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未验证 提交 c239f15a 编写于 作者: Z zhiboniu 提交者： GitHub
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tensor fluid code transfer part2 (#41096)

上级 1e56ca8a
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python/paddle/fft.py
浏览文件 @ c239f15a
......@@ -15,7 +15,8 @@
from typing import Sequence
import numpy as np
import paddle
from .tensor.attribute import is_complex, is_floating_point, is_integer, _real_to_complex_dtype, _complex_to_real_dtype
from .tensor.attribute import is_complex, is_floating_point, is_integer
from .tensor.creation import _real_to_complex_dtype, _complex_to_real_dtype
from .fluid.framework import _non_static_mode
from . import _C_ops
from .fluid.data_feeder import check_variable_and_dtype
......
python/paddle/fluid/tests/unittests/test_crop_tensor_op.py
浏览文件 @ c239f15a
......@@ -17,6 +17,7 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
......@@ -225,31 +226,30 @@ class TestCropTensorException(unittest.TestCase):
offset = fluid.data(name='offset', shape=[1], dtype='int32')
def attr_shape_type():
out = fluid.layers.crop_tensor(input1, shape=3)
out = paddle.crop(input1, shape=3)
def attr_shape_dtype():
out = fluid.layers.crop_tensor(input1, shape=[2, 2.0, 3, 3])
out = paddle.crop(input1, shape=[2, 2.0, 3, 3])
def attr_shape_value1():
out = fluid.layers.crop_tensor(input1, shape=[2, -2, dim, 3])
out = paddle.crop(input1, shape=[2, -2, dim, 3])
def attr_shape_value2():
out = fluid.layers.crop_tensor(input1, shape=[2, 0, dim, 3])
out = paddle.crop(input1, shape=[2, 0, dim, 3])
def attr_offsets_type():
out = fluid.layers.crop_tensor(
input1, shape=[2, 2, 3, 3], offsets=0)
out = paddle.crop(input1, shape=[2, 2, 3, 3], offsets=0)
def attr_offsets_dtype():
out = fluid.layers.crop_tensor(
out = paddle.crop(
input1, shape=[2, 2, 3, 3], offsets=[0, 1.0, 0, 0])
def attr_offsets_value():
out = fluid.layers.crop_tensor(
out = paddle.crop(
input1, shape=[2, 2, 3, 3], offsets=[0, -1, offset, 0])
def input_dtype():
out = fluid.layers.crop_tensor(input2, shape=[2, 2, 3, 3])
out = paddle.crop(input2, shape=[2, 2, 3, 3])
self.assertRaises(TypeError, attr_shape_type)
self.assertRaises(TypeError, attr_shape_dtype)
......
python/paddle/fluid/tests/unittests/test_slice_op.py
浏览文件 @ c239f15a
......@@ -534,13 +534,13 @@ class TestSliceAPI(unittest.TestCase):
# value_int64 is greater than 2147483647 which is the max of int32
value_int64 = fluid.layers.fill_constant([1], "int64", 2147483648)
out_1 = fluid.layers.slice(
out_1 = paddle.slice(
x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[value_int64, 100, -1])
out_2 = fluid.layers.slice(
out_2 = paddle.slice(
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1])
out_3 = fluid.layers.slice(
out_3 = paddle.slice(
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1])
out_4 = fluid.layers.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)
out_4 = paddle.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)
out_5 = x[-3:3, 0:100, 2:-1]
out_6 = x[minus_3:3, 0:100, :, 2:-1]
......
python/paddle/fluid/tests/unittests/test_strided_slice_op.py
浏览文件 @ c239f15a
......@@ -534,25 +534,25 @@ class TestStridedSliceAPI(unittest.TestCase):
shape=[3, 4, 5, 6],
append_batch_size=False,
dtype="float64")
out_1 = fluid.layers.strided_slice(
out_1 = paddle.strided_slice(
x,
axes=[0, 1, 2],
starts=[-3, 0, 2],
ends=[3, 100, -1],
strides=[1, 1, 1])
out_2 = fluid.layers.strided_slice(
out_2 = paddle.strided_slice(
x,
axes=[0, 1, 3],
starts=[minus_3, 0, 2],
ends=[3, 100, -1],
strides=[1, 1, 1])
out_3 = fluid.layers.strided_slice(
out_3 = paddle.strided_slice(
x,
axes=[0, 1, 3],
starts=[minus_3, 0, 2],
ends=[3, 100, minus_1],
strides=[1, 1, 1])
out_4 = fluid.layers.strided_slice(
out_4 = paddle.strided_slice(
x, axes=[0, 1, 2], starts=starts, ends=ends, strides=strides)
out_5 = x[-3:3, 0:100:2, -1:2:-1]
......
python/paddle/tensor/attribute.py
浏览文件 @ c239f15a
......@@ -14,37 +14,128 @@
from __future__ import print_function
from ..framework import core
from ..fluid.layer_helper import LayerHelper
from ..framework import core, _non_static_mode
from ..framework import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype
from ..fluid.data_feeder import check_type
from .creation import assign
from .creation import _complex_to_real_dtype
# TODO: define functions to get tensor attributes
from ..fluid.layers import rank # noqa: F401
from ..fluid.layers import shape # noqa: F401
import paddle
from paddle import _C_ops
from paddle.static import Variable
from ..static import Variable
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
import numpy as np
__all__ = []
def _complex_to_real_dtype(dtype):
if dtype == core.VarDesc.VarType.COMPLEX64:
return core.VarDesc.VarType.FP32
elif dtype == core.VarDesc.VarType.COMPLEX128:
return core.VarDesc.VarType.FP64
else:
return dtype
def rank(input):
"""
The OP returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
Args:
input (Tensor): The input N-D tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary.
Returns:
Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor.
Examples:
.. code-block:: python
import paddle
input = paddle.rand((3, 100, 100))
rank = paddle.rank(input)
print(rank)
# 3
"""
check_type(input, 'input', (Variable), 'input')
ndims = len(input.shape)
out = assign(np.array(ndims, 'int32'))
return out
def shape(input):
"""
:alias_main: paddle.shape
:alias: paddle.shape,paddle.tensor.shape,paddle.tensor.attribute.shape
:old_api: paddle.fluid.layers.shape
**Shape Layer**
Get the shape of the input.
.. code-block:: text
Case1:
Given N-D Tensor:
input = [ [1, 2, 3, 4], [5, 6, 7, 8] ]
Then:
input.shape = [2, 4]
Case2:
Given SelectedRows:
input.rows = [0, 4, 19]
input.height = 20
input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor
Then:
input.shape = [3, 2]
Args:
input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64.
If input variable is type of SelectedRows, returns the shape of it's inner tensor.
Returns:
Variable (Tensor): The shape of the input variable.
Examples:
.. code-block:: python
def _real_to_complex_dtype(dtype):
if dtype == core.VarDesc.VarType.FP32:
return core.VarDesc.VarType.COMPLEX64
elif dtype == core.VarDesc.VarType.FP64:
return core.VarDesc.VarType.COMPLEX128
else:
return dtype
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32")
output = fluid.layers.shape(inputs)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
img = np.ones((3, 100, 100)).astype(np.float32)
res = exe.run(fluid.default_main_program(), feed={'x':img}, fetch_list=[output])
print(res) # [array([ 3, 100, 100], dtype=int32)]
"""
if in_dygraph_mode():
out = _C_ops.final_state_shape(input)
out.stop_gradient = True
return out
if _in_legacy_dygraph():
out = _C_ops.shape(input)
out.stop_gradient = True
return out
check_variable_and_dtype(input, 'input', [
'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64',
'complex128'
], 'shape')
helper = LayerHelper('shape', **locals())
out = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type='shape',
inputs={'Input': input},
outputs={'Out': out},
stop_gradient=True)
return out
def is_complex(x):
......
python/paddle/tensor/creation.py
浏览文件 @ c239f15a
......@@ -14,27 +14,138 @@
from __future__ import print_function
import numpy as np
import math
from paddle.common_ops_import import fill_constant
from ..fluid.layers import utils
from ..fluid.layers import tensor
from ..static import Variable, device_guard
from ..framework import _current_expected_place, _get_paddle_place
from ..framework import dygraph_only
from ..framework import core
from ..fluid.layer_helper import LayerHelper
from ..framework import in_dygraph_mode, _non_static_mode
from ..framework import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
from ..framework import convert_np_dtype_to_dtype_, _varbase_creator, OpProtoHolder
from paddle.tensor.attribute import _complex_to_real_dtype, _real_to_complex_dtype
# TODO: define functions to get create a tensor
from ..fluid.layers import linspace # noqa: F401
import paddle
from paddle import _C_ops
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode, _in_eager_without_dygraph_check
from ..fluid.framework import _in_legacy_dygraph, _in_eager_without_dygraph_check
import warnings
__all__ = []
def _complex_to_real_dtype(dtype):
if dtype == core.VarDesc.VarType.COMPLEX64:
return core.VarDesc.VarType.FP32
elif dtype == core.VarDesc.VarType.COMPLEX128:
return core.VarDesc.VarType.FP64
else:
return dtype
def _real_to_complex_dtype(dtype):
if dtype == core.VarDesc.VarType.FP32:
return core.VarDesc.VarType.COMPLEX64
elif dtype == core.VarDesc.VarType.FP64:
return core.VarDesc.VarType.COMPLEX128
else:
return dtype
def linspace(start, stop, num, dtype=None, name=None):
r"""
This OP return fixed number of evenly spaced values within a given interval.
