|
|
|
|
@ -33,12 +33,12 @@ class UpSample(layers.Layer):
|
|
|
|
|
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the
|
|
|
|
|
future and only use :attr:`out_shape` instead.
|
|
|
|
|
Supporting resample methods:
|
|
|
|
|
'LINEAR' : linear interpolation
|
|
|
|
|
'BILINEAR' : Bilinear interpolation
|
|
|
|
|
'TRILINEAR' : Trilinear interpolation
|
|
|
|
|
'NEAREST' : Nearest neighbor interpolation
|
|
|
|
|
'BICUBIC' : Bicubic interpolation
|
|
|
|
|
|
|
|
|
|
'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.
|
|
|
|
|
|
|
|
|
|
@ -51,6 +51,11 @@ class UpSample(layers.Layer):
|
|
|
|
|
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,
|
|
|
|
|
@ -59,24 +64,26 @@ class UpSample(layers.Layer):
|
|
|
|
|
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.
|
|
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
|
|
|
|
|
.. code-block:: text
|
|
|
|
|
|
|
|
|
|
For scale:
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
@ -91,26 +98,17 @@ class UpSample(layers.Layer):
|
|
|
|
|
output: (N,C,H_out,W_out) where:
|
|
|
|
|
H_out = round(H_{in} * scale_{factor})
|
|
|
|
|
W_out = round(W_{in} * scale_{factor})
|
|
|
|
|
|
|
|
|
|
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}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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}
|
|
|
|
|
@ -123,6 +121,7 @@ class UpSample(layers.Layer):
|
|
|
|
|
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:
|
|
|
|
|
@ -144,73 +143,76 @@ class UpSample(layers.Layer):
|
|
|
|
|
H_out = H_{in} * scale_{factor}
|
|
|
|
|
W_out = W_{in} * scale_{factor}
|
|
|
|
|
|
|
|
|
|
https://en.wikipedia.org/wiki/Linear_interpolation.
|
|
|
|
|
For details of linear interpolation, please refer to Wikipedia:
|
|
|
|
|
|
|
|
|
|
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/Linear_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.
|
|
|
|
|
For details of bicubic interpolation, please refer to Wikipedia:
|
|
|
|
|
https://en.wikipedia.org/wiki/Bicubic_interpolation
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Parameters:
|
|
|
|
|
input (Variable): 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`.
|
|
|
|
|
out_shape(list|tuple|Variable|None): Output shape of image resize
|
|
|
|
|
layer, the shape is (out_w, ) when input is 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, each element can be an integer or a Tensor Variable of shape: [1].
|
|
|
|
|
size (list|tuple|Variable|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, each element can be an integer or a Tensor Variable of shape: [1].
|
|
|
|
|
If a Tensor Variable, its dimensions size should be a 1.
|
|
|
|
|
scale(float|Variable|None): The multiplier for the input height or width. At
|
|
|
|
|
least one of :attr:`out_shape` or :attr:`scale` must be set.
|
|
|
|
|
And :attr:`out_shape` has a higher priority than :attr:`scale`.
|
|
|
|
|
scale_factor (float|Variable|None): The multiplier for the input height or width. At
|
|
|
|
|
least one of :attr:`out_shape` or :attr:`scale_factor` must be set.
|
|
|
|
|
And :attr:`out_shape` has a higher priority than :attr:`scale_factor`.
|
|
|
|
|
Default: None.
|
|
|
|
|
name(str|None): A name for this layer(optional). If set None, the layer
|
|
|
|
|
will be named automatically.
|
|
|
|
|
resample(str): The resample method. It supports 'LINEAR', 'BILINEAR', 'TRILINEAR' ,
|
|
|
|
|
'BICUBIC' and 'NEAREST' currently. Default: 'BILINEAR'
|
|
|
|
|
mode (str): The resample method. It supports 'linear', 'nearst', '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.
|
|
|
|
|
Default: True
|
|
|
|
|
align_mode(int) : An optional for bilinear interpolation. can be \'0\'
|
|
|
|
|
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
|
|
|
|
|
src_idx = scale*dst_index.
|
|
|
|
|
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', `"NCHW"`, `"NHWC"`, `"NCDHW"`,
|
|
|
|
|
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).
|
|
|
|
|
|
|
|
|
|
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).
|
|
|
|
|
Raises:
|
|
|
|
|
TypeError: out_shape should be a list or tuple or Variable.
