Remove unused archs
This commit is contained in:
James Betker
parent
a1c8300052
commit
2e0bbda640
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@ -1,267 +0,0 @@
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import math
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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 models.archs.srg2_classic import Interpolate
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from utils.util import checkpoint
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class ResidualDenseBlock(nn.Module):
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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):
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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 + x
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# Linear block wrapper with custom weights and lrelu activation suited for use with AdaIN.
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class EqualLinear(nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
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else:
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self.bias = None
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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self.lrelu = nn.LeakyReLU(.2)
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def forward(self, input):
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out = F.linear(input, self.weight * self.scale)
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# Biased and scaled leaky relu.
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lrelu_bias = self.bias * self.lr_mul
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lrelu_dim = [1] * (out.ndim - lrelu_bias.ndim - 1)
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lrelu_scale = 2 ** .5
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out = self.lrelu(out + lrelu_bias.view(1, lrelu_bias.shape[0], *lrelu_dim)) * lrelu_scale
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return out
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def __repr__(self):
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return (
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
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)
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class AdaIn(nn.Module):
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def __init__(self, channels, latent_nf):
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super(AdaIn, self).__init__()
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self.norm = nn.InstanceNorm2d(channels)
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self.latent_reducer = nn.Linear(latent_nf, channels * 2)
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self.channels = channels
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def forward(self, x, latent):
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xn = self.norm(x)
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latent = self.latent_reducer(latent)
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latent_bias = latent[:, :self.channels].view(x.shape[0], self.channels, 1, 1)
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latent_scale = latent[:, -self.channels:].view(x.shape[0], self.channels, 1, 1)
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return xn * latent_scale + latent_bias
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class RRDBWithAdaIn(nn.Module):
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def __init__(self, mid_channels, growth_channels=32, latent_nf=256):
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super(RRDBWithAdaIn, self).__init__()
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self.adain1 = AdaIn(mid_channels, latent_nf)
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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.adain2 = AdaIn(mid_channels, latent_nf)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.adain3 = AdaIn(mid_channels, latent_nf)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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def forward(self, x, latent):
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out = self.rdb1(self.adain1(x, latent))
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out = self.rdb2(self.adain2(out, latent))
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out = self.rdb3(self.adain3(out, latent))
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residual = out * .2
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return residual + x, residual
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class ConvLatentEncoder(nn.Module):
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def __init__(self, latent_size):
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super(ConvLatentEncoder, self).__init__()
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layers = [EqualLinear(latent_size, latent_size, lr_mul=.01) for _ in range(8)]
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self.stack = nn.Sequential(*layers)
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def forward(self, latent):
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return self.stack(latent)
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class AdaRRDBNet(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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bottom_latent_only=False):
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super(AdaRRDBNet, self).__init__()
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self.latent_encoder = ConvLatentEncoder(256)
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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.nf = mid_channels
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self.bottom_latent_only = bottom_latent_only
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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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RRDBWithAdaIn,
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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_nf=256)
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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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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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self.latent_mean = 0
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self.latent_std = 0
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self.latent_var = 0
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self.block_residual_means = []
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self.block_residual_stds = []
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def forward(self, x, latent=None, ref=None):
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latent_was_none = latent
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if latent is None:
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latent = torch.randn((x.shape[0], 256), device=x.device)
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latent = self.latent_encoder(latent)
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if latent_was_none is not None:
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self.latent_mean = torch.mean(latent).detach().cpu()
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self.latent_std = torch.std(latent).detach().cpu()
