86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""Helper methods for writing metadata into TFLite models."""
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from typing import List
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from mediapipe.tasks.metadata import schema_py_generated as _schema_fb
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def get_input_tensor_names(model_buffer: bytearray) -> List[str]:
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"""Gets a list of the input tensor names."""
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subgraph = get_subgraph(model_buffer)
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tensor_names = []
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for i in range(subgraph.InputsLength()):
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index = subgraph.Inputs(i)
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tensor_names.append(subgraph.Tensors(index).Name().decode("utf-8"))
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return tensor_names
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def get_output_tensor_names(model_buffer: bytearray) -> List[str]:
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"""Gets a list of the output tensor names."""
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subgraph = get_subgraph(model_buffer)
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tensor_names = []
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for i in range(subgraph.OutputsLength()):
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index = subgraph.Outputs(i)
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tensor_names.append(subgraph.Tensors(index).Name().decode("utf-8"))
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return tensor_names
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def get_input_tensor_types(
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model_buffer: bytearray) -> List[_schema_fb.TensorType]:
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"""Gets a list of the input tensor types."""
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subgraph = get_subgraph(model_buffer)
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tensor_types = []
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for i in range(subgraph.InputsLength()):
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index = subgraph.Inputs(i)
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tensor_types.append(subgraph.Tensors(index).Type())
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return tensor_types
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def get_output_tensor_types(
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model_buffer: bytearray) -> List[_schema_fb.TensorType]:
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"""Gets a list of the output tensor types."""
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subgraph = get_subgraph(model_buffer)
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tensor_types = []
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for i in range(subgraph.OutputsLength()):
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index = subgraph.Outputs(i)
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tensor_types.append(subgraph.Tensors(index).Type())
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return tensor_types
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def get_subgraph(model_buffer: bytearray) -> _schema_fb.SubGraph:
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"""Gets the subgraph of the model.
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TFLite does not support multi-subgraph. A model should have exactly one
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subgraph.
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Args:
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model_buffer: valid buffer of the model file.
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Returns:
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The subgraph of the model.
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Raises:
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ValueError: if the model has more than one subgraph or has no subgraph.
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"""
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model = _schema_fb.Model.GetRootAsModel(model_buffer, 0)
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# Use the first subgraph as default. TFLite Interpreter doesn't support
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# multiple subgraphs yet, but models with mini-benchmark may have multiple
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# subgraphs for acceleration evaluation purpose.
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return model.Subgraphs(0)
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