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getDenseNetProtoTxt.py
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3191 lines (3086 loc) · 57.1 KB
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def getDenseNet121Init():
string = '\
layer {\n\
name: "conv1"\n\
type: "Convolution"\n\
bottom: "image"\n\
top: "conv1"\n\
convolution_param {\n\
num_output: 64\n\
bias_term: false\n\
pad: 3\n\
kernel_size: 7\n\
stride: 2\n\
}\n\
}\n\
layer {\n\
name: "conv1/bn"\n\
type: "BatchNorm"\n\
bottom: "conv1"\n\
top: "conv1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv1/scale"\n\
type: "Scale"\n\
bottom: "conv1/bn"\n\
top: "conv1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu1"\n\
type: "ReLU"\n\
bottom: "conv1/bn"\n\
top: "conv1/bn"\n\
}\n\
layer {\n\
name: "pool1"\n\
type: "Pooling"\n\
bottom: "conv1/bn"\n\
top: "pool1"\n\
pooling_param {\n\
pool: MAX\n\
kernel_size: 3\n\
stride: 2\n\
pad: 1\n\
# ceil_mode: false\n\
round_mode: FLOOR\n\
}\n\
}\n\
layer {\n\
name: "conv2_1/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "pool1"\n\
top: "conv2_1/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_1/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_1/x1/bn"\n\
top: "conv2_1/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_1/x1"\n\
type: "ReLU"\n\
bottom: "conv2_1/x1/bn"\n\
top: "conv2_1/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_1/x1"\n\
type: "Convolution"\n\
bottom: "conv2_1/x1/bn"\n\
top: "conv2_1/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_1/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_1/x1"\n\
top: "conv2_1/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_1/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_1/x2/bn"\n\
top: "conv2_1/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_1/x2"\n\
type: "ReLU"\n\
bottom: "conv2_1/x2/bn"\n\
top: "conv2_1/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_1/x2"\n\
type: "Convolution"\n\
bottom: "conv2_1/x2/bn"\n\
top: "conv2_1/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_1"\n\
type: "Concat"\n\
bottom: "pool1"\n\
bottom: "conv2_1/x2"\n\
top: "concat_2_1"\n\
}\n\
layer {\n\
name: "conv2_2/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_1"\n\
top: "conv2_2/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_2/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_2/x1/bn"\n\
top: "conv2_2/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_2/x1"\n\
type: "ReLU"\n\
bottom: "conv2_2/x1/bn"\n\
top: "conv2_2/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_2/x1"\n\
type: "Convolution"\n\
bottom: "conv2_2/x1/bn"\n\
top: "conv2_2/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_2/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_2/x1"\n\
top: "conv2_2/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_2/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_2/x2/bn"\n\
top: "conv2_2/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_2/x2"\n\
type: "ReLU"\n\
bottom: "conv2_2/x2/bn"\n\
top: "conv2_2/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_2/x2"\n\
type: "Convolution"\n\
bottom: "conv2_2/x2/bn"\n\
top: "conv2_2/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_2"\n\
type: "Concat"\n\
bottom: "concat_2_1"\n\
bottom: "conv2_2/x2"\n\
top: "concat_2_2"\n\
}\n\
layer {\n\
name: "conv2_3/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_2"\n\
top: "conv2_3/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_3/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_3/x1/bn"\n\
top: "conv2_3/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_3/x1"\n\
type: "ReLU"\n\
bottom: "conv2_3/x1/bn"\n\
top: "conv2_3/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_3/x1"\n\
type: "Convolution"\n\
bottom: "conv2_3/x1/bn"\n\
top: "conv2_3/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_3/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_3/x1"\n\
top: "conv2_3/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_3/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_3/x2/bn"\n\
top: "conv2_3/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_3/x2"\n\
type: "ReLU"\n\
bottom: "conv2_3/x2/bn"\n\
top: "conv2_3/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_3/x2"\n\
type: "Convolution"\n\
bottom: "conv2_3/x2/bn"\n\
top: "conv2_3/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_3"\n\
type: "Concat"\n\
bottom: "concat_2_2"\n\
bottom: "conv2_3/x2"\n\
top: "concat_2_3"\n\
}\n\
layer {\n\
name: "conv2_4/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_3"\n\
top: "conv2_4/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_4/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_4/x1/bn"\n\
top: "conv2_4/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_4/x1"\n\
type: "ReLU"\n\
bottom: "conv2_4/x1/bn"\n\
top: "conv2_4/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_4/x1"\n\
type: "Convolution"\n\
bottom: "conv2_4/x1/bn"\n\
top: "conv2_4/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_4/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_4/x1"\n\
