718 lines
30 KiB
Python
718 lines
30 KiB
Python
"""Validated, causal pooled and frame-set local-feature sidecars.
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No model/decoder imports: these utilities are also used by CPU head training.
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Sidecars store encoder-native vectors, never a randomly initialized projection.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import math
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from pathlib import Path
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from typing import Any
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import torch
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LOCAL_FORMAT = "qwen-local-pooled-v1"
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ORACLE_FORMAT = "easycom-oracle-person-history-v1"
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HIGHRATE_PERSON_AV_FORMAT = "causal-highrate-person-av-v2"
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MODALITIES = {"visual": ("visual",), "audio": ("audio",), "av": ("visual", "audio")}
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@dataclass(frozen=True)
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class FramePersonFeatures:
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"""Ragged person sets indexed by aligned 5 Hz rows without frame duplication."""
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features: torch.Tensor
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frame_offsets: torch.LongTensor
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row_to_frame: torch.LongTensor
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chosen_frame_times: torch.Tensor
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previous_observation: torch.LongTensor | None = None
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def __post_init__(self) -> None:
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if (
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not isinstance(self.features, torch.Tensor)
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or self.features.ndim != 2
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or not self.features.is_floating_point()
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or not torch.isfinite(self.features).all()
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or self.features.shape[1] <= 0
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):
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raise ValueError("Person features must be finite floating [N,D]")
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if (
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not isinstance(self.frame_offsets, torch.Tensor)
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or self.frame_offsets.dtype != torch.long
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or self.frame_offsets.ndim != 1
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or not len(self.frame_offsets)
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or int(self.frame_offsets[0]) != 0
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or int(self.frame_offsets[-1]) != len(self.features)
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or bool((self.frame_offsets[1:] < self.frame_offsets[:-1]).any())
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):
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raise ValueError("Invalid person frame_offsets")
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if (
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not isinstance(self.row_to_frame, torch.Tensor)
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or self.row_to_frame.dtype != torch.long
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or self.row_to_frame.ndim != 1
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):
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raise ValueError("row_to_frame must be int64 [T]")
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num_frames = len(self.frame_offsets) - 1
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if bool(((self.row_to_frame < -1) | (self.row_to_frame >= num_frames)).any()):
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raise ValueError("row_to_frame contains an invalid frame index")
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if (
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not isinstance(self.chosen_frame_times, torch.Tensor)
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or self.chosen_frame_times.dtype != torch.float64
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or self.chosen_frame_times.shape != self.row_to_frame.shape
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):
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raise ValueError("chosen_frame_times must be float64 [T]")
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missing = self.row_to_frame < 0
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if not torch.equal(torch.isnan(self.chosen_frame_times), missing):
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raise ValueError("Only rows without an available frame may have NaN frame time")
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if not torch.isfinite(self.chosen_frame_times[~missing]).all():
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raise ValueError("Chosen person frame times must be finite")
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if self.previous_observation is not None:
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previous = self.previous_observation
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if (
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not isinstance(previous, torch.Tensor)
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or previous.dtype != torch.long
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or previous.shape != (len(self.features),)
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):
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raise ValueError("previous_observation must be int64 [N]")
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indices = torch.arange(len(previous), dtype=torch.long)
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if bool(((previous < -1) | (previous >= indices)).any()):
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raise ValueError(
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"Each previous observation must be -1 or point strictly backward"
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)
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def __len__(self) -> int:
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return len(self.row_to_frame)
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@property
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def dimension(self) -> int:
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return int(self.features.shape[1])
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@property
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def max_people(self) -> int:
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counts = self.frame_offsets[1:] - self.frame_offsets[:-1]
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return int(counts.max()) if len(counts) else 0
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def select_rows(self, indices: torch.Tensor) -> "FramePersonFeatures":
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indices = torch.as_tensor(indices)
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if indices.dtype != torch.long or indices.ndim != 1:
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raise ValueError("Person row selection must be int64 [N]")
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if len(indices) and (int(indices.min()) < 0 or int(indices.max()) >= len(self)):
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raise IndexError("Person row selection is out of range")
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return FramePersonFeatures(
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self.features,
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self.frame_offsets,
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self.row_to_frame[indices],
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self.chosen_frame_times[indices],
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self.previous_observation,
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)
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def _padded_observation_indices(
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self,
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row_indices: torch.Tensor | None = None,
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) -> tuple[torch.LongTensor, torch.BoolTensor]:
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"""Return current-frame observation indices and a real-person mask."""
