add transer_RRDB_models

transer_RRDB_models.py

@@ -0,0 +1,55 @@
+import os
+import torch
+import RRDBNet_arch as arch
+
+pretrained_net = torch.load('./models/RRDB_ESRGAN_x4.pth')
+save_path = './models/RRDB_ESRGAN_x4.pth'
+
+crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+crt_net = crt_model.state_dict()
+
+load_net_clean = {}
+for k, v in pretrained_net.items():
+    if k.startswith('module.'):
+        load_net_clean[k[7:]] = v
+    else:
+        load_net_clean[k] = v
+pretrained_net = load_net_clean
+
+print('###################################\n')
+tbd = []
+for k, v in crt_net.items():
+    tbd.append(k)
+
+# directly copy
+for k, v in crt_net.items():
+    if k in pretrained_net and pretrained_net[k].size() == v.size():
+        crt_net[k] = pretrained_net[k]
+        tbd.remove(k)
+
+crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+for k in tbd.copy():
+    if 'RDB' in k:
+        ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+        if '.weight' in k:
+            ori_k = ori_k.replace('.weight', '.0.weight')
+        elif '.bias' in k:
+            ori_k = ori_k.replace('.bias', '.0.bias')
+        crt_net[k] = pretrained_net[ori_k]
+        tbd.remove(k)
+
+crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+torch.save(crt_net, save_path)
+print('Saving to ', save_path)