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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from PIL import Image as PILImage
from tensordict import tensorclass
@tensorclass
class BatchedVideoMetaData:
"""
This class represents metadata about a batch of videos.
Attributes:
unique_objects_identifier: A tensor of shape Bx3 containing unique identifiers for each object in the batch. Index consists of (video_id, obj_id, frame_id)
frame_orig_size: A tensor of shape Bx2 containing the original size of each frame in the batch.
"""
unique_objects_identifier: torch.LongTensor
frame_orig_size: torch.LongTensor
@tensorclass
class BatchedVideoDatapoint:
"""
This class represents a batch of videos with associated annotations and metadata.
Attributes:
img_batch: A [TxBxCxHxW] tensor containing the image data for each frame in the batch, where T is the number of frames per video, and B is the number of videos in the batch.
obj_to_frame_idx: A [TxOx2] tensor containing the image_batch index which the object belongs to. O is the number of objects in the batch.
masks: A [TxOxHxW] tensor containing binary masks for each object in the batch.
metadata: An instance of BatchedVideoMetaData containing metadata about the batch.
dict_key: A string key used to identify the batch.
"""
img_batch: torch.FloatTensor
obj_to_frame_idx: torch.IntTensor
masks: torch.BoolTensor
metadata: BatchedVideoMetaData
dict_key: str
def pin_memory(self, device=None):
return self.apply(torch.Tensor.pin_memory, device=device)
@property
def num_frames(self) -> int:
"""
Returns the number of frames per video.
"""
return self.batch_size[0]
@property
def num_videos(self) -> int:
"""
Returns the number of videos in the batch.
"""
return self.img_batch.shape[1]
@property
def flat_obj_to_img_idx(self) -> torch.IntTensor:
"""
Returns a flattened tensor containing the object to img index.
The flat index can be used to access a flattened img_batch of shape [(T*B)xCxHxW]
"""
frame_idx, video_idx = self.obj_to_frame_idx.unbind(dim=-1)
flat_idx = video_idx * self.num_frames + frame_idx
return flat_idx
@property
def flat_img_batch(self) -> torch.FloatTensor:
"""
Returns a flattened img_batch_tensor of shape [(B*T)xCxHxW]
"""
return self.img_batch.transpose(0, 1).flatten(0, 1)
@dataclass
class Object:
# Id of the object in the media
object_id: int
# Index of the frame in the media (0 if single image)
frame_index: int
segment: Union[torch.Tensor, dict] # RLE dict or binary mask
@dataclass
class Frame:
data: Union[torch.Tensor, PILImage.Image]
objects: List[Object]
@dataclass
class VideoDatapoint:
"""Refers to an image/video and all its annotations"""
frames: List[Frame]
video_id: int
size: Tuple[int, int]
def collate_fn(
batch: List[VideoDatapoint],
dict_key,
) -> BatchedVideoDatapoint:
"""
Args:
batch: A list of VideoDatapoint instances.
dict_key (str): A string key used to identify the batch.
"""
img_batch = []
for video in batch:
img_batch += [torch.stack([frame.data for frame in video.frames], dim=0)]
img_batch = torch.stack(img_batch, dim=0).permute((1, 0, 2, 3, 4))
T = img_batch.shape[0]
# Prepare data structures for sequential processing. Per-frame processing but batched across videos.
step_t_objects_identifier = [[] for _ in range(T)]
step_t_frame_orig_size = [[] for _ in range(T)]
step_t_masks = [[] for _ in range(T)]
step_t_obj_to_frame_idx = [
[] for _ in range(T)
] # List to store frame indices for each time step
for video_idx, video in enumerate(batch):
orig_video_id = video.video_id
orig_frame_size = video.size
for t, frame in enumerate(video.frames):
objects = frame.objects
for obj in objects:
orig_obj_id = obj.object_id
orig_frame_idx = obj.frame_index
step_t_obj_to_frame_idx[t].append(
torch.tensor([t, video_idx], dtype=torch.int)
)
step_t_masks[t].append(obj.segment.to(torch.bool))
step_t_objects_identifier[t].append(
torch.tensor([orig_video_id, orig_obj_id, orig_frame_idx])
)
step_t_frame_orig_size[t].append(torch.tensor(orig_frame_size))
obj_to_frame_idx = torch.stack(
[
torch.stack(obj_to_frame_idx, dim=0)
for obj_to_frame_idx in step_t_obj_to_frame_idx
],
dim=0,
)
masks = torch.stack([torch.stack(masks, dim=0) for masks in step_t_masks], dim=0)
objects_identifier = torch.stack(
[torch.stack(id, dim=0) for id in step_t_objects_identifier], dim=0
)
frame_orig_size = torch.stack(
[torch.stack(id, dim=0) for id in step_t_frame_orig_size], dim=0
)
return BatchedVideoDatapoint(
img_batch=img_batch,
obj_to_frame_idx=obj_to_frame_idx,
masks=masks,
metadata=BatchedVideoMetaData(
unique_objects_identifier=objects_identifier,
frame_orig_size=frame_orig_size,
),
dict_key=dict_key,
batch_size=[T],
)
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