# 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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"""Tests for lite.py."""
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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 os
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import tempfile
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import numpy as np
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from tensorflow.lite.python import lite
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from tensorflow.lite.python import lite_constants
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from tensorflow.lite.python.interpreter import Interpreter
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from tensorflow.python import keras
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer
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from tensorflow.python.platform import gfile
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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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from tensorflow.python.saved_model import saved_model
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from tensorflow.python.training.training_util import write_graph
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class FromConstructor(test_util.TensorFlowTestCase):
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# Tests invalid constructors using a dummy value for the GraphDef.
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def testInvalidConstructor(self):
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message = ('If input_tensors and output_tensors are None, both '
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'input_arrays_with_shape and output_arrays must be defined.')
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# `output_arrays` is not defined.
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with self.assertRaises(ValueError) as error:
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lite.TFLiteConverter(
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None, None, [], input_arrays_with_shape=[('input', [3, 9])])
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self.assertEqual(message, str(error.exception))
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# `input_arrays_with_shape` is not defined.
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with self.assertRaises(ValueError) as error:
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lite.TFLiteConverter(None, [], None, output_arrays=['output'])
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self.assertEqual(message, str(error.exception))
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# Tests valid constructors using a dummy value for the GraphDef.
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def testValidConstructor(self):
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converter = lite.TFLiteConverter(
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None,
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None,
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None,
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input_arrays_with_shape=[('input', [3, 9])],
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output_arrays=['output'])
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self.assertFalse(converter._has_valid_tensors())
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self.assertEqual(converter.get_input_arrays(), ['input'])
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with self.assertRaises(ValueError) as error:
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converter._set_batch_size(1)
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self.assertEqual(
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'The batch size cannot be set for this model. Please use '
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'input_shapes parameter.', str(error.exception))
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converter = lite.TFLiteConverter(None, ['input_tensor'], ['output_tensor'])
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self.assertTrue(converter._has_valid_tensors())
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@test_util.run_v1_only('b/120545219')
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class FromSessionTest(test_util.TensorFlowTestCase):
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def testFloat(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((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('add', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testString(self):
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in_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.string)
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out_tensor = array_ops.reshape(in_tensor, shape=[2, 2])
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.string_, input_details[0]['dtype'])
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self.assertTrue(([4] == input_details[0]['shape']).all())
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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('Reshape', output_details[0]['name'])
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self.assertEqual(np.string_, output_details[0]['dtype'])
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self.assertTrue(([2, 2] == output_details[0]['shape']).all())
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# TODO(b/122659643): Test setting/getting string data via the python
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# interpreter API after support has been added.
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def testQuantization(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(
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sess, [in_tensor_1, in_tensor_2], [out_tensor])
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converter.inference_type = lite_constants.QUANTIZED_UINT8
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converter.quantized_input_stats = {
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'inputA': (0., 1.),
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'inputB': (0., 1.)
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} # mean, std_dev
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('inputA', input_details[0]['name'])
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self.assertEqual(np.uint8, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((1., 0.),
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input_details[0]['quantization']) # scale, zero_point
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self.assertEqual('inputB', input_details[1]['name'])
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self.assertEqual(np.uint8, input_details[1]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
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self.assertEqual((1., 0.),
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input_details[1]['quantization']) # scale, zero_point
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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, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertTrue(output_details[0]['quantization'][0] > 0) # scale
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def testQuantizationInvalid(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(
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sess, [in_tensor_1, in_tensor_2], [out_tensor])
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converter.inference_type = lite_constants.QUANTIZED_UINT8
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converter.quantized_input_stats = {'inputA': (0., 1.)} # mean, std_dev
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with self.assertRaises(ValueError) as error:
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converter.convert()
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self.assertEqual(
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'Quantization input stats are not available for input tensors '
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'\'inputB\'.', str(error.exception))
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def testIntermediateInputArray(self):
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"""Convert a model from an intermediate input array."""
