File size: 5,815 Bytes
f53b39e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
# 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],
)
|