forked from mindspore-Ecosystem/mindspore
upgrade Ascend software package 09 Sep 21
This commit is contained in:
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Subproject commit 6cc2d0293685edd22a1e3ef2a6af393779a0283d
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Subproject commit 329eb8bbed9137a1ed6aca07ec68cdee6ae2975f
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# 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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""" test lazy adam """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.nn.optim import LazyAdam
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from mindspore.ops import operations as P
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@pytest.fixture(scope="module", autouse=True)
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def setup_teardown():
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context.set_context(enable_sparse=True)
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yield
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context.set_context(enable_sparse=False)
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
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self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
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self.matmul = P.MatMul()
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self.biasAdd = P.BiasAdd()
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def construct(self, x):
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x = self.biasAdd(self.matmul(x, self.weight), self.bias)
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return x
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class NetWithSparseGatherV2(nn.Cell):
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""" NetWithSparseGatherV2 definition """
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def __init__(self):
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super(NetWithSparseGatherV2, self).__init__()
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self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
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self.weight2 = Parameter(Tensor(np.ones([2, 1, 2]).astype((np.float32))), name="weight2")
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self.axis = 0
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self.gather = P.SparseGatherV2()
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def construct(self, indices, label):
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return self.gather(self.weight1, indices, self.axis) + self.weight2
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def test_lazy_adam_compile():
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""" test lazy adam compile """
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inputs = Tensor(np.ones([1, 64]).astype(np.float32))
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label = Tensor(np.zeros([1, 10]).astype(np.float32))
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net = Net()
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net.set_train()
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loss = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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_cell_graph_executor.compile(train_network, inputs, label)
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def test_spares_lazy_adam_compile():
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""" test sparse adam compile """
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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optimizer.target = 'CPU'
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train_network = TrainOneStepCell(net, optimizer)
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_cell_graph_executor.compile(train_network, indices, label)
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def test_spares_lazy_adam():
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""" test sparse adam"""
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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optimizer.target = 'Ascend'
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train_network = TrainOneStepCell(net, optimizer)
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_cell_graph_executor.compile(train_network, indices, label)
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def test_lazy_adam_error():
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net = Net()
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with pytest.raises(ValueError):
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LazyAdam(net.get_parameters(), learning_rate=-0.1)
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with pytest.raises(TypeError):
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LazyAdam(net.get_parameters(), learning_rate=0.1, beta1=2)
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# 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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""" test lazy adam """
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import pytest
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import numpy as np
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from mindspore.nn.optim import LazyAdam, FTRL, Adam, ProximalAdagrad
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter, context
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from mindspore.ops import operations as P
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@pytest.fixture(scope="module", autouse=True)
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def setup_teardown():
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context.set_context(enable_sparse=True)
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yield
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context.set_context(enable_sparse=False)
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class NetWithSparseGatherV2(nn.Cell):
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""" NetWithSparseGatherV2 definition """
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def __init__(self):
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super(NetWithSparseGatherV2, self).__init__()
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self.weight1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="weight1")
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self.weight2 = Parameter(Tensor(np.ones([2, 1, 2]).astype((np.float32))), name="weight2")
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self.axis = 0
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self.gather = P.SparseGatherV2()
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def construct(self, indices, label):
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return self.gather(self.weight1, indices, self.axis) + self.weight2
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def test_ftrl_target():
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""" test_ftrl_target """
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
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if optimizer.target not in ('CPU', 'Ascend'):
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raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
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def test_lazyadam_target():
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""" test_lazyadam_target """
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = LazyAdam(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
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if optimizer.target not in ('CPU', 'Ascend'):
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raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
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def test_adam_target():
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""" test_adam_target """
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
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if optimizer.target not in ('CPU', 'Ascend'):
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raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
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def test_proximal_target():
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""" test_proximal_target """
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net = NetWithSparseGatherV2()
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net.set_train()
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optimizer = ProximalAdagrad(net.trainable_params(), weight_decay=0.9, loss_scale=1024.0)
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if optimizer.target not in ('CPU', 'Ascend'):
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raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
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# 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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"""
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Test nn.probability.distribution.gumbel.
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"""
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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import dtype
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from mindspore import Tensor
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def test_gumbel_shape_errpr():
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"""
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Invalid shapes.
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"""
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with pytest.raises(ValueError):
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msd.Gumbel([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
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def test_type():
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with pytest.raises(TypeError):
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msd.Gumbel(0., 1., dtype=dtype.int32)
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def test_name():
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with pytest.raises(TypeError):
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msd.Gumbel(0., 1., name=1.0)
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def test_seed():
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with pytest.raises(TypeError):
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msd.Gumbel(0., 1., seed='seed')
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def test_scale():
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with pytest.raises(ValueError):
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msd.Gumbel(0., 0.)
