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Support variant input sizes and scales

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This commit is contained in:
James Betker 2020-04-22 00:39:55 -06:00
parent ebda70fcba
commit 05aafef938
2 changed files with 6 additions and 4 deletions
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8
codes/models/archs/RRDBNet_arch.py
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@ -46,7 +46,7 @@ class RRDB(nn.Module):
class RRDBNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, interpolation_scale_factor=2):
super(RRDBNet, self).__init__()
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
@ -61,13 +61,15 @@ class RRDBNet(nn.Module):
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.interpolation_scale_factor = interpolation_scale_factor
def forward(self, x):
fea = self.conv_first(x)
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return out

2
codes/models/archs/discriminator_vgg_arch.py
View File

@ -32,7 +32,7 @@ class Discriminator_VGG_128(nn.Module):
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
self.linear1 = nn.Linear(512 * 4 * input_img_factor * 4 * input_img_factor, 100)
self.linear1 = nn.Linear(int(512 * 4 * input_img_factor * 4 * input_img_factor), 100)
self.linear2 = nn.Linear(100, 1)
# activation function