Args:
start(int|float|Tensor): The input :attr:`start` is start variable of range. It is a scalar, \
or a Tensor of shape [1] with input data type int32, int64, float32 or float64.
stop(int|float|Tensor): The input :attr:`stop` is start variable of range. It is a scalar, \
or a Tensor of shape [1] with input data type int32, int64, float32 or float64.
num(int|Tensor): The input :attr:`num` is given num of the sequence. It is an int scalar, \
or a Tensor of shape [1] with data type int32.
dtype(np.dtype|str, optional): The data type of output tensor, it could be
int32, int64, float32 and float64. Default: if None, the data type is float32.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.Default: None.
Returns:
Tensor: the output data type will be float32, float64. The 1-D tensor with fixed number of evenly spaced values, \
the data shape of this tensor is :math:`[num]` . If the :attr:`num` is set 1, the output tensor just has \
the value with input :attr:`start`.
Examples:
.. code-block:: python
import paddle
data = paddle.linspace(0, 10, 5, 'float32') # [0.0, 2.5, 5.0, 7.5, 10.0]
data = paddle.linspace(0, 10, 1, 'float32') # [0.0]
"""
if dtype is None:
dtype = 'float32'
tensor_num = num
tensor_start = start
tensor_stop = stop
if not isinstance(num, Variable):
check_type(num, 'num', (int), 'linspace')
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if not isinstance(start, Variable):
with device_guard("cpu"):
tensor_start = fill_constant([1], dtype, start)
if not isinstance(stop, Variable):
with device_guard("cpu"):
tensor_stop = fill_constant([1], dtype, stop)
if not isinstance(num, Variable):
with device_guard("cpu"):
tensor_num = fill_constant([1], 'int32', num)
if _non_static_mode():
return _C_ops.linspace(tensor_start, tensor_stop, tensor_num, 'dtype',
dtype)
helper = LayerHelper("linspace", **locals())
start_dtype = convert_dtype(tensor_start.dtype)
stop_dtype = convert_dtype(tensor_stop.dtype)
out_dtype = convert_dtype(dtype)
if isinstance(start, Variable):
check_dtype(start.dtype, 'start',
['float32', 'float64', 'int32', 'int64'], 'linspace')
else:
check_type(start, 'start', (int, float), 'linspace')
if isinstance(stop, Variable):
check_dtype(stop.dtype, 'stop',
['float32', 'float64', 'int32', 'int64'], 'linspace')
else:
check_type(stop, 'stop', (int, float), 'linspace')
if isinstance(num, Variable):
check_dtype(num.dtype, 'num', ['int32'], 'linspace')
check_dtype(dtype, 'dtype', ['int32', 'int64', 'float32', 'float64'],
'linspace')
if ((stop_dtype == "float64" or start_dtype == "float64") and
out_dtype in ["float32", "int32"]) or ((stop_dtype == "int64" or
start_dtype == "int64") and
out_dtype == "int32"):
raise ValueError(
"The dtype of start/stop is {}/{} but the attr(dtype) of linspace is {}, "
"which may cause data type overflows. Please reset attr(dtype) of linspace."
.format(start_dtype, stop_dtype, dtype))
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='linspace',
inputs={'Start': tensor_start,
'Stop': tensor_stop,
'Num': tensor_num},
attrs={'dtype': dtype},
outputs={'Out': [out]})
if isinstance(num, int):
out.desc.set_shape((num, ))
return out
@dygraph_only
def to_tensor(data, dtype=None, place=None, stop_gradient=True):
r"""
......@@ -60,7 +171,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
Tensor: A Tensor constructed from ``data`` .
Raises:
TypeError: If the data type of ``data`` is not scalar, list, tuple, numpy.ndarray, paddle.Tensor
TypeError: If the data type of ``data`` is not scalar, list, tuple, np.ndarray, paddle.Tensor
ValueError: If ``data`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]
TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string.
......@@ -152,7 +263,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
return data
else:
raise TypeError(
"Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor".
"Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|np.ndarray|paddle.Tensor".
format(type(data)))
if not dtype:
if data.dtype in [
......@@ -439,11 +550,39 @@ def eye(num_rows, num_columns=None, dtype=None, name=None):
dtype = 'float32'
if num_columns is None:
num_columns = num_rows
return paddle.fluid.layers.eye(num_rows=num_rows,
num_columns=num_columns,
batch_shape=None,
dtype=dtype,
name=name)
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if num_columns is not None:
if not isinstance(num_columns, int) or num_columns < 0:
raise TypeError("num_columns should be a non-negative int")
else:
num_columns = num_rows
if _non_static_mode():
out = _C_ops.eye('dtype', dtype, 'num_rows', num_rows, 'num_columns',
num_columns)
else:
helper = LayerHelper("eye", **locals())
check_dtype(dtype, 'dtype',
['float16', 'float32', 'float64', 'int32', 'int64'], 'eye')
if not isinstance(num_rows, int) or num_rows < 0:
raise TypeError("num_rows should be a non-negative int")
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='eye',
inputs={},
outputs={'Out': [out]},
attrs={
'num_rows': num_rows,
'num_columns': num_columns,
'dtype': dtype
},
stop_gradient=True)
out.stop_gradient = True
return out
def full(shape, fill_value, dtype=None, name=None):
......@@ -564,7 +703,53 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
end = start
start = 0
return paddle.fluid.layers.range(start, end, step, dtype, name)
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if not isinstance(start, Variable):
with device_guard("cpu"):
start = fill_constant([1], dtype, start, force_cpu=True)
elif start.dtype != dtype:
start = paddle.cast(start, dtype)
if not isinstance(end, Variable):
with device_guard("cpu"):
end = fill_constant([1], dtype, end, force_cpu=True)
elif end.dtype != dtype:
end = paddle.cast(end, dtype)
if not isinstance(step, Variable):
with device_guard("cpu"):
step = fill_constant([1], dtype, step, force_cpu=True)
elif step.dtype != dtype:
step = paddle.cast(step, dtype)
if in_dygraph_mode():
return _C_ops.final_state_arange(start, end, step, dtype,
_current_expected_place())
if _in_legacy_dygraph():
out = _C_ops.range(start, end, step)
out.stop_gradient = True
return out
out_shape = None
if not isinstance(start, Variable) and not isinstance(
end, Variable) and not isinstance(step, Variable):
out_shape = [int(math.ceil((end - start) / step))]
check_dtype(dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'],
'range/arange')
helper = LayerHelper('range', **locals())
out = helper.create_variable_for_type_inference(dtype, shape=out_shape)
helper.append_op(
type='range',
inputs={'Start': start,
'End': end,
'Step': step},
outputs={'Out': out})
out.stop_gradient = True
return out
def _tril_triu_op(helper):
......@@ -1187,7 +1372,7 @@ def assign(x, output=None):
The OP copies the :attr:`x` to the :attr:`output`.
Parameters:
x (Tensor|numpy.ndarray|list|tuple|scalar): A tensor, numpy ndarray, tuple/list of scalar,
x (Tensor|np.ndarray|list|tuple|scalar): A tensor, numpy ndarray, tuple/list of scalar,
or scalar. Its data type supports float16, float32, float64, int32, int64, and bool.
Note: the float64 data will be converted to float32 because of current platform protobuf
data limitation.
