diff --git a/codes/models/archs/rrdb_with_adain_latent.py b/codes/models/archs/rrdb_with_adain_latent.py deleted file mode 100644 index afaae256..00000000 --- a/codes/models/archs/rrdb_with_adain_latent.py +++ /dev/null @@ -1,267 +0,0 @@ -import math -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): - 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): - 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 + x - - -# Linear block wrapper with custom weights and lrelu activation suited for use with AdaIN. -class EqualLinear(nn.Module): - def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1): - super().__init__() - self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) - if bias: - self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) - else: - self.bias = None - self.scale = (1 / math.sqrt(in_dim)) * lr_mul - self.lr_mul = lr_mul - self.lrelu = nn.LeakyReLU(.2) - - def forward(self, input): - out = F.linear(input, self.weight * self.scale) - # Biased and scaled leaky relu. - lrelu_bias = self.bias * self.lr_mul - lrelu_dim = [1] * (out.ndim - lrelu_bias.ndim - 1) - lrelu_scale = 2 ** .5 - out = self.lrelu(out + lrelu_bias.view(1, lrelu_bias.shape[0], *lrelu_dim)) * lrelu_scale - return out - - def __repr__(self): - return ( - f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' - ) - - -class AdaIn(nn.Module): - def __init__(self, channels, latent_nf): - super(AdaIn, self).__init__() - self.norm = nn.InstanceNorm2d(channels) - self.latent_reducer = nn.Linear(latent_nf, channels * 2) - self.channels = channels - - def forward(self, x, latent): - xn = self.norm(x) - latent = self.latent_reducer(latent) - latent_bias = latent[:, :self.channels].view(x.shape[0], self.channels, 1, 1) - latent_scale = latent[:, -self.channels:].view(x.shape[0], self.channels, 1, 1) - return xn * latent_scale + latent_bias - - -class RRDBWithAdaIn(nn.Module): - def __init__(self, mid_channels, growth_channels=32, latent_nf=256): - super(RRDBWithAdaIn, self).__init__() - self.adain1 = AdaIn(mid_channels, latent_nf) - self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) - self.adain2 = AdaIn(mid_channels, latent_nf) - self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) - self.adain3 = AdaIn(mid_channels, latent_nf) - self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) - - def forward(self, x, latent): - out = self.rdb1(self.adain1(x, latent)) - out = self.rdb2(self.adain2(out, latent)) - out = self.rdb3(self.adain3(out, latent)) - residual = out * .2 - return residual + x, residual - - -class ConvLatentEncoder(nn.Module): - def __init__(self, latent_size): - super(ConvLatentEncoder, self).__init__() - layers = [EqualLinear(latent_size, latent_size, lr_mul=.01) for _ in range(8)] - self.stack = nn.Sequential(*layers) - - def forward(self, latent): - return self.stack(latent) - - -class AdaRRDBNet(nn.Module): - 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(AdaRRDBNet, self).__init__() - self.latent_encoder = ConvLatentEncoder(256) - 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( - RRDBWithAdaIn, - num_blocks, - mid_channels=mid_channels, - growth_channels=growth_channels, - latent_nf=256) - self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) - # upsample - self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) - self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) - self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1) - self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - for m in [ - self.conv_first, self.conv_body, self.conv_up1, - self.conv_up2, self.conv_hr, self.conv_last - ]: - default_init_weights(m, 0.1) - self.latent_mean = 0 - self.latent_std = 0 - self.latent_var = 0 - self.block_residual_means = [] - self.block_residual_stds = [] - - def forward(self, x, latent=None, ref=None): - latent_was_none = latent - if latent is None: - latent = torch.randn((x.shape[0], 256), device=x.device) - latent = self.latent_encoder(latent) - if latent_was_none is not 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 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} - - -class LinearLatentEstimator(nn.Module): - def __init__(self, in_nc, nf): - super(LinearLatentEstimator, 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.bottom_channels = nf * 8 * 2 * 2 - self.l = nn.Linear(self.bottom_channels, 1024) - self.l2 = nn.Linear(1024, 256) - - self.lrelu = nn.LeakyReLU(.2, inplace=True) - self.norm = nn.LayerNorm(256) - - 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))) - - return fea - - 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) - o = o.view(o.shape[0], self.bottom_channels) - o = self.lrelu(self.l(o)) - return self.norm(self.l2(o)) - diff --git a/codes/models/archs/rrdb_with_latent.py b/codes/models/archs/rrdb_with_latent.py deleted file mode 100644 index 2be4ec18..00000000 --- a/codes/models/archs/rrdb_with_latent.py +++ /dev/null @@ -1,373 +0,0 @@ -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, - 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} - diff --git a/codes/models/networks.py b/codes/models/networks.py index 3714ace8..8b4b462f 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -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 [] diff --git a/codes/train.py b/codes/train.py index 118155d2..36fae2c5 100644 --- a/codes/train.py +++ b/codes/train.py @@ -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() diff --git a/codes/train2.py b/codes/train2.py index c8ffb242..95e8c32d 100644 --- a/codes/train2.py +++ b/codes/train2.py @@ -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()