support gather_v2 tensor and function interface
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zengzitao
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@ -552,7 +552,28 @@ mindspore.Tensor
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**异常：**
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- **ValueError** - 如果输入Tensor的shape长度小于 `indices` 的最后一个维度。
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.. py:method:: gather(input_indices, axis)
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返回指定 `axis` 上 `input_indices` 的元素对应的输入Tensor切片。为了方便描述，对于输入Tensor记为`input_params`。
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.. note::
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1. input_indices 的值必须在`[0, input_params.shape[axis])`的范围内，结果未定义超出范围。
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2. 当前在Ascend平台，input_params的值不能是 `bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`类型。
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**参数：**
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- **input_indices**(Tensor) - 待切片的索引张量，其形状为 :math:`(y_1, y_2, ..., y_S)`，代表指定原始张量元素的索引，其数据类型包括：int32，int64。
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- **axis**(int) - 指定维度索引的轴以搜集切片。
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**返回：**
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Tensor,其中shape维度为 :math:`input\_params.shape[:axis] + input\_indices.shape + input\_params.shape[axis + 1:]`。
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**异常：**
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- **TypeError** - 如果`axis`不是一个整数。
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- **TypeError** - 如果`input_indices`不是一个整数类型的Tensor。
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.. py:method:: ger(x)
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计算两个Tensor的外积，即计算此Tensor 和 `x` 的外积。如果此Tensor shape为 :math:`(m,)` ，`x` shape为 :math:`(n,)` ，
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@ -233,6 +233,7 @@ BuiltInTypeMap &GetMethodMap() {
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{"padding", std::string("padding")}, // padding()
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{"searchsorted", std::string("searchsorted")}, // P.Select()
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{"take", std::string("take")}, // P.GatherNd()
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{"gather", std::string("gather")}, // P.Gather()
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{"scatter_add", std::string("tensor_scatter_add")}, // P.TensorScatterAdd()
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{"scatter_mul", std::string("tensor_scatter_mul")}, // tensor_scatter_mul()
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{"scatter_sub", std::string("tensor_scatter_sub")}, // P.TensorScatterSub()
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@ -2324,6 +2324,14 @@ def gather_nd(input_x, indices):
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return F.gather_nd(input_x, indices)
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def gather(input_x, input_indices, axis):
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r"""
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Returns the slice of the input tensor corresponding to the elements of `input_indices` on the specified `axis`.
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Refer to :func:`mindspore.ops.gather` for more detail.
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"""
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return F.gather(input_x, input_indices, axis)
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def pdist(x, p=2.0):
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r"""
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Computes the p-norm distance between each pair of row vectors in the input.
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@ -1640,8 +1640,6 @@ 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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@ -3366,6 +3364,81 @@ class Tensor(Tensor_):
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validator.check_value_type('indices', indices, (Tensor_,), 'Tensor.gather_nd')
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return tensor_operator_registry.get('gather_nd')(self, indices)
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def gather(self, input_indices, axis):
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r"""
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Returns the slice of the input tensor corresponding to the elements of `input_indices` on the specified `axis`.
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The shape of input tensor is :math:`(x_1, x_2, ..., x_R)`. For convenience, define it as `input_params`
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The following figure shows the calculation process of Gather commonly:
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.. image:: Gather.png
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where params represents the input `input_params`, and indices represents the index to be sliced `input_indices`.
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.. note::
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1. The value of input_indices must be in the range of `[0, input_param.shape[axis])`, the result
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is undefined out of range.
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2. The data type of input_params cannot be
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`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore.html#mindspore.dtype>`_ on Ascend
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platform currently.
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Args:
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input_indices (Tensor): Index tensor to be sliced, the shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
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Specifies the indices of elements of the original Tensor. The data type can be int32 or int64.
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axis (int): Specifies the dimension index to gather indices.
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Returns:
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Tensor, the shape of tensor is
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:math:`input\_params.shape[:axis] + input\_indices.shape + input\_params.shape[axis + 1:]`.
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Raises:
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TypeError: If `axis` is not an int.
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TypeError: If `input_indices` is not a tensor of type int.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> # case1: input_indices is a Tensor with shape (5, ).
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>>> input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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>>> input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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>>> axis = 0
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>>> output = input_params.gather(input_indices, axis)
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>>> print(output)
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[1. 3. 5. 3. 7.]
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>>> # case2: input_indices is a Tensor with shape (2, 2). When the input_params has one dimension,
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>>> # the output shape is equal to the input_indices shape.
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>>> input_indices = Tensor(np.array([[0, 2], [2, 6]]), mindspore.int32)
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>>> axis = 0
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>>> output = input_params.gather(input_indices, axis)
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>>> print(output)
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[[ 1. 3.]
