Minor bugfixes

Signed-off-by: Krishna Murthy <[email protected]>

cache_dataset.py

@@ -10,6 +10,8 @@
 import torch
 from tqdm import tqdm
 
+from load_blender import load_blender_data
+from load_llff import load_llff_data
 from nerf_helpers import get_ray_bundle, meshgrid_xy
 
 

config/lego.yml

@@ -1,13 +1,13 @@
 # Parameters to setup experiment.
 experiment:
   # Unique experiment identifier
-  id: lego
+  id: lego-lowres
   # Experiment logs will be stored at "logdir"/"id"
   logdir: logs
   # Seed for random number generators (for repeatability).
   randomseed: 42  # Cause, why not?
   # Number of training iterations.
-  train_iters: 250000
+  train_iters: 200000
   # Number of training iterations after which to validate.
   validate_every: 100
   # Number of training iterations after which to checkpoint.
@@ -23,12 +23,12 @@ dataset:
   basedir: cache/nerf_synthetic/lego
   # Optionally, provide a path to the pre-cached dataset dir. This
   # overrides the other dataset options.
-  cachedir: cache/legocache/legofull
+  cachedir: cache/legocache200
   # For the Blender datasets (synthetic), optionally return images
   # at half the original resolution of 800 x 800, to save space.
   half_res: True
   # Stride (include one per "testskip" images in the dataset).
-  testskip: 10
+  testskip: 1
   # Do not use NDC (normalized device coordinates). Usually True for
   # synthetic (Blender) datasets.
   no_ndc: True
@@ -116,7 +116,7 @@ nerf:
   train:
     # Number of random rays to retain from each image.
     # These sampled rays are used for training, and the others are discarded.
-    num_random_rays: 8192  # 32 * 32 * 4
+    num_random_rays: 1024  # 32 * 32 * 4
     # Size of each chunk (rays are batched into "chunks" and passed through
     # the network)
     chunksize: 131072  # 131072  # 1024 * 32

train_nerf.py

@@ -168,17 +168,21 @@ def encode_direction_fn(x):
     with open(os.path.join(logdir, "config.yml"), "w") as f:
         f.write(cfg.dump())  # cfg, f, default_flow_style=False)
 
+    # By default, start at iteration 0 (unless a checkpoint is specified).
+    start_iter = 0
+
     # Load an existing checkpoint, if a path is specified.
     if os.path.exists(configargs.load_checkpoint):
         checkpoint = torch.load(configargs.load_checkpoint)
         model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
         if checkpoint["model_fine_state_dict"]:
             model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
-        # optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+        optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+        start_iter = checkpoint["iter"]
 
     # # TODO: Prepare raybatch tensor if batching random rays
 
-    for i in trange(cfg.experiment.train_iters):
+    for i in trange(start_iter, cfg.experiment.train_iters):
 
         model_coarse.train()
         if model_fine: