332 lines
12 KiB
Python
332 lines
12 KiB
Python
# 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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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 NetUnique(nn.Cell):
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def __init__(self):
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super(NetUnique, self).__init__()
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self.unique = P.Unique()
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def construct(self, x):
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x_unique, x_idx = self.unique(x)
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return x_unique, x_idx
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class NetUniqueDynamic(nn.Cell):
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def __init__(self):
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super(NetUniqueDynamic, self).__init__()
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self.convert = inner.GpuConvertToDynamicShape()
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self.unique = P.Unique()
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self.split = P.Split(0, 2)
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def construct(self, x):
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x_convert = self.convert(x)
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x_unique, x_idx = self.unique(x_convert)
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x_split = self.split(x_unique)
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return x_unique, x_idx, x_split
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d():
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x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5]).astype(np.float32))
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exp_output = np.array([1, 2, 3, 4, 5]).astype(np.float32)
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exp_idx = np.array([3, 4, 0, 1, 2, 2, 3, 4]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_float():
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x = Tensor(np.array([0.4, 0.5, 1.23, 2.2, 12.43, 12.43, 0.4, 0.5]).astype(np.float32))
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exp_output = np.array([0.4, 0.5, 1.23, 2.2, 12.43]).astype(np.float32)
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exp_idx = np.array([0, 1, 2, 3, 4, 4, 0, 1]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_sorted():
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x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.float32))
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exp_output = np.array([1, 2, 4, 7, 8]).astype(np.float32)
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exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_zeros():
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x = Tensor(np.zeros(1000).astype(np.float32))
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exp_output = np.zeros(1).astype(np.float32)
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exp_idx = np.zeros(1000).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_large():
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x_np1 = np.arange(100)
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x_np2 = np.arange(100, 200)
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x_np3 = np.arange(200, 300)
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x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
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x = Tensor(x_np.astype(np.float32))
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exp_output = np.arange(300).astype(np.float32)
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exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_half():
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x = Tensor(np.array([0.4, 0.5, 1.23, 2.2, 12.43, 12.43, 0.4, 0.5]).astype(np.float16))
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exp_output = np.array([0.4, 0.5, 1.23, 2.2, 12.43]).astype(np.float16)
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exp_idx = np.array([0, 1, 2, 3, 4, 4, 0, 1]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_sorted_half():
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x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.float16))
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exp_output = np.array([1, 2, 4, 7, 8]).astype(np.float16)
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exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_zeros_half():
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x = Tensor(np.zeros(1000).astype(np.float16))
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exp_output = np.zeros(1).astype(np.float16)
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exp_idx = np.zeros(1000).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_large_half():
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x_np1 = np.arange(100)
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x_np2 = np.arange(100, 200)
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x_np3 = np.arange(200, 300)
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x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
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x = Tensor(x_np.astype(np.float16))
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exp_output = np.arange(300).astype(np.float16)
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exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_int32():
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x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5]).astype(np.int32))
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exp_output = np.array([1, 2, 3, 4, 5]).astype(np.int32)
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exp_idx = np.array([3, 4, 0, 1, 2, 2, 3, 4]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_sorted_int32():
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x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.int32))
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exp_output = np.array([1, 2, 4, 7, 8]).astype(np.int32)
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exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_zeros_int32():
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x = Tensor(np.zeros(1000).astype(np.int32))
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exp_output = np.zeros(1).astype(np.int32)
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exp_idx = np.zeros(1000).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_large_int32():
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x_np1 = np.arange(100)
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x_np2 = np.arange(100, 200)
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x_np3 = np.arange(200, 300)
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x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
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x = Tensor(x_np.astype(np.int32))
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exp_output = np.arange(300).astype(np.int32)
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exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_dynamic():
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x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5, 6]).astype(np.float32))
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expt_unique = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
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expt_index = np.array([3, 4, 0, 1, 2, 2, 3, 4, 5]).astype(np.int32)
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expt_split = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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x2 = Tensor(np.array([1, 1, 4, 4, 7, 8, 8]).astype(np.float32))
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expt_unique2 = np.array([1, 4, 7, 8]).astype(np.float32)
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expt_index2 = np.array([0, 0, 1, 1, 2, 3, 3]).astype(np.int32)
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expt_split2 = np.array([[1, 4], [7, 8]]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUniqueDynamic()
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x_unique, x_idx, x_split = net(x)
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assert (x_unique.asnumpy() == expt_unique).all()
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assert (x_idx.asnumpy() == expt_index).all()
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for i, out in enumerate(x_split):
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assert (out.asnumpy() == expt_split[i]).all()
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x_unique2, x_idx2, x_split2 = net(x2)
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assert (x_unique2.asnumpy() == expt_unique2).all()
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assert (x_idx2.asnumpy() == expt_index2).all()
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for i, out in enumerate(x_split2):
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assert (out.asnumpy() == expt_split2[i]).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_int64():
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x = Tensor(np.array([4, 5, 1, 2, 3, 3, 4, 5]).astype(np.int64))
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exp_output = np.array([1, 2, 3, 4, 5]).astype(np.int64)
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exp_idx = np.array([3, 4, 0, 1, 2, 2, 3, 4]).astype(np.int64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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print(x_unique)
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print(x_idx)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_1d_sorted_int64():
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x = Tensor(np.array([1, 1, 2, 4, 4, 4, 7, 8, 8]).astype(np.int64))
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exp_output = np.array([1, 2, 4, 7, 8]).astype(np.int64)
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exp_idx = np.array([0, 0, 1, 2, 2, 2, 3, 4, 4]).astype(np.int64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_zeros_int64():
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x = Tensor(np.zeros(1000).astype(np.int64))
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exp_output = np.zeros(1).astype(np.int64)
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exp_idx = np.zeros(1000).astype(np.int64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_unique_large_int64():
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x_np1 = np.arange(100)
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x_np2 = np.arange(100, 200)
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x_np3 = np.arange(200, 300)
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x_np = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3))
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x = Tensor(x_np.astype(np.int64))
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exp_output = np.arange(300).astype(np.int64)
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exp_idx = np.concatenate((x_np1, x_np2, x_np3, x_np1, x_np2, x_np3, x_np1, x_np2, x_np3)).astype(np.int64)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = NetUnique()
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x_unique, x_idx = net(x)
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assert (x_unique.asnumpy() == exp_output).all()
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assert (x_idx.asnumpy() == exp_idx).all()
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