--- a
+++ b/model/network/basic_blocks.py
@@ -0,0 +1,28 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class BasicConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=False, **kwargs)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return F.leaky_relu(x, inplace=True)
+
+
+class SetBlock(nn.Module):
+    def __init__(self, forward_block, pooling=False):
+        super(SetBlock, self).__init__()
+        self.forward_block = forward_block
+        self.pooling = pooling
+        if pooling:
+            self.pool2d = nn.MaxPool2d(2)
+    def forward(self, x):
+        n, s, c, h, w = x.size()
+        x = self.forward_block(x.view(-1,c,h,w))
+        if self.pooling:
+            x = self.pool2d(x)
+        _, c, h, w = x.size()
+        return x.view(n, s, c, h ,w)