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in_tensor_init = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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in_tensor_final = in_tensor_init + in_tensor_init
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out_tensor = in_tensor_final + in_tensor_final
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor_final],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('add', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((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('add_1', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testSizeNoneInvalid(self):
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in_tensor = array_ops.placeholder(dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test None as shape.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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with self.assertRaises(ValueError) as error:
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converter.convert()
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self.assertEqual('Provide an input shape for input array \'Placeholder\'.',
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str(error.exception))
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def testScalarValid(self):
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# Construct a graph using a scalar (empty shape) input.
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in_tensor = array_ops.placeholder(dtype=dtypes.float32, shape=[])
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test conversion with the scalar input shape.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([] == input_details[0]['shape']).all())
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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('add', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([] == input_details[0]['shape']).all())
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# Validate inference using the scalar inputs/outputs.
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test_input = np.array(4.0, dtype=np.float32)
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expected_output = np.array(8.0, 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 testSizeInvalid(self):
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in_tensor = array_ops.placeholder(
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shape=[1, None, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test invalid shape. None after 1st dimension.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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with self.assertRaises(ValueError) as error:
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converter.convert()
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self.assertEqual(
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'None is only supported in the 1st dimension. Tensor '
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'\'Placeholder\' has invalid shape \'[1, None, 16, 3]\'.',
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str(error.exception))
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def testBatchSizeValid(self):
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in_tensor = array_ops.placeholder(
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shape=[None, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((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('add', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testFreezeGraph(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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var = variable_scope.get_variable(
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'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + var
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sess = session.Session()
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sess.run(_global_variables_initializer())
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.float32, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((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('add', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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# TODO(nupurgarg): Verify value of contents in GraphViz.
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def testGraphviz(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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converter.output_format = lite_constants.GRAPHVIZ_DOT
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graphviz_output = converter.convert()
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self.assertTrue(graphviz_output)
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# TODO(nupurgarg): Verify value of contents in GraphViz.
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def testDumpGraphviz(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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graphviz_dir = self.get_temp_dir()
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converter.dump_graphviz_dir = graphviz_dir
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Ensure interpreter is able to allocate and check graphviz data.
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interpreter = Interpreter(model_content=tflite_model)
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interpreter.allocate_tensors()
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num_items_graphviz = len(os.listdir(graphviz_dir))
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self.assertTrue(num_items_graphviz)
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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graphviz_dir = self.get_temp_dir()
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converter.dump_graphviz_dir = graphviz_dir
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converter.dump_graphviz_video = True
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Ensure graphviz folder has more data after using video flag.
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num_items_graphviz_video = len(os.listdir(graphviz_dir))
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self.assertTrue(num_items_graphviz_video > num_items_graphviz)
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def testInferenceInputType(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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converter.inference_input_type = lite_constants.QUANTIZED_UINT8
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converter.quantized_input_stats = {'Placeholder': (0., 1.)} # mean, std_dev
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.uint8, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((1., 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('add', output_details[0]['name'])
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self.assertEqual(np.float32, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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def testDefaultRangesStats(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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[out_tensor])
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converter.inference_type = lite_constants.QUANTIZED_UINT8
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converter.quantized_input_stats = {'Placeholder': (0., 1.)} # mean, std_dev
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converter.default_ranges_stats = (0, 6) # min, max
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tflite_model = converter.convert()
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self.assertTrue(tflite_model)
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# Check values from converted model.
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interpreter = Interpreter(model_content=tflite_model)
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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('Placeholder', input_details[0]['name'])
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self.assertEqual(np.uint8, input_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
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self.assertEqual((1., 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('add', output_details[0]['name'])
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self.assertEqual(np.uint8, output_details[0]['dtype'])
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertTrue(output_details[0]['quantization'][0] > 0) # scale
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def testPostTrainingQuantizeDeprecatedAttribute(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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quantized_converter = lite.TFLiteConverter.from_session(
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sess, [in_tensor_1], [out_tensor])
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self.assertFalse(quantized_converter.post_training_quantize)
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quantized_converter.post_training_quantize = True
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self.assertTrue(quantized_converter.post_training_quantize)
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self.assertEqual(quantized_converter.optimizations,
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[lite.Optimize.OPTIMIZE_FOR_SIZE])
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quantized_tflite = quantized_converter.convert()
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self.assertTrue(quantized_tflite)
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def testPostTrainingQuantize(self):
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np.random.seed(0)
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# We need the tensor to have more than 1024 elements for quantize_weights
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# to kick in. Thus, the [33, 33] shape.
