wehub-resource-sync
687 lines
28 KiB
Python
687 lines
28 KiB
Python
# Copyright 2018 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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"""TensorFlow Lite Python Interface: Sanity check."""
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import ctypes
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import io
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import pathlib
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import sys
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from unittest import mock
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import numpy as np
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import tensorflow as tf
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# Force loaded shared object symbols to be globally visible. This is needed so
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# that the interpreter_wrapper, in one .so file, can see the test_registerer,
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# in a different .so file. Note that this may already be set by default.
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# pylint: disable=g-import-not-at-top
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if hasattr(sys, 'setdlopenflags') and hasattr(sys, 'getdlopenflags'):
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sys.setdlopenflags(sys.getdlopenflags() | ctypes.RTLD_GLOBAL)
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from tensorflow.lite.python import interpreter as interpreter_wrapper
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from tensorflow.lite.python import lite
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from tensorflow.lite.python.metrics import metrics
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from tensorflow.lite.python.testdata import _pywrap_test_registerer as test_registerer
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from tensorflow.python.framework import test_util
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from tensorflow.python.platform import resource_loader
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from tensorflow.python.platform import test
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# pylint: enable=g-import-not-at-top
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class InterpreterCustomOpsTest(test_util.TensorFlowTestCase):
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def testRegistererByName(self):
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interpreter = interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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custom_op_registerers=['TF_TestRegisterer'])
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self.assertTrue(interpreter._safe_to_run())
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self.assertEqual(test_registerer.get_num_test_registerer_calls(), 1)
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def testRegistererByFunc(self):
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interpreter = interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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custom_op_registerers=[test_registerer.TF_TestRegisterer])
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self.assertTrue(interpreter._safe_to_run())
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self.assertEqual(test_registerer.get_num_test_registerer_calls(), 1)
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def testRegistererFailure(self):
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bogus_name = 'CompletelyBogusRegistererName'
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with self.assertRaisesRegex(
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ValueError, 'Looking up symbol \'' + bogus_name + '\' failed'):
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interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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custom_op_registerers=[bogus_name])
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# Register GenAI Ops is only supported when using LiteRT wheel.
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def testRegisterGenAIOpsFailure(self):
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genai_ops_name = 'pywrap_genai_ops.GenAIOpsRegisterer'
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with self.assertRaisesRegex(
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ValueError,
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"Loading library 'pywrap_genai_ops.so' failed with error"
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" 'pywrap_genai_ops.so: cannot open shared object file: No such file or"
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" directory'",
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):
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interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'
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),
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custom_op_registerers=[genai_ops_name],
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)
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def testNoCustomOps(self):
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interpreter = interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'))
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self.assertTrue(interpreter._safe_to_run())
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class InterpreterTest(test_util.TensorFlowTestCase):
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def assertQuantizationParamsEqual(self, scales, zero_points,
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quantized_dimension, params):
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self.assertAllEqual(scales, params['scales'])
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self.assertAllEqual(zero_points, params['zero_points'])
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self.assertEqual(quantized_dimension, params['quantized_dimension'])
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def testPathLikeModel(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=pathlib.Path(
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resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'
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)
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),
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)
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interpreter.allocate_tensors()
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def testThreads_NegativeValue(self):
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with self.assertRaisesRegex(ValueError, 'num_threads should >= 1'):
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interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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num_threads=-1)
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def testThreads_WrongType(self):
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with self.assertRaisesRegex(ValueError,
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'type of num_threads should be int'):
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interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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num_threads=4.2)
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def testNotSupportedOpResolverTypes(self):
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with self.assertRaisesRegex(
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ValueError, 'Unrecognized passed in op resolver type: test'):
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interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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experimental_op_resolver_type='test')
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def testFloatWithDifferentOpResolverTypes(self):
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op_resolver_types = [
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interpreter_wrapper.OpResolverType.BUILTIN,
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interpreter_wrapper.OpResolverType.BUILTIN_REF,
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interpreter_wrapper.OpResolverType.BUILTIN_WITHOUT_DEFAULT_DELEGATES
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]
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for op_resolver_type in op_resolver_types:
