Merge branch 'master' into xygrid_infotext_improvements

1 files changed, 0 insertions, 80 deletions

diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py
deleted file mode 100644
index e413d36e..00000000
--- a/modules/esrgam_model_arch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# this file is taken from https://github.com/xinntao/ESRGAN

-

-import functools

-import torch

-import torch.nn as nn

-import torch.nn.functional as F

-

-

-def make_layer(block, n_layers):

-    layers = []

-    for _ in range(n_layers):

-        layers.append(block())

-    return nn.Sequential(*layers)

-

-

-class ResidualDenseBlock_5C(nn.Module):

-    def __init__(self, nf=64, gc=32, bias=True):

-        super(ResidualDenseBlock_5C, self).__init__()

-        # gc: growth channel, i.e. intermediate channels

-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)

-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)

-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)

-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)

-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)

-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

-

-        # initialization

-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)

-

-    def forward(self, x):

-        x1 = self.lrelu(self.conv1(x))

-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))

-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))

-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))

-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))

-        return x5 * 0.2 + x

-

-

-class RRDB(nn.Module):

-    '''Residual in Residual Dense Block'''

-

-    def __init__(self, nf, gc=32):

-        super(RRDB, self).__init__()

-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)

-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)

-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)

-

-    def forward(self, x):

-        out = self.RDB1(x)

-        out = self.RDB2(out)

-        out = self.RDB3(out)

-        return out * 0.2 + x

-

-

-class RRDBNet(nn.Module):

-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):

-        super(RRDBNet, self).__init__()

-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)

-

-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)

-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)

-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        #### upsampling

-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)

-

-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

-

-    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')))

-        out = self.conv_last(self.lrelu(self.HRconv(fea)))

-

-        return out