Source code for mmcv.ops.upfirdn2d
# 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 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
[docs]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)
