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a/data/VOC0712/create_data_fisheye.sh b/data/VOC0712/create_data_fisheye.sh deleted file mode 100644 index 8aae816..0000000 --- a/data/VOC0712/create_data_fisheye.sh +++ /dev/null @@ -1,28 +0,0 @@ -cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) -root_dir=$cur_dir/../.. - -cd $root_dir - -redo=1 -data_root_dir="data/VOCdevkit" -dataset_name="fisheye" -mapfile="$root_dir/data/$dataset_name/labelmap_fisheye.prototxt" -anno_type="detection" -db="lmdb" -min_dim=0 -max_dim=0 -width=0 -height=0 - -extra_cmd="--encode-type=jpg --encoded" -if [ $redo ] -then - extra_cmd="$extra_cmd --redo" -fi -for subset in test trainval -do - python $root_dir/scripts/create_annoset.py --anno-type=$anno_type \ - --label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim --resize-width=$width \ - --resize-height=$height --check-label $extra_cmd $data_root_dir $root_dir/data/$dataset_name/$subset.txt \ - $data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$db examples/$dataset_name -done diff --git a/data/VOC0712/create_list_fisheye.sh b/data/VOC0712/create_list_fisheye.sh deleted file mode 100644 index 2859c87..0000000 --- a/data/VOC0712/create_list_fisheye.sh +++ /dev/null @@ -1,51 +0,0 @@ -#!/bin/bash - -root_dir="../../data/VOCdevkit" -sub_dir=ImageSets/Main -bash_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -for dataset in trainval test -do - dst_file=$bash_dir/$dataset.txt - if [ -f $dst_file ] - then - rm -f $dst_file - fi - for name in fisheye #VOC2007 VOC2012 - do - if [[ $dataset == "test" && $name == "VOC2012" ]] - then - continue - fi - echo "Create list for $name $dataset..." - dataset_file=$root_dir/$name/$sub_dir/$dataset.txt - - img_file=$bash_dir/$dataset"_img.txt" - cp $dataset_file $img_file - sed -i "s/^/$name\/JPEGImages\//g" $img_file - sed -i "s/$/.jpg/g" $img_file - - label_file=$bash_dir/$dataset"_label.txt" - cp $dataset_file $label_file - sed -i "s/^/$name\/Annotations\//g" $label_file - sed -i "s/$/.xml/g" $label_file - - paste -d' ' $img_file $label_file >> $dst_file - - rm -f $label_file - rm -f $img_file - done - - # Generate image name and size infomation. - if [ $dataset == "test" ] - then - $bash_dir/../../build/tools/get_image_size $root_dir $dst_file $bash_dir/$dataset"_name_size.txt" - fi - - # Shuffle trainval file. - if [ $dataset == "trainval" ] - then - rand_file=$dst_file.random - cat $dst_file | perl -MList::Util=shuffle -e 'print shuffle();' > $rand_file - mv $rand_file $dst_file - fi -done diff --git a/data/VOC0712/detect_test.txt b/data/VOC0712/detect_test.txt deleted file mode 100644 index 284b53e..0000000 --- a/data/VOC0712/detect_test.txt +++ /dev/null @@ -1,5 +0,0 @@ -C:\private\000166.jpg -C:\private\001852.jpg -C:\private\002597.jpg -C:\private\004030.jpg -C:\private\005225.jpg \ No newline at end of file diff --git a/data/VOC0712/labelmap_fisheye.prototxt b/data/VOC0712/labelmap_fisheye.prototxt deleted file mode 100644 index 9aae09b..0000000 --- a/data/VOC0712/labelmap_fisheye.prototxt +++ /dev/null @@ -1,45 +0,0 @@ -item { - name: "none_of_the_above" - label: 0 - display_name: "background" -} -item { - name: "g" - label: 1 - display_name: "g" -} -item { - name: "l" - label: 2 - display_name: "l" -} -item { - name: "b" - label: 3 - display_name: "b" -} -item { - name: "c" - label: 4 - display_name: "c" -} -item { - name: "m" - label: 5 - display_name: "m" -} -item { - name: "y" - label: 6 - display_name: "y" -} -item { - name: "p" - label: 7 - display_name: "p" -} -item { - name: "t" - label: 8 - display_name: "t" -} \ No newline at end of file diff --git a/data/VOC0712/test.txt b/data/VOC0712/test.txt deleted file mode 100644 index 58570f8..0000000 --- a/data/VOC0712/test.txt +++ /dev/null @@ -1,500 +0,0 @@ -fisheye/JPEGImages/B123456_N3chn320180508194217561.jpg fisheye/Annotations/B123456_N3chn320180508194217561.xml -fisheye/JPEGImages/B123456_N1chn120180501163942059.jpg fisheye/Annotations/B123456_N1chn120180501163942059.xml 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-fisheye/JPEGImages/B0123_N4chn420180522122903158.jpg fisheye/Annotations/B0123_N4chn420180522122903158.xml -fisheye/JPEGImages/B0123_N1chn120180521154440359.jpg fisheye/Annotations/B0123_N1chn120180521154440359.xml -fisheye/JPEGImages/B0123_N4chn420180518192503233.jpg fisheye/Annotations/B0123_N4chn420180518192503233.xml diff --git a/include/caffe/layers/yolov3_layer.hpp b/include/caffe/layers/yolov3_layer.hpp index a7f48d6..fc668a4 100644 --- a/include/caffe/layers/yolov3_layer.hpp +++ b/include/caffe/layers/yolov3_layer.hpp @@ -50,12 +50,15 @@ class Yolov3Layer : public LossLayer { int side_; int num_class_; int num_; + int biases_size_; int anchors_scale_; + int time_count_; float object_scale_; float class_scale_; float noobject_scale_; float coord_scale_; float thresh_; + bool use_logic_gradient_; vector biases_; vector mask_; Blob diff_; diff --git a/models/darknet/.gitignore b/models/darknet/.gitignore new file mode 100644 index 0000000..be71866 --- /dev/null +++ b/models/darknet/.gitignore @@ -0,0 +1 @@ +*.caffemodel diff --git a/models/yolov3/mobilenet_yolov3_lite_deploy_iter_57000.caffemodel b/models/yolov3/mobilenet_yolov3_lite_deploy_iter_57000.caffemodel new file mode 100644 index 0000000..3f5466f Binary files /dev/null and b/models/yolov3/mobilenet_yolov3_lite_deploy_iter_57000.caffemodel differ diff --git a/models/yolov3/mobilenet_yolo_solver.prototxt b/models/yolov3/mobilenet_yolov3_lite_solver.prototxt similarity index 59% rename from models/yolov3/mobilenet_yolo_solver.prototxt rename to models/yolov3/mobilenet_yolov3_lite_solver.prototxt index 6c8fa95..6bb6002 100644 --- a/models/yolov3/mobilenet_yolo_solver.prototxt +++ b/models/yolov3/mobilenet_yolov3_lite_solver.prototxt @@ -1,5 +1,5 @@ -train_net: "models/yolov3/mobilenet_yolo_train.prototxt" -test_net: "models/yolov3/mobilenet_yolo_test.prototxt" +train_net: "models/yolov3/mobilenet_yolov3_lite_train.prototxt" +test_net: "models/yolov3/mobilenet_yolov3_lite_test.prototxt" test_iter: 4952 test_interval: 1000 base_lr: 0.0005 @@ -9,15 +9,15 @@ lr_policy: "multistep" gamma: 0.5 weight_decay: 0.00005 snapshot: 1000 -snapshot_prefix: "models/yolov3/mobilenet_yolo_deploy" +snapshot_prefix: "models/yolov3/mobilenet_yolov3_lite_deploy" solver_mode: GPU debug_info: false snapshot_after_train: true test_initialization: false average_loss: 10 -stepvalue: 30000 -stepvalue: 60000 -iter_size: 2 +stepvalue: 25000 +stepvalue: 50000 +iter_size: 4 type: "RMSProp" eval_type: "detection" ap_version: "11point" diff --git a/models/yolov3/mobilenet_yolo_test.prototxt b/models/yolov3/mobilenet_yolov3_lite_test.prototxt similarity index 91% rename from models/yolov3/mobilenet_yolo_test.prototxt rename to models/yolov3/mobilenet_yolov3_lite_test.prototxt index e4af15d..7d759ad 100644 --- a/models/yolov3/mobilenet_yolo_test.prototxt +++ b/models/yolov3/mobilenet_yolov3_lite_test.prototxt @@ -1834,38 +1834,14 @@ layer { top: "conv13" } -#layer { -# name: "concat1" -# type: "Concat" -# bottom: "conv11" -# top: "concat1" -#} - -#layer { -# name: "reorg1" -# type: "Reorg" -# bottom: "concat1" -# top: "reorg1" -# reorg_param { -# stride: 2 -# } -#} - -#layer { -# name: "concat2" -# type: "Concat" -# bottom: "reorg1" -# bottom: "conv13" -# top: "concat2" -#} layer { name: "conv16/dw" type: "DepthwiseConvolution" bottom: "conv13" top: "conv16/dw" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -1903,11 +1879,11 @@ layer { bottom: "conv16/dw" top: "conv16/dw" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -1927,13 +1903,13 @@ layer { top: "conv16/dw" } layer { - name: "conv17" + name: "conv16" type: "Convolution" bottom: "conv16/dw" - top: "conv17" + top: "conv16" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -1945,10 +1921,10 @@ layer { } } layer { - name: "conv17/bn" + name: "conv16/bn" type: "BatchNorm" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" param { lr_mult: 0 decay_mult: 0 @@ -1963,16 +1939,16 @@ layer { } } layer { - name: "conv17/scale" + name: "conv16/scale" type: "Scale" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -1986,15 +1962,15 @@ layer { } } layer { - name: "conv17/relu" + name: "conv16/relu" type: "ReLU" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" } layer { name: "upsample" type: "Deconvolution" - bottom: "conv17" + bottom: "conv16" top: "upsample" param { lr_mult: 0 decay_mult: 0 } convolution_param { @@ -2006,20 +1982,153 @@ layer { } } layer { - name: "conv_18/sum" - type: "Eltwise" + name: "conv17/dw" + type: "DepthwiseConvolution" bottom: "conv11" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/dw/bn" + type: "BatchNorm" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/dw/scale" + type: "Scale" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/dw/relu" + type: "ReLU" + bottom: "conv17/dw" + top: "conv17/dw" +} +layer { + name: "conv17" + type: "Convolution" + bottom: "conv17/dw" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/bn" + type: "BatchNorm" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/scale" + type: "Scale" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/relu" + type: "ReLU" + bottom: "conv17" + top: "conv17" +} +layer { + name: "conv17/sum" + type: "Eltwise" + bottom: "conv17" bottom: "upsample" - top: "conv_18/sum" + top: "conv17/sum" } layer { - name: "conv19/dw" + name: "conv18/dw" type: "DepthwiseConvolution" - bottom: "conv_18/sum" - top: "conv19/dw" + bottom: "conv17/sum" + top: "conv18/dw" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 512 @@ -2034,10 +2143,10 @@ layer { } } layer { - name: "conv19/dw/bn" + name: "conv18/dw/bn" type: "BatchNorm" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" param { lr_mult: 0 decay_mult: 0 @@ -2052,16 +2161,16 @@ layer { } } layer { - name: "conv19/dw/scale" + name: "conv18/dw/scale" type: "Scale" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2075,19 +2184,19 @@ layer { } } layer { - name: "conv19/dw/relu" + name: "conv18/dw/relu" type: "ReLU" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" } layer { - name: "conv20" + name: "conv18" type: "Convolution" - bottom: "conv19/dw" - top: "conv20" + bottom: "conv18/dw" + top: "conv18" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -2099,10 +2208,10 @@ layer { } } layer { - name: "conv20/bn" + name: "conv18/bn" type: "BatchNorm" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" param { lr_mult: 0 decay_mult: 0 @@ -2117,16 +2226,16 @@ layer { } } layer { - name: "conv20/scale" + name: "conv18/scale" type: "Scale" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2140,17 +2249,18 @@ layer { } } layer { - name: "conv20/relu" + name: "conv18/relu" type: "ReLU" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" } + layer { - name: "conv22_indoor" + name: "conv20" type: "Convolution" - bottom: "conv17" - top: "conv22" + bottom: "conv16" + top: "conv20" param { lr_mult: 1 decay_mult: 1 @@ -2172,11 +2282,12 @@ layer { } } } + layer { - name: "conv23_indoor" + name: "conv21" type: "Convolution" - bottom: "conv20" - top: "conv23" + bottom: "conv18" + top: "conv21" param { lr_mult: 1 decay_mult: 1 @@ -2199,17 +2310,23 @@ layer { } } + layer { name: "detection_out" type: "Yolov3DetectionOutput" - bottom: "conv22" - bottom: "conv23" + bottom: "conv20" + bottom: "conv21" top: "detection_out" include { phase: TEST } yolov3_detection_output_param { + confidence_threshold: 0.01 + nms_threshold: 0.45 num_classes: 20 + + + #10,14, 23,27, 37,58, 81,82, 135,169, 344,319 biases: 10 biases: 14 biases: 23 @@ -2220,9 +2337,9 @@ layer { biases: 82 biases: 135 biases: 169 - biases: 344 + biases: 344 biases: 319 - + mask:3 mask:4 mask:5 diff --git a/models/yolov3/mobilenet_yolo_train.prototxt b/models/yolov3/mobilenet_yolov3_lite_train.prototxt similarity index 91% rename from models/yolov3/mobilenet_yolo_train.prototxt rename to models/yolov3/mobilenet_yolov3_lite_train.prototxt index 3a47481..f4b232d 100644 --- a/models/yolov3/mobilenet_yolo_train.prototxt +++ b/models/yolov3/mobilenet_yolov3_lite_train.prototxt @@ -125,12 +125,12 @@ layer { } expand_param { prob: 0.5 - max_expand_ratio: 2.0 + max_expand_ratio: 3.0 } } data_param { source: "examples/VOC0712/VOC0712_trainval_lmdb" - batch_size: 6 + batch_size: 4 backend: LMDB } annotated_data_param { @@ -1941,38 +1941,14 @@ layer { top: "conv13" } -#layer { -# name: "concat1" -# type: "Concat" -# bottom: "conv11" -# top: "concat1" -#} - -#layer { -# name: "reorg1" -# type: "Reorg" -# bottom: "concat1" -# top: "reorg1" -# reorg_param { -# stride: 2 -# } -#} - -#layer { -# name: "concat2" -# type: "Concat" -# bottom: "reorg1" -# bottom: "conv13" -# top: "concat2" -#} layer { name: "conv16/dw" type: "DepthwiseConvolution" bottom: "conv13" top: "conv16/dw" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -2010,11 +1986,11 @@ layer { bottom: "conv16/dw" top: "conv16/dw" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2034,13 +2010,13 @@ layer { top: "conv16/dw" } layer { - name: "conv17" + name: "conv16" type: "Convolution" bottom: "conv16/dw" - top: "conv17" + top: "conv16" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -2052,10 +2028,10 @@ layer { } } layer { - name: "conv17/bn" + name: "conv16/bn" type: "BatchNorm" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" param { lr_mult: 0 decay_mult: 0 @@ -2070,16 +2046,16 @@ layer { } } layer { - name: "conv17/scale" + name: "conv16/scale" type: "Scale" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2093,15 +2069,15 @@ layer { } } layer { - name: "conv17/relu" + name: "conv16/relu" type: "ReLU" - bottom: "conv17" - top: "conv17" + bottom: "conv16" + top: "conv16" } layer { name: "upsample" type: "Deconvolution" - bottom: "conv17" + bottom: "conv16" top: "upsample" param { lr_mult: 0 decay_mult: 0 } convolution_param { @@ -2113,20 +2089,153 @@ layer { } } layer { - name: "conv_18/sum" - type: "Eltwise" + name: "conv17/dw" + type: "DepthwiseConvolution" bottom: "conv11" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/dw/bn" + type: "BatchNorm" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/dw/scale" + type: "Scale" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/dw/relu" + type: "ReLU" + bottom: "conv17/dw" + top: "conv17/dw" +} +layer { + name: "conv17" + type: "Convolution" + bottom: "conv17/dw" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/bn" + type: "BatchNorm" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/scale" + type: "Scale" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/relu" + type: "ReLU" + bottom: "conv17" + top: "conv17" +} +layer { + name: "conv17/sum" + type: "Eltwise" + bottom: "conv17" bottom: "upsample" - top: "conv_18/sum" + top: "conv17/sum" } layer { - name: "conv19/dw" + name: "conv18/dw" type: "DepthwiseConvolution" - bottom: "conv_18/sum" - top: "conv19/dw" + bottom: "conv17/sum" + top: "conv18/dw" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 512 @@ -2141,10 +2250,10 @@ layer { } } layer { - name: "conv19/dw/bn" + name: "conv18/dw/bn" type: "BatchNorm" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" param { lr_mult: 0 decay_mult: 0 @@ -2159,16 +2268,16 @@ layer { } } layer { - name: "conv19/dw/scale" + name: "conv18/dw/scale" type: "Scale" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2182,19 +2291,19 @@ layer { } } layer { - name: "conv19/dw/relu" + name: "conv18/dw/relu" type: "ReLU" - bottom: "conv19/dw" - top: "conv19/dw" + bottom: "conv18/dw" + top: "conv18/dw" } layer { - name: "conv20" + name: "conv18" type: "Convolution" - bottom: "conv19/dw" - top: "conv20" + bottom: "conv18/dw" + top: "conv18" param { - lr_mult: 0.1 - decay_mult: 0.1 + lr_mult: 1 + decay_mult: 1 } convolution_param { num_output: 1024 @@ -2206,10 +2315,10 @@ layer { } } layer { - name: "conv20/bn" + name: "conv18/bn" type: "BatchNorm" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" param { lr_mult: 0 decay_mult: 0 @@ -2224,16 +2333,16 @@ layer { } } layer { - name: "conv20/scale" + name: "conv18/scale" type: "Scale" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" param { - lr_mult: 0.1 + lr_mult: 1 decay_mult: 0.0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0.0 } scale_param { @@ -2247,17 +2356,18 @@ layer { } } layer { - name: "conv20/relu" + name: "conv18/relu" type: "ReLU" - bottom: "conv20" - top: "conv20" + bottom: "conv18" + top: "conv18" } + layer { - name: "conv22_indoor" + name: "conv20" type: "Convolution" - bottom: "conv17" - top: "conv22" + bottom: "conv16" + top: "conv20" param { lr_mult: 1 decay_mult: 1 @@ -2279,11 +2389,12 @@ layer { } } } + layer { - name: "conv23_indoor" + name: "conv21" type: "Convolution" - bottom: "conv20" - top: "conv23" + bottom: "conv18" + top: "conv21" param { lr_mult: 1 decay_mult: 1 @@ -2305,10 +2416,12 @@ layer { } } } + + layer { name: "Yolov3Loss1" type: "Yolov3" - bottom: "conv22" + bottom: "conv20" bottom: "label" top: "det_loss1" loss_weight: 1 @@ -2320,8 +2433,9 @@ layer { noobject_scale: 1.0 class_scale: 1.0 coord_scale: 1.0 - thresh: 0.7 + thresh: 0.6 anchors_scale : 32 + use_logic_gradient : false #10,14, 23,27, 37,58, 81,82, 135,169, 344,319 biases: 10 biases: 14 @@ -2344,20 +2458,21 @@ layer { layer { name: "Yolov3Loss2" type: "Yolov3" - bottom: "conv23" + bottom: "conv21" bottom: "label" top: "det_loss2" loss_weight: 1 yolov3_param { - side: 13 + side: 26 num_class: 20 num: 3 object_scale: 5.0 noobject_scale: 1.0 class_scale: 1.0 coord_scale: 1.0 - thresh: 0.7 + thresh: 0.6 anchors_scale : 16 + use_logic_gradient : false #10,14, 23,27, 37,58, 81,82, 135,169, 344,319 biases: 10 biases: 14 @@ -2376,5 +2491,4 @@ layer { mask:1 mask:2 } -} - +} \ No newline at end of file diff --git a/models/yolov3/mobilenet_yolov3_solver.prototxt b/models/yolov3/mobilenet_yolov3_solver.prototxt new file mode 100644 index 0000000..48d6555 --- /dev/null +++ b/models/yolov3/mobilenet_yolov3_solver.prototxt @@ -0,0 +1,25 @@ +train_net: "models/yolov3/mobilenet_yolov3_train.prototxt" +test_net: "models/yolov3/mobilenet_yolov3_test.prototxt" +test_iter: 4952 +test_interval: 2000 +base_lr: 0.0005 +display: 10 +max_iter: 120000 +lr_policy: "multistep" +gamma: 0.5 +weight_decay: 0.00005 +snapshot: 2000 +snapshot_prefix: "models/yolov3/mobilenet_yolov3_deploy" +solver_mode: GPU +debug_info: false +snapshot_after_train: true +test_initialization: false +average_loss: 10 +stepvalue: 25000 +stepvalue: 50000 +stepvalue: 75000 +iter_size: 4 +type: "RMSProp" +eval_type: "detection" +ap_version: "11point" +show_per_class_result: true \ No newline at end of file diff --git