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if row_indices is None:
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frames = self.row_to_frame
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else:
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row_indices = torch.as_tensor(row_indices)
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if row_indices.dtype != torch.long or row_indices.ndim != 1:
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raise ValueError("Batch row indices must be int64 [B]")
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if len(row_indices) and (
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int(row_indices.min()) < 0 or int(row_indices.max()) >= len(self)
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):
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raise IndexError("Batch person row index is out of range")
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frames = self.row_to_frame[row_indices]
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valid_frames = frames >= 0
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safe_frames = frames.clamp_min(0)
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starts = torch.zeros_like(frames)
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counts = torch.zeros_like(frames)
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if bool(valid_frames.any()):
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starts[valid_frames] = self.frame_offsets[safe_frames[valid_frames]]
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counts[valid_frames] = (
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self.frame_offsets[safe_frames[valid_frames] + 1]
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- starts[valid_frames]
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)
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width = int(counts.max()) if len(counts) else 0
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mask = torch.arange(width)[None, :] < counts[:, None]
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if width == 0:
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return torch.zeros((len(frames), 0), dtype=torch.long), mask
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indices = starts[:, None] + torch.arange(width)[None, :]
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return indices.masked_fill(~mask, 0), mask
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def padded(
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self,
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row_indices: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.BoolTensor]:
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"""Materialize only one batch as [B,P,D] plus a True-for-real mask."""
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safe_indices, mask = self._padded_observation_indices(row_indices)
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if safe_indices.shape[1] == 0:
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return self.features.new_zeros((len(safe_indices), 0, self.dimension)), mask
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values = self.features[safe_indices]
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return values.masked_fill(~mask[..., None], 0), mask
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def padded_history(
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self,
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row_indices: torch.Tensor | None = None,
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*,
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max_observations: int,
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) -> tuple[torch.Tensor, torch.BoolTensor]:
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"""Return causal per-current-person histories as ``[B,P,L,D]``.
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Histories are chronological and left aligned. Track links are built
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from provided IDs with an explicit causal gap/reset policy when the
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sidecar is loaded; no identity value is exposed to the model.
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"""
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if max_observations <= 0:
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raise ValueError("max_observations must be positive")
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if self.previous_observation is None:
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raise ValueError("Track histories were not constructed for this person set")
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current, current_mask = self._padded_observation_indices(row_indices)
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batch, people = current.shape
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if people == 0:
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return (
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self.features.new_zeros(
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(batch, 0, max_observations, self.dimension)
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),
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torch.zeros(
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(batch, 0, max_observations), dtype=torch.bool
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),
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)
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reverse_indices = torch.zeros(
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(batch, people, max_observations), dtype=torch.long
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)
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reverse_mask = torch.zeros(
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(batch, people, max_observations), dtype=torch.bool
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)
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cursor = current.clone()
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cursor_valid = current_mask.clone()
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for step in range(max_observations):
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reverse_indices[:, :, step] = cursor.masked_fill(~cursor_valid, 0)
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reverse_mask[:, :, step] = cursor_valid