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with pytest.raises(ValueError):
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msd.Gumbel(0., -1.)
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def test_arguments():
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"""
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args passing during initialization.
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"""
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l = msd.Gumbel([3.0], [4.0], dtype=dtype.float32)
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assert isinstance(l, msd.Distribution)
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class GumbelProb(nn.Cell):
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"""
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Gumbel distribution: initialize with loc/scale.
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"""
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def __init__(self):
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super(GumbelProb, self).__init__()
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self.gumbel = msd.Gumbel(3.0, 4.0, dtype=dtype.float32)
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def construct(self, value):
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prob = self.gumbel.prob(value)
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log_prob = self.gumbel.log_prob(value)
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cdf = self.gumbel.cdf(value)
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log_cdf = self.gumbel.log_cdf(value)
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sf = self.gumbel.survival_function(value)
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log_sf = self.gumbel.log_survival(value)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_gumbel_prob():
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"""
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Test probability functions: passing value through construct.
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"""
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net = GumbelProb()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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ans = net(value)
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assert isinstance(ans, Tensor)
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class KL(nn.Cell):
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"""
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Test kl_loss.
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"""
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def __init__(self):
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super(KL, self).__init__()
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self.gumbel = msd.Gumbel(3.0, 4.0)
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def construct(self, mu, s):
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kl = self.gumbel.kl_loss('Gumbel', mu, s)
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cross_entropy = self.gumbel.cross_entropy('Gumbel', mu, s)
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return kl + cross_entropy
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def test_kl_cross_entropy():
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"""
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Test kl_loss and cross_entropy.
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"""
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from mindspore import context
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context.set_context(device_target="Ascend")
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net = KL()
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loc_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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scale_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(loc_b, scale_b)
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assert isinstance(ans, Tensor)
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class GumbelBasics(nn.Cell):
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"""
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Test class: basic loc/scale function.
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"""
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def __init__(self):
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super(GumbelBasics, self).__init__()
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self.gumbel = msd.Gumbel(3.0, 4.0, dtype=dtype.float32)
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def construct(self):
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mean = self.gumbel.mean()
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sd = self.gumbel.sd()
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mode = self.gumbel.mode()
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entropy = self.gumbel.entropy()
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return mean + sd + mode + entropy
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def test_bascis():
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"""
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Test mean/sd/mode/entropy functionality of Gumbel.
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"""
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net = GumbelBasics()
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ans = net()
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assert isinstance(ans, Tensor)
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class GumbelConstruct(nn.Cell):
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"""
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Gumbel distribution: going through construct.
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"""
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def __init__(self):
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super(GumbelConstruct, self).__init__()
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self.gumbel = msd.Gumbel(3.0, 4.0)
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def construct(self, value):
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prob = self.gumbel('prob', value)
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prob1 = self.gumbel.prob(value)
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return prob + prob1
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def test_gumbel_construct():
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"""
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Test probability function going through construct.
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"""
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net = GumbelConstruct()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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ans = net(value)
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assert isinstance(ans, Tensor)
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# 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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""" Test Dropout """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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context.set_context(device_target="Ascend")
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def test_check_dropout_3():
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Tensor(np.ones([20, 16, 50]).astype(np.int32))
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with pytest.raises(ValueError):
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nn.Dropout(3)
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class Net_dropout(nn.Cell):
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def __init__(self):
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super(Net_dropout, self).__init__()
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self.dropout = nn.Dropout(0.5)
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def construct(self, x):
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return self.dropout(x)
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@ -1,62 +0,0 @@
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# 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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""" test image gradients """
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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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.common.api import _cell_graph_executor
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from mindspore.common.api import ms_function
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context.set_context(device_target="Ascend")
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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.image_gradients = nn.ImageGradients()
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@ms_function
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def construct(self, x):
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return self.image_gradients(x)
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def test_compile():
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# input shape 1 x 1 x 2 x 2
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image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
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net = Net()
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_cell_graph_executor.compile(net, image)
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def test_compile_multi_channel():
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# input shape 4 x 2 x 2 x 2
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dtype = mstype.int32
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image = Tensor(np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
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[[[3, 5], [7, 9]], [[11, 13], [15, 17]]],
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[[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
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[[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
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net = Net()
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_cell_graph_executor.compile(net, image)
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def test_invalid_5d_input():
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dtype = mstype.float32
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image = Tensor(np.random.random([4, 1, 16, 16, 1]), dtype=dtype)
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net = Net()
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with pytest.raises(ValueError):
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_cell_graph_executor.compile(net, image)
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