forked from mindspore-Ecosystem/mindspore
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
# Copyright 2020 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
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_square_normal():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
output_ms = P.Square()(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
x_np = np.random.rand(2, 3, 1, 5, 4, 4).astype(np.float32)
|
|
output_ms = P.Square()(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
x_np = np.random.rand(2,).astype(np.float32)
|
|
output_ms = P.Square()(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
|
|
|
|
# Dynamic Shape Testing
|
|
class SqaureNetDynamic(nn.Cell):
|
|
def __init__(self):
|
|
super(SqaureNetDynamic, self).__init__()
|
|
self.square = P.Square()
|
|
self.gpu_convert_to_dynamic_shape = inner.GpuConvertToDynamicShape()
|
|
|
|
def construct(self, x):
|
|
x_dyn = self.gpu_convert_to_dynamic_shape(x)
|
|
return self.square(x_dyn)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_square_dynamic():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
net = SqaureNetDynamic()
|
|
x_np = np.random.rand(1, 3, 4, 4, 1).astype(np.float32)
|
|
output_ms = net(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
x_np = np.random.rand(2, 3, 4, 4, 8, 9).astype(np.float16)
|
|
output_ms = net(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|
|
x_np = np.random.rand(1).astype(np.float32)
|
|
output_ms = net(Tensor(x_np))
|
|
output_np = np.square(x_np)
|
|
assert np.allclose(output_ms.asnumpy(), output_np)
|