......@@ -1211,9 +1396,91 @@ def assign(x, output=None):
result2 = paddle.assign(data) # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
"""
check_type(x, 'x', (Variable, np.ndarray, list, tuple, float, int, bool),
'assign')
return tensor.assign(x, output)
input = x
helper = LayerHelper('assign', **locals())
check_type(input, 'input', (Variable, np.ndarray, list, tuple, float, int,
bool), 'assign')
is_inplace = True if output is not None else False
if np.isscalar(input) and not isinstance(input, str):
input = np.array([input])
elif isinstance(input, (list, tuple)):
input = np.array(input)
# NOTE(Aurelius84): Why we judge core.VarBase?
# In case of @to_static, a VarBase can be as input of `assign`,
# but _non_static_mode()==False under @to_static, which means
# isinstance(VarBase, Variable) == False. It will cause return None
# after this api.
if isinstance(input, (Variable, core.VarBase)):
if _non_static_mode():
if output is None:
if _in_legacy_dygraph():
output = core.VarBase()
else:
output = core.eager.Tensor()
_C_ops.assign(input, output)
else:
check_dtype(input.dtype, 'input', [
'float16', 'uint16', 'float32', 'float64', 'int32', 'int64',
'uint8', 'bool'
], 'assign', '(When the type of input in assign is Variable.)')
if output is None:
output = helper.create_variable_for_type_inference(
dtype=input.dtype)
helper.append_op(
type='assign', inputs={'X': [input]},
outputs={'Out': [output]})
elif isinstance(input, np.ndarray):
# Not support [var, var, ...] currently.
if len(input.shape) > 0 and any(isinstance(x, Variable) for x in input):
raise TypeError(
"Required type(input) numpy.ndarray, but found `list(Variable)` in input."
)
dtype = convert_np_dtype_to_dtype_(input.dtype)
if dtype == core.VarDesc.VarType.FP64:
# Setting FP64 numpy data is not supported in Paddle, so we
# use FP32 here
warnings.warn(
"paddle.assign doesn't support float64 input now due "
"to current platform protobuf data limitation, we convert "
"it to float32")
dtype = core.VarDesc.VarType.FP32
if dtype == core.VarDesc.VarType.BOOL:
value_name = "bool_values"
values = [int(v) for v in input.flat]
elif dtype == core.VarDesc.VarType.FP32:
value_name = "fp32_values"
values = [float(v) for v in input.flat]
elif dtype == core.VarDesc.VarType.INT32:
value_name = "int32_values"
values = [int(v) for v in input.flat]
elif dtype == core.VarDesc.VarType.INT64:
value_name = "int64_values"
values = [int(v) for v in input.flat]
else:
raise TypeError(
"When the type of 'input' in assign is numpy.ndarray, "
"the data type of 'input' must be bool, float32, int32 or int64, but "
"received %s." % convert_dtype(dtype))
if input.size > 1024 * 1024:
raise ValueError("The size of input is too big. Please consider "
"saving it to file and 'load_op' to load it")
if output is None:
output = helper.create_variable_for_type_inference(
dtype=input.dtype)
helper.append_op(
type='assign_value',
outputs={'Out': [output]},
attrs={
'dtype': dtype,
'shape': list(input.shape),
value_name: values
})
if is_inplace and _non_static_mode():
output._bump_inplace_version()
return output
def clone(x, name=None):
......
python/paddle/tensor/linalg.py
浏览文件 @ c239f15a
......@@ -13,14 +13,16 @@
# limitations under the License.
import numpy as np
from ..fluid.layer_helper import LayerHelper
from ..framework import LayerHelper
from ..framework import _varbase_creator, _dygraph_tracer, in_dygraph_mode, _non_static_mode
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
from ..static import Variable
from ..fluid.framework import _in_legacy_dygraph
from .manipulation import cast
from .math import multiply, add
from .logic import logical_not
from .creation import full
from ..fluid import layers
import paddle
from paddle.common_ops_import import core
from paddle.common_ops_import import VarDesc
......@@ -2532,11 +2534,11 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
y = paddle.to_tensor(y, dtype=x.dtype)
condition = s > cutoff
cond_int = layers.cast(condition, s.dtype)
cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)
out1 = layers.elementwise_mul(1 / s, cond_int)
out2 = layers.elementwise_mul(1 / y, cond_not_int)
singular = layers.elementwise_add(out1, out2)
cond_int = cast(condition, s.dtype)
cond_not_int = cast(logical_not(condition), s.dtype)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])
dims = list(range(len(vt.shape)))
......@@ -2559,11 +2561,11 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
y = paddle.to_tensor(y, dtype=s.dtype)
condition = s_abs > cutoff
cond_int = layers.cast(condition, s.dtype)
cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)
out1 = layers.elementwise_mul(1 / s, cond_int)
out2 = layers.elementwise_mul(1 / y, cond_not_int)
singular = layers.elementwise_add(out1, out2)
cond_int = cast(condition, s.dtype)
cond_not_int = cast(logical_not(condition), s.dtype)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])
out_1 = u * st
......@@ -2597,17 +2599,17 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
'keep_dim': True,
'reduce_all': False})
rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)
rcond = full(shape=[1], fill_value=rcond, dtype=dtype)
cutoff = rcond * max_singular_val
y = float('inf')
y = layers.fill_constant(shape=[1], value=y, dtype=dtype)
y = full(shape=[1], fill_value=y, dtype=dtype)
condition = s > cutoff
cond_int = layers.cast(condition, dtype)
cond_not_int = layers.cast(layers.logical_not(condition), dtype)
out1 = layers.elementwise_mul(1 / s, cond_int)
out2 = layers.elementwise_mul(1 / y, cond_not_int)
singular = layers.elementwise_add(out1, out2)
cond_int = cast(condition, dtype)
cond_not_int = cast(logical_not(condition), dtype)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st = helper.create_variable_for_type_inference(dtype=dtype)
st_shape = helper.create_variable_for_type_inference(dtype=dtype)
......@@ -2682,17 +2684,17 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None):
'keep_dim': True,
'reduce_all': False})
rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)
rcond = full(shape=[1], fill_value=rcond, dtype=s_type)
cutoff = rcond * max_singular_val
y = float('inf')
y = layers.fill_constant(shape=[1], value=y, dtype=s_type)
y = full(shape=[1], fill_value=y, dtype=s_type)
condition = s_abs > cutoff
cond_int = layers.cast(condition, s_type)
cond_not_int = layers.cast(layers.logical_not(condition), s_type)
out1 = layers.elementwise_mul(1 / s, cond_int)
out2 = layers.elementwise_mul(1 / y, cond_not_int)
singular = layers.elementwise_add(out1, out2)
cond_int = cast(condition, s_type)
cond_not_int = cast(logical_not(condition), s_type)
out1 = multiply(1 / s, cond_int)
out2 = multiply(1 / y, cond_not_int)
singular = add(out1, out2)
st = helper.create_variable_for_type_inference(dtype=s_type)
st_shape = helper.create_variable_for_type_inference(dtype=s_type)
......
python/paddle/tensor/manipulation.py
浏览文件 @ c239f15a
......@@ -16,32 +16,723 @@ from __future__ import print_function
from collections import Counter
from ..static import Variable, device_guard
from ..framework import core
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode, _in_eager_without_dygraph_check, _non_static_mode
from ..fluid.layer_helper import LayerHelper
from ..framework import core, in_dygraph_mode
from ..fluid.framework import _in_legacy_dygraph, _in_eager_without_dygraph_check, _non_static_mode
from ..framework import LayerHelper
from ..framework import OpProtoHolder, convert_np_dtype_to_dtype_, dygraph_only
from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
from ..fluid.layers import utils
import numpy as np
# TODO: define functions to manipulate a tensor
from ..fluid.layers import cast # noqa: F401
from ..fluid.layers import slice # noqa: F401
from ..fluid.layers import transpose # noqa: F401
from ..fluid.layers import unstack # noqa: F401
from ..fluid.layers import scatter_nd # noqa: F401
from ..fluid.layers import shard_index # noqa: F401
from ..fluid.layers import crop_tensor as crop # noqa: F401
from ..fluid.layers.nn import _elementwise_op_in_dygraph
from ..fluid import layers
from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
import paddle
from paddle import _C_ops
from paddle.tensor.attribute import _complex_to_real_dtype, _real_to_complex_dtype
from ..common_ops_import import dygraph_utils, fill_constant, _varbase_creator
import warnings
from .creation import zeros
from .creation import _complex_to_real_dtype
from .creation import _real_to_complex_dtype
__all__ = []
def cast(x, dtype):
"""
This OP takes in the Tensor :attr:`x` with :attr:`x.dtype` and casts it
to the output with :attr:`dtype`. It's meaningless if the output dtype
equals the input dtype, but it's fine if you do so.
