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| # modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 | |
| # Copyright (c) 2021, NVIDIA Corporation. All rights reserved. | |
| # NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator | |
| # 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): | |
| 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 | |
| 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): | |
| 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 | |
| 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) | |