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self.latent_var = torch.var(latent).detach().cpu()
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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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self.block_residual_means = []
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self.block_residual_stds = []
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for bl in self.body:
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body_feat, residual = checkpoint(bl, body_feat, latent)
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self.block_residual_means.append(torch.mean(residual).cpu())
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self.block_residual_stds.append(torch.std(residual).cpu())
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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 get_debug_values(self, s, n):
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blk_stds, blk_means = {}, {}
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for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)):
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blk_stds['block_%i' % (i+1,)] = s
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blk_means['block_%i' % (i+1,)] = m
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return {'encoded_latent_mean': self.latent_mean,
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'encoded_latent_std': self.latent_std,
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'encoded_latent_var': self.latent_var,
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'blocks_mean': blk_means,
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'blocks_std': blk_stds}
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class LinearLatentEstimator(nn.Module):
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def __init__(self, in_nc, nf):
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super(LinearLatentEstimator, 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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# [256, 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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# [256, 4, 4]
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self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn5_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn5_1 = nn.BatchNorm2d(nf * 8, affine=True)
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self.bottom_channels = nf * 8 * 2 * 2
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self.l = nn.Linear(self.bottom_channels, 1024)
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self.l2 = nn.Linear(1024, 256)
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self.lrelu = nn.LeakyReLU(.2, inplace=True)
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self.norm = nn.LayerNorm(256)
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def compute_body(self, x):
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fea = self.lrelu(self.bn1_0(self.conv1_0(x)))
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fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
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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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fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
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fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
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return fea
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def forward(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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o = checkpoint(self.compute_body, fea)
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o = o.view(o.shape[0], self.bottom_channels)
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o = self.lrelu(self.l(o))
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return self.norm(self.l2(o))
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@ -1,373 +0,0 @@
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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 models.archs.srg2_classic import Interpolate
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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):
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super(RRDBWithBypassAndLatent, self).__init__()
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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, latent):
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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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residual = out * .2 * bypass
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return residual + x, residual
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class ConvLatentEncoder(nn.Module):
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def __init__(self, nf):
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super(ConvLatentEncoder, self).__init__()
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latent_filters = [nf * 4, nf * 2, nf]
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layers = []
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for i in range(len(latent_filters)-1):
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layers.append(nn.Sequential(
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ConvGnLelu(latent_filters[i], latent_filters[i], kernel_size=1, activation=True, bias=False, norm=True),
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Interpolate(2),
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ConvGnLelu(latent_filters[i], latent_filters[i+1], kernel_size=1, activation=True, bias=False, norm=True)))
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self.final = nn.Sequential(
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ConvGnLelu(nf, nf, kernel_size=1, activation=True, bias=True, norm=True),
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ConvGnLelu(nf, nf, kernel_size=1, activation=False, bias=True, norm=False))
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self.layers = nn.ModuleList(layers)
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def forward(self, latents):
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assert len(latents) == 3
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out = torch.zeros_like(latents[0])
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for i in range(2):
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out = out + latents[i]
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out = self.layers[i](out)
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out = out + latents[2]
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return self.final(out)
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class RRDBNetWithLatent(nn.Module):
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# 8-layer MLP in the vein of StyleGAN.
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def create_linear_latent_encoder(self, latent_size):
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return 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),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True),
|
||||
nn.Linear(latent_size, latent_size),
|
||||
nn.BatchNorm1d(latent_size),
|
||||
nn.LeakyReLU(negative_slope=0.2, inplace=True))
