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syntax = "proto2";
package object_detection.protos;
import "object_detection/protos/optimizer.proto";
import "object_detection/protos/preprocessor.proto";
// Message for configuring DetectionModel training jobs (train.py).
// Next id: 28
message TrainConfig {
// Effective batch size to use for training.
// For TPU (or sync SGD jobs), the batch size per core (or GPU) is going to be
// `batch_size` / number of cores (or `batch_size` / number of GPUs).
optional uint32 batch_size = 1 [default=32];
// Data augmentation options.
repeated PreprocessingStep data_augmentation_options = 2;
// Whether to synchronize replicas during training.
optional bool sync_replicas = 3 [default=false];
// How frequently to keep checkpoints.
optional float keep_checkpoint_every_n_hours = 4 [default=10000.0];
// Optimizer used to train the DetectionModel.
optional Optimizer optimizer = 5;
// If greater than 0, clips gradients by this value.
optional float gradient_clipping_by_norm = 6 [default=0.0];
// Checkpoint to restore variables from. Typically used to load feature
// extractor variables trained outside of object detection.
optional string fine_tune_checkpoint = 7 [default=""];
// Type of checkpoint to restore variables from, e.g. 'classification' or
// 'detection'. Provides extensibility to from_detection_checkpoint.
// Typically used to load feature extractor variables from trained models.
optional string fine_tune_checkpoint_type = 22 [default=""];
// [Deprecated]: use fine_tune_checkpoint_type instead.
// Specifies if the finetune checkpoint is from an object detection model.
// If from an object detection model, the model being trained should have
// the same parameters with the exception of the num_classes parameter.
// If false, it assumes the checkpoint was a object classification model.
optional bool from_detection_checkpoint = 8 [default=false, deprecated=true];
// Whether to load all checkpoint vars that match model variable names and
// sizes. This option is only available if `from_detection_checkpoint` is
// True.
optional bool load_all_detection_checkpoint_vars = 19 [default = false];
// Number of steps to train the DetectionModel for. If 0, will train the model
// indefinitely.
optional uint32 num_steps = 9 [default=0];
// Number of training steps between replica startup.
// This flag must be set to 0 if sync_replicas is set to true.
optional float startup_delay_steps = 10 [default=15];
// If greater than 0, multiplies the gradient of bias variables by this
// amount.
optional float bias_grad_multiplier = 11 [default=0];
// Variables that should be updated during training. Note that variables which
// also match the patterns in freeze_variables will be excluded.
repeated string update_trainable_variables = 25;
// Variables that should not be updated during training. If
// update_trainable_variables is not empty, only eliminates the included
// variables according to freeze_variables patterns.
repeated string freeze_variables = 12;
// Number of replicas to aggregate before making parameter updates.
optional int32 replicas_to_aggregate = 13 [default=1];
// Maximum number of elements to store within a queue.
optional int32 batch_queue_capacity = 14 [default=150, deprecated=true];
// Number of threads to use for batching.
optional int32 num_batch_queue_threads = 15 [default=8, deprecated=true];
// Maximum capacity of the queue used to prefetch assembled batches.
optional int32 prefetch_queue_capacity = 16 [default=5, deprecated=true];
// If true, boxes with the same coordinates will be merged together.
// This is useful when each box can have multiple labels.
// Note that only Sigmoid classification losses should be used.
optional bool merge_multiple_label_boxes = 17 [default=false];
// If true, will use multiclass scores from object annotations as ground
// truth. Currently only compatible with annotated image inputs.
optional bool use_multiclass_scores = 24 [default = false];
// Whether to add regularization loss to `total_loss`. This is true by
// default and adds all regularization losses defined in the model to
// `total_loss`.
// Setting this option to false is very useful while debugging the model and
// losses.
optional bool add_regularization_loss = 18 [default=true];
// Maximum number of boxes used during training.
// Set this to at least the maximum amount of boxes in the input data.
// Otherwise, it may cause "Data loss: Attempted to pad to a smaller size
// than the input element" errors.
optional int32 max_number_of_boxes = 20 [default=100, deprecated=true];
// Whether to remove padding along `num_boxes` dimension of the groundtruth
// tensors.
optional bool unpad_groundtruth_tensors = 21 [default=true];
// Whether to retain original images (i.e. not pre-processed) in the tensor
// dictionary, so that they can be displayed in Tensorboard. Note that this
// will lead to a larger memory footprint.
optional bool retain_original_images = 23 [default=false];
// Whether to use bfloat16 for training. This is currently only supported for
// TPUs.
optional bool use_bfloat16 = 26 [default=false];
// Whether to summarize gradients.
optional bool summarize_gradients = 27 [default=false];
}
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