forked from mindspore-Ecosystem/mindspore
!27800 add testcase for ops
Merge pull request !27800 from zhanlijun/addtest1
This commit is contained in:
Gitee
commit
0158db12e6
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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 control ops """
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import os
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import numpy as np
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import pytest
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import mindspore as ms
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from mindspore import Tensor
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from mindspore import context
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from mindspore import nn
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from mindspore.common import dtype as mstype
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter
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context.set_context(mode=context.GRAPH_MODE)
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grad_by_list = C.GradOperation(get_by_list=True)
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grad_all = C.GradOperation(get_all=True)
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grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
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def if_compile_test(x_init, y_init):
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"""
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Feature: if compile test.
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Description: if compile test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.square = P.Square()
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self.add = P.Add()
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self.value = Tensor(3, dtype=ms.float32)
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self.switch = P.GeSwitch()
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self.merge = P.Merge()
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self.less = P.Less()
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def construct(self, x, y):
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cond = self.less(x, y)
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ret = self.value
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if cond:
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ret = self.add(x, ret)
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ret = self.add(y, ret)
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else:
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ret = self.square(self.value)
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return ret
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x = Tensor(x_init, dtype=ms.float32)
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y = Tensor(y_init, dtype=ms.float32)
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net = Net()
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output = net(x, y)
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return output
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def test_if_nested_compile():
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"""
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Feature: if nested compile test.
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Description: if nested compile test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self, auto_prefix=True):
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""""""
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super().__init__(auto_prefix=auto_prefix)
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self.squre = P.Square()
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self.value = Tensor(3, dtype=ms.float32)
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def construct(self, x, y):
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res = self.value
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if x <= y:
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res = x + res
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res = y + res
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else:
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if x == y:
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res = self.squre(self.value * y)
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else:
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res = self.squre(self.value)
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return res
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x = Tensor(1.0, dtype=ms.float32)
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y = Tensor(2.0, dtype=ms.float32)
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net = Net()
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net(x, y)
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def test_if_inside_for():
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"""
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Feature: if inside test.
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Description: if inside test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self, auto_prefix=True):
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""""""
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super().__init__(auto_prefix=auto_prefix)
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self.squre = P.Square()
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self.value = Tensor(3, dtype=ms.float32)
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self.count = 4
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def construct(self, x, y):
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res = 0
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for i in range(self.count):
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if i == x:
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res = res + x
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else:
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res = res - y
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return res
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c1 = Tensor(1, dtype=ms.int32)
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c2 = Tensor(1, dtype=ms.int32)
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net = Net()
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net(c1, c2)
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def test_while_with_weight_in_condition():
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"""
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Feature: while with weight in condition test.
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Description: while with weight in condition test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.loop = Parameter(Tensor(1, dtype=ms.float32), name="loop")
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def construct(self, x):
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while self.loop < 5:
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self.loop += 1
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x += 1
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return x
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net = Net()
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x = Tensor(-1, dtype=ms.float32)
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grad_all(net)(x)
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def test_while_add():
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"""
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Feature: while add test.
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Description: while add test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self, data):
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""""""
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super(Net, self).__init__()
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self.start = Tensor(0, dtype=mstype.int32)
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self.end = Tensor(2, dtype=mstype.int32)
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self.out = Tensor(np.zeros([2, 3], dtype=np.float32))
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self.add = P.Add()
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def construct(self, inputs):
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idx = self.start
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end = self.end
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out = self.out
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while idx < end:
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xi = inputs[idx, :, :]
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out = self.add(out, xi)
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idx = idx + 1
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return out
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x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32))
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net = Net(x)
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net(x)
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def test_tensor_all_construct_lack_branch():
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"""
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Feature: tensor all construct lack test.
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Description: tensor all construct lack test
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Expectation: compile done without error.
