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common.py
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common.py
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# Copyright (c) 2020 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
#
# Unless 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.
import warnings
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.tensor import fill_constant
from ...tensor import concat
from ...tensor.creation import zeros
from paddle.static import Variable
from ...fluid import dygraph_utils
# TODO: define the common functions to build a neural network
from ...tensor.manipulation import squeeze
from ...tensor.manipulation import unsqueeze
from ...tensor import clip
from ...tensor import sum
from ...tensor import sqrt
from ...fluid.data_feeder import (
check_variable_and_dtype,
check_dtype,
check_type,
)
from ...fluid.framework import (
_varbase_creator,
_in_legacy_dygraph,
in_dygraph_mode,
_non_static_mode,
)
from ...fluid import dygraph_utils
from paddle import _C_ops, _legacy_C_ops
from paddle.framework import in_dynamic_mode
from paddle.tensor.creation import full
from paddle.framework import core
from paddle.fluid.framework import _in_legacy_dygraph
from paddle.static import default_main_program
__all__ = []
def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
r"""
Return a col buffer of sliding local blocks of input x, also known
as im2col for batched 2D image tensors. For each block under the convolution filter,
all element will be rearranged as a column. While the convolution filter sliding over
the input feature map, a series of such columns will be formed.
For each input :math:`x` with shape [N, C, H, W], the output shape [N, Cout, Lout]
can be calculated as following.
.. math::
dkernel[0] &= dilations[0] \times (kernel\_sizes[0] - 1) + 1
dkernel[1] &= dilations[1] \times (kernel\_sizes[1] - 1) + 1
hout &= \frac{H + paddings[0] + paddings[2] - dkernel[0]}{strides[0]} + 1
wout &= \frac{W + paddings[1] + paddings[3] - dkernel[1]}{strides[1]} + 1
Cout &= C \times kernel\_sizes[0] \times kernel\_sizes[1]
Lout &= hout \times wout
Parameters:
x(Tensor): 4-D Tensor, input tensor of format [N, C, H, W],
data type can be float32 or float64
kernel_sizes(int|list): The size of convolution kernel, should be [k_h, k_w]
or an integer k treated as [k, k].
strides(int|list): The strides, should be [stride_h, stride_w]
or an integer stride treated as [sride, stride].
For default, strides will be [1, 1].
paddings(int|list): The paddings of each dimension, should be
[padding_top, padding_left, padding_bottom, padding_right]
or [padding_h, padding_w] or an integer padding.
If [padding_h, padding_w] was given, it will expanded to
[padding_h, padding_w, padding_h, padding_w]. If an integer
padding was given, [padding, padding, padding, padding] will
be used. For default, paddings will be [0, 0, 0, 0]
dilations(int|list): the dilations of convolution kernel, should be
[dilation_h, dilation_w], or an integer dilation treated as
[dilation, dilation]. For default, it will be [1, 1].
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 tensor corresponding to the sliding local blocks.
The output shape is [N, Cout, Lout] as decriabled above.
Cout is the total number of values within each block,
and Lout is the total number of such blocks.
The data type of output is the same as the input :math:`x`
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.randn((100,3,224,224))
y = F.unfold(x, [3, 3], 1, 1, 1)
"""
helper = LayerHelper("unfold", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'unfold')
assert len(x.shape) == 4, "input should be the format of [N, C, H, W]"
if isinstance(kernel_sizes, int):
kernel_sizes = [kernel_sizes, kernel_sizes]
else:
assert isinstance(kernel_sizes, list) and (
len(kernel_sizes) == 2
), "kernel_sizes should either be an integer or a list of two integers"
if isinstance(strides, int):
strides = [strides, strides]
else:
assert isinstance(strides, list) and (
len(strides) == 2
), "strides should either be an integer or a list of two integers"
if isinstance(dilations, int):
dilations = [dilations, dilations]
else:
assert isinstance(dilations, list) and (
len(dilations) == 2
), "dilations should either be an integer or a list of two integers"
if isinstance(paddings, int):
paddings = [paddings] * 4
elif isinstance(paddings, list):
if len(paddings) == 2:
paddings = paddings * 2
elif len(paddings) == 4:
pass
else:
raise ValueError(
"paddings should either be an integer or a list of 2 or 4 integers"
)
else:
raise ValueError(
"Unexpected type of paddings, it should be either an integer or a list"
"of 2 or 4 integers"
)
if in_dygraph_mode():
return _C_ops.unfold(x, kernel_sizes, strides, paddings, dilations)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="unfold",
inputs={"X": x},
outputs={"Y": out},
attrs={
"kernel_sizes": kernel_sizes,
"strides": strides,
"paddings": paddings,
"dilations": dilations,
},
)
return out
def interpolate(
x,
size=None,
scale_factor=None,
mode='nearest',
align_corners=False,
align_mode=0,
data_format='NCHW',
name=None,
):
"""
This API resizes a batch of images.
