annotator/uniformer/mmcv/ops/upfirdn2d.py

# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501

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# Augmentation (ADA)
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import torch
from torch.autograd import Function
from torch.nn import functional as F

from annotator.uniformer.mmcv.utils import to_2tuple
from ..utils import ext_loader

upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d'])


class UpFirDn2dBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad,
                in_size, out_size):

        up_x, up_y = up
        down_x, down_y = down
        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad

        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)

        grad_input = upfirdn2d_ext.upfirdn2d(
            grad_output,
            grad_kernel,
            up_x=down_x,
            up_y=down_y,
            down_x=up_x,
            down_y=up_y,
            pad_x0=g_pad_x0,
            pad_x1=g_pad_x1,
            pad_y0=g_pad_y0,
            pad_y1=g_pad_y1)
        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2],
                                     in_size[3])

        ctx.save_for_backward(kernel)

        pad_x0, pad_x1, pad_y0, pad_y1 = pad

        ctx.up_x = up_x
        ctx.up_y = up_y
        ctx.down_x = down_x
        ctx.down_y = down_y
        ctx.pad_x0 = pad_x0
        ctx.pad_x1 = pad_x1
        ctx.pad_y0 = pad_y0
        ctx.pad_y1 = pad_y1
        ctx.in_size = in_size
        ctx.out_size = out_size

        return grad_input

    @staticmethod
    def backward(ctx, gradgrad_input):
        kernel, = ctx.saved_tensors

        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2],
                                                ctx.in_size[3], 1)

        gradgrad_out = upfirdn2d_ext.upfirdn2d(
            gradgrad_input,
            kernel,
            up_x=ctx.up_x,
            up_y=ctx.up_y,
            down_x=ctx.down_x,
            down_y=ctx.down_y,
            pad_x0=ctx.pad_x0,
            pad_x1=ctx.pad_x1,
            pad_y0=ctx.pad_y0,
            pad_y1=ctx.pad_y1)
        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
        #                                  ctx.out_size[1], ctx.in_size[3])
        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1],
                                         ctx.out_size[0], ctx.out_size[1])

        return gradgrad_out, None, None, None, None, None, None, None, None


class UpFirDn2d(Function):

    @staticmethod
    def forward(ctx, input, kernel, up, down, pad):
        up_x, up_y = up
        down_x, down_y = down
        pad_x0, pad_x1, pad_y0, pad_y1 = pad

        kernel_h, kernel_w = kernel.shape
        batch, channel, in_h, in_w = input.shape
        ctx.in_size = input.shape

        input = input.reshape(-1, in_h, in_w, 1)

        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))

        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
        ctx.out_size = (out_h, out_w)

        ctx.up = (up_x, up_y)
        ctx.down = (down_x, down_y)
        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)

        g_pad_x0 = kernel_w - pad_x0 - 1
        g_pad_y0 = kernel_h - pad_y0 - 1
        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1

        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)

        out = upfirdn2d_ext.upfirdn2d(
            input,
            kernel,
            up_x=up_x,
            up_y=up_y,
            down_x=down_x,
            down_y=down_y,
            pad_x0=pad_x0,
            pad_x1=pad_x1,
            pad_y0=pad_y0,
            pad_y1=pad_y1)
        # out = out.view(major, out_h, out_w, minor)
        out = out.view(-1, channel, out_h, out_w)

        return out

    @staticmethod
    def backward(ctx, grad_output):
        kernel, grad_kernel = ctx.saved_tensors

        grad_input = UpFirDn2dBackward.apply(
            grad_output,
            kernel,
            grad_kernel,
            ctx.up,
            ctx.down,
            ctx.pad,
            ctx.g_pad,
            ctx.in_size,
            ctx.out_size,
        )

        return grad_input, None, None, None, None


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    """UpFRIDn for 2d features.

    UpFIRDn is short for upsample, apply FIR filter and downsample. More
    details can be found in:
    https://www.mathworks.com/help/signal/ref/upfirdn.html

    Args:
        input (Tensor): Tensor with shape of (n, c, h, w).
        kernel (Tensor): Filter kernel.
        up (int | tuple[int], optional): Upsampling factor. If given a number,
            we will use this factor for the both height and width side.
            Defaults to 1.
        down (int | tuple[int], optional): Downsampling factor. If given a
            number, we will use this factor for the both height and width side.
            Defaults to 1.
        pad (tuple[int], optional): Padding for tensors, (x_pad, y_pad) or
            (x_pad_0, x_pad_1, y_pad_0, y_pad_1). Defaults to (0, 0).

    Returns:
        Tensor: Tensor after UpFIRDn.
    """
    if input.device.type == 'cpu':
        if len(pad) == 2:
            pad = (pad[0], pad[1], pad[0], pad[1])

        up = to_2tuple(up)

        down = to_2tuple(down)

        out = upfirdn2d_native(input, kernel, up[0], up[1], down[0], down[1],
                               pad[0], pad[1], pad[2], pad[3])
    else:
        _up = to_2tuple(up)

        _down = to_2tuple(down)

        if len(pad) == 4:
            _pad = pad
        elif len(pad) == 2:
            _pad = (pad[0], pad[1], pad[0], pad[1])

        out = UpFirDn2d.apply(input, kernel, _up, _down, _pad)

    return out


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
                     pad_y0, pad_y1):
    _, channel, in_h, in_w = input.shape
    input = input.reshape(-1, in_h, in_w, 1)

    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(
        out,
        [0, 0,
         max(pad_x0, 0),
         max(pad_x1, 0),
         max(pad_y0, 0),
         max(pad_y1, 0)])
    out = out[:,
              max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
              max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape(
        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]

    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1

    return out.view(-1, channel, out_h, out_w)