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"""
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class NetConditionLackBranch(nn.Cell):
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def __init__(self):
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""""""
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super(NetConditionLackBranch, self).__init__()
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self.logicaland = P.LogicalAnd()
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self.logicalor = P.LogicalOr()
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def construct(self, input1, input2):
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if input1.all():
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return self.logicaland(input1, input2)
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while input1.any():
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return self.logicalor(input1, input2)
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# NOTICE: here missing return statement, default return None
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input_np_1 = np.random.choice([True], size=(2, 3, 4, 5))
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input_tensor_1 = Tensor(input_np_1)
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input_np_2 = np.random.choice([True, False], size=(2, 3, 4, 5))
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input_tensor_2 = Tensor(input_np_2)
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net = NetConditionLackBranch()
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with pytest.raises(Exception):
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net(input_tensor_1, input_tensor_2)
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def test_parser_switch_layer_func_primitive():
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"""
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Feature: parser switch layer func primitive test.
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Description: parser switch layer func primitive test
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Expectation: compile done without error.
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"""
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class FinalNet(nn.Cell):
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def __init__(self, funcs):
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""""""
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super().__init__()
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self.funcs = funcs
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def construct(self, i, input1):
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x = self.funcs[i](input1)
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return x
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func1 = P.ReLU()
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func2 = P.Softmax()
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funcs = (func1, func2)
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net = FinalNet(funcs)
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input1 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
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i = Tensor(1, mstype.int32)
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with pytest.raises(ValueError):
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net(i, input1)
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def test_large_for_loop():
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"""
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Feature: large for loop test.
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Description: large for loop test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.flatten = P.ReLU() # nn.Flatten()
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def construct(self, x):
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for elem in range(1, 1900):
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x = self.flatten(x + elem)
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return x
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t = Tensor(np.ones([2, 3], dtype=np.float32))
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net = Net()
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '1'
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old_max_call_depth = context.get_context('max_call_depth')
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context.set_context(max_call_depth=60)
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with pytest.raises(RuntimeError) as err:
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net(t)
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context.set_context(max_call_depth=old_max_call_depth)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '0'
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assert 'Exceed function call depth limit 60' in str(err.value)
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def test_large_for_loop_with_continue_break():
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"""
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Feature: large for loop with continue break test.
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Description: large for loop with continue break test
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Expectation: compile done without error.
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"""
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class Net(nn.Cell):
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def __init__(self):
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""""""
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super(Net, self).__init__()
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self.flatten = P.ReLU() # nn.Flatten()
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def construct(self, x):
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idx = 0
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for elem1 in range(200):
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idx = idx + 1
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if idx < 10:
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x = x + 0.5
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continue
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if idx > 500:
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break
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x = self.flatten(x + elem1)
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return x
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '1'
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old_max_call_depth = context.get_context('max_call_depth')
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context.set_context(max_call_depth=2000)
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t = Tensor(np.ones([2, 3], dtype=np.float32))
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net = Net()
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net(t)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '0'
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context.set_context(max_call_depth=old_max_call_depth)
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def test_recursive_call():
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"""
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Feature: recursive call test.
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Description: recursive call test
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Expectation: compile done without error.
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"""
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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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""""""
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super(Net, self).__init__()
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self.fc = nn.Dense(10, 10) # padding=0
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# self.net2 = Net2()
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def construct(self, x):
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net2 = Net2()
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x = net2(x)
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out = self.fc(x)
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return out
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class Net2(nn.Cell):
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def __init__(self):
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super(Net2, self).__init__()
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self.net = Net()
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self.fc = nn.Dense(10, 10)
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def construct(self, x):
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x = self.net(x)
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out = self.fc(x)
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return out
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context.set_context(mode=context.GRAPH_MODE)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '1'
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old_max_call_depth = context.get_context('max_call_depth')
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context.set_context(max_call_depth=80)
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input_data = Tensor(np.identity(10).astype(np.float32))
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net = Net2()
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with pytest.raises(RuntimeError):
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net(input_data)
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os.environ['MS_DEV_RECURSIVE_EVAL'] = '0'
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context.set_context(max_call_depth=old_max_call_depth)
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def test_pow():
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"""
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Feature: pow test.
