diff --git a/codes/models/RRDBNet_arch.py b/codes/models/RRDBNet_arch.py index 1fe25f0f..faeec8b7 100644 --- a/codes/models/RRDBNet_arch.py +++ b/codes/models/RRDBNet_arch.py @@ -6,10 +6,14 @@ 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_2 import UResNet50_2 +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 import SwitchedConv class ResidualDenseBlock(nn.Module): @@ -303,82 +307,91 @@ class RRDBNet(nn.Module): torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1))) - -class DiscRDB(nn.Module): - def __init__(self, mid_channels=64, growth_channels=32): - super(DiscRDB, self).__init__() - for i in range(5): - out_channels = mid_channels if i == 4 else growth_channels - actnorm = i != 5 - self.add_module( - f'conv{i+1}', - ConvGnLelu(mid_channels + i * growth_channels, out_channels, kernel_size=3, norm=actnorm, activation=actnorm, bias=True) - ) - self.lrelu = nn.LeakyReLU(negative_slope=.2) - for i in range(5): - default_init_weights(getattr(self, f'conv{i+1}'), 1) - - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv2(torch.cat((x, x1), 1)) - x3 = self.conv3(torch.cat((x, x1, x2), 1)) - x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return self.lrelu(x5 + x) - - -class DiscRRDB(nn.Module): - def __init__(self, mid_channels, growth_channels=32): - super(DiscRRDB, self).__init__() - self.rdb1 = DiscRDB(mid_channels, growth_channels) - self.rdb2 = DiscRDB(mid_channels, growth_channels) - self.rdb3 = DiscRDB(mid_channels, growth_channels) - self.gn = nn.GroupNorm(num_groups=8, num_channels=mid_channels) - - def forward(self, x): - out = self.rdb1(x) - out = self.rdb2(out) - out = self.rdb3(out) - return self.gn(out + x) - - -class RRDBDiscriminator(nn.Module): +class RRDBNetSwitchedConv(nn.Module): def __init__(self, in_channels, + out_channels, mid_channels=64, num_blocks=23, growth_channels=32, - blocks_per_checkpoint=1 + 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(RRDBDiscriminator, self).__init__() + super().__init__() self.num_blocks = num_blocks self.blocks_per_checkpoint = blocks_per_checkpoint + self.scale = scale self.in_channels = in_channels - self.conv_first = ConvGnLelu(in_channels, mid_channels, 3, stride=4, activation=False, norm=False, bias=True) + 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( - DiscRRDB, + body_block, num_blocks, mid_channels=mid_channels, - growth_channels=growth_channels) - self.tail = nn.Sequential( - ConvGnLelu(mid_channels, mid_channels // 2, kernel_size=1, activation=True, norm=False, bias=True), - ConvGnLelu(mid_channels // 2, mid_channels // 4, kernel_size=1, activation=True, norm=False, bias=True), - ConvGnLelu(mid_channels // 4, 1, kernel_size=1, activation=False, norm=False, bias=True) - ) - self.pred_ = None + 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) - def forward(self, x): - feat = self.conv_first(x) + 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) - pred = checkpoint(self.tail, feat) - self.pred_ = pred.detach().clone() - return pred + 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): - if self.pred_ is not None: - self.pred_ = F.sigmoid(self.pred_) - torchvision.utils.save_image(self.pred_.cpu().float(), os.path.join(path, "%i_predictions.png" % (step,))) + 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 @@ -404,4 +417,16 @@ def register_RRDBNet(opt_net, opt): 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) \ No newline at end of file + 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']) diff --git a/codes/models/switched_conv.py b/codes/models/switched_conv.py new file mode 100644 index 00000000..91315e44 --- /dev/null +++ b/codes/models/switched_conv.py @@ -0,0 +1,125 @@ +import functools +import math +from collections import OrderedDict + +import torch +import torch.nn as nn +from torch.nn import init, Conv2d +import torch.nn.functional as F + + +class