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fix: unpack video_metadata from tuples and pass separately to processor

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The Qwen3.5 processor requires video_metadata as a separate parameter,
not embedded in the video tensors. Use return_video_metadata=True from
process_vision_info, then unpack the (tensor, metadata) tuples into
separate videos and video_metadata lists for the processor call.

Made-with: Cursor
This commit is contained in:
Pepijn
2026-03-30 17:37:59 +02:00
parent 72692525da
commit 5f85b572d7
1 changed files with 41 additions and 33 deletions
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src/lerobot/data_processing/data_annotations/vlm_annotations.py
+41 -33
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@@ -87,6 +87,17 @@ class BaseVLM(ABC):
pass
def _unpack_video_inputs(
video_inputs: list | None,
) -> tuple[list | None, list[dict] | None]:
"""Unpack (tensor, metadata) tuples returned by process_vision_info with return_video_metadata=True."""
if not video_inputs:
return None, None
videos = [v[0] for v in video_inputs]
metadata = [v[1] for v in video_inputs]
return videos, metadata
def create_skill_segmentation_prompt(
coarse_goal: str | None = None,
subtask_labels: list[str] | None = None,
@@ -159,13 +170,14 @@ class Qwen2VL(BaseVLM):
]
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
videos, video_metadata = _unpack_video_inputs(video_inputs)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)
@@ -210,22 +222,20 @@ class Qwen2VL(BaseVLM):
all_messages.append(messages)
all_texts = []
all_image_inputs = []
all_video_inputs = []
all_video_tuples = []
for messages in all_messages:
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
all_texts.append(text)
all_image_inputs.extend(image_inputs or [])
all_video_inputs.extend(video_inputs or [])
all_video_tuples.extend(video_inputs or [])
videos, video_metadata = _unpack_video_inputs(all_video_tuples or None)
inputs = self.processor(
text=all_texts,
images=all_image_inputs if all_image_inputs else None,
videos=all_video_inputs if all_video_inputs else None,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)
@@ -334,13 +344,14 @@ class Qwen3VL(BaseVLM):
]
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
videos, video_metadata = _unpack_video_inputs(video_inputs)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)
@@ -384,22 +395,20 @@ class Qwen3VL(BaseVLM):
all_messages.append(messages)
all_texts = []
all_image_inputs = []
all_video_inputs = []
all_video_tuples = []
for messages in all_messages:
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
all_texts.append(text)
all_image_inputs.extend(image_inputs or [])
all_video_inputs.extend(video_inputs or [])
all_video_tuples.extend(video_inputs or [])
videos, video_metadata = _unpack_video_inputs(all_video_tuples or None)
inputs = self.processor(
text=all_texts,
images=all_image_inputs if all_image_inputs else None,
videos=all_video_inputs if all_video_inputs else None,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)
@@ -502,13 +511,14 @@ class Qwen35VL(BaseVLM):
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
videos, video_metadata = _unpack_video_inputs(video_inputs)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)
@@ -551,24 +561,22 @@ class Qwen35VL(BaseVLM):
all_messages.append(messages)
all_texts = []
all_image_inputs = []
all_video_inputs = []
all_video_tuples = []
for messages in all_messages:
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
image_inputs, video_inputs = self.process_vision_info(messages)
image_inputs, video_inputs = self.process_vision_info(messages, return_video_metadata=True)
all_texts.append(text)
all_image_inputs.extend(image_inputs or [])
all_video_inputs.extend(video_inputs or [])
all_video_tuples.extend(video_inputs or [])
videos, video_metadata = _unpack_video_inputs(all_video_tuples or None)
inputs = self.processor(
text=all_texts,
images=all_image_inputs if all_image_inputs else None,
videos=all_video_inputs if all_video_inputs else None,
videos=videos,
video_metadata=video_metadata,
do_sample_frames=False,
fps=1.0,
padding=True,
return_tensors="pt",
).to(self.device)