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Description: pow test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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power = Tensor(np.array(3.0, np.int64))
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testpow = P.Pow()
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expect = np.array([[8, 8], [27, 27]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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def test_pow1():
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"""
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Feature: pow one test.
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Description: pow one test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[2, 2], [2, 2]]))
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power = Tensor(np.array(3.0, np.int64))
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testpow = P.Pow()
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expect = np.array([[8, 8], [8, 8]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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def test_pow2():
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"""
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Feature: pow two test.
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Description: pow two test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[1, 1], [2, 2]]))
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power = Tensor(np.array(3.0, np.int64))
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testpow = P.Pow()
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expect = np.array([[1, 1], [8, 8]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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def test_pow3():
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"""
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Feature: pow three test.
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Description: pow three test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[2, 2], [1, 1]]))
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power = Tensor(np.array(3.0, np.int64))
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testpow = P.Pow()
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expect = np.array([[8, 8], [1, 1]])
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result = testpow(input_tensor, power)
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assert np.all(result.asnumpy() == expect)
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def test_exp():
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"""
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Feature: exp test.
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Description: exp test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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testexp = P.Exp()
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result = testexp(input_tensor)
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expect = np.exp(np.array([[2, 2], [3, 3]]))
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assert np.all(result.asnumpy() == expect)
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def test_exp1():
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"""
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Feature: exp one test.
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Description: exp one test
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Expectation: compile done without error.
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"""
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input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
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testexp = P.Exp()
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result = testexp(input_tensor)
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expect = np.exp(np.array([[2, 2], [3, 3]]))
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assert np.all(result.asnumpy() == expect)
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def test_realdiv():
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"""
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Feature: realdiv test.
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Description: realdiv test
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Expectation: compile done without error.
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"""
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x = Tensor(2048.0)
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y = Tensor(128.0)
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div = P.RealDiv()
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result = div(x, y)
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x = x.asnumpy()
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y = y.asnumpy()
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expect = x / y
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assert np.all(result.asnumpy() == expect)
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def test_realdiv1():
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"""
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Feature: realdiv one test.
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Description: realdiv one test
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Expectation: compile done without error.
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"""
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x = Tensor(256.0)
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y = Tensor(128.0)
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div = P.RealDiv()
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result = div(x, y)
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x = x.asnumpy()
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y = y.asnumpy()
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expect = x / y
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assert np.all(result.asnumpy() == expect)
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def test_eye():
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"""
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Feature: eye test.
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Description: eye test
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||||
Expectation: compile done without error.
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||||
"""
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||||
x = np.arange(3)
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expect = np.ones_like(x)
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expect = np.diag(expect)
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eye = P.Eye()
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||||
eye_output = eye(3, 3, ms.float32)
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||||
assert np.all(eye_output.asnumpy() == expect)
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||||
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||||
def test_sub():
|
||||
"""
|
||||
Feature: sub test.
|
||||
Description: sub test
|
||||
Expectation: compile done without error.
|
||||
"""
|
||||
input_x = Tensor(np.ones(shape=[3]))
|
||||
input_y = Tensor(np.zeros(shape=[3]))
|
||||
|
||||
sub = P.Sub()
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||||
result = sub(input_x, input_y)
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expect = np.ones(shape=[3])
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||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_square():
|
||||
"""
|
||||
Feature: square test.
|
||||
Description: square test
|
||||
Expectation: compile done without error.
|
||||
"""
|
||||
input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
|
||||
square = P.Square()
|
||||
result = square(input_tensor)
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||||
expect = np.array([[1, 4, 9], [16, 25, 36]])
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_sqrt():
|
||||
"""
|
||||
Feature: sqrt test.
|
||||
Description: sqrt test
|
||||
Expectation: compile done without error.
|
||||
"""
|
||||
input_tensor = Tensor(np.array([[4, 4], [9, 9]]))
|
||||
|
||||
sqrt = P.Sqrt()
|
||||
expect = np.array([[2, 2], [3, 3]])
|
||||
result = sqrt(input_tensor)
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
Loading…
Reference in New Issue