Support 2x RRDB with 4x srflow

codes/models/archs/srflow_orig/FlowUpsamplerNet.py

@@ -48,7 +48,7 @@ class FlowUpsamplerNet(nn.Module):
                 4: 'fea_up0'
             }
 
-        elif opt['scale'] == 4:
+        elif opt['scale'] == 4 or opt['scale'] == 2:
             self.levelToName = {
                 0: 'fea_up4',
                 1: 'fea_up2',

codes/models/archs/srflow_orig/RRDBNet_arch.py

@@ -186,6 +186,7 @@ class RRDBNet(nn.Module):
 
         out = self.conv_last(self.lrelu(self.conv_hr(fea)))
 
+        if self.scale >= 4:
             results = {'last_lr_fea': last_lr_fea,
                        'fea_up1': last_lr_fea,
                        'fea_up2': fea_up2,
@@ -201,6 +202,28 @@ class RRDBNet(nn.Module):
             fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
             if fea_upn1_en:
                 results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
+        elif self.scale == 2:
+            # "Pretend" this is is 4x by shuffling around the inputs a bit.
+            half = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
+            quarter = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
+            eighth = F.interpolate(last_lr_fea, scale_factor=1/8, mode='bilinear', align_corners=False, recompute_scale_factor=True)
+            results = {'last_lr_fea': half,
+                       'fea_up1': half,
+                       'fea_up2': last_lr_fea,
+                       'fea_up4': fea_up2,
+                       'fea_up8': fea_up4,
+                       'fea_up16': fea_up8,
+                       'fea_up32': fea_up16,
+                       'out': out}
+
+            fea_up0_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up0']) or False
+            if fea_up0_en:
+                results['fea_up0'] = quarter
+            fea_upn1_en = opt_get(self.opt, ['networks', 'generator','flow', 'fea_up-1']) or False
+            if fea_upn1_en:
+                results['fea_up-1'] = eighth
+        else:
+            raise NotImplementedError
 
         if get_steps:
             for k, v in block_results.items():

codes/models/archs/srflow_orig/SRFlowNet_arch.py

@@ -27,6 +27,7 @@ class SRFlowNet(nn.Module):
         hidden_channels = opt_get(opt, ['networks', 'generator','flow', 'hidden_channels'])
         hidden_channels = hidden_channels or 64
         self.RRDB_training = opt_get(self.opt, ['networks', 'generator','train_RRDB'], default=False)
+        self.flow_scale = opt_get(self.opt, ['networks', 'generator', 'flow_scale'], default=opt['scale'])  # <!-- hack to enable RRDB to do 2x scaling while retaining the flow architecture of 4x.
 
         self.flowUpsamplerNet = \
             FlowUpsamplerNet((160, 160, 3), hidden_channels, K,
@@ -41,8 +42,8 @@ class SRFlowNet(nn.Module):
         if seed: torch.manual_seed(seed)
         if opt_get(self.opt, ['networks', 'generator', 'flow', 'split', 'enable']):
             C = self.flowUpsamplerNet.C
-            H = int(self.opt['scale'] * lr_shape[2] // self.flowUpsamplerNet.scaleH)
-            W = int(self.opt['scale'] * lr_shape[3] // self.flowUpsamplerNet.scaleW)
+            H = int(self.flow_scale * lr_shape[2] // (self.flowUpsamplerNet.scaleH * self.flow_scale / self.RRDB.scale))
+            W = int(self.flow_scale * lr_shape[3] // (self.flowUpsamplerNet.scaleW * self.flow_scale / self.RRDB.scale))
 
             size = (batch_size, C, H, W)
             if heat == 0:
@@ -149,9 +150,9 @@ class SRFlowNet(nn.Module):
                     keys.append('fea_up0')
                 if 'fea_up-1' in rrdbResults.keys():
                     keys.append('fea_up-1')
-                if self.opt['scale'] >= 8:
+                if self.flow_scale >= 8:
                     keys.append('fea_up8')
-                if self.opt['scale'] == 16:
+                if self.flow_scale == 16:
                     keys.append('fea_up16')
                 for k in keys:
                     h = rrdbResults[k].shape[2]
@@ -166,7 +167,7 @@ class SRFlowNet(nn.Module):
 
     def reverse_flow(self, lr, z, y_onehot, eps_std, epses=None, lr_enc=None, add_gt_noise=True):
         logdet = torch.zeros_like(lr[:, 0, 0, 0])
-        pixels = thops.pixels(lr) * self.opt['scale'] ** 2
+        pixels = thops.pixels(lr) * self.flow_scale ** 2
 
         if add_gt_noise:
             logdet = logdet - float(-np.log(self.quant) * pixels)

codes/train2.py

@@ -291,7 +291,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_teco_vix_srg2_classic_proper_disc.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_srflow.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()