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https://github.com/vee1e/tflite-micro.git
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f22888af65
Second OP for the TFLM Signal library, Real-Valued Fast Fourier Transform. The RFFT OP provides three resolutions: `FLOAT, INT16, INT32` Similar usage as to previous Window OP: * `op_resolver.AddRfft()` (which will add all resolutions, and determine the type at runtime) * `op_resolver.AddRfftFloat()`, `op_resolver.AddRfftInt16()`, `op_resolver.AddRfftInt32()` for a specific resolution type. * or via python as can be seen in `fft_ops_test.py` 3 testing options are provided: * Micro(C++): bazel run signal/micro/kernels:fft_test * Tensorflow/Micro(Python): bazel run python/tflite_micro/signal:fft_ops_test * Makefile(C++): make -f tensorflow/lite/micro/tools/make/Makefile test_kernel_fft_test BUG=[287346710](http://b/287346710)
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# Copyright 2023 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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"""Python utility functions."""
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import tensorflow as tf
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from tensorflow.python.framework import load_library
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from tensorflow.python.platform import resource_loader
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from tflite_micro.python.tflite_micro import runtime
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# TODO(b/286889497): find better name and place for this function.
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def get_tflm_interpreter(concrete_function, trackable_obj):
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"""Initialize a TFLite interpreter with a concerte function.
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Args:
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concrete_function: A concrete function
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Returns:
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TFLite interpreter object
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"""
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converter = tf.lite.TFLiteConverter.from_concrete_functions(
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[concrete_function], trackable_obj)
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converter.allow_custom_ops = True
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tflite_model = converter.convert()
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return runtime.Interpreter.from_bytes(tflite_model, arena_size=500000)
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def load_custom_op(name):
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return load_library.load_op_library(
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resource_loader.get_path_to_datafile('../ops/_' + name))
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