More latent work

codes/models/archs/rrdb_with_latent.py

@@ -134,13 +134,15 @@ class RRDBNetWithLatent(nn.Module):
                  num_blocks=23,
                  growth_channels=32,
                  blocks_per_checkpoint=4,
-                 scale=4):
+                 scale=4,
+                 bottom_latent_only=False):
         super(RRDBNetWithLatent, self).__init__()
         self.num_blocks = num_blocks
         self.blocks_per_checkpoint = blocks_per_checkpoint
         self.scale = scale
         self.in_channels = in_channels
         self.nf = mid_channels
+        self.bottom_latent_only = bottom_latent_only
         first_conv_stride = 1 if in_channels <= 4 else scale
         first_conv_ksize = 3 if first_conv_stride == 1 else 7
         first_conv_padding = 1 if first_conv_stride == 1 else 3
@@ -172,6 +174,9 @@ class RRDBNetWithLatent(nn.Module):
             mults = [4, 2, 1]
             b, f, h, w = x.shape
             latent = [torch.randn((b, self.nf * m, h // m, w // m), dtype=torch.float, device=x.device) for m in mults]
+            if self.bottom_latent_only:
+                latent[1] = torch.zeros_like(latent[1])
+                latent[2] = torch.zeros_like(latent[2])
         latent = self.latent_encoder(latent)
         if latent_was_none is None:
             self.latent_mean = torch.mean(latent).detach().cpu()
@@ -289,3 +294,80 @@ class LatentEstimator(nn.Module):
                 'latent_estimator_std': self.latent_std,
                 'latent_estimator_var': self.latent_var}
 
+
+class LatentEstimator2(nn.Module):
+    def __init__(self, in_nc, nf):
+        super(LatentEstimator2, self).__init__()
+        # [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)
+        self.bn0_1 = nn.BatchNorm2d(nf, affine=True)
+        # [64, 64, 64]
+        self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
+        self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
+        self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
+        self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
+
+        # [128, 32, 32]
+        self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
+        self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
+        self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
+        self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
+
+        # [256, 16, 16]
+        self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False)
+        self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
+        self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
+        self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
+
+        # [256, 8, 8]
+        self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
+        self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
+        self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
+        self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
+
+        # [256, 4, 4]
+        self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
+        self.bn5_0 = nn.BatchNorm2d(nf * 8, affine=True)
+        self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
+        self.bn5_1 = nn.BatchNorm2d(nf * 8, affine=True)
+        self.l = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, activation=True, norm=True, bias=True)
+        self.l2 = ConvGnLelu(nf * 4, nf * 4, kernel_size=1, activation=False, norm=False, bias=True)
+
+        self.lrelu = nn.LeakyReLU(.2, inplace=True)
+        self.norm = nn.InstanceNorm2d(nf*4)
+
+    def compute_body(self, x):
+        fea = self.lrelu(self.bn1_0(self.conv1_0(x)))
+        fea = self.lrelu(self.bn1_1(self.conv1_1(fea)))
+
+        fea = self.lrelu(self.bn2_0(self.conv2_0(fea)))
+        fea = self.lrelu(self.bn2_1(self.conv2_1(fea)))
+
+        fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
+        fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
+        fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
+        fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
+        fea = self.lrelu(self.bn5_0(self.conv5_0(fea)))
+        fea = self.lrelu(self.bn5_1(self.conv5_1(fea)))
+        o3 = self.norm(self.l2(self.l(fea)))
+
+        return F.interpolate(o3, scale_factor=4, mode="nearest")
+
+    def forward(self, x):
+        fea = self.lrelu(self.conv0_0(x))
+        fea = self.lrelu(self.bn0_1(self.conv0_1(fea)))
+        o = checkpoint(self.compute_body, fea)
+        out = [o,\
+               torch.zeros((o.shape[0],128,16,16), device=o.device),\
+               torch.zeros((o.shape[0],64,32,32), device=o.device)]
+        self.latent_mean = torch.mean(out[-1])
+        self.latent_std = torch.std(out[-1])
+        self.latent_var = torch.var(out[-1])
+        return out
+
+    def get_debug_values(self, s, n):
+        return {'latent_estimator_mean': self.latent_mean,
+                'latent_estimator_std': self.latent_std,
+                'latent_estimator_var': self.latent_var}
+

codes/models/networks.py

@@ -20,7 +20,7 @@ import models.archs.rcan as rcan
 import models.archs.ChainedEmbeddingGen as chained
 from models.archs import srg2_classic
 from models.archs.pyramid_arch import BasicResamplingFlowNet
-from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent
+from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2
 from models.archs.teco_resgen import TecoGen
 
 logger = logging.getLogger('base')
@@ -122,10 +122,15 @@ def define_G(opt, net_key='network_G', scale=None):
     elif which_model == "rrdb_with_latent":
         netG = RRDBNetWithLatent(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
                                   mid_channels=opt_net['nf'], num_blocks=opt_net['nb'],
-                                  blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], scale=opt_net['scale'])
+                                  blocks_per_checkpoint=opt_net['blocks_per_checkpoint'],
+                                  scale=opt_net['scale'],
+                                  bottom_latent_only=opt_net['bottom_latent_only'])
     elif which_model == "latent_estimator":
-        overwrite = [1,2] if opt_net['only_base_level'] else []
+        if opt_net['version'] == 2:
-        netG = LatentEstimator(in_nc=3, nf=opt_net['nf'], overwrite_levels=overwrite)
+            netG = LatentEstimator2(in_nc=3, nf=opt_net['nf'])
+        else:
+            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/train2.py

@@ -280,7 +280,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_latent_mi1_rrdb4x_6bl.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_latent_mi1_rrdb4x_6bl_lower_signal_2.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     args = parser.parse_args()
     opt = option.parse(args.opt, is_train=True)