Args:
x(Tensor): An input N-D Tensor with data type bool, float16,
float32, float64, int32, int64, uint8.
dtype(np.dtype|str): Data type of the output:
bool, float16, float32, float64, int8, int32, int64, uint8.
Returns:
Tensor: A Tensor with the same shape as input's.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([2, 3, 4], 'float64')
y = paddle.cast(x, 'uint8')
"""
if in_dygraph_mode():
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return _C_ops.final_state_cast(x, dtype)
if _non_static_mode():
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
out = _C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
return out
check_variable_and_dtype(x, 'x', [
'bool', 'float16', 'float32', 'float64', 'int16', 'int32', 'int64',
'uint8', 'uint16'
], 'cast')
check_dtype(dtype, 'dtype', [
'bool', 'float16', 'float32', 'float64', 'int8', 'int16', 'int32',
'int64', 'uint8', 'uint16'
], 'cast')
helper = LayerHelper('cast', **locals())
out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=x.stop_gradient)
helper.append_op(
type='cast',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'in_dtype': x.dtype,
'out_dtype': out.dtype})
return out
def slice(input, axes, starts, ends):
"""
This operator produces a slice of ``input`` along multiple axes. Similar to numpy:
https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and
end dimension for each axis in the list of axes and Slice uses this information
to slice the input data tensor. If a negative value is passed to
``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the
axis :math:`i-1` (here 0 is the initial position).
If the value passed to ``starts`` or ``ends`` is greater than n
(the number of elements in this dimension), it represents n.
For slicing to the end of a dimension with unknown size, it is recommended
to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``.
Following examples will explain how slice works:
.. code-block:: text
Case1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Case2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [0, 1]
ends = [-1, 1000] # -1 denotes the reverse 0th position of dimension 0.
Then:
result = [ [2, 3, 4], ] # result = data[0:1, 1:4]
Args:
input (Tensor): A ``Tensor`` . The data type is ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``.
axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to .
starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of
it should be integers or Tensors with shape [1]. If ``starts`` is an Tensor, it should be an 1-D Tensor.
It represents starting indices of corresponding axis in ``axes``.
ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of
it should be integers or Tensors with shape [1]. If ``ends`` is an Tensor, it should be an 1-D Tensor .
It represents ending indices of corresponding axis in ``axes``.
Returns:
Tensor: A ``Tensor``. The data type is same as ``input``.
Raises:
TypeError: The type of ``starts`` must be list, tuple or Tensor.
TypeError: The type of ``ends`` must be list, tuple or Tensor.
Examples:
.. code-block:: python
import paddle
input = paddle.rand(shape=[4, 5, 6], dtype='float32')
# example 1:
# attr starts is a list which doesn't contain tensor.
axes = [0, 1, 2]
starts = [-3, 0, 2]
ends = [3, 2, 4]
sliced_1 = paddle.slice(input, axes=axes, starts=starts, ends=ends)
# sliced_1 is input[0:3, 0:2, 2:4].
# example 2:
# attr starts is a list which contain tensor.
minus_3 = paddle.full([1], -3, "int32")
sliced_2 = paddle.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends)
# sliced_2 is input[0:3, 0:2, 2:4].
"""
if in_dygraph_mode():
attrs = ()
starts_tensor = None
ends_tensor = None
if isinstance(axes, (list, tuple)):
axes = list(axes)
if len(axes) == 0:
raise ValueError(
"Input axes should not be an empty list/tuple.")
for i in range(len(axes)):
if axes[i] < 0:
axes[i] = max(0, axes[i] + len(input.shape))
else:
axes[i] = min(len(input.shape) - 1, axes[i])
else:
raise ValueError(
"Input axes must be a python list or tuple, but reveived {}".
format(type(axes)))
infer_flags = list(1 for i in range(len(axes)))
tmp_tensor_type = core.eager.Tensor
if isinstance(starts, (list, tuple)):
starts = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type) else item
for item in starts
]
attrs += ('starts', starts)
elif isinstance(starts, tmp_tensor_type):
starts_tensor = starts
starts.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
if isinstance(ends, (list, tuple)):
ends = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type) else item for item in ends
]
attrs += ('ends', ends)
elif isinstance(ends, tmp_tensor_type):
ends_tensor = ends
ends_tensor.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
return _C_ops.slice(input, starts_tensor, ends_tensor, None, None,
'axes', axes, 'infer_flags', infer_flags, *attrs)
else:
if _in_legacy_dygraph():
attrs = ()
starts_tensor = None
ends_tensor = None
if isinstance(axes, (list, tuple)):
axes = list(axes)
if len(axes) == 0:
raise ValueError(
"Input axes should not be an empty list/tuple.")
for i in range(len(axes)):
if axes[i] < 0:
axes[i] = max(0, axes[i] + len(input.shape))
else:
axes[i] = min(len(input.shape) - 1, axes[i])
else:
raise ValueError(
"Input axes must be a python list or tuple, but reveived {}".
format(type(axes)))
infer_flags = list(1 for i in range(len(axes)))
tmp_tensor_type = Variable
if isinstance(starts, (list, tuple)):
starts = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type) else item
for item in starts
]
attrs += ('starts', starts)
elif isinstance(starts, tmp_tensor_type):
starts_tensor = starts
starts.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
if isinstance(ends, (list, tuple)):
ends = [
item.numpy().item(0)
if isinstance(item, tmp_tensor_type) else item
for item in ends
]
attrs += ('ends', ends)
elif isinstance(ends, tmp_tensor_type):
ends_tensor = ends
ends_tensor.stop_gradient = True
infer_flags = list(-1 for i in range(len(axes)))
return _C_ops.slice(input, starts_tensor, ends_tensor, None, None,
'axes', axes, 'infer_flags', infer_flags,
*attrs)
if not isinstance(starts, (list, tuple, Variable)):
raise ValueError(
"Input starts must be an Variable, python list or tuple.")
if not isinstance(ends, (list, tuple, Variable)):
raise ValueError(
"Input ends must be an Variable, python list or tuple.")
helper = LayerHelper('slice', **locals())
inputs = {'Input': input}
attrs = {'axes': axes}
infer_flags = list(1 for i in range(len(axes)))
# starts
if isinstance(starts, Variable):
starts.stop_gradient = True
inputs['StartsTensor'] = starts
infer_flags = list(-1 for i in range(len(axes)))
elif isinstance(starts, (list, tuple)):
attrs['starts'] = []
if utils._contain_var(starts):
inputs['StartsTensorList'] = utils._convert_to_tensor_list(starts)
for i, dim in enumerate(starts):
if isinstance(dim, Variable):
attrs['starts'].append(-1)
infer_flags[i] = -1
else:
attrs['starts'].append(dim)
else:
attrs['starts'] = starts
# ends
if isinstance(ends, Variable):
ends.stop_gradient = True
inputs['EndsTensor'] = ends
infer_flags = list(-1 for i in range(len(axes)))
elif isinstance(ends, (list, tuple)):
attrs['ends'] = []
if utils._contain_var(ends):
inputs['EndsTensorList'] = utils._convert_to_tensor_list(ends)
for i, dim in enumerate(ends):
if isinstance(dim, Variable):
attrs['ends'].append(-1)
infer_flags[i] = -1
else:
attrs['ends'].append(dim)
else:
attrs['ends'] = ends
# infer_flags
attrs['infer_flags'] = infer_flags
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('input'))
helper.append_op(
type='slice', inputs=inputs, attrs=attrs, outputs={'Out': out})
return out
def transpose(x, perm, name=None):
"""
Permute the data dimensions of `input` according to `perm`.
The `i`-th dimension of the returned tensor will correspond to the
perm[i]-th dimension of `input`.
Args:
x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32.
perm (list|tuple): Permute the input according to the data of perm.
name (str): The name of this layer. It is optional.
Returns:
Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.
