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import torch.nn as nn
import torch.nn.functional as F
def mlp(
input_dim,
hidden_dim,
output_dim,
hidden_depth,
output_mod=None,
batchnorm=False,
activation=nn.ReLU,
):
if hidden_depth == 0:
mods = [nn.Linear(input_dim, output_dim)]
else:
mods = (
[nn.Linear(input_dim, hidden_dim), activation(inplace=True)]
if not batchnorm
else [
nn.Linear(input_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
activation(inplace=True),
]
)
for _ in range(hidden_depth - 1):
mods += (
[nn.Linear(hidden_dim, hidden_dim), activation(inplace=True)]
if not batchnorm
else [
nn.Linear(hidden_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
activation(inplace=True),
]
)
mods.append(nn.Linear(hidden_dim, output_dim))
if output_mod is not None:
mods.append(output_mod)
trunk = nn.Sequential(*mods)
return trunk
def weight_init(m):
"""Custom weight init for Conv2D and Linear layers."""
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
if hasattr(m.bias, "data"):
m.bias.data.fill_(0.0)
class MLP(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
output_dim,
hidden_depth,
output_mod=None,
batchnorm=False,
activation=nn.ReLU,
):
super().__init__()
self.trunk = mlp(
input_dim,
hidden_dim,
output_dim,
hidden_depth,
output_mod,
batchnorm=batchnorm,
activation=activation,
)
self.apply(weight_init)
def forward(self, x):
return self.trunk(x)
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