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| from collections import abc | |
| import torch | |
| from torch.nn import functional as F | |
| def upfirdn2d(inputs, kernel, up=1, down=1, pad=(0, 0)): | |
| if not isinstance(up, abc.Iterable): | |
| up = (up, up) | |
| if not isinstance(down, abc.Iterable): | |
| down = (down, down) | |
| if len(pad) == 2: | |
| pad = (pad[0], pad[1], pad[0], pad[1]) | |
| return upfirdn2d_native(inputs, kernel, *up, *down, *pad) | |
| def upfirdn2d_native( | |
| inputs, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 | |
| ): | |
| _, channel, in_h, in_w = inputs.shape | |
| inputs = inputs.reshape(-1, in_h, in_w, 1) | |
| _, in_h, in_w, minor = inputs.shape | |
| kernel_h, kernel_w = kernel.shape | |
| out = inputs.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) // down_y | |
| out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x | |
| return out.view(-1, channel, out_h, out_w) |