5_6/x1" | |
type: "Convolution" | |
bottom: "conv5_6/x1/bn" | |
top: "conv5_6/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_6/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_6/x1" | |
top: "conv5_6/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_6/x2/scale" | |
type: "Scale" | |
bottom: "conv5_6/x2/bn" | |
top: "conv5_6/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_6/x2" | |
type: "ReLU" | |
bottom: "conv5_6/x2/bn" | |
top: "conv5_6/x2/bn" | |
} | |
layer { | |
name: "conv5_6/x2" | |
type: "Convolution" | |
bottom: "conv5_6/x2/bn" | |
top: "conv5_6/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_6" | |
type: "Concat" | |
bottom: "concat_5_5" | |
bottom: "conv5_6/x2" | |
top: "concat_5_6" | |
} | |
layer { | |
name: "conv5_7/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_6" | |
top: "conv5_7/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_7/x1/scale" | |
type: "Scale" | |
bottom: "conv5_7/x1/bn" | |
top: "conv5_7/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_7/x1" | |
type: "ReLU" | |
bottom: "conv5_7/x1/bn" | |
top: "conv5_7/x1/bn" | |
} | |
layer { | |
name: "conv5_7/x1" | |
type: "Convolution" | |
bottom: "conv5_7/x1/bn" | |
top: "conv5_7/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_7/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_7/x1" | |
top: "conv5_7/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_7/x2/scale" | |
type: "Scale" | |
bottom: "conv5_7/x2/bn" | |
top: "conv5_7/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_7/x2" | |
type: "ReLU" | |
bottom: "conv5_7/x2/bn" | |
top: "conv5_7/x2/bn" | |
} | |
layer { | |
name: "conv5_7/x2" | |
type: "Convolution" | |
bottom: "conv5_7/x2/bn" | |
top: "conv5_7/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_7" | |
type: "Concat" | |
bottom: "concat_5_6" | |
bottom: "conv5_7/x2" | |
top: "concat_5_7" | |
} | |
layer { | |
name: "conv5_8/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_7" | |
top: "conv5_8/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_8/x1/scale" | |
type: "Scale" | |
bottom: "conv5_8/x1/bn" | |
top: "conv5_8/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_8/x1" | |
type: "ReLU" | |
bottom: "conv5_8/x1/bn" | |
top: "conv5_8/x1/bn" | |
} | |
layer { | |
name: "conv5_8/x1" | |
type: "Convolution" | |
bottom: "conv5_8/x1/bn" | |
top: "conv5_8/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_8/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_8/x1" | |
top: "conv5_8/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_8/x2/scale" | |
type: "Scale" | |
bottom: "conv5_8/x2/bn" | |
top: "conv5_8/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_8/x2" | |
type: "ReLU" | |
bottom: "conv5_8/x2/bn" | |
top: "conv5_8/x2/bn" | |
} | |
layer { | |
name: "conv5_8/x2" | |
type: "Convolution" | |
bottom: "conv5_8/x2/bn" | |
top: "conv5_8/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_8" | |
type: "Concat" | |
bottom: "concat_5_7" | |
bottom: "conv5_8/x2" | |
top: "concat_5_8" | |
} | |
layer { | |
name: "conv5_9/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_8" | |
top: "conv5_9/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_9/x1/scale" | |
type: "Scale" | |
bottom: "conv5_9/x1/bn" | |
top: "conv5_9/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_9/x1" | |
type: "ReLU" | |
bottom: "conv5_9/x1/bn" | |
top: "conv5_9/x1/bn" | |
} | |
layer { | |
name: "conv5_9/x1" | |
type: "Convolution" | |
bottom: "conv5_9/x1/bn" | |
top: "conv5_9/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_9/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_9/x1" | |
top: "conv5_9/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_9/x2/scale" | |
type: "Scale" | |
bottom: "conv5_9/x2/bn" | |
top: "conv5_9/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_9/x2" | |
type: "ReLU" | |
bottom: "conv5_9/x2/bn" | |
top: "conv5_9/x2/bn" | |
} | |
layer { | |
name: "conv5_9/x2" | |
type: "Convolution" | |
bottom: "conv5_9/x2/bn" | |
top: "conv5_9/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_9" | |
type: "Concat" | |
bottom: "concat_5_8" | |
bottom: "conv5_9/x2" | |
top: "concat_5_9" | |
} | |
layer { | |
name: "conv5_10/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_9" | |
top: "conv5_10/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_10/x1/scale" | |