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in_tensor_1 = array_ops.placeholder(
|
shape=[33, 33], dtype=dtypes.float32, name='inputA')
|
in_tensor_2 = constant_op.constant(
|
np.random.uniform(low=-10., high=10., size=(33, 33)),
|
shape=[33, 33],
|
dtype=dtypes.float32,
|
name='inputB')
|
out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
|
sess = session.Session()
|
|
# Convert float model.
|
float_converter = lite.TFLiteConverter.from_session(sess, [in_tensor_1],
|
[out_tensor])
|
float_tflite = float_converter.convert()
|
self.assertTrue(float_tflite)
|
|
# Convert quantized weights model.
|
quantized_converter = lite.TFLiteConverter.from_session(
|
sess, [in_tensor_1], [out_tensor])
|
quantized_converter.optimizations = [lite.Optimize.OPTIMIZE_FOR_SIZE]
|
quantized_tflite = quantized_converter.convert()
|
self.assertTrue(quantized_tflite)
|
|
# Ensure that the quantized weights tflite model is smaller.
|
self.assertTrue(len(quantized_tflite) < len(float_tflite))
|
|
def testPostTrainingCalibrateAndQuantize(self):
|
np.random.seed(0)
|
# Create a mobilenet like model.
|
output_channel = 16
|
depth_multiplier = 1
|
inp = array_ops.placeholder(dtype=dtypes.float32, shape=(1, 5, 5, 3))
|
conv = nn_ops.conv2d(
|
inp,
|
filter=array_ops.zeros([3, 3, 3, output_channel]),
|
strides=[1, 1, 1, 1],
|
padding='SAME')
|
dconv = nn_ops.depthwise_conv2d_native(
|
conv,
|
filter=array_ops.zeros(
|
[16, 16, output_channel, output_channel * depth_multiplier]),
|
strides=[1, 1, 1, 1],
|
padding='SAME')
|
pool = nn_ops.pool(
|
dconv, window_shape=[2, 2], pooling_type='AVG', padding='SAME')
|
max_pool = nn_ops.pool(
|
pool, window_shape=[2, 2], pooling_type='MAX', padding='SAME')
|
output = nn_ops.softmax(max_pool)
|
|
def calibration_gen():
|
for _ in range(10):
|
yield [np.random.uniform(-1, 1, size=(1, 5, 5, 3)).astype(np.float32)]
|
|
sess = session.Session()
|
|
# Convert float model.
|
float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
|
float_tflite = float_converter.convert()
|
self.assertTrue(float_tflite)
|
|
# Convert quantized weights model.
|
quantized_converter = lite.TFLiteConverter.from_session(
|
sess, [inp], [output])
|
quantized_converter.optimizations = [lite.Optimize.OPTIMIZE_FOR_SIZE]
|
quantized_converter.representative_dataset = lite.RepresentativeDataset(
|
calibration_gen)
|
quantized_tflite = quantized_converter.convert()
|
self.assertTrue(quantized_tflite)
|
|
# Ensure that the quantized weights tflite model is smaller.
|
self.assertTrue(len(quantized_tflite) < len(float_tflite))
|
|
def testFloatTocoConverter(self):
|
"""Tests deprecated test TocoConverter."""
