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https://github.com/vee1e/tflite-micro.git
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18b9e6f2a8
An upcoming change switches compress() to emitting DECODE-based models. The Python ops resolver registers the DECODE operator unconditionally, so those models load successfully even in a build without compression support. That breaks this test's original approach, which ran a model through compress() and expected the load to fail. Rewrite it to instead inject a raw COMPRESSION_METADATA entry into the flatbuffer via model_editor, directly exercising the HasCompressionMetadata() detection path for legacy-compressed models. Decoupling the test from compress() output lets it verify the legacy-rejection behavior independently of what compress() emits, so it passes both before and after the switch. BUG=part of #3256
93 lines
3.5 KiB
Python
93 lines
3.5 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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# ==============================================================================
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"""Test legacy compression metadata detection when compression is disabled."""
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import os
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import numpy as np
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import tensorflow as tf
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from tflite_micro.python.tflite_micro import runtime
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from tflite_micro.tensorflow.lite.micro.compression import model_editor
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def _create_test_model():
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"""Create a simple quantized model for testing."""
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(10, input_shape=(5, ), activation='relu'),
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tf.keras.layers.Dense(5, activation='softmax')
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])
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
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converter = tf.lite.TFLiteConverter.from_keras_model(model)
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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def representative_dataset():
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for _ in range(10):
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yield [np.random.randn(1, 5).astype(np.float32)]
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converter.representative_dataset = representative_dataset
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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converter.inference_input_type = tf.uint8
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converter.inference_output_type = tf.uint8
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tflite_model = converter.convert()
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return bytes(tflite_model) if isinstance(tflite_model,
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bytearray) else tflite_model
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def _inject_compression_metadata(model_data):
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"""Inject raw COMPRESSION_METADATA into a model's flatbuffer metadata.
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This simulates a legacy-compressed model (one that uses the
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COMPRESSION_METADATA metadata entry and kernel-level decompression) without
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going through compress(), which now produces DECODE-based output.
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"""
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model = model_editor.read(model_data)
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model.metadata["COMPRESSION_METADATA"] = b"\x00"
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return bytes(model.build())
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class LegacyCompressionDetectionTest(tf.test.TestCase):
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"""Test that legacy COMPRESSION_METADATA is rejected without the flag."""
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def test_regular_model_loads_successfully(self):
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"""Non-compressed models should load without issues."""
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model_data = _create_test_model()
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interpreter = runtime.Interpreter.from_bytes(model_data)
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self.assertIsNotNone(interpreter)
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def test_legacy_compressed_model_raises_runtime_error(self):
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"""Models with COMPRESSION_METADATA should raise RuntimeError."""
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model_data = _create_test_model()
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legacy_model = _inject_compression_metadata(model_data)
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with self.assertRaises(RuntimeError):
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runtime.Interpreter.from_bytes(legacy_model)
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def test_can_load_regular_after_legacy_failure(self):
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"""Verify regular models still load after a legacy-compressed failure."""
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model_data = _create_test_model()
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legacy_model = _inject_compression_metadata(model_data)
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with self.assertRaises(RuntimeError):
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runtime.Interpreter.from_bytes(legacy_model)
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interpreter = runtime.Interpreter.from_bytes(model_data)
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self.assertIsNotNone(interpreter)
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if __name__ == '__main__':
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
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tf.test.main()
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