wehub-resource-sync
84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
# Copyright 2020 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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# ==============================================================================
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r"""Randomize all weights in a tflite file."""
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from absl import app
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from absl import flags
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from tensorflow.lite.tools import flatbuffer_utils
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FLAGS = flags.FLAGS
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flags.DEFINE_string('input_tflite_file', None,
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'Full path name to the input TFLite file.')
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flags.DEFINE_string('output_tflite_file', None,
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'Full path name to the output randomized TFLite file.')
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flags.DEFINE_multi_integer(
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'buffers_to_skip', [], 'Buffer indices in the TFLite model to be skipped, '
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'i.e., to be left unmodified.')
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flags.DEFINE_multi_string(
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'ops_to_skip', [], 'Ops in the TFLite model to be skipped / unmodified.')
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flags.DEFINE_multi_string(
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'ops_operands_to_skip',
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[],
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'Op operand indices in the TFLite model to be skipped / unmodified. It'
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' should be specified in the format'
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' <op_name>:<operand_index>[,<operand_index>]. For example,'
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' TRANSPOSE_CONV:0,2 stands for skipping the TRANSPOSE_CONV operands'
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' indexed 0 and 2',
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)
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flags.DEFINE_integer('random_seed', 0, 'Input to the random number generator.')
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flags.mark_flag_as_required('input_tflite_file')
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flags.mark_flag_as_required('output_tflite_file')
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def main(_):
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buffers_to_skip = FLAGS.buffers_to_skip
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ops_to_skip = [op.upper() for op in FLAGS.ops_to_skip]
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ops_operands_to_skip = {}
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for op_operands_to_skip in FLAGS.ops_operands_to_skip:
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op_name, indices = op_operands_to_skip.split(':')
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op_name_upper = op_name.upper()
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if op_name_upper in ops_operands_to_skip:
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raise ValueError(
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'Indices for the same op must be specified only once multiple'
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f' specification for op {op_name}.'
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)
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ops_operands_to_skip[op_name_upper] = list(map(int, indices.split(',')))
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model = flatbuffer_utils.read_model(FLAGS.input_tflite_file)
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# Add in buffers for ops in ops_to_skip or ops_operands_to_skip to the list of
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# skipped buffers.
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for graph in model.subgraphs:
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for op in graph.operators:
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op_name = flatbuffer_utils.opcode_to_name(model, op.opcodeIndex)
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op_name_upper = op_name.upper()
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if op_name_upper in ops_to_skip:
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for input_idx in op.inputs:
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buffers_to_skip.append(graph.tensors[input_idx].buffer)
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if op_name_upper in ops_operands_to_skip:
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for operand_idx in ops_operands_to_skip[op_name_upper]:
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buffers_to_skip.append(graph.tensors[op.inputs[operand_idx]].buffer)
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flatbuffer_utils.randomize_weights(model, FLAGS.random_seed,
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FLAGS.buffers_to_skip)
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flatbuffer_utils.write_model(model, FLAGS.output_tflite_file)
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if __name__ == '__main__':
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app.run(main)
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