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// Copyright 2021 The Deeplab2 Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto2";
package deeplab2;
option java_multiple_files = true;
// Configure the solver options.
// Next ID: 17
message SolverOptions {
optional bool use_sync_batchnorm = 1 [default = true];
optional float batchnorm_momentum = 14 [default = 0.99];
optional float batchnorm_epsilon = 15 [default = 0.001];
// Set the learning rate policy for training. Available policies: 'poly',
// 'cosine'.
optional string learning_policy = 2 [default = 'poly'];
// Set the base learning rate for model training.
optional float base_learning_rate = 3 [default = 1e-3];
// Set the power value used in the poly learning policy.
optional float poly_learning_power = 4 [default = 0.9];
// End learning rate for polynomial learning rate schedule.
optional float poly_end_learning_rate = 5 [default = 0.0];
// Set the number of steps for the warmup phase. We currently only
// support linear warmup, i.e., if global_step < warmup_steps, the
// learning rate will be `global_step / warmup_steps * base_learning_rate`.
optional int32 warmup_steps = 6 [default = 0];
// Set the optimizer method. Supported types: 'adam', 'sgd'.
optional string optimizer = 7 [default = 'adam'];
// Set the value of the weight decay for training.
optional float weight_decay = 8 [default = 0];
// Set whether to use gradient clipping or not.
optional bool use_gradient_clipping = 9 [default = false];
// Set the norm used in gradient clipping.
optional float clip_gradient_norm = 10 [default = 10.0];
// Set the number of steps for training.
optional int32 training_number_of_steps = 11 [default = 60000];
// Set the backbone learning rate multiplier when different learning rates
// are desired for the backbone and for the other layers. For example,
// MaX-DeepLab uses this field to set a 0.1x learning rate for the pretrained
// backbone parameters.
optional float backbone_learning_rate_multiplier = 16 [default = 1.0];
}
/********** Submessages used to config loss options **********/
// Configure the loss options.
message LossOptions {
message SingleLossOptions {
// Set the name of the loss.
optional string name = 1;
// Set the global weight of the loss used to weight the contribution of this
// loss with respect to all other losses.
optional float weight = 2 [default = 1.0];
// Set the percentage of top-k pixels to be used for backpropagation.
optional float top_k_percent = 3 [default = 1.0];
}
// Set the loss options for the semantic segmentation output.
optional SingleLossOptions semantic_loss = 1;
// Set the loss options for the center head.
optional SingleLossOptions center_loss = 2;
// Set the loss options for the regression head.
optional SingleLossOptions regression_loss = 3;
// Set the loss options for the motion head.
optional SingleLossOptions motion_loss = 4;
// Set the loss options for the next regression head.
optional SingleLossOptions next_regression_loss = 5;
// Set the loss options for the PQ-style loss.
optional SingleLossOptions pq_style_loss = 6;
// Set the loss options for the mask id cross entropy loss.
optional SingleLossOptions mask_id_cross_entropy_loss = 7;
// Set the loss options for the instance discrimination loss.
optional SingleLossOptions instance_discrimination_loss = 8;
}
// Configure the trainer options.
message TrainerOptions {
// Set the maximum number of checkpoints to keep.
optional int32 num_checkpoints_to_keep = 1 [default = 5];
// Set the number of steps after which a checkpoint should be made.
optional int32 save_checkpoints_steps = 2 [default = 1000];
// Set after how many steps the summary should be written. Must be a multiple
// of steps_per_loop.
optional int32 save_summaries_steps = 3 [default = 1000];
// Set how many steps one `inner` train loop should have. This relates to the
// orbit framework:
// https://github.com/tensorflow/models/blob/master/orbit/controller.py#L33
optional int32 steps_per_loop = 4 [default = 1000];
// Set the loss options.
optional LossOptions loss_options = 5;
// Set the solver options.
optional SolverOptions solver_options = 6;
}
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