For Example:
.. code-block:: text
x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]]
[[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
shape(x) = [2,3,4]
# Example 1
perm0 = [1,0,2]
y_perm0 = [[[ 1 2 3 4] [13 14 15 16]]
[[ 5 6 7 8] [17 18 19 20]]
[[ 9 10 11 12] [21 22 23 24]]]
shape(y_perm0) = [3,2,4]
# Example 2
perm1 = [2,1,0]
y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]]
[[ 2 14] [ 6 18] [10 22]]
[[ 3 15] [ 7 19] [11 23]]
[[ 4 16] [ 8 20] [12 24]]]
shape(y_perm1) = [4,3,2]
Examples:
.. code-block:: python
import paddle
x = paddle.randn([2, 3, 4])
x_transposed = paddle.transpose(x, perm=[1, 0, 2])
print(x_transposed.shape)
# [3L, 2L, 4L]
"""
if in_dygraph_mode():
return _C_ops.final_state_transpose(x, perm)
else:
if _in_legacy_dygraph():
out, _ = _C_ops.transpose2(x, 'axis', perm)
return out
check_variable_and_dtype(x, 'x', [
'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64',
'complex128'
], 'transpose')
check_type(perm, 'perm', (list, tuple), 'transpose')
if isinstance(perm, tuple):
perm = list(perm)
if len(perm) != len(x.shape):
raise ValueError(
"Input(perm) is the permutation of dimensions of Input(x), "
"its length should be equal to dimensions of Input(x), "
"but received dimension of Input(x) is %s, "
"the length of Input(perm) is %s." % (len(x.shape), len(perm)))
for idx, dim in enumerate(perm):
if dim >= len(x.shape):
raise ValueError(
"Each element in Input(perm) should be less than Input(x)'s dimension, "
"but %d-th element in Input(perm) is %d which exceeds Input(x)'s "
"dimension %d." % (idx, perm[idx], len(x.shape)))
helper = LayerHelper('transpose', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='transpose2',
inputs={'X': [x]},
outputs={'Out': [out],
'XShape': [x_shape]},
attrs={'axis': perm})
return out
def unstack(x, axis=0, num=None):
"""
:alias_main: paddle.unstack
:alias: paddle.unstack,paddle.tensor.unstack,paddle.tensor.manipulation.unstack
:old_api: paddle.fluid.layers.unstack
**UnStack Layer**
This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
raised.
Args:
x (Tensor): Input Tensor. It is a N-D Tensors of data types float32, float64, int32, int64.
axis (int): The axis along which the input is unstacked.
num (int|None): The number of output variables.
Returns:
list(Tensor): The unstacked Tensors list. The list elements are N-D Tensors of data types float32, float64, int32, int64.
Raises:
ValueError: If x.shape[axis] <= 0 or axis is not in range [-D, D).
Examples:
.. code-block:: python
import paddle
x = paddle.ones(name='x', shape=[2, 3, 5], dtype='float32') # create a tensor with shape=[2, 3, 5]
y = paddle.unstack(x, axis=1) # unstack with second axis, which results 3 tensors with shape=[2, 5]
"""
if _non_static_mode():
if num == None:
num = x.shape[axis]
if num == 0:
return []
return _C_ops.unstack(x, num, 'axis', int(axis), 'num', num)
helper = LayerHelper('unstack', **locals())
if num is None:
if axis is None or x.shape[axis] <= 0:
raise ValueError('unknown unstack number')
else:
num = x.shape[axis]
outs = []
for _ in range(num):
outs.append(helper.create_variable_for_type_inference(x.dtype))
helper.append_op(
type='unstack',
inputs={'X': [x]},
outputs={'Y': outs},
attrs={'axis': axis,
'num': num})
return outs
def shard_index(input, index_num, nshards, shard_id, ignore_value=-1):
"""
Reset the values of `input` according to the shard it beloning to.
Every value in `input` must be a non-negative integer, and
the parameter `index_num` represents the integer above the maximum
value of `input`. Thus, all values in `input` must be in the range
[0, index_num) and each value can be regarded as the offset to the beginning
of the range. The range is further split into multiple shards. Specifically,
we first compute the `shard_size` according to the following formula,
which represents the number of integers each shard can hold. So for the
i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size).
::
shard_size = (index_num + nshards - 1) // nshards
For each value `v` in `input`, we reset it to a new value according to the
following formula:
::
v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value
That is, the value `v` is set to the new offset within the range represented by the shard `shard_id`
if it in the range. Otherwise, we reset it to be `ignore_value`.
Args:
input (Tensor): Input tensor with data type int64 or int32. It's last dimension must be 1.
index_num (int): An integer represents the integer above the maximum value of `input`.
nshards (int): The number of shards.
shard_id (int): The index of the current shard.
ignore_value (int): An integer value out of sharded index range.
Returns:
Tensor.
Examples:
.. code-block:: python
import paddle
label = paddle.to_tensor([[16], [1]], "int64")
shard_label = paddle.shard_index(input=label,
index_num=20,
nshards=2,
shard_id=0)
print(shard_label)
# [[-1], [1]]
"""
if in_dygraph_mode():
return _C_ops.final_state_shard_index(input, index_num, nshards,
shard_id, ignore_value)
check_variable_and_dtype(input, 'input', ['int64', 'int32'], 'shard_index')
op_type = 'shard_index'
helper = LayerHelper(op_type, **locals())
if shard_id < 0 or shard_id >= nshards:
raise ValueError('The shard_id(%d) should be in [0, %d)' %
(shard_id, nshards))
out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=op_type,
inputs={'X': [input]},
outputs={'Out': out},
attrs={
'index_num': index_num,
'nshards': nshards,
'shard_id': shard_id,
'ignore_value': ignore_value
},
stop_gradient=True)
return out
def crop(x, shape=None, offsets=None, name=None):
"""
Crop input into output, as specified by offsets and shape.
.. code-block:: text
* Case 1 (input is a 2-D Tensor):
Input:
X.shape = [3, 5]
X.data = [[0, 1, 2, 0, 0],
[0, 3, 4, 0, 0],
[0, 0, 0, 0, 0]]
Parameters:
shape = [2, 2]
offsets = [0, 1]
Output:
Out.shape = [2, 2]
Out.data = [[1, 2],
[3, 4]]
* Case 2 (input is a 3-D Tensor):
Input:
X.shape = [2, 3, 4]
X.data = [[[0, 1, 2, 3],
[0, 5, 6, 7],
[0, 0, 0, 0]],
[[0, 3, 4, 5],
[0, 6, 7, 8],
[0, 0, 0, 0]]]
Parameters:
shape = [2, 2, -1]
offsets = [0, 0, 1]
Output:
Out.shape = [2, 2, 3]
Out.data = [[[1, 2, 3],
[5, 6, 7]],
[[3, 4, 5],
[6, 7, 8]]]
Parameters:
x (Tensor): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64.
shape (list|tuple|Tensor): The output shape is specified
by `shape`. Its data type is int32. If a list/tuple, it's length must be
the same as the dimension size of `x`. If a Tensor, it should be a 1-D Tensor.
When it is a list, each element can be an integer or a Tensor of shape: [1].
If Variable contained, it is suitable for the case that the shape may
be changed each iteration.
offsets (list|tuple|Variable, optional): Specifies the cropping
offsets at each dimension. Its data type is int32. If a list/tuple, it's length
must be the same as the dimension size of `x`. If a Tensor, it should be a 1-D
Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1].
If Variable contained, it is suitable for the case that the offsets may be changed
each iteration. Default: None, the offsets are 0 at each dimension.
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 cropped Tensor has same data type with `x`.
Examples:
.. code-block:: python
:name: code-example1
import paddle
x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# x.shape = [3, 3]
# x = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
# shape can be a 1-D Tensor or list or tuple.
shape = paddle.to_tensor([2, 2], dtype='int32')
# shape = [2, 2]
# shape = (2, 2)
out = paddle.crop(x, shape)
# out.shape = [2, 2]
# out = [[1,2], [4,5]]
# offsets can be a 1-D Tensor or list or tuple.
offsets = paddle.to_tensor([0, 1], dtype='int32')
# offsets = [1, 0]
# offsets = (1, 1)
out = paddle.crop(x, shape, offsets)
# out.shape = [2, 2]
# if offsets = [0, 0], out = [[1,2], [4,5]]
# if offsets = [0, 1], out = [[2,3], [5,6]]
# if offsets = [1, 0], out = [[4,5], [7,8]]
# if offsets = [1, 1], out = [[5,6], [8,9]]
"""
helper = LayerHelper('crop_tensor', **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'crop_tensor')
check_type(shape, 'shape', (list, tuple, Variable), 'crop_tensor')
check_type(offsets, 'offsets', (list, tuple, Variable, type(None)),
'crop_tensor')
if offsets is None:
offsets = [0] * len(x.shape)
out = helper.create_variable_for_type_inference(x.dtype)
ipts = {'X': x}
attrs = {}
def _attr_shape_check(shape_val):
if not isinstance(shape_val, int):
raise TypeError(
"Attr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s."
