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2ea2835 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | """Stride-4 latent-aligned contiguous-clip sampler.
From a ``num_pixel_frames``-frame, ``source_fps``-fps video, select a phase
``o ~ Uniform{0,1,2,3}`` and take every ``stride``-th frame
(``I = {o + stride*k}``), giving an effective ``source_fps/stride`` fps stream;
then take a random contiguous ``clip_frames``-frame window of that stream.
For the canonical setting (81 frames @ 16 fps, stride 4, clip 16) this yields 16
frames at an effective 4 fps. The *same* sampler is used for target-statistic
precomputation, generated training, and evaluation (random phase + random valid
start), with an optional deterministic mode for tests/debugging.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import List, Optional
import torch
def stride4_candidate_indices(phase: int, stride: int, num_pixel_frames: int) -> List[int]:
"""All frame indices ``phase + stride*k`` within ``[0, num_pixel_frames-1]``."""
last = num_pixel_frames - 1
return [phase + stride * k for k in range(num_pixel_frames) if phase + stride * k <= last]
@dataclass
class ClipSpec:
"""One sampled clip: a set of frame indices applied across the batch.
``crop_id`` / ``encoder_name`` are optional bookkeeping fields set by the
caller (the spatial crop is applied by the encoder adapter, not here).
"""
phase: int
start: int
frame_indices: List[int]
stride: int = 4
clip_frames: int = 16
crop_id: Optional[str] = None
encoder_name: Optional[str] = None
def to_dict(self) -> dict:
return {
"phase": int(self.phase),
"start": int(self.start),
"frame_indices": list(self.frame_indices),
"stride": int(self.stride),
"clip_frames": int(self.clip_frames),
"crop_id": self.crop_id,
"encoder_name": self.encoder_name,
}
def index_tensor(self, device=None) -> torch.Tensor:
return torch.tensor(self.frame_indices, dtype=torch.long, device=device)
class Stride4ClipSampler:
"""Sampler for stride-4 contiguous clips.
Parameters
----------
num_pixel_frames, source_fps, stride, clip_frames:
temporal geometry (defaults: 81, 16, 4, 16 -> 16 frames @ 4 fps).
random_phase / random_start:
if ``False`` and a ``deterministic`` value is given, use it.
deterministic / phase / start:
debug mode -- always return ``frame_indices`` for the fixed phase/start.
"""
def __init__(
self,
num_pixel_frames: int = 81,
source_fps: int = 16,
stride: int = 4,
clip_frames: int = 16,
random_phase: bool = True,
random_start: bool = True,
deterministic: bool = False,
phase: int = 0,
start: int = 0,
):
if clip_frames <= 0:
raise ValueError("clip_frames must be positive")
self.num_pixel_frames = int(num_pixel_frames)
self.source_fps = int(source_fps)
self.stride = int(stride)
self.clip_frames = int(clip_frames)
self.random_phase = bool(random_phase)
self.random_start = bool(random_start)
self.deterministic = bool(deterministic)
self.fixed_phase = int(phase)
self.fixed_start = int(start)
# validate feasibility: the shortest phase stream must still hold a clip
min_candidates = min(
len(stride4_candidate_indices(p, self.stride, self.num_pixel_frames))
for p in range(self.stride)
)
if min_candidates < self.clip_frames:
raise ValueError(
f"stride={self.stride}, clip_frames={self.clip_frames} infeasible: "
f"some phase yields only {min_candidates} candidate frames "
f"(< {self.clip_frames}) for {self.num_pixel_frames} pixel frames"
)
@property
def effective_fps(self) -> float:
return self.source_fps / self.stride
def _sample_one(self, generator: Optional[torch.Generator]) -> ClipSpec:
if self.deterministic:
phase = self.fixed_phase
start = self.fixed_start
else:
if self.random_phase:
phase = int(torch.randint(0, self.stride, (), generator=generator).item())
else:
phase = self.fixed_phase
candidate = stride4_candidate_indices(phase, self.stride, self.num_pixel_frames)
max_start = len(candidate) - self.clip_frames
if self.random_start and max_start > 0:
start = int(torch.randint(0, max_start + 1, (), generator=generator).item())
else:
start = min(self.fixed_start, max_start) if not self.random_start else 0
candidate = stride4_candidate_indices(phase, self.stride, self.num_pixel_frames)
max_start = len(candidate) - self.clip_frames
start = max(0, min(start, max_start))
frame_indices = candidate[start: start + self.clip_frames]
return ClipSpec(
phase=phase,
start=start,
frame_indices=frame_indices,
stride=self.stride,
clip_frames=self.clip_frames,
)
def sample(
self,
batch_size: int = 1,
num_clips: int = 1,
generator: Optional[torch.Generator] = None,
) -> List[ClipSpec]:
"""Return ``num_clips`` clip specs (each applied across the whole batch).
``batch_size`` is accepted for API symmetry; the same frame indices are
applied to all batch items of a given clip (see ``gather_frames``).
"""
return [self._sample_one(generator) for _ in range(max(1, num_clips))]
def config_dict(self) -> dict:
"""Sampler config to embed in every statistics file (spec section 1.2)."""
return {
"source_fps": self.source_fps,
"num_pixel_frames": self.num_pixel_frames,
"stride": self.stride,
"effective_fps": self.effective_fps,
"clip_frames": self.clip_frames,
"phase_distribution": "uniform_0_1_2_3" if self.stride == 4 else f"uniform_0_{self.stride - 1}",
"start_distribution": "uniform_valid_contiguous_start",
"deterministic": self.deterministic,
}
@classmethod
def from_config(cls, cfg: dict) -> "Stride4ClipSampler":
return cls(
num_pixel_frames=int(cfg.get("num_pixel_frames", 81)),
source_fps=int(cfg.get("source_fps", 16)),
stride=int(cfg.get("stride", 4)),
clip_frames=int(cfg.get("clip_frames", 16)),
random_phase=bool(cfg.get("random_phase", True)),
random_start=bool(cfg.get("random_start", True)),
deterministic=bool(cfg.get("deterministic", cfg.get("deterministic_debug", False))),
phase=int(cfg.get("phase", 0)),
start=int(cfg.get("start", 0)),
)
def gather_frames(video: torch.Tensor, frame_indices, time_dim: int = 1) -> torch.Tensor:
"""Select ``frame_indices`` along ``time_dim`` of ``video`` (differentiable)."""
if not torch.is_tensor(frame_indices):
frame_indices = torch.tensor(list(frame_indices), dtype=torch.long, device=video.device)
else:
frame_indices = frame_indices.to(device=video.device, dtype=torch.long)
return video.index_select(time_dim, frame_indices)
def scatter_add_frames(target: torch.Tensor, frame_indices, values: torch.Tensor, time_dim: int = 1) -> torch.Tensor:
"""Accumulate ``values`` into ``target`` at ``frame_indices`` along ``time_dim``.
Uses ``index_add`` so that a pixel frame selected by multiple clips / crops /
encoders correctly accumulates gradients (spec section 11.3).
"""
if not torch.is_tensor(frame_indices):
frame_indices = torch.tensor(list(frame_indices), dtype=torch.long, device=target.device)
else:
frame_indices = frame_indices.to(device=target.device, dtype=torch.long)
target.index_add_(time_dim, frame_indices, values.to(target.dtype))
return target
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