Support manually set val_incides and train_indices

Author: haodongw101

configs/actorshq.toml

@@ -16,6 +16,9 @@ scene_id = 0
 resolution = 4 # Resolution of the actorshq dataset (1, 2, 4)
 data_factor = 1  # Downsample factor for the dataset. ActorsHQ dataset is already downsampled with correct intrinsics.
 test_every = 8  # Every N images is a test image
+# val_indices = [0, 8, 16, 24]    # Manual validation view indices (overrides test_every)
+# train_indices = [1, 2, 3, 5, 7]  # Manual training view indices (overrides test_every)
+
 # patch_size = null  # Random crop size for training (experimental)
 # global_scale = 1.0  # Global scale factor for scene size
 # normalize_world_space = true  # Normalize the world space

examples/config.py

@@ -57,6 +57,10 @@ class Config:
     result_dir: str = "results/garden"
     # Every N images there is a test image
     test_every: int = 8
+    # Manual validation view indices (overrides test_every)
+    val_indices: Optional[List[int]] = None
+    # Manual training view indices (overrides test_every)
+    train_indices: Optional[List[int]] = None
     # Random crop size for training  (experimental)
     patch_size: Optional[int] = None
     # A global scaler that applies to the scene size related parameters

examples/datasets/colmap.py

@@ -68,6 +68,8 @@ def __init__(
         factor: int = 1,
         normalize: bool = False,
         test_every: int = 8,
+        val_indices: Optional[List[int]] = None,
+        train_indices: Optional[List[int]] = None,
     ):
         self.data_dir = data_dir
         self.factor = factor
@@ -271,11 +273,19 @@ def __init__(
         self.point_indices = point_indices  # Dict[str, np.ndarray], image_name -> [M,]
         self.transform = transform  # np.ndarray, (4, 4)
 
-        # Randomly select validation indices to avoid systematic bias in selecting validation cameras
+        # Determine train/val split
         indices = np.arange(len(self.image_names))
-        num_val = len(self.image_names) // self.test_every
-        self.val_indices = np.random.choice(indices, size=num_val, replace=False)
-        print(f"Randomly selected {num_val} validation cameras to avoid systematic bias")
+        if val_indices is not None:
+            self.val_indices = np.array(val_indices, dtype=np.int64)
+            print(f"[Parser] Using {len(self.val_indices)} manual validation indices: {sorted(self.val_indices.tolist())}")
+        elif train_indices is not None:
+            train_set = set(train_indices)
+            self.val_indices = np.array([i for i in indices if i not in train_set], dtype=np.int64)
+            print(f"[Parser] Using {len(train_indices)} manual training indices; {len(self.val_indices)} views assigned to validation")
+        else:
+            num_val = len(self.image_names) // self.test_every
+            self.val_indices = np.random.choice(indices, size=num_val, replace=False)
+            print(f"[Parser] Randomly selected {num_val} validation cameras (test_every={self.test_every})")
 
         # load one image to check the size. In the case of tanksandtemples dataset, the
         # intrinsics stored in COLMAP corresponds to 2x upsampled images.

examples/simple_trainer.py

@@ -199,6 +199,8 @@ def __init__(
             factor=cfg.data_factor,
             normalize=cfg.normalize_world_space,
             test_every=cfg.test_every,
+            val_indices=cfg.val_indices,
+            train_indices=cfg.train_indices,
         )
         self.trainset = Dataset(
             self.parser,

scripts/batch_run.py

@@ -8,8 +8,8 @@
 import subprocess
 import os
 import itertools
-from dataclasses import dataclass
-from typing import List, Tuple
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple
 from concurrent.futures import ProcessPoolExecutor, as_completed
 
 
@@ -26,14 +26,18 @@
         "frame_ids": [1],
         "resolution": 4,
         "config_path": "./configs/actorshq.toml",
+        "test_every": 8,
     },
     "neural3d": {
         "base_data_dir": "/synology/Neural_3D_Video",
         "sequences": ["coffee_martini", "cook_spinach", "cut_roasted_beef",
-                      # "flame_salmon_1", "flame_steak", "sear_steak"
+                      "flame_salmon_1", "flame_steak", "sear_steak"
                       ],
         "frame_ids": [0],
         "config_path": "./configs/actorshq.toml",
+        # "test_every": 8,
+        "val_indices": [0, 20],
+        "train_indices": [i for i in range(0, 21) if i not in [0, 20]],
     },
 }
 # =========================================================
@@ -50,6 +54,9 @@ class JobConfig:
     config_path: str = "./configs/actorshq.toml"
     run_script_path: str = ""
     root_run_path: str = ""
+    test_every: Optional[int] = None
+    val_indices: Optional[List[int]] = None
+    train_indices: Optional[List[int]] = None
 
