RRDB with latent
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James Betker
parent
df47d6cbbb
commit
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240
codes/models/archs/rrdb_with_latent.py
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240
codes/models/archs/rrdb_with_latent.py
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@ -0,0 +1,240 @@
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision
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from torch.utils.checkpoint import checkpoint_sequential
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from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
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from utils.util import checkpoint
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class ResidualDenseBlock(nn.Module):
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"""Residual Dense Block.
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Used in RRDB block in ESRGAN.
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Args:
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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels=64, growth_channels=32):
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super(ResidualDenseBlock, self).__init__()
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for i in range(5):
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out_channels = mid_channels if i == 4 else growth_channels
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self.add_module(
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f'conv{i+1}',
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nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
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1, 1))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for i in range(5):
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default_init_weights(getattr(self, f'conv{i+1}'), 0.1)
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def forward(self, x, identity=None):
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if identity is None:
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identity = x
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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return x5 * 0.2 + identity
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class RRDBWithBypassAndLatent(nn.Module):
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def __init__(self, mid_channels, growth_channels=32, latent_dim=256):
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super(RRDBWithBypassAndLatent, self).__init__()
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self.latent_process = nn.Sequential(nn.Linear(latent_dim, latent_dim//2, bias=False),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_dim//2, mid_channels, bias=False),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(mid_channels, mid_channels, bias=True))
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self.latent_join = nn.Sequential(ConvGnLelu(mid_channels*2, mid_channels*2, activation=True, norm=False, bias=False),
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ConvGnLelu(mid_channels*2, mid_channels, activation=False, norm=False, bias=False))
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True),
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ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False),
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ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False),
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nn.Sigmoid())
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def forward(self, x, original_latent):
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b, f, h, w = x.shape
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latent = self.latent_process(original_latent)
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b, l = latent.shape
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latent = latent.view(b, l, 1, 1)
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latent = latent.repeat(1, 1, h, w)
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out = self.latent_join(torch.cat([x, latent], dim=1))
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out = self.rdb1(out, x)
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out = self.rdb2(out)
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out = self.rdb3(out)
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bypass = self.bypass(torch.cat([x, out], dim=1))
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self.bypass_map = bypass.detach().clone()
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return out * 0.2 * bypass + x
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class RRDBNetWithLatent(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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mid_channels=64,
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num_blocks=23,
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growth_channels=32,
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blocks_per_checkpoint=4,
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scale=4,
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latent_size=256):
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super(RRDBNetWithLatent, self).__init__()
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self.num_blocks = num_blocks
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self.blocks_per_checkpoint = blocks_per_checkpoint
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self.scale = scale
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self.in_channels = in_channels
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self.latent_size = latent_size
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first_conv_stride = 1 if in_channels <= 4 else scale
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first_conv_ksize = 3 if first_conv_stride == 1 else 7
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first_conv_padding = 1 if first_conv_stride == 1 else 3
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self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
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self.body = make_layer(
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RRDBWithBypassAndLatent,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels,
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latent_dim=latent_size)
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self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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# 8-layer MLP in the vein of StyleGAN.
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self.latent_encoder = nn.Sequential(nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True))
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for m in [
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self.conv_first, self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_hr, self.conv_last
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]:
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default_init_weights(m, 0.1)
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def forward(self, x, latent=None, ref=None):
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if latent is None:
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latent = torch.randn((x.shape[0], self.latent_size), dtype=torch.float, device=x.device)
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latent = checkpoint(self.latent_encoder, latent)
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if self.in_channels > 4:
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x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic")
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if ref is None:
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ref = torch.zeros_like(x_lg)
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x_lg = torch.cat([x_lg, ref], dim=1)
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else:
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x_lg = x
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feat = self.conv_first(x_lg)
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body_feat = feat
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for bl in self.body:
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body_feat = checkpoint(bl, body_feat, latent)
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body_feat = self.conv_body(body_feat)
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feat = feat + body_feat
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# upsample
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feat = self.lrelu(
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self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
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if self.scale == 4:
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feat = self.lrelu(
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self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
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else:
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feat = self.lrelu(self.conv_up2(feat))
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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def visual_dbg(self, step, path):
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for i, bm in enumerate(self.body):
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torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
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# Based heavily on the same VGG arch used for the discriminator.
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class LatentEstimator(nn.Module):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, in_nc, nf, latent_size=256):
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super(LatentEstimator, self).__init__()
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# [64, 128, 128]
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self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
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# [64, 64, 64]
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self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
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self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
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# [128, 32, 32]
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self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
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self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
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# [256, 16, 16]
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self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
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self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
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# [512, 8, 8]
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self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
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final_nf = nf * 8
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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self.linear1 = nn.Linear(int(final_nf * 4 * 4), latent_size*2)
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self.linear2 = nn.Linear(latent_size*2, latent_size)
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def compute_body(self, x):
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fea = self.lrelu(self.conv0_0(x))
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fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
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#fea = torch.cat([fea, skip_med], dim=1)
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fea = self.lrelu(self.bn1_0(self.conv1_0(fea)))
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fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
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#fea = torch.cat([fea, skip_lo], dim=1)
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fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
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fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
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fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
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fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
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fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
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fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
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return fea
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def forward(self, x):
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fea = checkpoint(self.compute_body, x)
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fea = fea.contiguous().view(fea.size(0), -1)
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fea = self.linear1(fea)
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out = self.linear2(fea)
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return out
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@ -20,6 +20,7 @@ import models.archs.rcan as rcan
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import models.archs.ChainedEmbeddingGen as chained
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from models.archs import srg2_classic
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from models.archs.pyramid_arch import BasicResamplingFlowNet
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from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent
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from models.archs.teco_resgen import TecoGen
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logger = logging.getLogger('base')
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@ -118,6 +119,12 @@ def define_G(opt, net_key='network_G', scale=None):
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netG = TecoGen(opt_net['nf'], opt_net['scale'])
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elif which_model == "basic_resampling_flow_predictor":
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netG = BasicResamplingFlowNet(opt_net['nf'], resample_scale=opt_net['resample_scale'])
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elif which_model == "rrdb_with_latent":
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netG = RRDBNetWithLatent(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
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mid_channels=opt_net['nf'], num_blocks=opt_net['nb'],
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blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], scale=opt_net['scale'])
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elif which_model == "latent_estimator":
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netG = LatentEstimator(in_nc=3, nf=opt_net['nf'])
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else:
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raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
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return netG
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@ -26,7 +26,7 @@ class ConfigurableStep(Module):
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self.optimizers = None
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self.scaler = GradScaler(enabled=self.opt['fp16'])
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self.grads_generated = False
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self.min_total_loss = opt_step['min_total_loss'] if 'min_total_loss' in opt_step.keys() else 0
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self.min_total_loss = opt_step['min_total_loss'] if 'min_total_loss' in opt_step.keys() else -999999999
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self.injectors = []
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if 'injectors' in self.step_opt.keys():
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