def index_select(x, index, axis=0, name=None):
"""
Returns a new tensor which indexes the ``input`` tensor along dimension ``axis`` using
the entries in ``index`` which is a Tensor. The returned tensor has the same number
of dimensions as the original ``x`` tensor. The dim-th dimension has the same
size as the length of ``index``; other dimensions have the same size as in the ``x`` tensor.
Args:
x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float32, float64, int32, int64.
index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64.
axis (int, optional): The dimension in which we index. Default: if None, the ``axis`` is 0.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor with same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]])
index = paddle.to_tensor([0, 1, 1], dtype='int32')
out_z1 = paddle.index_select(x=x, index=index)
#[[1. 2. 3. 4.]
# [5. 6. 7. 8.]
# [5. 6. 7. 8.]]
out_z2 = paddle.index_select(x=x, index=index, axis=1)
#[[ 1. 2. 2.]
# [ 5. 6. 6.]
# [ 9. 10. 10.]]
"""
if in_dygraph_mode():
return _C_ops.index_select(x, index, 'dim', axis)
helper = LayerHelper("index_select", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.index_select')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'paddle.tensor.search.index_select')
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(type='index_select',
inputs={
'X': x,
'Index': index
},
outputs={'Out': out},
attrs={'dim': axis})
return out
def mode(x, axis=-1, keepdim=False, name=None):
"""
This OP is used to find values and indices of the modes at the optional axis.
Args:
x(Tensor): Tensor, an input N-D Tensor with type float32, float64, int32, int64.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. Default is -1.
keepdim(bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimentions is one fewer than x since the axis is squeezed. Default is False.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64.
Examples:
.. code-block:: python
import paddle
tensor = paddle.to_tensor([[[1,2,2],[2,3,3]],[[0,5,5],[9,9,0]]], dtype=paddle.float32)
res = paddle.mode(tensor, 2)
print(res)
# (Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[2., 3.],
# [5., 9.]]), Tensor(shape=[2, 2], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[1, 1],
# [1, 0]]))
"""
if in_dygraph_mode():
return _C_ops.mode(x, "axis", axis, "keepdim", keepdim)
helper = LayerHelper("mode", **locals())
inputs = {"X": [x]}
attrs = {}
attrs['axis'] = axis
attrs['keepdim'] = keepdim
values = helper.create_variable_for_type_inference(dtype=x.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(type="mode",
inputs=inputs,
outputs={
"Out": [values],
"Indices": [indices]
},
attrs=attrs)
indices.stop_gradient = True
return values, indices
def masked_select(x, mask, name=None):
"""
This OP Returns a new 1-D tensor which indexes the input tensor according to the ``mask``
which is a tensor with data type of bool.
Args:
x (Tensor): The input Tensor, the data type can be int32, int64, float32, float64.
mask (Tensor): The Tensor containing the binary mask to index with, it's data type is bool.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns: A 1-D Tensor which is the same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]])
mask = paddle.to_tensor([[True, False, False, False],
[True, True, False, False],
[True, False, False, False]])
out = paddle.masked_select(x, mask)
#[1.0 5.0 6.0 9.0]
"""
if in_dygraph_mode():
return _C_ops.masked_select(x, mask)
helper = LayerHelper("masked_select", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.mask_select')
check_variable_and_dtype(mask, 'mask', ['bool'],
'paddle.tensor.search.masked_select')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='masked_select',
inputs={
'X': x,
'Mask': mask
},
outputs={'Y': out})
return out
def kthvalue(x, k, axis=None, keepdim=False, name=None):
"""
This OP is used to find values and indices of the k-th smallest at the axis.
Args:
x(Tensor): A N-D Tensor with type float32, float64, int32, int64.
k(int): The k for the k-th smallest number to look for along the axis.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. The default is None. And if the axis is None, it will computed as -1 by default.
keepdim(bool, optional): Whether to keep the given axis in output. If it is True, the dimensions will be same as input x and with size one in the axis. Otherwise the output dimentions is one fewer than x since the axis is squeezed. Default is False.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64.