The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
Where in_w is width of the input tensor, in_h is the height of the input tensor,
in_d is the depth of the intput tensor.
and the resizing only applies on the three dimensions(depth, height and width).
Supporting resample methods:
- 'linear' : Linear interpolation
- 'bilinear' : Bilinear interpolation
- 'trilinear' : Trilinear interpolation
- 'nearest' : Nearest neighbor interpolation
- 'bicubic' : Bicubic interpolation
- 'area': Area interpolation
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in both the 3rd dimension(in height direction) and the 4th dimension(in width
direction) on input tensor.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions.
align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
Bicubic interpolation is an extension of cubic interpolation for interpolating
data points on a two-dimensional regular grid. The interpolated surface is
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Area interpolation is to perform area interpolation
in both the 3rd dimension(in height direction) , the 4th dimension(in width
direction) and the 5th dimension(in depth direction) on input tensor. Set to
area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or
`paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`.
Example:
.. code-block:: text
# For scale_factor:
if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0)
else:
scale_factor = float(in_size/out_size)
# Linear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = W_{in} * scale_{factor}
# Nearest neighbor interpolation:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = floor (H_{in} * scale_{factor})
W_out = floor (W_{in} * scale_{factor})
# Bilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
# Bicubic interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
# Trilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor}
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of linear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation.
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
Parameters:
x (Tensor): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
size (list|tuple|Tensor|None): Output shape of image resize
layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1].
If a Tensor, its dimensions size should be a 1.
scale_factor (float|Tensor|list|tuple|None): The multiplier for the input height or width. At
least one of :attr:`size` or :attr:`scale_factor` must be set.
And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor.
Default: None.
mode (str): The resample method. It supports 'linear', 'area', 'nearest', 'bilinear',
'bicubic' and 'trilinear' currently. Default: 'nearest'
align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the
corner pixels.This only has an effect when 'linear', 'bilinear', 'bicubic' or 'trilinear'.
Default: False
align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above,
it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale_factor*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
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 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
output_1 = F.interpolate(x=input_data, size=[12,12])
print(output_1.shape)
# [2L, 3L, 12L, 12L]
# given scale
output_2 = F.interpolate(x=input_data, scale_factor=[2,1])
print(output_2.shape)
# [2L, 3L, 12L, 10L]
# bilinear interp
output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear")
print(output_2.shape)
# [2L, 3L, 12L, 10L]
"""
data_format = data_format.upper()
resample = mode.upper()
resample_type = mode.lower()
resample_methods = [
'LINEAR',
'BILINEAR',
'TRILINEAR',
'NEAREST',
'BICUBIC',
'AREA',
]
if resample not in resample_methods:
raise ValueError(
"The 'resample' of image_resize can only be 'area', 'linear', 'bilinear', 'trilinear', "
" 'bicubic' or 'nearest' currently."
)
if resample in ['LINEAR'] and len(x.shape) != 3:
raise ValueError("'linear' only support 3-D tensor.")
if resample in ['NEAREST'] and len(x.shape) != 4 and len(x.shape) != 5:
raise ValueError("'NEAREST' only support 4-D or 5-D tensor.")
if resample in ['BILINEAR', 'BICUBIC'] and len(x.shape) != 4:
raise ValueError("'bilinear' and 'bicubic' only support 4-D tensor.")
if resample == 'TRILINEAR' and len(x.shape) != 5:
raise ValueError("'trilinear'only support 5-D tensor.")
if size is None and scale_factor is None:
raise ValueError("One of size and scale_factor must not be None.")
if not isinstance(align_corners, bool):
raise TypeError("Attr align_corners should be a bool value")
if align_mode != 0 and align_mode != 1:
raise ValueError("align_mode can only be 0 or 1")
if align_corners != 0 and resample == 'NEAREST':
raise ValueError(
"align_corners option can only be set with the interpolating modes: linear | bilinear | bicubic | trilinear"
)
if resample == 'AREA':
if (
isinstance(size, list)
or isinstance(size, tuple)
or isinstance(size, Variable)
):
if len(size) == 0:
raise ValueError("output size can not be empty")
if len(x.shape) == 3:
return paddle.nn.functional.adaptive_avg_pool1d(x, size)
elif len(x.shape) == 4:
return paddle.nn.functional.adaptive_avg_pool2d(x, size)
elif len(x.shape) == 5:
return paddle.nn.functional.adaptive_avg_pool3d(x, size)
helper = LayerHelper('{}_interp_v2'.format(resample_type), **locals())
dtype = helper.input_dtype(input_param_name='x')
if len(x.shape) == 3 and data_format not in ['NCW', 'NWC']:
raise ValueError(
"Got wrong value for param `data_format`: "
+ data_format
+ " received but only `NCW` or `NWC` supported for 3-D input."