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safe = cursor.masked_fill(~cursor_valid, 0)
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cursor = self.previous_observation[safe]
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cursor_valid &= cursor >= 0
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lengths = reverse_mask.sum(dim=-1)
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output_positions = torch.arange(max_observations)[None, None, :]
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source_positions = lengths[:, :, None] - 1 - output_positions
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history_mask = output_positions < lengths[:, :, None]
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source_positions = source_positions.clamp_min(0)
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chronological = reverse_indices.gather(dim=-1, index=source_positions)
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chronological = chronological.masked_fill(~history_mask, 0)
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histories = self.features[chronological]
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histories = histories.masked_fill(~history_mask[..., None], 0)
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return histories, history_mask
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@classmethod
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def concatenate(
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cls,
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parts: list["FramePersonFeatures"],
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) -> "FramePersonFeatures":
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if not parts:
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raise ValueError("Cannot concatenate no person feature sets")
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dimension = parts[0].dimension
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if any(part.dimension != dimension for part in parts):
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raise ValueError("Person feature dimensions differ across sessions")
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feature_parts = []
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offset_parts = [torch.zeros(1, dtype=torch.long)]
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row_parts = []
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time_parts = []
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feature_base = 0
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frame_base = 0
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previous_parts = []
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has_track_links = [part.previous_observation is not None for part in parts]
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if any(has_track_links) and not all(has_track_links):
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raise ValueError("Track history links are missing for some sessions")
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for part in parts:
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feature_parts.append(part.features)
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offset_parts.append(part.frame_offsets[1:] + feature_base)
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rows = part.row_to_frame.clone()
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valid = rows >= 0
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rows[valid] += frame_base
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row_parts.append(rows)
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time_parts.append(part.chosen_frame_times)
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if part.previous_observation is not None:
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previous = part.previous_observation.clone()
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valid_previous = previous >= 0
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previous[valid_previous] += feature_base
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previous_parts.append(previous)
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feature_base += len(part.features)
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frame_base += len(part.frame_offsets) - 1
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return cls(
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torch.cat(feature_parts, dim=0),
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torch.cat(offset_parts, dim=0),
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torch.cat(row_parts, dim=0),
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torch.cat(time_parts, dim=0),
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torch.cat(previous_parts, dim=0) if previous_parts else None,
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)
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def reference_alignment(payload: dict[str, Any]) -> tuple[torch.Tensor, torch.Tensor]:
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"""Require the same full, zero-origin 5 Hz timeline used by head training."""
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states = payload.get("states")
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if not isinstance(states, torch.Tensor) or states.ndim not in (2, 3) or not len(states):
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raise ValueError("Reference must contain nonempty [T,D] or [T,L,D] states")
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if float(payload.get("start", 0)) != 0:
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raise ValueError("Local features require a zero-origin reference (start=0)")
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indices = torch.as_tensor(payload.get("label_indices", []))
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if indices.dtype != torch.long or not torch.equal(indices, torch.arange(len(states))):
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raise ValueError("Reference label_indices must be dense int64 [0,...,T-1]")
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times = torch.as_tensor(payload.get("times", []), dtype=torch.float64)
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expected = (torch.arange(len(states), dtype=torch.float64) + 1) / 5
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# Existing feature files may have serialized times as float32.