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
out_tensor = in_tensor + in_tensor
|
sess = session.Session()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Ensure the interpreter is able to load.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
def testMultipleOutputNodeNames(self):
|
"""Tests converting a graph with an op that have multiple outputs."""
|
input_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.float32)
|
out0, out1, out2, out3 = array_ops.split(input_tensor, [1, 1, 1, 1], axis=0)
|
sess = session.Session()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TFLiteConverter.from_session(sess, [input_tensor],
|
[out0, out1, out2, out3])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
interpreter.set_tensor(input_details[0]['index'],
|
np.asarray([1.0, 2.0, 3.0, 4.0], dtype=np.float32))
|
interpreter.invoke()
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(4, len(output_details))
|
self.assertEqual(1.0, interpreter.get_tensor(output_details[0]['index']))
|
self.assertEqual(2.0, interpreter.get_tensor(output_details[1]['index']))
|
self.assertEqual(3.0, interpreter.get_tensor(output_details[2]['index']))
|
self.assertEqual(4.0, interpreter.get_tensor(output_details[3]['index']))
|
|
|
@test_util.run_v1_only('b/120545219')
|
class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
|
def testFloat(self):
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
_ = in_tensor + in_tensor
|
sess = session.Session()
|
|
# Write graph to file.
|
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
write_graph(sess.graph_def, '', graph_def_file, False)
|
sess.close()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TFLiteConverter.from_frozen_graph(graph_def_file,
|
['Placeholder'], ['add'])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('Placeholder', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('add', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
def testFloatWithShapesArray(self):
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
_ = in_tensor + in_tensor
|
sess = session.Session()
|
|
# Write graph to file.
|
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
write_graph(sess.graph_def, '', graph_def_file, False)
|
sess.close()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TFLiteConverter.from_frozen_graph(
|
graph_def_file, ['Placeholder'], ['add'],
|
input_shapes={'Placeholder': [1, 16, 16, 3]})
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
|
def testFreezeGraph(self):
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
var = variable_scope.get_variable(
|
'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
_ = in_tensor + var
|
sess = session.Session()
|
|
# Write graph to file.
|
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
write_graph(sess.graph_def, '', graph_def_file, False)
|
sess.close()
|
|
# Ensure the graph with variables cannot be converted.
|
with self.assertRaises(ValueError) as error:
|
lite.TFLiteConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
|
['add'])
|
self.assertEqual('Please freeze the graph using freeze_graph.py.',
|
str(error.exception))
|
|
def testPbtxt(self):
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
_ = in_tensor + in_tensor
|
sess = session.Session()
|
|
# Write graph to file.
|
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pbtxt')
|
write_graph(sess.graph_def, '', graph_def_file, True)
|
sess.close()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TFLiteConverter.from_frozen_graph(graph_def_file,
|
['Placeholder'], ['add'])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('Placeholder', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('add', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
def testInvalidFileNotFound(self):
|
with self.assertRaises(IOError) as error:
|
lite.TFLiteConverter.from_frozen_graph('invalid_file', ['Placeholder'],
|
['add'])
|
self.assertEqual('File \'invalid_file\' does not exist.',
|
str(error.exception))
|
|
def testInvalidFileBadData(self):
|
graph_def_file = os.path.join(self.get_temp_dir(), 'invalid_file')
|
with gfile.Open(graph_def_file, 'wb') as temp_file:
|
temp_file.write('bad data')
|
temp_file.flush()
|
|
# Attempts to convert the invalid model.
|
with self.assertRaises(IOError) as error:
|
lite.TFLiteConverter.from_frozen_graph(graph_def_file, ['Placeholder'],
|
['add'])
|
self.assertEqual(
|
'Unable to parse input file \'{}\'.'.format(graph_def_file),
|
str(error.exception))
|
|
# TODO(nupurgarg): Test model loading in open source.
|
def _initObjectDetectionArgs(self):
|
# Initializes the arguments required for the object detection model.
|
# Looks for the model file which is saved in a different location internally
|
# and externally.
|
filename = resource_loader.get_path_to_datafile('testdata/tflite_graph.pb')
|
if not os.path.exists(filename):
|
filename = os.path.join(
|
resource_loader.get_root_dir_with_all_resources(),
|
'../tflite_mobilenet_ssd_quant_protobuf/tflite_graph.pb')
|
if not os.path.exists(filename):
|
raise IOError("File '{0}' does not exist.".format(filename))
|
|
self._graph_def_file = filename
|
self._input_arrays = ['normalized_input_image_tensor']
|
self._output_arrays = [
|
'TFLite_Detection_PostProcess', 'TFLite_Detection_PostProcess:1',
|
'TFLite_Detection_PostProcess:2', 'TFLite_Detection_PostProcess:3'
|
]
|
self._input_shapes = {'normalized_input_image_tensor': [1, 300, 300, 3]}
|
|
def testTFLiteGraphDef(self):
