forked from huawei/mindspore2022
420 lines
13 KiB
Python
420 lines
13 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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""" test array ops """
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import functools
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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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import mindspore.context as context
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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.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.ops import prim_attr_register
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from mindspore.ops.operations import _inner_ops as inner
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from mindspore.ops.primitive import PrimitiveWithInfer
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from mindspore.ops.signature import sig_rw, sig_dtype, make_sig
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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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from ....mindspore_test_framework.pipeline.forward.verify_exception \
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import pipeline_for_verify_exception_for_case_by_case_config
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context.set_context(mode=context.PYNATIVE_MODE)
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def test_expand_dims():
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input_tensor = Tensor(np.array([[2, 2], [2, 2]]))
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expand_dims = P.ExpandDims()
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output = expand_dims(input_tensor, 0)
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assert output.asnumpy().shape == (1, 2, 2)
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def test_cast():
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_x = Tensor(input_np)
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td = ms.int32
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cast = P.Cast()
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result = cast(input_x, td)
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expect = input_np.astype(np.int32)
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assert np.all(result.asnumpy() == expect)
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def test_ones():
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ones = P.Ones()
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output = ones((2, 3), mstype.int32)
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assert output.asnumpy().shape == (2, 3)
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assert np.sum(output.asnumpy()) == 6
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def test_ones_1():
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ones = P.Ones()
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output = ones(2, mstype.int32)
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assert output.asnumpy().shape == (2,)
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assert np.sum(output.asnumpy()) == 2
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def test_zeros():
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zeros = P.Zeros()
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output = zeros((2, 3), mstype.int32)
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assert output.asnumpy().shape == (2, 3)
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assert np.sum(output.asnumpy()) == 0
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def test_zeros_1():
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zeros = P.Zeros()
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output = zeros(2, mstype.int32)
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assert output.asnumpy().shape == (2,)
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assert np.sum(output.asnumpy()) == 0
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@non_graph_engine
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def test_reshape():
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input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]))
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shp = (3, 2)
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reshape = P.Reshape()
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output = reshape(input_tensor, shp)
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assert output.asnumpy().shape == (3, 2)
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def test_transpose():
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input_tensor = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
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perm = (0, 2, 1)
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expect = np.array([[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]])
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transpose = P.Transpose()
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output = transpose(input_tensor, perm)
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assert np.all(output.asnumpy() == expect)
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def test_squeeze():
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input_tensor = Tensor(np.ones(shape=[3, 2, 1]))
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squeeze = P.Squeeze(2)
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output = squeeze(input_tensor)
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assert output.asnumpy().shape == (3, 2)
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def test_invert_permutation():
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invert_permutation = P.InvertPermutation()
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x = (3, 4, 0, 2, 1)
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output = invert_permutation(x)
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expect = (2, 4, 3, 0, 1)
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assert np.all(output == expect)
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def test_select():
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select = P.Select()
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cond = Tensor(np.array([[True, False, False], [False, True, True]]))
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x = Tensor(np.array([[1, 2, 3], [4, 5, 6]]))
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y = Tensor(np.array([[7, 8, 9], [10, 11, 12]]))
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output = select(cond, x, y)
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expect = np.array([[1, 8, 9], [10, 5, 6]])
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assert np.all(output.asnumpy() == expect)
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def test_argmin_invalid_output_type():
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P.Argmin(-1, mstype.int64)
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P.Argmin(-1, mstype.int32)
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with pytest.raises(TypeError):
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P.Argmin(-1, mstype.float32)
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with pytest.raises(TypeError):
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P.Argmin(-1, mstype.float64)
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with pytest.raises(TypeError):
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P.Argmin(-1, mstype.uint8)
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with pytest.raises(TypeError):
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P.Argmin(-1, mstype.bool_)
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class CustomOP(PrimitiveWithInfer):
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__mindspore_signature__ = (sig_dtype.T, sig_dtype.T, sig_dtype.T1,
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sig_dtype.T1, sig_dtype.T2, sig_dtype.T2,
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sig_dtype.T2, sig_dtype.T3, sig_dtype.T4)
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@prim_attr_register
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def __init__(self):
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pass
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def __call__(self, p1, p2, p3, p4, p5, p6, p7, p8, p9):
