mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
db550c38c9
Add a tensor_type module that holds the single mapping from a TFLite TensorType to a numpy dtype, and convert view.py to use it. The mapping was inlined in view.py; centralizing it gives the compression tooling one place to maintain as more callers need to read tensor buffers as numpy arrays. tensor_type.to_numpy() raises ValueError for types with no clean numpy equivalent (STRING, RESOURCE, VARIANT, BFLOAT16, and the sub-byte integer types) instead of silently returning a wrong dtype. Only types with an unambiguous little-endian numpy representation are mapped. BUG=part of #3256
421 lines
12 KiB
Python
421 lines
12 KiB
Python
# Copyright 2025 The TensorFlow 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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# This development tool prints compressed and uncompressed .tflite models to
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# stdout in a human-readable, searchable, structured, text format. Helpful
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# annotations (indexes of lists, names of operators, etc.) derived from the
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# model are added as virtual fields with names beginning with an _underscore.
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#
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# Example usage:
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# bazel run //tensorflow/lite/micro/compression:view -- $(realpath model.tflite)
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#
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# Theory of operation:
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# Convert the model into a Python dictionary, expressing the hierarchical nature
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# of the model, and pretty print the dictionary. Please extend as needed for
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# your use case.
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from dataclasses import dataclass
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from enum import Enum
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import bitarray
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import bitarray.util
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import numpy as np
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import os
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import prettyprinter
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import prettyprinter.doc
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import sys
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import textwrap
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import absl.app
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from tensorflow.lite.micro.compression import metadata_py_generated as compression_schema
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from tensorflow.lite.micro.compression import tensor_type
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from tensorflow.lite.python import schema_py_generated as tflite_schema
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# Detect if running under Bazel by checking for BAZEL environment variables
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is_bazel = 'BUILD_WORKING_DIRECTORY' in os.environ or 'BAZEL_TEST' in os.environ
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if is_bazel:
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USAGE = textwrap.dedent("""\
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Usage: bazel run //tensorflow/lite/micro/compression:view -- <MODEL_PATH>
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Print a human-readable visualization of a .tflite model.
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Note: When running through Bazel, MODEL_PATH must be an absolute path.
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Example: bazel run //tensorflow/lite/micro/compression:view -- $(realpath model.tflite)"""
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)
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else:
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USAGE = textwrap.dedent(f"""\
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Usage: {os.path.basename(sys.argv[0])} <MODEL_PATH>
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Print a human-readable visualization of a .tflite model.""")
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def print_model(model_path):
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with open(model_path, 'rb') as flatbuffer:
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d = create_dictionary(memoryview(flatbuffer.read()))
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prettyprinter.cpprint(d)
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def main(argv):
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try:
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model_path = argv[1]
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except IndexError:
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sys.stderr.write(USAGE)
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sys.exit(1)
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print_model(model_path)
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@dataclass
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class MetadataReader:
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model: tflite_schema.ModelT
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buffer_index: int
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metadata: compression_schema.MetadataT
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@classmethod
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def build(cls, model: tflite_schema.ModelT):
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if model.metadata is None:
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return None
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for item in model.metadata:
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if _decode_name(item.name) == "COMPRESSION_METADATA":
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buffer_index = item.buffer
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buffer = model.buffers[buffer_index]
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metadata = compression_schema.MetadataT.InitFromPackedBuf(
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buffer.data, 0)
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if metadata.subgraphs is None:
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raise ValueError("Invalid compression metadata")
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return cls(model, buffer_index, metadata)
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else:
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return None
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def unpack(self):
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result = []
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for index, subgraph in enumerate(self.metadata.subgraphs):
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result.append({
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"_index": index,
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"lut_tensors": unpack_lut_metadata(subgraph.lutTensors),
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})
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return {"subgraphs": result}
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def unpack_operators(model: tflite_schema.ModelT,
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operators: list[tflite_schema.OperatorT]):
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result = []
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for index, op in enumerate(operators):
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opcode = model.operatorCodes[op.opcodeIndex]
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name = OPERATOR_NAMES[opcode.builtinCode]
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d = {
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"_operator": index,
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"opcode_index": op.opcodeIndex,
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"_opcode_name": name,
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"inputs": op.inputs,
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"outputs": op.outputs,
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}
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result.append(d)
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return result
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def unpack_TensorType(type):
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attrs = [
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attr for attr in dir(tflite_schema.TensorType)
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if not attr.startswith("__")
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]
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lut = {getattr(tflite_schema.TensorType, attr): attr for attr in attrs}
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return lut[type]
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def _decode_name(name):
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"""Returns name as a str or 'None'.