|
# Tests the object detection model that cannot be loaded in TensorFlow.
|
self._initObjectDetectionArgs()
|
|
converter = lite.TFLiteConverter.from_frozen_graph(
|
self._graph_def_file, self._input_arrays, self._output_arrays,
|
self._input_shapes)
|
converter.allow_custom_ops = True
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('normalized_input_image_tensor', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 300, 300, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(4, len(output_details))
|
self.assertEqual('TFLite_Detection_PostProcess', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 10, 4] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
self.assertEqual('TFLite_Detection_PostProcess:1',
|
output_details[1]['name'])
|
self.assertTrue(([1, 10] == output_details[1]['shape']).all())
|
self.assertEqual('TFLite_Detection_PostProcess:2',
|
output_details[2]['name'])
|
self.assertTrue(([1, 10] == output_details[2]['shape']).all())
|
self.assertEqual('TFLite_Detection_PostProcess:3',
|
output_details[3]['name'])
|
self.assertTrue(([1] == output_details[3]['shape']).all())
|
|
def testTFLiteGraphDefMissingShape(self):
|
# Tests invalid cases for the model that cannot be loaded in TensorFlow.
|
self._initObjectDetectionArgs()
|
|
# Missing `input_shapes`.
|
with self.assertRaises(ValueError) as error:
|
lite.TFLiteConverter.from_frozen_graph(
|
self._graph_def_file, self._input_arrays, self._output_arrays)
|
self.assertEqual('input_shapes must be defined for this model.',
|
str(error.exception))
|
|
def testTFLiteGraphDefInvalidShape(self):
|
# Tests invalid cases for the model that cannot be loaded in TensorFlow.
|
self._initObjectDetectionArgs()
|
|
# `input_shapes` does not contain the names in `input_arrays`.
|
with self.assertRaises(ValueError) as error:
|
lite.TFLiteConverter.from_frozen_graph(
|
self._graph_def_file,
|
self._input_arrays,
|
self._output_arrays,
|
input_shapes={'invalid-value': [1, 19]})
|
self.assertEqual(
|
'input_shapes must contain a value for each item in input_array.',
|
str(error.exception))
|
|
def testFloatTocoConverter(self):
|
in_tensor = array_ops.placeholder(
|
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
_ = in_tensor + in_tensor
|
sess = session.Session()
|
|
# Write graph to file.
|
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
write_graph(sess.graph_def, '', graph_def_file, False)
|
sess.close()
|
|
# Convert model and ensure model is not None.
|
converter = lite.TocoConverter.from_frozen_graph(graph_def_file,
|
['Placeholder'], ['add'])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Ensure the model is able to load.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
|
@test_util.run_v1_only('b/120545219')
|
class FromSavedModelTest(test_util.TensorFlowTestCase):
|
|
def _createSavedModel(self, shape):
|
"""Create a simple SavedModel."""
|
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
|
with session.Session() as sess:
|
in_tensor_1 = array_ops.placeholder(
|
shape=shape, dtype=dtypes.float32, name='inputB')
|
in_tensor_2 = array_ops.placeholder(
|
shape=shape, dtype=dtypes.float32, name='inputA')
|
out_tensor = in_tensor_1 + in_tensor_2
|
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
|
outputs = {'z': out_tensor}
|
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
|
return saved_model_dir
|
|
def testSimpleModel(self):
|
"""Test a SavedModel."""
|
saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
|
|
# Convert model and ensure model is not None.
|
converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(2, len(input_details))
|
self.assertEqual('inputA', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
self.assertEqual('inputB', input_details[1]['name'])
|
self.assertEqual(np.float32, input_details[1]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
|
self.assertEqual((0., 0.), input_details[1]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('add', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
def testNoneBatchSize(self):
|
"""Test a SavedModel, with None in input tensor's shape."""