Examples:
.. code-block:: python
import paddle
x = paddle.randn((2,3,2))
# Tensor(shape=[2, 3, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[[ 0.22954939, -0.01296274],
# [ 1.17135799, -0.34493217],
# [-0.19550551, -0.17573971]],
#
# [[ 0.15104349, -0.93965352],
# [ 0.14745511, 0.98209465],
# [ 0.10732264, -0.55859774]]])
y = paddle.kthvalue(x, 2, 1)
# (Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[ 0.22954939, -0.17573971],
# [ 0.14745511, -0.55859774]]), Tensor(shape=[2, 2], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[0, 2],
# [1, 2]]))
"""
if in_dygraph_mode():
if axis is not None:
return _C_ops.kthvalue(x, 'k', k, "axis", axis, "keepdim", keepdim)
else:
return _C_ops.kthvalue(x, 'k', k, "keepdim", keepdim)
helper = LayerHelper("kthvalue", **locals())
inputs = {"X": [x]}
attrs = {'k': k}
if axis is not None:
attrs['axis'] = axis
values = helper.create_variable_for_type_inference(dtype=x.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(type="kthvalue",
inputs=inputs,
outputs={
"Out": [values],
"Indices": [indices]
},
attrs=attrs)
indices.stop_gradient = True
return values, indices
def searchsorted(sorted_sequence,
values,
out_int32=False,
right=False,
name=None):
"""
This OP is used to find the index of the corresponding `sorted_sequence` in the innermost dimension based on the given `values`.
Args:
sorted_sequence(Tensor): An input N-D or 1-D tensor with type int32, int64, float32, float64. The value of the tensor monotonically increases in the innermost dimension.
values(Tensor): An input N-D tensor value with type int32, int64, float32, float64.
out_int32(bool, optional): Data type of the output tensor which can be int32, int64. The default value is False, and it indicates that the output data type is int64.
right(bool, optional): Find the upper or lower bounds of the sorted_sequence range in the innermost dimension based on the given `values`. If the value of the sorted_sequence is nan or inf, return the size of the innermost dimension.
The default value is False and it shows the lower bounds.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor（the same sizes of the `values`）, return the tensor of int32 if set :attr:`out_int32` is True, otherwise return the tensor of int64.
Examples:
.. code-block:: python
import paddle
sorted_sequence = paddle.to_tensor([[1, 3, 5, 7, 9, 11],
[2, 4, 6, 8, 10, 12]], dtype='int32')
values = paddle.to_tensor([[3, 6, 9, 10], [3, 6, 9, 10]], dtype='int32')
out1 = paddle.searchsorted(sorted_sequence, values)
print(out1)
# Tensor(shape=[2, 4], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[1, 3, 4, 5],
# [1, 2, 4, 4]])
out2 = paddle.searchsorted(sorted_sequence, values, right=True)
print(out2)
# Tensor(shape=[2, 4], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[2, 3, 5, 5],
# [1, 3, 4, 5]])
sorted_sequence_1d = paddle.to_tensor([1, 3, 5, 7, 9, 11, 13])
out3 = paddle.searchsorted(sorted_sequence_1d, values)
print(out3)
# Tensor(shape=[2, 4], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [[1, 3, 4, 5],
# [1, 3, 4, 5]])
"""
if in_dygraph_mode():
return _C_ops.searchsorted(sorted_sequence, values, "out_int32",
out_int32, "right", right)
check_variable_and_dtype(sorted_sequence, 'SortedSequence',
['float32', 'float64', 'int32', 'int64'],
'paddle.searchsorted')
check_variable_and_dtype(values, 'Values',
['float32', 'float64', 'int32', 'int64'],
'paddle.searchsorted')
helper = LayerHelper('searchsorted', **locals())
out_type = 'int32' if out_int32 else 'int64'
out = helper.create_variable_for_type_inference(dtype=out_type)
helper.append_op(type='searchsorted',
inputs={
'SortedSequence': sorted_sequence,
"Values": values
},
outputs={'Out': out},
attrs={
"out_int32": out_int32,
"right": right
})
return out
def topk(x, k, axis=None, largest=True, sorted=True, name=None):
"""
This OP is used to find values and indices of the k largest or smallest at the optional axis.
If the input is a 1-D Tensor, finds the k largest or smallest values and indices.
If the input is a Tensor with higher rank, this operator computes the top k values and indices along the :attr:`axis`.