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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experimental_op_resolver_type=op_resolver_type)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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self.assertEqual(1, len(input_details))
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self.assertEqual('input', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([1, 4] == input_details[0]['shape']).all())
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self.assertEqual((0.0, 0), input_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[], [], 0, input_details[0]['quantization_parameters'])
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output_details = interpreter.get_output_details()
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self.assertEqual(1, len(output_details))
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self.assertEqual('output', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 4] == output_details[0]['shape']).all())
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self.assertEqual((0.0, 0), output_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[], [], 0, output_details[0]['quantization_parameters'])
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test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
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expected_output = np.array([[4.0, 3.0, 2.0, 1.0]], dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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def testFloatWithTwoThreads(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'),
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num_threads=2)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
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expected_output = np.array([[4.0, 3.0, 2.0, 1.0]], dtype=np.float32)
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_details = interpreter.get_output_details()
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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def testUint8(self):
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model_path = resource_loader.get_path_to_datafile(
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'testdata/permute_uint8.tflite')
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with io.open(model_path, 'rb') as model_file:
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data = model_file.read()
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interpreter = interpreter_wrapper.Interpreter(model_content=data)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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self.assertEqual(1, len(input_details))
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self.assertEqual('input', input_details[0]['name'])
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self.assertEqual(np.uint8, input_details[0]['dtype'])
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self.assertTrue(([1, 4] == input_details[0]['shape']).all())
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self.assertEqual((1.0, 0), input_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[1.0], [0], 0, input_details[0]['quantization_parameters'])
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output_details = interpreter.get_output_details()
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self.assertEqual(1, len(output_details))
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self.assertEqual('output', output_details[0]['name'])
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self.assertEqual(np.uint8, output_details[0]['dtype'])
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self.assertTrue(([1, 4] == output_details[0]['shape']).all())
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self.assertEqual((1.0, 0), output_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[1.0], [0], 0, output_details[0]['quantization_parameters'])
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test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8)
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expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8)
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interpreter.resize_tensor_input(input_details[0]['index'], test_input.shape)
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interpreter.allocate_tensors()
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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def testString(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/gather_string.tflite'))
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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self.assertEqual(2, len(input_details))
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self.assertEqual('input', input_details[0]['name'])
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self.assertEqual(np.bytes_, input_details[0]['dtype'])
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self.assertTrue(([10] == input_details[0]['shape']).all())
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self.assertEqual((0.0, 0), input_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[], [], 0, input_details[0]['quantization_parameters'])
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self.assertEqual('indices', input_details[1]['name'])
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self.assertEqual(np.int64, input_details[1]['dtype'])
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self.assertTrue(([3] == input_details[1]['shape']).all())
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self.assertEqual((0.0, 0), input_details[1]['quantization'])
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self.assertQuantizationParamsEqual(
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[], [], 0, input_details[1]['quantization_parameters'])
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output_details = interpreter.get_output_details()
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self.assertEqual(1, len(output_details))
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self.assertEqual('output', output_details[0]['name'])
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self.assertEqual(np.bytes_, output_details[0]['dtype'])
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self.assertTrue(([3] == output_details[0]['shape']).all())
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self.assertEqual((0.0, 0), output_details[0]['quantization'])
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self.assertQuantizationParamsEqual(
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[], [], 0, output_details[0]['quantization_parameters'])
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test_input = np.array([1, 2, 3], dtype=np.int64)
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interpreter.set_tensor(input_details[1]['index'], test_input)
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test_input = np.array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])
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expected_output = np.array([b'b', b'c', b'd'])
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interpreter.set_tensor(input_details[0]['index'], test_input)
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interpreter.invoke()
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output_data = interpreter.get_tensor(output_details[0]['index'])
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self.assertTrue((expected_output == output_data).all())
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def testStringZeroDim(self):
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data = b'abcd' + bytes(16)
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/gather_string_0d.tflite'))
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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interpreter.set_tensor(input_details[0]['index'], np.array(data))
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test_input_tensor = interpreter.get_tensor(input_details[0]['index'])
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self.assertEqual(len(data), len(test_input_tensor.item(0)))
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def testPerChannelParams(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile('testdata/pc_conv.bin'))
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interpreter.allocate_tensors()
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# Tensor index 1 is the weight.
