60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import os
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import torch
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from torch.nn import functional as F
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module_path = os.path.dirname(__file__)
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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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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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) |