nodes/hao-ai-lab-FastVideo
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[Fix] remove video ratio limitation (#1069)

Browse Source
This commit is contained in:
Jinzhe Pan
2026-02-05 20:33:08 -08:00
committed by GitHub
parent 98308db7e0
commit a321b95a8a
2 changed files with 3 additions and 93 deletions
Download Patch File Download Diff File Expand all files Collapse all files
fastvideo/dataset/preprocessing_datasets.py
+1 -70
View File
@@ -148,62 +148,6 @@ class DataValidationStage(DatasetFilterStage):
return batch
class ResolutionFilterStage(DatasetFilterStage):
"""Stage for filtering data items based on resolution constraints."""
def __init__(self,
max_h_div_w_ratio: float = 17 / 16,
min_h_div_w_ratio: float = 8 / 16,
max_height: int = 1024,
max_width: int = 1024):
self.max_h_div_w_ratio = max_h_div_w_ratio
self.min_h_div_w_ratio = min_h_div_w_ratio
self.max_height = max_height
self.max_width = max_width
def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
"""
Check if data item passes resolution filtering.
Args:
batch: Dataset batch with resolution information
Returns:
True if passes filter, False otherwise
"""
# Only apply to videos
if not batch.is_video:
return True
if batch.resolution is None:
return False
height = batch.resolution.get("height", None)
width = batch.resolution.get("width", None)
if height is None or width is None:
return False
# Check aspect ratio
aspect = self.max_height / self.max_width
hw_aspect_thr = 1.5
return self.filter_resolution(
height,
width,
max_h_div_w_ratio=hw_aspect_thr * aspect,
min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
)
def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
"""Process does nothing for resolution filtering - filtering is handled by should_keep."""
return batch
def filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
min_h_div_w_ratio: float) -> bool:
"""Filter based on height/width ratio."""
return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
class FrameSamplingStage(DatasetFilterStage):
"""Stage for temporal frame sampling and indexing."""
@@ -328,11 +272,6 @@ class VideoTransformStage(DatasetStage):
video = rearrange(video, "t c h w -> c t h w")
video = video.to(torch.uint8)
h, w = video.shape[-2:]
assert (
h / w <= 17 / 16 and h / w >= 8 / 16
), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({batch.path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
video = video.float() / 127.5 - 1.0
batch.pixel_values = video
return batch
@@ -489,8 +428,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
tokenizer) -> None:
"""Initialize all processing stages."""
self.validation_stage = DataValidationStage()
self.resolution_filter_stage = ResolutionFilterStage(
max_height=args.max_height, max_width=args.max_width)
self.frame_sampling_stage = FrameSamplingStage(
num_frames=args.num_frames,
train_fps=args.train_fps,
@@ -543,7 +480,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
# Initialize counters
filter_counts = {
"validation_failed": 0,
"resolution_failed": 0,
"frame_sampling_failed": 0
}
sample_num_frames: list[int] = []
@@ -579,10 +515,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
filter_counts["validation_failed"] += 1
return False
if not self.resolution_filter_stage.should_keep(batch):
filter_counts["resolution_failed"] += 1
return False
if not self.frame_sampling_stage.should_keep(batch):
filter_counts["frame_sampling_failed"] += 1
return False
@@ -594,10 +526,9 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
after_count: int):
"""Log filtering statistics."""
logger.info(
"validation_failed: %d, resolution_failed: %d, frame_sampling_failed: %d, "
"validation_failed: %d, frame_sampling_failed: %d, "
"Counter(sample_num_frames): %s, before filter: %d, after filter: %d",
filter_counts['validation_failed'],
filter_counts['resolution_failed'],
filter_counts['frame_sampling_failed'], Counter(sample_num_frames),
before_count, after_count)
fastvideo/workflow/preprocess/components.py
+2 -23
View File
@@ -26,24 +26,18 @@ class PreprocessingDataValidator:
def __init__(self,
max_height: int = 1024,
max_width: int = 1024,
max_h_div_w_ratio: float = 17 / 16,
min_h_div_w_ratio: float = 8 / 16,
num_frames: int = 16,
train_fps: int = 24,
speed_factor: float = 1.0,
video_length_tolerance_range: float = 5.0,
drop_short_ratio: float = 0.0,
hw_aspect_threshold: float = 1.5):
drop_short_ratio: float = 0.0):
self.max_height = max_height
self.max_width = max_width
self.max_h_div_w_ratio = max_h_div_w_ratio
self.min_h_div_w_ratio = min_h_div_w_ratio
self.num_frames = num_frames
self.train_fps = train_fps
self.speed_factor = speed_factor
self.video_length_tolerance_range = video_length_tolerance_range
self.drop_short_ratio = drop_short_ratio
self.hw_aspect_threshold = hw_aspect_threshold
self.validators: dict[str, Callable[[dict[str, Any]], bool]] = {}
self.filter_counts: dict[str, int] = {}
@@ -86,26 +80,11 @@ class PreprocessingDataValidator:
def _validate_resolution(self, batch: dict[str, Any]) -> bool:
"""Validate resolution constraints"""
aspect = self.max_height / self.max_width
if batch["resolution"] is not None:
height = batch["resolution"].get("height", None)
width = batch["resolution"].get("width", None)
if height is None or width is None:
return False
return self._filter_resolution(
height,
width,
max_h_div_w_ratio=self.hw_aspect_threshold * aspect,
min_h_div_w_ratio=1 / self.hw_aspect_threshold * aspect,
)
def _filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
min_h_div_w_ratio: float) -> bool:
"""Filter based on aspect ratio"""
return (min_h_div_w_ratio <= h / w <= max_h_div_w_ratio) and (
self.min_h_div_w_ratio <= h / w <= self.max_h_div_w_ratio)
return not (height is None or width is None)
def _validate_frame_sampling(self, batch: dict[str, Any]) -> bool:
"""Validate frame sampling constraints"""