type: "Scale" | |
bottom: "conv5_10/x1/bn" | |
top: "conv5_10/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_10/x1" | |
type: "ReLU" | |
bottom: "conv5_10/x1/bn" | |
top: "conv5_10/x1/bn" | |
} | |
layer { | |
name: "conv5_10/x1" | |
type: "Convolution" | |
bottom: "conv5_10/x1/bn" | |
top: "conv5_10/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_10/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_10/x1" | |
top: "conv5_10/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_10/x2/scale" | |
type: "Scale" | |
bottom: "conv5_10/x2/bn" | |
top: "conv5_10/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_10/x2" | |
type: "ReLU" | |
bottom: "conv5_10/x2/bn" | |
top: "conv5_10/x2/bn" | |
} | |
layer { | |
name: "conv5_10/x2" | |
type: "Convolution" | |
bottom: "conv5_10/x2/bn" | |
top: "conv5_10/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_10" | |
type: "Concat" | |
bottom: "concat_5_9" | |
bottom: "conv5_10/x2" | |
top: "concat_5_10" | |
} | |
layer { | |
name: "conv5_11/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_10" | |
top: "conv5_11/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_11/x1/scale" | |
type: "Scale" | |
bottom: "conv5_11/x1/bn" | |
top: "conv5_11/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_11/x1" | |
type: "ReLU" | |
bottom: "conv5_11/x1/bn" | |
top: "conv5_11/x1/bn" | |
} | |
layer { | |
name: "conv5_11/x1" | |
type: "Convolution" | |
bottom: "conv5_11/x1/bn" | |
top: "conv5_11/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_11/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_11/x1" | |
top: "conv5_11/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_11/x2/scale" | |
type: "Scale" | |
bottom: "conv5_11/x2/bn" | |
top: "conv5_11/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_11/x2" | |
type: "ReLU" | |
bottom: "conv5_11/x2/bn" | |
top: "conv5_11/x2/bn" | |
} | |
layer { | |
name: "conv5_11/x2" | |
type: "Convolution" | |
bottom: "conv5_11/x2/bn" | |
top: "conv5_11/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_11" | |
type: "Concat" | |
bottom: "concat_5_10" | |
bottom: "conv5_11/x2" | |
top: "concat_5_11" | |
} | |
layer { | |
name: "conv5_12/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_11" | |
top: "conv5_12/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_12/x1/scale" | |
type: "Scale" | |
bottom: "conv5_12/x1/bn" | |
top: "conv5_12/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_12/x1" | |
type: "ReLU" | |
bottom: "conv5_12/x1/bn" | |
top: "conv5_12/x1/bn" | |
} | |
layer { | |
name: "conv5_12/x1" | |
type: "Convolution" | |
bottom: "conv5_12/x1/bn" | |
top: "conv5_12/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_12/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_12/x1" | |
top: "conv5_12/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_12/x2/scale" | |
type: "Scale" | |
bottom: "conv5_12/x2/bn" | |
top: "conv5_12/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_12/x2" | |
type: "ReLU" | |
bottom: "conv5_12/x2/bn" | |
top: "conv5_12/x2/bn" | |
} | |
layer { | |
name: "conv5_12/x2" | |
type: "Convolution" | |
bottom: "conv5_12/x2/bn" | |
top: "conv5_12/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_12" | |
type: "Concat" | |
bottom: "concat_5_11" | |
bottom: "conv5_12/x2" | |
top: "concat_5_12" | |
} | |
layer { | |
name: "conv5_13/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_12" | |
top: "conv5_13/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_13/x1/scale" | |
type: "Scale" | |
bottom: "conv5_13/x1/bn" | |
top: "conv5_13/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_13/x1" | |
type: "ReLU" | |
bottom: "conv5_13/x1/bn" | |
top: "conv5_13/x1/bn" | |
} | |
layer { | |
name: "conv5_13/x1" | |
type: "Convolution" | |
bottom: "conv5_13/x1/bn" | |
top: "conv5_13/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_13/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_13/x1" | |
top: "conv5_13/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_13/x2/scale" | |
type: "Scale" | |
bottom: "conv5_13/x2/bn" | |
top: "conv5_13/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_13/x2" | |
type: "ReLU" | |
bottom: "conv5_13/x2/bn" | |
top: "conv5_13/x2/bn" | |
} | |
layer { | |
name: "conv5_13/x2" | |
type: "Convolution" | |
