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test_elementwise_mod_op.py
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test_elementwise_mod_op.py
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# 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 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()