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tensor.py
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tensor.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unlessf required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from ..layer_helper import LayerHelper
from ..param_attr import ParamAttr
from ..framework import convert_np_dtype_to_dtype_
from ..framework import Variable
from ..initializer import Constant, force_init_on_cpu
from ..core import VarDesc
from ..imperative import base as imperative_base
from .layer_function_generator import templatedoc
import numpy
__all__ = [
'create_tensor', 'create_parameter', 'create_global_var', 'cast',
'tensor_array_to_tensor', 'concat', 'sums', 'assign',
'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite'
]
def create_tensor(dtype, name=None, persistable=False):
"""
Create an variable, which will hold a LoDTensor with data type dtype.
Args:
dtype(string): 'float32'|'int32'|..., the data type of the
created tensor.
name(string): The name of the created tensor, if not set,
the name will be a random unique one.
persistable(bool): Set the persistable flag of the create tensor.
Returns:
Variable: The tensor variable storing the created tensor.
Examples:
.. code-block:: python
tensor = fluid.layers.create_tensor(dtype='float32')
"""
helper = LayerHelper("create_tensor", **locals())
return helper.create_variable(
name=helper.name, dtype=dtype, persistable=persistable)
def create_parameter(shape,
dtype,
name=None,
attr=None,
is_bias=False,
default_initializer=None):
"""
Create a parameter. The parameter is a learnable variable, which can have
gradient, and can be optimized.
NOTE: this is a very low-level API. This API is useful when you create
operator by your self. instead of using layers.
Args:
shape(list[int]): shape of the parameter
dtype(string): element type of the parameter
attr(ParamAttr): attributes of the parameter
is_bias(bool): This can affect which default initializer is chosen
when default_initializer is None. If is_bias,
initializer.Constant(0.0) will be used. Otherwise,
Xavier() will be used.
default_initializer(Initializer): initializer for the parameter
Returns:
the created parameter.
Examples:
>>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
>>> data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False)
>>> hidden = fluid.layers.matmul(x=data, y=W)
"""
helper = LayerHelper("create_parameter", **locals())
if attr is None:
attr = ParamAttr(name=name)
return helper.create_parameter(attr, shape, dtype, is_bias,
default_initializer)
def create_global_var(shape,
value,
dtype,
persistable=False,
force_cpu=False,
name=None):
"""
Create a new tensor variable with value in the global block(block 0).
Args:
shape(list[int]): shape of the variable
value(float): the value of the variable. The new created
variable will be filled with it.
dtype(string): data type of the variable
persistable(bool): if this variable is persistable.
Default: False
force_cpu(bool): force this variable to be on CPU.
Default: False
name(str|None): The name of the variable. If set to None the variable
name will be generated automatically.
Default: None
Returns:
Variable: the created Variable
Examples:
.. code-block:: python
var = fluid.create_global_var(shape=[2,3], value=1.0, dtype='float32',
persistable=True, force_cpu=True, name='new_var')
"""
helper = LayerHelper("global_var", **locals())
var = helper.create_global_variable(
dtype=dtype,
shape=shape,
persistable=persistable,
name=name,
stop_gradient=True)
helper.set_variable_initializer(
var, initializer=Constant(
value=float(value), force_cpu=force_cpu))
return var
def cast(x, dtype):
"""
This layer takes in the Variable :attr:`x` with :attr:`x.dtype` and casts
it to the output with :attr:`dtype`.
Args:
x (Variable): The input Variable for casting.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output Variable.
Returns:
Variable: The output Variable after casting.
Examples:
.. code-block:: python
data = fluid.layers.data(name='x', shape=[13], dtype='float32')
result = fluid.layers.cast(x=data, dtype='float64')
"""
helper = LayerHelper('cast', **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='cast',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'in_dtype': x.dtype,
'out_dtype': out.dtype})
return out
def concat(input, axis=0, name=None):
"""
**Concat**
This function concatenates the input along the axis mentioned
and returns that as the output.
Args:
input(list): List of tensors to be concatenated
axis(int): Integer axis along which the tensors will be concatenated
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: Output variable of the concatenation
Examples:
.. code-block:: python
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
"""
helper = LayerHelper('concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='concat',
inputs={'X': input},
outputs={'Out': [out]},
attrs={'axis': axis})
return out
def tensor_array_to_tensor(input, axis=1, name=None):
"""
This function concatenates the input LodTensorArray along the axis mentioned
and returns that as the output.
A simple example as below:
.. code-block:: text
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
Args:
input(list): Input LodTensorArray
axis(int): Integer axis along which the tensors will be concatenated
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: Output variable of the concatenation
Variable: The input LodTensorArray items' dims along the axis
Examples:
.. code-block:: python
output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array)
"""
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
out_index = helper.create_variable_for_type_inference(dtype="int32")
helper.append_op(
type='tensor_array_to_tensor',
inputs={'X': input},
outputs={'Out': [out],
'OutIndex': [out_index]},
attrs={'axis': axis})
return out, out_index
def sums(input, out=None):
"""
This function performs the sum operation on the input and returns the
result as the output.