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raise NotImplementedError
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class CustomOP2(PrimitiveWithInfer):
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__mindspore_signature__ = (
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make_sig('p1', sig_rw.RW_WRITE, dtype=sig_dtype.T),
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make_sig('p2', dtype=sig_dtype.T),
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make_sig('p3', dtype=sig_dtype.T),
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)
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@prim_attr_register
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def __init__(self):
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pass
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def __call__(self, p1, p2, p3):
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raise NotImplementedError
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class CustNet1(Cell):
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def __init__(self):
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super(CustNet1, self).__init__()
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self.op = CustomOP()
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self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
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self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
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self.int1 = 3
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self.float1 = 5.1
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def construct(self):
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x = self.op(self.t1, self.t1, self.int1,
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self.float1, self.int1, self.float1,
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self.t2, self.t1, self.int1)
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return x
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class CustNet2(Cell):
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def __init__(self):
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super(CustNet2, self).__init__()
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self.op = CustomOP2()
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self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
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self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
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self.int1 = 3
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def construct(self):
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return self.op(self.t1, self.t2, self.int1)
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class CustNet3(Cell):
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def __init__(self):
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super(CustNet3, self).__init__()
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self.op = P.ReduceSum()
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self.t1 = Tensor(np.ones([2, 2]), dtype=ms.int32)
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self.t2 = Tensor(np.ones([1, 5]), dtype=ms.float16)
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self.t2 = 1
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def construct(self):
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return self.op(self.t1, self.t2)
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class MathBinaryNet1(Cell):
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def __init__(self):
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super(MathBinaryNet1, self).__init__()
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self.add = P.Add()
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self.mul = P.Mul()
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self.max = P.Maximum()
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self.number = 3
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def construct(self, x):
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return self.add(x, self.number) + self.mul(x, self.number) + self.max(x, self.number)
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class MathBinaryNet2(Cell):
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def __init__(self):
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super(MathBinaryNet2, self).__init__()
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self.less_equal = P.LessEqual()
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self.greater = P.Greater()
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self.logic_or = P.LogicalOr()
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self.logic_and = P.LogicalAnd()
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self.number = 3
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self.flag = True
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def construct(self, x):
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ret_less_equal = self.logic_and(self.less_equal(x, self.number), self.flag)
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ret_greater = self.logic_or(self.greater(x, self.number), self.flag)
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return self.logic_or(ret_less_equal, ret_greater)
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class BatchToSpaceNet(Cell):
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def __init__(self):
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super(BatchToSpaceNet, self).__init__()
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block_size = 2
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crops = [[0, 0], [0, 0]]
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self.batch_to_space = P.BatchToSpace(block_size, crops)
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def construct(self, x):
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return self.batch_to_space(x)
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class SpaceToBatchNet(Cell):
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def __init__(self):
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super(SpaceToBatchNet, self).__init__()
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block_size = 2
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paddings = [[0, 0], [0, 0]]
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self.space_to_batch = P.SpaceToBatch(block_size, paddings)
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def construct(self, x):
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return self.space_to_batch(x)
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class PackNet(Cell):
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def __init__(self):
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super(PackNet, self).__init__()
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self.stack = P.Stack()
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def construct(self, x):
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return self.stack((x, x))
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class UnpackNet(Cell):
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def __init__(self):
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super(UnpackNet, self).__init__()
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self.unstack = P.Unstack()
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def construct(self, x):
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return self.unstack(x)
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class SpaceToDepthNet(Cell):
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def __init__(self):
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super(SpaceToDepthNet, self).__init__()
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block_size = 2
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self.space_to_depth = P.SpaceToDepth(block_size)
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def construct(self, x):
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return self.space_to_depth(x)
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class DepthToSpaceNet(Cell):
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def __init__(self):
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super(DepthToSpaceNet, self).__init__()
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block_size = 2
