forked from huawei/mindspore2022
116 lines
3.3 KiB
Python
116 lines
3.3 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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from mindspore import Tensor, nn
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from mindspore.common import dtype as mstype
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from mindspore.ops.composite import GradOperation
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class Grad(nn.Cell):
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def __init__(self, net):
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super().__init__()
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self.grad = GradOperation(get_all=False)
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self.net = net
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def construct(self, x, y):
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grad_net = self.grad(self.net)
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grad = grad_net(x, y)
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return grad
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class CaseNet(nn.Cell):
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def __init__(self):
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super(CaseNet, self).__init__()
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self.conv = nn.Conv2d(1, 1, 3)
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self.relu = nn.ReLU()
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self.relu1 = nn.ReLU()
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self.softmax = nn.Softmax()
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self.layers1 = (self.relu, self.softmax)
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self.layers2 = (self.conv, self.relu1)
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def construct(self, x, index1, index2):
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x = self.layers1[index1](x)
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x = self.layers2[index2](x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_switch_layer():
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context.set_context(mode=context.GRAPH_MODE)
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net = CaseNet()
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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value = net(data, idx, idx2)
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relu = nn.ReLU()
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true_value = relu(data)
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ret = np.allclose(value.asnumpy(), true_value.asnumpy())
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assert ret
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_cell_in_list():
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"""
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Feature: Switch layer in while.
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Description: test recursive switch layer.
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Expectation: success if grad and output are correct.
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"""
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class TestCell(nn.Cell):
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def __init__(self, i):
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super().__init__()
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self.i = i
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def construct(self, x):
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return self.i * x
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class CellInList(nn.Cell):
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def __init__(self):
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super().__init__()
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self.cell_list = nn.CellList()
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self.cell_list.append(TestCell(4))
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self.cell_list.append(TestCell(5))
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self.cell_list.append(TestCell(6))
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def construct(self, t, x):
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out = t
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while x < 3:
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add = self.cell_list[x](t)
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out = out + add
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x += 1
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return out
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net = CellInList()
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t = Tensor(10, mstype.int32)
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x = Tensor(0, mstype.int32)
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out = net(t, x)
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grad_net = Grad(net)
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grad_out = grad_net(t, x)
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assert out == Tensor(160, mstype.int32)
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assert grad_out == Tensor(16, mstype.int32)
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