Add a simple PatchGAN

map2map/models/patchgan.py

@@ -0,0 +1,18 @@
+import torch.nn as nn
+
+from .conv import ConvBlock
+
+
+class PatchGAN(nn.Module):
+    def __init__(self, in_chan, out_chan=1):
+        super().__init__()
+
+        self.convs = nn.Sequential(
+            ConvBlock(in_chan, 64, seq='CA'),
+            ConvBlock(64, 128, seq='CBA'),
+            ConvBlock(128, 256, seq='CBA'),
+            nn.Conv3d(256, out_chan, 1)
+        )
+
+    def forward(self, x):
+        return self.convs(x)