sync

msl_net.py

+from typing import List
+import torch
+import torch.nn as nn
+from .pytorch_prototyping.pytorch_prototyping import *
+from .my import util
+from .my import device
+
+
+class FcNet(nn.Module):
+
+    def __init__(self, in_chns, out_chns, nf, n_layers):
+        super().__init__()
+        self.layers = list()
+        self.layers.append(nn.Linear(in_chns, nf))
+        self.layers.append(nn.LeakyReLU())
+        for _ in range(1, n_layers):
+            self.layers.append(nn.Linear(nf, nf))
+            self.layers.append(nn.LeakyReLU())
+        self.layers.append(nn.Linear(nf, out_chns))
+        self.net = nn.Sequential(*self.layers)
+
+    def forward(self, x):
+        return self.net(x)
+
+class Rendering(nn.Module):
+
+    def __init__(self, n_sphere_layers):
+        super().__init__()
+        self.n_sl = n_sphere_layers
+
+    def forward(self, net, pos, dir):
+        """
+        [summary]
+
+        :param pos: B x 3, position of a ray
+        :param dir: B x 3, direction of a ray
+        """
+        
+
+class MslNet(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):

run_lf_syn.py

@@ -27,7 +27,7 @@ BATCH_SIZE = 8
 TEST_BATCH_SIZE = 10
 NUM_EPOCH = 1000
 MODE = "Silence"  # "Perf"
-EPOCH_BEGIN = 0
+EPOCH_BEGIN = 600
 
 
 def train():
@@ -58,7 +58,7 @@ def train():
     # 3. Train
     model.train()
     epoch = EPOCH_BEGIN
-    iters = EPOCH_BEGIN * len(train_data_loader)
+    iters = EPOCH_BEGIN * len(train_data_loader) * BATCH_SIZE
 
     util.CreateDirIfNeed(RUN_DIR)