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weight_details = interpreter.get_tensor_details()[1]
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qparams = weight_details['quantization_parameters']
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# Ensure that we retrieve per channel quantization params correctly.
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self.assertEqual(len(qparams['scales']), 128)
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def testDenseTensorAccess(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile('testdata/pc_conv.bin'))
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interpreter.allocate_tensors()
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weight_details = interpreter.get_tensor_details()[1]
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s_params = weight_details['sparsity_parameters']
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self.assertEqual(s_params, {})
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def testSparseTensorAccess(self):
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interpreter = interpreter_wrapper.InterpreterWithCustomOps(
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model_path=resource_loader.get_path_to_datafile(
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'../testdata/sparse_tensor.bin'),
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custom_op_registerers=['TF_TestRegisterer'])
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interpreter.allocate_tensors()
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# Tensor at index 0 is sparse.
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compressed_buffer = interpreter.get_tensor(0)
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# Ensure that the buffer is of correct size and value.
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self.assertEqual(len(compressed_buffer), 12)
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sparse_value = [1, 0, 0, 4, 2, 3, 0, 0, 5, 0, 0, 6]
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self.assertAllEqual(compressed_buffer, sparse_value)
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tensor_details = interpreter.get_tensor_details()[0]
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s_params = tensor_details['sparsity_parameters']
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# Ensure sparsity parameter returned is correct
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self.assertAllEqual(s_params['traversal_order'], [0, 1, 2, 3])
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self.assertAllEqual(s_params['block_map'], [0, 1])
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dense_dim_metadata = {'format': 0, 'dense_size': 2}
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self.assertAllEqual(s_params['dim_metadata'][0], dense_dim_metadata)
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self.assertAllEqual(s_params['dim_metadata'][2], dense_dim_metadata)
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self.assertAllEqual(s_params['dim_metadata'][3], dense_dim_metadata)
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self.assertEqual(s_params['dim_metadata'][1]['format'], 1)
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self.assertAllEqual(s_params['dim_metadata'][1]['array_segments'],
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[0, 2, 3])
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self.assertAllEqual(s_params['dim_metadata'][1]['array_indices'], [0, 1, 1])
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@mock.patch.object(metrics.TFLiteMetrics,
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'increase_counter_interpreter_creation')
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def testCreationCounter(self, increase_call):
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interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'))
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increase_call.assert_called_once()
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class InterpreterTestErrorPropagation(test_util.TensorFlowTestCase):
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# Model must have at least 7 bytes to hold model identifier
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def testTooShortModelContent(self):
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with self.assertRaisesRegex(ValueError,
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'The model is not a valid Flatbuffer buffer'):
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interpreter_wrapper.Interpreter(model_content=b'short')
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def testInvalidModelContent(self):
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with self.assertRaisesRegex(ValueError,
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'The model is not a valid Flatbuffer buffer'):
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interpreter_wrapper.Interpreter(model_content=b'wrong_identifier')
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def testInvalidModelFile(self):
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with self.assertRaisesRegex(ValueError,
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'Could not open \'totally_invalid_file_name\''):
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interpreter_wrapper.Interpreter(model_path='totally_invalid_file_name')
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def testInvokeBeforeReady(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'))
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with self.assertRaisesRegex(RuntimeError,
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'Invoke called on model that is not ready'):
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interpreter.invoke()
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def testInvalidModelFileContent(self):
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with self.assertRaisesRegex(
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ValueError, '`model_path` or `model_content` must be specified.'):
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interpreter_wrapper.Interpreter(model_path=None, model_content=None)
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def testInvalidIndex(self):
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interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'))
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interpreter.allocate_tensors()
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# Invalid tensor index passed.