bottom: "conv5_13/x2/bn" | |
top: "conv5_13/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_13" | |
type: "Concat" | |
bottom: "concat_5_12" | |
bottom: "conv5_13/x2" | |
top: "concat_5_13" | |
} | |
layer { | |
name: "conv5_14/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_13" | |
top: "conv5_14/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_14/x1/scale" | |
type: "Scale" | |
bottom: "conv5_14/x1/bn" | |
top: "conv5_14/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_14/x1" | |
type: "ReLU" | |
bottom: "conv5_14/x1/bn" | |
top: "conv5_14/x1/bn" | |
} | |
layer { | |
name: "conv5_14/x1" | |
type: "Convolution" | |
bottom: "conv5_14/x1/bn" | |
top: "conv5_14/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_14/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_14/x1" | |
top: "conv5_14/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_14/x2/scale" | |
type: "Scale" | |
bottom: "conv5_14/x2/bn" | |
top: "conv5_14/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_14/x2" | |
type: "ReLU" | |
bottom: "conv5_14/x2/bn" | |
top: "conv5_14/x2/bn" | |
} | |
layer { | |
name: "conv5_14/x2" | |
type: "Convolution" | |
bottom: "conv5_14/x2/bn" | |
top: "conv5_14/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_14" | |
type: "Concat" | |
bottom: "concat_5_13" | |
bottom: "conv5_14/x2" | |
top: "concat_5_14" | |
} | |
layer { | |
name: "conv5_15/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_14" | |
top: "conv5_15/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_15/x1/scale" | |
type: "Scale" | |
bottom: "conv5_15/x1/bn" | |
top: "conv5_15/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_15/x1" | |
type: "ReLU" | |
bottom: "conv5_15/x1/bn" | |
top: "conv5_15/x1/bn" | |
} | |
layer { | |
name: "conv5_15/x1" | |
type: "Convolution" | |
bottom: "conv5_15/x1/bn" | |
top: "conv5_15/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_15/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_15/x1" | |
top: "conv5_15/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_15/x2/scale" | |
type: "Scale" | |
bottom: "conv5_15/x2/bn" | |
top: "conv5_15/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_15/x2" | |
type: "ReLU" | |
bottom: "conv5_15/x2/bn" | |
top: "conv5_15/x2/bn" | |
} | |
layer { | |
name: "conv5_15/x2" | |
type: "Convolution" | |
bottom: "conv5_15/x2/bn" | |
top: "conv5_15/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_15" | |
type: "Concat" | |
bottom: "concat_5_14" | |
bottom: "conv5_15/x2" | |
top: "concat_5_15" | |
} | |
layer { | |
name: "conv5_16/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_15" | |
top: "conv5_16/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_16/x1/scale" | |
type: "Scale" | |
bottom: "conv5_16/x1/bn" | |
top: "conv5_16/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_16/x1" | |
type: "ReLU" | |
bottom: "conv5_16/x1/bn" | |
top: "conv5_16/x1/bn" | |
} | |
layer { | |
name: "conv5_16/x1" | |
type: "Convolution" | |
bottom: "conv5_16/x1/bn" | |
top: "conv5_16/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_16/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_16/x1" | |
top: "conv5_16/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_16/x2/scale" | |
type: "Scale" | |
bottom: "conv5_16/x2/bn" | |
top: "conv5_16/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_16/x2" | |
type: "ReLU" | |
bottom: "conv5_16/x2/bn" | |
top: "conv5_16/x2/bn" | |
} | |
layer { | |
name: "conv5_16/x2" | |
type: "Convolution" | |
bottom: "conv5_16/x2/bn" | |
top: "conv5_16/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_16" | |
type: "Concat" | |
bottom: "concat_5_15" | |
bottom: "conv5_16/x2" | |
top: "concat_5_16" | |
} | |
layer { | |
name: "conv5_17/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_16" | |
top: "conv5_17/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_17/x1/scale" | |
type: "Scale" | |
bottom: "conv5_17/x1/bn" | |
top: "conv5_17/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_17/x1" | |
type: "ReLU" | |
bottom: "conv5_17/x1/bn" | |
top: "conv5_17/x1/bn" | |
} | |
layer { | |
name: "conv5_17/x1" | |
type: "Convolution" | |
bottom: "conv5_17/x1/bn" | |
top: "conv5_17/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_17/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_17/x1" | |