)
elif len(x.shape) == 4 and data_format not in ['NCHW', 'NHWC']:
raise ValueError(
"Got wrong value for param `data_format`: "
+ data_format
+ " received but only `NCHW` or `NHWC` supported for 4-D input."
)
elif len(x.shape) == 5 and data_format not in ['NCDHW', 'NDHWC']:
raise ValueError(
"Got wrong value for param `data_format`: "
+ data_format
+ " received but only `NCDHW` or `NDHWC` supported for 5-D input."
)
def _is_list_or_turple_(data):
return isinstance(data, list) or isinstance(data, tuple)
if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW':
data_layout = 'NCHW'
if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC':
data_layout = 'NHWC'
if resample == 'NEAREST':
align_corners = False
inputs = {"X": x}
attrs = {
"out_d": -1,
"out_h": -1,
"out_w": -1,
"interp_method": resample_type,
"align_corners": align_corners,
"align_mode": align_mode,
"data_layout": data_layout,
}
out_shape = size
scale = scale_factor
if out_shape is not None and scale is not None:
raise ValueError("Only one of size or scale_factor should be defined.")
if out_shape is not None:
if isinstance(out_shape, Variable) and not in_dynamic_mode():
out_shape.stop_gradient = True
inputs['OutSize'] = out_shape
else:
if in_dynamic_mode():
if isinstance(out_shape, Variable):
out_shape = list(out_shape.numpy())
else:
out_shape = list(out_shape)
for i, dim in enumerate(out_shape):
if isinstance(dim, Variable):
out_shape[i] = dim.numpy()[0]
if not (_is_list_or_turple_(out_shape)):
raise TypeError("size should be a list or tuple or Variable.")
# Validate the shape
contain_var = False
for dim_idx, dim_size in enumerate(out_shape):
if isinstance(dim_size, Variable):
contain_var = True
continue
assert (
dim_size > 0
), "Each dimension size given in out_shape must be greater than 0."
if contain_var:
new_size_tensor = []
size_list = []
for dim in out_shape:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_size_tensor.append(dim)
size_list.append(-1)
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_size_tensor.append(temp_out)
size_list.append(dim)
inputs['SizeTensor'] = new_size_tensor
if len(x.shape) == 3:
if len(out_shape) != 1:
raise ValueError(
"size length should be 2 for input 3-D tensor"
)
if contain_var:
attrs['out_w'] = size_list[0]
else:
out_shape = list(map(int, out_shape))
attrs['out_w'] = out_shape[0]
if len(x.shape) == 4:
if len(out_shape) != 2:
raise ValueError(
"size length should be 2 for " "input 4-D tensor."
)
if contain_var:
attrs['out_h'] = size_list[0]
attrs['out_w'] = size_list[1]
else:
out_shape = list(map(int, out_shape))
attrs['out_h'] = out_shape[0]
attrs['out_w'] = out_shape[1]
if len(x.shape) == 5:
if len(out_shape) != 3:
raise ValueError(
"size length should be 3 for " "input 5-D tensor."
)
if contain_var:
attrs['out_d'] = size_list[0]
attrs['out_h'] = size_list[1]
attrs['out_w'] = size_list[2]
else:
out_shape = list(map(int, out_shape))
attrs['out_d'] = out_shape[0]
attrs['out_h'] = out_shape[1]
attrs['out_w'] = out_shape[2]
else:
if in_dynamic_mode() and isinstance(scale, Variable):
scale = list(scale.numpy())
if isinstance(scale, Variable):
scale.stop_gradient = True
inputs["Scale"] = scale
elif isinstance(scale, float) or isinstance(scale, int):
if scale <= 0:
raise ValueError("Attr(scale) should be greater than zero.")
scale_list = []
for i in range(len(x.shape) - 2):
scale_list.append(scale)
attrs['scale'] = list(map(float, scale_list))
elif isinstance(scale, list) or isinstance(scale, tuple):
if len(scale) != len(x.shape) - 2:
raise ValueError(
"scale_shape length should be {} for "
"input {}-D tensor.".format(len(x.shape) - 2, len(x.shape))
)
for value in scale:
if value <= 0:
raise ValueError("Attr(scale) should be greater than zero.")
attrs['scale'] = list(map(float, scale))
else:
raise TypeError(
"Attr(scale)'s type should be float, int, list, tuple, or Tensor."