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if times.shape != expected.shape or not torch.allclose(times, expected, atol=5e-4, rtol=0):
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raise ValueError("Reference times must be [0.2,0.4,...] seconds at 5 Hz")
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return times, indices
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def _load_validated_payload(
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path: Path,
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reference: dict[str, Any],
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*,
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sample_id: str,
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dataset: str,
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modalities: str,
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expected_contract: dict[str, Any] | None,
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oracle_analysis: bool = False,
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highrate_person_av: bool = False,
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allow_highrate_audio: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor, dict[str, Any], dict[str, Any], dict[str, Any]]:
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if modalities not in MODALITIES:
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raise ValueError(f"Unknown local modalities: {modalities}")
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times, indices = reference_alignment(reference)
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payload = torch.load(path, map_location="cpu", weights_only=False)
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if (
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allow_highrate_audio
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and modalities == "audio"
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and isinstance(payload, dict)
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and payload.get("format") == HIGHRATE_PERSON_AV_FORMAT
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):
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highrate_person_av = True
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if oracle_analysis and highrate_person_av:
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raise ValueError("Oracle and learned high-rate local formats are distinct")
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expected_format = (
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ORACLE_FORMAT
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if oracle_analysis
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else HIGHRATE_PERSON_AV_FORMAT
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if highrate_person_av
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else LOCAL_FORMAT
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)
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if not isinstance(payload, dict) or payload.get("format") != expected_format:
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raise ValueError(f"Invalid local feature format: {path}")
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if payload.get("sample_id") != sample_id or payload.get("dataset") != dataset:
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raise ValueError(f"Local sample/dataset mismatch: {path}")
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if payload.get("partial", False):
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raise ValueError(f"Partial smoke-test features cannot be used for training: {path}")
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local_indices = torch.as_tensor(payload.get("label_indices", []))
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local_times = torch.as_tensor(payload.get("times", []), dtype=torch.float64)
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if local_indices.dtype != torch.long or not torch.equal(indices, local_indices):
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raise ValueError(f"Local label_indices mismatch: {path}")
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if local_times.shape != times.shape or not torch.allclose(local_times, times, atol=1e-7, rtol=0):
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raise ValueError(f"Local times mismatch: {path}")
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config = payload.get("config", {})
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if oracle_analysis:
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if (
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dataset != "easycom"
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or config.get("causal_policy") != "egospeak-right-edge-current-state"
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or config.get("uses_speech_labels") is not True
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or config.get("oracle_analysis") is not True
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):
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raise ValueError(
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f"Oracle features lack the explicit causal/oracle contract: {path}"
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)
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elif highrate_person_av:
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if (
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config.get("causal_policy") != "right-edge-causal-highrate-asd"
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or config.get("uses_speech_labels") is not False
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or config.get("model_fps") != 25.0
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or config.get("output_hz") != 5.0
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or config.get("person_feature_kind")
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!= "muvap-asd-causal-visual-plus-activity-logit"
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or config.get("audio_feature_kind")
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!= "muvap-cpc-causal-audio-state"
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or config.get("audio_source") != "single-shared-traditional-cpc-stream"
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or config.get("person_visual_dim") != config.get("visual_dim") - 1
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or config.get("person_activity_logit_dim") != 1
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or config.get("person_tokens_duplicate_audio") is not False
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):
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raise ValueError(
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f"High-rate person AV features lack the causal ASD contract: {path}"
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)
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elif (
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config.get("causal_policy") != "half-open-trailing-window"
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or config.get("uses_speech_labels") is not False
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):
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raise ValueError(f"Local features lack the causal/no-speech-label contract: {path}")
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contract = {"format": expected_format, "modalities": modalities, "config": config}
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if expected_contract is not None and contract != expected_contract:
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raise ValueError(f"Local encoder/pooling contract differs across sessions/checkpoint: {path}")
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return times, indices, payload, config, contract
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def load_local_features(
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path: Path,
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reference: dict[str, Any],
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*,
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sample_id: str,
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dataset: str,
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modalities: str,
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expected_contract: dict[str, Any] | None = None,
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) -> tuple[torch.Tensor, dict[str, Any]]:
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"""Pack each requested modality as [raw vector, validity bit]. Fail closed."""