top: "conv5_17/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_17/x2/scale" | |
type: "Scale" | |
bottom: "conv5_17/x2/bn" | |
top: "conv5_17/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_17/x2" | |
type: "ReLU" | |
bottom: "conv5_17/x2/bn" | |
top: "conv5_17/x2/bn" | |
} | |
layer { | |
name: "conv5_17/x2" | |
type: "Convolution" | |
bottom: "conv5_17/x2/bn" | |
top: "conv5_17/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_17" | |
type: "Concat" | |
bottom: "concat_5_16" | |
bottom: "conv5_17/x2" | |
top: "concat_5_17" | |
} | |
layer { | |
name: "conv5_18/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_17" | |
top: "conv5_18/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_18/x1/scale" | |
type: "Scale" | |
bottom: "conv5_18/x1/bn" | |
top: "conv5_18/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_18/x1" | |
type: "ReLU" | |
bottom: "conv5_18/x1/bn" | |
top: "conv5_18/x1/bn" | |
} | |
layer { | |
name: "conv5_18/x1" | |
type: "Convolution" | |
bottom: "conv5_18/x1/bn" | |
top: "conv5_18/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_18/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_18/x1" | |
top: "conv5_18/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_18/x2/scale" | |
type: "Scale" | |
bottom: "conv5_18/x2/bn" | |
top: "conv5_18/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_18/x2" | |
type: "ReLU" | |
bottom: "conv5_18/x2/bn" | |
top: "conv5_18/x2/bn" | |
} | |
layer { | |
name: "conv5_18/x2" | |
type: "Convolution" | |
bottom: "conv5_18/x2/bn" | |
top: "conv5_18/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_18" | |
type: "Concat" | |
bottom: "concat_5_17" | |
bottom: "conv5_18/x2" | |
top: "concat_5_18" | |
} | |
layer { | |
name: "conv5_19/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_18" | |
top: "conv5_19/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_19/x1/scale" | |
type: "Scale" | |
bottom: "conv5_19/x1/bn" | |
top: "conv5_19/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_19/x1" | |
type: "ReLU" | |
bottom: "conv5_19/x1/bn" | |
top: "conv5_19/x1/bn" | |
} | |
layer { | |
name: "conv5_19/x1" | |
type: "Convolution" | |
bottom: "conv5_19/x1/bn" | |
top: "conv5_19/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_19/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_19/x1" | |
top: "conv5_19/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_19/x2/scale" | |
type: "Scale" | |
bottom: "conv5_19/x2/bn" | |
top: "conv5_19/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_19/x2" | |
type: "ReLU" | |
bottom: "conv5_19/x2/bn" | |
top: "conv5_19/x2/bn" | |
} | |
layer { | |
name: "conv5_19/x2" | |
type: "Convolution" | |
bottom: "conv5_19/x2/bn" | |
top: "conv5_19/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_19" | |
type: "Concat" | |
bottom: "concat_5_18" | |
bottom: "conv5_19/x2" | |
top: "concat_5_19" | |
} | |
layer { | |
name: "conv5_20/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_19" | |
top: "conv5_20/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_20/x1/scale" | |
type: "Scale" | |
bottom: "conv5_20/x1/bn" | |
top: "conv5_20/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_20/x1" | |
type: "ReLU" | |
bottom: "conv5_20/x1/bn" | |
top: "conv5_20/x1/bn" | |
} | |
layer { | |
name: "conv5_20/x1" | |
type: "Convolution" | |
bottom: "conv5_20/x1/bn" | |
top: "conv5_20/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_20/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_20/x1" | |
top: "conv5_20/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_20/x2/scale" | |
type: "Scale" | |
bottom: "conv5_20/x2/bn" | |
top: "conv5_20/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_20/x2" | |
type: "ReLU" | |
bottom: "conv5_20/x2/bn" | |
top: "conv5_20/x2/bn" | |
} | |
layer { | |
name: "conv5_20/x2" | |
type: "Convolution" | |
bottom: "conv5_20/x2/bn" | |
top: "conv5_20/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_20" | |
type: "Concat" | |
bottom: "concat_5_19" | |
bottom: "conv5_20/x2" | |
top: "concat_5_20" | |
} | |
layer { | |
name: "conv5_21/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_20" | |
top: "conv5_21/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_21/x1/scale" | |
type: "Scale" | |
bottom: "conv5_21/x1/bn" | |
