mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
bbf70db499
Add a fluent builder API for creating compression specifications
without writing YAML strings. This is useful in scripts and
Jupyter notebooks.
Example usage:
spec = (compression.SpecBuilder()
.add_tensor(subgraph=0, tensor=2)
.with_lut(index_bitwidth=4)
.build())
BUG=#3125
Co-authored-by: suleshahid <110432064+suleshahid@users.noreply.github.com>
79 lines
2.5 KiB
Python
79 lines
2.5 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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# A simple test to check whether the tflite_micro package works after it is
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# installed.
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# To test from the perspective of a package user, use import paths to locations
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# in the Python installation environment rather than to locations in the tflm
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# source tree.
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from tflite_micro import runtime
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from tflite_micro import compression
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import numpy as np
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import pkg_resources
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import sys
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import tempfile
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import os
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def runtime_test():
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"""Test the runtime interpreter functionality."""
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# Create an interpreter with a sine model
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model = pkg_resources.resource_filename(__name__, "sine_float.tflite")
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interpreter = runtime.Interpreter.from_file(model)
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OUTPUT_INDEX = 0
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INPUT_INDEX = 0
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input_shape = interpreter.get_input_details(INPUT_INDEX).get("shape")
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# The interpreter infers sin(x)
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def infer(x):
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tensor = np.array(x, np.float32).reshape(input_shape)
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interpreter.set_input(tensor, INPUT_INDEX)
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interpreter.invoke()
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return interpreter.get_output(OUTPUT_INDEX).squeeze()
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# Check a few inferred values against a numerical computation
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PI = 3.14
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inputs = (0.0, PI / 2, PI, 3 * PI / 2, 2 * PI)
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outputs = [infer(x) for x in inputs]
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goldens = np.sin(inputs)
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return np.allclose(outputs, goldens, atol=0.05)
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def compression_test():
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"""Test that the compression module is available and functional."""
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# Test that compress function is available
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# We don't actually compress here as it requires a properly structured model
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# with compressible tensors, but we verify the function is importable
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assert callable(compression.compress)
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# Test availability of the SpecBuilder
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_ = (compression.SpecBuilder().add_tensor(
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subgraph=0, tensor=0).with_lut(index_bitwidth=4).build())
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return True
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def passed():
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"""Run all postinstall checks."""
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runtime_passed = runtime_test()
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compression_passed = compression_test()
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return runtime_passed and compression_passed
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if __name__ == "__main__":
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sys.exit(0 if passed() else 1)
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