195 lines
5.5 KiB
Python
195 lines
5.5 KiB
Python
# pylint: skip-file
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# type: ignore
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# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501
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import os
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import torch
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from torch.autograd import Function
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from torch.nn import functional as F
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upfirdn2d_ext = None
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class UpFirDn2dBackward(Function):
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@staticmethod
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def forward(
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ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
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):
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up_x, up_y = up
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down_x, down_y = down
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g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
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grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
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grad_input = upfirdn2d_ext.upfirdn2d(
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grad_output,
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grad_kernel,
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down_x,
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down_y,
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up_x,
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up_y,
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g_pad_x0,
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g_pad_x1,
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g_pad_y0,
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g_pad_y1,
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)
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grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
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ctx.save_for_backward(kernel)
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pad_x0, pad_x1, pad_y0, pad_y1 = pad
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ctx.up_x = up_x
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ctx.up_y = up_y
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ctx.down_x = down_x
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ctx.down_y = down_y
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ctx.pad_x0 = pad_x0
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ctx.pad_x1 = pad_x1
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ctx.pad_y0 = pad_y0
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ctx.pad_y1 = pad_y1
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ctx.in_size = in_size
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ctx.out_size = out_size
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return grad_input
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@staticmethod
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def backward(ctx, gradgrad_input):
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(kernel,) = ctx.saved_tensors
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gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
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gradgrad_out = upfirdn2d_ext.upfirdn2d(
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gradgrad_input,
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kernel,
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ctx.up_x,
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ctx.up_y,
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ctx.down_x,
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ctx.down_y,
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ctx.pad_x0,
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ctx.pad_x1,
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ctx.pad_y0,
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ctx.pad_y1,
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)
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# gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
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# ctx.out_size[1], ctx.in_size[3])
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gradgrad_out = gradgrad_out.view(
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ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
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)
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return gradgrad_out, None, None, None, None, None, None, None, None
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class UpFirDn2d(Function):
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@staticmethod
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def forward(ctx, input, kernel, up, down, pad):
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up_x, up_y = up
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down_x, down_y = down
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pad_x0, pad_x1, pad_y0, pad_y1 = pad
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kernel_h, kernel_w = kernel.shape
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_, channel, in_h, in_w = input.shape
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ctx.in_size = input.shape
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input = input.reshape(-1, in_h, in_w, 1)
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ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
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out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
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out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
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ctx.out_size = (out_h, out_w)
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ctx.up = (up_x, up_y)
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ctx.down = (down_x, down_y)
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ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
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g_pad_x0 = kernel_w - pad_x0 - 1
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g_pad_y0 = kernel_h - pad_y0 - 1
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g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
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g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
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ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
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out = upfirdn2d_ext.upfirdn2d(
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input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
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)
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# out = out.view(major, out_h, out_w, minor)
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out = out.view(-1, channel, out_h, out_w)
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return out
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@staticmethod
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def backward(ctx, grad_output):
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kernel, grad_kernel = ctx.saved_tensors
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grad_input = UpFirDn2dBackward.apply(
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grad_output,
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kernel,
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grad_kernel,
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ctx.up,
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ctx.down,
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ctx.pad,
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ctx.g_pad,
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ctx.in_size,
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ctx.out_size,
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)
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return grad_input, None, None, None, None
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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if input.device.type == "cpu":
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out = upfirdn2d_native(
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input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
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)
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else:
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out = UpFirDn2d.apply(
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input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
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)
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return out
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def upfirdn2d_native(
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input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
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):
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_, channel, in_h, in_w = input.shape
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input = input.reshape(-1, in_h, in_w, 1)
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_, in_h, in_w, minor = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, in_h, 1, in_w, 1, minor)
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out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
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out = out.view(-1, in_h * up_y, in_w * up_x, minor)
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out = F.pad(
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out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
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)
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out = out[
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:,
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max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
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max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
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:,
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]
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out = out.permute(0, 3, 1, 2)
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out = out.reshape(
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[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
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)
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(
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-1,
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minor,
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in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
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in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
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)
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out = out.permute(0, 2, 3, 1)
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out = out[:, ::down_y, ::down_x, :]
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out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
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out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
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return out.view(-1, channel, out_h, out_w)
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