nbrdf mlp model source code

nbrdf-release.py

@@ -0,0 +1,59 @@
+'''
+    NBRDF MLP model
+    - input_size   3
+    - hidden_size  21
+    - hidden_layer 3
+    - output_size  3
+
+    @author
+    Copyright (c) 2024-2025 Peter HU.
+
+    @file
+    reference: https://github.com/asztr/Neural-BRDF
+    
+'''
+# --- built in ---
+import sys
+import path
+# --- 3rd party ---
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import random
+
+# --- related module ---
+device = torch.device(
+    "cuda" if torch.cuda.is_available()
+    else torch.device("mps") if torch.backends.mps.is_available()
+    else "cpu")
+
+
+class MLP(nn.Module):
+    '''Pytorch NBRDF MLP model'''
+    def __init__(self, input_size, hidden_size, output_size) -> None:
+        super().__init__()
+        # Initialize separately
+        self.fc1 = nn.Linear(input_size, hidden_size, bias=True)
+        self.fc2 = nn.Linear(hidden_size, hidden_size, bias=True)
+        self.fc3 = nn.Linear(hidden_size, output_size, bias=True)
+        # initialize the weight
+
+        # Reproducibility for generation purpose
+        torch.manual_seed(0)
+        random.seed(0)
+        with torch.no_grad():
+            for func in [self.fc1, self.fc2, self.fc3]:
+                func.bias.zero_()
+                func.weight.uniform_(0.0, 0.02)
+
+
+    def forward(self, x):        
+        out = self.fc1(x)
+        out = F.leaky_relu(out)
+        out = self.fc2(out)
+        out = F.leaky_relu(out)
+        out = self.fc3(out)
+        out = F.relu(torch.exp(out) - 1.0)
+
+        return out