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118 lines
3.9 KiB
118 lines
3.9 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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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class NetGatherD(nn.Cell):
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def __init__(self, dim=1):
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super(NetGatherD, self).__init__()
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self.gatherd = P.GatherD()
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self.dim = int(dim)
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def construct(self, x, index):
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return self.gatherd(x, self.dim, index)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_gatherd_fp32():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(5, 5, 5).astype(np.float32) * prop
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index = np.random.randint(0, 5, (5, 3, 5)).astype(np.int32)
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dim = 1
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gatherd = NetGatherD(dim)
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output = gatherd(Tensor(x), Tensor(index))
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expect = np.zeros(index.shape).astype(np.float32)
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for i in range(index.shape[0]):
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for j in range(index.shape[1]):
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for k in range(index.shape[2]):
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expect[i, j, k] = x[i, index[i, j, k], k]
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error = np.ones(shape=expect.shape) * 1.0e-6
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assert np.all(np.abs(output.asnumpy() - expect) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_gatherd_fp16():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(5, 5, 5).astype(np.float16) * prop
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index = np.random.randint(0, 5, (3, 5, 5)).astype(np.int64)
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dim = 0
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gatherd = NetGatherD(dim)
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output = gatherd(Tensor(x), Tensor(index))
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expect = np.zeros(index.shape).astype(np.float16)
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for i in range(index.shape[0]):
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for j in range(index.shape[1]):
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for k in range(index.shape[2]):
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expect[i, j, k] = x[index[i, j, k], j, k]
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error = np.ones(shape=expect.shape) * 1.0e-6
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assert np.all(np.abs(output.asnumpy() - expect) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_gatherd_int32():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(5, 5, 5).astype(np.int32) * prop
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index = np.random.randint(0, 5, (5, 5, 8)).astype(np.int32)
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dim = -1
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gatherd = NetGatherD(dim)
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output = gatherd(Tensor(x), Tensor(index))
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expect = np.zeros(index.shape).astype(np.int32)
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for i in range(index.shape[0]):
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for j in range(index.shape[1]):
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for k in range(index.shape[2]):
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expect[i, j, k] = x[i, j, index[i, j, k]]
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_gatherd_bool():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(5, 5, 5).astype(np.int32) * prop
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x = (x >= 0).astype(np.bool)
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index = np.random.randint(0, 5, (5, 5, 8)).astype(np.int32)
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dim = -1
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gatherd = NetGatherD(dim)
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output = gatherd(Tensor(x), Tensor(index))
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expect = np.zeros(index.shape).astype(np.bool)
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for i in range(index.shape[0]):
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for j in range(index.shape[1]):
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for k in range(index.shape[2]):
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expect[i, j, k] = x[i, j, index[i, j, k]]
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assert np.all(output.asnumpy() == expect)
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