% type(shape_val))
if shape_val == 0:
raise ValueError(
"Attr(shape) of Op(crop_tensor) should not be zero, but received: %s."
% str(shape_val))
if shape_val < -1:
raise ValueError(
"When the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s."
% str(shape_val))
def _attr_offsets_check(offset_val):
if not isinstance(offset_val, int):
raise TypeError(
"Attr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s."
% type(offset_val))
if offset_val < 0:
raise ValueError(
"Attr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s."
% str(offset_val))
if isinstance(offsets, Variable):
offsets.stop_gradient = True
ipts['Offsets'] = offsets
attrs['offsets'] = [-1] * len(x.shape)
elif utils._contain_var(offsets):
new_offsets_tensor = []
offsets_attr = []
for dim in offsets:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_offsets_tensor.append(dim)
offsets_attr.append(-1)
else:
_attr_offsets_check(dim)
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
new_offsets_tensor.append(temp_out)
offsets_attr.append(dim)
ipts['OffsetsTensor'] = new_offsets_tensor
attrs['offsets'] = offsets_attr
else:
for offset in offsets:
_attr_offsets_check(offset)
attrs['offsets'] = offsets
if isinstance(shape, Variable):
shape.stop_gradient = True
ipts['Shape'] = shape
elif utils._contain_var(shape):
new_shape_tensor = []
shape_attr = []
for dim_size in shape:
if isinstance(dim_size, Variable):
dim_size.stop_gradient = True
new_shape_tensor.append(dim_size)
shape_attr.append(0)
else:
_attr_shape_check(dim_size)
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant(
[1], 'int32', dim_size, force_cpu=True, out=temp_out)
new_shape_tensor.append(temp_out)
shape_attr.append(dim_size)
ipts['ShapeTensor'] = new_shape_tensor
attrs['shape'] = shape_attr
else:
for dim_size in shape:
_attr_shape_check(dim_size)
attrs['shape'] = shape
helper.append_op(
type='crop_tensor',
inputs=ipts,
outputs={'Out': out},
attrs=None if len(attrs) == 0 else attrs)
return out
@dygraph_only
def fill_(x, value):
"""
......@@ -328,7 +1019,74 @@ def concat(x, axis=0, name=None):
# [11 12 13]
# [14 15 16]]
"""
return paddle.fluid.layers.concat(input=x, axis=axis, name=name)
input = x
if in_dygraph_mode():
if isinstance(axis, Variable):
axis = axis.numpy()
axis = axis.item(0)
if not isinstance(input, Variable):
input = [t for t in input if t.shape.count(0) == 0]
return _C_ops.final_state_concat(input, axis)
if _in_legacy_dygraph():
if isinstance(axis, Variable):
axis = axis.numpy()
axis = axis.item(0)
if not isinstance(input, Variable):
input = [t for t in input if t.shape.count(0) == 0]
out = _varbase_creator()
_C_ops.concat(input, out, 'axis', axis)
return out
check_type(input, 'input', (list, tuple, Variable), 'concat')
if not isinstance(input, Variable):
for id, x in enumerate(input):
check_variable_and_dtype(
x, 'input[' + str(id) + ']',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'concat')
if x.dtype != input[0].dtype:
raise TypeError(
"All the Tensors in the input must have the same data type.")
else:
input = [input]
check_type(axis, 'axis', (int, Variable), 'concat')
if isinstance(axis, Variable):
check_dtype(
axis.dtype, 'axis', ['int32', 'int64'], 'concat',
"The data type of axis must be int32 or int64 when axis is a Tensor")
helper = LayerHelper('concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if input[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
# NOTE(liym27): Don't remove this if branch!
# This feature is supported for Dynamic-to-Static, because after transformed, the type of inputs[0]
# is LOD_TENSOR_ARRAY in some scenarios. And this feature can be used in static mode.
assert len(input) == 1, "If the elements of 'input' in concat are Variable(LoDTensorArray), " \
"number of the elements must be 1, but received %s." % len(input)
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': input[0]},
outputs={'Out': [out],
'OutIndex': [out_index]},
attrs={'axis': axis,
'use_stack': False})
else:
inputs = {'X': input}
attrs = {}
if isinstance(axis, Variable):
axis.stop_gradient = True
inputs['AxisTensor'] = axis
else:
attrs['axis'] = axis
helper.append_op(
type='concat', inputs=inputs, outputs={'Out': [out]}, attrs=attrs)
return out
def broadcast_tensors(input, name=None):
......@@ -900,7 +1658,53 @@ def stack(x, axis=0, name=None):
# [3., 4.],
# [5., 6.]]]
"""
return layers.stack(x, axis, name)
axis = 0 if axis is None else axis
if in_dygraph_mode():
return _C_ops.final_state_stack(x, axis)
if _in_legacy_dygraph():
return _C_ops.stack(x, 'axis', axis)
if not isinstance(x, list) and not isinstance(x, tuple):
# NOTE:(zhiqiu) Only support Variable as input if the Variable is a LOD_TENSOR_ARRAY create by create_array, array_write, array_read, etc.
# In that case, Variable is array of tensors indeed.
if isinstance(x, Variable) and x.desc.type(
) == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
x = [x]
else:
raise TypeError("The type of '%s' in %s must be %s, but received %s"
% ('x', 'stack',
'list[Tensor], tuple[Tensor] or TensorArray',
type(x)))
helper = LayerHelper('stack', **locals())
out = helper.create_variable_for_type_inference(x[0].dtype)
if x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
assert len(x) == 1, "If the elements of 'x' in stack are Variable(LoDTensorArray), " \
"number of the elements must be 1, but received %s." % len(x)
out_index = helper.create_variable_for_type_inference(dtype="int32")
for i in x:
check_variable_and_dtype(i, 'x', \
['float16', 'float32', 'float64', 'int32', 'int64'], 'stack')
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': x[0]},
outputs={'Out': [out],
'OutIndex': [out_index]},
attrs={'axis': axis,
'use_stack': True})
else:
helper.append_op(
type='stack',
inputs={'X': x},
outputs={'Y': out},
attrs={'axis': axis})
return out
def split(x, num_or_sections, axis=0, name=None):
......@@ -951,8 +1755,110 @@ def split(x, num_or_sections, axis=0, name=None):
print(out1.shape) # [3, 3, 5]
print(out2.shape) # [3, 3, 5]
"""
return paddle.fluid.layers.split(
input=x, num_or_sections=num_or_sections, dim=axis, name=name)
input = x
dim = axis
if _non_static_mode():
num = None
attrs = ()
if isinstance(dim, Variable):
dim = dim.numpy()
dim = dim.item(0)
assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
dim = (len(input.shape) + dim) if dim < 0 else dim
attrs += ('axis', dim)
if isinstance(num_or_sections, int):
num = num_or_sections
attrs += ('num', num_or_sections)
elif isinstance(num_or_sections, (list, tuple)):
num = len(num_or_sections)
if utils._contain_var(num_or_sections):
for index, item in enumerate(num_or_sections):
if isinstance(item, Variable):
num_or_sections[index] = num_or_sections[index].numpy()[
0]
attrs += ('sections', list(num_or_sections))
else:
attrs += ('sections', list(num_or_sections))
else:
raise TypeError(
"The type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but "
"received %s." % (type(num_or_sections)))
out = [_varbase_creator() for n in range(num)]
_C_ops.split(input, out, *attrs)
return out
check_variable_and_dtype(
input, 'input',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'], 'split')
check_type(num_or_sections, 'num_or_sections', (list, int, tuple), 'split')
check_type(dim, 'dim', (int, Variable), 'split')
if isinstance(dim, Variable):
check_dtype(dim.dtype, 'dim', ['int32', 'int64'], 'split')
helper = LayerHelper('split', **locals())
input_shape = input.shape
inputs = {'X': input}
attrs = {'num': num_or_sections if isinstance(num_or_sections, int) else 0}
def _get_SectionsTensorList(one_list):
tensor_list = []
unk_dim_idx = -1
for idx, dim_size in enumerate(one_list):
if isinstance(dim_size, Variable):
dim_size.stop_gradient = True
tensor_list.append(dim_size)
else:
assert (isinstance(dim_size, int))
if dim_size == -1:
assert unk_dim_idx == -1, (
"Only one value of 'num_or_section' in split can "
"be -1. But received num_or_section[%d] is also -1." %
idx)
unk_dim_idx = idx
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant(
[1], 'int32', dim_size, force_cpu=True, out=temp_out)
tensor_list.append(temp_out)
return tensor_list
if isinstance(dim, Variable):
dim.stop_gradient = True
inputs['AxisTensor'] = dim
else:
assert len(input.shape) + dim >= 0, "(rank(x) + axis) must >= 0"
dim = (len(input_shape) + dim) if dim < 0 else dim
attrs['axis'] = dim
if isinstance(num_or_sections, int):
assert num_or_sections > 1, 'num_or_sections must be more than 1.'
