forked from mindspore-Ecosystem/mindspore
!35916 [OP]delete space_to_batch_nd tensor api
Merge pull request !35916 from zichun_ye/del_space_to_batch_nd_tensor
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@ -1195,31 +1195,6 @@ mindspore.Tensor
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- **TypeError** - `y` 不是Tensor、int或者float。
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- **ValueError** - 输入的shape不相同。
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.. py:method:: space_to_batch_nd(block_shape, paddings)
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将空间维度划分为对应大小的块，然后在批次维度重排张量。
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更多参考详见 :func:`mindspore.ops.space_to_batch_nd`。
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**参数：**
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- **block_shape** (list[int], tuple[int], int) - 块形状描述空间维度为分割的个数。
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- **paddings** (tuple, list) - 空间维度的填充大小。
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**返回：**
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Tensor，经过划分排列之后的结果。
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**异常：**
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- **TypeError** - 如果 `block_shape` 不是 list, tuple 或者 int。
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- **TypeError** - 如果 `paddings` 不是 list 或者 tuple。
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- **ValueError** - 如果当 `block_shape` 为 list 或 tuple， `block_shape` 不是一维。
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- **ValueError** - 如果 Ascend 平台上 `block_shape` 长度不是2。
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- **ValueError** - 如果 `paddings` 的形状不是 (2, M), 其中 M 为 `block_shape` 的长度。
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- **ValueError** - 如果 `block_shape` 的元素不是大于一的整数。
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- **ValueError** - 如果 `paddings` 的元素不是非负的整数。
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.. py:method:: shape
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:property:
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@ -221,7 +221,6 @@ BuiltInTypeMap &GetMethodMap() {
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{"argmin", std::string("argmin")}, // P.Argmax()
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{"resize", std::string("resize")}, // P.Reshape()
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{"select", std::string("select")}, // P.Select()
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{"space_to_batch_nd", std::string("space_to_batch_nd")}, // P.SpaceToBatchND
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{"batch_to_space_nd", std::string("batch_to_space_nd")}, // P.BatchToSpaceND
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{"choose", std::string("choose")}, // P.Select()
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{"diagonal", std::string("diagonal")}, // P.Eye()
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@ -2191,15 +2191,6 @@ def filter_(fun, iter_):
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return result
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def space_to_batch_nd(x, block_shape, paddings):
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"""
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Divides spatial dimensions into blocks and combines the block size with the original batch.
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Refer to :func:`mindspore.ops.space_to_batch_nd` for more detail.
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"""
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return P.SpaceToBatchND(block_shape, paddings)(x)
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def batch_to_space_nd(x, block_shape, crops):
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r"""
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions.
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@ -1566,8 +1566,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('col2im')(self, output_size, kernel_size, dilation, padding_value, stride)
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def reshape(self, *shape):
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"""
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Give a new shape to a tensor without changing its data.
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@ -3141,48 +3139,6 @@ class Tensor(Tensor_):
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j = tensor_operator_registry.get('select')(mask, mid, j)
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return j
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def space_to_batch_nd(self, block_shape, paddings):
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r"""
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Divides spatial dimensions into blocks and combines the block size with the original batch.
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Refer to :func:`mindspore.ops.space_to_batch_nd` for more detail.
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Args:
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block_shape (Union[list(int), tuple(int), int]): The block size of dividing block with all value greater
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than 1.
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paddings (Union[tuple, list]): The padding values for spatial dimensions, containing M subtraction list.
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Each contains 2 integer values.
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Returns:
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Tensor, the output tensor with the same data type as the input tensor.
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Raises:
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ValueError: If `block_shape` is not one dimensional when `block_shape` is a list or tuple.
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ValueError: If the length of `block_shape` is not 2 on Ascend.
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ValueError: If the element of `block_shape` is not an integer larger than 1.
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ValueError: If shape of `paddings` is not (2, M), where M is the length of `block_shape`.
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ValueError: If the element of `paddings` is not an integer larger than 0.
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TypeError: If `block_shape` is not one of list, tuple, int.
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TypeError: If `paddings` is neither list nor tuple.
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Supported Platforms:
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``Ascend`` ``CPU``
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Examples:
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>>> import numpy as np
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>>> from mindspore import Tensor
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>>> block_shape = [2, 2]
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>>> paddings = [[0, 0], [0, 0]]
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>>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
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>>> output = input_x.space_to_batch_nd(block_shape, paddings)
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>>> print(output)
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[[[[1.]]]
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[[[2.]]]
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[[[3.]]]
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[[[4.]]]]
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"""
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return tensor_operator_registry.get('space_to_batch_nd')(block_shape, paddings)(self)
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def batch_to_space_nd(self, block_shape, crops):
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"""
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions.
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@ -950,7 +950,6 @@ tensor_operator_registry.register('sum', P.ReduceSum)
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tensor_operator_registry.register('split', P.Split)
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tensor_operator_registry.register('select', P.Select)
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tensor_operator_registry.register('zeros_like', P.ZerosLike)
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tensor_operator_registry.register('space_to_batch_nd', P.SpaceToBatchND)
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tensor_operator_registry.register('batch_to_space_nd', P.BatchToSpaceND)
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tensor_operator_registry.register('one_hot', P.OneHot)
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tensor_operator_registry.register('masked_fill', masked_fill)
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@ -117,43 +117,6 @@ def test_space_to_batch_nd_function():
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np.testing.assert_array_equal(output.asnumpy(), expect)
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class SpaceToBatchNDTensorNet(nn.Cell):
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def __init__(self, block_size=2):
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super(SpaceToBatchNDTensorNet, self).__init__()
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self.block_size = block_size
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def construct(self, x):
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return x.space_to_batch_nd(self.block_size, [[0, 0], [0, 0]])
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_space_to_batch_nd_tensor():
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"""
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Feature: test SpaceToBatchND tensor interface.
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Description: test tensor interface.
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Expectation: the result match with numpy result
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"""
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net = SpaceToBatchNDTensorNet(2)
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input_x = Tensor(np.arange(16).reshape((1, 1, 4, 4)).astype(np.float32), mindspore.float32)
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expect = np.array([[[[0, 2],
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[8, 10]]],
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[[[1, 3],
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[9, 11]]],
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[[[4, 6],
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[12, 14]]],
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[[[5, 7],
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[13, 15]]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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output = net(input_x)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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output = net(input_x)
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assert (output.asnumpy() == expect).all()
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class SpaceToBatchNDDynamicShapeNetMS(nn.Cell):
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def __init__(self, block_size, paddings, axis=0):
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super().__init__()
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