Args:
input (Variable|list): The input tensor that has the elements
that need to be summed up.
out (Variable|None): Output parameter. The sum result.
Default: None
Returns:
Variable: the sum of input. The same as the argument 'out'
Examples:
.. code-block:: python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
a0 = layers.array_read(array=tmp, i=i)
i = layers.increment(x=i)
a1 = layers.array_read(array=tmp, i=i)
mean_a0 = layers.mean(a0)
mean_a1 = layers.mean(a1)
a_sum = layers.sums(input=[mean_a0, mean_a1])
"""
helper = LayerHelper('sum', **locals())
if out is None:
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='sum',
inputs={'X': input},
outputs={'Out': out},
attrs={'use_mkldnn': False})
return out
def assign(input, output=None):
"""
**Assign**
This function copies the *input* Variable to the *output* Variable.
Args:
input(Variable|numpy.ndarray): The source variable
output(Variable|None): The destination variable
Returns:
Variable: The destination variable that was supplied as the *output*.
Examples:
.. code-block:: python
out = fluid.layers.create_tensor(dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
fluid.layers.assign(hidden, out)
"""
helper = LayerHelper('assign', **locals())
if output is None:
output = helper.create_variable_for_type_inference(dtype=input.dtype)
if isinstance(input, Variable):
helper.append_op(
type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
elif isinstance(input, numpy.ndarray):
dtype = convert_np_dtype_to_dtype_(input.dtype)
if dtype == VarDesc.VarType.FP32:
value_name = "fp32_values"
values = [float(v) for v in input.flat]
elif dtype == VarDesc.VarType.INT32:
value_name = "int32_values"
values = [int(v) for v in input.flat]
else:
raise ValueError("Unsupported dtype %s", input.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")
helper.append_op(
type='assign_value',
outputs={'Out': [output]},
attrs={
'dtype': dtype,
'shape': list(input.shape),
value_name: values
})
else:
raise ValueError("Wrong type for assign input: %s" % type(input))
return output
def fill_constant(shape, dtype, value, force_cpu=False, out=None):
"""
**fill_constant**
This function creates a tensor with specified `shape` and `dtype`, and
initializes it with a constant specifed by `value`.
The attribute `stop_gradient` of the created tensor is set to True.
Args:
shape(tuple|list|None): Shape of the output tensor.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor.
value(float): The constant value used to initialize the output tensor.
out(Variable): The output tensor.
force_cpu(True|False): data should be on CPU if set true.
Returns:
Variable: The tensor variable storing the output.
Examples:
.. code-block:: python
data = fluid.layers.fill_constant(shape=[1], value=0, dtype='int64')
"""
helper = LayerHelper("fill_constant", **locals())
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant',
inputs={},
outputs={'Out': [out]},
attrs={
'shape': shape,
'dtype': out.dtype,
'value': float(value),
'force_cpu': force_cpu or force_init_on_cpu()
},
stop_gradient=True)
out.stop_gradient = True
return out
@templatedoc()
def fill_constant_batch_size_like(input,
shape,
dtype,
value,
input_dim_idx=0,
output_dim_idx=0):
"""
${comment}
It also sets *stop_gradient* to True.
Args:
input(${input_type}): ${input_comment}.
shape(${shape_type}): ${shape_comment}.
dtype(${dtype_type}): ${dtype_comment}.
value(${value_type}): ${value_comment}.
input_dim_idx(${input_dim_idx_type}): ${input_dim_idx_comment}.
output_dim_idx(${output_dim_idx_type}): ${output_dim_idx_comment}.
Returns:
${out_comment}.
Examples:
.. code-block:: python
data = fluid.layers.fill_constant_batch_size_like(
input=like, shape=[1], value=0, dtype='int64')
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={'Input': input},
outputs={'Out': [out]},
attrs={
'shape': shape,
'dtype': out.dtype,
'value': float(value),
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx
})
out.stop_gradient = True
return out
def argmin(x, axis=0):
"""
**argmin**
This function computes the indices of the min elements
of the input tensor's element along the provided axis.
Args:
x(Variable): The input to compute the indices of
the min elements.
axis(int): Axis to compute indices along.
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
out = fluid.layers.argmin(x=in, axis=0)
out = fluid.layers.argmin(x=in, axis=-1)
"""
helper = LayerHelper("arg_min", **locals())
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_min',
inputs={'X': x},
outputs={'Out': [out]},
attrs={'axis': axis})
return out
def argmax(x, axis=0):
"""
**argmax**
This function computes the indices of the max elements
of the input tensor's element along the provided axis.
Args:
x(Variable): The input to compute the indices of
the max elements.
axis(int): Axis to compute indices along.