a/models/yolov3/mobilenet_yolov3_test.prototxt b/models/yolov3/mobilenet_yolov3_test.prototxt new file mode 100644 index 0000000..92088c6 --- /dev/null +++ b/models/yolov3/mobilenet_yolov3_test.prototxt @@ -0,0 +1,2962 @@ +name: "MobileNet-YOLO" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + scale: 0.007843 + mean_value: 127.5 + mean_value: 127.5 + mean_value: 127.5 + resize_param { + prob: 1.0 + resize_mode: WARP + height: 416 + width: 416 + interp_mode: LINEAR + } + } + data_param { + source: "examples/VOC0712/VOC0712_test_lmdb" + batch_size: 1 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} + + +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "DepthwiseConvolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/dw/bn" + type: "BatchNorm" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/dw/scale" + type: "Scale" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/dw/relu" + type: "ReLU" + bottom: "conv1/dw" + top: "conv1/dw" +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv1/dw" + top: "conv1" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/relu" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "conv2/dw" + type: "DepthwiseConvolution" + bottom: "conv1" + top: "conv2/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 64 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/dw/bn" + type: "BatchNorm" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/dw/scale" + type: "Scale" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/dw/relu" + type: "ReLU" + bottom: "conv2/dw" + top: "conv2/dw" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "conv2/dw" + top: "conv2" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/bn" + type: "BatchNorm" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/scale" + type: "Scale" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/relu" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "conv3/dw" + type: "DepthwiseConvolution" + bottom: "conv2" + top: "conv3/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/dw/bn" + type: "BatchNorm" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/dw/scale" + type: "Scale" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/dw/relu" + type: "ReLU" + bottom: "conv3/dw" + top: "conv3/dw" +} +layer { + name: "conv3" + type: "Convolution" + bottom: "conv3/dw" + top: "conv3" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/bn" + type: "BatchNorm" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/scale" + type: "Scale" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/relu" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "conv4/dw" + type: "DepthwiseConvolution" + bottom: "conv3" + top: "conv4/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 128 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/dw/bn" + type: "BatchNorm" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/dw/scale" + type: "Scale" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/dw/relu" + type: "ReLU" + bottom: "conv4/dw" + top: "conv4/dw" +} +layer { + name: "conv4" + type: "Convolution" + bottom: "conv4/dw" + top: "conv4" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/bn" + type: "BatchNorm" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/scale" + type: "Scale" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/relu" + type: "ReLU" + bottom: "conv4" + top: "conv4" +} +layer { + name: "conv5/dw" + type: "DepthwiseConvolution" + bottom: "conv4" + top: "conv5/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/dw/bn" + type: "BatchNorm" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/dw/scale" + type: "Scale" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/dw/relu" + type: "ReLU" + bottom: "conv5/dw" + top: "conv5/dw" +} +layer { + name: "conv5" + type: "Convolution" + bottom: "conv5/dw" + top: "conv5" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/bn" + type: "BatchNorm" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/scale" + type: "Scale" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/relu" + type: "ReLU" + bottom: "conv5" + top: "conv5" +} +layer { + name: "conv6/dw" + type: "DepthwiseConvolution" + bottom: "conv5" + top: "conv6/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/dw/bn" + type: "BatchNorm" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/dw/scale" + type: "Scale" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/dw/relu" + type: "ReLU" + bottom: "conv6/dw" + top: "conv6/dw" +} +layer { + name: "conv6" + type: "Convolution" + bottom: "conv6/dw" + top: "conv6" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/bn" + type: "BatchNorm" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/scale" + type: "Scale" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/relu" + type: "ReLU" + bottom: "conv6" + top: "conv6" +} +layer { + name: "conv7/dw" + type: "DepthwiseConvolution" + bottom: "conv6" + top: "conv7/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/dw/bn" + type: "BatchNorm" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/dw/scale" + type: "Scale" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/dw/relu" + type: "ReLU" + bottom: "conv7/dw" + top: "conv7/dw" +} +layer { + name: "conv7" + type: "Convolution" + bottom: "conv7/dw" + top: "conv7" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/bn" + type: "BatchNorm" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/scale" + type: "Scale" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/relu" + type: "ReLU" + bottom: "conv7" + top: "conv7" +} +layer { + name: "conv8/dw" + type: "DepthwiseConvolution" + bottom: "conv7" + top: "conv8/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/dw/bn" + type: "BatchNorm" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/dw/scale" + type: "Scale" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/dw/relu" + type: "ReLU" + bottom: "conv8/dw" + top: "conv8/dw" +} +layer { + name: "conv8" + type: "Convolution" + bottom: "conv8/dw" + top: "conv8" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/bn" + type: "BatchNorm" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/scale" + type: "Scale" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/relu" + type: "ReLU" + bottom: "conv8" + top: "conv8" +} +layer { + name: "conv9/dw" + type: "DepthwiseConvolution" + bottom: "conv8" + top: "conv9/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/dw/bn" + type: "BatchNorm" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/dw/scale" + type: "Scale" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/dw/relu" + type: "ReLU" + bottom: "conv9/dw" + top: "conv9/dw" +} +layer { + name: "conv9" + type: "Convolution" + bottom: "conv9/dw" + top: "conv9" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/bn" + type: "BatchNorm" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/scale" + type: "Scale" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/relu" + type: "ReLU" + bottom: "conv9" + top: "conv9" +} +layer { + name: "conv10/dw" + type: "DepthwiseConvolution" + bottom: "conv9" + top: "conv10/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/dw/bn" + type: "BatchNorm" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/dw/scale" + type: "Scale" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/dw/relu" + type: "ReLU" + bottom: "conv10/dw" + top: "conv10/dw" +} +layer { + name: "conv10" + type: "Convolution" + bottom: "conv10/dw" + top: "conv10" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/bn" + type: "BatchNorm" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/scale" + type: "Scale" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/relu" + type: "ReLU" + bottom: "conv10" + top: "conv10" +} +layer { + name: "conv11/dw" + type: "DepthwiseConvolution" + bottom: "conv10" + top: "conv11/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/dw/bn" + type: "BatchNorm" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/dw/scale" + type: "Scale" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/dw/relu" + type: "ReLU" + bottom: "conv11/dw" + top: "conv11/dw" +} +layer { + name: "conv11" + type: "Convolution" + bottom: "conv11/dw" + top: "conv11" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/bn" + type: "BatchNorm" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/scale" + type: "Scale" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/relu" + type: "ReLU" + bottom: "conv11" + top: "conv11" +} +layer { + name: "conv12/dw" + type: "DepthwiseConvolution" + bottom: "conv11" + top: "conv12/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/dw/bn" + type: "BatchNorm" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/dw/scale" + type: "Scale" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/dw/relu" + type: "ReLU" + bottom: "conv12/dw" + top: "conv12/dw" +} +layer { + name: "conv12" + type: "Convolution" + bottom: "conv12/dw" + top: "conv12" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/bn" + type: "BatchNorm" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/scale" + type: "Scale" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/relu" + type: "ReLU" + bottom: "conv12" + top: "conv12" +} +layer { + name: "conv13/dw" + type: "DepthwiseConvolution" + bottom: "conv12" + top: "conv13/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/dw/bn" + type: "BatchNorm" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/dw/scale" + type: "Scale" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/dw/relu" + type: "ReLU" + bottom: "conv13/dw" + top: "conv13/dw" +} +layer { + name: "conv13" + type: "Convolution" + bottom: "conv13/dw" + top: "conv13" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/bn" + type: "BatchNorm" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/scale" + type: "Scale" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/relu" + type: "ReLU" + bottom: "conv13" + top: "conv13" +} + +layer { + name: "conv16/dw" + type: "DepthwiseConvolution" + bottom: "conv13" + top: "conv16/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16/dw/bn" + type: "BatchNorm" + bottom: "conv16/dw" + top: "conv16/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16/dw/scale" + type: "Scale" + bottom: "conv16/dw" + top: "conv16/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16/dw/relu" + type: "ReLU" + bottom: "conv16/dw" + top: "conv16/dw" +} +layer { + name: "conv16" + type: "Convolution" + bottom: "conv16/dw" + top: "conv16" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16/bn" + type: "BatchNorm" + bottom: "conv16" + top: "conv16" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16/scale" + type: "Scale" + bottom: "conv16" + top: "conv16" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16/relu" + type: "ReLU" + bottom: "conv16" + top: "conv16" +} +layer { + name: "upsample" + type: "Deconvolution" + bottom: "conv16" + top: "upsample" + param { lr_mult: 0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 4 stride: 2 pad: 1 + group: 512 + weight_filler: { type: "bilinear" } + bias_term: false + } +} +layer { + name: "conv17/dw" + type: "DepthwiseConvolution" + bottom: "conv11" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/dw/bn" + type: "BatchNorm" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/dw/scale" + type: "Scale" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/dw/relu" + type: "ReLU" + bottom: "conv17/dw" + top: "conv17/dw" +} +layer { + name: "conv17" + type: "Convolution" + bottom: "conv17/dw" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/bn" + type: "BatchNorm" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/scale" + type: "Scale" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/relu" + type: "ReLU" + bottom: "conv17" + top: "conv17" +} +layer { + name: "conv17/sum" + type: "Eltwise" + bottom: "conv17" + bottom: "upsample" + top: "conv17/sum" +} +layer { + name: "conv18/dw" + type: "DepthwiseConvolution" + bottom: "conv17/sum" + top: "conv18/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv18/dw/bn" + type: "BatchNorm" + bottom: "conv18/dw" + top: "conv18/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv18/dw/scale" + type: "Scale" + bottom: "conv18/dw" + top: "conv18/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv18/dw/relu" + type: "ReLU" + bottom: "conv18/dw" + top: "conv18/dw" +} +layer { + name: "conv18" + type: "Convolution" + bottom: "conv18/dw" + top: "conv18" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv18/bn" + type: "BatchNorm" + bottom: "conv18" + top: "conv18" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv18/scale" + type: "Scale" + bottom: "conv18" + top: "conv18" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv18/relu" + type: "ReLU" + bottom: "conv18" + top: "conv18" +} + +layer { + name: "upsample_conv18" + type: "Deconvolution" + bottom: "conv18" + top: "upsample_conv18" + param { lr_mult: 0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 4 stride: 2 pad: 1 + group: 512 + weight_filler: { type: "bilinear" } + bias_term: false + } +} + + +layer { + name: "conv19/dw" + type: "DepthwiseConvolution" + bottom: "conv5" + top: "conv19/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv19/dw/bn" + type: "BatchNorm" + bottom: "conv19/dw" + top: "conv19/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv19/dw/scale" + type: "Scale" + bottom: "conv19/dw" + top: "conv19/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv19/dw/relu" + type: "ReLU" + bottom: "conv19/dw" + top: "conv19/dw" +} +layer { + name: "conv19" + type: "Convolution" + bottom: "conv19/dw" + top: "conv19" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv19/bn" + type: "BatchNorm" + bottom: "conv19" + top: "conv19" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv19/scale" + type: "Scale" + bottom: "conv19" + top: "conv19" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv19/relu" + type: "ReLU" + bottom: "conv19" + top: "conv19" +} +layer { + name: "conv20/dw" + type: "DepthwiseConvolution" + bottom: "conv19" + top: "conv20/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv20/dw/bn" + type: "BatchNorm" + bottom: "conv20/dw" + top: "conv20/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv20/dw/scale" + type: "Scale" + bottom: "conv20/dw" + top: "conv20/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv20/dw/relu" + type: "ReLU" + bottom: "conv20/dw" + top: "conv20/dw" +} +layer { + name: "conv20" + type: "Convolution" + bottom: "conv20/dw" + top: "conv20" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv20/bn" + type: "BatchNorm" + bottom: "conv20" + top: "conv20" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv20/scale" + type: "Scale" + bottom: "conv20" + top: "conv20" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv20/relu" + type: "ReLU" + bottom: "conv20" + top: "conv20" +} +layer { + name: "conv21/dw" + type: "DepthwiseConvolution" + bottom: "conv20" + top: "conv21/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv21/dw/bn" + type: "BatchNorm" + bottom: "conv21/dw" + top: "conv21/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv21/dw/scale" + type: "Scale" + bottom: "conv21/dw" + top: "conv21/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv21/dw/relu" + type: "ReLU" + bottom: "conv21/dw" + top: "conv21/dw" +} +layer { + name: "conv21" + type: "Convolution" + bottom: "conv21/dw" + top: "conv21" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv21/bn" + type: "BatchNorm" + bottom: "conv21" + top: "conv21" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv21/scale" + type: "Scale" + bottom: "conv21" + top: "conv21" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv21/relu" + type: "ReLU" + bottom: "conv21" + top: "conv21" +} +layer { + name: "conv21/cat" + type: "Concat" + bottom: "conv21" + bottom: "upsample_conv18" + top: "conv21/cat" +} +layer { + name: "conv22/dw" + type: "DepthwiseConvolution" + bottom: "conv21/cat" + top: "conv22/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 768 + bias_term: false + pad: 1 + kernel_size: 3 + group: 768 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv22/dw/bn" + type: "BatchNorm" + bottom: "conv22/dw" + top: "conv22/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv22/dw/scale" + type: "Scale" + bottom: "conv22/dw" + top: "conv22/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv22/dw/relu" + type: "ReLU" + bottom: "conv22/dw" + top: "conv22/dw" +} +layer { + name: "conv22" + type: "Convolution" + bottom: "conv22/dw" + top: "conv22" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv22/bn" + type: "BatchNorm" + bottom: "conv22" + top: "conv22" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv22/scale" + type: "Scale" + bottom: "conv22" + top: "conv22" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv22/relu" + type: "ReLU" + bottom: "conv22" + top: "conv22" +} + +layer { + name: "conv23" + type: "Convolution" + bottom: "conv16" + top: "conv23" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} + +layer { + name: "conv24" + type: "Convolution" + bottom: "conv18" + top: "conv24" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} + +layer { + name: "conv25" + type: "Convolution" + bottom: "conv22" + top: "conv25" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} +layer { + name: "detection_out" + type: "Yolov3DetectionOutput" + bottom: "conv23" + bottom: "conv24" + bottom: "conv25" + top: "detection_out" + include { + phase: TEST + } + yolov3_detection_output_param { + confidence_threshold: 0.01 + nms_threshold: 0.45 + num_classes: 20 + + #10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + + biases: 10 + biases: 13 + biases: 16 + biases: 30 + biases: 33 + biases: 23 + biases: 30 + biases: 61 + biases: 62 + biases: 45 + biases: 59 + biases: 119 + biases: 116 + biases: 90 + biases: 156 + biases: 198 + biases: 373 + biases: 326 + + mask:6 + mask:7 + mask:8 + mask:3 + mask:4 + mask:5 + mask:0 + mask:1 + mask:2 + anchors_scale:32 + anchors_scale:16 + anchors_scale:8 + mask_group_num:3 + } +} +layer { + name: "detection_eval" + type: "DetectionEvaluate" + bottom: "detection_out" + bottom: "label" + top: "detection_eval" + include { + phase: TEST + } + detection_evaluate_param { + num_classes: 21 + background_label_id: 0 + overlap_threshold: 0.5 + evaluate_difficult_gt: false + } +} + diff --git a/models/yolov3/mobilenet_yolov3_train.prototxt b/models/yolov3/mobilenet_yolov3_train.prototxt new file mode 100644 index 0000000..2bdfb78 --- /dev/null +++ b/models/yolov3/mobilenet_yolov3_train.prototxt @@ -0,0 +1,3135 @@ +name: "MobileNet-YOLO" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TRAIN + } + + transform_param { + scale: 0.007843 + mirror: true + mean_value: 127.5 + mean_value: 127.5 + mean_value: 127.5 + + resize_param { + prob: 0.1 + resize_mode: WARP + height: 608 + width: 608 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 416 + width: 416 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 320 + width: 320 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 352 + width: 352 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 384 + width: 384 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 448 + width: 448 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 480 + width: 480 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 512 + width: 512 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + resize_param { + prob: 0.1 + resize_mode: WARP + height: 576 + width: 576 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: LANCZOS4 + } + + + emit_constraint { + emit_type: CENTER + } + distort_param { + brightness_prob: 0.5 + brightness_delta: 32.0 + contrast_prob: 0.5 + contrast_lower: 0.5 + contrast_upper: 1.5 + hue_prob: 0.5 + hue_delta: 18.0 + saturation_prob: 0.5 + saturation_lower: 0.5 + saturation_upper: 1.5 + random_order_prob: 0.0 + } + expand_param { + prob: 0.5 + max_expand_ratio: 4.0 + } + } + data_param { + source: "examples/VOC0712/VOC0712_trainval_lmdb" + batch_size: 4 + backend: LMDB + } + annotated_data_param { + yolo_data_type : 1 + yolo_data_jitter : 0.3 + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} + +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "DepthwiseConvolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/dw/bn" + type: "BatchNorm" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/dw/scale" + type: "Scale" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/dw/relu" + type: "ReLU" + bottom: "conv1/dw" + top: "conv1/dw" +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv1/dw" + top: "conv1" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/relu" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "conv2/dw" + type: "DepthwiseConvolution" + bottom: "conv1" + top: "conv2/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 64 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/dw/bn" + type: "BatchNorm" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/dw/scale" + type: "Scale" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/dw/relu" + type: "ReLU" + bottom: "conv2/dw" + top: "conv2/dw" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "conv2/dw" + top: "conv2" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/bn" + type: "BatchNorm" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/scale" + type: "Scale" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/relu" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "conv3/dw" + type: "DepthwiseConvolution" + bottom: "conv2" + top: "conv3/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/dw/bn" + type: "BatchNorm" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/dw/scale" + type: "Scale" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/dw/relu" + type: "ReLU" + bottom: "conv3/dw" + top: "conv3/dw" +} +layer { + name: "conv3" + type: "Convolution" + bottom: "conv3/dw" + top: "conv3" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/bn" + type: "BatchNorm" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/scale" + type: "Scale" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/relu" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "conv4/dw" + type: "DepthwiseConvolution" + bottom: "conv3" + top: "conv4/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 128 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/dw/bn" + type: "BatchNorm" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/dw/scale" + type: "Scale" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/dw/relu" + type: "ReLU" + bottom: "conv4/dw" + top: "conv4/dw" +} +layer { + name: "conv4" + type: "Convolution" + bottom: "conv4/dw" + top: "conv4" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/bn" + type: "BatchNorm" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/scale" + type: "Scale" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/relu" + type: "ReLU" + bottom: "conv4" + top: "conv4" +} +layer { + name: "conv5/dw" + type: "DepthwiseConvolution" + bottom: "conv4" + top: "conv5/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/dw/bn" + type: "BatchNorm" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/dw/scale" + type: "Scale" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/dw/relu" + type: "ReLU" + bottom: "conv5/dw" + top: "conv5/dw" +} +layer { + name: "conv5" + type: "Convolution" + bottom: "conv5/dw" + top: "conv5" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/bn" + type: "BatchNorm" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/scale" + type: "Scale" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/relu" + type: "ReLU" + bottom: "conv5" + top: "conv5" +} +layer { + name: "conv6/dw" + type: "DepthwiseConvolution" + bottom: "conv5" + top: "conv6/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/dw/bn" + type: "BatchNorm" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/dw/scale" + type: "Scale" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/dw/relu" + type: "ReLU" + bottom: "conv6/dw" + top: "conv6/dw" +} +layer { + name: "conv6" + type: "Convolution" + bottom: "conv6/dw" + top: "conv6" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/bn" + type: "BatchNorm" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/scale" + type: "Scale" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/relu" + type: "ReLU" + bottom: "conv6" + top: "conv6" +} +layer { + name: "conv7/dw" + type: "DepthwiseConvolution" + bottom: "conv6" + top: "conv7/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/dw/bn" + type: "BatchNorm" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/dw/scale" + type: "Scale" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/dw/relu" + type: "ReLU" + bottom: "conv7/dw" + top: "conv7/dw" +} +layer { + name: "conv7" + type: "Convolution" + bottom: "conv7/dw" + top: "conv7" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/bn" + type: "BatchNorm" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/scale" + type: "Scale" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/relu" + type: "ReLU" + bottom: "conv7" + top: "conv7" +} +layer { + name: "conv8/dw" + type: "DepthwiseConvolution" + bottom: "conv7" + top: "conv8/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/dw/bn" + type: "BatchNorm" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/dw/scale" + type: "Scale" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/dw/relu" + type: "ReLU" + bottom: "conv8/dw" + top: "conv8/dw" +} +layer { + name: "conv8" + type: "Convolution" + bottom: "conv8/dw" + top: "conv8" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/bn" + type: "BatchNorm" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/scale" + type: "Scale" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/relu" + type: "ReLU" + bottom: "conv8" + top: "conv8" +} +layer { + name: "conv9/dw" + type: "DepthwiseConvolution" + bottom: "conv8" + top: "conv9/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/dw/bn" + type: "BatchNorm" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/dw/scale" + type: "Scale" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/dw/relu" + type: "ReLU" + bottom: "conv9/dw" + top: "conv9/dw" +} +layer { + name: "conv9" + type: "Convolution" + bottom: "conv9/dw" + top: "conv9" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/bn" + type: "BatchNorm" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/scale" + type: "Scale" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/relu" + type: "ReLU" + bottom: "conv9" + top: "conv9" +} +layer { + name: "conv10/dw" + type: "DepthwiseConvolution" + bottom: "conv9" + top: "conv10/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/dw/bn" + type: "BatchNorm" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/dw/scale" + type: "Scale" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/dw/relu" + type: "ReLU" + bottom: "conv10/dw" + top: "conv10/dw" +} +layer { + name: "conv10" + type: "Convolution" + bottom: "conv10/dw" + top: "conv10" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/bn" + type: "BatchNorm" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/scale" + type: "Scale" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/relu" + type: "ReLU" + bottom: "conv10" + top: "conv10" +} +layer { + name: "conv11/dw" + type: "DepthwiseConvolution" + bottom: "conv10" + top: "conv11/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/dw/bn" + type: "BatchNorm" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/dw/scale" + type: "Scale" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/dw/relu" + type: "ReLU" + bottom: "conv11/dw" + top: "conv11/dw" +} +layer { + name: "conv11" + type: "Convolution" + bottom: "conv11/dw" + top: "conv11" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/bn" + type: "BatchNorm" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/scale" + type: "Scale" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/relu" + type: "ReLU" + bottom: "conv11" + top: "conv11" +} +layer { + name: "conv12/dw" + type: "DepthwiseConvolution" + bottom: "conv11" + top: "conv12/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/dw/bn" + type: "BatchNorm" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/dw/scale" + type: "Scale" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/dw/relu" + type: "ReLU" + bottom: "conv12/dw" + top: "conv12/dw" +} +layer { + name: "conv12" + type: "Convolution" + bottom: "conv12/dw" + top: "conv12" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/bn" + type: "BatchNorm" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/scale" + type: "Scale" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/relu" + type: "ReLU" + bottom: "conv12" + top: "conv12" +} +layer { + name: "conv13/dw" + type: "DepthwiseConvolution" + bottom: "conv12" + top: "conv13/dw" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/dw/bn" + type: "BatchNorm" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/dw/scale" + type: "Scale" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/dw/relu" + type: "ReLU" + bottom: "conv13/dw" + top: "conv13/dw" +} +layer { + name: "conv13" + type: "Convolution" + bottom: "conv13/dw" + top: "conv13" + param { + lr_mult: 0.1 + decay_mult: 0.1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/bn" + type: "BatchNorm" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/scale" + type: "Scale" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/relu" + type: "ReLU" + bottom: "conv13" + top: "conv13" +} + +layer { + name: "conv16/dw" + type: "DepthwiseConvolution" + bottom: "conv13" + top: "conv16/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16/dw/bn" + type: "BatchNorm" + bottom: "conv16/dw" + top: "conv16/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16/dw/scale" + type: "Scale" + bottom: "conv16/dw" + top: "conv16/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16/dw/relu" + type: "ReLU" + bottom: "conv16/dw" + top: "conv16/dw" +} +layer { + name: "conv16" + type: "Convolution" + bottom: "conv16/dw" + top: "conv16" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16/bn" + type: "BatchNorm" + bottom: "conv16" + top: "conv16" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16/scale" + type: "Scale" + bottom: "conv16" + top: "conv16" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16/relu" + type: "ReLU" + bottom: "conv16" + top: "conv16" +} +layer { + name: "upsample" + type: "Deconvolution" + bottom: "conv16" + top: "upsample" + param { lr_mult: 0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 4 stride: 2 pad: 1 + group: 512 + weight_filler: { type: "bilinear" } + bias_term: false + } +} +layer { + name: "conv17/dw" + type: "DepthwiseConvolution" + bottom: "conv11" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/dw/bn" + type: "BatchNorm" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/dw/scale" + type: "Scale" + bottom: "conv17/dw" + top: "conv17/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/dw/relu" + type: "ReLU" + bottom: "conv17/dw" + top: "conv17/dw" +} +layer { + name: "conv17" + type: "Convolution" + bottom: "conv17/dw" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17/bn" + type: "BatchNorm" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17/scale" + type: "Scale" + bottom: "conv17" + top: "conv17" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17/relu" + type: "ReLU" + bottom: "conv17" + top: "conv17" +} +layer { + name: "conv17/sum" + type: "Eltwise" + bottom: "conv17" + bottom: "upsample" + top: "conv17/sum" +} +layer { + name: "conv18/dw" + type: "DepthwiseConvolution" + bottom: "conv17/sum" + top: "conv18/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv18/dw/bn" + type: "BatchNorm" + bottom: "conv18/dw" + top: "conv18/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv18/dw/scale" + type: "Scale" + bottom: "conv18/dw" + top: "conv18/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv18/dw/relu" + type: "ReLU" + bottom: "conv18/dw" + top: "conv18/dw" +} +layer { + name: "conv18" + type: "Convolution" + bottom: "conv18/dw" + top: "conv18" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv18/bn" + type: "BatchNorm" + bottom: "conv18" + top: "conv18" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv18/scale" + type: "Scale" + bottom: "conv18" + top: "conv18" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv18/relu" + type: "ReLU" + bottom: "conv18" + top: "conv18" +} + +layer { + name: "upsample_conv18" + type: "Deconvolution" + bottom: "conv18" + top: "upsample_conv18" + param { lr_mult: 0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 4 stride: 2 pad: 1 + group: 512 + weight_filler: { type: "bilinear" } + bias_term: false + } +} + + +layer { + name: "conv19/dw" + type: "DepthwiseConvolution" + bottom: "conv5" + top: "conv19/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv19/dw/bn" + type: "BatchNorm" + bottom: "conv19/dw" + top: "conv19/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv19/dw/scale" + type: "Scale" + bottom: "conv19/dw" + top: "conv19/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv19/dw/relu" + type: "ReLU" + bottom: "conv19/dw" + top: "conv19/dw" +} +layer { + name: "conv19" + type: "Convolution" + bottom: "conv19/dw" + top: "conv19" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv19/bn" + type: "BatchNorm" + bottom: "conv19" + top: "conv19" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv19/scale" + type: "Scale" + bottom: "conv19" + top: "conv19" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv19/relu" + type: "ReLU" + bottom: "conv19" + top: "conv19" +} +layer { + name: "conv20/dw" + type: "DepthwiseConvolution" + bottom: "conv19" + top: "conv20/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv20/dw/bn" + type: "BatchNorm" + bottom: "conv20/dw" + top: "conv20/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv20/dw/scale" + type: "Scale" + bottom: "conv20/dw" + top: "conv20/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv20/dw/relu" + type: "ReLU" + bottom: "conv20/dw" + top: "conv20/dw" +} +layer { + name: "conv20" + type: "Convolution" + bottom: "conv20/dw" + top: "conv20" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv20/bn" + type: "BatchNorm" + bottom: "conv20" + top: "conv20" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv20/scale" + type: "Scale" + bottom: "conv20" + top: "conv20" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv20/relu" + type: "ReLU" + bottom: "conv20" + top: "conv20" +} +layer { + name: "conv21/dw" + type: "DepthwiseConvolution" + bottom: "conv20" + top: "conv21/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv21/dw/bn" + type: "BatchNorm" + bottom: "conv21/dw" + top: "conv21/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv21/dw/scale" + type: "Scale" + bottom: "conv21/dw" + top: "conv21/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv21/dw/relu" + type: "ReLU" + bottom: "conv21/dw" + top: "conv21/dw" +} +layer { + name: "conv21" + type: "Convolution" + bottom: "conv21/dw" + top: "conv21" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv21/bn" + type: "BatchNorm" + bottom: "conv21" + top: "conv21" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv21/scale" + type: "Scale" + bottom: "conv21" + top: "conv21" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv21/relu" + type: "ReLU" + bottom: "conv21" + top: "conv21" +} +layer { + name: "conv21/cat" + type: "Concat" + bottom: "conv21" + bottom: "upsample_conv18" + top: "conv21/cat" +} +layer { + name: "conv22/dw" + type: "DepthwiseConvolution" + bottom: "conv21/cat" + top: "conv22/dw" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 768 + bias_term: false + pad: 1 + kernel_size: 3 + group: 768 + engine: CAFFE + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv22/dw/bn" + type: "BatchNorm" + bottom: "conv22/dw" + top: "conv22/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv22/dw/scale" + type: "Scale" + bottom: "conv22/dw" + top: "conv22/dw" + param { + lr_mult: 1 + decay_mult: 0.0 + } + param { + lr_mult: 2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv22/dw/relu" + type: "ReLU" + bottom: "conv22/dw" + top: "conv22/dw" +} +layer { + name: "conv22" + type: "Convolution" + bottom: "conv22/dw" + top: "conv22" + param { + lr_mult: 1 + decay_mult: 1 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv22/bn" + type: "BatchNorm" + bottom: "conv22" + top: "conv22" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv22/scale" + type: "Scale" + bottom: "conv22" + top: "conv22" + param { + lr_mult: 0.1 + decay_mult: 0.0 + } + param { + lr_mult: 0.2 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv22/relu" + type: "ReLU" + bottom: "conv22" + top: "conv22" +} + +layer { + name: "conv23" + type: "Convolution" + bottom: "conv16" + top: "conv23" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} + +layer { + name: "conv24" + type: "Convolution" + bottom: "conv18" + top: "conv24" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} + +layer { + name: "conv25" + type: "Convolution" + bottom: "conv22" + top: "conv25" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 75 + kernel_size: 1 + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + value: 0 + } + } +} + +layer { + name: "Yolov3Loss1" + type: "Yolov3" + bottom: "conv23" + bottom: "label" + top: "det_loss1" + loss_weight: 1 + yolov3_param { + side: 13 + num_class: 