top: "conv2_4/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_4/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_4/x2/bn"\n\
top: "conv2_4/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_4/x2"\n\
type: "ReLU"\n\
bottom: "conv2_4/x2/bn"\n\
top: "conv2_4/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_4/x2"\n\
type: "Convolution"\n\
bottom: "conv2_4/x2/bn"\n\
top: "conv2_4/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_4"\n\
type: "Concat"\n\
bottom: "concat_2_3"\n\
bottom: "conv2_4/x2"\n\
top: "concat_2_4"\n\
}\n\
layer {\n\
name: "conv2_5/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_4"\n\
top: "conv2_5/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_5/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_5/x1/bn"\n\
top: "conv2_5/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_5/x1"\n\
type: "ReLU"\n\
bottom: "conv2_5/x1/bn"\n\
top: "conv2_5/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_5/x1"\n\
type: "Convolution"\n\
bottom: "conv2_5/x1/bn"\n\
top: "conv2_5/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_5/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_5/x1"\n\
top: "conv2_5/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_5/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_5/x2/bn"\n\
top: "conv2_5/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_5/x2"\n\
type: "ReLU"\n\
bottom: "conv2_5/x2/bn"\n\
top: "conv2_5/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_5/x2"\n\
type: "Convolution"\n\
bottom: "conv2_5/x2/bn"\n\
top: "conv2_5/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_5"\n\
type: "Concat"\n\
bottom: "concat_2_4"\n\
bottom: "conv2_5/x2"\n\
top: "concat_2_5"\n\
}\n\
layer {\n\
name: "conv2_6/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_5"\n\
top: "conv2_6/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_6/x1/scale"\n\
type: "Scale"\n\
bottom: "conv2_6/x1/bn"\n\
top: "conv2_6/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_6/x1"\n\
type: "ReLU"\n\
bottom: "conv2_6/x1/bn"\n\
top: "conv2_6/x1/bn"\n\
}\n\
layer {\n\
name: "conv2_6/x1"\n\
type: "Convolution"\n\
bottom: "conv2_6/x1/bn"\n\
top: "conv2_6/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv2_6/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv2_6/x1"\n\
top: "conv2_6/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_6/x2/scale"\n\
type: "Scale"\n\
bottom: "conv2_6/x2/bn"\n\
top: "conv2_6/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_6/x2"\n\
type: "ReLU"\n\
bottom: "conv2_6/x2/bn"\n\
top: "conv2_6/x2/bn"\n\
}\n\
layer {\n\
name: "conv2_6/x2"\n\
type: "Convolution"\n\
bottom: "conv2_6/x2/bn"\n\
top: "conv2_6/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_2_6"\n\
type: "Concat"\n\
bottom: "concat_2_5"\n\
bottom: "conv2_6/x2"\n\
top: "concat_2_6"\n\
}\n\
layer {\n\
name: "conv2_blk/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_2_6"\n\
top: "conv2_blk/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv2_blk/scale"\n\
type: "Scale"\n\
bottom: "conv2_blk/bn"\n\
top: "conv2_blk/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu2_blk"\n\
type: "ReLU"\n\
bottom: "conv2_blk/bn"\n\
top: "conv2_blk/bn"\n\
}\n\
layer {\n\
name: "conv2_blk"\n\
type: "Convolution"\n\
bottom: "conv2_blk/bn"\n\
top: "conv2_blk"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "pool2"\n\
type: "Pooling"\n\
bottom: "conv2_blk"\n\
top: "pool2"\n\
pooling_param {\n\
pool: AVE\n\
kernel_size: 2\n\
stride: 2\n\
}\n\
}\n\
layer {\n\
name: "conv3_1/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "pool2"\n\
top: "conv3_1/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_1/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_1/x1/bn"\n\
top: "conv3_1/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_1/x1"\n\
type: "ReLU"\n\
bottom: "conv3_1/x1/bn"\n\
top: "conv3_1/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_1/x1"\n\
type: "Convolution"\n\
bottom: "conv3_1/x1/bn"\n\
top: "conv3_1/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_1/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_1/x1"\n\
top: "conv3_1/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_1/x2/scale"\n\
type: "Scale"\n\
bottom: "conv3_1/x2/bn"\n\
top: "conv3_1/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_1/x2"\n\
type: "ReLU"\n\
bottom: "conv3_1/x2/bn"\n\
top: "conv3_1/x2/bn"\n\
}\n\
layer {\n\
name: "conv3_1/x2"\n\
type: "Convolution"\n\
bottom: "conv3_1/x2/bn"\n\
top: "conv3_1/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_3_1"\n\
type: "Concat"\n\
bottom: "pool2"\n\
bottom: "conv3_1/x2"\n\
top: "concat_3_1"\n\
}\n\
layer {\n\
name: "conv3_2/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_3_1"\n\
top: "conv3_2/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_2/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_2/x1/bn"\n\
top: "conv3_2/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_2/x1"\n\
type: "ReLU"\n\
bottom: "conv3_2/x1/bn"\n\
