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import os
from typing import Dict, List
import numpy as np
import torch
import torch.distributed as dist
import yaml
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs, set_seed
from safetensors.torch import load_file
from tqdm.auto import tqdm
from dataloader import create_inference_loader
from models.model import CaFNet
DEFAULT_CONFIG = {
# Packaged evaluation dataset.
"base_dir": "",
"split_json": None,
"test_base_dir": None,
"test_split": "train",
"test_split_json": None,
# Input and radar processing
"input_height": 288,
"input_width": 512,
"radar_max_depth_m": 11.2,
"max_dist_correspondence": 0.5,
"patch_size": None,
# Model
"encoder": "resnet34_bts",
"encoder_radar": "resnet18",
"radar_input_channels": 1,
"bts_size": 512,
"max_depth": 11.2,
# Runtime
"batch_size": 8,
# Windows uses spawn-based multiprocessing; keep the public evaluation
# entry point portable and deterministic by default.
"num_workers": 0,
"seed": 42,
"cpu": False,
"mixed_precision": "fp16",
"checkpoint_path": "checkpoints/cafnet.safetensors",
"prediction_dir": "prediction",
}
def parse_args():
parser = argparse.ArgumentParser(description="Run CaFNet inference on Smoke-Eval.")
parser.add_argument("--config", type=str, required=True, help="Path to YAML config")
return parser.parse_args()
def load_config(path):
with open(path, "r") as f:
cfg = yaml.safe_load(f) or {}
if not isinstance(cfg, dict):
raise ValueError("Config must be a YAML mapping (key-value pairs).")
merged = dict(DEFAULT_CONFIG)
merged.update(cfg)
if not merged["test_base_dir"]:
raise ValueError("Config must define 'test_base_dir'.")
if not merged["checkpoint_path"]:
raise ValueError("Config must define 'checkpoint_path'.")
if not os.path.isfile(merged["checkpoint_path"]):
raise FileNotFoundError(f"Checkpoint not found: {merged['checkpoint_path']}")
if merged.get("radar_input_channels", 1) != 1:
raise ValueError("radar_input_channels must be 1 for this setup.")
return argparse.Namespace(**merged)
def build_model_args(args):
return argparse.Namespace(
encoder=args.encoder,
encoder_radar=args.encoder_radar,
radar_input_channels=args.radar_input_channels,
input_height=args.input_height,
input_width=args.input_width,
max_depth=args.max_depth,
bts_size=args.bts_size,
)
def _extract_model_state(checkpoint):
if isinstance(checkpoint, dict) and isinstance(checkpoint.get("model"), dict):
return checkpoint["model"]
if isinstance(checkpoint, dict):
return checkpoint
raise ValueError("Unsupported checkpoint format.")
def _gather_objects(accelerator, obj):
if accelerator.num_processes == 1:
return [obj]
if not dist.is_available() or not dist.is_initialized():
return [obj]
gathered = [None for _ in range(accelerator.num_processes)]
dist.all_gather_object(gathered, obj)
return gathered
def _merge_predictions(all_rank_predictions):
merged: Dict[str, Dict[int, np.ndarray]] = {}
for rank_dict in all_rank_predictions:
if not rank_dict:
continue
for seq_name, frame_map in rank_dict.items():
seq_slot = merged.setdefault(seq_name, {})
for frame_idx, pred in frame_map.items():
frame_idx = int(frame_idx)
if frame_idx not in seq_slot:
seq_slot[frame_idx] = pred
return merged
def _save_sequence_predictions(predictions, out_dir):
os.makedirs(out_dir, exist_ok=True)
for seq_name in sorted(predictions.keys()):
frame_map = predictions[seq_name]
ordered_frames = sorted(frame_map.keys())
if not ordered_frames:
pred_stack = np.zeros((0,), dtype=np.float32)
else:
pred_stack = np.stack([frame_map[k] for k in ordered_frames], axis=0).astype(
np.float32,
copy=False,
)
np.save(os.path.join(out_dir, f"{seq_name.lower()}_pred.npy"), pred_stack)
def _run_loader_inference(accelerator, model, loader, samples, save_dir, desc):
model.eval()
local_preds: Dict[str, Dict[int, np.ndarray]] = {}
with torch.no_grad():
pbar = tqdm(
loader,
desc=desc,
disable=not accelerator.is_local_main_process,
dynamic_ncols=True,
leave=False,
)
for batch in pbar:
sample_idx, image, depth_gt, radar, radar_gt = batch
image = image.to(accelerator.device, non_blocking=True)
radar = radar.to(accelerator.device, non_blocking=True)
# Kept for parity with validation loop structure.
_ = depth_gt.to(accelerator.device, non_blocking=True)
_ = radar_gt.to(accelerator.device, non_blocking=True)
focal = torch.ones((image.size(0),), device=image.device)
_, _, _, _, depth_est, _, _ = model(image, radar, focal)
pred_np = depth_est.detach().float().cpu().numpy()
if pred_np.ndim == 4 and pred_np.shape[1] == 1:
pred_np = pred_np[:, 0]
if torch.is_tensor(sample_idx):
sample_idx_list = sample_idx.detach().cpu().tolist()
else:
sample_idx_list = list(sample_idx)
for local_i, sample_i in enumerate(sample_idx_list):
seq_name, frame_idx = samples[int(sample_i)]
seq_slot = local_preds.setdefault(seq_name, {})
frame_idx = int(frame_idx)
if frame_idx not in seq_slot:
seq_slot[frame_idx] = pred_np[local_i].astype(np.float32, copy=False)
gathered = _gather_objects(accelerator, local_preds)
if accelerator.is_main_process:
merged = _merge_predictions(gathered)
_save_sequence_predictions(merged, save_dir)
accelerator.wait_for_everyone()
def main():
cli = parse_args()
args = load_config(cli.config)
set_seed(args.seed)
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
mixed_precision=None if args.mixed_precision in ("no", "none") else args.mixed_precision,
cpu=args.cpu,
kwargs_handlers=[ddp_kwargs],
)
test_loader = create_inference_loader(
args,
pin_memory=(accelerator.device.type == "cuda"),
)
test_samples: List = test_loader.dataset.samples
model = CaFNet(build_model_args(args))
model, test_loader = accelerator.prepare(model, test_loader)
state_dict = load_file(args.checkpoint_path, device="cpu")
accelerator.unwrap_model(model).load_state_dict(state_dict, strict=True)
_run_loader_inference(
accelerator=accelerator,
model=model,
loader=test_loader,
samples=test_samples,
save_dir=args.prediction_dir,
desc="Inference",
)
if accelerator.is_main_process:
print(f"Saved predictions to: {args.prediction_dir}")
if __name__ == "__main__":
main()
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