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Convert input frames to float on GPU #300

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Merged
yondonfu merged 1 commit into from
Jan 2, 2026
Merged

Convert input frames to float on GPU #300

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4 changes: 2 additions & 2 deletions src/scope/core/pipelines/process.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,8 @@ def preprocess_chunk(
frames = []

for frame in chunk:
# Move to pipeline device
frame = frame.to(device=device, dtype=dtype)
# Move to pipeline device first (likely as uint8), then convert dtype on device
frame = frame.to(device=device).to(dtype=dtype)
frame = rearrange(frame, "T H W C -> T C H W")

_, _, H, W = frame.shape
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11 changes: 5 additions & 6 deletions src/scope/server/frame_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -836,15 +836,14 @@ def prepare_chunk(self, chunk_size: int) -> list[torch.Tensor]:
for _ in range(last_idx + 1):
self.frame_buffer.popleft()

# Convert VideoFrames to tensors
# Convert VideoFrames to tensors (keep as uint8, GPU will handle dtype conversion)
tensor_frames = []
for video_frame in video_frames:
# Convert VideoFrame into (1, H, W, C) tensor on cpu
# Convert VideoFrame into (1, H, W, C) uint8 tensor on cpu
# The T=1 dimension is expected by preprocess_chunk which rearranges T H W C -> T C H W
tensor = (
torch.from_numpy(video_frame.to_ndarray(format="rgb24"))
.float()
.unsqueeze(0)
# Note: We keep uint8 here and let pipeline preprocess chunk to target dtype on GPU
tensor = torch.from_numpy(video_frame.to_ndarray(format="rgb24")).unsqueeze(
0
)
tensor_frames.append(tensor)

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