Args:
x(Tensor): Tensor, an input N-D Tensor with type float32, float64, int32, int64.
k(int, Tensor): The number of top elements to look for along the axis.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. Default is -1.
largest(bool, optional) : largest is a flag, if set to true,
algorithm will sort by descending order, otherwise sort by
ascending order. Default is True.
sorted(bool, optional): controls whether to return the elements in sorted order, default value is True. In gpu device, it always return the sorted value.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
tuple(Tensor), return the values and indices. The value data type is the same as the input `x`. The indices data type is int64.
Examples:
.. code-block:: python
import paddle
tensor_1 = paddle.to_tensor([1, 4, 5, 7])
value_1, indices_1 = paddle.topk(tensor_1, k=1)
print(value_1)
# [7]
print(indices_1)
# [3]
tensor_2 = paddle.to_tensor([[1, 4, 5, 7], [2, 6, 2, 5]])
value_2, indices_2 = paddle.topk(tensor_2, k=1)
print(value_2)
# [[7]
# [6]]
print(indices_2)
# [[3]
# [1]]
value_3, indices_3 = paddle.topk(tensor_2, k=1, axis=-1)
print(value_3)
# [[7]
# [6]]
print(indices_3)
# [[3]
# [1]]
value_4, indices_4 = paddle.topk(tensor_2, k=1, axis=0)
print(value_4)
# [[2 6 5 7]]
print(indices_4)
# [[1 1 0 0]]
"""
if in_dygraph_mode():
k = k.numpy().item(0) if isinstance(k, Variable) else k
if axis is None:
out, indices = _C_ops.top_k_v2(x, 'k', int(k), 'largest', largest,
'sorted', sorted)
else:
out, indices = _C_ops.top_k_v2(x, 'k', int(k), 'axis', axis,
'largest', largest, 'sorted',
sorted)
return out, indices
helper = LayerHelper("top_k_v2", **locals())
inputs = {"X": [x]}
attrs = {}
if isinstance(k, Variable):
inputs['K'] = [k]
else:
attrs = {'k': k}
attrs['largest'] = largest
attrs['sorted'] = sorted
if axis is not None:
attrs['axis'] = axis
values = helper.create_variable_for_type_inference(dtype=x.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(type="top_k_v2",
inputs=inputs,
outputs={
"Out": [values],
"Indices": [indices]
},
attrs=attrs)
indices.stop_gradient = True
return values, indices
def index_sample(x, index):
"""
**IndexSample Layer**
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
.. code-block:: text
Given:
X = [[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10]]
Index = [[0, 1, 3],
[0, 2, 4]]
Then:
Out = [[1, 2, 4],
[6, 8, 10]]
Args:
x (Tensor): The source input tensor with 2-D shape. Supported data type is
int32, int64, float32, float64.
index (Tensor): The index input tensor with 2-D shape, first dimension should be same with X.
Data type is int32 or int64.
Returns:
output (Tensor): The output is a tensor with the same shape as index.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]], dtype='float32')
index = paddle.to_tensor([[0, 1, 2],
[1, 2, 3],
[0, 0, 0]], dtype='int32')
target = paddle.to_tensor([[100, 200, 300, 400],
[500, 600, 700, 800],
[900, 1000, 1100, 1200]], dtype='int32')
out_z1 = paddle.index_sample(x, index)
print(out_z1)
#[[1. 2. 3.]
# [6. 7. 8.]
# [9. 9. 9.]]
# Use the index of the maximum value by topk op
# get the value of the element of the corresponding index in other tensors
top_value, top_index = paddle.topk(x, k=2)
out_z2 = paddle.index_sample(target, top_index)
print(top_value)
#[[ 4. 3.]
# [ 8. 7.]
# [12. 11.]]
print(top_index)
#[[3 2]
# [3 2]
# [3 2]]
print(out_z2)
#[[ 400 300]
# [ 800 700]
# [1200 1100]]
"""
if in_dygraph_mode():
return _C_ops.index_sample(x, index)
helper = LayerHelper("index_sample", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.index_sample')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'paddle.tensor.search.index_sample')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='index_sample',
inputs={
'X': x,
'Index': index
},
outputs={'Out': out})
return out
def where(condition, x=None, y=None, name=None):
r"""
Return a tensor of elements selected from either $x$ or $y$, depending on $condition$.
