update

nerf/network.py

@@ -0,0 +1,68 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from encoding import get_encoder
+
+
+class NeRFNetwork(nn.Module):
+ def __init__(self,
+ encoding="hashgrid",
+ encoding_view="frequency",
+ num_layers=3,
+ skips=[],
+ hidden_dim=64,
+ clip_sdf=None,
+ ):
+ super().__init__()
+
+ self.num_layers = num_layers
+ self.skips = skips
+ self.hidden_dim = hidden_dim
+ self.clip_sdf = clip_sdf
+
+ self.encoder, self.in_dim = get_encoder(encoding)
+
+ backbone = []
+
+ for l in range(num_layers):
+ if l == 0:
+ in_dim = self.in_dim
+ elif l in self.skips:
+ in_dim = self.hidden_dim + self.in_dim
+ else:
+ in_dim = self.hidden_dim
+
+ if l == num_layers - 1:
+ out_dim = 1
+ else:
+ out_dim = self.hidden_dim
+
+ backbone.append(nn.Linear(in_dim, out_dim, bias=False))
+
+ self.backbone = nn.ModuleList(backbone)
+
+
+ def forward(self, x):
+ # x: [B, 3]
+
+ #print('forward: x', x.shape, x.min().item(), x.max().item())
+
+ x = self.encoder(x)
+
+ #print('forward: enc(x)', x.shape, x.min().item(), x.max().item())
+
+ h = x
+ for l in range(self.num_layers):
+ if l in self.skips:
+ h = torch.cat([h, x], dim=-1)
+ h = self.backbone[l](h)
+ if l != self.num_layers - 1:
+ h = F.relu(h, inplace=True)
+
+ if self.clip_sdf is not None:
+ h = h.clamp(-self.clip_sdf, self.clip_sdf)
+
+ #print('forward: y', h.shape, h.min().item(), h.max().item())
+
+ return h

nerf/provider.py

@@ -0,0 +1,46 @@
+import os
+import time
+import glob
+import numpy as np
+
+import cv2
+from PIL import Image
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+# NeRF dataset
+import json
+
+
+class NeRFDataset(Dataset):
+ def __init__(self, path):
+ super().__init__()
+
+ self.path = path
+
+ # load cameras
+ transform_path = os.path.join(self.path, 'transforms.json')
+ with open(transform_path, 'r') as f:
+ transform = json.load(f)
+
+ self.images = []
+ self.cameras = []
+ self.intrinsics = []
+
+
+
+
+
+ def __len__(self):
+ return len(self.images)
+
+ def __getitem__(self, index):
+
+
+
+ results = {
+
+ }
+
+ return results