|
||||
|
||||
# Creates a 2D latent by iterating through the provided latent_filters and doubling the
|
||||
# image size each step.
|
||||
def create_conv_latent_encoder(self, latent_filters):
|
||||
return ConvLatentEncoder(latent_filters)
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
mid_channels=64,
|
||||
num_blocks=23,
|
||||
growth_channels=32,
|
||||
blocks_per_checkpoint=4,
|
||||
scale=4,
|
||||
bottom_latent_only=False):
|
||||
super(RRDBNetWithLatent, self).__init__()
|
||||
self.num_blocks = num_blocks
|
||||
self.blocks_per_checkpoint = blocks_per_checkpoint
|
||||
self.scale = scale
|
||||
self.in_channels = in_channels
|
||||
self.nf = mid_channels
|
||||
self.bottom_latent_only = bottom_latent_only
|
||||
first_conv_stride = 1 if in_channels <= 4 else scale
|
||||
first_conv_ksize = 3 if first_conv_stride == 1 else 7
|
||||
first_conv_padding = 1 if first_conv_stride == 1 else 3
|
||||
self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
|
||||
self.body = make_layer(
|
||||
RRDBWithBypassAndLatent,
|
||||
num_blocks,
|
||||
mid_channels=mid_channels,
|
||||
growth_channels=growth_channels)
|
||||
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)
|
||||
|
||||
self.latent_encoder = self.create_conv_latent_encoder(mid_channels)
|
||||
|
||||
for m in [
|
||||
self.conv_first, self.conv_body, self.conv_up1,
|
||||
self.conv_up2, self.conv_hr, self.conv_last
|
||||
]:
|
||||
default_init_weights(m, 0.1)
|
||||
|
||||
def forward(self, x, latent=None, ref=None):
|
||||
latent_was_none = latent
|
||||
if latent is None:
|
||||
mults = [4, 2, 1]
|
||||
b, f, h, w = x.shape
|
||||
latent = [torch.randn((b, self.nf * m, h // m, w // m), dtype=torch.float, device=x.device) for m in mults]
|
||||
if self.bottom_latent_only:
|
||||
latent[1] = torch.zeros_like(latent[1])
|
||||
latent[2] = torch.zeros_like(latent[2])
|
||||
latent = self.latent_encoder(latent)
|
||||
if latent_was_none is None:
|
||||
self.latent_mean = torch.mean(latent).detach().cpu()
|
||||
self.latent_std = torch.std(latent).detach().cpu()
|
||||
self.latent_var = torch.var(latent).detach().cpu()
|
||||
if self.in_channels > 4:
|
||||
x_lg = F.interpolate(x, scale_factor=self.scale, mode="bicubic")
|
||||
if ref is None:
|
||||
ref = torch.zeros_like(x_lg)
|
||||
x_lg = torch.cat([x_lg, ref], dim=1)
|
||||
else:
|
||||
x_lg = x
|
||||
feat = self.conv_first(x_lg)
|
||||
body_feat = feat
|
||||
self.block_residual_means = []
|
||||
self.block_residual_stds = []
|
||||
for bl in self.body:
|
||||
body_feat, residual = checkpoint(bl, body_feat, latent)
|
||||
self.block_residual_means.append(torch.mean(residual).cpu())
|
||||
self.block_residual_stds.append(torch.std(residual).cpu())
|
||||
body_feat = self.conv_body(body_feat)
|
||||
feat = feat + body_feat
|
||||
# upsample
|
||||
feat = self.lrelu(
|
||||
self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
|
||||
if self.scale == 4:
|
||||
feat = self.lrelu(
|
||||
self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
|
||||
else:
|
||||
feat = self.lrelu(self.conv_up2(feat))
|
||||
out = self.conv_last(self.lrelu(self.conv_hr(feat)))
|
||||
return out
|
||||
|
||||
def visual_dbg(self, step, path):
|
||||
for i, bm in enumerate(self.body):
|
||||
torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
|
||||
|
||||
def get_debug_values(self, s, n):
|
||||
blk_stds, blk_means = {}, {}
|
||||
for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)):
