https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/190

models/networks.py

@@ -76,9 +76,13 @@ def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, in
     norm_layer = get_norm_layer(norm_type=norm)
 
     if netG == 'resnet_9blocks':
-        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9,use_deconvolution=True)
+    elif netG == 'resnet_9blocks+':
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9,use_deconvolution=False)
     elif netG == 'resnet_6blocks':
-        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6,use_deconvolution=True)
+    elif netG == 'resnet_6blocks+':
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6,use_deconvolution=False)
     elif netG == 'unet_128':
         net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
     elif netG == 'unet_256':
@@ -140,7 +144,7 @@ def __call__(self, input, target_is_real):
 # Code and idea originally from Justin Johnson's architecture.
 # https://github.com/jcjohnson/fast-neural-style/
 class ResnetGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
+    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect',use_deconvolution=False):
         assert(n_blocks >= 0)
         super(ResnetGenerator, self).__init__()
         self.input_nc = input_nc
@@ -171,12 +175,18 @@ def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_d
 
         for i in range(n_downsampling):
             mult = 2**(n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
-                                         kernel_size=3, stride=2,
-                                         padding=1, output_padding=1,
-                                         bias=use_bias),
-                      norm_layer(int(ngf * mult / 2)),
-                      nn.ReLU(True)]
+            if use_deconvolution:
+                model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
+                             kernel_size=3, stride=2,
+                             padding=1, output_padding=1,
+                             bias=use_bias)]
+            else:
+                model += [nn.Upsample(scale_factor = 2, mode='bilinear',align_corners=True),
+                          nn.ReflectionPad2d(1),
+                          nn.Conv2d(ngf * mult, int(ngf * mult / 2),kernel_size=3, stride=1, padding=0)]
+
+            model += [norm_layer(int(ngf * mult / 2)),nn.ReLU(True)]
+
         model += [nn.ReflectionPad2d(3)]
         model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
         model += [nn.Tanh()]