|
saved_model_dir = self._createSavedModel(shape=[None, 16, 16, 3])
|
|
converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(2, len(input_details))
|
self.assertEqual('inputA', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
self.assertEqual('inputB', input_details[1]['name'])
|
self.assertEqual(np.float32, input_details[1]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
|
self.assertEqual((0., 0.), input_details[1]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('add', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
def testOrderInputArrays(self):
|
"""Test a SavedModel ordering of input arrays."""
|
saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
|
|
converter = lite.TFLiteConverter.from_saved_model(
|
saved_model_dir, input_arrays=['inputB', 'inputA'])
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(2, len(input_details))
|
self.assertEqual('inputA', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
self.assertEqual('inputB', input_details[1]['name'])
|
self.assertEqual(np.float32, input_details[1]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
|
self.assertEqual((0., 0.), input_details[1]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('add', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
def testSubsetInputArrays(self):
|
"""Test a SavedModel with a subset of the input array names of the model."""
|
saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
|
|
# Check case where input shape is given.
|
converter = lite.TFLiteConverter.from_saved_model(
|
saved_model_dir,
|
input_arrays=['inputA'],
|
input_shapes={'inputA': [1, 16, 16, 3]})
|
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check case where input shape is None.
|
converter = lite.TFLiteConverter.from_saved_model(
|
saved_model_dir, input_arrays=['inputA'], input_shapes={'inputA': None})
|
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
def testSimpleModelTocoConverter(self):
|
"""Test a SavedModel with deprecated TocoConverter."""
|
saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])
|
|
# Convert model and ensure model is not None.
|
converter = lite.TocoConverter.from_saved_model(saved_model_dir)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Ensure the model is able to load.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
|
@test_util.run_v1_only('b/120545219')
|
class FromKerasFile(test_util.TensorFlowTestCase):
|
|
def setUp(self):
|
keras.backend.clear_session()
|
|
def _getSequentialModel(self):
|
with session.Session().as_default():
|
model = keras.models.Sequential()
|
model.add(keras.layers.Dense(2, input_shape=(3,)))
|
model.add(keras.layers.RepeatVector(3))
|
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
|
model.compile(
|
loss=keras.losses.MSE,
|
optimizer=keras.optimizers.RMSprop(),
|
metrics=[keras.metrics.categorical_accuracy],
|
sample_weight_mode='temporal')
|
x = np.random.random((1, 3))
|
y = np.random.random((1, 3, 3))
|
model.train_on_batch(x, y)
|
model.predict(x)
|
|
try:
|
fd, keras_file = tempfile.mkstemp('.h5')
|
keras.models.save_model(model, keras_file)
|
finally:
|
os.close(fd)
|
return keras_file
|
|
def testSequentialModel(self):
|
"""Test a Sequential tf.keras model with default inputs."""
|
keras_file = self._getSequentialModel()
|
|
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check tensor details of converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('dense_input', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
# Check inference of converted model.
|
input_data = np.array([[1, 2, 3]], dtype=np.float32)
|
interpreter.set_tensor(input_details[0]['index'], input_data)
|
interpreter.invoke()
|
tflite_result = interpreter.get_tensor(output_details[0]['index'])
|
|
keras_model = keras.models.load_model(keras_file)
|
keras_result = keras_model.predict(input_data)
|
|
np.testing.assert_almost_equal(tflite_result, keras_result, 5)
|
os.remove(keras_file)
|
|
def testSequentialModelInputArray(self):
|
"""Test a Sequential tf.keras model testing input arrays argument."""
|
keras_file = self._getSequentialModel()
|
|
# Invalid input array raises error.
|
with self.assertRaises(ValueError) as error:
|
lite.TFLiteConverter.from_keras_model_file(
|
keras_file, input_arrays=['invalid-input'])
|
self.assertEqual("Invalid tensors 'invalid-input' were found.",
|
str(error.exception))
|
|
# Valid input array.
|
converter = lite.TFLiteConverter.from_keras_model_file(
|
keras_file, input_arrays=['dense_input'])
|
tflite_model = converter.convert()
|
os.remove(keras_file)
|
self.assertTrue(tflite_model)
|
|
def testSequentialModelInputShape(self):
|
"""Test a Sequential tf.keras model testing input shapes argument."""
