// Copyright 2019 The MediaPipe 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 mediapipe;
import "mediapipe/framework/calculator.proto";
message TensorFlowInferenceCalculatorOptions {
extend mediapipe.CalculatorOptions {
optional TensorFlowInferenceCalculatorOptions ext = 113766539;
}
// The signature_name specifies the mapping between stream tags and TensorFlow
// session tensors. The mapping between tags and tensors is encoded as a
// ModelManifest signature. The signatures are keyed by name and the
// named_signature matching signature_name is used by the calculator to
// match stream tags to tensors. The named_signature must be a
// ModelManifest.generic_signature with map keys that are valid tags (i.e.
// [A-Z0-9]*).
optional string signature_name = 1;
// How many elements to batch together and feed into the graph.
// Setting the batch_size to 1 disables batching entirely. You still may or
// may not need to add the batch dimension via the option below depending on
// the input data shape and the model's expectations.
optional int32 batch_size = 2;
// Whether to add a 0th dimension to the input tensors for batching.
// If the 0th dimension is the batch dimension, then the tensors are
// concatenated on that dimension. If the 0th is a data dimension, then a 0th
// dimension is added before concatenating. If added, the extra dimension is
// removed before outputting the tensor. Examples of each case: If you want
// to batch spectra of audio over time for an LSTM, a time-frequency
// representation has a 0th dimension as the batch dimension. If you want to
// batch frames of video that are [width, height, channels], the batch
// dimension needs to be added.
optional bool add_batch_dim_to_tensors = 3 [default = true];
// Whether to pad the last batch to batch_size or run inference on a partial
// batch.
// Setting this to false is useful for TPU models that use in-graph batching
// as padding in MediaPipe conflicts with merging of batches in tensorflows
// batch ops.
optional bool pad_to_batch_size = 8 [default = true];
// These pairs represent feed and fetch tensors for handling recurrent state.
// Each entry is a colon separated pair of strings. The first half of each
// string is the signature tag for the feed tensor for recurrent state. The
// second half of the string is the signature tag for the fetch tensor for the
// recurrent state. More than two colon separated strings is an error. During
// inference, The fetch tensor is fetched at every timestep and will be output
// if there is a corresponding output stream. The behavior of the feed tensor
// is determined by the following conditions in order: If the MediaPipe input
// stream with the matching tag has a packet available, then the input
// packet's tensor is passed in. If no input packet is available and we have
// fetched a tensor from the previous time step, we will feed the tensor from
// the previous timestep back in. If neither tensor is available, no tensor
// will be fed into the model.
// If this flag is set, batch_size must be 1. Do not list recurrent_tag_pair
// tags as initial_state_tags because those are only fed once.
repeated string recurrent_tag_pair = 4;
// If set to true, skips input for which any of the features are missing.
// If set to false, requires that all input features to be available. If not,
// it will report an error for the calculator.
optional bool skip_on_missing_features = 5 [default = false];
// Maximum allowed concurrent Tensorflow session run calls in the calculator
// to avoid overloading local compute hardware such as TPU. Note that this
// only works in the local process, not "globally" across multiple processes
// or replicas (if any). Default to 0, i.e. no limit.
optional int32 max_concurrent_session_runs = 6 [default = 0];
// If turned on, the Calculator expects a vector of batched packages as input.
// This will make sure that you can turn on max_in_flight for batch_size
// greater than 1. Otherwise it results in problems of none-monotonically
// increasing timestamps.
// Use BatchSequentialCalculator to create the batches. The batch_size
// should agree for both calculators. All the data in a batch is processed
// together. The BatchSequentialCalculator can't run with max_in_flight.
optional bool batched_input = 7;
}
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