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[ 3. 7.]]
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>>> # case3: input_indices is a Tensor with shape (2, ) and
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>>> # input_params is a Tensor with shape (3, 4) and axis is 0.
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>>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
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>>> input_indices = Tensor(np.array([0, 2]), mindspore.int32)
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>>> axis = 0
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>>> output = input_params.gather(input_indices, axis)
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>>> print(output)
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[[1. 2. 3. 4.]
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[9. 10. 11. 12.]]
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>>> # case4: input_indices is a Tensor with shape (2, ) and
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>>> # input_params is a Tensor with shape (3, 4) and axis is 1.
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>>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
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>>> input_indices = Tensor(np.array([0, 2]), mindspore.int32)
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>>> axis = 1
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>>> output = input_params.gather(input_indices, axis)
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>>> print(output)
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[[1. 3.]
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[5. 7.]
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[9. 11.]]
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"""
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self._init_check()
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validator.check_is_int(axis, 'axis')
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return tensor_operator_registry.get('gather')(self, input_indices, axis)
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def var(self, axis=None, ddof=0, keepdims=False):
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"""
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Compute the variance along the specified axis.
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@ -998,6 +998,7 @@ tensor_operator_registry.register('eye', eye)
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tensor_operator_registry.register('reduce_sum', reduce_sum)
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tensor_operator_registry.register('tensor_slice', tensor_slice)
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tensor_operator_registry.register('select', select)
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tensor_operator_registry.register('gather', gather)
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tensor_operator_registry.register('gather_d', gather_d)
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tensor_operator_registry.register('gather_elements', gather_elements)
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tensor_operator_registry.register('gather_nd', gather_nd)
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@ -0,0 +1,144 @@
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# Copyright 2022 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 numpy as np
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import pytest
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import mindspore
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class GatherTensorTestNet(nn.Cell):
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def construct(self, x, indices, axis):
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return x.gather(indices, axis)
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class GatherNet(nn.Cell):
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def __init__(self):
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super(GatherNet, self).__init__()
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self.gather = P.Gather()
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def construct(self, input_x, indices, axis):
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return self.gather(input_x, indices, axis)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_tensor_graph_mode():
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"""
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Feature: gather tensor test on graph mode.
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Description: test gather tensor's interface on graph mode.
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Expectation: the result equal to expect.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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axis = 0
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net = GatherTensorTestNet()
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output = net(input_params, input_indices, axis)
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expect_np = np.array([1., 3., 5., 3., 7.])
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rtol = 1.e-4
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atol = 1.e-4
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assert np.allclose(output.asnumpy(), expect_np, rtol, atol, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_tensor_pynative_mode():
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"""
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Feature: gather tensor test on pynative mode.
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Description: test gather tensor's interface on pynative mode.
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Expectation: the result equal to expect.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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axis = 0
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net = GatherTensorTestNet()
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output = net(input_params, input_indices, axis)
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expect_np = np.array([1., 3., 5., 3., 7.])
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rtol = 1.e-4
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atol = 1.e-4
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assert np.allclose(output.asnumpy(), expect_np, rtol, atol, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_functional_pynative_mode():
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"""
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Feature: gather functional test on pynative mode.
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Description: test gather_nd functional's interface on pynative mode.
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Expectation: the result equal to expect.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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axis = 0
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output = ops.gather(input_params, input_indices, axis)
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expect_np = np.array([1., 3., 5., 3., 7.])
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rtol = 1.e-4
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atol = 1.e-4
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assert np.allclose(output.asnumpy(), expect_np, rtol, atol, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_functional_graph_mode():
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"""
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Feature: gather functional test on graph mode.
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Description: test gather functional's interface on graph mode.
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Expectation: the result equal to expect.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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axis = 0
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output = ops.gather(input_params, input_indices, axis)
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expect_np = np.array([1., 3., 5., 3., 7.])
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rtol = 1.e-4
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atol = 1.e-4
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assert np.allclose(output.asnumpy(), expect_np, rtol, atol, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_gather_static_pynative_mode():
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"""
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Feature: gather static shape test on pynative mode.
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Description: test static shape for gather on pynative mode.
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Expectation: the result equal to expect.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32)
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input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32)
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axis = 0
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net = GatherNet()
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output = net(input_params, input_indices, axis)
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expect_np = np.array([1., 3., 5., 3., 7.])
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rtol = 1.e-4
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atol = 1.e-4
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assert np.allclose(output.asnumpy(), expect_np, rtol, atol, equal_nan=True)
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