SwitchedConv(nn.Module): + def __init__(self, + in_channels: int, + out_channels: int, + kernel_size: int, + switch_breadth: int, + stride: int = 1, + padding: int = 0, + dilation: int = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = 'zeros', + include_coupler: bool = False, # A 'coupler' is a latent converter which can make any bxcxhxw tensor a compatible switchedconv selector by performing a linear 1x1 conv, softmax and interpolate. + coupler_dim_in: int = 0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.padding_mode = padding_mode + self.groups = groups + + if include_coupler: + self.coupler = Conv2d(coupler_dim_in, switch_breadth, kernel_size=1) + else: + self.coupler = None + + self.weights = nn.ParameterList([nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size)) for _ in range(switch_breadth)]) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self) -> None: + for w in self.weights: + init.kaiming_uniform_(w, a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = init._calculate_fan_in_and_fan_out(self.weights[0]) + bound = 1 / math.sqrt(fan_in) + init.uniform_(self.bias, -bound, bound) + + def forward(self, inp, selector): + if self.coupler: + selector = F.softmax(self.coupler(selector), dim=1) + out_shape = [s // self.stride for s in inp.shape[2:]] + if selector.shape[2] != out_shape[0] or selector.shape[3] != out_shape[1]: + selector = F.interpolate(selector, size=out_shape, mode="nearest") + + conv_results = [] + for i, w in enumerate(self.weights): + conv_results.append(F.conv2d(inp, w, self.bias, self.stride, self.padding, self.dilation, self.groups) * selector[:, i].unsqueeze(1)) + return torch.stack(conv_results, dim=-1).sum(dim=-1) + + + +# Given a state_dict and the module that that sd belongs to, strips out all Conv2d.weight parameters and replaces them +# with the equivalent SwitchedConv.weight parameters. Does not create coupler params. +def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_list=[]): + state_dict = module.state_dict() + for name, m in module.named_modules(): + ignored = False + for smod in ignore_list: + if smod in name: + ignored = True + continue + if ignored: + continue + if isinstance(m, nn.Conv2d): + if name == '': + basename = 'weight' + modname = 'weights' + else: + basename = f'{name}.weight' + modname = f'{name}.weights' + cnv_weights = state_dict[basename] + del state_dict[basename] + for j in range(switch_breadth): + state_dict[f'{modname}.{j}'] = cnv_weights.clone() + return state_dict + + +def test_net(): + base_conv = Conv2d(32, 64, 3, stride=2, padding=1, bias=True).to('cuda') + mod_conv = SwitchedConv(32, 64, 3, switch_breadth=8, stride=2, padding=1, bias=True, include_coupler=True, coupler_dim_in=128).to('cuda') + mod_sd = convert_conv_net_state_dict_to_switched_conv(base_conv, 8) + mod_conv.load_state_dict(mod_sd, strict=False) + inp = torch.randn((8,32,128,128), device='cuda') + sel = torch.randn((8,128,32,32), device='cuda') + out1 = base_conv(inp) + out2 = mod_conv(inp, sel) + assert(torch.max(torch.abs(out1-out2)) < 1e-6) + +def perform_conversion(): + sd = torch.load("../experiments/rrdb_imgset_226500_generator.pth") + load_net_clean = OrderedDict() # remove unnecessary 'module.' + for k, v in sd.items(): + if k.startswith('module.'): + load_net_clean[k.replace('module.', '')] = v + else: + load_net_clean[k] = v + sd = load_net_clean + import models.RRDBNet_arch as rrdb + block = functools.partial(rrdb.RRDBWithBypass) + mod = rrdb.RRDBNet(in_channels=3, out_channels=3, + mid_channels=64, num_blocks=23, body_block=block, scale=2, initial_stride=2) + mod.load_state_dict(sd) + converted = convert_conv_net_state_dict_to_switched_conv(mod, 8, ['body.','conv_first','resnet_encoder']) + torch.save(converted, "../experiments/rrdb_imgset_226500_generator_converted.pth") + + +if __name__ == '__main__': + perform_conversion()