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self.depth_to_space = P.DepthToSpace(block_size)
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def construct(self, x):
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return self.depth_to_space(x)
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class BatchToSpaceNDNet(Cell):
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def __init__(self):
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super(BatchToSpaceNDNet, self).__init__()
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block_shape = [2, 2]
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crops = [[0, 0], [0, 0]]
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self.batch_to_space_nd = P.BatchToSpaceND(block_shape, crops)
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def construct(self, x):
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return self.batch_to_space_nd(x)
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class SpaceToBatchNDNet(Cell):
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def __init__(self):
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super(SpaceToBatchNDNet, self).__init__()
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block_shape = [2, 2]
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paddings = [[0, 0], [0, 0]]
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self.space_to_batch_nd = P.SpaceToBatchND(block_shape, paddings)
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def construct(self, x):
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return self.space_to_batch_nd(x)
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class TensorShapeNet(Cell):
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def __init__(self):
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super(TensorShapeNet, self).__init__()
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self.shape = P.TensorShape()
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self.unique = P.Unique()
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def construct(self, x):
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x, _ = self.unique(x)
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return self.shape(x)
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class RangeNet(Cell):
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def __init__(self):
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super(RangeNet, self).__init__()
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self.range_ops = inner.Range(1.0, 8.0, 2.0)
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def construct(self, x):
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return self.range_ops(x)
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test_case_array_ops = [
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('CustNet1', {
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'block': CustNet1(),
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'desc_inputs': []}),
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('CustNet2', {
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'block': CustNet2(),
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'desc_inputs': []}),
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('CustNet3', {
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'block': CustNet3(),
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'desc_inputs': []}),
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('MathBinaryNet1', {
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'block': MathBinaryNet1(),
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'desc_inputs': [Tensor(np.ones([2, 2]), dtype=ms.int32)]}),
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('MathBinaryNet2', {
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'block': MathBinaryNet2(),
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'desc_inputs': [Tensor(np.ones([2, 2]), dtype=ms.int32)]}),
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('BatchToSpaceNet', {
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'block': BatchToSpaceNet(),
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'desc_inputs': [Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]).astype(np.float16))]}),
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('SpaceToBatchNet', {
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'block': SpaceToBatchNet(),
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'desc_inputs': [Tensor(np.array([[[[1, 2], [3, 4]]]]).astype(np.float16))]}),
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('PackNet', {
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'block': PackNet(),
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'desc_inputs': [Tensor(np.array([[[1, 2], [3, 4]]]).astype(np.float16))]}),
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('UnpackNet', {
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'block': UnpackNet(),
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'desc_inputs': [Tensor(np.array([[1, 2], [3, 4]]).astype(np.float16))]}),
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('SpaceToDepthNet', {
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'block': SpaceToDepthNet(),
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'desc_inputs': [Tensor(np.random.rand(1, 3, 2, 2).astype(np.float16))]}),
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('DepthToSpaceNet', {
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'block': DepthToSpaceNet(),
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'desc_inputs': [Tensor(np.random.rand(1, 12, 1, 1).astype(np.float16))]}),
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('SpaceToBatchNDNet', {
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'block': SpaceToBatchNDNet(),
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'desc_inputs': [Tensor(np.random.rand(1, 1, 2, 2).astype(np.float16))]}),
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('BatchToSpaceNDNet', {
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'block': BatchToSpaceNDNet(),
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'desc_inputs': [Tensor(np.random.rand(4, 1, 1, 1).astype(np.float16))]}),
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('RangeNet', {
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'block': RangeNet(),
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'desc_inputs': [Tensor(np.array([1, 2, 3, 2]), ms.int32)]}),
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('TensorShapeNet', {'block': TensorShapeNet(), 'desc_inputs': [Tensor(np.array([1, 2, 3, 2]), ms.int32)]})
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]
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test_case_lists = [test_case_array_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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@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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raise_set = [
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('Squeeze_1_Error', {
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'block': (lambda x: P.Squeeze(axis=1.2), {'exception': TypeError}),
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'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
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('Squeeze_2_Error', {
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'block': (lambda x: P.Squeeze(axis=((1.2, 1.3))), {'exception': TypeError}),
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'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
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('ReduceSum_Error', {
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'block': (lambda x: P.ReduceSum(keep_dims=1), {'exception': TypeError}),
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'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5]))]}),
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('TensorShapeNet_Error', {'block': (lambda x: P.TensorSHape(), {'exception': TypeError}),
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'desc_inputs': [(Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[3, 1, 5])))]})
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]
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@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
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def test_check_exception():
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return raise_set
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