|
|
|
|
|
TypeError: actual_shape should either be Variable or None.
|
|
|
|
|
ValueError: The 'resample' of image_resize can only be 'BILINEAR',
|
|
|
|
|
'TRILINEAR', 'BICUBIC', or 'NEAREST' currently.
|
|
|
|
|
ValueError: 'BILINEAR', 'BICUBIC' and 'NEAREST' only support 4-D tensor.
|
|
|
|
|
ValueError: 'TRILINEAR' only support 5-D tensor.
|
|
|
|
|
ValueError: One of out_shape and scale must not be None.
|
|
|
|
|
ValueError: out_shape length should be 2 for input 4-D tensor.
|
|
|
|
|
ValueError: out_shape length should be 3 for input 5-D tensor.
|
|
|
|
|
ValueError: scale should be greater than zero.
|
|
|
|
|
TypeError: size should be a list or tuple or Variable.
|
|
|
|
|
ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear',
|
|
|
|
|
'trilinear', 'bicubic', or 'nearest' currently.
|
|
|
|
|
ValueError: 'linear' only support 3-D tensor.
|
|
|
|
|
ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.
|
|
|
|
|
ValueError: 'trilinear' only support 5-D tensor.
|
|
|
|
|
ValueError: One of size and scale_factor must not be None.
|
|
|
|
|
ValueError: size length should be 1 for input 3-D tensor.
|
|
|
|
|
ValueError: size length should be 2 for input 4-D tensor.
|
|
|
|
|
ValueError: size length should be 3 for input 5-D tensor.
|
|
|
|
|
ValueError: scale_factor should be greater than zero.
|
|
|
|
|
TypeError: align_corners should be a bool value
|
|
|
|
|
ValueError: align_mode can only be '0' or '1'
|
|
|
|
|
ValueError: data_format can only be 'NCW', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
|
|
|
|
|
ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
|
|
|
|
|
|
|
|
|
|
Examples:
|
|
|
|
|
.. code-block:: python
|
|
|
|
|
import paddle
|
|
|
|
|
import numpy as np
|
|
|
|
|
import paddle.fluid.dygraph as dg
|
|
|
|
|
upsample_op = paddle.nn.UpSample(out_shape=[12,12])
|
|
|
|
|
upsample_op = paddle.nn.UpSample(size=[12,12])
|
|
|
|
|
input_data = np.random.rand(2,3,6,10).astype("float32")
|
|
|
|
|
place = paddle.fluid.CPUPlace()
|
|
|
|
|
with dg.guard(place) as g:
|
|
|
|
|
@ -221,16 +223,16 @@ class UpSample(layers.Layer):
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self,
|
|
|
|
|
out_shape=None,
|
|
|
|
|
scale=None,
|
|
|
|
|
resample='BILINEAR',
|
|
|
|
|
align_corners=True,
|
|
|
|
|
size=None,
|
|
|
|
|
scale_factor=None,
|
|
|
|
|
mode='nearest',
|
|
|
|
|
align_corners=False,
|
|
|
|
|
align_mode=1,
|
|
|
|
|
data_format='NCHW'):
|
|
|
|
|
super(UpSample, self).__init__()
|
|
|
|
|
self.out_shape = out_shape
|
|
|
|
|
self.scale = scale
|
|
|
|
|
self.resample = resample
|
|
|
|
|
self.size = size
|
|
|
|
|
self.scale_factor = scale_factor
|
|
|
|
|
self.mode = mode.lower()
|
|
|
|
|
self.align_corners = align_corners
|
|
|
|
|
self.align_mode = align_mode
|
|
|
|
|
self.data_format = data_format
|
|
|
|
|
@ -238,9 +240,9 @@ class UpSample(layers.Layer):
|
|
|
|
|
def forward(self, input):
|
|
|
|
|
out = F.interpolate(
|
|
|
|
|
input,
|
|
|
|
|
out_shape=self.out_shape,
|
|
|
|
|
scale=self.scale,
|
|
|
|
|
resample=self.resample,
|
|
|
|
|
size=self.size,
|
|
|
|
|
scale_factor=self.scale_factor,
|
|
|
|
|
mode=self.mode,
|
|
|
|
|
align_corners=self.align_corners,
|
|
|
|
|
align_mode=self.align_mode,
|
|
|
|
|
data_format=self.data_format)
|
|
|
|
|
|
FDInSky
5 years ago
committed by
GitHub