mindspore/tests/st/pynative/test_parser_tensor_assign.py

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# 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.
# ============================================================================
2020-09-16 16:02:18 +08:00
import pytest
import numpy as np
import mindspore as ms
from mindspore.nn import ReLU
from mindspore.nn import Cell
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_parser_tensor_assign_slice():
class Net(Cell):
def __init__(self, U):
super(Net, self).__init__()
self.relu = ReLU()
self.U = U
def construct(self, x):
x = self.relu(x)
x[..., :2] = U
return x
input_np_x = np.random.rand(4, 4, 4)
input_me_x = Tensor(input_np_x, ms.float32)
U = 1.0
net = Net(U)
out_me = net(input_me_x)
input_np_x[..., :2] = U
assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
def test_parser_tensor_assign_slice_002():
class Net(Cell):
def __init__(self, U):
super(Net, self).__init__()
self.relu = ReLU()
self.U = U
def construct(self, x):
x = self.relu(x)
x[::, :, :1] = self.U
return x
input_np_x = np.random.rand(4, 4, 4)
input_me_x = Tensor(input_np_x, ms.float32)
U = 1.0
net = Net(U)
out_me = net(input_me_x)
input_np_x[::, :, :1] = U
assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_parser_tensor_assign_bool():
class Net(Cell):
def __init__(self, U):
super(Net, self).__init__()
self.relu = ReLU()
self.U = U
def construct(self, x, tensorB):
x = self.relu(x)
x[tensorB] = self.U
return x
input_np_x = np.random.rand(4, 4, 4)
input_me_x = Tensor(input_np_x, ms.float32)
numpy_B = np.random.randn(4, 4, 4) > 0
tensor_B = Tensor(numpy_B)
U = np.array([1])
net = Net(Tensor(U))
out_me = net(input_me_x, tensor_B)
input_np_x[numpy_B] = U
assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)
def test_parser_tensor_assign_bool_002():
class Net(Cell):
def __init__(self, U):
super(Net, self).__init__()
self.relu = ReLU()
self.U = U
self.fill = P.Fill()
def construct(self, x, tensorB):
x = self.relu(x)
x[tensorB] = self.U
return x
input_np_x = np.random.rand(2, 2, 2)
input_me_x = Tensor(input_np_x, ms.float32)
numpy_B = np.random.randn(2, 2, 2) > 0
tensor_B = Tensor(numpy_B)
U = 1
net = Net(U)
out_me = net(input_me_x, tensor_B)
input_np_x[numpy_B] = U
assert np.allclose(out_me.asnumpy(), input_np_x, rtol=0.01, atol=0.01)