| # Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Tests for Dequantize Operations.""" |
| |
| import numpy as np |
| |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.platform import test |
| |
| |
| class DequantizeOpTest(test.TestCase): |
| |
| def __init__(self, method_name="runTest"): |
| super(DequantizeOpTest, self).__init__(method_name) |
| |
| def _testDequantizeOp(self, inputs, min_range, max_range, dtype, |
| mode="MIN_COMBINED", narrow_range=False): |
| with self.cached_session(): |
| input_op = constant_op.constant(inputs, shape=[len(inputs)], dtype=dtype) |
| dequantized = array_ops.dequantize(input_op, min_range, max_range, |
| mode=mode, narrow_range=narrow_range) |
| tf_ans = self.evaluate(dequantized) |
| |
| # TODO(vrv): Add support for DT_QINT32 quantization if needed. |
| type_dict = { |
| dtypes.quint8: np.uint8, |
| dtypes.qint8: np.int8, |
| dtypes.quint16: np.uint16, |
| dtypes.qint16: np.int16 |
| } |
| self.assertIn(dtype, type_dict.keys()) |
| v_max = np.iinfo(type_dict[dtype]).max |
| v_min = np.iinfo(type_dict[dtype]).min |
| self.assertGreaterEqual(min_range, v_min) |
| self.assertLessEqual(max_range, v_max) |
| type_range = v_max - v_min |
| |
| if mode == "MIN_COMBINED": |
| if v_min < 0: |
| half_range = (type_range + 1) / 2 |
| else: |
| half_range = 0.0 |
| np_ans = ((inputs.astype(np.float32) + half_range) * |
| (max_range - min_range) / type_range) + min_range |
| elif mode == "SCALED": |
| if narrow_range: |
| v_min += 1 |
| scale_factor = max(min_range / v_min, max_range / v_max) |
| np_ans = inputs.astype(np.float32) * scale_factor |
| |
| self.assertAllClose(tf_ans, np_ans, rtol=1e-5, atol=1e-5) |
| |
| def testBasicQuint8(self): |
| self._testDequantizeOp(np.array([0, 128, 255]), 0.0, 6.0, dtypes.quint8) |
| self._testDequantizeOp(np.array([0, 128, 255]), 0.0, 123.456, dtypes.quint8) |
| self._testDequantizeOp( |
| np.array([0, 4, 42, 108, 243]), 5.0, 200.2, dtypes.quint8) |
| |
| def testBasicQint8(self): |
| self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8) |
| self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8) |
| self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8) |
| |
| def testScaledMode(self): |
| self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8, |
| mode="SCALED") |
| self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8, |
| mode="SCALED") |
| self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8, |
| mode="SCALED") |
| |
| def testNarrowRange(self): |
| self._testDequantizeOp(np.array([-128, 0, 127]), -1.0, 2.0, dtypes.qint8, |
| mode="SCALED", narrow_range=True) |
| self._testDequantizeOp(np.array([-2, 4, -17]), -5.0, -3.0, dtypes.qint8, |
| mode="SCALED", narrow_range=True) |
| self._testDequantizeOp(np.array([0, -4, 42, -108]), 5.0, 40.0, dtypes.qint8, |
| mode="SCALED", narrow_range=True) |
| |
| def testAxis(self): |
| # Generates a tensor of the specified `shape` using values from `values` |
| # scaled by (slice_idx + 1) along `axis` dimension. |
| def scale_per_slice(shape, axis, values): |
| # Note: repeats the values if the shape is larger than values. |
| out = np.take(values, np.remainder(np.arange(np.prod(shape)), |
| len(values))).reshape(shape) |
| if axis is not None: |
| scale_shape = [1] * len(shape) |
| scale_shape[axis] = shape[axis] |
| out *= np.arange(1, shape[axis] + 1).reshape(scale_shape) |
| return out |
| |
| shape = np.array([2, 3, 4, 5]) |
| values = np.array([-128, -64, 0, 38, 102, 71, 64], dtype=np.int32) |
| dequant_values = np.array([-2, -1.0, 0, 0.59375, 1.59375, 1.109375, 1.0], |
| dtype=np.float32) |
| for axis in [None, 0, 1, 2, 3]: |
| inputs = constant_op.constant( |
| scale_per_slice(shape, None, values), dtype=dtypes.qint8) |
| expected_dequantized = scale_per_slice(shape, axis, dequant_values) |
| if axis is None: |
| min_range, max_range = -2.0, 1.6 |
| else: |
| num_slices = shape[axis] |
| min_range, max_range = [], [] |
| for slice_idx in range(num_slices): |
| min_range.append(-2.0 * (slice_idx + 1)) |
| max_range.append(1.6 * (slice_idx + 1)) |
| dequantized = self.evaluate( |
| array_ops.dequantize( |
| inputs, min_range, max_range, mode="SCALED", axis=axis)) |
| self.assertAllEqual(dequantized, expected_dequantized) |
| if axis is not None: |
| dequantized = self.evaluate( |
| array_ops.dequantize( |
| inputs, min_range, max_range, mode="SCALED", axis=(axis - 4))) |
| self.assertAllClose(dequantized, expected_dequantized) |
| |
| if __name__ == "__main__": |
| test.main() |