)
if in_dynamic_mode():
attr_list = []
for k, v in attrs.items():
attr_list.append(k)
attr_list.append(v)
dy_attr = tuple(attr_list)
if resample_type == "linear":
if in_dygraph_mode():
out = _C_ops.linear_interp(
x,
inputs['OutSize'] if 'OutSize' in inputs else None,
inputs['SizeTensor'] if 'SizeTensor' in inputs else None,
inputs['Scale'] if 'Scale' in inputs else None,
attrs['data_layout'],
attrs['out_d'],
attrs['out_h'],
attrs['out_w'],
attrs['scale'] if 'scale' in attrs else [],
attrs['interp_method'],
attrs['align_corners'],
attrs['align_mode'],
)
else:
out = _legacy_C_ops.linear_interp_v2(x, *dy_attr)
elif resample_type == "bilinear":
if in_dygraph_mode():
out = _C_ops.bilinear_interp(
x,
inputs['OutSize'] if 'OutSize' in inputs else None,
inputs['SizeTensor'] if 'SizeTensor' in inputs else None,
inputs['Scale'] if 'Scale' in inputs else None,
attrs['data_layout'],
attrs['out_d'],
attrs['out_h'],
attrs['out_w'],
attrs['scale'] if 'scale' in attrs else [],
attrs['interp_method'],
attrs['align_corners'],
attrs['align_mode'],
)
else:
out = _legacy_C_ops.bilinear_interp_v2(x, *dy_attr)
elif resample_type == "trilinear":
if in_dygraph_mode():
out = _C_ops.trilinear_interp(
x,
inputs['OutSize'] if 'OutSize' in inputs else None,
inputs['SizeTensor'] if 'SizeTensor' in inputs else None,
inputs['Scale'] if 'Scale' in inputs else None,
attrs['data_layout'],
attrs['out_d'],
attrs['out_h'],
attrs['out_w'],
attrs['scale'] if 'scale' in attrs else [],
attrs['interp_method'],
attrs['align_corners'],
attrs['align_mode'],
)
else:
out = _legacy_C_ops.trilinear_interp_v2(x, *dy_attr)
elif resample_type == "nearest":
if in_dygraph_mode():
out = _C_ops.nearest_interp(
x,
inputs['OutSize'] if 'OutSize' in inputs else None,
inputs['SizeTensor'] if 'SizeTensor' in inputs else None,
inputs['Scale'] if 'Scale' in inputs else None,
attrs['data_layout'],
attrs['out_d'],
attrs['out_h'],
attrs['out_w'],
attrs['scale'] if 'scale' in attrs else [],
attrs['interp_method'],
attrs['align_corners'],
attrs['align_mode'],
)
else:
out = _legacy_C_ops.nearest_interp_v2(x, *dy_attr)
elif resample_type == "bicubic":
if in_dygraph_mode():
out = _C_ops.bicubic_interp(
x,
inputs['OutSize'] if 'OutSize' in inputs else None,
inputs['SizeTensor'] if 'SizeTensor' in inputs else None,
inputs['Scale'] if 'Scale' in inputs else None,
attrs['data_layout'],
attrs['out_d'],
attrs['out_h'],
attrs['out_w'],
attrs['scale'] if 'scale' in attrs else [],
attrs['interp_method'],
attrs['align_corners'],
attrs['align_mode'],
)
else:
out = _legacy_C_ops.bicubic_interp_v2(x, *dy_attr)
return out
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='{}_interp_v2'.format(resample_type),
inputs=inputs,
outputs={"Out": out},
attrs=attrs,
)
return out
def upsample(
x,
size=None,
scale_factor=None,
mode='nearest',
align_corners=False,
align_mode=0,
data_format='NCHW',
name=None,
):
"""
This API resizes a batch of images.
The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
Where in_w is width of the input tensor, in_h is the height of the input tensor,
in_d is the depth of the intput tensor.
and the resizing only applies on the three dimensions(depth, height and width).
Supporting resample methods:
- 'linear' : Linear interpolation
- 'bilinear' : Bilinear interpolation
- 'trilinear' : Trilinear interpolation
- 'nearest' : Nearest neighbor interpolation
- 'bicubic' : Bicubic interpolation
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in both the 3rd dimension(in height direction) and the 4th dimension(in width
direction) on input tensor.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
Bicubic interpolation is an extension of cubic interpolation for interpolating
data points on a two-dimensional regular grid. The interpolated surface is
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions.
align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
Area interpolation is to perform area interpolation
in both the 3rd dimension(in height direction) , the 4th dimension(in width
direction) and the 5th dimension(in depth direction) on input tensor. Set to
area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or
`paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`.
