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| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| class FusedLeakyReLU(nn.Module): | |
| def __init__(self, channel, bias=True, negative_slope=0.2, scale=2**0.5): | |
| super().__init__() | |
| if bias: | |
| self.bias = nn.Parameter(torch.zeros(channel)) | |
| else: | |
| self.bias = None | |
| self.negative_slope = negative_slope | |
| self.scale = scale | |
| def forward(self, input): | |
| return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) | |
| def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2**0.5): | |
| if input.dtype == torch.float16: | |
| bias = bias.half() | |
| if bias is not None: | |
| rest_dim = [1] * (input.ndim - bias.ndim - 1) | |
| return F.leaky_relu( | |
| input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2 | |
| ) * scale | |
| else: | |
| return F.leaky_relu(input, negative_slope=0.2) * scale | |
| def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): | |
| up_x, up_y = up, up | |
| down_x, down_y = down, down | |
| pad_x0, pad_x1, pad_y0, pad_y1 = pad[0], pad[1], pad[0], pad[1] | |
| _, 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) | |