if isinstance(dim, int) and input_shape[dim] > 0:
assert input_shape[dim] % num_or_sections ==0, \
"The input's size along the split dimension " \
"must be evenly divisible by Attr(num_or_sections). " \
"But %d is not evenly divisible by %d. " % (num_or_sections,input_shape[dim])
num = num_or_sections
else:
if isinstance(dim, int) and input_shape[dim] > 0:
assert len(num_or_sections) <= input_shape[
dim], 'len(num_or_sections) must not be more than input.shape[dim].'
num = len(num_or_sections)
attrs['sections'] = list(
map(lambda ele: -1 if isinstance(ele, Variable) else ele,
num_or_sections))
if utils._contain_var(num_or_sections):
inputs['SectionsTensorList'] = _get_SectionsTensorList(
num_or_sections)
outs = [
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
for i in range(num)
]
helper.append_op(
type='split', inputs=inputs, outputs={'Out': outs}, attrs=attrs)
return outs
def squeeze(x, axis=None, name=None):
......@@ -1035,7 +1941,30 @@ def squeeze(x, axis=None, name=None):
elif isinstance(axis, tuple):
axis = list(axis)
return layers.squeeze(x, axis, name)
input = x
axes = axis
if in_dygraph_mode():
return _C_ops.final_state_squeeze(input, axes)[1]
if _in_legacy_dygraph():
out, _ = _C_ops.squeeze2(input, 'axes', axes)
return out
helper = LayerHelper("squeeze", **locals())
check_variable_and_dtype(input, 'input', [
'float16', 'float32', 'float64', 'bool', 'int8', 'int32', 'int64',
'complex64', 'complex128'
], 'squeeze')
check_type(axes, 'axis/axes', (list, tuple), 'squeeze')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="squeeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out,
"XShape": x_shape})
return out
@inplace_apis_in_dygraph_only
......@@ -1335,8 +2264,61 @@ def unsqueeze(x, axis, name=None):
print(out3[0, 0, 0, 0, 0]) # [10.]
"""
input = x
axes = axis
if _non_static_mode():
if isinstance(axes, int):
axes = [axes]
elif isinstance(axes, Variable):
axes = axes.numpy().tolist()
elif isinstance(axes, (list, tuple)):
axes = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in axes
]
if _in_legacy_dygraph():
out, _ = _C_ops.unsqueeze2(input, 'axes', axes)
return out
return _C_ops.final_state_unsqueeze(input, axes)[1]
check_type(axes, 'axis/axes', (int, list, tuple, Variable), 'unsqueeze')
check_variable_and_dtype(input, 'input', [
'float16',
'float32',
'float64',
'bool',
'int8',
'int16',
'int32',
'int64',
'complex64',
'complex128',
], 'unsqueeze')
helper = LayerHelper("unsqueeze2", **locals())
inputs = {"X": input}
attrs = {}
if isinstance(axes, int):
axes = [axes]
if isinstance(axes, Variable):
axes.stop_gradient = True
inputs["AxesTensor"] = axes
elif isinstance(axes, (list, tuple)):
if utils._contain_var(axes):
inputs["AxesTensorList"] = utils._convert_to_tensor_list(axes)
else:
attrs["axes"] = axes
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="unsqueeze2",
inputs=inputs,
attrs=attrs,
outputs={"Out": out,
"XShape": x_shape})
return layers.unsqueeze(x, axis, name)
return out
@inplace_apis_in_dygraph_only
......@@ -1680,7 +2662,70 @@ def scatter_nd_add(x, index, updates, name=None):
index = paddle.to_tensor(index_data)
output = paddle.scatter_nd_add(x, index, updates)
"""
return layers.scatter_nd_add(x, index, updates, name=None)
if in_dygraph_mode():
op = getattr(_C_ops, 'scatter_nd_add')
return op(x, index, updates)
else:
if _in_legacy_dygraph():
op = getattr(_C_ops, 'scatter_nd_add')
return op(x, index, updates)
else:
if x.dtype != updates.dtype:
raise ValueError("x and updates must have same data type.")
helper = LayerHelper('scatter_nd_add', **locals())
dtype = helper.input_dtype(input_param_name='x')
output = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="scatter_nd_add",
inputs={"X": x,
"Index": index,
"Updates": updates},
outputs={"Out": output})
return output
def scatter_nd(index, updates, shape, name=None):
"""
**Scatter_nd Layer**
Output is obtained by scattering the :attr:`updates` in a new tensor according
to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the
tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)`
is equal to :code:`scatter_nd_add(paddle.zeros(shape, updates.dtype), index, updates)` .
If :attr:`index` has repeated elements, then the corresponding updates are accumulated.
Because of the numerical approximation issues, the different order of repeated elements
in :attr:`index` may cause different results. The specific calculation method can be
seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op.
Args:
index (Tensor): The index input with ndim > 1 and index.shape[-1] <= len(shape).
Its dtype should be int32 or int64 as it is used as indexes.
updates (Tensor): The updated value of scatter_nd op. Its dtype should be float32, float64.
It must have the shape index.shape[:-1] + shape[index.shape[-1]:]
shape(tuple|list): Shape of output tensor.
name (str|None): The output Tensor name. If set None, the layer will be named automatically.
Returns:
output (Tensor): The output is a tensor with the same type as :attr:`updates` .
Examples:
.. code-block:: python
import paddle
import numpy as np
index_data = np.array([[1, 1],
[0, 1],
[1, 3]]).astype(np.int64)
index = paddle.to_tensor(index_data)
updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
shape = [3, 5, 9, 10]
output = paddle.scatter_nd(index, updates, shape)
"""
return scatter_nd_add(zeros(shape, updates.dtype), index, updates, name)
def chunk(x, chunks, axis=0, name=None):
......@@ -1722,8 +2767,7 @@ def chunk(x, chunks, axis=0, name=None):
# out2.shape [3, 3, 5]
"""
check_type(chunks, 'chunks', (int), 'chunk')
return paddle.fluid.layers.split(
input=x, num_or_sections=chunks, dim=axis, name=name)
return split(x, num_or_sections=chunks, axis=axis, name=name)
def tile(x, repeat_times, name=None):
......@@ -2136,7 +3180,124 @@ def reshape(x, shape, name=None):
# the value is [10.]
"""
return paddle.fluid.layers.reshape(x=x, shape=shape, name=name)
actual_shape = None
act = None
inplace = False
if in_dygraph_mode():
tmp_tensor_type = core.eager.Tensor
#TODO(zhiqiu): enable inplace in dygraph mode.
if inplace:
warnings.warn(
"Inplace on reshape is not allowed and will be discarded in dygraph mode currently."
)
if isinstance(shape, (list, tuple)):
shape = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in shape
]
out, _ = _C_ops.reshape2(x, None, 'shape', shape)
elif isinstance(shape, tmp_tensor_type):
shape.stop_gradient = True
out, _ = _C_ops.reshape2(x, shape)
else:
raise ValueError(
"shape must be an instance of `list`, `tuple` or `Variable`,"
" got '{}.'".format(type(shape)))
return dygraph_utils._append_activation_in_dygraph(out, act)
else:
if _in_legacy_dygraph():
tmp_tensor_type = Variable
if inplace:
warnings.warn(
"Inplace on reshape is not allowed and will be discarded in dygraph mode currently."