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times, _, payload, config, contract = _load_validated_payload(
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path,
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reference,
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sample_id=sample_id,
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dataset=dataset,
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modalities=modalities,
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expected_contract=expected_contract,
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allow_highrate_audio=True,
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)
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chunks = []
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for name in MODALITIES[modalities]:
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values = payload.get(name)
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valid = payload.get(f"{name}_valid")
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dimension = config.get(f"{name}_dim")
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if not isinstance(dimension, int) or dimension <= 0:
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raise ValueError(f"Missing {name}_dim: {path}")
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if not isinstance(values, torch.Tensor) or values.shape != (len(times), dimension):
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raise ValueError(f"Invalid {name} shape: {path}")
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if not values.is_floating_point() or not torch.isfinite(values).all():
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raise ValueError(f"Non-finite/non-floating {name} features: {path}")
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if not isinstance(valid, torch.Tensor) or valid.dtype != torch.bool or valid.shape != (len(times),):
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raise ValueError(f"Invalid {name}_valid mask: {path}")
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if torch.count_nonzero(values[~valid]):
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raise ValueError(f"Invalid {name} rows must be stored as zeros: {path}")
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chunks.extend((values.float(), valid[:, None].float()))
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return torch.cat(chunks, dim=-1), contract
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def load_frame_set_features(
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path: Path,
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reference: dict[str, Any],
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*,
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sample_id: str,
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dataset: str,
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expected_contract: dict[str, Any] | None = None,
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track_max_gap_seconds: float | None = None,
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oracle_analysis: bool = False,
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highrate_person_av: bool = False,
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) -> tuple[torch.Tensor, FramePersonFeatures, dict[str, Any]]:
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"""Load direct audio and the latest causal per-frame person set per 5 Hz row."""
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times, _, payload, config, contract = _load_validated_payload(
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path,
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reference,
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sample_id=sample_id,
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dataset=dataset,
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modalities="av",
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expected_contract=expected_contract,
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oracle_analysis=oracle_analysis,
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highrate_person_av=highrate_person_av,
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)
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audio = payload.get("audio")
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audio_valid = payload.get("audio_valid")
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audio_dim = config.get("audio_dim")
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if not isinstance(audio_dim, int) or audio_dim <= 0:
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raise ValueError(f"Missing audio_dim: {path}")
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if not isinstance(audio, torch.Tensor) or audio.shape != (len(times), audio_dim):
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raise ValueError(f"Invalid audio shape: {path}")
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if not audio.is_floating_point() or not torch.isfinite(audio).all():
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raise ValueError(f"Non-finite/non-floating audio features: {path}")
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if (
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not isinstance(audio_valid, torch.Tensor)
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or audio_valid.dtype != torch.bool
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or audio_valid.shape != (len(times),)
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):
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raise ValueError(f"Invalid audio_valid mask: {path}")
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if torch.count_nonzero(audio[~audio_valid]):
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raise ValueError(f"Invalid audio rows must be stored as zeros: {path}")
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visual_dim = config.get("visual_dim")
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persons = payload.get("persons")
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|
if not isinstance(visual_dim, int) or visual_dim <= 0:
|
|
raise ValueError(f"Missing visual_dim: {path}")
|
|
if not isinstance(persons, dict):
|
|
raise ValueError(f"Missing ragged person features: {path}")
|
|
features = persons.get("features")
|
|
frame_times = persons.get("frame_times")
|
|
frame_offsets = persons.get("frame_offsets")
|
|
records = persons.get("records")
|
|
if (
|
|
not isinstance(features, torch.Tensor)
|
|
or features.ndim != 2
|
|
or features.shape[1] != visual_dim
|
|
or not features.is_floating_point()
|
|
or not torch.isfinite(features).all()
|
|
):
|
|
raise ValueError(f"Invalid person feature shape/content: {path}")
|
|
if (
|
|
not isinstance(frame_times, torch.Tensor)
|
|
or frame_times.ndim != 1
|
|
or not frame_times.is_floating_point()
|
|
):
|
|
raise ValueError(f"Invalid person frame_times: {path}")
|
|
frame_times = frame_times.to(dtype=torch.float64)
|
|
if (
|
|
not torch.isfinite(frame_times).all()
|
|
or bool((frame_times < 0).any())
|
|
or bool((frame_times[1:] <= frame_times[:-1]).any())
|
|
):
|
|
raise ValueError(f"Person frame_times must be finite, nonnegative and increasing: {path}")
|
|
if (
|
|
not isinstance(frame_offsets, torch.Tensor)
|
|
or frame_offsets.dtype != torch.long
|
|
or frame_offsets.shape != (len(frame_times) + 1,)
|
|
or not len(frame_offsets)
|
|
or int(frame_offsets[0]) != 0
|
|
or int(frame_offsets[-1]) != len(features)
|
|
or bool((frame_offsets[1:] < frame_offsets[:-1]).any())
|
|
):
|
|
raise ValueError(f"Invalid person frame_offsets: {path}")
|
|
if not isinstance(records, list) or len(records) != len(features):
|
|
raise ValueError(f"Person records/features length mismatch: {path}")
|
|
if len(frame_times) and float(frame_times[-1]) > float(times[-1]) + 1e-8:
|
|
raise ValueError(f"Person frame timeline extends beyond the reference: {path}")
