# 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) # ======================================================================= # 1. 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If you bring or threaten to bring a patent claim # against any Licensor (including any claim, cross-claim or # counterclaim in a lawsuit) to enforce any patents that you allege # are infringed by any Work, then your rights under this License from # such Licensor (including the grant in Section 2.1) will terminate # immediately. # 3.5 Trademarks. This License does not grant any rights to use any # Licensor’s or its affiliates’ names, logos, or trademarks, except # as necessary to reproduce the notices described in this License. # 3.6 Termination. If you violate any term of this License, then your # rights under this License (including the grant in Section 2.1) will # terminate immediately. # 4. Disclaimer of Warranty. # THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR # NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER # THIS LICENSE. # 5. Limitation of Liability. # EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL # THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE # SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, # INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF # OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK # (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, # LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER # COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF # THE POSSIBILITY OF SUCH DAMAGES. # ======================================================================= 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)