add videoprism example

src/lerobot/policies/videovla/videoprism/test.py

@@ -0,0 +1,50 @@
+import torch
+import numpy as np
+from torchcodec.decoders import VideoDecoder
+
+from lerobot.policies.videovla.videoprism import VideoPrismVideoProcessor
+from lerobot.policies.videovla.videoprism import VideoPrismVisionModel
+processor = VideoPrismVideoProcessor.from_pretrained(
+    "MHRDYN7/videoprism-base-f16r288"
+)
+
+model = VideoPrismVisionModel.from_pretrained(
+    "MHRDYN7/videoprism-base-f16r288",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa",
+)
+
+video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"
+
+vr = VideoDecoder(video_url)
+frame_idx = np.arange(0, 64)
+video = vr.get_frames_at(indices=frame_idx).data  # T x C x H x W
+
+video = processor(video, return_tensors="pt")
+video = {k: v.to(model.device, model.dtype) for k, v in video.items()}
+outputs = model(**video)
+encoder_outputs = outputs.last_hidden_state
+print(encoder_outputs.shape) # 
+
+import time
+import torch
+
+# warmup
+for _ in range(10):
+    _ = model(**video)
+
+times = []
+for _ in range(50):
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    _ = model(**video)
+
+    torch.cuda.synchronize()
+    t1 = time.perf_counter()
+    times.append(t1 - t0)
+
+print(f"Mean: {1000*sum(times)/len(times):.2f} ms")
+print(f"Min : {1000*min(times):.2f} ms")
+print(f"Max : {1000*max(times):.2f} ms")