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221 lines
6.0 KiB
221 lines
6.0 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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import functools
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from ..ut_filter import non_graph_engine
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from ....mindspore_test_framework.mindspore_test import mindspore_test
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from ....mindspore_test_framework.pipeline.forward.compile_forward \
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import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
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def test_list_equal():
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class Net(nn.Cell):
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def __init__(self, z: list):
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z == [1, 2, 3]:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [1, 2, 3]
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net = Net(z)
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assert net(x, y) == x
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def test_list_not_equal():
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class Net(nn.Cell):
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def __init__(self, z: list):
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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if self.z == [3, 4, 5]:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [1, 2, 3]
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net = Net(z)
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assert net(x, y) == y
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def test_list_expansion():
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class Net(nn.Cell):
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def __init__(self, z: list):
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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a, b, c = self.z
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if a == 1 and b == 2 and c == 3:
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ret = x
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else:
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ret = y
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [1, 2, 3]
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net = Net(z)
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assert net(x, y) == x
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def test_list_append():
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class Net(nn.Cell):
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def __init__(self, z: list):
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super(Net, self).__init__()
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self.z = z
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def construct(self, x, y):
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z = [[1, 2], 3]
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z[0].append(88)
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z[0].append(99)
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if z[0][3] == 99:
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ret = y
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else:
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ret = x
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [1, 2, 3]
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net = Net(z)
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assert net(x, y) == y
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def test_list_append_2():
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class Net(nn.Cell):
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def __init__(self, z: list):
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super(Net, self).__init__()
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self.z = z
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self.x = 9
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def construct(self, x, y):
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self.z[0].append(88)
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self.z[0].append(99)
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if self.z[0][3] == 88:
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ret = y
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else:
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ret = x
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return ret
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x = Tensor(np.ones([6, 8, 10], np.int32))
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y = Tensor(np.zeros([3, 4, 5], np.int32))
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z = [[1, 2], 3]
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net = Net(z)
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assert net(x, y) == x
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class ListOperate(nn.Cell):
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def __init__(self, ):
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super(ListOperate, self).__init__()
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def construct(self, t, l):
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x = [1, 2, 3, 4, 5, 6]
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x[2] = 9
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x[1] = x[3] + 11
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x[3] = x[1] + x[0]
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x[0] = x[2] * x[4]
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x[5] = x[1] - x[2]
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x[4] = x[3] / x[2]
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x.append(8)
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x.append(8)
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x.append(t)
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x.append(l)
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x.append(l)
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return x
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class AxisListNet(nn.Cell):
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def __init__(self):
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super(AxisListNet, self).__init__()
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self.reduce_sum = P.ReduceSum()
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self.reduce_mean = P.ReduceMean()
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self.reduce_max = P.ReduceMax()
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self.reduce_min = P.ReduceMin()
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self.add_n = P.AddN()
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self.axis = [0, 1, 2]
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def construct(self, x):
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ret_sum = self.reduce_sum(x, self.axis)
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ret_mean = self.reduce_mean(x, self.axis)
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ret_max = self.reduce_max(x, self.axis)
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ret_min = self.reduce_min(x, self.axis)
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ret = [ret_sum, ret_mean, ret_max, ret_min]
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return self.add_n(ret) + ret_sum
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class AxisListEmptyNet(nn.Cell):
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def __init__(self):
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super(AxisListEmptyNet, self).__init__()
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self.reduce_sum = P.ReduceSum()
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self.axis = []
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def construct(self, x):
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return self.reduce_sum(x, self.axis)
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class AxisListDefaultNet(nn.Cell):
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def __init__(self):
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super(AxisListDefaultNet, self).__init__()
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self.reduce_sum = P.ReduceSum()
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def construct(self, x):
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return self.reduce_sum(x)
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test_case_ops = [
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('ListOperate', {
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'block': ListOperate(),
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'desc_inputs': [Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32)),
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[2, 3, 4]]}),
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('AxisList', {
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'block': AxisListNet(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))]}),
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('AxisListEmpty', {
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'block': AxisListEmptyNet(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))]}),
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('AxisListDefault', {
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'block': AxisListDefaultNet(),
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'desc_inputs': [Tensor(np.ones([6, 8, 10], np.int32))]}),
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]
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test_case_lists = [test_case_ops]
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test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
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# use -k to select certain testcast
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# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm
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import mindspore.context as context
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@non_graph_engine
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@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
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def test_exec():
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context.set_context(mode=context.GRAPH_MODE)
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return test_exec_case
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