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with self.assertRaisesRegex(
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ValueError, 'Invalid tensor index 4 exceeds max tensor index 3'
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):
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interpreter._get_tensor_details(4, 0)
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with self.assertRaisesRegex(ValueError, 'Invalid node index'):
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interpreter._get_op_details(4)
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def testEmptyInputTensor(self):
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class TestModel(tf.keras.models.Model):
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@tf.function(
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input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
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def TestSum(self, x):
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return tf.raw_ops.Sum(input=x, axis=[0])
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test_model = TestModel()
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converter = lite.TFLiteConverterV2.from_concrete_functions([
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test_model.TestSum.get_concrete_function(
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tf.TensorSpec([None], tf.float32))
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], test_model)
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model = converter.convert()
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interpreter = lite.Interpreter(model_content=model)
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# Make sure that passing empty tensor doesn't cause any errors.
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interpreter.get_signature_runner()(x=tf.zeros([0], tf.float32))
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class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase):
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def setUp(self):
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super(InterpreterTensorAccessorTest, self).setUp()
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self.interpreter = interpreter_wrapper.Interpreter(
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model_path=resource_loader.get_path_to_datafile(
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'testdata/permute_float.tflite'))
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self.interpreter.allocate_tensors()
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self.input0 = self.interpreter.get_input_details()[0]['index']
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self.initial_data = np.array([[-1., -2., -3., -4.]], np.float32)
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def testTensorAccessor(self):
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"""Check that tensor returns a reference."""
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array_ref = self.interpreter.tensor(self.input0)
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np.copyto(array_ref(), self.initial_data)
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self.assertAllEqual(array_ref(), self.initial_data)
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self.assertAllEqual(
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self.interpreter.get_tensor(self.input0), self.initial_data)
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def testGetTensorAccessor(self):
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"""Check that get_tensor returns a copy."""
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self.interpreter.set_tensor(self.input0, self.initial_data)
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array_initial_copy = self.interpreter.get_tensor(self.input0)
|
|
new_value = np.add(1., array_initial_copy)
|
|
self.interpreter.set_tensor(self.input0, new_value)
|
|
self.assertAllEqual(array_initial_copy, self.initial_data)
|
|
self.assertAllEqual(self.interpreter.get_tensor(self.input0), new_value)
|
|
|
|
def testBase(self):
|
|
self.assertTrue(self.interpreter._safe_to_run())
|
|
_ = self.interpreter.tensor(self.input0)
|
|
self.assertTrue(self.interpreter._safe_to_run())
|
|
in0 = self.interpreter.tensor(self.input0)()
|
|
self.assertFalse(self.interpreter._safe_to_run())
|
|
in0b = self.interpreter.tensor(self.input0)()
|
|
self.assertFalse(self.interpreter._safe_to_run())
|
|
# Now get rid of the buffers so that we can evaluate.
|
|
del in0
|
|
del in0b
|
|
self.assertTrue(self.interpreter._safe_to_run())
|
|
|
|
def testBaseProtectsFunctions(self):
|
|
in0 = self.interpreter.tensor(self.input0)()
|
|
# Make sure we get an exception if we try to run an unsafe operation
|
|
with self.assertRaisesRegex(RuntimeError, 'There is at least 1 reference'):
|
|
_ = self.interpreter.allocate_tensors()
|
|
# Make sure we get an exception if we try to run an unsafe operation
|
|
with self.assertRaisesRegex(RuntimeError, 'There is at least 1 reference'):
|
|
_ = self.interpreter.invoke() # pylint: disable=assignment-from-no-return
|
|
# Now test that we can run
|
|
del in0 # this is our only buffer reference, so now it is safe to change
|
|
in0safe = self.interpreter.tensor(self.input0)
|
|
_ = self.interpreter.allocate_tensors()
|
|
del in0safe # make sure in0Safe is held but lint doesn't complain
|
|
|
|
|
|
class InterpreterNodeAccessTest(test_util.TensorFlowTestCase):
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=resource_loader.get_path_to_datafile(
|
|
'testdata/permute_float.tflite'
|
|
)
|
|
)
|
|
self.interpreter.allocate_tensors()
|
|
self.input0 = self.interpreter.get_input_details()[0]['index']
|
|
self.initial_data = np.array([[-1.0, -2.0, -3.0, -4.0]], np.float32)
|
|
|
|
def testValidNode(self):
|
|
"""Check that tensor returns a reference."""