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The flatbuffer library returns names as bytes objects or None. This function
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returns a str, decoded from the bytes object, or None.
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"""
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if name is None:
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return None
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else:
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return str(name, encoding="utf-8")
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@dataclass
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class TensorCoordinates:
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subgraph_ix: int
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tensor_index: int
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class CompressionMethod(Enum):
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LUT = "LUT"
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OPERATOR_NAMES = {
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code: name
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for name, code in tflite_schema.BuiltinOperator.__dict__.items()
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}
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class Codec:
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def __init__(self, reader: MetadataReader, model: tflite_schema.ModelT):
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self.reader = reader
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self.model = model
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def _tensor_metadata(self, tensor: TensorCoordinates):
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subgraph = self.reader.metadata.subgraphs[tensor.subgraph_ix]
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for metadata in subgraph.lutTensors:
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if tensor.tensor_index == metadata.tensor:
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return metadata
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else:
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return None
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def list_compressions(
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self, coordinates: TensorCoordinates) -> list[CompressionMethod]:
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metadata = self._tensor_metadata(coordinates)
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if metadata:
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return [CompressionMethod.LUT]
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else:
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return []
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def lookup_tables(self, coordinates: TensorCoordinates) -> np.ndarray:
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metadata = self._tensor_metadata(coordinates)
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if not metadata:
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return np.array([])
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model_subgraph = self.model.subgraphs[coordinates.subgraph_ix]
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model_tensor = model_subgraph.tensors[coordinates.tensor_index]
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value_buffer = self.model.buffers[metadata.valueBuffer]
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values = np.frombuffer(bytes(value_buffer.data),
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dtype=tensor_type.to_numpy(model_tensor.type))
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values_per_table = 2**metadata.indexBitwidth
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tables = len(values) // values_per_table
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values = values.reshape((tables, values_per_table))
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return values
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def unpack_tensors(tensors, subgraph_index: int, codec: Codec | None):
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result = []
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for index, t in enumerate(tensors):
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d = {
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"_tensor": index,
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"name": _decode_name(t.name),
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"type": unpack_TensorType(t.type),
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"shape": t.shape,
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"buffer": t.buffer,
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}
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if t.isVariable:
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d["is_variable"] = True
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else:
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# don't display this unusual field
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pass
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if t.quantization is not None and t.quantization.scale is not None:
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d["quantization"] = {
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"scale": t.quantization.scale,
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"zero": t.quantization.zeroPoint,
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"dimension": t.quantization.quantizedDimension,
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}
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result.append(d)
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if codec is not None:
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coordinates = TensorCoordinates(subgraph_ix=subgraph_index,
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tensor_index=index)
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d |= unpack_compression(coordinates, codec)
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return result
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def unpack_compression(tensor: TensorCoordinates, codec: Codec) -> dict:
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result = {}
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compressions = codec.list_compressions(tensor)
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if compressions:
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result["_compressed"] = [c.name for c in compressions]
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metadata = codec._tensor_metadata(tensor)
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assert metadata is not None
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result["_value_buffer"] = metadata.valueBuffer
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result["_lookup_tables"] = codec.lookup_tables(tensor)
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return result
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def unpack_subgraphs(model: tflite_schema.ModelT, codec: Codec | None):
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result = []
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for index, s in enumerate(model.subgraphs):
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d = {
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"_subgraph": index,
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"_operator_count": len(s.operators),
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"_tensor_count": len(s.tensors),
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"name": _decode_name(s.name),
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"operators": unpack_operators(model, s.operators),
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"tensors": unpack_tensors(s.tensors, subgraph_index=index,
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codec=codec),
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}
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result.append(d)
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return result
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def unpack_opcodes(opcodes: list[tflite_schema.OperatorCodeT]) -> list:
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result = []
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for index, opcode in enumerate(opcodes):
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d: dict = {
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"_opcode_index": index,
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"_name": OPERATOR_NAMES[opcode.builtinCode],
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"builtin_code": opcode.builtinCode,
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"version": opcode.version,
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}
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if opcode.customCode is not None:
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d["custom_code"] = opcode.customCode
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del d["_name"]
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result.append(d)
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return result
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def unpack_metadata(model: tflite_schema.ModelT):
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entries = []
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compression = MetadataReader.build(model)
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if model.metadata is None:
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return entries
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for m in model.metadata:
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d = {"name": _decode_name(m.name), "buffer": m.buffer}
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if compression and compression.buffer_index == m.buffer:
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d["_compression_metadata"] = compression.unpack()
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entries.append(d)
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return entries
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def unpack_lut_metadata(lut_tensors):
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return [{
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"tensor": t.tensor,
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"value_buffer": t.valueBuffer,
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"index_bitwidth": t.indexBitwidth,
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} for t in sorted(lut_tensors, key=lambda x: x.tensor)]
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def find_lut_info_for_buffer(buffer_index, model, compression_data):
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"""Find LUT metadata for a given buffer index.