**Note**:
``paddle.where(condition)`` is identical to ``paddle.nonzero(condition, as_tuple=True)``.
.. math::
out_i =
\begin{cases}
x_i, \quad \text{if} \ condition_i \ is \ True \\
y_i, \quad \text{if} \ condition_i \ is \ False \\
\end{cases}
Args:
condition(Tensor): The condition to choose x or y.
x(Tensor, optional): x is a Tensor with data type float32, float64, int32, int64. Either both or neither of x and y should be given.
y(Tensor, optional): y is a Tensor with data type float32, float64, int32, int64. Either both or neither of x and y should be given.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: A Tensor with the same data dype as x.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([0.9383, 0.1983, 3.2, 1.2])
y = paddle.to_tensor([1.0, 1.0, 1.0, 1.0])
out = paddle.where(x>1, x, y)
print(out)
#out: [1.0, 1.0, 3.2, 1.2]
out = paddle.where(x>1)
print(out)
#out: (Tensor(shape=[2, 1], dtype=int64, place=CPUPlace, stop_gradient=True,
# [[2],
# [3]]),)
"""
if x is None and y is None:
return nonzero(condition, as_tuple=True)
if x is None or y is None:
raise ValueError("either both or neither of x and y should be given")
if not in_dygraph_mode():
check_variable_and_dtype(condition, 'condition', ['bool'], 'where')
check_variable_and_dtype(x, 'x',
['float32', 'float64', 'int32', 'int64'],
'where')
check_variable_and_dtype(y, 'y',
['float32', 'float64', 'int32', 'int64'],
'where')
condition_shape = list(condition.shape)
x_shape = list(x.shape)
y_shape = list(y.shape)
if x_shape == y_shape and condition_shape == x_shape:
if in_dygraph_mode():
return _C_ops.where(condition, x, y)
else:
helper = LayerHelper("where", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type='where',
inputs={
'Condition': condition,
'X': x,
'Y': y
},
outputs={'Out': [out]})
return out
else:
cond_int = layers.cast(condition, x.dtype)
cond_not_int = layers.cast(layers.logical_not(condition), x.dtype)
out1 = layers.elementwise_mul(x, cond_int)
out2 = layers.elementwise_mul(y, cond_not_int)
out = layers.elementwise_add(out1, out2)
return out
def sort(x, axis=-1, descending=False, name=None):
"""
This OP sorts the input along the given axis, and returns the sorted output tensor. The default sort algorithm is ascending, if you want the sort algorithm to be descending, you must set the :attr:`descending` as True.
Args:
x(Tensor): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(x). when axis<0, it works the same way
as axis+R. Default is 0.
descending(bool, optional) : Descending is a flag, if set to true,
algorithm will sort by descending order, else sort by
ascending order. Default is false.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: sorted tensor(with the same shape and data type as ``x``).
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[[5,8,9,5],
[0,0,1,7],
[6,9,2,4]],
[[5,2,4,2],
[4,7,7,9],
[1,7,0,6]]],
dtype='float32')
out1 = paddle.sort(x=x, axis=-1)
out2 = paddle.sort(x=x, axis=0)
out3 = paddle.sort(x=x, axis=1)
print(out1)
#[[[5. 5. 8. 9.]
# [0. 0. 1. 7.]
# [2. 4. 6. 9.]]
# [[2. 2. 4. 5.]
# [4. 7. 7. 9.]
# [0. 1. 6. 7.]]]
print(out2)
#[[[5. 2. 4. 2.]
# [0. 0. 1. 7.]
# [1. 7. 0. 4.]]
# [[5. 8. 9. 5.]
# [4. 7. 7. 9.]
# [6. 9. 2. 6.]]]
print(out3)
#[[[0. 0. 1. 4.]
