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- syntax = "proto2";
-
- package object_detection.protos;
-
- // Configuration proto for the convolution op hyperparameters to use in the
- // object detection pipeline.
- message Hyperparams {
-
- // Operations affected by hyperparameters.
- enum Op {
- // Convolution, Separable Convolution, Convolution transpose.
- CONV = 1;
-
- // Fully connected
- FC = 2;
- }
- optional Op op = 1 [default = CONV];
-
- // Regularizer for the weights of the convolution op.
- optional Regularizer regularizer = 2;
-
- // Initializer for the weights of the convolution op.
- optional Initializer initializer = 3;
-
- // Type of activation to apply after convolution.
- enum Activation {
- // Use None (no activation)
- NONE = 0;
-
- // Use tf.nn.relu
- RELU = 1;
-
- // Use tf.nn.relu6
- RELU_6 = 2;
- }
- optional Activation activation = 4 [default = RELU];
-
- oneof normalizer_oneof {
- // Note that if nothing below is selected, then no normalization is applied
- // BatchNorm hyperparameters.
- BatchNorm batch_norm = 5;
- // GroupNorm hyperparameters. This is only supported on a subset of models.
- // Note that the current implementation of group norm instantiated in
- // tf.contrib.group.layers.group_norm() only supports fixed_size_resizer
- // for image preprocessing.
- GroupNorm group_norm = 7;
- }
-
- // Whether depthwise convolutions should be regularized. If this parameter is
- // NOT set then the conv hyperparams will default to the parent scope.
- optional bool regularize_depthwise = 6 [default = false];
- }
-
- // Proto with one-of field for regularizers.
- message Regularizer {
- oneof regularizer_oneof {
- L1Regularizer l1_regularizer = 1;
- L2Regularizer l2_regularizer = 2;
- }
- }
-
- // Configuration proto for L1 Regularizer.
- // See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l1_regularizer
- message L1Regularizer {
- optional float weight = 1 [default = 1.0];
- }
-
- // Configuration proto for L2 Regularizer.
- // See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l2_regularizer
- message L2Regularizer {
- optional float weight = 1 [default = 1.0];
- }
-
- // Proto with one-of field for initializers.
- message Initializer {
- oneof initializer_oneof {
- TruncatedNormalInitializer truncated_normal_initializer = 1;
- VarianceScalingInitializer variance_scaling_initializer = 2;
- RandomNormalInitializer random_normal_initializer = 3;
- }
- }
-
- // Configuration proto for truncated normal initializer. See
- // https://www.tensorflow.org/api_docs/python/tf/truncated_normal_initializer
- message TruncatedNormalInitializer {
- optional float mean = 1 [default = 0.0];
- optional float stddev = 2 [default = 1.0];
- }
-
- // Configuration proto for variance scaling initializer. See
- // https://www.tensorflow.org/api_docs/python/tf/contrib/layers/
- // variance_scaling_initializer
- message VarianceScalingInitializer {
- optional float factor = 1 [default = 2.0];
- optional bool uniform = 2 [default = false];
- enum Mode {
- FAN_IN = 0;
- FAN_OUT = 1;
- FAN_AVG = 2;
- }
- optional Mode mode = 3 [default = FAN_IN];
- }
-
- // Configuration proto for random normal initializer. See
- // https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer
- message RandomNormalInitializer {
- optional float mean = 1 [default = 0.0];
- optional float stddev = 2 [default = 1.0];
- }
-
- // Configuration proto for batch norm to apply after convolution op. See
- // https://www.tensorflow.org/api_docs/python/tf/contrib/layers/batch_norm
- message BatchNorm {
- optional float decay = 1 [default = 0.999];
- optional bool center = 2 [default = true];
- optional bool scale = 3 [default = false];
- optional float epsilon = 4 [default = 0.001];
- // Whether to train the batch norm variables. If this is set to false during
- // training, the current value of the batch_norm variables are used for
- // forward pass but they are never updated.
- optional bool train = 5 [default = true];
- }
-
- // Configuration proto for group normalization to apply after convolution op.
- // https://arxiv.org/abs/1803.08494
- message GroupNorm {
- }
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