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Returns a dict with tensor_index, subgraph_index, and index_bitwidth if the
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buffer contains compressed indices, otherwise returns None.
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"""
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if compression_data is None:
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return None
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for subgraph_idx, subgraph in enumerate(compression_data.metadata.subgraphs):
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for lut_tensor in subgraph.lutTensors:
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# Get the tensor to find which buffer contains the compressed indices
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tensor = model.subgraphs[subgraph_idx].tensors[lut_tensor.tensor]
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if tensor.buffer == buffer_index:
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return {
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"tensor_index": lut_tensor.tensor,
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"subgraph_index": subgraph_idx,
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"index_bitwidth": lut_tensor.indexBitwidth,
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}
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return None
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def unpack_buffers(model, compression_data):
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buffers = []
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for index, buffer in enumerate(model.buffers):
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native = {
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"_buffer": index,
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"_bytes": len(buffer.data) if buffer.data is not None else 0,
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}
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if compression_data is not None and index == compression_data.buffer_index:
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native["_compression_metadata"] = True
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native["data"] = buffer.data
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# Check if this buffer contains compressed indices
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lut_info = find_lut_info_for_buffer(index, model, compression_data)
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if lut_info and buffer.data is not None:
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# Decode the indices from the buffer
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bstring = bitarray.bitarray()
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bstring.frombytes(bytes(buffer.data))
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bitwidth = lut_info["index_bitwidth"]
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chunks = [
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bstring[i:i + bitwidth]
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for i in range(0,
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len(bstring) - bitwidth + 1, bitwidth)
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]
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indices = [bitarray.util.ba2int(chunk) for chunk in chunks]
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# Convert indices to numpy array to match data field formatting
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indices_array = np.array(indices, dtype=np.uint8)
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native["_lut_indices"] = {
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"tensor": lut_info["tensor_index"],
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"bitwidth": bitwidth,
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"indices": indices_array,
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}
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buffers.append(native)
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return buffers
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def create_dictionary(flatbuffer: memoryview) -> dict:
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"""Returns a human-readable dictionary from the provided model flatbuffer.
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This function transforms a .tflite model flatbuffer into a Python dictionary.
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When pretty-printed, this dictionary offers an easily interpretable view of
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the model.
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"""
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model = tflite_schema.ModelT.InitFromPackedBuf(flatbuffer, 0)
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compression_metadata = MetadataReader.build(model)
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codec = Codec(compression_metadata, model) if compression_metadata else None
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output = {
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"description": model.description,
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"version": model.version,
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"operator_codes": unpack_opcodes(model.operatorCodes),
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"metadata": unpack_metadata(model),
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"subgraphs": unpack_subgraphs(model, codec),
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"buffers": unpack_buffers(model, compression_metadata),
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}
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return output
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@prettyprinter.register_pretty(np.ndarray)
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def pretty_numpy_array(array, ctx):
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# Format array without ellipsis, similar to how buffer data is displayed
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string = np.array2string(array,
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threshold=np.inf,
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max_line_width=78,
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separator=' ',
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suppress_small=True)
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lines = string.splitlines()
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if len(lines) == 1:
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return lines[0]
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parts = list()
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parts.append(prettyprinter.doc.HARDLINE)
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for line in lines:
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parts.append(line)
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parts.append(prettyprinter.doc.HARDLINE)
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return prettyprinter.doc.nest(ctx.indent, prettyprinter.doc.concat(parts))
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if __name__ == "__main__":
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sys.modules['__main__'].__doc__ = USAGE
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absl.app.run(main)
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