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163 lines
4.9 KiB
163 lines
4.9 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations.array_ops as P
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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class StackNet(nn.Cell):
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def __init__(self, nptype):
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super(StackNet, self).__init__()
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self.stack = P.Stack(axis=2)
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self.data_np = np.array([0] * 16).astype(nptype)
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self.data_np = np.reshape(self.data_np, (2, 2, 2, 2))
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self.x1 = Parameter(initializer(
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Tensor(self.data_np), [2, 2, 2, 2]), name='x1')
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self.x2 = Parameter(initializer(
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Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype)), [2, 2, 2, 2]), name='x2')
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@ms_function
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def construct(self):
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return self.stack((self.x1, self.x2))
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def stack(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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stack_ = StackNet(nptype)
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output = stack_()
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expect = np.array([[[[[0, 0],
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[0, 0]],
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[[0, 1],
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[2, 3]]],
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[[[0, 0],
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[0, 0]],
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[[4, 5],
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[6, 7]]]],
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[[[[0, 0],
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[0, 0]],
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[[8, 9],
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[10, 11]]],
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[[[0, 0],
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[0, 0]],
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[[12, 13],
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[14, 15]]]]]).astype(nptype)
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assert (output.asnumpy() == expect).all()
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def stack_pynative(nptype):
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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x1 = np.array([0] * 16).astype(nptype)
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x1 = np.reshape(x1, (2, 2, 2, 2))
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x1 = Tensor(x1)
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x2 = Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype))
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expect = np.array([[[[[0, 0],
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[0, 0]],
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[[0, 1],
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[2, 3]]],
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[[[0, 0],
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[0, 0]],
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[[4, 5],
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[6, 7]]]],
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[[[[0, 0],
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[0, 0]],
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[[8, 9],
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[10, 11]]],
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[[[0, 0],
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[0, 0]],
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[[12, 13],
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[14, 15]]]]]).astype(nptype)
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output = P.Stack(axis=2)((x1, x2))
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_float32():
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stack(np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_float16():
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stack(np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_int32():
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stack(np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_int16():
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stack(np.int16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_uint8():
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stack(np.uint8)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_graph_bool():
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stack(np.bool)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_float32():
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stack_pynative(np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_float16():
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stack_pynative(np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_int32():
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stack_pynative(np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_int16():
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stack_pynative(np.int16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_uint8():
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stack_pynative(np.uint8)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stack_pynative_bool():
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stack_pynative(np.bool)
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