# upsample¶

paddle.nn.functional. upsample ( x, size=None, scale_factor=None, mode='nearest', align_corners=False, align_mode=0, data_format='NCHW', name=None ) [source]

This op 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

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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

Unexpected indentation.

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}

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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/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 `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: . 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 `size` or `scale_factor` must be set. And `size` has a higher priority than `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’, ‘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. 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 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).

Raises
• TypeError – size should be a list or tuple or Tensor.

• 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’, ‘NWC’, ‘NCHW’, ‘NHWC’, ‘NCDHW’ or ‘NDHWC’.

• Examples

• . code-block: – python: import paddle import numpy as np import paddle.nn.functional as F input_data = np.random.rand(2,3,6,10).astype(“float32”) input = paddle.to_tensor(input_data) output = F.upsample(x=input, size=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L]