import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torch.utils.checkpoint import checkpoint_sequential from models.archs.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu from models.archs.srg2_classic import Interpolate from utils.util import checkpoint class ResidualDenseBlock(nn.Module): """Residual Dense Block. Used in RRDB block in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels=64, growth_channels=32): super(ResidualDenseBlock, self).__init__() for i in range(5): out_channels = mid_channels if i == 4 else growth_channels self.add_module( f'conv{i+1}', nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3, 1, 1)) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) for i in range(5): default_init_weights(getattr(self, f'conv{i+1}'), 0.1) def forward(self, x, identity=None): if identity is None: identity = 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)) return x5 * 0.2 + identity class RRDBWithBypassAndLatent(nn.Module): def __init__(self, mid_channels, growth_channels=32): super(RRDBWithBypassAndLatent, self).__init__() self.latent_join = nn.Sequential(ConvGnLelu(mid_channels*2, mid_channels*2, activation=True, norm=False, bias=False), ConvGnLelu(mid_channels*2, mid_channels, activation=False, norm=False, bias=False)) self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) self.bypass = nn.Sequential(ConvGnSilu(mid_channels*2, mid_channels, kernel_size=3, bias=True, activation=True, norm=True), ConvGnSilu(mid_channels, mid_channels//2, kernel_size=3, bias=False, activation=True, norm=False), ConvGnSilu(mid_channels//2, 1, kernel_size=3, bias=False, activation=False, norm=False), nn.Sigmoid()) def forward(self, x, latent): out = self.latent_join(torch.cat([x, latent], dim=1)) out = self.rdb1(out, x) out = self.rdb2(out) out = self.rdb3(out) bypass = self.bypass(torch.cat([x, out], dim=1)) self.bypass_map = bypass.detach().clone() residual = out * .2 * bypass return residual + x, residual class ConvLatentEncoder(nn.Module): def __init__(self, nf): super(ConvLatentEncoder, self).__init__() latent_filters = [nf * 4, nf * 2, nf] layers = [] for i in range(len(latent_filters)-1): layers.append(nn.Sequential( ConvGnLelu(latent_filters[i], latent_filters[i], kernel_size=1, activation=True, bias=False, norm=True), Interpolate(2), ConvGnLelu(latent_filters[i], latent_filters[i+1], kernel_size=1, activation=True, bias=False, norm=True))) self.final = nn.Sequential( ConvGnLelu(nf, nf, kernel_size=1, activation=True, bias=True, norm=True), ConvGnLelu(nf, nf, kernel_size=1, activation=False, bias=True, norm=False)) self.layers = nn.ModuleList(layers) def forward(self, latents): assert len(latents) == 3 out = torch.zeros_like(latents[0]) for i in range(2): out = out + latents[i] out = self.layers[i](out) out = out + latents[2] return self.final(out) class RRDBNetWithLatent(nn.Module): # 8-layer MLP in the vein of StyleGAN. def create_linear_latent_encoder(self, latent_size): return nn.Sequential(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), 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): 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 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] 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): super(LatentEstimator, 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) 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)) 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}