forked from mrq/DL-Art-School
Misc RRDB changes
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@ -4,10 +4,9 @@ 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 utils.util import checkpoint
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from utils.util import checkpoint, sequential_checkpoint
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class ResidualDenseBlock(nn.Module):
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@ -251,7 +250,7 @@ class RRDBNet(nn.Module):
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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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feat = checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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feat = feat[:, :self.reduce_ch]
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body_feat = self.conv_body(feat)
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feat = feat + body_feat
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@ -353,7 +352,7 @@ class RRDBDiscriminator(nn.Module):
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def forward(self, x):
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feat = self.conv_first(x)
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feat = checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
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pred = checkpoint(self.tail, feat)
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self.pred_ = pred.detach().clone()
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return pred
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@ -119,7 +119,7 @@ class RRDBWithBypass(nn.Module):
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class RRDBNet(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, opt=None):
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, initial_conv_stride=1, opt=None):
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self.opt = opt
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super(RRDBNet, self).__init__()
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@ -130,7 +130,10 @@ class RRDBNet(nn.Module):
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RRDB_block_f = functools.partial(RRDB, mid_channels=nf, growth_channels=gc)
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self.scale = scale
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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if initial_conv_stride == 1:
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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else:
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self.conv_first = nn.Conv2d(in_nc, nf, 7, stride=initial_conv_stride, padding=3, bias=True)
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self.body = mutil.make_layer(RRDB_block_f, nb)
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self.conv_body = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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#### upsampling
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@ -204,26 +207,6 @@ class RRDBNet(nn.Module):
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fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
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if fea_upn1_en:
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results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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elif self.scale == 2:
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# "Pretend" this is is 4x by shuffling around the inputs a bit.
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half = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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quarter = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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eighth = F.interpolate(last_lr_fea, scale_factor=1/8, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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results = {'last_lr_fea': half,
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'fea_up1': half,
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'fea_up2': last_lr_fea,
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'fea_up4': fea_up2,
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'fea_up8': fea_up4,
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'fea_up16': fea_up8,
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'fea_up32': fea_up16,
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'out': out}
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fea_up0_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up0']) or False
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if fea_up0_en:
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results['fea_up0'] = quarter
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fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
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if fea_upn1_en:
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results['fea_up-1'] = eighth
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else:
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raise NotImplementedError
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@ -171,7 +171,8 @@ def define_G(opt, opt_net, scale=None):
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elif which_model == 'rrdb_srflow':
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from models.archs.srflow_orig.RRDBNet_arch import RRDBNet
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netG = RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'])
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nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
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initial_conv_stride=opt_net['initial_stride'])
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else:
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raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
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return netG
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@ -21,7 +21,7 @@ import models.networks as networks
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def forward_pass(model, output_dir, alteration_suffix=''):
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model.feed_data(data, need_GT=need_GT)
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model.feed_data(data, 0, need_GT=need_GT)
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model.test()
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visuals = model.get_current_visuals(need_GT)['rlt'].cpu()
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@ -53,7 +53,7 @@ def forward_pass(model, output_dir, alteration_suffix=''):
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if __name__ == "__main__":
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#### options
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torch.backends.cudnn.benchmark = True
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srg_analyze = False
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_4x_psnr.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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@ -95,6 +95,7 @@ if __name__ == "__main__":
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tq = tqdm(test_loader)
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for data in tq:
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need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
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need_GT = need_GT and want_metrics
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fea_loss, psnr_loss = forward_pass(model, dataset_dir, opt['name'])
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fea_loss += fea_loss
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@ -291,7 +291,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb4x_6bl_multires.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_rrdb_2stride.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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@ -291,7 +291,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgsetext_srflow_frompsnr.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_exd_imgsetext_rrdb4x_6bl_2stride/train_exd_imgsetext_rrdb4x_6bl_2stride.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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