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| @@ -0,0 +1,228 @@ | ||
| name: "VGG_ILSVRC_16_layers" | ||
| layers { | ||
| name: "data" type: MIL_DATA top: "data" top: "label" | ||
| mil_data_param { | ||
| images_per_batch: 1 n_classes: 1000 | ||
|
|
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| label_file: "protofiles/v39/labels_train.all.h5" | ||
| # source: "protofiles/v39/train.all_train.txt" | ||
| source: "protofiles/v39/train.all_all.txt" | ||
| randomize: 1 | ||
|
|
||
| root_dir: "/data0/sgupta/msrdata/JPEGImages/" | ||
| # root_dir: "/work5/sgupta/msr-post-cvpr/coco-data/JPEGImages/" | ||
| ext: "jpg" | ||
| } | ||
| transform_param { | ||
| mirror: true | ||
| crop_size: 565 | ||
| mean_value: 103.939 mean_value: 116.779 mean_value: 123.68 | ||
| } | ||
| include: { phase: TRAIN } | ||
| } | ||
|
|
||
| layers { | ||
| name: "data" | ||
| type: MIL_DATA | ||
| top: "data" | ||
| top: "label" | ||
| mil_data_param { | ||
| images_per_batch: 1 | ||
| n_classes: 1000 | ||
|
|
||
| # source: "protofiles/v39/val.all_all.txt" | ||
| # label_file: "protofiles/v39/labels_val.all.h5" | ||
| # randomize: 0 | ||
| # root_dir: "/data0/sgupta/msrdata/JPEGImages/" | ||
|
|
||
| # label_file: "protofiles/v39/labels_val.all.h5" | ||
| # source: "protofiles/v39/val.all_all.txt" | ||
| # randomize: 0 | ||
| # root_dir: "/data0/sgupta/msrdata/JPEGImages/" | ||
|
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| # label_file: "protofiles/v39/labels_val.new.h5" | ||
| # source: "protofiles/v39/val.new_all.txt" | ||
| # randomize: 0 | ||
| # root_dir: "/data1/sgupta/coco/images/" | ||
|
|
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| label_file: "protofiles/v39/labels_all.pascal.h5" | ||
| source: "protofiles/v39/all.pascal_all.txt" | ||
| randomize: 0 | ||
| root_dir: "/data1/sgupta/pascal1k/JPEGImages/" | ||
|
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| ext: "jpg" | ||
|
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| num_scales: 1 | ||
| scale_factor: 0.707 | ||
| } | ||
| transform_param { | ||
| mirror: true | ||
| crop_size: 565 | ||
| mean_value: 103.939 mean_value: 116.779 mean_value: 123.68 | ||
| } | ||
| include: { phase: TEST } | ||
| } | ||
|
|
||
| layer { | ||
| name: "conv1_1" | ||
| type: "Convolution" | ||
| bottom: "data" | ||
| top: "conv1_1" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 64 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } | ||
| layer { | ||
| name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 64 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } | ||
| layer { | ||
| name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 128 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } | ||
| layer { | ||
| name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 128 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } | ||
| layer { | ||
| name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } | ||
| layer { | ||
| name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } | ||
| layer { | ||
| name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } | ||
| layer { | ||
| name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } | ||
| layer { | ||
| name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } | ||
| layer { | ||
| name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } | ||
| layer { | ||
| name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } | ||
| layer { | ||
| name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } | ||
| layer { | ||
| name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } | ||
|
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| layer { | ||
| bottom: "conv5_3" top: "pool5" name: "pool5" type: "Pooling" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
|
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| layer { | ||
| bottom: "pool5" top: "fc6-conv" name: "fc6-conv" type: "Convolution" | ||
| convolution_param { num_output: 4096 pad: 0 kernel_size: 7 } | ||
| } | ||
|
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| layer { bottom: "fc6-conv" top: "fc6-conv" name: "relu6" type: "ReLU" } | ||
|
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| layer { | ||
| bottom: "fc6-conv" top: "fc6-conv" name: "drop6" type: "Dropout" | ||
| dropout_param { dropout_ratio: 0.5 } | ||
| } | ||
| layer { | ||
| bottom: "fc6-conv" top: "fc7-conv" name: "fc7-conv" type: "Convolution" | ||
| convolution_param { num_output: 4096 pad: 0 kernel_size: 1 } | ||
| } | ||
| layer { bottom: "fc7-conv" top: "fc7-conv" name: "relu7" type: "ReLU" } | ||
| layer { | ||
| bottom: "fc7-conv" top: "fc7-conv" name: "drop7" type: "Dropout" | ||
| dropout_param { dropout_ratio: 0.5 } | ||
| } | ||
|
|
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| layer { | ||
