222 lines
7.9 KiB
Python
222 lines
7.9 KiB
Python
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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from models.arch_util import ConvGnLelu, default_init_weights, make_layer
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from models.diffusion.nn import timestep_embedding
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from trainer.networks import register_model
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from utils.util import checkpoint
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import torch_intermediary as ml
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# Conditionally uses torch's checkpoint functionality if it is enabled in the opt file.
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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, embedding=False, init_weight=.1):
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super(ResidualDenseBlock, self).__init__()
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self.embedding = embedding
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if embedding:
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self.first_conv = ConvGnLelu(mid_channels, mid_channels, activation=True, norm=False, bias=True)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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ml.Linear(
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mid_channels*4,
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mid_channels,
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),
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)
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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}'), init_weight)
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default_init_weights(self.conv5, 0)
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self.normalize = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
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def forward(self, x, emb):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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if self.embedding:
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x0 = self.first_conv(x)
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emb_out = self.emb_layers(emb).type(x0.dtype)
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while len(emb_out.shape) < len(x0.shape):
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emb_out = emb_out[..., None]
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x0 = x0 + emb_out
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else:
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x0 = x
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x1 = self.lrelu(self.conv1(x0))
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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 self.normalize(x5 * .2 + x)
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class RRDB(nn.Module):
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"""Residual in Residual Dense Block.
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Used in RRDB-Net 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, growth_channels=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels, embedding=True)
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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.normalize = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
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self.residual_mult = nn.Parameter(torch.FloatTensor([.1]))
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def forward(self, x, emb):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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out = self.rdb1(x, emb)
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out = self.rdb2(out, emb)
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out = self.rdb3(out, emb)
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return self.normalize(out * self.residual_mult + x)
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class RRDBNet(nn.Module):
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"""Networks consisting of Residual in Residual Dense Block, which is used
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in ESRGAN.
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
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Currently, it supports x4 upsampling scale factor.
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Args:
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in_channels (int): Channel number of inputs.
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out_channels (int): Channel number of outputs.
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mid_channels (int): Channel number of intermediate features.
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Default: 64
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num_blocks (int): Block number in the trunk network. Defaults: 23
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growth_channels (int): Channels for each growth. Default: 32.
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"""
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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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body_block=RRDB,
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):
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super(RRDBNet, self).__init__()
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self.num_blocks = num_blocks
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self.in_channels = in_channels
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self.mid_channels = mid_channels
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# The diffusion RRDB starts with a full resolution image and downsamples into a .25 working space
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self.input_block = ConvGnLelu(in_channels, mid_channels, kernel_size=7, stride=1, activation=True, norm=False, bias=True)
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self.down1 = ConvGnLelu(mid_channels, mid_channels, kernel_size=3, stride=2, activation=True, norm=False, bias=True)
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self.down2 = ConvGnLelu(mid_channels, mid_channels, kernel_size=3, stride=2, activation=True, norm=False, bias=True)
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# Guided diffusion uses a time embedding.
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time_embed_dim = mid_channels * 4
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self.time_embed = nn.Sequential(
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ml.Linear(mid_channels, time_embed_dim),
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nn.SiLU(),
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ml.Linear(time_embed_dim, time_embed_dim),
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)
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self.body = make_layer(
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body_block,
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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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self.conv_body = nn.Conv2d(self.mid_channels, self.mid_channels, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(self.mid_channels, self.mid_channels, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(self.mid_channels*2, self.mid_channels, 3, 1, 1)
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self.conv_up3 = None
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self.conv_hr = nn.Conv2d(self.mid_channels*2, self.mid_channels, 3, 1, 1)
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self.conv_last = nn.Conv2d(self.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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self.normalize = nn.GroupNorm(num_groups=8, num_channels=self.mid_channels)
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for m in [
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self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_hr
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]:
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if m is not None:
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default_init_weights(m, 1.0)
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default_init_weights(self.conv_last, 0)
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def forward(self, x, timesteps, low_res, correction_factors=None):
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emb = self.time_embed(timestep_embedding(timesteps, self.mid_channels))
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_, _, new_height, new_width = x.shape
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upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
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x = torch.cat([x, upsampled], dim=1)
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if correction_factors is not None:
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correction_factors = correction_factors.view(x.shape[0], -1, 1, 1).repeat(1, 1, new_height, new_width)
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else:
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correction_factors = torch.zeros((b, self.num_corruptions, new_height, new_width), dtype=torch.float, device=x.device)
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x = torch.cat([x, correction_factors], dim=1)
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d1 = self.input_block(x)
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d2 = self.down1(d1)
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feat = self.down2(d2)
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for bl in self.body:
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feat = checkpoint(bl, feat, emb)
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feat = feat[:, :self.mid_channels]
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feat = self.conv_body(feat)
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# upsample
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out = torch.cat([self.lrelu(
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self.normalize(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))),
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d2], dim=1)
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out = torch.cat([self.lrelu(
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self.normalize(self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest')))),
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d1], dim=1)
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out = self.conv_last(self.normalize(self.lrelu(self.conv_hr(out))))
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return out
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@register_model
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def register_rrdb_diffusion(opt_net, opt):
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return RRDBNet(**opt_net['args'])
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if __name__ == '__main__':
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model = RRDBNet(6,6)
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x = torch.randn(1,3,128,128)
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l = torch.randn(1,3,32,32)
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t = torch.LongTensor([555])
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y = model(x, t, l)
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print(y.shape, y.mean(), y.std(), y.min(), y.max())
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