|
|
|
|
# Normal visual observations are instantaneous samples and must be strictly
|
|
# earlier than the cutoff. High-rate ASD states instead summarize complete
|
|
# 25 Hz intervals ending at the stored right edge, so equality with a 5 Hz
|
|
# cutoff is causal and intentional. Oracle current state has the same
|
|
# lookup geometry but remains a separate, analysis-only format.
|
|
# Oracle speech state is defined and sampled at the cutoff itself, so its
|
|
# explicit analysis-only format uses an inclusive current-state lookup.
|
|
row_to_frame = torch.searchsorted(
|
|
frame_times,
|
|
times,
|
|
right=oracle_analysis or highrate_person_av,
|
|
) - 1
|
|
chosen = torch.full(times.shape, float("nan"), dtype=torch.float64)
|
|
has_frame = row_to_frame >= 0
|
|
chosen[has_frame] = frame_times[row_to_frame[has_frame]]
|
|
if oracle_analysis or highrate_person_av:
|
|
if bool((chosen[has_frame] > times[has_frame]).any()):
|
|
raise ValueError(f"Oracle person mapping selected future evidence: {path}")
|
|
elif bool((chosen[has_frame] >= times[has_frame]).any()):
|
|
raise ValueError(f"Person frame mapping selected future visual evidence: {path}")
|
|
previous_observation = None
|
|
if track_max_gap_seconds is not None:
|
|
if not math.isfinite(track_max_gap_seconds) or track_max_gap_seconds <= 0:
|
|
raise ValueError("track_max_gap_seconds must be finite and positive")
|
|
previous_observation = _build_previous_track_links(
|
|
records,
|
|
frame_times,
|
|
frame_offsets,
|
|
max_gap_seconds=track_max_gap_seconds,
|
|
path=path,
|
|
)
|
|
frame_sets = FramePersonFeatures(
|
|
features,
|
|
frame_offsets,
|
|
row_to_frame.to(dtype=torch.long),
|
|
chosen,
|
|
previous_observation,
|
|
)
|
|
direct_audio = torch.cat(
|
|
(audio.float(), audio_valid[:, None].float()),
|
|
dim=-1,
|
|
)
|
|
return direct_audio, frame_sets, contract
|
|
|
|
|
|
def load_highrate_person_av_features(
|
|
path: Path,
|
|
reference: dict[str, Any],
|
|
*,
|
|
sample_id: str,
|
|
dataset: str,
|
|
expected_contract: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, FramePersonFeatures, dict[str, Any]]:
|
|
"""Load frozen 25 Hz causal ASD states sampled at 5 Hz right edges.
|
|
|
|
The returned dense tensor contains the single shared causal CPC audio state
|
|
and its validity bit. The visual dimension in the contract belongs to the
|
|
ragged, variable-cardinality set of visual-track state plus ASD score; it is
|
|
not a pooled image observation consumed by the head.