 
 def build_data_dir_actorshq(cfg: dict, actor: str, sequence: str, frame_id: int) -> str:
@@ -70,6 +77,9 @@ def create_jobs_actorshq(cfg: dict, method: str, cuda_devices: List[str],
     sequences = cfg.get("sequences", [])
     frame_ids = cfg.get("frame_ids", [])
     config_path = cfg.get("config_path", "./configs/actorshq.toml")
+    test_every = cfg.get("test_every", None)
+    val_indices = cfg.get("val_indices", None)
+    train_indices = cfg.get("train_indices", None)
 
     all_combinations = list(itertools.product(actors, sequences, frame_ids))
     num_gpus = len(cuda_devices)
@@ -91,6 +101,9 @@ def create_jobs_actorshq(cfg: dict, method: str, cuda_devices: List[str],
             config_path=config_path,
             run_script_path=run_script_path,
             root_run_path=root_run_path,
+            test_every=test_every,
+            val_indices=val_indices,
+            train_indices=train_indices,
         )
         jobs.append(job)
 
@@ -103,6 +116,9 @@ def create_jobs_neural3d(cfg: dict, method: str, cuda_devices: List[str],
     sequences = cfg.get("sequences", [])
     frame_ids = cfg.get("frame_ids", [])
     config_path = cfg.get("config_path", "./configs/actorshq.toml")
+    test_every = cfg.get("test_every", None)
+    val_indices = cfg.get("val_indices", None)
+    train_indices = cfg.get("train_indices", None)
 
     all_combinations = list(itertools.product(sequences, frame_ids))
     num_gpus = len(cuda_devices)
@@ -124,6 +140,9 @@ def create_jobs_neural3d(cfg: dict, method: str, cuda_devices: List[str],
             config_path=config_path,
             run_script_path=run_script_path,
             root_run_path=root_run_path,
+            test_every=test_every,
+            val_indices=val_indices,
+            train_indices=train_indices,
         )
         jobs.append(job)
 
@@ -158,6 +177,12 @@ def run_single_experiment(config: JobConfig) -> int:
         "--config", config.config_path,
         "--disable_viewer",
     ]
+    if config.test_every is not None:
+        cmd.extend(["--test_every", str(config.test_every)])
+    if config.val_indices is not None:
+        cmd.extend(["--val_indices", ",".join(str(i) for i in config.val_indices)])
+    if config.train_indices is not None:
+        cmd.extend(["--train_indices", ",".join(str(i) for i in config.train_indices)])
 
     result = subprocess.run(cmd, env=env, cwd=config.root_run_path)
     return result.returncode

scripts/run_actorshq.py

@@ -27,6 +27,12 @@ def parse_args():
                         help="Path to config file")
     parser.add_argument("--disable_viewer", action="store_true",
                         help="Disable the viewer")
+    parser.add_argument("--test_every", type=int, default=None,
+                        help="Every N images is a test image (overrides config)")
+    parser.add_argument("--val_indices", type=str, default=None,
+                        help="Comma-separated validation view indices (overrides test_every)")
+    parser.add_argument("--train_indices", type=str, default=None,
+                        help="Comma-separated training view indices (overrides test_every)")
     return parser.parse_args()
 
 def run_experiment(config: Config, dist=False):
@@ -149,6 +155,14 @@ def build_exp_name(cfg: Config, prefix: str = "actorshq") -> str:
     # Build experiment name
     exp_name = build_exp_name(cfg, args.exp_name_prefix)
 
+    # Override test_every / val/train indices if provided via CLI
+    if args.test_every is not None:
+        cfg.test_every = args.test_every
+    if args.val_indices is not None:
+        cfg.val_indices = [int(x) for x in args.val_indices.split(",")]
+    if args.train_indices is not None:
+        cfg.train_indices = [int(x) for x in args.train_indices.split(",")]
+
     # Set viewer
     cfg.disable_viewer = args.disable_viewer