improve non-cuda-ray mode

main.py

@@ -28,8 +28,8 @@
     parser.add_argument('--ckpt', type=str, default='latest')
     parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch")
     parser.add_argument('--max_steps', type=int, default=1024, help="max num steps sampled per ray (only valid when using --cuda_ray)")
-    parser.add_argument('--num_steps', type=int, default=128, help="num steps sampled per ray (only valid when not using --cuda_ray)")
-    parser.add_argument('--upsample_steps', type=int, default=0, help="num steps up-sampled per ray (only valid when not using --cuda_ray)")
+    parser.add_argument('--num_steps', type=int, default=64, help="num steps sampled per ray (only valid when not using --cuda_ray)")
+    parser.add_argument('--upsample_steps', type=int, default=64, help="num steps up-sampled per ray (only valid when not using --cuda_ray)")
     parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)")
     parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when not using --cuda_ray)")
     parser.add_argument('--albedo_iters', type=int, default=15000, help="training iters that only use albedo shading")
@@ -40,8 +40,8 @@
     parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
     parser.add_argument('--backbone', type=str, default='grid', help="nerf backbone, choose from [grid, tcnn, vanilla]")
     # rendering resolution in training, decrease this if CUDA OOM.
-    parser.add_argument('--w', type=int, default=128, help="render width for NeRF in training")
-    parser.add_argument('--h', type=int, default=128, help="render height for NeRF in training")
+    parser.add_argument('--w', type=int, default=64, help="render width for NeRF in training")
+    parser.add_argument('--h', type=int, default=64, help="render height for NeRF in training")
     parser.add_argument('--jitter_pose', action='store_true', help="add jitters to the randomly sampled camera poses")
 
     ### dataset options
@@ -55,6 +55,7 @@
     parser.add_argument('--angle_front', type=float, default=30, help="[0, angle_front] is the front region, [180, 180+angle_front] the back region, otherwise the side region.")
 
     parser.add_argument('--lambda_entropy', type=float, default=1e-4, help="loss scale for alpha entropy")
+    parser.add_argument('--lambda_opacity', type=float, default=0, help="loss scale for alpha value")
     parser.add_argument('--lambda_orient', type=float, default=1e-2, help="loss scale for orientation")
 
     ### GUI options
@@ -72,10 +73,16 @@
     if opt.O:
         opt.fp16 = True
         opt.dir_text = True
+        # use occupancy grid to prune ray sampling, faster rendering.
         opt.cuda_ray = True
+        opt.lambda_entropy = 1e-4
+        opt.lambda_opacity = 0
+
     elif opt.O2:
         opt.fp16 = True
         opt.dir_text = True
+        opt.lambda_entropy = 1e-3
+        opt.lambda_opacity = 1e-3 # no occupancy grid, so use a stronger opacity loss.
 
     if opt.backbone == 'vanilla':
         from nerf.network import NeRFNetwork

nerf/sd.py

@@ -20,7 +20,7 @@ def __init__(self, device):
                 print(f'[INFO] loaded hugging face access token from ./TOKEN!')
         except FileNotFoundError as e:
             self.token = True
-            print(f'[INFO] try to load hugging face access token from the default plase, make sure you have run `huggingface-cli login`.')
+            print(f'[INFO] try to load hugging face access token from the default place, make sure you have run `huggingface-cli login`.')
 
         self.device = device
         self.num_train_timesteps = 1000

nerf/utils.py

@@ -330,11 +330,11 @@ def train_step(self, data):
             if rand > 0.8: 
                 shading = 'albedo'
                 ambient_ratio = 1.0
-            elif rand > 0.4: 
-                shading = 'lambertian'
-                ambient_ratio = 0.1
+            # elif rand > 0.4: 
+            #     shading = 'textureless'
+            #     ambient_ratio = 0.1
             else: 
-                shading = 'textureless'
+                shading = 'lambertian'
                 ambient_ratio = 0.1
 
         # _t = time.time()
@@ -355,22 +355,24 @@ def train_step(self, data):
 
         # encode pred_rgb to latents
         # _t = time.time()
-        loss_guidance = self.guidance.train_step(text_z, pred_rgb)
+        loss = self.guidance.train_step(text_z, pred_rgb)
         # torch.cuda.synchronize(); print(f'[TIME] total guiding {time.time() - _t:.4f}s')
 
         # occupancy loss
         pred_ws = outputs['weights_sum'].reshape(B, 1, H, W)
-        # mask_ws = outputs['mask'].reshape(B, 1, H, W) # near < far
 
-        # loss_ws = (pred_ws ** 2 + 0.01).sqrt().mean()
+        if self.opt.lambda_opacity > 0:
+            loss_opacity = (pred_ws ** 2).mean()
+            loss = loss + self.opt.lambda_opacity * loss_opacity
 
-        alphas = (pred_ws).clamp(1e-5, 1 - 1e-5)
-        # alphas = alphas ** 2 # skewed entropy, favors 0 over 1
-        loss_entropy = (- alphas * torch.log2(alphas) - (1 - alphas) * torch.log2(1 - alphas)).mean()
-
-        loss = loss_guidance + self.opt.lambda_entropy * loss_entropy
+        if self.opt.lambda_entropy > 0:
+            alphas = (pred_ws).clamp(1e-5, 1 - 1e-5)
+            # alphas = alphas ** 2 # skewed entropy, favors 0 over 1
+            loss_entropy = (- alphas * torch.log2(alphas) - (1 - alphas) * torch.log2(1 - alphas)).mean()
+
+            loss = loss + self.opt.lambda_entropy * loss_entropy
 
-        if 'loss_orient' in outputs:
+        if self.opt.lambda_orient > 0 and 'loss_orient' in outputs:
             loss_orient = outputs['loss_orient']
             loss = loss + self.opt.lambda_orient * loss_orient