61 lines
2.3 KiB
Protocol Buffer
61 lines
2.3 KiB
Protocol Buffer
/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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syntax = "proto2";
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package mediapipe.tasks.components.containers.proto;
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option java_package = "com.google.mediapipe.tasks.components.containers.proto";
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option java_outer_classname = "EmbeddingsProto";
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// Defines a dense floating-point embedding.
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message FloatEmbedding {
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repeated float values = 1 [packed = true];
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}
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// Defines a dense scalar-quantized embedding.
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message QuantizedEmbedding {
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optional bytes values = 1;
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}
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// Embedding result for a given embedder head.
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message Embedding {
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// The actual embedding, either floating-point or quantized.
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oneof embedding {
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FloatEmbedding float_embedding = 1;
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QuantizedEmbedding quantized_embedding = 2;
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}
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// The index of the embedder head that produced this embedding. This is useful
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// for multi-head models.
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optional int32 head_index = 3;
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// The name of the embedder head, which is the corresponding tensor metadata
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// name (if any). This is useful for multi-head models.
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optional string head_name = 4;
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}
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// Embedding results for a given embedder model.
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message EmbeddingResult {
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// The embedding results for each model head, i.e. one for each output tensor.
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repeated Embedding embeddings = 1;
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// The optional timestamp (in milliseconds) of the start of the chunk of data
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// corresponding to these results.
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//
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// This is only used for embedding extraction on time series (e.g. audio
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// embedding). In these use cases, the amount of data to process might
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// exceed the maximum size that the model can process: to solve this, the
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// input data is split into multiple chunks starting at different timestamps.
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optional int64 timestamp_ms = 2;
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}
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