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| name: "AlexNet for Office" | |
| # ----------------------------------------------------------------------------- | |
| # ----------------------------------------------------------------- Data layer | |
| # ----------------------------------------------------------------------------- | |
| # ---------------------------------------------------------------------- Source | |
| # Train phase | |
| layer { | |
| name: "source_data" | |
| type: "Data" | |
| top: "source_data" | |
| top: "lp_labels" | |
| data_param { | |
| source: "train_0_lmdb" | |
| backend: LMDB | |
| batch_size: 64 | |
| cursor: SHUFFLING | |
| } | |
| transform_param { | |
| crop_size: 227 | |
| mean_file: "imagenet_mean_path" | |
| mirror: true | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "source_domain_labels" | |
| type: "DummyData" | |
| top: "source_domain_labels" | |
| dummy_data_param { | |
| data_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| num: 64 | |
| channels: 1 | |
| height: 1 | |
| width: 1 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| # ---------------------------------------------------------------------- Target | |
| # Train phase | |
| layer { | |
| name: "target_data" | |
| type: "Data" | |
| top: "target_data" | |
| data_param { | |
| source: "_train_0_lmdb" | |
| backend: LMDB | |
| batch_size: 64 | |
| cursor: SHUFFLING | |
| } | |
| transform_param { | |
| crop_size: 227 | |
| mean_file: "imagenet_mean_path" | |
| mirror: true | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "target_domain_labels" | |
| type: "DummyData" | |
| top: "target_domain_labels" | |
| dummy_data_param { | |
| data_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| num: 64 | |
| channels: 1 | |
| height: 1 | |
| width: 1 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| # Test phase | |
| layer { | |
| name: "target_data" | |
| type: "Data" | |
| top: "data" | |
| top: "lp_labels" | |
| data_param { | |
| source: "train_0_lmdb" | |
| backend: LMDB | |
| batch_size: 1 | |
| } | |
| transform_param { | |
| crop_size: 227 | |
| mean_file: "imagenet_mean_path" | |
| } | |
| include: { phase: TEST } | |
| } | |
| layer { | |
| name: "target_domain_labels" | |
| type: "DummyData" | |
| top: "dc_labels" | |
| dummy_data_param { | |
| data_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| num: 1 | |
| channels: 1 | |
| height: 1 | |
| width: 1 | |
| } | |
| include: { phase: TEST } | |
| } | |
| # ---------------------------------------------------------- Data concatenation | |
| layer { | |
| name: "concat_data" | |
| type: "Concat" | |
| bottom: "source_data" | |
| bottom: "target_data" | |
| top: "data" | |
| concat_param { | |
| concat_dim: 0 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| layer { | |
| name: "concat_domain_labels" | |
| type: "Concat" | |
| bottom: "source_domain_labels" | |
| bottom: "target_domain_labels" | |
| top: "dc_labels" | |
| concat_param { | |
| concat_dim: 0 | |
| } | |
| include: { phase: TRAIN } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "conv1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "norm1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "norm2" | |
| type: "LRN" | |
| bottom: "conv2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "norm2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "fc6" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "fc6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "fc7" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "bottleneck" | |
| type: "InnerProduct" | |
| bottom: "fc7" | |
| top: "bottleneck" | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 20 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.1 | |
| } | |
| } | |
| } | |
| # ----------------------------------------------------------------------------- | |
| # ------------------------------------------------------------- Label predictor | |
| # ----------------------------------------------------------------------------- | |
| # ------------------------------------------------------ Exclude target samples | |
| # ----------------------------------------------------------------------------- | |
| # ----------------------------------------------------------- Gradient reversal | |
| # ----------------------------------------------------------------------------- | |
| layer { | |
| name: "grl" | |
| type: "GradientScaler" | |
| bottom: "bottleneck" | |
| top: "grl" | |
| gradient_scaler_param { | |
| lower_bound: 0.0 | |
| upper_bound: 1.0 | |
| alpha: 0.5 | |
| max_iter: 123 | |
| } | |
| } | |
| # ----------------------------------------------------------------------------- | |
| # ----------------------------------------------------------- Domain classifier | |
| # ----------------------------------------------------------------------------- | |
| layer { | |
| name: "dc_ip1" | |
| type: "InnerProduct" | |
| bottom: "grl" | |
| top: "dc_ip1" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dc_relu1" | |
| type: "ReLU" | |
| bottom: "dc_ip1" | |
| top: "dc_ip1" | |
| } | |
| layer { | |
| name: "dc_drop1" | |
| type: "Dropout" | |
| bottom: "dc_ip1" | |
| top: "dc_ip1" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "dc_ip2" | |
| type: "InnerProduct" | |
| bottom: "dc_ip1" | |
| top: "dc_ip2" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dc_relu2" | |
| type: "ReLU" | |
| bottom: "dc_ip2" | |
| top: "dc_ip2" | |
| } | |
| layer { | |
| name: "dc_drop2" | |
| type: "Dropout" | |
| bottom: "dc_ip2" | |
| top: "dc_ip2" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| # ---------------------------------------------------------------------------- | |
| layer { | |
| name: "dc_ip3" | |
| type: "InnerProduct" | |
| bottom: "dc_ip2" | |
| top: "dc_ip3" | |
| param { | |
| lr_mult: 10 | |
| } | |
| param { | |
| lr_mult: 20 | |
| } | |
| inner_product_param { | |
| num_output: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.3 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "dc_loss" | |
| type: "SigmoidCrossEntropyLoss" | |
| bottom: "dc_ip3" | |
| bottom: "dc_labels" | |
| top: "dc_loss" | |
| loss_weight: 0.1 | |
| } |
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