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298 lines
7.7 KiB
298 lines
7.7 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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""" test cell """
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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
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from mindspore.common.api import _executor
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class ModA(nn.Cell):
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def __init__(self, tensor):
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super(ModA, self).__init__()
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self.weight = Parameter(tensor, name="weight")
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def construct(self, *inputs):
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pass
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class ModB(nn.Cell):
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def __init__(self, tensor):
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super(ModB, self).__init__()
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self.weight = Parameter(tensor, name="weight")
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def construct(self, *inputs):
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pass
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class ModC(nn.Cell):
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def __init__(self, ta, tb):
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super(ModC, self).__init__()
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self.mod1 = ModA(ta)
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self.mod2 = ModB(tb)
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def construct(self, *inputs):
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pass
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class Net(nn.Cell):
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""" Net definition """
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name_len = 4
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cells_num = 3
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def __init__(self, ta, tb):
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super(Net, self).__init__()
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self.mod1 = ModA(ta)
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self.mod2 = ModB(tb)
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self.mod3 = ModC(ta, tb)
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def construct(self, *inputs):
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pass
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class Net2(nn.Cell):
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def __init__(self, ta, tb):
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super(Net2, self).__init__(auto_prefix=False)
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self.mod1 = ModA(ta)
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self.mod2 = ModB(tb)
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self.mod3 = ModC(ta, tb)
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def construct(self, *inputs):
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pass
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class ConvNet(nn.Cell):
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""" ConvNet definition """
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image_h = 224
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image_w = 224
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output_ch = 64
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def __init__(self, num_classes=10):
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super(ConvNet, self).__init__()
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self.conv1 = nn.Conv2d(3, ConvNet.output_ch, kernel_size=7, stride=2, pad_mode="pad", padding=3)
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self.bn1 = nn.BatchNorm2d(ConvNet.output_ch)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(
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int(ConvNet.image_h * ConvNet.image_w * ConvNet.output_ch / (4 * 4)),
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num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def test_basic():
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net(ta, tb)
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names = list(n.parameters_dict().keys())
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assert len(names) == n.name_len
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assert names[0] == "mod1.weight"
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assert names[1] == "mod2.weight"
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assert names[2] == "mod3.mod1.weight"
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assert names[3] == "mod3.mod2.weight"
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def test_parameter_name():
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""" test_parameter_name """
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net(ta, tb)
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names = []
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for m in n.parameters_and_names():
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if m[0]:
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names.append(m[0])
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assert names[0] == "mod1.weight"
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assert names[1] == "mod2.weight"
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assert names[2] == "mod3.mod1.weight"
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assert names[3] == "mod3.mod2.weight"
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def test_cell_name():
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""" test_cell_name """
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net(ta, tb)
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n.insert_child_to_cell('modNone', None)
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names = []
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for m in n.cells_and_names():
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if m[0]:
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names.append(m[0])
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assert names[0] == "mod1"
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assert names[1] == "mod2"
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assert names[2] == "mod3"
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assert names[3] == "mod3.mod1"
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assert names[4] == "mod3.mod2"
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def test_cells():
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net(ta, tb)
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ch = list(n.cells())
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assert len(ch) == n.cells_num
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def test_exceptions():
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""" test_exceptions """
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t = Tensor(np.ones([2, 3]))
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class ModError(nn.Cell):
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def __init__(self, tensor):
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self.weight = Parameter(tensor, name="weight")
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super(ModError, self).__init__()
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def construct(self, *inputs):
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pass
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with pytest.raises(AttributeError):
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ModError(t)
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class ModError1(nn.Cell):
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def __init__(self, tensor):
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super().__init__()
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self.weight = Parameter(tensor, name="weight")
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self.weight = None
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self.weight = ModA(tensor)
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def construct(self, *inputs):
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pass
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with pytest.raises(TypeError):
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ModError1(t)
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class ModError2(nn.Cell):
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def __init__(self, tensor):
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super().__init__()
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self.mod = ModA(tensor)
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self.mod = None
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self.mod = tensor
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def construct(self, *inputs):
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pass
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with pytest.raises(TypeError):
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ModError2(t)
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m = nn.Cell()
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with pytest.raises(NotImplementedError):
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m.construct()
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def test_del():
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""" test_del """
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net(ta, tb)
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names = list(n.parameters_dict().keys())
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assert len(names) == n.name_len
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del n.mod1
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names = list(n.parameters_dict().keys())
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assert len(names) == n.name_len - 1
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with pytest.raises(AttributeError):
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del n.mod1.weight
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del n.mod2.weight
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names = list(n.parameters_dict().keys())
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assert len(names) == n.name_len - 2
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with pytest.raises(AttributeError):
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del n.mod
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def test_add_attr():
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""" test_add_attr """
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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p = Parameter(ta, name="weight")
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m = nn.Cell()
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m.insert_param_to_cell('weight', p)
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with pytest.raises(TypeError):
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m.insert_child_to_cell("network", p)
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with pytest.raises(KeyError):
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m.insert_param_to_cell('', p)
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with pytest.raises(KeyError):
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m.insert_param_to_cell('a.b', p)
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m.insert_param_to_cell('weight', p)
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with pytest.raises(KeyError):
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m.insert_child_to_cell('', ModA(ta))
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with pytest.raises(KeyError):
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m.insert_child_to_cell('a.b', ModB(tb))
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with pytest.raises(TypeError):
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m.insert_child_to_cell('buffer', tb)
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with pytest.raises(TypeError):
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m.insert_param_to_cell('w', ta)
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with pytest.raises(TypeError):
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m.insert_child_to_cell('m', p)
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class ModAddCellError(nn.Cell):
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def __init__(self, tensor):
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self.mod = ModA(tensor)
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super().__init__()
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def construct(self, *inputs):
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pass
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with pytest.raises(AttributeError):
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ModAddCellError(ta)
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def test_train_eval():
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m = nn.Cell()
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assert not m.training
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m.set_train()
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assert m.training
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m.set_train(False)
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assert not m.training
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def test_stop_update_name():
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ta = Tensor(np.ones([2, 3]))
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tb = Tensor(np.ones([1, 4]))
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n = Net2(ta, tb)
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names = list(n.parameters_dict().keys())
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assert names[0] == "weight"
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assert names[1] == "mod1.weight"
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assert names[2] == "mod2.weight"
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class ModelName(nn.Cell):
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def __init__(self, tensor):
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super(ModelName, self).__init__()
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self.w2 = Parameter(tensor, name="weight")
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self.w1 = Parameter(tensor, name="weight")
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self.w3 = Parameter(tensor, name=None)
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self.w4 = Parameter(tensor, name=None)
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def construct(self, *inputs):
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pass
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def test_cell_names():
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ta = Tensor(np.ones([2, 3]))
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mn = ModelName(ta)
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with pytest.raises(ValueError):
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_executor.compile(mn)
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