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kernel_predictor.py
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kernel_predictor.py
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from math import ceil
from torch import nn
class KernelPredictor(nn.Module):
def __init__(self, in_channels, out_channels, n_groups, style_channels, kernel_size):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.w_channels = style_channels
self.n_groups = n_groups
self.kernel_size = kernel_size
padding = (kernel_size - 1) / 2
self.spatial = nn.Conv2d(style_channels,
in_channels * out_channels // n_groups,
kernel_size=kernel_size,
padding=(ceil(padding), ceil(padding)),
padding_mode='reflect')
self.pointwise = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(style_channels,
out_channels * out_channels // n_groups,
kernel_size=1)
)
self.bias = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(style_channels,
out_channels,
kernel_size=1)
)
def forward(self, w):
w_spatial = self.spatial(w)
w_spatial = w_spatial.reshape(len(w),
self.out_channels,
self.in_channels // self.n_groups,
self.kernel_size, self.kernel_size)
w_pointwise = self.pointwise(w)
w_pointwise = w_pointwise.reshape(len(w),
self.out_channels,
self.out_channels // self.n_groups,
1, 1)
bias = self.bias(w)
bias = bias.reshape(len(w),
self.out_channels)
return w_spatial, w_pointwise, bias