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@carrierlxk
carrierlxk authored Nov 29, 2018
1 parent da7c483 commit 2550cc9
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56 changes: 28 additions & 28 deletions prototxt/vgg_se_res_layer.prototxt
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Original file line number Diff line number Diff line change
Expand Up @@ -11,34 +11,34 @@ input_dim: 1
input_dim: 512 #
input_dim: 7 #105#
input_dim: 5 #162#
layer { name: "conv4_3_global_pool" type: "Pooling" bottom: "fea_map1" top: "conv4_3_global_pool"
pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
layer { name: "conv4_3_1x1_down" type: "Convolution" bottom: "conv4_3_global_pool" top: "conv4_3_1x1_down"
param { name: "conv4_3_1x1_down_w" lr_mult: 1 } param { name: "conv4_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv4_3_1x1_down/relu" type: "ReLU" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_down"}
layer { name: "conv4_3_1x1_up" type: "Convolution" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_up"
param { name: "conv4_3_1x1_up_w" lr_mult: 1 } param { name: "conv4_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv4_3_prob" type: "Sigmoid" bottom: "conv4_3_1x1_up" top: "conv4_3_1x1_up" }
layer { name: "conv4_3_prob_reshape" type: "Reshape" bottom: "conv4_3_1x1_up" top: "conv4_3_prob_reshape"
reshape_param { shape { dim: 0 dim: 0 } }}
layer { name: "conv4_3_scale" type: "Scale" bottom: "fea_map1" bottom: "conv4_3_prob_reshape" top: "conv4_3_output"
scale_param { axis: 0 bias_term: false } }
layer { name: "conv5_3_global_pool" type: "Pooling" bottom: "fea_map2" top: "conv5_3_global_pool"
pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
layer { name: "conv5_3_1x1_down" type: "Convolution" bottom: "conv5_3_global_pool" top: "conv5_3_1x1_down"
param { name: "conv5_3_1x1_down_w" lr_mult: 1 } param { name: "conv5_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv5_3_1x1_down/relu" type: "ReLU" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_down"}
layer { name: "conv5_3_1x1_up" type: "Convolution" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_up"
param { name: "conv5_3_1x1_up_w" lr_mult: 1 } param { name: "conv5_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv5_3_prob" type: "Sigmoid" bottom: "conv5_3_1x1_up" top: "conv5_3_1x1_up" }
layer { name: "conv5_3_prob_reshape" type: "Reshape" bottom: "conv5_3_1x1_up" top: "conv5_3_prob_reshape"
reshape_param { shape { dim: 0 dim: 0 } }}
layer { name: "conv5_3_scale" type: "Scale" bottom: "fea_map2" bottom: "conv5_3_prob_reshape" top: "conv5_3_output"
scale_param { axis: 0 bias_term: false } }
layer { name: "feature_red1" type: "Convolution" bottom: "conv4_3_output" top: "feature_red1"
param { name: "feature_red1_w" lr_mult: 1 } convolution_param { num_output: 128 bias_term: false pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "feature_red2" type: "Convolution" bottom: "conv5_3_output" top: "feature_red2"
param { name: "feature_red2_w" lr_mult: 1 } convolution_param { num_output: 128 bias_term: false pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_global_pool" type: "Pooling" bottom: "fea_map1" top: "conv4_3_global_pool"
# pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
#layer { name: "conv4_3_1x1_down" type: "Convolution" bottom: "conv4_3_global_pool" top: "conv4_3_1x1_down"
# param { name: "conv4_3_1x1_down_w" lr_mult: 1 } param { name: "conv4_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_1x1_down/relu" type: "ReLU" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_down"}
#layer { name: "conv4_3_1x1_up" type: "Convolution" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_up"
# param { name: "conv4_3_1x1_up_w" lr_mult: 1 } param { name: "conv4_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_prob" type: "Sigmoid" bottom: "conv4_3_1x1_up" top: "conv4_3_1x1_up" }
#layer { name: "conv4_3_prob_reshape" type: "Reshape" bottom: "conv4_3_1x1_up" top: "conv4_3_prob_reshape"
# reshape_param { shape { dim: 0 dim: 0 } }}
#layer { name: "conv4_3_scale" type: "Scale" bottom: "fea_map1" bottom: "conv4_3_prob_reshape" top: "conv4_3_output"
# scale_param { axis: 0 bias_term: false } }
#layer { name: "conv5_3_global_pool" type: "Pooling" bottom: "fea_map2" top: "conv5_3_global_pool"
# pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
#layer { name: "conv5_3_1x1_down" type: "Convolution" bottom: "conv5_3_global_pool" top: "conv5_3_1x1_down"
# param { name: "conv5_3_1x1_down_w" lr_mult: 1 } param { name: "conv5_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv5_3_1x1_down/relu" type: "ReLU" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_down"}
#layer { name: "conv5_3_1x1_up" type: "Convolution" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_up"
# param { name: "conv5_3_1x1_up_w" lr_mult: 1 } param { name: "conv5_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv5_3_prob" type: "Sigmoid" bottom: "conv5_3_1x1_up" top: "conv5_3_1x1_up" }
#layer { name: "conv5_3_prob_reshape" type: "Reshape" bottom: "conv5_3_1x1_up" top: "conv5_3_prob_reshape"
# reshape_param { shape { dim: 0 dim: 0 } }}
#layer { name: "conv5_3_scale" type: "Scale" bottom: "fea_map2" bottom: "conv5_3_prob_reshape" top: "conv5_3_output"
# scale_param { axis: 0 bias_term: false } }
layer { name: "feature_red1" type: "Convolution" bottom: "fea_map1" top: "feature_red1"
param { name: "feature_red1_w" lr_mult: 1 } convolution_param { num_output: 128 bias_term: false pad:1 kernel_size: 3 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "feature_red2" type: "Convolution" bottom: "fea_map2" top: "feature_red2"
param { name: "feature_red2_w" lr_mult: 1 } convolution_param { num_output: 128 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "P4" type: "Eltwise" bottom: "feature_red1" bottom: "feature_red2" top: "P4" eltwise_param { operation: SUM coeff: 0.8 coeff: 0.2}}
layer { bottom: "P4" top: "conv6_1" name: "conv6_1" type: "Convolution" param { name: "conv6_1_w" lr_mult: 1 decay_mult: 1 } param { name: "conv6_1_b" lr_mult: 2 decay_mult:0} convolution_param { weight_filler {type: "gaussian" std:1e-7} bias_filler {type: "constant" value: 0} num_output: 1 pad: 0
kernel_h: 1
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56 changes: 28 additions & 28 deletions prototxt/vgg_se_res_layer_test.prototxt
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Original file line number Diff line number Diff line change
Expand Up @@ -11,34 +11,34 @@ input_dim: 1
input_dim: 512 #
input_dim: 7 #105#
input_dim: 5 #162#
layer { name: "conv4_3_global_pool" type: "Pooling" bottom: "fea_map1" top: "conv4_3_global_pool"
pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
layer { name: "conv4_3_1x1_down" type: "Convolution" bottom: "conv4_3_global_pool" top: "conv4_3_1x1_down"
param { name: "conv4_3_1x1_down_w" lr_mult: 0 } param { name: "conv4_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv4_3_1x1_down/relu" type: "ReLU" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_down"}
layer { name: "conv4_3_1x1_up" type: "Convolution" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_up"
param { name: "conv4_3_1x1_up_w" lr_mult: 0 } param { name: "conv4_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv4_3_prob" type: "Sigmoid" bottom: "conv4_3_1x1_up" top: "conv4_3_1x1_up" }
layer { name: "conv4_3_prob_reshape" type: "Reshape" bottom: "conv4_3_1x1_up" top: "conv4_3_prob_reshape"
reshape_param { shape { dim: 0 dim: 0 } }}
layer { name: "conv4_3_scale" type: "Scale" bottom: "fea_map1" bottom: "conv4_3_prob_reshape" top: "conv4_3_output"
scale_param { axis: 0 bias_term: false } }
layer { name: "conv5_3_global_pool" type: "Pooling" bottom: "fea_map2" top: "conv5_3_global_pool"
pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
layer { name: "conv5_3_1x1_down" type: "Convolution" bottom: "conv5_3_global_pool" top: "conv5_3_1x1_down"
param { name: "conv5_3_1x1_down_w" lr_mult: 0 } param { name: "conv5_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv5_3_1x1_down/relu" type: "ReLU" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_down"}
layer { name: "conv5_3_1x1_up" type: "Convolution" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_up"
param { name: "conv5_3_1x1_up_w" lr_mult: 0 } param { name: "conv5_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "conv5_3_prob" type: "Sigmoid" bottom: "conv5_3_1x1_up" top: "conv5_3_1x1_up" }
layer { name: "conv5_3_prob_reshape" type: "Reshape" bottom: "conv5_3_1x1_up" top: "conv5_3_prob_reshape"
reshape_param { shape { dim: 0 dim: 0 } }}
layer { name: "conv5_3_scale" type: "Scale" bottom: "fea_map2" bottom: "conv5_3_prob_reshape" top: "conv5_3_output"