Example:
.. code-block:: text
For scale_factor:
if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0)
else:
scale_factor = float(in_size/out_size)
Linear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = W_{in} * scale_{factor}
Nearest neighbor interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = floor (H_{in} * scale_{factor})
W_out = floor (W_{in} * scale_{factor})
else:
align_corners = True
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
Bilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
Bicubic interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
Trilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor}
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of linear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation.
Parameters:
x (Tensor): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
size (list|tuple|Tensor|None, optional): Output shape of image resize
layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
Default: None. If a list/tuple, each element can be an integer or a Tensor of shape: [1].
If a Tensor , its dimensions size should be a 1.
scale_factor (float|Tensor|list|tuple|None, optional): The multiplier for the input height or width. At
least one of :attr:`size` or :attr:`scale_factor` must be set.
And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if
it is either a list or a tuple or a Tensor.
Default: None.
mode (str, optional): The resample method. It supports 'linear', 'nearest', 'bilinear',
'bicubic' and 'trilinear' currently. Default: 'nearest'
align_corners(bool, optional) : An optional bool, If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the
corner pixels.
Default: False
align_mode(int, optional) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above,
it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale_factor*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
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 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
upsample_out = paddle.nn.Upsample(size=[12,12])
output = upsample_out(x=input_data)
print(output.shape)
# [2L, 3L, 12L, 12L]
"""
return interpolate(
x, size, scale_factor, mode, align_corners, align_mode, data_format
)
def bilinear(x1, x2, weight, bias=None, name=None):
"""
This layer performs bilinear on two inputs.
See :ref:`api_nn_Bilinear` for details and output shape.
Parameters:
x1 (Tensor): the first input tensor, it's data type should be float32, float64.
x2 (Tensor): the second input tensor, it's data type should be float32, float64.
weight (Parameter): The learnable weights of this layer, shape is [out_features, in1_features, in2_features].
bias (Parameter, optional): The learnable bias(Bias) of this layer, shape is [1, out_features]. If it is set to None, no bias will be added to the output units. The default value is None.
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`. Default: None.
Returns:
Tensor: A 2-D Tensor of shape [batch_size, out_features].
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x1 = paddle.randn((5, 5)).astype(paddle.float32)
x2 = paddle.randn((5, 4)).astype(paddle.float32)
w = paddle.randn((1000, 5, 4)).astype(paddle.float32)
b = paddle.randn((1, 1000)).astype(paddle.float32)
result = F.bilinear(x1, x2, w, b)
print(result.shape)
# [5, 1000]
"""
if in_dygraph_mode():
return _C_ops.bilinear_tensor_product(x1, x2, weight, bias)
elif _non_static_mode():
return _legacy_C_ops.bilinear_tensor_product(x1, x2, weight, bias)
check_variable_and_dtype(x1, 'x1', ['float32', 'float64'], 'bilinear')
check_variable_and_dtype(x2, 'x2', ['float32', 'float64'], 'bilinear')
inputs = {"X": x1, "Y": x2, "Weight": weight}
if bias is not None:
inputs["Bias"] = bias
helper = LayerHelper("bilinear", **locals())
out = helper.create_variable_for_type_inference(dtype=x1.dtype)
helper.append_op(
type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out}
)
return out
def dropout(
x, p=0.5, axis=None, training=True, mode="upscale_in_train", name=None
):
"""
Dropout is a regularization technique for reducing overfitting by preventing
neuron co-adaption during training. The dropout operator randomly sets the
outputs of some units to zero, while upscale others according to the given
dropout probability.
Args:
x (Tensor): The input tensor. The data type is float32 or float64.
p (float|int, optional): Probability of setting units to zero. Default 0.5.
axis (int|list|tuple, optional): The axis along which the dropout is performed. Default None.
training (bool, optional): A flag indicating whether it is in train phrase or not. Default True.
mode(str, optional): ['upscale_in_train'(default) | 'downscale_in_infer'].
1. upscale_in_train(default), upscale the output at training time
- train: out = input * mask / ( 1.0 - dropout_prob )
- inference: out = input
2. downscale_in_infer, downscale the output at inference
- train: out = input * mask
- inference: out = input * (1.0 - dropout_prob)
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor representing the dropout, has same shape and data type as `x` .
Examples:
We use ``p=0.5`` in the following description for simplicity.
1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly.
.. code-block:: text