|
|
ops_details = self.interpreter._get_ops_details()
|
|
self.assertEqual(ops_details[0]['index'], 0)
|
|
self.assertEqual(ops_details[0]['op_name'], 'FULLY_CONNECTED')
|
|
self.assertAllEqual(ops_details[0]['inputs'], [0, 1, -1])
|
|
self.assertAllEqual(ops_details[0]['outputs'], [2])
|
|
self.assertAllEqual(
|
|
ops_details[0]['operand_types'], [np.float32, np.float32]
|
|
)
|
|
self.assertAllEqual(ops_details[0]['result_types'], [np.float32])
|
|
|
|
def testInvalidNode(self):
|
|
with self.assertRaisesRegex(ValueError, 'Invalid node index'):
|
|
self.interpreter._get_op_details(4)
|
|
|
|
|
|
class InterpreterDelegateTest(test_util.TensorFlowTestCase):
|
|
|
|
def setUp(self):
|
|
super(InterpreterDelegateTest, self).setUp()
|
|
self._delegate_file = resource_loader.get_path_to_datafile(
|
|
'testdata/test_delegate.so')
|
|
self._model_file = resource_loader.get_path_to_datafile(
|
|
'testdata/permute_float.tflite')
|
|
|
|
# Load the library to reset the counters.
|
|
library = ctypes.pydll.LoadLibrary(self._delegate_file)
|
|
library.initialize_counters()
|
|
|
|
def _TestInterpreter(self, model_path, options=None):
|
|
"""Test wrapper function that creates an interpreter with the delegate."""
|
|
delegate = interpreter_wrapper.load_delegate(self._delegate_file, options)
|
|
return interpreter_wrapper.Interpreter(
|
|
model_path=model_path, experimental_delegates=[delegate])
|
|
|
|
def testDelegate(self):
|
|
"""Tests the delegate creation and destruction."""
|
|
interpreter = self._TestInterpreter(model_path=self._model_file)
|
|
lib = interpreter._delegates[0]._library
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 1)
|
|
|
|
del interpreter
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 1)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 1)
|
|
|
|
def testMultipleInterpreters(self):
|
|
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
|
|
lib = delegate._library
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 0)
|
|
|
|
interpreter_a = interpreter_wrapper.Interpreter(
|
|
model_path=self._model_file, experimental_delegates=[delegate])
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 1)
|
|
|
|
interpreter_b = interpreter_wrapper.Interpreter(
|
|
model_path=self._model_file, experimental_delegates=[delegate])
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 2)
|
|
|
|
del delegate
|
|
del interpreter_a
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 2)
|
|
|
|
del interpreter_b
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 1)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 2)
|
|
|
|
def testDestructionOrder(self):
|
|
"""Make sure internal _interpreter object is destroyed before delegate."""
|
|
self.skipTest('TODO(b/142136355): fix flakiness and re-enable')
|
|
# Track which order destructions were doned in
|
|
destructions = []
|
|
|
|
def register_destruction(x):
|
|
destructions.append(x if isinstance(x, str) else x.decode('utf-8'))
|
|
return 0
|
|
|
|
# Make a wrapper for the callback so we can send this to ctypes
|
|
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
|
|
# Make an interpreter with the delegate
|
|
interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=resource_loader.get_path_to_datafile(
|
|
'testdata/permute_float.tflite'),
|
|
experimental_delegates=[delegate])
|
|
|
|
class InterpreterDestroyCallback:
|
|
|
|
def __del__(self):
|
|
register_destruction('interpreter')
|
|
|
|
interpreter._interpreter.stuff = InterpreterDestroyCallback()
|
|
# Destroy both delegate and interpreter
|
|
library = delegate._library
|
|
prototype = ctypes.CFUNCTYPE(ctypes.c_int, (ctypes.c_char_p))
|
|
library.set_destroy_callback(prototype(register_destruction))
|
|
del delegate
|
|
del interpreter
|
|
library.set_destroy_callback(None)
|
|
# check the interpreter was destroyed before the delegate
|
|
self.assertEqual(destructions, ['interpreter', 'test_delegate'])
|
|
|
|
def testOptions(self):
|
|
delegate_a = interpreter_wrapper.load_delegate(self._delegate_file)
|
|
lib = delegate_a._library
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 1)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 0)
|
|
self.assertEqual(lib.get_options_counter(), 0)
|
|
|
|
delegate_b = interpreter_wrapper.load_delegate(
|
|
self._delegate_file, options={
|
|
'unused': False,
|
|
'options_counter': 2
|
|
})
|
|
lib = delegate_b._library
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 2)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 0)
|
|
self.assertEqual(lib.get_options_counter(), 2)
|
|
|
|
del delegate_a
|
|
del delegate_b
|
|
|
|
self.assertEqual(lib.get_num_delegates_created(), 2)
|
|
self.assertEqual(lib.get_num_delegates_destroyed(), 2)
|
|
self.assertEqual(lib.get_num_delegates_invoked(), 0)
|
|
self.assertEqual(lib.get_options_counter(), 2)
|
|
|
|
def testFail(self):
