LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2019 Rajeev Yasarla
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

models/face_fed.py

@@ -592,9 +592,9 @@ class scale_kernel_conf(nn.Module):
         x4=self.conv3(x3)
         x4 = self.trans_block4(x4)
         #print(x4.size())
-        residual = self.sig(self.conv_refin(self.sig(F.adaptive_avg_pool2d(x4,(1,1)))))
+        residual = self.sig(self.conv_refin(self.sig(F.avg_pool2d(x4,16))))
         #print(residual)
-        residual = F.interpolate(residual, size=x.size()[2:])
+        residual = F.upsample_nearest(residual, scale_factor=128)
         #print(residual.size())
         return residual
 

requirements.txt

@@ -1,5 +1,5 @@
-numpy==1.22.0
+numpy==1.18.0
-opencv-python>=4.2.0.32
+opencv-python==4.1.2.30
 Pillow>=6.2.2
 six==1.13.0
 torch==0.4.1.post2

test_face_deblur.py

@@ -57,7 +57,7 @@ parser.add_argument('--workers', type=int, help='number of data loading workers'
 parser.add_argument('--exp', default='sample', help='folder to output images and model checkpoints')
 parser.add_argument('--display', type=int, default=5, help='interval for displaying train-logs')
 parser.add_argument('--evalIter', type=int, default=500, help='interval for evauating(generating) images from valDataroot')
-opt = parser.parse_args()#pt.originalSize
+opt = parser.parse_args()
 print(opt)
 
 
@@ -253,27 +253,27 @@ for epoch in range(1):
             
             #x_hatcls1,x_hatcls2,x_hatcls3,x_hatcls4,x_lst1,x_lst2,x_lst3,x_lst4 = netG(val_inputv,val_inputv_256,smaps_i,smaps_i64,class1,class2,class3,class4)
             smaps,smaps64 = netS(val_inputv,val_inputv_256)
-            class1 = torch.zeros([1,1,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class1 = torch.zeros([1,1,128,128], dtype=torch.float32)
             class1[:,0,:,:] = smaps[:,0,:,:]
-            class2 = torch.zeros([1,1,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class2 = torch.zeros([1,1,128,128], dtype=torch.float32)
             class2[:,0,:,:] = smaps[:,1,:,:]
-            class3 = torch.zeros([1,1,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class3 = torch.zeros([1,1,128,128], dtype=torch.float32)
             class3[:,0,:,:] = smaps[:,2,:,:]
-            class4 = torch.zeros([1,1,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class4 = torch.zeros([1,1,128,128], dtype=torch.float32)
             class4[:,0,:,:] = smaps[:,3,:,:]
-            class_msk1 = torch.zeros([1,3,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class_msk1 = torch.zeros([1,3,128,128], dtype=torch.float32)
             class_msk1[:,0,:,:] = smaps[:,0,:,:] 
             class_msk1[:,1,:,:] = smaps[:,0,:,:] 
             class_msk1[:,2,:,:] = smaps[:,0,:,:] 
-            class_msk2 = torch.zeros([1,3,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class_msk2 = torch.zeros([1,3,128,128], dtype=torch.float32)
             class_msk2[:,0,:,:] = smaps[:,1,:,:]
             class_msk2[:,1,:,:] = smaps[:,1,:,:]
             class_msk2[:,2,:,:] = smaps[:,1,:,:]
-            class_msk3 = torch.zeros([1,3,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class_msk3 = torch.zeros([1,3,128,128], dtype=torch.float32)
             class_msk3[:,0,:,:] = smaps[:,2,:,:] 
             class_msk3[:,1,:,:] = smaps[:,2,:,:] 
             class_msk3[:,2,:,:] = smaps[:,2,:,:] 
-            class_msk4 = torch.zeros([1,3,opt.originalSize,opt.originalSize], dtype=torch.float32)
+            class_msk4 = torch.zeros([1,3,128,128], dtype=torch.float32)
             class_msk4[:,0,:,:] = smaps[:,3,:,:]  
             class_msk4[:,1,:,:] = smaps[:,3,:,:]
             class_msk4[:,2,:,:] = smaps[:,3,:,:]
@@ -310,3 +310,5 @@ for epoch in range(1):
 
             im.save(filename)
       
+      
+