latent work

codes/models/archs/rrdb_with_latent.py

@@ -224,8 +224,9 @@ class RRDBNetWithLatent(nn.Module):
 # 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):
+    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)
@@ -276,6 +277,8 @@ class LatentEstimator(nn.Module):
         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])

codes/models/networks.py

@@ -124,7 +124,8 @@ def define_G(opt, net_key='network_G', scale=None):
                                   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":
-        netG = LatentEstimator(in_nc=3, nf=opt_net['nf'])
+        overwrite = [1,2] if opt_net['only_base_level'] else []
+        netG = LatentEstimator(in_nc=3, nf=opt_net['nf'], overwrite_levels=overwrite)
     else:
         raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
     return netG

codes/train.py

@@ -265,7 +265,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_mi1_rrdb_6bypass.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_latent_mi1_rrdb4x_6bl_lower_signal.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()

codes/utils/util.py

@@ -382,3 +382,13 @@ def recursively_detach(v):
         for k, t in v.items():
             out[k] = recursively_detach(t)
         return out
+
+def opt_get(opt, keys, default=None):
+    if opt is None:
+        return default
+    ret = opt
+    for k in keys:
+        ret = ret.get(k, None)
+        if ret is None:
+            return default
+    return ret