| name: "fc8_coco" type: "Convolution" bottom: "fc7-conv" top: "fc8-conv" | ||
| param{ lr_mult: 10 decay_mult: 1 } | ||
| param{ lr_mult: 20 decay_mult: 0 } | ||
| convolution_param { | ||
| num_output: 1000 kernel_size: 1 | ||
| weight_filler { type: "gaussian" std: 0.001 } | ||
| bias_filler { type: "constant" value: -6.58 } | ||
| } | ||
| } | ||
|
|
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| layer { name: "sigmoid" type: "Sigmoid" bottom: "fc8-conv" top: | ||
| "fc8-conv-sigmoid" } | ||
|
|
||
| layer{ | ||
| name: "mil" type: "MIL" bottom: "fc8-conv-sigmoid" top: "mil" | ||
| mil_param{ type: NOR } | ||
| } | ||
|
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| layer{ | ||
| name: "mil-max" type: "MIL" bottom: "fc8-conv" top: "mil_max" | ||
| mil_param{ type: MAX } | ||
| } | ||
|
|
||
| layers { name: "loss" type: CROSS_ENTROPY_LOSS bottom: "mil" bottom: "label" | ||
| loss_weight: 20 } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,167 @@ | ||
| name: "VGG_ILSVRC_16_layers" | ||
| input: "data" | ||
| input_dim: 1 | ||
| input_dim: 3 | ||
| input_dim: 565 | ||
| input_dim: 565 | ||
|
|
||
| layer { | ||
| name: "conv1_1" | ||
| type: "Convolution" | ||
| bottom: "data" | ||
| top: "conv1_1" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 64 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } | ||
| layer { | ||
| name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 64 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } | ||
| layer { | ||
| name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 128 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } | ||
| layer { | ||
| name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| param { lr_mult: 0 decay_mult: 0 } | ||
| convolution_param { num_output: 128 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } | ||
| layer { | ||
| name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } | ||
| layer { | ||
| name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } | ||
| layer { | ||
| name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 256 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } | ||
| layer { | ||
| name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } | ||
| layer { | ||
| name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } | ||
| layer { | ||
| name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } | ||
| layer { | ||
| name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
| layer { | ||
| name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } | ||
| layer { | ||
| name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } | ||
| layer { | ||
| name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" | ||
| param { lr_mult: 1 decay_mult: 1 } | ||
| param { lr_mult: 2 decay_mult: 0 } | ||
| convolution_param { num_output: 512 pad: 1 kernel_size: 3 } | ||
| } | ||
| layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } | ||
|
|
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| layer { | ||
| bottom: "conv5_3" top: "pool5" name: "pool5" type: "Pooling" | ||
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } | ||
| } | ||
|
|
||
| layer { | ||
| bottom: "pool5" top: "fc6-conv" name: "fc6-conv" type: "Convolution" | ||
| convolution_param { num_output: 4096 pad: 0 kernel_size: 7 } | ||
| } | ||
|
|
||
| layer { bottom: "fc6-conv" top: "fc6-conv" name: "relu6" type: "ReLU" } | ||
|
|
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| layer { | ||
| bottom: "fc6-conv" top: "fc6-conv" name: "drop6" type: "Dropout" | ||
| dropout_param { dropout_ratio: 0.5 } | ||
| } | ||
| layer { | ||
| bottom: "fc6-conv" top: "fc7-conv" name: "fc7-conv" type: "Convolution" | ||
| convolution_param { num_output: 4096 pad: 0 kernel_size: 1 } | ||
| } | ||
| layer { bottom: "fc7-conv" top: "fc7-conv" name: "relu7" type: "ReLU" } | ||
| layer { | ||
| bottom: "fc7-conv" top: "fc7-conv" name: "drop7" type: "Dropout" | ||
| dropout_param { dropout_ratio: 0.5 } | ||
| } | ||
|
|
||
| layer { | ||
| name: "fc8_coco" type: "Convolution" bottom: "fc7-conv" top: "fc8-conv" | ||
| param{ lr_mult: 10 decay_mult: 1 } | ||
| param{ lr_mult: 20 decay_mult: 0 } | ||
| convolution_param { | ||
| num_output: 1000 kernel_size: 1 | ||
| weight_filler { type: "gaussian" std: 0.001 } | ||
| bias_filler { type: "constant" value: -6.58 } | ||
| } | ||
| } | ||
|
|
||
| layer { name: "sigmoid" type: "Sigmoid" bottom: "fc8-conv" top: | ||
| "fc8-conv-sigmoid" } | ||
|
|
||
| layer{ | ||
| name: "mil" type: "MIL" bottom: "fc8-conv-sigmoid" top: "mil" | ||
| mil_param{ type: NOR } | ||
| } | ||
|
|
||
| layer{ | ||
| name: "mil-max" type: "MIL" bottom: "fc8-conv" top: "mil_max" | ||
| mil_param{ type: MAX } | ||
| } |
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