forked from mrq/DL-Art-School
374 lines
17 KiB
Python
374 lines
17 KiB
Python
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),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True),
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nn.Linear(latent_size, latent_size),
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nn.BatchNorm1d(latent_size),
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nn.LeakyReLU(negative_slope=0.2, inplace=True))
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# Creates a 2D latent by iterating through the provided latent_filters and doubling the
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# image size each step.
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def create_conv_latent_encoder(self, latent_filters):
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return ConvLatentEncoder(latent_filters)
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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(RRDBNetWithLatent, self).__init__()
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self.num_blocks = num_blocks
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self.blocks_per_checkpoint = blocks_per_checkpoint
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self.scale = scale
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self.in_channels = in_channels
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self.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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RRDBWithBypassAndLatent,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels)
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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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self.latent_encoder = self.create_conv_latent_encoder(mid_channels)
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for m in [
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self.conv_first, self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_hr, self.conv_last
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]:
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default_init_weights(m, 0.1)
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def forward(self, x, latent=None, ref=None):
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latent_was_none = latent
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if latent is None:
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mults = [4, 2, 1]
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b, f, h, w = x.shape
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latent = [torch.randn((b, self.nf * m, h // m, w // m), dtype=torch.float, device=x.device) for m in mults]
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if self.bottom_latent_only:
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latent[1] = torch.zeros_like(latent[1])
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latent[2] = torch.zeros_like(latent[2])
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latent = self.latent_encoder(latent)
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if latent_was_none is 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 visual_dbg(self, step, path):
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for i, bm in enumerate(self.body):
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torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
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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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# Based heavily on the same VGG arch used for the discriminator.
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class LatentEstimator(nn.Module):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, in_nc, nf, overwrite_levels=[]):
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super(LatentEstimator, self).__init__()
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self.overwrite_levels = overwrite_levels
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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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self.d1p1 = ConvGnLelu(nf * 2, nf, kernel_size=1, activation=True, norm=True, bias=True)
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self.d1p2 = ConvGnLelu(nf, nf, kernel_size=1, activation=False, norm=False, bias=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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self.d2p1 = ConvGnLelu(nf * 4, nf * 2, kernel_size=1, activation=True, norm=True, bias=True)
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self.d2p2 = ConvGnLelu(nf * 2, nf * 2, kernel_size=1, activation=False, norm=False, bias=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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self.d3p1 = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, activation=True, norm=True, bias=True)
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self.d3p2 = ConvGnLelu(nf * 4, nf * 4, kernel_size=1, activation=False, norm=False, bias=True)
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self.lrelu = nn.LeakyReLU(.2, inplace=True)
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self.tanh = nn.Tanh()
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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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o1 = self.tanh(self.d1p2(self.d1p1(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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o2 = self.tanh(self.d2p2(self.d2p1(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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o3 = self.tanh(self.d3p2(self.d3p1(fea)))
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return o3, o2, o1
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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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out = list(checkpoint(self.compute_body, fea))
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for lvl in self.overwrite_levels:
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out[lvl] = torch.zeros_like(out[lvl])
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self.latent_mean = torch.mean(out[-1])
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self.latent_std = torch.std(out[-1])
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self.latent_var = torch.var(out[-1])
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return out
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def get_debug_values(self, s, n):
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return {'latent_estimator_mean': self.latent_mean,
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'latent_estimator_std': self.latent_std,
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'latent_estimator_var': self.latent_var}
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class LatentEstimator2(nn.Module):
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def __init__(self, in_nc, nf):
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super(LatentEstimator2, 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.l = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, activation=True, norm=True, bias=True)
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self.l2 = ConvGnLelu(nf * 4, nf * 4, kernel_size=1, activation=False, norm=False, bias=True)
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self.lrelu = nn.LeakyReLU(.2, inplace=True)
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self.norm = nn.InstanceNorm2d(nf*4)
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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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o3 = self.norm(self.l2(self.l(fea)))
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return F.interpolate(o3, scale_factor=4, mode="nearest")
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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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out = [o,\
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torch.zeros((o.shape[0],128,16,16), device=o.device),\
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torch.zeros((o.shape[0],64,32,32), device=o.device)]
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self.latent_mean = torch.mean(out[-1])
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self.latent_std = torch.std(out[-1])
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self.latent_var = torch.var(out[-1])
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return out
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|
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def get_debug_values(self, s, n):
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return {'latent_estimator_mean': self.latent_mean,
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'latent_estimator_std': self.latent_std,
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'latent_estimator_var': self.latent_var}
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|
|