# 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import io
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import numpy as np
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import six
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from tensorflow.lite.python import interpreter as interpreter_wrapper
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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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class InterpreterTest(test_util.TensorFlowTestCase):
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def testFloat(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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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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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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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 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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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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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'],
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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.string_, 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.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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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.string_, 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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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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class InterpreterTestErrorPropagation(test_util.TensorFlowTestCase):
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def testInvalidModelContent(self):
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with self.assertRaisesRegexp(ValueError,
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'Model provided has model identifier \''):
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interpreter_wrapper.Interpreter(model_content=six.b('garbage'))
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def testInvalidModelFile(self):
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with self.assertRaisesRegexp(
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ValueError, 'Could not open \'totally_invalid_file_name\''):
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interpreter_wrapper.Interpreter(
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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.assertRaisesRegexp(RuntimeError,
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'Invoke called on model that is not ready'):
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interpreter.invoke()
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class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase):
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def setUp(self):
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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)
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new_value = np.add(1., array_initial_copy)
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self.interpreter.set_tensor(self.input0, new_value)
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self.assertAllEqual(array_initial_copy, self.initial_data)
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self.assertAllEqual(self.interpreter.get_tensor(self.input0), new_value)
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def testBase(self):
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self.assertTrue(self.interpreter._safe_to_run())
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_ = self.interpreter.tensor(self.input0)
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self.assertTrue(self.interpreter._safe_to_run())
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in0 = self.interpreter.tensor(self.input0)()
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self.assertFalse(self.interpreter._safe_to_run())
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in0b = self.interpreter.tensor(self.input0)()
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self.assertFalse(self.interpreter._safe_to_run())
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# Now get rid of the buffers so that we can evaluate.
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del in0
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del in0b
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self.assertTrue(self.interpreter._safe_to_run())
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def testBaseProtectsFunctions(self):
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in0 = self.interpreter.tensor(self.input0)()
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# Make sure we get an exception if we try to run an unsafe operation
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with self.assertRaisesRegexp(
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RuntimeError, 'There is at least 1 reference'):
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_ = self.interpreter.allocate_tensors()
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# Make sure we get an exception if we try to run an unsafe operation
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with self.assertRaisesRegexp(
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RuntimeError, 'There is at least 1 reference'):
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_ = self.interpreter.invoke()
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# Now test that we can run
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del in0 # this is our only buffer reference, so now it is safe to change
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in0safe = self.interpreter.tensor(self.input0)
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_ = self.interpreter.allocate_tensors()
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del in0safe # make sure in0Safe is held but lint doesn't complain
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if __name__ == '__main__':
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test.main()
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