Delete rrdbnet_arch.py

rrdbnet_arch.py

@@ -1,121 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_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))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        if scale == 8:
-            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        if self.scale == 8:
-            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out