20 + num: 3 + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + thresh: 0.6 + anchors_scale : 32 + use_logic_gradient : false + #10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + + biases: 10 + biases: 13 + biases: 16 + biases: 30 + biases: 33 + biases: 23 + biases: 30 + biases: 61 + biases: 62 + biases: 45 + biases: 59 + biases: 119 + biases: 116 + biases: 90 + biases: 156 + biases: 198 + biases: 373 + biases: 326 + + mask:6 + mask:7 + mask:8 + } +} +layer { + name: "Yolov3Loss2" + type: "Yolov3" + bottom: "conv24" + bottom: "label" + top: "det_loss2" + loss_weight: 1 + yolov3_param { + side: 26 + num_class: 20 + num: 3 + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + thresh: 0.6 + anchors_scale : 16 + use_logic_gradient : false + #10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + + biases: 10 + biases: 13 + biases: 16 + biases: 30 + biases: 33 + biases: 23 + biases: 30 + biases: 61 + biases: 62 + biases: 45 + biases: 59 + biases: 119 + biases: 116 + biases: 90 + biases: 156 + biases: 198 + biases: 373 + biases: 326 + + mask:3 + mask:4 + mask:5 + } +} + +layer { + name: "Yolov3Loss3" + type: "Yolov3" + bottom: "conv25" + bottom: "label" + top: "det_loss3" + loss_weight: 1 + yolov3_param { + side: 52 + num_class: 20 + num: 3 + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + thresh: 0.6 + anchors_scale : 8 + use_logic_gradient : false + #10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + + biases: 10 + biases: 13 + biases: 16 + biases: 30 + biases: 33 + biases: 23 + biases: 30 + biases: 61 + biases: 62 + biases: 45 + biases: 59 + biases: 119 + biases: 116 + biases: 90 + biases: 156 + biases: 198 + biases: 373 + biases: 326 + + mask:0 + mask:1 + mask:2 + } +} \ No newline at end of file diff --git a/scripts/create_annoset.py b/scripts/create_annoset.py new file mode 100644 index 0000000..8eb863a --- /dev/null +++ b/scripts/create_annoset.py @@ -0,0 +1,167 @@ +import argparse +import os +import shutil +import subprocess +import sys + +from caffe.proto import caffe_pb2 +from google.protobuf import text_format + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Create AnnotatedDatum database") + parser.add_argument("root", + help="The root directory which contains the images and annotations.") + parser.add_argument("listfile", + help="The file which contains image paths and annotation info.") + parser.add_argument("outdir", + help="The output directory which stores the database file.") + parser.add_argument("exampledir", + help="The directory to store the link of the database files.") + parser.add_argument("--redo", default = False, action = "store_true", + help="Recreate the database.") + parser.add_argument("--anno-type", default = "classification", + help="The type of annotation {classification, detection}.") + parser.add_argument("--label-type", default = "xml", + help="The type of label file format for detection {xml, json, txt}.") + parser.add_argument("--backend", default = "lmdb", + help="The backend {lmdb, leveldb} for storing the result") + parser.add_argument("--check-size", default = False, action = "store_true", + help="Check that all the datum have the same size.") + parser.add_argument("--encode-type", default = "", + help="What type should we encode the image as ('png','jpg',...).") + parser.add_argument("--encoded", default = False, action = "store_true", + help="The encoded image will be save in datum.") + parser.add_argument("--gray", default = False, action = "store_true", + help="Treat images as grayscale ones.") + parser.add_argument("--label-map-file", default = "", + help="A file with LabelMap protobuf message.") + parser.add_argument("--min-dim", default = 0, type = int, + help="Minimum dimension images are resized to.") + parser.add_argument("--max-dim", default = 0, type = int, + help="Maximum dimension images are resized to.") + parser.add_argument("--resize-height", default = 0, type = int, + help="Height images are resized to.") + parser.add_argument("--resize-width", default = 0, type = int, + help="Width images are resized to.") + parser.add_argument("--shuffle", default = False, action = "store_true", + help="Randomly shuffle the order of images and their labels.") + parser.add_argument("--check-label", default = False, action = "store_true", + help="Check that there is no duplicated name/label.") + + args = parser.parse_args() + root_dir = args.root + list_file = args.listfile + out_dir = args.outdir + example_dir = args.exampledir + + redo = args.redo + anno_type = args.anno_type + label_type = args.label_type + backend = args.backend + check_size = args.check_size + encode_type = args.encode_type + encoded = args.encoded + gray = args.gray + label_map_file = args.label_map_file + min_dim = args.min_dim + max_dim = args.max_dim + resize_height = args.resize_height + resize_width = args.resize_width + shuffle = args.shuffle + check_label = args.check_label + + # check if root directory exists + if not os.path.exists(root_dir): + print("root directory: {} does not exist".format(root_dir)) + sys.exit() + # add "/" to root directory if needed + if root_dir[-1] != "/": + root_dir += "/" + # check if list file exists + if not os.path.exists(list_file): + print("list file: {} does not exist".format(list_file)) + sys.exit() + # check list file format is correct + with open(list_file, "r") as lf: + for line in lf.readlines(): + img_file, anno = line.strip("\n").split(" ") + if not os.path.exists(root_dir + img_file): + print("image file: {} does not exist".format(root_dir + img_file)) + if anno_type == "classification": + if not anno.isdigit(): + print("annotation: {} is not an integer".format(anno)) + elif anno_type == "detection": + if not os.path.exists(root_dir + anno): + print("annofation file: {} does not exist".format(root_dir + anno)) + sys.exit() + break + # check if label map file exist + if anno_type == "detection": + if not os.path.exists(label_map_file): + print("label map file: {} does not exist".format(label_map_file)) + sys.exit() + label_map = caffe_pb2.LabelMap() + lmf = open(label_map_file, "r") + try: + text_format.Merge(str(lmf.read()), label_map) + except: + print("Cannot parse label map file: {}".format(label_map_file)) + sys.exit() + out_parent_dir = os.path.dirname(out_dir) + if not os.path.exists(out_parent_dir): + os.makedirs(out_parent_dir) + if os.path.exists(out_dir) and not redo: + print("{} already exists and I do not hear redo".format(out_dir)) + sys.exit() + if os.path.exists(out_dir): + shutil.rmtree(out_dir) + + # get caffe root directory + caffe_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + if anno_type == "detection": + cmd = "{}/build/tools/convert_annoset" \ + " --anno_type={}" \ + " --label_type={}" \ + " --label_map_file={}" \ + " --check_label={}" \ + " --min_dim={}" \ + " --max_dim={}" \ + " --resize_height={}" \ + " --resize_width={}" \ + " --backend={}" \ + " --shuffle={}" \ + " --check_size={}" \ + " --encode_type={}" \ + " --encoded={}" \ + " --gray={}" \ + " {} {} {}" \ + .format(caffe_root, anno_type, label_type, label_map_file, check_label, + min_dim, max_dim, resize_height, resize_width, backend, shuffle, + check_size, encode_type, encoded, gray, root_dir, list_file, out_dir) + elif anno_type == "classification": + cmd = "{}/build/tools/convert_annoset" \ + " --anno_type={}" \ + " --min_dim={}" \ + " --max_dim={}" \ + " --resize_height={}" \ + " --resize_width={}" \ + " --backend={}" \ + " --shuffle={}" \ + " --check_size={}" \ + " --encode_type={}" \ + " --encoded={}" \ + " --gray={}" \ + " {} {} {}" \ + .format(caffe_root, anno_type, min_dim, max_dim, resize_height, + resize_width, backend, shuffle, check_size, encode_type, encoded, + gray, root_dir, list_file, out_dir) + print(cmd) + process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE) + output = process.communicate()[0] + + if not os.path.exists(example_dir): + os.makedirs(example_dir) + link_dir = os.path.join(example_dir, os.path.basename(out_dir)) + if os.path.exists(link_dir): + os.unlink(link_dir) + os.symlink(out_dir, link_dir) diff --git a/src/caffe/layers/yolov3_detection_output_layer.cpp b/src/caffe/layers/yolov3_detection_output_layer.cpp index ac6d408..5270363 100644 --- a/src/caffe/layers/yolov3_detection_output_layer.cpp +++ b/src/caffe/layers/yolov3_detection_output_layer.cpp @@ -1,3 +1,10 @@ +/* +* @Author: Eric612 +* @Date: 2018-08-20 +* @https://github.com/eric612/Caffe-YOLOv2-Windows +* @https://github.com/eric612/MobileNet-YOLO +* Avisonic +*/ #include #include // NOLINT(readability/streams) #include @@ -234,6 +241,7 @@ void Yolov3DetectionOutputLayer::Forward_cpu( vector< PredictionResult > predicts; predicts.clear(); Dtype *class_score = new Dtype[num_class_]; + for (int t = 0; t < bottom.size(); t++) { side_ = bottom[t]->width(); int stride = side_*side_; @@ -270,28 +278,40 @@ void Yolov3DetectionOutputLayer::Forward_cpu( Dtype obj_score = swap_data[index + 4 * stride]; //LOG(INFO) << obj_score; get_region_box2(pred, swap_data, biases_, mask_[n + mask_offset], index, x2, y2, side_, side_, side_*anchors_scale_[t], side_*anchors_scale_[t], stride); + //LOG(INFO)< predict; for (int c = 0; c < num_class_; ++c) { class_score[c] *= obj_score; //LOG(INFO) << class_score[c]; if (class_score[c] > confidence_threshold_) - { - PredictionResult