# Copyright (c) 2019 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 import paddle import paddle.fluid as fluid import paddle.fluid.core as core from op_test import OpTest import random class TestElementwiseModOp(OpTest): def init_kernel_type(self): self.use_mkldnn = False def setUp(self): self.op_type = "elementwise_mod" self.python_api = paddle.remainder self.axis = -1 self.init_dtype() self.init_input_output() self.init_kernel_type() self.init_axis() self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(self.x), 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) } self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn} self.outputs = {'Out': self.out} def test_check_output(self): if self.attrs['axis'] == -1: self.check_output(check_eager=True) else: self.check_output(check_eager=False) def init_input_output(self): self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype) self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype) self.out = np.mod(self.x, self.y) def init_dtype(self): self.dtype = np.int32 def init_axis(self): pass class TestElementwiseModOp_scalar(TestElementwiseModOp): def init_input_output(self): scale_x = random.randint(0, 100000000) scale_y = random.randint(1, 100000000) self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype) self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype) self.out = np.mod(self.x, self.y) class TestElementwiseModOpFloat(TestElementwiseModOp): def init_dtype(self): self.dtype = np.float32 def init_input_output(self): self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype) self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype) self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y) def test_check_output(self): if self.attrs['axis'] == -1: self.check_output(check_eager=True) else: self.check_output(check_eager=False) class TestElementwiseModOpFp16(TestElementwiseModOp): def init_dtype(self): self.dtype = np.float16 def init_input_output(self): self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype) self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype) self.out = np.mod(self.x, self.y) def test_check_output(self): if self.attrs['axis'] == -1: self.check_output(check_eager=True) else: self.check_output(check_eager=False) class TestElementwiseModOpDouble(TestElementwiseModOpFloat): def init_dtype(self): self.dtype = np.float64 class TestRemainderOp(unittest.TestCase): def _executed_api(self, x, y, name=None): return paddle.remainder(x, y, name) def test_name(self): with fluid.program_guard(fluid.Program()): x = fluid.data(name="x", shape=[2, 3], dtype="int64") y = fluid.data(name='y', shape=[2, 3], dtype='int64') y_1 = self._executed_api(x, y, name='div_res') self.assertEqual(('div_res' in y_1.name), True) def test_dygraph(self): with fluid.dygraph.guard(): np_x = np.array([2, 3, 8, 7]).astype('int64') np_y = np.array([1, 5, 3, 3]).astype('int64') x = paddle.to_tensor(np_x) y = paddle.to_tensor(np_y) z = self._executed_api(x, y) np_z = z.numpy() z_expected = np.array([0, 3, 2, 1]) self.assertEqual((np_z == z_expected).all(), True) np_x = np.array([-3.3, 11.5, -2, 3.5]) np_y = np.array([-1.2, 2., 3.3, -2.3]) x = paddle.to_tensor(np_x) y = paddle.to_tensor(np_y) z = x % y z_expected = np.array([-0.9, 1.5, 1.3, -1.1]) np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05) np_x = np.array([-3, 11, -2, 3]) np_y = np.array([-1, 2, 3, -2]) x = paddle.to_tensor(np_x, dtype="int64") y = paddle.to_tensor(np_y, dtype="int64") z = x % y z_expected = np.array([0, 1, 1, -1]) np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05) class TestRemainderInplaceOp(TestRemainderOp): def _executed_api(self, x, y, name=None): return x.remainder_(y, name) class TestRemainderInplaceBroadcastSuccess(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(2, 3, 4).astype('float') self.y_numpy = np.random.rand(3, 4).astype('float') def test_broadcast_success(self): paddle.disable_static() self.init_data() x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) inplace_result = x.remainder_(y) numpy_result = self.x_numpy % self.y_numpy self.assertEqual((inplace_result.numpy() == numpy_result).all(), True) paddle.enable_static() class TestRemainderInplaceBroadcastSuccess2(TestRemainderInplaceBroadcastSuccess ): def init_data(self): self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float') self.y_numpy = np.random.rand(3, 1).astype('float') class TestRemainderInplaceBroadcastSuccess3(TestRemainderInplaceBroadcastSuccess ): def init_data(self): self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float') self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float') if __name__ == '__main__': unittest.main()