Optimize examples for better performance (#59)

* zeros -> emtpy in cuda

* disable cuda hint if is been built

* update examples with better perf

* bump version to 0.2.0

* update readme

* update index and readm

* clean up doc

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "nerfacc"
-version = "0.1.8"
+version = "0.2.0"
 description = "A General NeRF Acceleration Toolbox."
 readme = "README.md"
 authors = [{name = "Ruilong", email = "ruilongli94@gmail.com"}]

scripts/run_profiler.py

+from typing import Callable
+
+import torch
+
+import nerfacc
+
+
+class Profiler:
+    def __init__(self, warmup=10, repeat=1000):
+        self.warmup = warmup
+        self.repeat = repeat
+
+    def __call__(self, func: Callable):
+        # warmup
+        for _ in range(self.warmup):
+            func()
+        torch.cuda.synchronize()
+
+        # profile
+        with torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+            profile_memory=True,
+        ) as prof:
+            for _ in range(self.repeat):
+                func()
+            torch.cuda.synchronize()
+
+        # return
+        events = prof.key_averages()
+        self_cpu_time_total = (
+            sum([event.self_cpu_time_total for event in events]) / self.repeat
+        )
+        self_cuda_time_total = (
+            sum([event.self_cuda_time_total for event in events]) / self.repeat
+        )
+        self_cuda_memory_usage = max(
+            [event.self_cuda_memory_usage for event in events]
+        )
+        return (
+            self_cpu_time_total,  # in us
+            self_cuda_time_total,  # in us
+            self_cuda_memory_usage,  # in bytes
+        )
+
+
+def main():
+    device = "cuda:0"
+    torch.manual_seed(42)
+    profiler = Profiler(warmup=10, repeat=1000)
+
+    # contract
+    print("* contract")
+    x = torch.rand([1024, 3], device=device)
+    roi = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.float32, device=device)
+    fn = lambda: nerfacc.contract(
+        x, roi=roi, type=nerfacc.ContractionType.UN_BOUNDED_TANH
+    )
+    cpu_t, cuda_t, cuda_bytes = profiler(fn)
+    print(f"{cpu_t:.2f} us, {cuda_t:.2f} us, {cuda_bytes / 1024 / 1024:.2f} MB")
+
+
+if __name__ == "__main__":
+    main()