In PyTorch, F.interpolatethe function itself has no learnable parameters. It is a function used to resize the input tensor, usually used for upsampling or downsampling operations on images.
F.interpolateThe function performs an interpolation operation on the input tensor according to the provided adjustment method (such as interpolation method, scaling factor, etc.) to obtain an output tensor of the target size. This process is calculated based on the input data without additional learnable parameters .
However, F.interpolatefunctions can be combined with models that have learnable parameters. For example, in a convolutional neural network (CNN), the F.interpolateinput image can be resized and then passed to a convolutional layer or a fully connected layer with learnable parameters for further processing.
In this case, the learnable parameters exist in the convolutional or fully connected layers of the model, rather than in F.interpolatethe function itself . F.interpolateThe function is just a data processing tool used to perform sizing operations in the model.
It should be noted that F.interpolatethe function is usually fixed during the training process and the parameters are not updated based on backpropagation . If you need to learn resizable parameters during training, consider using a layer with learnable parameters, e.g. nn.Upsampleor a custom learnable resizing layer.