|
keras_file = self._getSequentialModel()
|
|
# Passing in shape of invalid input array raises error.
|
with self.assertRaises(ValueError) as error:
|
converter = lite.TFLiteConverter.from_keras_model_file(
|
keras_file, input_shapes={'invalid-input': [2, 3]})
|
self.assertEqual(
|
"Invalid tensor 'invalid-input' found in tensor shapes map.",
|
str(error.exception))
|
|
# Passing in shape of valid input array.
|
converter = lite.TFLiteConverter.from_keras_model_file(
|
keras_file, input_shapes={'dense_input': [2, 3]})
|
tflite_model = converter.convert()
|
os.remove(keras_file)
|
self.assertTrue(tflite_model)
|
|
# Check input shape from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('dense_input', input_details[0]['name'])
|
self.assertTrue(([2, 3] == input_details[0]['shape']).all())
|
|
def testSequentialModelOutputArray(self):
|
"""Test a Sequential tf.keras model testing output arrays argument."""
|
keras_file = self._getSequentialModel()
|
|
# Invalid output array raises error.
|
with self.assertRaises(ValueError) as error:
|
lite.TFLiteConverter.from_keras_model_file(
|
keras_file, output_arrays=['invalid-output'])
|
self.assertEqual("Invalid tensors 'invalid-output' were found.",
|
str(error.exception))
|
|
# Valid output array.
|
converter = lite.TFLiteConverter.from_keras_model_file(
|
keras_file, output_arrays=['time_distributed/Reshape_1'])
|
tflite_model = converter.convert()
|
os.remove(keras_file)
|
self.assertTrue(tflite_model)
|
|
def testFunctionalModel(self):
|
"""Test a Functional tf.keras model with default inputs."""
|
with session.Session().as_default():
|
inputs = keras.layers.Input(shape=(3,), name='input')
|
x = keras.layers.Dense(2)(inputs)
|
output = keras.layers.Dense(3)(x)
|
|
model = keras.models.Model(inputs, output)
|
model.compile(
|
loss=keras.losses.MSE,
|
optimizer=keras.optimizers.RMSprop(),
|
metrics=[keras.metrics.categorical_accuracy])
|
x = np.random.random((1, 3))
|
y = np.random.random((1, 3))
|
model.train_on_batch(x, y)
|
|
model.predict(x)
|
fd, keras_file = tempfile.mkstemp('.h5')
|
try:
|
keras.models.save_model(model, keras_file)
|
finally:
|
os.close(fd)
|
|
# Convert to TFLite model.
|
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check tensor details of converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
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, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('dense_1/BiasAdd', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
# Check inference of converted model.
|
input_data = np.array([[1, 2, 3]], dtype=np.float32)
|
interpreter.set_tensor(input_details[0]['index'], input_data)
|
interpreter.invoke()
|
tflite_result = interpreter.get_tensor(output_details[0]['index'])
|
|
keras_model = keras.models.load_model(keras_file)
|
keras_result = keras_model.predict(input_data)
|
|
np.testing.assert_almost_equal(tflite_result, keras_result, 5)
|
os.remove(keras_file)
|
|
def testFunctionalModelMultipleInputs(self):
|
"""Test a Functional tf.keras model with multiple inputs and outputs."""
