From e343722d37d39c852ea715e60f24b5037a51a4d6 Mon Sep 17 00:00:00 2001 From: James Betker Date: Tue, 1 Dec 2020 11:11:15 -0700 Subject: [PATCH] Add stepped rrdb --- codes/models/archs/multi_res_rrdb.py | 127 ++++++++++++++++++++++++++- 1 file changed, 125 insertions(+), 2 deletions(-) diff --git a/codes/models/archs/multi_res_rrdb.py b/codes/models/archs/multi_res_rrdb.py index d95a678e..59f35e42 100644 --- a/codes/models/archs/multi_res_rrdb.py +++ b/codes/models/archs/multi_res_rrdb.py @@ -2,8 +2,8 @@ import torch.nn as nn import torch.nn.functional as F from models.archs.RRDBNet_arch import RRDB -from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu -from utils.util import checkpoint +from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu, PixelUnshuffle +from utils.util import checkpoint, sequential_checkpoint class MultiLevelRRDB(nn.Module): @@ -81,3 +81,126 @@ class MultiResRRDBNet(nn.Module): def visual_dbg(self, step, path): pass + + +class SteppedResRRDBNet(nn.Module): + def __init__(self, + in_channels, + out_channels, + mid_channels=64, + l1_blocks=3, + l2_blocks=3, + growth_channels=32, + scale=4, + ): + super().__init__() + self.scale = scale + self.in_channels = in_channels + + self.conv_first = nn.Conv2d(in_channels, mid_channels, 7, stride=2, padding=3) + self.conv_second = nn.Conv2d(mid_channels, mid_channels*2, 3, stride=2, padding=1) + + self.l1_blocks = nn.Sequential(*[RRDB(mid_channels*2, growth_channels*2) for _ in range(l1_blocks)]) + self.l1_upsample_conv = nn.Conv2d(mid_channels*2, mid_channels, 3, stride=1, padding=1) + self.l2_blocks = nn.Sequential(*[RRDB(mid_channels, growth_channels, 2) for _ in range(l2_blocks)]) + + self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + # upsample + self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + for m in [ + self.conv_first, self.conv_second, self.l1_upsample_conv, self.conv_body, self.conv_up1, + self.conv_up2, self.conv_hr, self.conv_last + ]: + if m is not None: + default_init_weights(m, 0.1) + + def forward(self, x): + trunk = self.conv_first(x) + trunk = self.conv_second(trunk) + trunk = sequential_checkpoint(self.l1_blocks, len(self.l2_blocks), trunk) + trunk = F.interpolate(trunk, scale_factor=2, mode="nearest") + trunk = self.l1_upsample_conv(trunk) + trunk = sequential_checkpoint(self.l2_blocks, len(self.l2_blocks), trunk) + body_feat = self.conv_body(trunk) + feat = trunk + body_feat + + # upsample + out = self.lrelu( + self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) + if self.scale == 4: + out = self.lrelu( + self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest'))) + else: + out = self.lrelu(self.conv_up2(out)) + out = self.conv_last(self.lrelu(self.conv_hr(out))) + + return out + + def visual_dbg(self, step, path): + pass + + +class PixelShufflingSteppedResRRDBNet(nn.Module): + def __init__(self, + in_channels, + out_channels, + mid_channels=64, + l1_blocks=3, + l2_blocks=3, + growth_channels=32, + scale=2, + ): + super().__init__() + self.scale = scale * 2 # This RRDB operates at half-scale resolution. + self.in_channels = in_channels + + self.pix_unshuffle = PixelUnshuffle(4) + self.conv_first = nn.Conv2d(4*4*in_channels, mid_channels*2, 3, stride=1, padding=1) + + self.l1_blocks = nn.Sequential(*[RRDB(mid_channels*2, growth_channels*2) for _ in range(l1_blocks)]) + self.l1_upsample_conv = nn.Conv2d(mid_channels*2, mid_channels, 3, stride=1, padding=1) + self.l2_blocks = nn.Sequential(*[RRDB(mid_channels, growth_channels, 2) for _ in range(l2_blocks)]) + + self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + # upsample + self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) + self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + for m in [ + self.conv_first, self.l1_upsample_conv, self.conv_body, self.conv_up1, + self.conv_up2, self.conv_hr, self.conv_last + ]: + if m is not None: + default_init_weights(m, 0.1) + + def forward(self, x): + trunk = self.conv_first(self.pix_unshuffle(x)) + trunk = sequential_checkpoint(self.l1_blocks, len(self.l1_blocks), trunk) + trunk = F.interpolate(trunk, scale_factor=2, mode="nearest") + trunk = self.l1_upsample_conv(trunk) + trunk = sequential_checkpoint(self.l2_blocks, len(self.l2_blocks), trunk) + body_feat = self.conv_body(trunk) + feat = trunk + body_feat + + # upsample + out = self.lrelu( + self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) + if self.scale == 4: + out = self.lrelu( + self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest'))) + else: + out = self.lrelu(self.conv_up2(out)) + out = self.conv_last(self.lrelu(self.conv_hr(out))) + + return out + + def visual_dbg(self, step, path): + pass