|
||||
blk_stds['block_%i' % (i+1,)] = s
|
||||
blk_means['block_%i' % (i+1,)] = m
|
||||
return {'encoded_latent_mean': self.latent_mean,
|
||||
'encoded_latent_std': self.latent_std,
|
||||
'encoded_latent_var': self.latent_var,
|
||||
'blocks_mean': blk_means,
|
||||
'blocks_std': blk_stds}
|
||||
|
||||
|
||||
# Based heavily on the same VGG arch used for the discriminator.
|
||||
class LatentEstimator(nn.Module):
|
||||
# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
|
||||
def __init__(self, in_nc, nf, overwrite_levels=[]):
|
||||
super(LatentEstimator, self).__init__()
|
||||
self.overwrite_levels = overwrite_levels
|
||||
# [64, 128, 128]
|
||||
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
||||
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
||||
self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
||||
# [64, 64, 64]
|
||||
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
||||
self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
||||
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
||||
self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
||||
self.d1p1 = ConvGnLelu(nf * 2, nf, kernel_size=1, activation=True, norm=True, bias=True)
|
||||
self.d1p2 = ConvGnLelu(nf, nf, kernel_size=1, activation=False, norm=False, bias=True)
|
||||
|
||||
# [128, 32, 32]
|
||||
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
||||
self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
||||
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
||||
self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
||||
self.d2p1 = ConvGnLelu(nf * 4, nf * 2, kernel_size=1, activation=True, norm=True, bias=True)
|
||||
self.d2p2 = ConvGnLelu(nf * 2, nf * 2, kernel_size=1, activation=False, norm=False, bias=True)
|
||||
|
||||
# [256, 16, 16]
|
||||
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
|
||||
self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
||||
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.d3p1 = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, activation=True, norm=True, bias=True)
|
||||
self.d3p2 = ConvGnLelu(nf * 4, nf * 4, kernel_size=1, activation=False, norm=False, bias=True)
|
||||
|
||||
self.lrelu = nn.LeakyReLU(.2, inplace=True)
|
||||
self.tanh = nn.Tanh()
|
||||
|
||||
def compute_body(self, x):
|
||||
fea = self.lrelu(self.bn1_0(self.conv1_0(x)))
|
||||
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
|
||||
o1 = self.tanh(self.d1p2(self.d1p1(fea)))
|
||||
|
||||
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
|
||||
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
|
||||
o2 = self.tanh(self.d2p2(self.d2p1(fea)))
|
||||
|
||||
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
||||
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
||||
o3 = self.tanh(self.d3p2(self.d3p1(fea)))
|
||||
|
||||
return o3, o2, o1
|
||||
|
||||
def forward(self, x):
|
||||
fea = self.lrelu(self.conv0_0(x))
|
||||
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
|
||||
out = list(checkpoint(self.compute_body, fea))
|
||||
for lvl in self.overwrite_levels:
|
||||
out[lvl] = torch.zeros_like(out[lvl])
|
||||
self.latent_mean = torch.mean(out[-1])
|
||||
self.latent_std = torch.std(out[-1])
|
||||
self.latent_var = torch.var(out[-1])
|
||||
return out
|
||||
|
||||
def get_debug_values(self, s, n):
|
||||
return {'latent_estimator_mean': self.latent_mean,
|
||||
'latent_estimator_std': self.latent_std,
|
||||
'latent_estimator_var': self.latent_var}
|
||||
|
||||
|
||||
class LatentEstimator2(nn.Module):
|
||||
def __init__(self, in_nc, nf):
|
||||
super(LatentEstimator2, self).__init__()
|
||||
# [64, 128, 128]
|
||||
self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
||||
self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
|
||||
self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
|
||||
# [64, 64, 64]
|
||||
self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
|
||||
self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
|
||||
self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
|
||||
self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
|
||||
|
||||
# [128, 32, 32]
|
||||
self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
|
||||
self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
|
||||
self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
|
||||
self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
|
||||
|
||||
# [256, 16, 16]
|
||||
self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
|
||||
self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
||||
self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
|
||||
# [256, 8, 8]
|
||||
self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
||||
self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
||||
self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
|
||||
# [256, 4, 4]
|
||||
self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
|
||||
self.bn5_0 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
