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55 lines
1.7 KiB
55 lines
1.7 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 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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class Net(nn.Cell):
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def __init__(self, multiples):
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super(Net, self).__init__()
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self.tile = P.Tile()
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self.multiples = multiples
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def construct(self, x):
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return self.tile(x, self.multiples)
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def get_output(x, multiples, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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net = Net(multiples)
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output = net(x)
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return output
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def test_tile(shape, dtype, multiples):
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x = Tensor(np.random.normal(0, 1, shape).astype(dtype))
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expect = get_output(x, multiples, False)
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output = get_output(x, multiples, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 0.0001, 0.0001)
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def test_tile_ascend():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_tile((24, 1), np.float16, (2, 2, 2))
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test_tile((24, 1), np.float32, (1, 2))
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