fary86
5 years ago
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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 ops """
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import functools
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import numpy as np
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from mindspore import ops
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _grad_ops as G
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import mindspore.ops.composite as C
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.common.parameter import Parameter
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from ..ut_filter import non_graph_engine
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from mindspore.common.api import _executor
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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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pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
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from ....mindspore_test_framework.pipeline.gradient.compile_gradient\
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import pipeline_for_compile_grad_ge_graph_for_case_by_case_config
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class ExpandDimsNet(nn.Cell):
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def __init__(self, axis):
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super(ExpandDimsNet, self).__init__()
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self.axis = axis
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self.op = P.ExpandDims()
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def construct(self, x):
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return self.op(x, self.axis)
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class IsInstanceNet(nn.Cell):
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def __init__(self, inst):
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super(IsInstanceNet, self).__init__()
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self.inst = inst
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self.op = P.IsInstance()
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def construct(self, t):
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return self.op(self.inst, t)
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class ReshapeNet(nn.Cell):
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def __init__(self, shape):
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super(ReshapeNet, self).__init__()
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self.shape = shape
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self.op = P.Reshape()
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def construct(self, x):
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return self.op(x, self.shape)
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raise_set = [
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# input is scala, not Tensor
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('ExpandDims0', {
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'block': (P.ExpandDims(), {'exception': TypeError, 'error_keywords': ['ExpandDims']}),
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'desc_inputs': [5.0, 1],
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'skip': ['backward']}),
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# axis is as a parameter
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('ExpandDims1', {
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'block': (P.ExpandDims(), {'exception': TypeError, 'error_keywords': ['ExpandDims']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), 1],
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'skip': ['backward']}),
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# axis as an attribute, but less then lower limit
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('ExpandDims2', {
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'block': (ExpandDimsNet(-4), {'exception': ValueError, 'error_keywords': ['ExpandDims']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
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'skip': ['backward']}),
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# axis as an attribute, but greater then upper limit
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('ExpandDims3', {
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'block': (ExpandDimsNet(3), {'exception': ValueError, 'error_keywords': ['ExpandDims']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
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'skip': ['backward']}),
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# input is scala, not Tensor
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('DType0', {
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'block': (P.DType(), {'exception': TypeError, 'error_keywords': ['DType']}),
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'desc_inputs': [5.0],
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'skip': ['backward']}),
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# input x scala, not Tensor
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('SameTypeShape0', {
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'block': (P.SameTypeShape(), {'exception': TypeError, 'error_keywords': ['SameTypeShape']}),
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'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
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'skip': ['backward']}),
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# input y scala, not Tensor
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('SameTypeShape1', {
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'block': (P.SameTypeShape(), {'exception': TypeError, 'error_keywords': ['SameTypeShape']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), 5.0],
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'skip': ['backward']}),
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# type of x and y not match
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('SameTypeShape2', {
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'block': (P.SameTypeShape(), {'exception': TypeError, 'error_keywords': ['SameTypeShape']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.int32))],
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'skip': ['backward']}),
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# shape of x and y not match
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('SameTypeShape3', {
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'block': (P.SameTypeShape(), {'exception': ValueError, 'error_keywords': ['SameTypeShape']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 3]).astype(np.float32))],
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'skip': ['backward']}),
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# sub_type is None
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('IsSubClass0', {
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'block': (P.IsSubClass(), {'exception': TypeError, 'error_keywords': ['IsSubClass']}),
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'desc_inputs': [None, mstype.number],
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'skip': ['backward']}),
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# type_ is None
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('IsSubClass1', {
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'block': (P.IsSubClass(), {'exception': TypeError, 'error_keywords': ['IsSubClass']}),
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'desc_inputs': [mstype.number, None],
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'skip': ['backward']}),
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# inst is var
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('IsInstance0', {
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'block': (P.IsInstance(), {'exception': ValueError, 'error_keywords': ['IsInstance']}),
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'desc_inputs': [5.0, mstype.number],
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'skip': ['backward']}),
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# t is not mstype.Type
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('IsInstance1', {
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'block': (IsInstanceNet(5.0), {'exception': TypeError, 'error_keywords': ['IsInstance']}),
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'desc_inputs': [None],
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'skip': ['backward']}),
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# input x is scalar, not Tensor
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('Reshape0', {
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'block': (P.Reshape(), {'exception': TypeError, 'error_keywords': ['Reshape']}),
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'desc_inputs': [5.0, (1, 2)],
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'skip': ['backward']}),
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# input shape is var
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('Reshape1', {
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'block': (P.Reshape(), {'exception': TypeError, 'error_keywords': ['Reshape']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), (2, 3, 2)],
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'skip': ['backward']}),
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# element of shape is not int
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('Reshape3', {
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'block': (ReshapeNet((2, 3.0, 2)), {'exception': TypeError, 'error_keywords': ['Reshape']}),
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'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32))],
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'skip': ['backward']}),
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]
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@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception)
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def test_check_exception():
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return raise_set
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