- class paddle.nn. UpsamplingBilinear2D ( size=None, scale_factor=None, data_format='NCHW', name=None ) [source]
This op upsamples a batch of images, using bilinear’ pixel values. The input must be a 4-D Tensor of the shape (num_batches, channels, in_h, in_w), where in_w is width of the input tensor, in_h is the height of the input tensor. And the upsampling only applies on the two dimensions(height and width). 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.
For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation.
x (Tensor) – 4-D Tensor, its data type is float32, float64, or uint8, its data format is specified by
size (list|tuple|Tensor|None) – Output shape of image resize layer, the shape is (out_h, out_w) when input is a 4-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|int|list|tuple|Tensor|None) – The multiplier for the input height or width. At least one of
scale_factormust be set. And
sizehas 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.
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
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
import paddle import paddle.nn as nn input_data = paddle.rand(shape=(2,3,6,10)).astype("float32") upsample_out = paddle.nn.UpsamplingBilinear2D(size=[12,12]) input = paddle.to_tensor(input_data) output = upsample_out(x=input) print(output.shape) # [2L, 3L, 12L, 12L]
Defines the computation performed at every call. Should be overridden by all subclasses.
*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments
Extra representation of this layer, you can have custom implementation of your own layer.