--- a
+++ b/v3/py2tfjs/meshnet.py
@@ -0,0 +1,183 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+MeshNet_38_or_64_kwargs = [
+    {
+        "in_channels": -1,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 2,
+        "stride": 1,
+        "dilation": 2,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 4,
+        "stride": 1,
+        "dilation": 4,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 8,
+        "stride": 1,
+        "dilation": 8,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 3,
+        "out_channels": 21,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 21,
+        "kernel_size": 1,
+        "out_channels": -1,
+        "padding": 0,
+        "stride": 1,
+        "dilation": 1,
+    },
+]
+
+MeshNet_68_kwargs = [
+    {
+        "in_channels": -1,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 2,
+        "stride": 1,
+        "dilation": 2,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 4,
+        "stride": 1,
+        "dilation": 4,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 8,
+        "stride": 1,
+        "dilation": 8,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 16,
+        "stride": 1,
+        "dilation": 16,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 3,
+        "out_channels": 71,
+        "padding": 1,
+        "stride": 1,
+        "dilation": 1,
+    },
+    {
+        "in_channels": 71,
+        "kernel_size": 1,
+        "out_channels": -1,
+        "padding": 0,
+        "stride": 1,
+        "dilation": 1,
+    },
+]
+
+
+def conv_w_bn_before_act(dropout_p=0, *args, **kwargs):
+    """Configurable Conv block with Batchnorm and Dropout"""
+    return nn.Sequential(
+        nn.Conv3d(*args, **kwargs),
+        nn.BatchNorm3d(kwargs["out_channels"]),
+        nn.ReLU(inplace=True),
+        nn.Dropout3d(dropout_p),
+    )
+
+
+def init_weights(model):
+    """Set weights to be xavier normal for all Convs"""
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d, nn.ConvTranspose3d)):
+            nn.init.xavier_normal_(m.weight, gain=nn.init.calculate_gain("relu"))
+            nn.init.constant_(m.bias, 0.0)
+
+
+class MeshNet(nn.Module):
+    """Configurable MeshNet from https://arxiv.org/pdf/1612.00940.pdf"""
+
+    def __init__(self, n_channels, n_classes, large=True, dropout_p=0):
+        """Init"""
+        if large:
+            params = MeshNet_68_kwargs
+        else:
+            params = MeshNet_38_or_64_kwargs
+
+        super(MeshNet, self).__init__()
+        params[0]["in_channels"] = n_channels
+        params[-1]["out_channels"] = n_classes
+        layers = [
+            conv_w_bn_before_act(dropout_p=dropout_p, **block_kwargs)
+            for block_kwargs in params[:-1]
+        ]
+        layers.append(nn.Conv3d(**params[-1]))
+        self.model = nn.Sequential(*layers)
+        init_weights(self.model,)
+
+    def forward(self, x):
+        """Forward pass"""
+        x = self.model(x)
+        return x