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191 lines
7.0 KiB
191 lines
7.0 KiB
syntax = "proto2";
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package object_detection.protos;
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import "object_detection/protos/hyperparams.proto";
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// Configuration proto for box predictor. See core/box_predictor.py for details.
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message BoxPredictor {
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oneof box_predictor_oneof {
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ConvolutionalBoxPredictor convolutional_box_predictor = 1;
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MaskRCNNBoxPredictor mask_rcnn_box_predictor = 2;
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RfcnBoxPredictor rfcn_box_predictor = 3;
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WeightSharedConvolutionalBoxPredictor
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weight_shared_convolutional_box_predictor = 4;
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}
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}
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// Configuration proto for Convolutional box predictor.
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// Next id: 13
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message ConvolutionalBoxPredictor {
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// Hyperparameters for convolution ops used in the box predictor.
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optional Hyperparams conv_hyperparams = 1;
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// Minimum feature depth prior to predicting box encodings and class
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// predictions.
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optional int32 min_depth = 2 [default = 0];
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// Maximum feature depth prior to predicting box encodings and class
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// predictions. If max_depth is set to 0, no additional feature map will be
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// inserted before location and class predictions.
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optional int32 max_depth = 3 [default = 0];
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// Number of the additional conv layers before the predictor.
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optional int32 num_layers_before_predictor = 4 [default = 0];
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// Whether to use dropout for class prediction.
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optional bool use_dropout = 5 [default = true];
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// Keep probability for dropout
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optional float dropout_keep_probability = 6 [default = 0.8];
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// Size of final convolution kernel. If the spatial resolution of the feature
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// map is smaller than the kernel size, then the kernel size is set to
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// min(feature_width, feature_height).
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optional int32 kernel_size = 7 [default = 1];
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// Size of the encoding for boxes.
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optional int32 box_code_size = 8 [default = 4];
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// Whether to apply sigmoid to the output of class predictions.
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// TODO(jonathanhuang): Do we need this since we have a post processing
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// module.?
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optional bool apply_sigmoid_to_scores = 9 [default = false];
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optional float class_prediction_bias_init = 10 [default = 0.0];
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// Whether to use depthwise separable convolution for box predictor layers.
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optional bool use_depthwise = 11 [default = false];
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}
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// Configuration proto for weight shared convolutional box predictor.
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// Next id: 18
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message WeightSharedConvolutionalBoxPredictor {
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// Hyperparameters for convolution ops used in the box predictor.
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optional Hyperparams conv_hyperparams = 1;
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// Number of the additional conv layers before the predictor.
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optional int32 num_layers_before_predictor = 4 [default = 0];
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// Output depth for the convolution ops prior to predicting box encodings
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// and class predictions.
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optional int32 depth = 2 [default = 0];
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// Size of final convolution kernel. If the spatial resolution of the feature
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// map is smaller than the kernel size, then the kernel size is set to
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// min(feature_width, feature_height).
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optional int32 kernel_size = 7 [default = 3];
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// Size of the encoding for boxes.
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optional int32 box_code_size = 8 [default = 4];
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// Bias initialization for class prediction. It has been show to stabilize
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// training where there are large number of negative boxes. See
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// https://arxiv.org/abs/1708.02002 for details.
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optional float class_prediction_bias_init = 10 [default = 0.0];
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// Whether to use dropout for class prediction.
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optional bool use_dropout = 11 [default = false];
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// Keep probability for dropout.
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optional float dropout_keep_probability = 12 [default = 0.8];
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// Whether to share the multi-layer tower between box prediction and class
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// prediction heads.
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optional bool share_prediction_tower = 13 [default = false];
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// Whether to use depthwise separable convolution for box predictor layers.
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optional bool use_depthwise = 14 [default = false];
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// Enum to specify how to convert the detection scores at inference time.
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enum ScoreConverter {
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// Input scores equals output scores.
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IDENTITY = 0;
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// Applies a sigmoid on input scores.
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SIGMOID = 1;
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}
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// Callable elementwise score converter at inference time.
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optional ScoreConverter score_converter = 16 [default = IDENTITY];
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// If specified, apply clipping to box encodings.
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message BoxEncodingsClipRange {
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optional float min = 1;
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optional float max = 2;
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}
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optional BoxEncodingsClipRange box_encodings_clip_range = 17;
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}
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// TODO(alirezafathi): Refactor the proto file to be able to configure mask rcnn
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// head easily.
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// Next id: 15
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message MaskRCNNBoxPredictor {
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// Hyperparameters for fully connected ops used in the box predictor.
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optional Hyperparams fc_hyperparams = 1;
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// Whether to use dropout op prior to the both box and class predictions.
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optional bool use_dropout = 2 [default = false];
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// Keep probability for dropout. This is only used if use_dropout is true.
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optional float dropout_keep_probability = 3 [default = 0.5];
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// Size of the encoding for the boxes.
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optional int32 box_code_size = 4 [default = 4];
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// Hyperparameters for convolution ops used in the box predictor.
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optional Hyperparams conv_hyperparams = 5;
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// Whether to predict instance masks inside detection boxes.
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optional bool predict_instance_masks = 6 [default = false];
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// The depth for the first conv2d_transpose op applied to the
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// image_features in the mask prediction branch. If set to 0, the value
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// will be set automatically based on the number of channels in the image
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// features and the number of classes.
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optional int32 mask_prediction_conv_depth = 7 [default = 256];
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// Whether to predict keypoints inside detection boxes.
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optional bool predict_keypoints = 8 [default = false];
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// The height and the width of the predicted mask.
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optional int32 mask_height = 9 [default = 15];
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optional int32 mask_width = 10 [default = 15];
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// The number of convolutions applied to image_features in the mask prediction
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// branch.
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optional int32 mask_prediction_num_conv_layers = 11 [default = 2];
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optional bool masks_are_class_agnostic = 12 [default = false];
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// Whether to use one box for all classes rather than a different box for each
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// class.
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optional bool share_box_across_classes = 13 [default = false];
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// Whether to apply convolutions on mask features before upsampling using
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// nearest neighbor resizing.
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// By default, mask features are resized to [`mask_height`, `mask_width`]
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// before applying convolutions and predicting masks.
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optional bool convolve_then_upsample_masks = 14 [default = false];
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}
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message RfcnBoxPredictor {
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// Hyperparameters for convolution ops used in the box predictor.
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optional Hyperparams conv_hyperparams = 1;
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// Bin sizes for RFCN crops.
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optional int32 num_spatial_bins_height = 2 [default = 3];
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optional int32 num_spatial_bins_width = 3 [default = 3];
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// Target depth to reduce the input image features to.
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optional int32 depth = 4 [default = 1024];
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// Size of the encoding for the boxes.
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optional int32 box_code_size = 5 [default = 4];
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// Size to resize the rfcn crops to.
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optional int32 crop_height = 6 [default = 12];
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optional int32 crop_width = 7 [default = 12];
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}
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