# Copyright (c) 2018 PaddlePaddle 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. from __future__ import print_function import unittest import numpy as np from op_test import OpTest class TestFakeQuantizeOp(OpTest): def setUp(self): self.op_type = "fake_quantize_abs_max" self.attrs = {'bit_length': 8} self.inputs = {'X': np.random.random((124, 240)).astype("float32"), } scale = np.max(np.abs(self.inputs['X'])).astype("float32") self.outputs = { 'Out': np.round(self.inputs['X'] / scale * ( (1 << (self.attrs['bit_length'] - 1)) - 1)), 'OutScale': np.array(scale).astype("float32"), } def test_check_output(self): self.check_output() class TestFakeQuantizeOp(OpTest): def setUp(self): self.op_type = "fake_quantize_range_abs_max" self.attrs = { 'bit_length': int(5), 'window_size': int(1), 'is_test': False } self.inputs = { 'X': np.random.random((8, 16, 7, 7)).astype("float32"), 'Iter': np.zeros(1).astype("int64"), 'InScale': np.zeros(1).astype("float32") } scale = np.max(np.abs(self.inputs['X'])).astype("float32") out_scales = np.zeros(self.attrs['window_size']).astype("float32") out_scales[0] = scale self.outputs = { 'Out': np.round(self.inputs['X'] / scale * ( (1 << (self.attrs['bit_length'] - 1)) - 1)), 'OutScale': scale, 'OutScales': out_scales, } def test_check_output(self): self.check_output() if __name__ == "__main__": unittest.main()