forked from mindspore-Ecosystem/mindspore
150 lines
4.7 KiB
Python
150 lines
4.7 KiB
Python
# 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 NetRelu(nn.Cell):
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def __init__(self):
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super(NetRelu, self).__init__()
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self.relu = P.ReLU()
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def construct(self, x):
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return self.relu(x)
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class NetReluDynamic(nn.Cell):
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def __init__(self):
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super(NetReluDynamic, self).__init__()
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self.conv = inner.GpuConvertToDynamicShape()
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self.relu = P.ReLU()
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def construct(self, x):
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x_conv = self.conv(x)
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return self.relu(x_conv)
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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_relu_float32():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.float32))
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expect = np.array([[[[0, 1, 10,],
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[1, 0, 1,],
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[10, 1, 0.]]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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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_relu_int8():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.int8))
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expect = np.array([[[[0, 1, 10,],
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[1, 0, 1,],
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[10, 1, 0.]]]]).astype(np.int8)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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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_relu_int32():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.int32))
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expect = np.array([[[[0, 1, 10,],
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[1, 0, 1,],
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[10, 1, 0.]]]]).astype(np.int32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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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_relu_int64():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.int64))
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expect = np.array([[[[0, 1, 10,],
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[1, 0, 1,],
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[10, 1, 0.]]]]).astype(np.int64)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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print(output.asnumpy(), expect)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu = NetRelu()
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output = relu(x)
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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_relu_int64_dynamic_shape():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.int64))
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expect = np.array([[[[0, 1, 10,],
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[1, 0, 1,],
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[10, 1, 0.]]]]).astype(np.int64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu_dynamic = NetReluDynamic()
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output = relu_dynamic(x)
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assert (output.asnumpy() == expect).all()
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