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
out = fluid.layers.argmax(x=in, axis=0)
out = fluid.layers.argmax(x=in, axis=-1)
"""
helper = LayerHelper("arg_max", **locals())
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_max',
inputs={'X': x},
outputs={'Out': [out]},
attrs={'axis': axis})
return out
def argsort(input, axis=-1, name=None):
"""
Performs sorting on the input Variable along the given axis, and outputs
sorted data Varibale and its corresponding index Variable with the same
shape as :attr:`input`.
.. code-block:: text
For example, the given axis is -1 and the input Variable
input = [[0.15849551, 0.45865775, 0.8563702 ],
[0.12070083, 0.28766365, 0.18776911]],
after argsort, the sorted Vairable becomes
out = [[0.15849551, 0.45865775, 0.8563702 ],
[0.12070083, 0.18776911, 0.28766365]],
and the sorted indices along the given axis turn outs to be
indices = [[0, 1, 2],
[0, 2, 1]]
Args:
input(Variable): The input Variable for sorting.
axis(int): The axis along which to sort the input Variable. When
:attr:`axis` < 0, the actual axis will be :attr:`axis` +
rank(:attr:`input`). Default -1, the last dimension.
name(str|None): (optional) A name for this layer. If set None, the
layer will be named automatically.
Returns:
tuple: A tuple of sorted data Variable and the sorted indices.
Examples:
.. code-block:: python
input = fluid.layers.data(data=[2, 3])
out, indices = fluid.layers.argsort(input, axis=0)
"""
helper = LayerHelper("argsort", **locals())
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=True)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': input},
outputs={'Out': out,
'Indices': ids},
attrs={'axis': axis})
return out, ids
def ones(shape, dtype, force_cpu=False):
"""
**ones**
This function creates a tensor of specified *shape* and
*dtype*, and initializes this with 1.
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
data = fluid.layers.ones(shape=[1], dtype='int64')
"""
return fill_constant(value=1.0, **locals())
def zeros(shape, dtype, force_cpu=False):
"""
**zeros**
This function creates a tensor of specified *shape* and
*dtype*, and initializes this with 0.
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor.
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor.
force_cpu(bool, default False): Whether to make output stay on CPU.
Returns:
Variable: The tensor variable storing the output.
Examples:
.. code-block:: python
data = fluid.layers.zeros(shape=[1], dtype='int64')
"""
return fill_constant(value=0.0, **locals())
def reverse(x, axis):
"""
**reverse**
This function reverse the input 'x' along given axises.
Args:
x(Vairbale): the input to be reversed.
axis(int|tuple|list): Axis that along which order of elements
is reversed. If it is a tuple or a list, reversing
will be apply on each axis in the tuple or list.
Returns:
Variable: The reversed tensor.
Examples:
.. code-block:: python
out = fluid.layers.reverse(x=in, axis=0)
# or:
out = fluid.layers.reverse(x=in, axis=[0,1])
"""
if isinstance(axis, int):
axis = [axis]
helper = LayerHelper("reverse", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reverse',
inputs={'X': x},
outputs={'Out': [out]},
attrs={'axis': axis})
return out
def save(x, file_path, overwrite=True):
"""
Saves a variable as a file.
Args:
x(variable): The Tensor/LoDTensor to be saved.
file_path(str): The file path where the variable will be saved.
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
"""
helper = LayerHelper("save", **locals())
helper.append_op(
type="save",
inputs={"input": x},
outputs={},
args={"file_path": file_path,
"overwrite": overwrite})
def save_combine(x, file_path, overwrite=True):
"""
Saves a list of variables into a single file.
Args:
x(list): A list of Tensor/LoDTensor variables to be saved together in
a single file.
file_path(str): The file path where variables will be saved.
overwrite(bool): Whether or not cover the given file when it has already
existed. If it's set 'False' and the file is existed, a runtime
error will be thrown.
Returns:
There is no return value.
Examples:
.. code-block:: python
v1 = fluid.layers.data(name="data",
shape=(4, 6),
dtype="float32")
v2 = fluid.layers.data(name="data",
shape=(6, 8, 4),
dtype="float32")
normed = fluid.layers.save_combine([v1, v2], file_path="output")
"""
helper = LayerHelper("save_combine", **locals())
helper.append_op(
type="save_combine",
inputs={"input": x},
outputs={},
args={"file_path": file_path,
"overwrite": overwrite})
def load_combine(out, file_path):
"""
Loads a list of vairables from a single file.
Args:
out(list): The list of variables to be read from the disk file.
file_path(str): The path of the disk file.
"""
helper = LayerHelper("load_combine", **locals())
helper.append_op(
type="load_combine",
inputs={},
output={"Out": out},
args={"file_path": file_path})
def has_inf(x):
"""
Test if any of x contains an infinity number
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isinf", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
return out
def has_nan(x):
"""
Test if any of x contains a NAN
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isnan", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
return out
def isfinite(x):
"""
Test if any of x contains an infinity/NAN number. If all the elements are finite,
returns true, else false.
Args:
x(variable): The Tensor/LoDTensor to be checked.
Returns:
Variable: The tensor variable storing the output, contains a bool value.
"""
helper = LayerHelper("isfinite", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out