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When a model contains COMPRESSION_METADATA but the interpreter was built without compression support, throw a RuntimeError with a helpful message directing users to build with --//:with_compression=true. The implementation uses inline functions in compression_utils.h that are optimized away when compression is disabled, ensuring all code paths remain compile-checked, and readable without preprocessor clutter. Includes test_compression_unsupported.py to verify the error detection, which only runs when compression is disabled. BUG=#3125
95 lines
3.7 KiB
Python
95 lines
3.7 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 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 import compression
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class CompressionDetectionTest(tf.test.TestCase):
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"""Test compression metadata detection when compression is disabled."""
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def _create_test_model(self):
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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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# Convert to quantized TFLite
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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 test_regular_model_loads_successfully(self):
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"""Non-compressed models should load without issues."""
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model_data = self._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_compressed_model_raises_runtime_error(self):
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"""Compressed models should raise RuntimeError when compression is disabled."""
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# Create and compress a model
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model_data = self._create_test_model()
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spec = (compression.SpecBuilder().add_tensor(
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subgraph=0, tensor=1).with_lut(index_bitwidth=4).build())
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compressed_model = compression.compress(model_data, spec)
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if isinstance(compressed_model, bytearray):
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compressed_model = bytes(compressed_model)
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# Should raise RuntimeError
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with self.assertRaises(RuntimeError):
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runtime.Interpreter.from_bytes(compressed_model)
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def test_can_load_regular_after_compressed_failure(self):
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"""Verify we can still load regular models after compressed model fails."""
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model_data = self._create_test_model()
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# First try compressed model (should fail)
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spec = (compression.SpecBuilder().add_tensor(
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subgraph=0, tensor=1).with_lut(index_bitwidth=4).build())
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compressed_model = compression.compress(model_data, spec)
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with self.assertRaises(RuntimeError):
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runtime.Interpreter.from_bytes(bytes(compressed_model))
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# Then load regular model (should succeed)
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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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# Set TF environment variables to suppress warnings
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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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