# [5. 8. 2. 5.]
# [6. 9. 9. 7.]]
# [[1. 2. 0. 2.]
# [4. 7. 4. 6.]
# [5. 7. 7. 9.]]]
"""
if in_dygraph_mode():
out, _ = _C_ops.argsort(x, 'axis', axis, 'descending', descending)
return out
helper = LayerHelper("sort", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype,
stop_gradient=False)
ids = helper.create_variable_for_type_inference(VarDesc.VarType.INT64,
stop_gradient=True)
helper.append_op(type='argsort',
inputs={'X': x},
outputs={
'Out': out,
'Indices': ids
},
attrs={
'axis': axis,
'descending': descending
})
return out
def nonzero(x, as_tuple=False):
"""
Return a tensor containing the indices of all non-zero elements of the `input`
tensor. If as_tuple is True, return a tuple of 1-D tensors, one for each dimension
in `input`, each containing the indices (in that dimension) of all non-zero elements
of `input`. Given a n-Dimensional `input` tensor with shape [x_1, x_2, ..., x_n], If
as_tuple is False, we can get a output tensor with shape [z, n], where `z` is the
number of all non-zero elements in the `input` tensor. If as_tuple is True, we can get
a 1-D tensor tuple of length `n`, and the shape of each 1-D tensor is [z, 1].
Args:
x (Tensor): The input tensor variable.
as_tuple (bool): Return type, Tensor or tuple of Tensor.
Returns:
Tensor. The data type is int64.
Examples:
.. code-block:: python
import paddle
x1 = paddle.to_tensor([[1.0, 0.0, 0.0],
[0.0, 2.0, 0.0],
[0.0, 0.0, 3.0]])
x2 = paddle.to_tensor([0.0, 1.0, 0.0, 3.0])
out_z1 = paddle.nonzero(x1)
print(out_z1)
#[[0 0]
# [1 1]
# [2 2]]
out_z1_tuple = paddle.nonzero(x1, as_tuple=True)
for out in out_z1_tuple:
print(out)
#[[0]
# [1]
# [2]]
#[[0]
# [1]
# [2]]
out_z2 = paddle.nonzero(x2)
print(out_z2)
#[[1]
# [3]]
out_z2_tuple = paddle.nonzero(x2, as_tuple=True)
for out in out_z2_tuple:
print(out)
#[[1]
# [3]]
"""
list_out = []
shape = x.shape
rank = len(shape)
if in_dygraph_mode():
outs = _C_ops.where_index(x)
else:
outs = layers.where(x)
if not as_tuple:
return outs
elif rank == 1:
return tuple([outs])
else:
for i in range(rank):
list_out.append(
layers.slice(outs, axes=[1], starts=[i], ends=[i + 1]))
return tuple(list_out)
def argmin(x, axis=None, keepdim=False, dtype="int64", name=None):
"""
This OP computes the indices of the min elements of the input tensor's
element along the provided axis.
Args:
x(Tensor): An input N-D Tensor with type float32, float64, int16,
int32, int64, uint8.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index.
keepdim(bool, optional): Keep the axis that selecting min. The defalut value is False.
dtype(str): Data type of the output tensor which can
be int32, int64. The default value is 'int64', and it will
return the int64 indices.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor, return the tensor of `int32` if set :attr:`dtype` is `int32`, otherwise return the tensor of `int64`
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[5,8,9,5],
[0,0,1,7],
[6,9,2,4]])
out1 = paddle.argmin(x)
print(out1) # 4
out2 = paddle.argmin(x, axis=1)
print(out2)
# [0 0 2]
out3 = paddle.argmin(x, axis=-1)
print(out3)
# [0 0 2]
"""
if axis is not None and not isinstance(axis, int):
raise TypeError(
"The type of 'axis' must be int or None in argmin, but received %s."
% (type(axis)))
if dtype is None:
raise ValueError(
"the value of 'dtype' in argmin could not be None, but received None"
)
var_dtype = convert_np_dtype_to_dtype_(dtype)
flatten = False
if axis is None:
flatten = True
axis = 0
if in_dygraph_mode():
out = _C_ops.arg_min(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
keepdim, 'flatten', flatten)
return out
helper = LayerHelper("argmin", **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
'paddle.argmin')
check_dtype(var_dtype, 'dtype', ['int32', 'int64'], 'argmin')
out = helper.create_variable_for_type_inference(var_dtype)
attrs = {}
attrs['keepdims'] = keepdim
attrs['axis'] = axis
attrs['flatten'] = flatten
attrs['dtype'] = var_dtype
helper.append_op(type='arg_min',
inputs={'X': x},
outputs={'Out': [out]},
attrs=attrs)
out.stop_gradient = True
return out