top: "conv5_21/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_21/x1" | |
type: "ReLU" | |
bottom: "conv5_21/x1/bn" | |
top: "conv5_21/x1/bn" | |
} | |
layer { | |
name: "conv5_21/x1" | |
type: "Convolution" | |
bottom: "conv5_21/x1/bn" | |
top: "conv5_21/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_21/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_21/x1" | |
top: "conv5_21/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_21/x2/scale" | |
type: "Scale" | |
bottom: "conv5_21/x2/bn" | |
top: "conv5_21/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_21/x2" | |
type: "ReLU" | |
bottom: "conv5_21/x2/bn" | |
top: "conv5_21/x2/bn" | |
} | |
layer { | |
name: "conv5_21/x2" | |
type: "Convolution" | |
bottom: "conv5_21/x2/bn" | |
top: "conv5_21/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_21" | |
type: "Concat" | |
bottom: "concat_5_20" | |
bottom: "conv5_21/x2" | |
top: "concat_5_21" | |
} | |
layer { | |
name: "conv5_22/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_21" | |
top: "conv5_22/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_22/x1/scale" | |
type: "Scale" | |
bottom: "conv5_22/x1/bn" | |
top: "conv5_22/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_22/x1" | |
type: "ReLU" | |
bottom: "conv5_22/x1/bn" | |
top: "conv5_22/x1/bn" | |
} | |
layer { | |
name: "conv5_22/x1" | |
type: "Convolution" | |
bottom: "conv5_22/x1/bn" | |
top: "conv5_22/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_22/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_22/x1" | |
top: "conv5_22/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_22/x2/scale" | |
type: "Scale" | |
bottom: "conv5_22/x2/bn" | |
top: "conv5_22/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_22/x2" | |
type: "ReLU" | |
bottom: "conv5_22/x2/bn" | |
top: "conv5_22/x2/bn" | |
} | |
layer { | |
name: "conv5_22/x2" | |
type: "Convolution" | |
bottom: "conv5_22/x2/bn" | |
top: "conv5_22/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_22" | |
type: "Concat" | |
bottom: "concat_5_21" | |
bottom: "conv5_22/x2" | |
top: "concat_5_22" | |
} | |
layer { | |
name: "conv5_23/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_22" | |
top: "conv5_23/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_23/x1/scale" | |
type: "Scale" | |
bottom: "conv5_23/x1/bn" | |
top: "conv5_23/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_23/x1" | |
type: "ReLU" | |
bottom: "conv5_23/x1/bn" | |
top: "conv5_23/x1/bn" | |
} | |
layer { | |
name: "conv5_23/x1" | |
type: "Convolution" | |
bottom: "conv5_23/x1/bn" | |
top: "conv5_23/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_23/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_23/x1" | |
top: "conv5_23/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_23/x2/scale" | |
type: "Scale" | |
bottom: "conv5_23/x2/bn" | |
top: "conv5_23/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_23/x2" | |
type: "ReLU" | |
bottom: "conv5_23/x2/bn" | |
top: "conv5_23/x2/bn" | |
} | |
layer { | |
name: "conv5_23/x2" | |
type: "Convolution" | |
bottom: "conv5_23/x2/bn" | |
top: "conv5_23/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_23" | |
type: "Concat" | |
bottom: "concat_5_22" | |
bottom: "conv5_23/x2" | |
top: "concat_5_23" | |
} | |
layer { | |
name: "conv5_24/x1/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_23" | |
top: "conv5_24/x1/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_24/x1/scale" | |
type: "Scale" | |
bottom: "conv5_24/x1/bn" | |
top: "conv5_24/x1/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_24/x1" | |
type: "ReLU" | |
bottom: "conv5_24/x1/bn" | |
top: "conv5_24/x1/bn" | |
} | |
layer { | |
name: "conv5_24/x1" | |
type: "Convolution" | |
bottom: "conv5_24/x1/bn" | |
top: "conv5_24/x1" | |
convolution_param { | |
num_output: 192 | |
bias_term: false | |
kernel_size: 1 | |
} | |
} | |
layer { | |
name: "conv5_24/x2/bn" | |
type: "BatchNorm" | |
bottom: "conv5_24/x1" | |
top: "conv5_24/x2/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_24/x2/scale" | |
type: "Scale" | |
bottom: "conv5_24/x2/bn" | |
top: "conv5_24/x2/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_24/x2" | |
type: "ReLU" | |
bottom: "conv5_24/x2/bn" | |
top: "conv5_24/x2/bn" | |
} | |
layer { | |
name: "conv5_24/x2" | |
type: "Convolution" | |
