You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
mindspore/tests/st/ops/gpu/test_equal_op.py

123 lines
4.0 KiB

# Copyright 2019 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
import numpy as np
import pytest
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.nn import Cell
from mindspore.ops import operations as P
class NetEqual(Cell):
def __init__(self):
super(NetEqual, self).__init__()
self.Equal = P.Equal()
def construct(self, x, y):
return self.Equal(x, y)
class NetNotEqual(Cell):
def __init__(self):
super(NetNotEqual, self).__init__()
self.NotEqual = P.NotEqual()
def construct(self, x, y):
return self.NotEqual(x, y)
class NetGreaterEqual(Cell):
def __init__(self):
super(NetGreaterEqual, self).__init__()
self.GreaterEqual = P.GreaterEqual()
def construct(self, x, y):
return self.GreaterEqual(x, y)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_equal():
x0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
x0 = Tensor(x0_np)
y0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
y0 = Tensor(y0_np)
expect0 = np.equal(x0_np, y0_np)
x1_np = np.array([0, 1, 3]).astype(np.float32)
x1 = Tensor(x1_np)
y1_np = np.array([0, 1, -3]).astype(np.float32)
y1 = Tensor(y1_np)
expect1 = np.equal(x1_np, y1_np)
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
equal = NetEqual()
output0 = equal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape
output1 = equal(x1, y1)
assert np.all(output1.asnumpy() == expect1)
assert output1.shape == expect1.shape
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
equal = NetEqual()
output0 = equal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape
output1 = equal(x1, y1)
assert np.all(output1.asnumpy() == expect1)
assert output1.shape == expect1.shape
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_notequal():
x0 = Tensor(np.array([[1.2, 1], [1, 0]]).astype(np.float32))
y0 = Tensor(np.array([[1, 2]]).astype(np.float32))
expect0 = np.array([[True, True], [False, True]])
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
notequal = NetNotEqual()
output0 = notequal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
notequal = NetNotEqual()
output0 = notequal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_greaterqual():
x0 = Tensor(np.array([[1.2, 1], [1, 0]]).astype(np.float32))
y0 = Tensor(np.array([[1, 2]]).astype(np.float32))
expect0 = np.array([[True, False], [True, False]])
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
gequal = NetGreaterEqual()
output0 = gequal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
gequal = NetGreaterEqual()
output0 = gequal(x0, y0)
assert np.all(output0.asnumpy() == expect0)
assert output0.shape == expect0.shape