)
if isinstance(shape, (list, tuple)):
shape = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in shape
]
out, _ = _C_ops.reshape2(x, None, 'shape', shape)
elif isinstance(shape, tmp_tensor_type):
shape.stop_gradient = True
out, _ = _C_ops.reshape2(x, shape)
else:
raise ValueError(
"shape must be an instance of `list`, `tuple` or `Variable`,"
" got '{}.'".format(type(shape)))
return dygraph_utils._append_activation_in_dygraph(out, act)
check_variable_and_dtype(x, 'x', [
'float16', 'float32', 'float64', 'int16', 'int32', 'int64', 'bool',
'uint16'
], 'reshape')
check_type(shape, 'shape', (list, tuple, Variable), 'reshape')
check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape')
helper = LayerHelper("reshape2", **locals())
def get_attr_shape(list_shape):
unk_dim_idx = -1
attrs_shape = []
for dim_idx, dim_size in enumerate(list_shape):
if isinstance(dim_size, Variable):
attrs_shape.append(-1)
else:
attrs_shape.append(dim_size)
if dim_size == -1:
assert unk_dim_idx == -1, (
"Only one dimension value of 'shape' in reshape can "
"be -1. But received shape[%d] is also -1.\n"
"\n\t# N = x.shape()[2]\t\t# N is an int. "
"(NOT recommend under @to_static)\n\tN = paddle.shape(x)[2]\t\t"
"# N is a Tensor. (Recommend)\n\tz = paddle.reshape([N, -1, 4])"
"\t# z.shape is [-1, -1, 4]\n\n"
" If your target shape in Reshape represents dynamic shape, "
"please turn it into a Tensor under @to_static. See above example for details."
% dim_idx)
unk_dim_idx = dim_idx
elif dim_size == 0:
assert dim_idx < len(x.shape), (
"The index of 0 in `shape` must be less than "
"the input tensor X's dimensions. "
"But received shape[%d] = 0, X's dimensions = %d." %
(dim_idx, len(x.shape)))
else:
assert dim_size > 0, (
"Each dimension value of 'shape' in reshape must not "
"be negative except one unknown dimension. "
"But received shape[%d] = %s." %
(dim_idx, str(dim_size)))
return attrs_shape
inputs = {"X": x}
attrs = {}
if isinstance(shape, Variable):
shape.stop_gradient = True
inputs["Shape"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, ("The size of 'shape' in reshape can't be zero, "
"but received %s." % len(shape))
attrs["shape"] = get_attr_shape(shape)
if utils._contain_var(shape):
inputs['ShapeTensor'] = utils._convert_to_tensor_list(shape)
elif isinstance(actual_shape, Variable):
actual_shape.stop_gradient = True
inputs["Shape"] = actual_shape
out = x if inplace else helper.create_variable_for_type_inference(
dtype=x.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="reshape2",
inputs=inputs,
attrs=attrs,
outputs={"Out": out,
"XShape": x_shape})
return helper.append_activation(out)
@inplace_apis_in_dygraph_only
......@@ -2231,8 +3392,24 @@ def gather_nd(x, index, name=None):
output = paddle.gather_nd(x, index) #[[3, 4]]
"""
return paddle.fluid.layers.gather_nd(input=x, index=index, name=name)
if in_dygraph_mode():
return _C_ops.final_state_gather_nd(x, index)
else:
if _in_legacy_dygraph():
return _C_ops.gather_nd(x, index)
check_variable_and_dtype(
x, 'x', ['bool', 'float32', 'float64', 'int16', 'int32', 'int64'],
'gather_np')
check_variable_and_dtype(index, 'index', ['int32', 'int64'], 'gather_np')
helper = LayerHelper('gather_nd', **locals())
dtype = helper.input_dtype()
output = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="gather_nd",
inputs={"X": x,
"Index": index},
outputs={"Out": output})
return output
def strided_slice(x, axes, starts, ends, strides, name=None):
......@@ -2318,8 +3495,115 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
# sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2].
"""
return paddle.fluid.layers.strided_slice(
input=x, axes=axes, starts=starts, ends=ends, strides=strides)
helper = LayerHelper('strided_slice', **locals())
check_variable_and_dtype(x, 'x',
['bool', 'float32', 'float64', 'int32', 'int64'],
'strided_slice')
check_type(axes, 'axes', (list, tuple), 'strided_slice')
check_type(starts, 'starts', (list, tuple, Variable), 'strided_slice')
check_type(ends, 'ends', (list, tuple, Variable), 'strided_slice')
check_type(strides, 'strides', (list, tuple, Variable), 'strided_slice')
def check_list_elements_dtype(list_input, input_name):
if isinstance(list_input, Variable):
check_dtype(list_input.dtype, input_name, ['int32'],
'strided_slice')
else:
for i, var in enumerate(list_input):
var_name = input_name + '[' + str(i) + ']'
if isinstance(var, Variable):
check_dtype(var.dtype, var_name, ['int32'], 'strided_slice')
check_list_elements_dtype(axes, 'axes')
check_list_elements_dtype(starts, 'starts')
check_list_elements_dtype(ends, 'ends')
check_list_elements_dtype(strides, 'strides')
def get_new_list_tensor(old_list):
new_list_tensor = []
for dim in old_list:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_list_tensor.append(dim)
else:
assert (isinstance(dim, int))
temp_out = helper.create_variable_for_type_inference('int32')
fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
new_list_tensor.append(temp_out)
return new_list_tensor
inputs = {'Input': x}
attrs = {'axes': axes}
infer_flags = list(1 for i in range(len(axes)))
if _non_static_mode():
inputs = {'Input': x}
attrs = {
'axes': axes,
'starts': starts,
'ends': ends,
'strides': strides,
'infer_flags': infer_flags
}
else:
# starts
if isinstance(starts, Variable):
starts.stop_gradient = True
inputs['StartsTensor'] = starts
elif isinstance(starts, (list, tuple)):
attrs['starts'] = []
if utils._contain_var(starts):
inputs['StartsTensorList'] = get_new_list_tensor(starts)
for i, dim in enumerate(starts):
if isinstance(dim, Variable):
attrs['starts'].append(-1)
infer_flags[i] = -1
else:
attrs['starts'].append(dim)
else:
attrs['starts'] = starts
# ends
if isinstance(ends, Variable):
ends.stop_gradient = True
inputs['EndsTensor'] = ends
elif isinstance(ends, (list, tuple)):
attrs['ends'] = []
if utils._contain_var(ends):
inputs['EndsTensorList'] = get_new_list_tensor(ends)
for i, dim in enumerate(ends):
if isinstance(dim, Variable):
attrs['ends'].append(-1)
infer_flags[i] = -1
else:
attrs['ends'].append(dim)
else:
attrs['ends'] = ends
# strides
if isinstance(strides, Variable):
strides.stop_gradient = True
inputs['StridesTensor'] = strides
elif isinstance(strides, (list, tuple)):
attrs['strides'] = []
if utils._contain_var(strides):
inputs['StridesTensorList'] = get_new_list_tensor(strides)
for i, dim in enumerate(strides):
if isinstance(dim, Variable):
attrs['strides'].append(-1)
infer_flags[i] = -1
else:
attrs['strides'].append(dim)
else:
attrs['strides'] = strides
attrs['infer_flags'] = infer_flags
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('x'))
helper.append_op(
type='strided_slice', inputs=inputs, attrs=attrs, outputs={'Out': out})
return out
def tensordot(x, y, axes=2, name=None):
......
python/paddle/tensor/random.py
浏览文件 @ c239f15a
......@@ -16,7 +16,7 @@
from ..framework import core
from ..framework import convert_np_dtype_to_dtype_, dygraph_only
from ..fluid.layer_helper import LayerHelper
from ..framework import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, check_shape
from ..fluid.layers import utils
import paddle
......
python/paddle/tensor/search.py
浏览文件 @ c239f15a
......@@ -14,7 +14,7 @@
from __future__ import print_function
import numpy as np
import paddle
from ..fluid.layer_helper import LayerHelper
from ..framework import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
from ..fluid import layers
from ..framework import core, in_dygraph_mode, _non_static_mode
......
python/paddle/tensor/stat.py
浏览文件 @ c239f15a
......@@ -16,7 +16,7 @@
import numpy as np
from ..static import Variable
from ..fluid.layer_helper import LayerHelper
from ..framework import LayerHelper
from ..framework import core
from paddle.fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from .search import where
......
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