186 lines
7.2 KiB
Python
186 lines
7.2 KiB
Python
# Lint as: python2, python3
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# Copyright 2019 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 Calibrator."""
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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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from absl.testing import parameterized
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import numpy as np
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from six.moves import range
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from tensorflow.lite.python import lite_constants as constants
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from tensorflow.lite.python.optimize import calibrator as _calibrator
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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 CalibratorTest(test_util.TensorFlowTestCase, parameterized.TestCase):
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@parameterized.named_parameters(
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# Activation type Int8
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('UseActivationTypeInt8', constants.INT8),
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# Activation type Int16
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('UseActivationTypeInt16', constants.INT16))
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def test_calibration_with_quantization(self, activations_type):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator for the model.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
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quantized_model = quantizer.calibrate_and_quantize(input_gen,
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constants.FLOAT,
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constants.FLOAT, False,
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activations_type)
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self.assertIsNotNone(quantized_model)
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@parameterized.named_parameters(
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# Activation type Int8
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('UseActivationTypeInt8', constants.INT8),
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# Activation type Int16
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('UseActivationTypeInt16', constants.INT16))
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def test_calibration_with_quantization_allow_float(self, activations_type):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator for the model.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
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quantized_model = quantizer.calibrate_and_quantize(input_gen,
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constants.FLOAT,
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constants.FLOAT, True,
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activations_type)
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self.assertIsNotNone(quantized_model)
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def test_calibration_with_quantization_single_op(self):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator for the model.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
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quantized_model = quantizer.calibrate_and_quantize_single(
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input_gen, constants.FLOAT, constants.FLOAT, True, 'conv2d_8/BiasAdd')
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self.assertIsNotNone(quantized_model)
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@parameterized.named_parameters(
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# Activation type Int8
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('UseActivationTypeInt8 - EnableMlirQuantizer', constants.INT8),
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# Activation type Int16
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('UseActivationTypeInt16 - DisableEnableMlirQuantizer', constants.INT16))
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def test_calibration_with_quantization_multiple_inputs(
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self, activations_type):
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# Load multi add model from test data.
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# This model has 4 inputs of size (1, 8, 8, 3).
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model_path = resource_loader.get_path_to_datafile(
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'../../testdata/multi_add.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator for the model.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 8, 8, 3), dtype=np.float32) for _ in range(4)]
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quantized_model = quantizer.calibrate_and_quantize(input_gen,
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constants.FLOAT,
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constants.FLOAT, False,
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activations_type)
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self.assertIsNotNone(quantized_model)
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def test_invalid_model_buffer(self):
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float_model = b'\0' * 100
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with self.assertRaisesRegex(ValueError, 'Failed to parse the model'):
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_calibrator.Calibrator(float_model)
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# TODO(fengliuai): enable mlir quantizer
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def test_empty_calibrator_gen(self):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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def empty_input_gen():
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for i in ():
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yield i
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with self.assertRaises(RuntimeError):
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quantizer.calibrate_and_quantize(empty_input_gen, constants.FLOAT,
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constants.FLOAT, False)
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def test_invalid_shape_calibrator_gen(self):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator with incorrect shape.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 2, 2, 3), dtype=np.float32)]
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with self.assertRaisesRegex(ValueError, 'Size mismatch'):
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quantizer.calibrate_and_quantize(input_gen, constants.FLOAT,
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constants.FLOAT, False, constants.INT8,
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False)
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def test_invalid_type_calibrator_gen(self):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator with incorrect type.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 5, 5, 3), dtype=np.int32)]
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with self.assertRaises(ValueError):
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quantizer.calibrate_and_quantize(input_gen, constants.FLOAT,
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constants.FLOAT, False, constants.INT8)
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def test_calibration(self):
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model_path = resource_loader.get_path_to_datafile(
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'test_data/mobilenet_like_model.bin')
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float_model = open(model_path, 'rb').read()
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quantizer = _calibrator.Calibrator(float_model)
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# Input generator for the model.
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def input_gen():
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for _ in range(10):
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yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
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quantized_model = quantizer.calibrate(input_gen)
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self.assertIsNotNone(quantized_model)
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if __name__ == '__main__':
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test.main()
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