|
||||
self.bn5_1 = nn.BatchNorm2d(nf * 8, affine=True)
|
||||
self.l = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, activation=True, norm=True, bias=True)
|
||||
self.l2 = ConvGnLelu(nf * 4, nf * 4, kernel_size=1, activation=False, norm=False, bias=True)
|
||||
|
||||
self.lrelu = nn.LeakyReLU(.2, inplace=True)
|
||||
self.norm = nn.InstanceNorm2d(nf*4)
|
||||
|
||||
def compute_body(self, x):
|
||||
fea = self.lrelu(self.bn1_0(self.conv1_0(x)))
|
||||
fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
|
||||
|
||||
fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
|
||||
fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
|
||||
|
||||
fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
|
||||
fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
|
||||
fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
|
||||
fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
|
||||
fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
|
||||
fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
|
||||
o3 = self.norm(self.l2(self.l(fea)))
|
||||
|
||||
return F.interpolate(o3, scale_factor=4, mode="nearest")
|
||||
|
||||
def forward(self, x):
|
||||
fea = self.lrelu(self.conv0_0(x))
|
||||
fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
|
||||
o = checkpoint(self.compute_body, fea)
|
||||
out = [o,\
|
||||
torch.zeros((o.shape[0],128,16,16), device=o.device),\
|
||||
torch.zeros((o.shape[0],64,32,32), device=o.device)]
|
||||
self.latent_mean = torch.mean(out[-1])
|
||||
self.latent_std = torch.std(out[-1])
|
||||
self.latent_var = torch.var(out[-1])
|
||||
return out
|
||||
|
||||
def get_debug_values(self, s, n):
|
||||
return {'latent_estimator_mean': self.latent_mean,
|
||||
'latent_estimator_std': self.latent_std,
|
||||
'latent_estimator_var': self.latent_var}
|
||||
|
||||
|
|
@ -21,8 +21,6 @@ import models.archs.rcan as rcan
|
|||
from models.archs import srg2_classic
|
||||
from models.archs.biggan.biggan_discriminator import BigGanDiscriminator
|
||||
from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
|
||||
from models.archs.rrdb_with_adain_latent import AdaRRDBNet, LinearLatentEstimator
|
||||
from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2
|
||||
from models.archs.teco_resgen import TecoGen
|
||||
|
||||
logger = logging.getLogger('base')
|
||||
|
|
@ -59,6 +57,12 @@ def define_G(opt, opt_net, scale=None):
|
|||
mid_channels=opt_net['nf'], l1_blocks=opt_net['l1'],
|
||||
l2_blocks=opt_net['l2'], l3_blocks=opt_net['l3'],
|
||||
growth_channels=opt_net['gc'], scale=opt_net['scale'])
|
||||
elif which_model == "twostep_rrdb":
|
||||
from models.archs.multi_res_rrdb import PixelShufflingSteppedResRRDBNet
|
||||
netG = PixelShufflingSteppedResRRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
|
||||
mid_channels=opt_net['nf'], l1_blocks=opt_net['l1'],
|
||||
l2_blocks=opt_net['l2'],
|
||||
growth_channels=opt_net['gc'], scale=opt_net['scale'])
|
||||
elif which_model == 'rcan':
|
||||
#args: n_resgroups, n_resblocks, res_scale, reduction, scale, n_feats
|
||||
opt_net['rgb_range'] = 255
|
||||
|
|
@ -122,25 +126,6 @@ def define_G(opt, opt_net, scale=None):
|
|||
netG = SwitchedGen_arch.BackboneResnet()
|
||||
elif which_model == "tecogen":
|
||||
netG = TecoGen(opt_net['nf'], opt_net['scale'])
|
||||
elif which_model == "rrdb_with_latent":
|
||||
netG = RRDBNetWithLatent(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
|
||||
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'],
|
||||
blocks_per_checkpoint=opt_net['blocks_per_checkpoint'],
|
||||
scale=opt_net['scale'],
|
||||
bottom_latent_only=opt_net['bottom_latent_only'])
|
||||
elif which_model == "adarrdb":
|
||||
netG = AdaRRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
|
||||
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'],
|
||||
blocks_per_checkpoint=opt_net['blocks_per_checkpoint'],
|
||||
scale=opt_net['scale'])
|
||||
elif which_model == "latent_estimator":
|
||||
if opt_net['version'] == 2:
|
||||
netG = LatentEstimator2(in_nc=3, nf=opt_net['nf'])
|
||||
else:
|
||||
overwrite = [1,2] if opt_net['only_base_level'] else []
|
||||
netG = LatentEstimator(in_nc=3, nf=opt_net['nf'], overwrite_levels=overwrite)
|
||||
elif which_model == "linear_latent_estimator":
|
||||
netG = LinearLatentEstimator(in_nc=3, nf=opt_net['nf'])
|
||||
elif which_model == 'stylegan2':
|
||||
is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False
|
||||
attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
|
||||
|
|
|
|||
|
|
@ -291,7 +291,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb_2stride.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb4x_6bl_bigbatch.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
|
|
@ -291,7 +291,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb4x_2stride_multiframe.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_srflow_frompsnr.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user