bottom: "conv5_24/x2/bn" | |
top: "conv5_24/x2" | |
convolution_param { | |
num_output: 48 | |
bias_term: false | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "concat_5_24" | |
type: "Concat" | |
bottom: "concat_5_23" | |
bottom: "conv5_24/x2" | |
top: "concat_5_24" | |
} | |
layer { | |
name: "conv5_blk/bn" | |
type: "BatchNorm" | |
bottom: "concat_5_24" | |
top: "conv5_blk/bn" | |
batch_norm_param { | |
eps: 1e-5 | |
} | |
} | |
layer { | |
name: "conv5_blk/scale" | |
type: "Scale" | |
bottom: "conv5_blk/bn" | |
top: "conv5_blk/bn" | |
scale_param { | |
bias_term: true | |
} | |
} | |
layer { | |
name: "relu5_blk" | |
type: "ReLU" | |
bottom: "conv5_blk/bn" | |
top: "conv5_blk/bn" | |
} | |
layer { | |
name: "pool5" | |
type: "Pooling" | |
bottom: "conv5_blk/bn" | |
top: "pool5" | |
pooling_param { | |
pool: AVE | |
global_pooling: true | |
} | |
} | |
layer { | |
bottom: "pool5" | |
top: "local" | |
name: "local" | |
type: "InnerProduct" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 1024 | |
} | |
} | |
layer { | |
bottom: "local" | |
top: "local" | |
name: "relu-local" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "pool5" | |
top: "global" | |
name: "global" | |
type: "InnerProduct" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 1024 | |
} | |
} | |
layer { | |
bottom: "global" | |
top: "global" | |
name: "relu-globall" | |
type: "ReLU" | |
} | |
layer { | |
bottom: "global" | |
top: "fc50" | |
name: "fc50" | |
type: "InnerProduct" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 50 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "class_loss" | |
type: "SoftmaxWithLoss" | |
bottom: "fc50" | |
bottom: "class_label" | |
top: "class_loss" | |
loss_weight: 1 | |
} | |
#layer { | |
# name: "svm_class_label" | |
# type: "HingeLoss" | |
# bottom: "fc50" | |
# bottom: "class_label" | |
# top: "svm_class_label" | |
# loss_weight: 1 | |
#} | |
layer { | |
name: "texture-label-score" | |
type: "InnerProduct" | |
bottom: "local" | |
top: "texture-label-score" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 156 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "texture-label-loss" | |
type: "SigmoidCrossEntropyLoss" | |
bottom: "texture-label-score" | |
bottom: "texture_label" | |
top: "texture-label-loss" | |
} | |
layer { | |
name: "fabric-label-score" | |
type: "InnerProduct" | |
bottom: "local" | |
top: "fabric-label-score" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 218 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "fabric-label-loss" | |
type: "SigmoidCrossEntropyLoss" | |
bottom: "fabric-label-score" | |
bottom: "fabric_label" | |
top: "fabric-label-loss" | |
} | |
layer { | |
name: "shape-label-score" | |
type: "InnerProduct" | |
bottom: "global" | |
top: "shape-label-score" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 180 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "shape-label-loss" | |
type: "SigmoidCrossEntropyLoss" | |
bottom: "shape-label-score" | |
bottom: "shape_label" | |
top: "shape-label-loss" | |
} | |
layer { | |
name: "part-label-score" | |
type: "InnerProduct" | |
bottom: "local" | |
top: "part-label-score" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 216 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "part-label-loss" | |
type: "SigmoidCrossEntropyLoss" | |
bottom: "part-label-score" | |
bottom: "part_label" | |
top: "part-label-loss" | |
} | |
layer { | |
name: "style-label-score" | |
type: "InnerProduct" | |
bottom: "global" | |
top: "style-label-score" | |
param { | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 230 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "style-label-loss" | |
type: "SigmoidCrossEntropyLoss" | |
bottom: "style-label-score" | |
bottom: "style_label" | |
top: "style-label-loss" | |
} | |
#layer { | |
# type: 'Python' | |
# name: 'weighted-multi-label-loss' | |
# top: 'weighted-multi-label-loss' | |
# bottom: "multi-label-score" | |
# bottom: "label" | |
# python_param { | |
# # the module name -- usually the filename -- that needs to be in $PYTHONPATH | |
# module: 'weightedsigmoidcrossentropyloss' | |
# # the layer name -- the class name in the module | |
# layer: 'WeightedSigmoidCrossentropyLoss' | |
# } | |
# loss_weight: 1 | |
#} |
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