import functools import os import random import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torchvision.models.resnet import Bottleneck from models.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu from models.pixel_level_contrastive_learning.resnet_unet_3 import UResNet50_3 from trainer.networks import register_model from utils.util import checkpoint, sequential_checkpoint, opt_get from models.switched_conv.switched_conv import SwitchedConv 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, init_weight=.1): 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}'), init_weight) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ 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)) # Emperically, we use 0.2 to scale the residual for better performance return x5 * 0.2 + x class RRDB(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels, growth_channels=32, reduce_to=None): super(RRDB, self).__init__() self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) if reduce_to is not None: self.reducer = ConvGnLelu(mid_channels, reduce_to, kernel_size=3, activation=False, norm=False, bias=True) self.recover_ch = mid_channels - reduce_to else: self.reducer = None def forward(self, x, return_residual=False): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) if self.reducer is not None: out = self.reducer(out) b, f, h, w = out.shape out = torch.cat([out, torch.zeros((b, self.recover_ch, h, w), device=out.device)], dim=1) if return_residual: return 0.2 * out else: # Empirically, we use 0.2 to scale the residual for better performance return out * 0.2 + x class RRDBWithBypass(nn.Module): """Residual in Residual Dense Block. Used in RRDB-Net in ESRGAN. Args: mid_channels (int): Channel number of intermediate features. growth_channels (int): Channels for each growth. """ def __init__(self, mid_channels, growth_channels=32, reduce_to=None, randomly_add_noise_to_bypass=False): super(RRDBWithBypass, self).__init__() self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels) self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels) if reduce_to is not None: self.reducer = ConvGnLelu(mid_channels, reduce_to, kernel_size=3, activation=False, norm=False, bias=True) self.recover_ch = mid_channels - reduce_to bypass_channels = mid_channels + reduce_to else: self.reducer = None bypass_channels = mid_channels * 2 self.bypass = nn.Sequential(ConvGnSilu(bypass_channels, 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()) self.randomly_add_bypass_noise = randomly_add_noise_to_bypass def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ out = self.rdb1(x) out = self.rdb2(out) out = self.rdb3(out) if self.reducer is not None: out = self.reducer(out) b, f, h, w = out.shape out = torch.cat([out, torch.zeros((b, self.recover_ch, h, w), device=out.device)], dim=1) bypass = self.bypass(torch.cat([x, out], dim=1)) # The purpose of random noise is to induce usage of bypass maps that would otherwise be "dead". Theoretically # if these maps provide value, the noise should trigger gradients to flow into the bypass conv network again. if self.randomly_add_bypass_noise and random.random() < .2: rnoise = torch.rand_like(bypass) * .02 bypass = (bypass + rnoise).clamp(0, 1) self.bypass_map = bypass.detach().clone() # Empirically, we use 0.2 to scale the residual for better performance return out * 0.2 * bypass + x class RRDBNet(nn.Module): """Networks consisting of Residual in Residual Dense Block, which is used in ESRGAN. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Currently, it supports x4 upsampling scale factor. Args: in_channels (int): Channel number of inputs. out_channels (int): Channel number of outputs. mid_channels (int): Channel number of intermediate features. Default: 64 num_blocks (int): Block number in the trunk network. Defaults: 23 growth_channels (int): Channels for each growth. Default: 32. """ def __init__(self, in_channels, out_channels, mid_channels=64, num_blocks=23, growth_channels=32, body_block=RRDB, blocks_per_checkpoint=1, scale=4, additive_mode="not", # Options: "not", "additive", "additive_enforced" headless=False, feature_channels=64, # Only applicable when headless=True. How many channels are used at the trunk level. output_mode="hq_only", # Options: "hq_only", "hq+features", "features_only" initial_stride=1, use_ref=False, # When set, a reference image is expected as input and synthesized if not found. Useful for video SR. ): super(RRDBNet, self).__init__() assert output_mode in ['hq_only', 'hq+features', 'features_only'] assert additive_mode in ['not', 'additive', 'additive_enforced'] self.num_blocks = num_blocks self.blocks_per_checkpoint = blocks_per_checkpoint self.scale = scale self.in_channels = in_channels self.output_mode = output_mode self.use_ref = use_ref first_conv_stride = initial_stride if not self.use_ref else scale first_conv_ksize = 3 if first_conv_stride == 1 else 7 first_conv_padding = 1 if first_conv_stride == 1 else 3 if headless: self.conv_first = None self.reduce_ch = feature_channels reduce_to = feature_channels self.conv_ref_first = ConvGnLelu(3, feature_channels, 7, stride=2, norm=False, activation=False, bias=True) else: self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding) self.reduce_ch = mid_channels reduce_to = None self.body = make_layer( body_block, num_blocks, mid_channels=mid_channels, growth_channels=growth_channels, reduce_to=reduce_to) self.conv_body = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1) # upsample self.conv_up1 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1) self.conv_up2 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1) if scale >= 8: self.conv_up3 = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1) else: self.conv_up3 = None self.conv_hr = nn.Conv2d(self.reduce_ch, self.reduce_ch, 3, 1, 1) self.conv_last = nn.Conv2d(self.reduce_ch, out_channels, 3, 1, 1) self.additive_mode = additive_mode if additive_mode == "additive_enforced": self.add_enforced_pool = nn.AvgPool2d(kernel_size=scale, stride=scale) 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_up3, self.conv_hr, self.conv_last ]: if m is not None: default_init_weights(m, 0.1) def forward(self, x, ref=None): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ if self.conv_first is None: # Headless mode -> embedding inputs. if ref is not None: ref = self.conv_ref_first(ref) feat = torch.cat([x, ref], dim=1) else: feat = x else: # "Normal" mode -> image input. if self.use_ref: 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) feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat) feat = feat[:, :self.reduce_ch] body_feat = self.conv_body(feat) feat = feat + body_feat if self.output_mode == "features_only": return feat # upsample out = self.lrelu( self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) if self.scale >= 4: out = self.lrelu( self.conv_up2(F.interpolate(out, scale_factor=2, mode='nearest'))) if self.scale >= 8: out = self.lrelu( self.conv_up3(F.interpolate(out, scale_factor=2, mode='nearest'))) else: out = self.lrelu(self.conv_up2(out)) out = self.conv_last(self.lrelu(self.conv_hr(out))) if "additive" in self.additive_mode: x_interp = F.interpolate(x, scale_factor=self.scale, mode='bilinear') if self.additive_mode == 'additive': out = out + x_interp elif self.additive_mode == 'additive_enforced': out_pooled = self.add_enforced_pool(out) out = out - F.interpolate(out_pooled, scale_factor=self.scale, mode='nearest') out = out + x_interp if self.output_mode == "hq+features": return out, feat return out def visual_dbg(self, step, path): for i, bm in enumerate(self.body): if hasattr(bm, 'bypass_map'): torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1))) class RRDBNetSwitchedConv(nn.Module): def __init__(self, in_channels, out_channels, mid_channels=64, num_blocks=23, growth_channels=32, body_block=RRDB, blocks_per_checkpoint=1, scale=4, initial_stride=1, use_ref=False, # When set, a reference image is expected as input and synthesized if not found. Useful for video SR. resnet_encoder_dict=None ): super().__init__() self.num_blocks = num_blocks self.blocks_per_checkpoint = blocks_per_checkpoint self.scale = scale self.in_channels = in_channels self.use_ref = use_ref first_conv_stride = initial_stride if not self.use_ref 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.reduce_ch = mid_channels reduce_to = None self.body = make_layer( body_block, num_blocks, mid_channels=mid_channels, growth_channels=growth_channels, reduce_to=reduce_to) self.conv_body = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) # upsample self.conv_up1 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) self.conv_up2 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) if scale >= 8: self.conv_up3 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) else: self.conv_up3 = None self.conv_hr = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) self.conv_last = SwitchedConv(self.reduce_ch, out_channels, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.resnet_encoder = UResNet50_3(Bottleneck, [3, 4, 6, 3], out_dim=64) if resnet_encoder_dict: self.resnet_encoder.load_state_dict(torch.load(resnet_encoder_dict)) for m in [ self.conv_first, self.conv_body, self.conv_up1, self.conv_up2, self.conv_up3, self.conv_hr, self.conv_last ]: if m is not None: default_init_weights(m, 0.1) def forward(self, x, ref=None): switch_enc = checkpoint(self.resnet_encoder, F.interpolate(x, scale_factor=2, mode="bilinear")) x_lg = x feat = self.conv_first(x_lg) feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat) feat = feat[:, :self.reduce_ch] body_feat = checkpoint(self.conv_body, feat, switch_enc) feat = feat + body_feat # upsample out = self.lrelu( checkpoint(self.conv_up1, F.interpolate(feat, scale_factor=2, mode='nearest'), switch_enc)) if self.scale >= 4: out = self.lrelu( checkpoint(self.conv_up2, F.interpolate(out, scale_factor=2, mode='nearest'), switch_enc)) if self.scale >= 8: out = self.lrelu( self.conv_up3(F.interpolate(out, scale_factor=2, mode='nearest'), switch_enc)) else: out = self.lrelu(checkpoint(self.conv_up2, out, switch_enc)) out = checkpoint(self.conv_hr, out, switch_enc) out = checkpoint(self.conv_last, self.lrelu(out), switch_enc) return out def visual_dbg(self, step, path): for i, bm in enumerate(self.body): if hasattr(bm, 'bypass_map'): torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1))) @register_model def register_RRDBNetBypass(opt_net, opt): additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not' output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only' gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32 initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1 bypass_noise = opt_get(opt_net, ['bypass_noise'], False) block = functools.partial(RRDBWithBypass, randomly_add_noise_to_bypass=bypass_noise) return RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'], mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode, output_mode=output_mode, body_block=block, scale=opt_net['scale'], growth_channels=gc, initial_stride=initial_stride) @register_model def register_RRDBNet(opt_net, opt): additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not' output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only' gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32 initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1 return RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'], mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode, output_mode=output_mode, body_block=RRDB, scale=opt_net['scale'], growth_channels=gc, initial_stride=initial_stride) @register_model def register_rrdb_switched_conv(opt_net, opt): gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32 initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1 bypass_noise = opt_get(opt_net, ['bypass_noise'], False) block = functools.partial(RRDBWithBypass, randomly_add_noise_to_bypass=bypass_noise) return RRDBNetSwitchedConv(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'], mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], body_block=block, scale=opt_net['scale'], growth_channels=gc, initial_stride=initial_stride, resnet_encoder_dict=opt_net['switch_encoder'])