top: "conv3_2/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_2/x1"\n\
type: "Convolution"\n\
bottom: "conv3_2/x1/bn"\n\
top: "conv3_2/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_2/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_2/x1"\n\
top: "conv3_2/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_2/x2/scale"\n\
type: "Scale"\n\
bottom: "conv3_2/x2/bn"\n\
top: "conv3_2/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_2/x2"\n\
type: "ReLU"\n\
bottom: "conv3_2/x2/bn"\n\
top: "conv3_2/x2/bn"\n\
}\n\
layer {\n\
name: "conv3_2/x2"\n\
type: "Convolution"\n\
bottom: "conv3_2/x2/bn"\n\
top: "conv3_2/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_3_2"\n\
type: "Concat"\n\
bottom: "concat_3_1"\n\
bottom: "conv3_2/x2"\n\
top: "concat_3_2"\n\
}\n\
layer {\n\
name: "conv3_3/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_3_2"\n\
top: "conv3_3/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_3/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_3/x1/bn"\n\
top: "conv3_3/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_3/x1"\n\
type: "ReLU"\n\
bottom: "conv3_3/x1/bn"\n\
top: "conv3_3/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_3/x1"\n\
type: "Convolution"\n\
bottom: "conv3_3/x1/bn"\n\
top: "conv3_3/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_3/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_3/x1"\n\
top: "conv3_3/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_3/x2/scale"\n\
type: "Scale"\n\
bottom: "conv3_3/x2/bn"\n\
top: "conv3_3/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_3/x2"\n\
type: "ReLU"\n\
bottom: "conv3_3/x2/bn"\n\
top: "conv3_3/x2/bn"\n\
}\n\
layer {\n\
name: "conv3_3/x2"\n\
type: "Convolution"\n\
bottom: "conv3_3/x2/bn"\n\
top: "conv3_3/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_3_3"\n\
type: "Concat"\n\
bottom: "concat_3_2"\n\
bottom: "conv3_3/x2"\n\
top: "concat_3_3"\n\
}\n\
layer {\n\
name: "conv3_4/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_3_3"\n\
top: "conv3_4/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_4/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_4/x1/bn"\n\
top: "conv3_4/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_4/x1"\n\
type: "ReLU"\n\
bottom: "conv3_4/x1/bn"\n\
top: "conv3_4/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_4/x1"\n\
type: "Convolution"\n\
bottom: "conv3_4/x1/bn"\n\
top: "conv3_4/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_4/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_4/x1"\n\
top: "conv3_4/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_4/x2/scale"\n\
type: "Scale"\n\
bottom: "conv3_4/x2/bn"\n\
top: "conv3_4/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_4/x2"\n\
type: "ReLU"\n\
bottom: "conv3_4/x2/bn"\n\
top: "conv3_4/x2/bn"\n\
}\n\
layer {\n\
name: "conv3_4/x2"\n\
type: "Convolution"\n\
bottom: "conv3_4/x2/bn"\n\
top: "conv3_4/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_3_4"\n\
type: "Concat"\n\
bottom: "concat_3_3"\n\
bottom: "conv3_4/x2"\n\
top: "concat_3_4"\n\
}\n\
layer {\n\
name: "conv3_5/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_3_4"\n\
top: "conv3_5/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_5/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_5/x1/bn"\n\
top: "conv3_5/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_5/x1"\n\
type: "ReLU"\n\
bottom: "conv3_5/x1/bn"\n\
top: "conv3_5/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_5/x1"\n\
type: "Convolution"\n\
bottom: "conv3_5/x1/bn"\n\
top: "conv3_5/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_5/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_5/x1"\n\
top: "conv3_5/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_5/x2/scale"\n\
type: "Scale"\n\
bottom: "conv3_5/x2/bn"\n\
top: "conv3_5/x2/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_5/x2"\n\
type: "ReLU"\n\
bottom: "conv3_5/x2/bn"\n\
top: "conv3_5/x2/bn"\n\
}\n\
layer {\n\
name: "conv3_5/x2"\n\
type: "Convolution"\n\
bottom: "conv3_5/x2/bn"\n\
top: "conv3_5/x2"\n\
convolution_param {\n\
num_output: 32\n\
bias_term: false\n\
pad: 1\n\
kernel_size: 3\n\
}\n\
}\n\
layer {\n\
name: "concat_3_5"\n\
type: "Concat"\n\
bottom: "concat_3_4"\n\
bottom: "conv3_5/x2"\n\
top: "concat_3_5"\n\
}\n\
layer {\n\
name: "conv3_6/x1/bn"\n\
type: "BatchNorm"\n\
bottom: "concat_3_5"\n\
top: "conv3_6/x1/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\
}\n\
layer {\n\
name: "conv3_6/x1/scale"\n\
type: "Scale"\n\
bottom: "conv3_6/x1/bn"\n\
top: "conv3_6/x1/bn"\n\
scale_param {\n\
bias_term: true\n\
}\n\
}\n\
layer {\n\
name: "relu3_6/x1"\n\
type: "ReLU"\n\
bottom: "conv3_6/x1/bn"\n\
top: "conv3_6/x1/bn"\n\
}\n\
layer {\n\
name: "conv3_6/x1"\n\
type: "Convolution"\n\
bottom: "conv3_6/x1/bn"\n\
top: "conv3_6/x1"\n\
convolution_param {\n\
num_output: 128\n\
bias_term: false\n\
kernel_size: 1\n\
}\n\
}\n\
layer {\n\
name: "conv3_6/x2/bn"\n\
type: "BatchNorm"\n\
bottom: "conv3_6/x1"\n\
top: "conv3_6/x2/bn"\n\
batch_norm_param {\n\
eps: 1e-5\n\
}\n\