|
|
"""
|
|
|
|
direct_audio, people, contract = load_frame_set_features(
|
|
path,
|
|
reference,
|
|
sample_id=sample_id,
|
|
dataset=dataset,
|
|
expected_contract=expected_contract,
|
|
highrate_person_av=True,
|
|
)
|
|
config = contract["config"]
|
|
if config.get("audio_source") != "single-shared-traditional-cpc-stream":
|
|
raise ValueError(f"Unexpected high-rate sidecar audio source: {path}")
|
|
return direct_audio, people, contract
|
|
|
|
|
|
def _build_previous_track_links(
|
|
records: list[Any],
|
|
frame_times: torch.Tensor,
|
|
frame_offsets: torch.LongTensor,
|
|
*,
|
|
max_gap_seconds: float,
|
|
path: Path,
|
|
) -> torch.LongTensor:
|
|
"""Link observations causally by provided track ID without exposing IDs.
|
|
|
|
A source-clip boundary always resets a track. Missing/unknown IDs are
|
|
isolated observations instead of being incorrectly merged into one track.
|
|
Duplicate known IDs within one frame fail closed.
|
|
"""
|
|
|
|
previous = torch.full((len(records),), -1, dtype=torch.long)
|
|
last_by_track: dict[tuple[int, str], tuple[int, float]] = {}
|
|
unknown_values = {"", "-1", "none", "null", "unknown"}
|
|
for frame_index, time_value in enumerate(frame_times.tolist()):
|
|
start = int(frame_offsets[frame_index])
|
|
end = int(frame_offsets[frame_index + 1])
|
|
seen: set[tuple[int, str]] = set()
|
|
for observation_index in range(start, end):
|
|
record = records[observation_index]
|
|
if not isinstance(record, dict):
|
|
raise ValueError(f"Invalid person record at {observation_index}: {path}")
|
|
raw_id = record.get("id")
|
|
text_id = "" if raw_id is None else str(raw_id).strip()
|
|
source_clip_index = record.get("source_clip_index", 0)
|
|
if isinstance(source_clip_index, bool) or not isinstance(
|
|
source_clip_index, int
|
|
):
|
|
raise ValueError(
|
|
f"Invalid source_clip_index at person record "
|
|
f"{observation_index}: {path}"
|
|
)
|
|
if text_id.lower() in unknown_values:
|
|
# Never join multiple anonymous detections under a shared -1.
|
|
continue
|
|
key = (source_clip_index, text_id)
|
|
if key in seen:
|
|
raise ValueError(
|
|
f"Duplicate person/track ID {text_id!r} in frame "
|
|
f"{frame_index}: {path}"
|
|
)
|
|
seen.add(key)
|
|
old = last_by_track.get(key)
|
|
if old is not None:
|
|
old_index, old_time = old
|
|
gap = float(time_value) - old_time
|
|
if gap <= 0:
|
|
raise ValueError(f"Non-causal person track order: {path}")
|
|
if gap <= max_gap_seconds + 1e-8:
|
|
previous[observation_index] = old_index
|
|
last_by_track[key] = (observation_index, float(time_value))
|
|
return previous
|
|
|
|
|
|
def load_track_features(
|
|
path: Path,
|
|
reference: dict[str, Any],
|
|
*,
|
|
sample_id: str,
|
|
dataset: str,
|
|
track_max_gap_seconds: float,
|
|
expected_contract: dict[str, Any] | None = None,
|
|
) -> tuple[torch.Tensor, FramePersonFeatures, dict[str, Any]]:
|
|
"""Load the exact pooled AV baseline plus causal anonymous track links."""
|
|
|
|
pooled, contract = load_local_features(
|
|
path,
|
|
reference,
|
|
sample_id=sample_id,
|
|
dataset=dataset,
|
|
modalities="av",
|
|
expected_contract=expected_contract,
|
|
)
|
|
_, people, second_contract = load_frame_set_features(
|
|
path,
|
|
reference,
|
|
sample_id=sample_id,
|
|
dataset=dataset,
|
|
expected_contract=contract,
|
|
track_max_gap_seconds=track_max_gap_seconds,
|
|
)
|
|
if second_contract != contract:
|
|
raise AssertionError("Track and pooled local contracts unexpectedly differ")
|
|
return pooled, people, contract
|
|
|
|
|
|
def local_head_dimensions(contract: dict[str, Any] | None) -> dict[str, int]:
|
|
if contract is None:
|
|
return {"local_visual_dim": 0, "local_audio_dim": 0}
|
|
selected = MODALITIES[contract["modalities"]]
|
|
return {
|
|
f"local_{name}_dim": int(contract["config"][f"{name}_dim"]) if name in selected else 0
|
|
for name in ("visual", "audio")
|
|
}
|
|
|
|
|
|
def causal_frame_ticks(end: float, fps: float) -> range:
|
|
"""Global fixed lattice [0,end), excluding the frame exactly at end."""
