Upload model/archs/mlp_head.py with huggingface_hub

model/archs/mlp_head.py

@@ -1,40 +1,40 @@
-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
-
+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
+