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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TensorFlow Lite Python Interface: Sanity check."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import io
import numpy as np
from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper
from tensorflow.python.framework import test_util
from tensorflow.python.platform import resource_loader
from tensorflow.python.platform import test
class InterpreterTest(test_util.TensorFlowTestCase):
def testFloat(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('input', input_details[0]['name'])
self.assertEqual(np.float32, input_details[0]['dtype'])
self.assertTrue(([1, 4] == input_details[0]['shape']).all())
self.assertEqual((0.0, 0), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('output', output_details[0]['name'])
self.assertEqual(np.float32, output_details[0]['dtype'])
self.assertTrue(([1, 4] == output_details[0]['shape']).all())
self.assertEqual((0.0, 0), output_details[0]['quantization'])
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
expected_output = np.array([[4.0, 3.0, 2.0, 1.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
def testUint8(self):
model_path = resource_loader.get_path_to_datafile(
'testdata/permute_uint8.tflite')
with io.open(model_path, 'rb') as model_file:
data = model_file.read()
interpreter = interpreter_wrapper.Interpreter(model_content=data)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('input', input_details[0]['name'])
self.assertEqual(np.uint8, input_details[0]['dtype'])
self.assertTrue(([1, 4] == input_details[0]['shape']).all())
self.assertEqual((1.0, 0), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('output', output_details[0]['name'])
self.assertEqual(np.uint8, output_details[0]['dtype'])
self.assertTrue(([1, 4] == output_details[0]['shape']).all())
self.assertEqual((1.0, 0), output_details[0]['quantization'])
test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8)
expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8)
interpreter.resize_tensor_input(input_details[0]['index'],
np.array(test_input.shape, dtype=np.int32))
interpreter.allocate_tensors()
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase):
def setUp(self):
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., -2., -3., -4.]], np.float32)
def testTensorAccessor(self):
"""Check that tensor returns a reference."""
array_ref = self.interpreter.tensor(self.input0)
np.copyto(array_ref(), self.initial_data)
self.assertAllEqual(array_ref(), self.initial_data)
self.assertAllEqual(
self.interpreter.get_tensor(self.input0), self.initial_data)
def testGetTensorAccessor(self):
"""Check that get_tensor returns a copy."""
self.interpreter.set_tensor(self.input0, self.initial_data)
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.assertRaisesRegexp(
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.assertRaisesRegexp(
RuntimeError, 'There is at least 1 reference'):
_ = self.interpreter.invoke()
# 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
if __name__ == '__main__':
test.main()
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