|
with session.Session().as_default():
|
a = keras.layers.Input(shape=(3,), name='input_a')
|
b = keras.layers.Input(shape=(3,), name='input_b')
|
dense = keras.layers.Dense(4, name='dense')
|
c = dense(a)
|
d = dense(b)
|
e = keras.layers.Dropout(0.5, name='dropout')(c)
|
|
model = keras.models.Model([a, b], [d, e])
|
model.compile(
|
loss=keras.losses.MSE,
|
optimizer=keras.optimizers.RMSprop(),
|
metrics=[keras.metrics.mae],
|
loss_weights=[1., 0.5])
|
|
input_a_np = np.random.random((10, 3))
|
input_b_np = np.random.random((10, 3))
|
output_d_np = np.random.random((10, 4))
|
output_e_np = np.random.random((10, 4))
|
model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
|
|
model.predict([input_a_np, input_b_np], batch_size=5)
|
fd, keras_file = tempfile.mkstemp('.h5')
|
try:
|
keras.models.save_model(model, keras_file)
|
finally:
|
os.close(fd)
|
|
# Convert to TFLite model.
|
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
os.remove(keras_file)
|
|
# Check values from converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(2, len(input_details))
|
self.assertEqual('input_a', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
self.assertEqual('input_b', input_details[1]['name'])
|
self.assertEqual(np.float32, input_details[1]['dtype'])
|
self.assertTrue(([1, 3] == input_details[1]['shape']).all())
|
self.assertEqual((0., 0.), input_details[1]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(2, len(output_details))
|
self.assertEqual('dense_1/BiasAdd', 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.), output_details[0]['quantization'])
|
|
self.assertEqual('dropout/Identity', output_details[1]['name'])
|
self.assertEqual(np.float32, output_details[1]['dtype'])
|
self.assertTrue(([1, 4] == output_details[1]['shape']).all())
|
self.assertEqual((0., 0.), output_details[1]['quantization'])
|
|
def testFunctionalSequentialModel(self):
|
"""Test a Functional tf.keras model containing a Sequential model."""
|
with session.Session().as_default():
|
model = keras.models.Sequential()
|
model.add(keras.layers.Dense(2, input_shape=(3,)))
|
model.add(keras.layers.RepeatVector(3))
|
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
|
model = keras.models.Model(model.input, model.output)
|
|
model.compile(
|
loss=keras.losses.MSE,
|
optimizer=keras.optimizers.RMSprop(),
|
metrics=[keras.metrics.categorical_accuracy],
|
sample_weight_mode='temporal')
|
x = np.random.random((1, 3))
|
y = np.random.random((1, 3, 3))
|
model.train_on_batch(x, y)
|
model.predict(x)
|
|
model.predict(x)
|
fd, keras_file = tempfile.mkstemp('.h5')
|
try:
|
keras.models.save_model(model, keras_file)
|
finally:
|
os.close(fd)
|
|
# Convert to TFLite model.
|
converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Check tensor details of converted model.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
input_details = interpreter.get_input_details()
|
self.assertEqual(1, len(input_details))
|
self.assertEqual('dense_input', input_details[0]['name'])
|
self.assertEqual(np.float32, input_details[0]['dtype'])
|
self.assertTrue(([1, 3] == input_details[0]['shape']).all())
|
self.assertEqual((0., 0.), input_details[0]['quantization'])
|
|
output_details = interpreter.get_output_details()
|
self.assertEqual(1, len(output_details))
|
self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
|
self.assertEqual(np.float32, output_details[0]['dtype'])
|
self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
|
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
|
# Check inference of converted model.
|
input_data = np.array([[1, 2, 3]], dtype=np.float32)
|
interpreter.set_tensor(input_details[0]['index'], input_data)
|
interpreter.invoke()
|
tflite_result = interpreter.get_tensor(output_details[0]['index'])
|
|
keras_model = keras.models.load_model(keras_file)
|
keras_result = keras_model.predict(input_data)
|
|
np.testing.assert_almost_equal(tflite_result, keras_result, 5)
|
os.remove(keras_file)
|
|
def testSequentialModelTocoConverter(self):
|
"""Test a Sequential tf.keras model with deprecated TocoConverter."""
|
keras_file = self._getSequentialModel()
|
|
converter = lite.TocoConverter.from_keras_model_file(keras_file)
|
tflite_model = converter.convert()
|
self.assertTrue(tflite_model)
|
|
# Ensure the model is able to load.
|
interpreter = Interpreter(model_content=tflite_model)
|
interpreter.allocate_tensors()
|
|
|
if __name__ == '__main__':
|
test.main()
|
