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chore: import upstream snapshot with attribution
2026-07-13 12:14:16 +08:00

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Python

# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Functions used by multiple tflite test files."""
from tensorflow.lite.python import schema_py_generated as schema_fb
from tensorflow.lite.python import schema_util
from tensorflow.lite.tools import visualize
def get_ops_list(model_data):
"""Returns a set of ops in the tflite model data."""
model = schema_fb.Model.GetRootAsModel(model_data, 0)
op_set = set()
for subgraph_idx in range(model.SubgraphsLength()):
subgraph = model.Subgraphs(subgraph_idx)
for op_idx in range(subgraph.OperatorsLength()):
op = subgraph.Operators(op_idx)
opcode = model.OperatorCodes(op.OpcodeIndex())
builtin_code = schema_util.get_builtin_code_from_operator_code(opcode)
if builtin_code == schema_fb.BuiltinOperator.CUSTOM:
opname = opcode.CustomCode().decode("utf-8")
op_set.add(opname)
else:
op_set.add(visualize.BuiltinCodeToName(builtin_code))
return op_set
def get_output_shapes(model_data):
"""Returns a list of output shapes in the tflite model data."""
model = schema_fb.Model.GetRootAsModel(model_data, 0)
output_shapes = []
for subgraph_idx in range(model.SubgraphsLength()):
subgraph = model.Subgraphs(subgraph_idx)
for output_idx in range(subgraph.OutputsLength()):
output_tensor_idx = subgraph.Outputs(output_idx)
output_tensor = subgraph.Tensors(output_tensor_idx)
output_shapes.append(output_tensor.ShapeAsNumpy().tolist())
return output_shapes