| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| class SdfMlp(nn.Module): |
| def __init__(self, input_dim, hidden_dim=512, bias=True): |
| super().__init__() |
| self.input_dim = input_dim |
| self.hidden_dim = hidden_dim |
|
|
| self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) |
| self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias) |
| self.fc3 = nn.Linear(hidden_dim, 4, bias=bias) |
|
|
|
|
| def forward(self, input): |
| x = F.relu(self.fc1(input)) |
| x = F.relu(self.fc2(x)) |
| out = self.fc3(x) |
| return out |
|
|
|
|
| class RgbMlp(nn.Module): |
| def __init__(self, input_dim, hidden_dim=512, bias=True): |
| super().__init__() |
| self.input_dim = input_dim |
| self.hidden_dim = hidden_dim |
|
|
| self.fc1 = nn.Linear(input_dim, hidden_dim, bias=bias) |
| self.fc2 = nn.Linear(hidden_dim, hidden_dim, bias=bias) |
| self.fc3 = nn.Linear(hidden_dim, 3, bias=bias) |
|
|
| def forward(self, input): |
| x = F.relu(self.fc1(input)) |
| x = F.relu(self.fc2(x)) |
| out = self.fc3(x) |
|
|
| return out |
|
|
| |
