109 lines
4.0 KiB
Python
109 lines
4.0 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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"""Functional tests for quantized 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 QuantizedOpsTest(test.TestCase):
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def __init__(self, method_name="runTest"):
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super(QuantizedOpsTest, self).__init__(method_name)
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def testQuantizeOp(self):
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expected_output = [1, 1, 2, 127, 255, 255]
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with self.session(use_gpu=False) as sess:
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x = constant_op.constant(
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[1.0, 1.25, 1.75, 127.0, 255.0, 500.0],
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shape=[6],
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dtype=dtypes.float32)
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x_min = 0.0
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x_max = 255.0
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op = array_ops.quantize(x, x_min, x_max, dtypes.quint8, mode="MIN_FIRST")
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value = self.evaluate(op)
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self.assertArrayNear(expected_output, value.output, 0.1)
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def testDequantizeOp(self):
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expected_output = [1.0, 2.0, 4.0, 8.0, 16.0, 255.0]
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inp = np.array([1, 2, 4, 8, 16, 255]).astype(np.uint8)
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with self.session(use_gpu=False) as sess:
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x = constant_op.constant(inp, shape=[6], dtype=dtypes.quint8)
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x_min = 0.0
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x_max = 255.0
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op = array_ops.dequantize(x, x_min, x_max, mode="MIN_FIRST")
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value = self.evaluate(op)
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self.assertArrayNear(expected_output, value, 0.1)
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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([-1, -0.5, 0, 0.3, 0.8, 0.555, 0.5], dtype=np.float32)
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quant_values = np.array([-128, -64, 0, 38, 102, 71, 64], dtype=np.int32)
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for axis in [None, 0, 1, 2, 3]:
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inputs = constant_op.constant(scale_per_slice(shape, axis, values))
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expected_quantized = scale_per_slice(shape, None, quant_values)
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if axis is None:
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min_range, max_range = -1.0, 0.8
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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(-1.0 * (slice_idx + 1))
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max_range.append(0.8 * (slice_idx + 1))
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quantized = self.evaluate(
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array_ops.quantize(
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inputs,
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min_range,
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max_range,
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T=dtypes.qint8,
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mode="SCALED",
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round_mode="HALF_TO_EVEN",
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axis=axis)).output
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self.assertAllEqual(quantized, expected_quantized)
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if axis is not None:
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quantized = self.evaluate(
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array_ops.quantize(
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inputs,
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min_range,
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max_range,
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T=dtypes.qint8,
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mode="SCALED",
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round_mode="HALF_TO_EVEN",
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axis=(axis - 4))).output
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self.assertAllClose(quantized, expected_quantized)
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if __name__ == "__main__":
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test.main()
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