predict; - predict.x = pred[0]; - predict.y = pred[1]; - predict.w = pred[2]; - predict.h = pred[3]; - predict.classType = c ; - predict.confidence = class_score[c]; - predicts.push_back(predict); + { + //if(class_score[c]>maxmima_score) + { + //maxmima_score = class_score[c]; + predict.x = pred[0]; + predict.y = pred[1]; + predict.w = pred[2]; + predict.h = pred[3]; + predict.classType = c ; + predict.confidence = class_score[c]; + predicts.push_back(predict); + } + //LOG(INFO) << predict.x << "," << predict.y << "," << predict.w << "," << predict.h; //LOG(INFO) << predict.confidence; } } + //if(maxmima_score> confidence_threshold_) + //{ + // predicts.push_back(predict); + //} } } } mask_offset += groups_num_; + } delete[] class_score; diff --git a/src/caffe/layers/yolov3_layer.cpp b/src/caffe/layers/yolov3_layer.cpp index d57d0a0..68a4182 100644 --- a/src/caffe/layers/yolov3_layer.cpp +++ b/src/caffe/layers/yolov3_layer.cpp @@ -127,13 +127,14 @@ namespace caffe { class_scale_ = param.class_scale(); //1.0 coord_scale_ = param.coord_scale(); //1.0 thresh_ = param.thresh(); //0.6 - + use_logic_gradient_ = param.use_logic_gradient(); for (int c = 0; c < param.biases_size(); ++c) { biases_.push_back(param.biases(c)); } for (int c = 0; c < param.mask_size(); ++c) { mask_.push_back(param.mask(c)); } + biases_size_ = param.biases_size()/2; int input_count = bottom[0]->count(1); //h*w*n*(classes+coords+1) = 13*13*5*(20+4+1) int label_count = bottom[1]->count(1); //30*5- // outputs: classes, iou, coordinates @@ -154,7 +155,15 @@ namespace caffe { diff_.ReshapeLike(*bottom[0]); real_diff_.ReshapeLike(*bottom[0]); } - + template + int int_index(vector a, int val, int n) + { + int i; + for (i = 0; i < n; ++i) { + if (a[i] == val) return i; + } + return -1; + } template void Yolov3Layer::Forward_cpu( const vector*>& bottom, const vector*>& top) { @@ -258,7 +267,7 @@ namespace caffe { truth.push_back(h); float best_iou = 0; int best_index = 0; - int best_n = 0; + int best_n = -1; int i = truth[0] * side_; int j = truth[1] * side_; int pos = j * side_ + i; @@ -269,40 +278,57 @@ namespace caffe { truth_shift.push_back(w); truth_shift.push_back(h); //int size = coords_ + num_class_ + 1; - - for (int n = 0; n < num_; ++n) { - int index2 = n*len*stride + pos + b * bottom[0]->count(1); + //LOG(INFO) << biases_size_; + for (int n = 0; n < biases_size_; ++n) { + //LOG(INFO) << index2; vector pred(4); - pred[2] = biases_[2 * mask_[n]] / (float)(side_*anchors_scale_); - pred[3] = biases_[2 * mask_[n] + 1] / (float)(side_*anchors_scale_); + pred[2] = biases_[2 * n] / (float)(side_*anchors_scale_); + pred[3] = biases_[2 * n + 1] / (float)(side_*anchors_scale_); pred[0] = 0; pred[1] = 0; float iou = box_iou(pred, truth_shift); if (iou > best_iou) { - best_index = index2; - best_iou = iou; + //best_index = index2; best_n = n; + best_iou = iou; } } - float iou; - iou = delta_region_box(truth, swap_data, biases_, mask_[best_n], best_index, i, j, side_, side_, side_*anchors_scale_, side_*anchors_scale_, diff, coord_scale_*(2 - truth[2] * truth[3]), stride); + //LOG(INFO) << best_n; + int mask_n = int_index(mask_, best_n, num_); + + //LOG(INFO) << mask_n; + + if (mask_n >= 0) { + float iou; + best_n = mask_n; + //LOG(INFO) << best_n; + best_index = best_n*len*stride + pos + b * bottom[0]->count(1); + + iou = delta_region_box(truth, swap_data, biases_,best_n, best_index, i, j, side_, side_, side_*anchors_scale_, side_*anchors_scale_, diff, coord_scale_*(2 - truth[2] * truth[3]), stride); - if (iou > 0.5) - recall += 1; - if (iou > 0.75) - recall75 += 1; - avg_iou += iou; - avg_obj += swap_data[best_index + 4 * stride]; + if (iou > 0.5) + recall += 1; + if (iou > 0.75) + recall75 += 1; + avg_iou += iou; + avg_obj += swap_data[best_index + 4 * stride]; + if (use_logic_gradient_) { + diff[best_index + 4 * stride] = (-1.0) * (1 - swap_data[best_index + 4 * stride]) * object_scale_; + } + else { + diff[best_index + 4 * stride] = (-1.0) * (1 - swap_data[best_index + 4 * stride]); + } - //diff[best_index + 4 * stride] = (-1.0) * object_scale_ * (1 - swap_data[best_index + 4 * stride]) * logistic_gradient(swap_data[best_index + 4 * stride]); - diff[best_index + 4 * stride] = (-1.0) * (1 - swap_data[best_index + 4 * stride]); + //diff[best_index + 4 * stride] = (-1.0) * (1 - swap_data[best_index + 4 * stride]) ; - delta_region_class_v3(swap_data, diff, best_index + 5 * stride, class_label, num_class_, class_scale_, &avg_cat, stride); //softmax_tree_ + delta_region_class_v3(swap_data, diff, best_index + 5 * stride, class_label, num_class_, class_scale_, &avg_cat, stride); //softmax_tree_ + + ++count; + ++class_count; + } - ++count; - ++class_count; } } @@ -315,18 +341,23 @@ namespace caffe { //LOG(INFO) << "iter: " << iter <<" loss: " << loss; if (!(iter_ % 10)) { - LOG(INFO) << "avg_noobj: " << score_.avg_anyobj / 10. << " avg_obj: " << score_.avg_obj / 10. << - " avg_iou: " << score_.avg_iou / 10. << " avg_cat: " << score_.avg_cat / 10. << " recall: " << score_.recall / 10. << " recall75: " << score_.recall75 / 10.; + LOG(INFO) << "avg_noobj: " << score_.avg_anyobj / 10. << " avg_obj: " << score_.avg_obj / time_count_ << + " avg_iou: " << score_.avg_iou / time_count_ << " avg_cat: " << score_.avg_cat / time_count_ << " recall: " << score_.recall / time_count_ << " recall75: " << score_.recall75 / time_count_; //LOG(INFO) << "avg_noobj: "<< avg_anyobj/(side_*side_*num_*bottom[0]->num()) << " avg_obj: " << avg_obj/count <<" avg_iou: " << avg_iou/count << " avg_cat: " << avg_cat/class_count << " recall: " << recall/count << " recall75: " << recall75 / count; score_.avg_anyobj = score_.avg_obj = score_.avg_iou = score_.avg_cat = score_.recall = score_.recall75 = 0; + time_count_ = 0; } else { score_.avg_anyobj += avg_anyobj / (side_*side_*num_*bottom[0]->num()); - score_.avg_obj += avg_obj / count; - score_.avg_iou += avg_iou / count; - score_.avg_cat += avg_cat / class_count; - score_.recall += recall / count; - score_.recall75 += recall75 / count; + if (count > 0) { + score_.avg_obj += avg_obj / count; + score_.avg_iou += avg_iou / count; + score_.avg_cat += avg_cat / count; + score_.recall += recall / count; + score_.recall75 += recall75 / count; + time_count_++; + } + } } @@ -338,34 +369,42 @@ namespace caffe { LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { - /*const Dtype* top_data = swap_.cpu_data(); - Dtype* diff = diff_.mutable_cpu_data(); - side_ = bottom[0]->width(); - int len = 4 + num_class_ + 1; - int stride = side_*side_; - //LOG(INFO)<num(); b++) { - for (int s = 0; s < side_*side_; s++) { - for (int n = 0; n < num_; n++) { - int index = n*len*stride + s + b*bottom[0]->count(1); - //LOG(INFO)< pred; - float best_iou = 0; - int best_class = -1; - vector best_truth; - for (int c = 0; c < len; ++c) { - int index2 = c*stride + index; - //LOG(INFO)<width(); + int len = 4 + num_class_ + 1; + int stride = side_*side_; + //LOG(INFO)<num(); b++) { + for (int s = 0; s < side_*side_; s++) { + for (int n = 0; n < num_; n++) { + int index = n*len*stride + s + b*bottom[0]->count(1); + //LOG(INFO)< pred; + float best_iou = 0; + int best_class = -1; + vector best_truth; + for (int c = 0; c < len; ++c) { + int index2 = c*stride + index; + //LOG(INFO)<cpu_diff()[0] / bottom[0]->num(); //const Dtype alpha(1.0); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 8471f41..75c2e63 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -465,6 +465,7 @@ message Yolov3Parameter{ optional uint32 anchors_scale = 10 [default = 32]; repeated float biases = 11; repeated uint32 mask = 12; + optional bool use_logic_gradient = 13 [default = false]; } message RegionLossParameter{ //Yolo 9000 @@ -1119,6 +1120,7 @@ message YoloDetectionOutputParameter { optional float nms_threshold = 6 [default = 0.45]; repeated float biases = 7; optional string label_map_file = 8; + } message Yolov3DetectionOutputParameter { // Yolo detection output layer @@ -1130,6 +1132,7 @@ message Yolov3DetectionOutputParameter { repeated uint32 anchors_scale = 6 ; optional uint32 mask_group_num = 7 [default = 2]; repeated uint32 mask = 8; + } message DropoutParameter { optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio diff --git a/train_yolov3.sh b/train_yolov3.sh new file mode 100644 index 0000000..6473892 --- /dev/null +++ b/train_yolov3.sh @@ -0,0 +1,5 @@ +#!/bin/bash +LOG=log/train-`date +%Y-%m-%d-%H-%M-%S`.log +./tools/caffe train --solver models/yolov3/mobilenet_yolov3_solver.prototxt --weights models/MobileNet/mobilenet_iter_73000.caffemodel --gpu=0 2>&1 | tee $LOG +#--weights models/MobileNet/mobilenet_iter_73000.caffemodel +#--snapshot models/yolov3/mobilenet_yolo_deploy_iter_1000.solverstate \ No newline at end of file diff --git a/train_yolov3_lite.sh b/train_yolov3_lite.sh new file mode 100644 index 0000000..b5b9162 --- /dev/null +++ b/train_yolov3_lite.sh @@ -0,0 +1,5 @@ +#!/bin/bash +LOG=log/train-`date +%Y-%m-%d-%H-%M-%S`.log +./tools/caffe train --solver models/yolov3/mobilenet_yolov3_lite_solver.prototxt --weights models/MobileNet/mobilenet_iter_73000.caffemodel --gpu=0 2>&1 | tee $LOG +#--weights models/MobileNet/mobilenet_iter_73000.caffemodel +#--snapshot models/yolov3/mobilenet_yolo_deploy_iter_1000.solverstate \ No newline at end of file