139 lines
5.7 KiB
Python
139 lines
5.7 KiB
Python
# Copyright 2015 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 Dequantize Operations."""
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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 numpy as np
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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.ops import array_ops
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from tensorflow.python.platform import test
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class DequantizeOpTest(test.TestCase):
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def __init__(self, method_name="runTest"):
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super(DequantizeOpTest, self).__init__(method_name)
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def _testDequantizeOp(self, inputs, min_range, max_range, dtype,
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mode="MIN_COMBINED", narrow_range=False):
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with self.cached_session():
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input_op = constant_op.constant(inputs, shape=[len(inputs)], dtype=dtype)
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dequantized = array_ops.dequantize(input_op, min_range, max_range,
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mode=mode, narrow_range=narrow_range)
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tf_ans = self.evaluate(dequantized)
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# TODO(vrv): Add support for DT_QINT32 quantization if needed.
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type_dict = {
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dtypes.quint8: np.uint8,
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dtypes.qint8: np.int8,
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dtypes.quint16: np.uint16,
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dtypes.qint16: np.int16
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}
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self.assertIn(dtype, type_dict.keys())
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v_max = np.iinfo(type_dict[dtype]).max
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v_min = np.iinfo(type_dict[dtype]).min
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self.assertGreaterEqual(min_range, v_min)
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self.assertLessEqual(max_range, v_max)
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type_range = v_max - v_min
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if mode == "MIN_COMBINED":
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if v_min < 0:
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half_range = (type_range + 1) / 2
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else:
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half_range = 0.0
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np_ans = ((inputs.astype(np.float32) + half_range) *
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(max_range - min_range) / type_range) + min_range
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elif mode == "SCALED":
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if narrow_range:
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v_min += 1
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scale_factor = max(min_range / v_min, max_range / v_max)
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np_ans = inputs.astype(np.float32) * scale_factor
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self.assertAllClose(tf_ans, np_ans, rtol=1e-5, atol=1e-5)
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def testBasicQuint8(self):
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self._testDequantizeOp(np.array([0, 128, 255]), 0.0, 6.0, dtypes.quint8)
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self._testDequantizeOp(np.array([0, 128, 255]), 0.0, 123.456, dtypes.quint8)
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self._testDequantizeOp(
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np.array([0, 4, 42, 108, 243]), 5.0, 200.2, dtypes.quint8)
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def testBasicQint8(self):
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self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8)
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self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8)
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self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8)
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def testScaledMode(self):
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self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8,
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mode="SCALED")
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self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8,
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mode="SCALED")
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self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8,
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mode="SCALED")
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def testNarrowRange(self):
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self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8,
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mode="SCALED", narrow_range=True)
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self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8,
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mode="SCALED", narrow_range=True)
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self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8,
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mode="SCALED", narrow_range=True)
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def testAxis(self):
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# Generates a tensor of the specified `shape` using values from `values`
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# scaled by (slice_idx + 1) along `axis` dimension.
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def scale_per_slice(shape, axis, values):
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# Note: repeats the values if the shape is larger than values.
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out = np.take(values, np.remainder(np.arange(np.prod(shape)),
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len(values))).reshape(shape)
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if axis is not None:
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scale_shape = [1] * len(shape)
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scale_shape[axis] = shape[axis]
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out *= np.arange(1, shape[axis] + 1).reshape(scale_shape)
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return out
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shape = np.array([2, 3, 4, 5])
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values = np.array([-128, -64, 0, 38, 102, 71, 64], dtype=np.int32)
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dequant_values = np.array([-2, -1.0, 0, 0.59375, 1.59375, 1.109375, 1.0],
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dtype=np.float32)
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for axis in [None, 0, 1, 2, 3]:
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inputs = constant_op.constant(
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scale_per_slice(shape, None, values), dtype=dtypes.qint8)
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expected_dequantized = scale_per_slice(shape, axis, dequant_values)
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if axis is None:
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min_range, max_range = -2.0, 1.6
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else:
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num_slices = shape[axis]
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min_range, max_range = [], []
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for slice_idx in range(num_slices):
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min_range.append(-2.0 * (slice_idx + 1))
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max_range.append(1.6 * (slice_idx + 1))
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dequantized = self.evaluate(
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array_ops.dequantize(
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inputs, min_range, max_range, mode="SCALED", axis=axis))
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self.assertAllEqual(dequantized, expected_dequantized)
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if axis is not None:
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dequantized = self.evaluate(
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array_ops.dequantize(
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inputs, min_range, max_range, mode="SCALED", axis=(axis - 4)))
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self.assertAllClose(dequantized, expected_dequantized)
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if __name__ == "__main__":
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test.main()
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