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142 lines
4.3 KiB
142 lines
4.3 KiB
# Copyright 2019 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.expand_dims = P.ExpandDims()
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def construct(self, tensor):
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return self.expand_dims(tensor, -1)
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class NetDynamic(nn.Cell):
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def __init__(self):
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super(NetDynamic, self).__init__()
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self.conv = inner.GpuConvertToDynamicShape()
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self.expand_dims = P.ExpandDims()
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def construct(self, x):
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x_conv = self.conv(x)
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return self.expand_dims(x_conv, -1)
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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_net_bool():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.bool)
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net = NetDynamic()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_int8():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.int8)
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net = NetDynamic()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_uint8():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_int16():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.int16)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_int32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.int32)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_int64():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.int64)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_float16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.float16)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_float32():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.float32)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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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_net_float64():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.random.randn(1, 16, 1, 1).astype(np.float64)
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net = Net()
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output = net(Tensor(x))
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assert np.all(output.asnumpy() == np.expand_dims(x, -1))
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