|
|
if end <= 0 or fps <= 0:
|
|
raise ValueError("end and fps must be positive")
|
|
return range(max(0, math.ceil(end * fps - 1e-8)))
|
|
|
|
|
|
def pooled_recent_visual(
|
|
frame_times: torch.Tensor,
|
|
frame_vectors: torch.Tensor,
|
|
frame_valid: torch.Tensor,
|
|
end: float,
|
|
window_seconds: float,
|
|
) -> tuple[torch.Tensor, bool]:
|
|
"""Equal frame weighting after equal person weighting within each frame."""
|
|
keep = ((frame_times >= max(0., end - window_seconds) - 1e-8)
|
|
& (frame_times < end - 1e-8) & frame_valid)
|
|
if not bool(keep.any()):
|
|
return frame_vectors.new_zeros(frame_vectors.shape[-1]), False
|
|
return frame_vectors[keep].float().mean(0), True
|
|
|
|
|
|
def easycom_boxes(rows: list[dict[str, Any]], kind: str) -> dict[int, list[dict[str, Any]]]:
|
|
"""Map EasyCom face (1-based) / head (0-based) records to decoded indices."""
|
|
if kind not in ("face", "head"):
|
|
raise ValueError(f"Unknown box kind: {kind}")
|
|
result = {}
|
|
for row in rows:
|
|
frame = int(row["Frame_Number"]) - (1 if kind == "face" else 0)
|
|
people = row.get("Participants", [])
|
|
if isinstance(people, dict):
|
|
people = [people]
|
|
if frame < 0 or frame in result or not isinstance(people, list):
|
|
raise ValueError("Invalid/duplicate EasyCom box frame")
|
|
result[frame] = [
|
|
{"id": str(p.get("Participant_ID", -1)),
|
|
"xyxy": [float(p[k]) for k in ("x1", "y1", "x2", "y2")]}
|
|
for p in people
|
|
]
|
|
return result
|
|
|
|
|
|
def crop_box(
|
|
xyxy: list[float], width: int, height: int, *,
|
|
offset_x: int = 0, offset_y: int = 0, expansion: float = 1.,
|
|
) -> tuple[int, int, int, int] | None:
|
|
"""Transform to camera coordinates, expand, clip; reject degenerate boxes."""
|
|
if len(xyxy) != 4 or not all(math.isfinite(v) for v in xyxy):
|
|
return None
|
|
x1, y1, x2, y2 = xyxy
|
|
if x2 <= x1 or y2 <= y1 or expansion <= 0:
|
|
return None
|
|
cx, cy = (x1 + x2) / 2 - offset_x, (y1 + y2) / 2 - offset_y
|
|
half_w, half_h = (x2 - x1) * expansion / 2, (y2 - y1) * expansion / 2
|
|
box = (max(0, math.floor(cx - half_w)), max(0, math.floor(cy - half_h)),
|
|
min(width, math.ceil(cx + half_w)), min(height, math.ceil(cy + half_h)))
|
|
w, h = box[2] - box[0], box[3] - box[1]
|
|
# Qwen's smart_resize rejects aspect ratios above 200 (e.g. a sliver of
|
|
# an offscreen box). Such a crop is not a usable person observation.
|
|
return box if min(w, h) >= 2 and max(w, h) / min(w, h) <= 200 else None
|