|
|
with self.assertRaisesRegex(
|
|
# Due to exception chaining in PY3, we can't be more specific here and
|
|
# check that the phrase 'Fail argument sent' is present.
|
|
ValueError, 'Failed to load delegate from'):
|
|
interpreter_wrapper.load_delegate(
|
|
self._delegate_file, options={'fail': 'fail'})
|
|
|
|
|
|
class InterpreterMultiSignatureTest(test_util.TensorFlowTestCase):
|
|
|
|
def setUp(self):
|
|
super(InterpreterMultiSignatureTest, self).setUp()
|
|
self._single_signature_file = resource_loader.get_path_to_datafile(
|
|
'testdata/permute_float.tflite'
|
|
)
|
|
self._double_signature_file = resource_loader.get_path_to_datafile(
|
|
'testdata/two_signatures.tflite'
|
|
)
|
|
|
|
def testNumSubgraphsSingleSignature(self):
|
|
single_signature_interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=self._single_signature_file
|
|
)
|
|
self.assertEqual(single_signature_interpreter.num_subgraphs(), 1)
|
|
|
|
def testNumSubgraphsDoubleSignature(self):
|
|
double_signature_interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=self._double_signature_file
|
|
)
|
|
self.assertEqual(double_signature_interpreter.num_subgraphs(), 2)
|
|
|
|
def testGetTensorDetailsSingleSignature(self):
|
|
single_signature_interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=self._single_signature_file
|
|
)
|
|
tensor_details = single_signature_interpreter.get_tensor_details()
|
|
self.assertLen(tensor_details, 3)
|
|
self.assertEqual(tensor_details[0]['name'], 'input')
|
|
|
|
with self.assertRaisesRegex(ValueError, 'subgraph_index is out of range'):
|
|
single_signature_interpreter.get_tensor_details(subgraph_index=1)
|
|
|
|
with self.assertRaisesRegex(ValueError, 'subgraph_index is out of range'):
|
|
single_signature_interpreter.get_tensor_details(subgraph_index=-1)
|
|
|
|
def testGetTensorDetailsDoubleSignature(self):
|
|
double_signature_interpreter = interpreter_wrapper.Interpreter(
|
|
model_path=self._double_signature_file
|
|
)
|
|
subgraph0_tensor_details = double_signature_interpreter.get_tensor_details(
|
|
subgraph_index=0
|
|
)
|
|
self.assertLen(subgraph0_tensor_details, 3)
|
|
self.assertEqual(subgraph0_tensor_details[0]['name'], 'add_x:0')
|
|
|
|
subgraph1_tensor_details = double_signature_interpreter.get_tensor_details(
|
|
subgraph_index=1
|
|
)
|
|
self.assertLen(subgraph1_tensor_details, 3)
|
|
self.assertEqual(subgraph1_tensor_details[0]['name'], 'multiply_x:0')
|
|
|
|
with self.assertRaisesRegex(ValueError, 'subgraph_index is out of range'):
|
|
double_signature_interpreter.get_tensor_details(subgraph_index=3)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test.main()
|