scale_param { axis: 0 bias_term: false } }
layer { name: "feature_red1" type: "Convolution" bottom: "conv4_3_output" top: "feature_red1"
param { name: "feature_red1_w" lr_mult: 0 } convolution_param { num_output: 128 bias_term: false pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "feature_red2" type: "Convolution" bottom: "conv5_3_output" top: "feature_red2"
param { name: "feature_red2_w" lr_mult: 0 } convolution_param { num_output: 128 bias_term: false pad: 0 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_global_pool" type: "Pooling" bottom: "fea_map1" top: "conv4_3_global_pool"
# pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
#layer { name: "conv4_3_1x1_down" type: "Convolution" bottom: "conv4_3_global_pool" top: "conv4_3_1x1_down"
# param { name: "conv4_3_1x1_down_w" lr_mult: 0 } param { name: "conv4_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_1x1_down/relu" type: "ReLU" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_down"}
#layer { name: "conv4_3_1x1_up" type: "Convolution" bottom: "conv4_3_1x1_down" top: "conv4_3_1x1_up"
# param { name: "conv4_3_1x1_up_w" lr_mult: 0 } param { name: "conv4_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv4_3_prob" type: "Sigmoid" bottom: "conv4_3_1x1_up" top: "conv4_3_1x1_up" }
#layer { name: "conv4_3_prob_reshape" type: "Reshape" bottom: "conv4_3_1x1_up" top: "conv4_3_prob_reshape"
# reshape_param { shape { dim: 0 dim: 0 } }}
#layer { name: "conv4_3_scale" type: "Scale" bottom: "fea_map1" bottom: "conv4_3_prob_reshape" top: "conv4_3_output"
# scale_param { axis: 0 bias_term: false } }
#layer { name: "conv5_3_global_pool" type: "Pooling" bottom: "fea_map2" top: "conv5_3_global_pool"
# pooling_param { pool: AVE engine: CAFFE global_pooling: true }}
#layer { name: "conv5_3_1x1_down" type: "Convolution" bottom: "conv5_3_global_pool" top: "conv5_3_1x1_down"
# param { name: "conv5_3_1x1_down_w" lr_mult: 0 } param { name: "conv5_3_1x1_down_b" lr_mult: 2 } convolution_param { num_output: 128 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv5_3_1x1_down/relu" type: "ReLU" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_down"}
#layer { name: "conv5_3_1x1_up" type: "Convolution" bottom: "conv5_3_1x1_down" top: "conv5_3_1x1_up"
# param { name: "conv5_3_1x1_up_w" lr_mult: 0 } param { name: "conv5_3_1x1_up_b" lr_mult: 2 } convolution_param { num_output: 512 kernel_size: 1 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
#layer { name: "conv5_3_prob" type: "Sigmoid" bottom: "conv5_3_1x1_up" top: "conv5_3_1x1_up" }
#layer { name: "conv5_3_prob_reshape" type: "Reshape" bottom: "conv5_3_1x1_up" top: "conv5_3_prob_reshape"
# reshape_param { shape { dim: 0 dim: 0 } }}
#layer { name: "conv5_3_scale" type: "Scale" bottom: "fea_map2" bottom: "conv5_3_prob_reshape" top: "conv5_3_output"
# scale_param { axis: 0 bias_term: false } }
layer { name: "feature_red1" type: "Convolution" bottom: "fea_map1" top: "feature_red1"
param { name: "feature_red1_w" lr_mult: 0 } convolution_param { num_output: 128 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "feature_red2" type: "Convolution" bottom: "fea_map2" top: "feature_red2"
param { name: "feature_red2_w" lr_mult: 0 } convolution_param { num_output: 128 bias_term: false pad: 1 kernel_size: 3 stride: 1 weight_filler { type: "msra" } bias_filler { type: "constant" } }}
layer { name: "P4" type: "Eltwise" bottom: "feature_red1" bottom: "feature_red2" top: "P4" eltwise_param { operation: SUM coeff: 0.8 coeff: 0.2}}
layer { bottom: "P4" top: "conv6_1" name: "conv6_1" type: "Convolution" param { name: "conv6_1_w" lr_mult: 1 decay_mult: 1 } param { name: "conv6_1_b" lr_mult: 2 decay_mult:0} convolution_param { weight_filler {type: "gaussian" std:1e-7} bias_filler {type: "constant" value: 0} num_output: 1 pad: 0
kernel_h: 1
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2 changes: 1 addition & 1 deletion prototxt/vgg_se_res_solver1.prototxt
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ net: 'vgg_layer2.prototxt'
#test_interval: 1000
##update_interval: 2
lr_policy: "step"
base_lr: 2e-9#5e-9#1e-8#5e-9#1e-7#5e-9#4e-7#4e-7#5e-8#
base_lr: 1e-8#2e-9#5e-9#1e-8#5e-9#1e-7#5e-9#4e-7#4e-7#5e-8#
gamma: 0.1
stepsize: 40000
display: 10
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