mmedit.models.editors.stylegan2.ada.grid_sample_gradfix
¶
Custom replacement for torch.nn.functional.grid_sample that supports arbitrarily high order gradients between the input and output.
Only works on 2D images and assumes mode=’bilinear’, padding_mode=’zeros’, align_corners=False.
Module Contents¶
Classes¶
Base class to create custom autograd.Function 

Base class to create custom autograd.Function 
Functions¶


Attributes¶
 class mmedit.models.editors.stylegan2.ada.grid_sample_gradfix._GridSample2dForward(*args, **kwargs)[source]¶
Bases:
torch.autograd.Function
Base class to create custom autograd.Function
To create a custom autograd.Function, subclass this class and implement the
forward()
andbackward()
static methods. Then, to use your custom op in the forward pass, call the class methodapply
. Do not callforward()
directly.To ensure correctness and best performance, make sure you are calling the correct methods on
ctx
and validating your backward function usingtorch.autograd.gradcheck()
.See extendingautograd for more details on how to use this class.
Examples:
>>> class Exp(Function): >>> @staticmethod >>> def forward(ctx, i): >>> result = i.exp() >>> ctx.save_for_backward(result) >>> return result >>> >>> @staticmethod >>> def backward(ctx, grad_output): >>> result, = ctx.saved_tensors >>> return grad_output * result >>> >>> # Use it by calling the apply method: >>> # xdoctest: +SKIP >>> output = Exp.apply(input)
 static forward(ctx, input, grid)[source]¶
Performs the operation.
This function is to be overridden by all subclasses.
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward()
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward()
if they are intended to be used for injvp
.
 static backward(ctx, grad_output)[source]¶
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward()
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward()
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward()
will havectx.needs_input_grad[0] = True
if the first input toforward()
needs gradient computated w.r.t. the output.
 class mmedit.models.editors.stylegan2.ada.grid_sample_gradfix._GridSample2dBackward(*args, **kwargs)[source]¶
Bases:
torch.autograd.Function
Base class to create custom autograd.Function
To create a custom autograd.Function, subclass this class and implement the
forward()
andbackward()
static methods. Then, to use your custom op in the forward pass, call the class methodapply
. Do not callforward()
directly.To ensure correctness and best performance, make sure you are calling the correct methods on
ctx
and validating your backward function usingtorch.autograd.gradcheck()
.See extendingautograd for more details on how to use this class.
Examples:
>>> class Exp(Function): >>> @staticmethod >>> def forward(ctx, i): >>> result = i.exp() >>> ctx.save_for_backward(result) >>> return result >>> >>> @staticmethod >>> def backward(ctx, grad_output): >>> result, = ctx.saved_tensors >>> return grad_output * result >>> >>> # Use it by calling the apply method: >>> # xdoctest: +SKIP >>> output = Exp.apply(input)
 static forward(ctx, grad_output, input, grid)[source]¶
Performs the operation.
This function is to be overridden by all subclasses.
It must accept a context ctx as the first argument, followed by any number of arguments (tensors or other types).
The context can be used to store arbitrary data that can be then retrieved during the backward pass. Tensors should not be stored directly on ctx (though this is not currently enforced for backward compatibility). Instead, tensors should be saved either with
ctx.save_for_backward()
if they are intended to be used inbackward
(equivalently,vjp
) orctx.save_for_forward()
if they are intended to be used for injvp
.
 static backward(ctx, grad2_grad_input, grad2_grad_grid)[source]¶
Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).
This function is to be overridden by all subclasses.
It must accept a context
ctx
as the first argument, followed by as many outputs as theforward()
returned (None will be passed in for non tensor outputs of the forward function), and it should return as many tensors, as there were inputs toforward()
. Each argument is the gradient w.r.t the given output, and each returned value should be the gradient w.r.t. the corresponding input. If an input is not a Tensor or is a Tensor not requiring grads, you can just pass None as a gradient for that input.The context can be used to retrieve tensors saved during the forward pass. It also has an attribute
ctx.needs_input_grad
as a tuple of booleans representing whether each input needs gradient. E.g.,backward()
will havectx.needs_input_grad[0] = True
if the first input toforward()
needs gradient computated w.r.t. the output.