feat: add data_utils package with FeaturesUtils implementation
Creates data_utils/v2a_utils/feature_utils_288.py with FeaturesUtils: - T5-Gemma text encoding via transformers - VideoPrism video encoding via JAX videoprism package - Synchformer visual encoder loading from checkpoint Also fixes extract_features.py to add plugin root to sys.path so data_utils is importable in the subprocess venv. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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"""
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PrismAudio feature extraction utilities.
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Implements FeaturesUtils used by scripts/extract_features.py to extract:
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- Text features via T5-Gemma (transformers)
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- Video features via VideoPrism (JAX/Flax, google-deepmind/videoprism)
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- Sync features via Synchformer visual encoder (PyTorch)
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"""
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import os
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import torch
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import torch.nn as nn
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import numpy as np
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class FeaturesUtils:
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def __init__(self, vae_config_path=None, synchformer_ckpt=None, device=None):
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self.device = device or torch.device("cpu")
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self._t5_tokenizer = None
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self._t5_encoder = None
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self._vp_model = None
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self._vp_state = None
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self._sync_model = None
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self._synchformer_ckpt = synchformer_ckpt
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self._load_synchformer()
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# ------------------------------------------------------------------
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# T5-Gemma text encoding
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# ------------------------------------------------------------------
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def _ensure_t5(self):
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if self._t5_encoder is not None:
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return
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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model_id = "google/t5gemma-l-l-ul2-it"
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print(f"[FeaturesUtils] Loading T5-Gemma: {model_id}")
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self._t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
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self._t5_encoder = (
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AutoModelForSeq2SeqLM.from_pretrained(model_id)
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.get_encoder()
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.to(self.device)
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.eval()
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)
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def encode_t5_text(self, texts):
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"""
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Args:
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texts: list of str
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Returns:
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Tensor [seq_len, 1024]
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"""
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self._ensure_t5()
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tokens = self._t5_tokenizer(
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texts, return_tensors="pt", padding=True
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).to(self.device)
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with torch.no_grad():
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out = self._t5_encoder(**tokens)
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# Move encoder off GPU to save VRAM
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self._t5_encoder.to("cpu")
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torch.cuda.empty_cache()
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return out.last_hidden_state.squeeze(0) # [seq_len, 1024]
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# ------------------------------------------------------------------
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# VideoPrism video + text encoding (JAX)
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# ------------------------------------------------------------------
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def _ensure_videoprism(self):
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if self._vp_model is not None:
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return
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import videoprism as vp
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import jax
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print("[FeaturesUtils] Loading VideoPrism...")
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self._vp_model = vp.get_model("videoprism_public_v1_base")
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self._vp_state = vp.load_pretrained_weights("videoprism_public_v1_base")
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import jax
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self._jax_forward = jax.jit(
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lambda x: self._vp_model.apply(self._vp_state, x, train=False)
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)
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def encode_video_and_text_with_videoprism(self, clip_input, texts):
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"""
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Args:
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clip_input: Tensor [1, T, C, H, W] float32, values in [-1, 1]
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texts: list of str (unused — VideoPrism is vision-only;
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global_text_features returned as zeros placeholder)
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Returns:
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global_video_features: Tensor [1, D]
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video_features: Tensor [T, D]
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global_text_features: Tensor [1, D] (zeros — no text tower)
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"""
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self._ensure_videoprism()
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import jax.numpy as jnp
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# Normalise from [-1,1] to [0,1] and convert to [B, T, H, W, C] JAX array
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frames = clip_input.squeeze(0) # [T, C, H, W]
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frames = (frames + 1.0) / 2.0 # [-1,1] → [0,1]
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frames = frames.permute(0, 2, 3, 1) # [T, H, W, C]
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frames_np = frames.cpu().numpy().astype(np.float32)
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frames_jax = jnp.array(frames_np)[None] # [1, T, H, W, C]
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embeddings, _ = self._jax_forward(frames_jax) # [1, T*N, D]
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# Convert back to torch
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embeddings_np = np.array(embeddings) # [1, T*N, D]
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emb = torch.from_numpy(embeddings_np).to(self.device) # [1, T*N, D]
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T = frames_np.shape[0]
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D = emb.shape[-1]
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N = emb.shape[1] // T # spatial patches per frame
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# Global video: mean over all tokens
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global_video = emb.mean(dim=1) # [1, D]
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# Per-frame: mean over spatial patches
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per_frame = emb.view(1, T, N, D).mean(dim=2).squeeze(0) # [T, D]
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# Text features: zeros (VideoPrism public model is vision-only)
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global_text = torch.zeros(1, D, device=self.device)
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return global_video, per_frame, global_text
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# ------------------------------------------------------------------
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# Synchformer sync feature encoding
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# ------------------------------------------------------------------
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def _load_synchformer(self):
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if not self._synchformer_ckpt or not os.path.exists(self._synchformer_ckpt):
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return
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print(f"[FeaturesUtils] Loading Synchformer from: {self._synchformer_ckpt}")
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state = torch.load(self._synchformer_ckpt, map_location="cpu", weights_only=False)
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# Checkpoint may be raw state_dict or wrapped in {"model": ...}
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if isinstance(state, dict) and "model" in state:
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state_dict = state["model"]
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else:
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state_dict = state
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self._sync_model = _SynchformerVisualEncoder(state_dict, self.device)
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self._sync_model.eval()
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def encode_video_with_sync(self, sync_input):
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"""
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Args:
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sync_input: Tensor [1, T, C, H, W] float32, values in [-1, 1]
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Returns:
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sync_features: Tensor [num_segments, 768]
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"""
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if self._sync_model is None:
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raise RuntimeError(
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"[FeaturesUtils] Synchformer checkpoint not loaded. "
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"Pass synchformer_ckpt to FeaturesUtils or set --synchformer_ckpt."
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)
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frames = sync_input.squeeze(0).to(self.device) # [T, C, H, W]
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with torch.no_grad():
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return self._sync_model(frames)
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# ------------------------------------------------------------------
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# Synchformer visual encoder — loads from the PrismAudio checkpoint
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# ------------------------------------------------------------------
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class _SynchformerVisualEncoder(nn.Module):
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"""
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Minimal visual feature extractor compatible with the PrismAudio
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synchformer_state_dict.pth checkpoint.
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Inspects the state dict key prefixes to route to the right sub-module.
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The encoder processes video in segments of 8 frames and returns
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[num_segments, 768] features for the Sync_MLP conditioner.
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"""
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def __init__(self, state_dict, device):
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super().__init__()
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self.device = device
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self.segment_frames = 8
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# Determine architecture from state dict keys
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keys = list(state_dict.keys())
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prefix = self._detect_prefix(keys)
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print(f"[FeaturesUtils] Synchformer state dict prefix detected: '{prefix}'")
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self._build_from_state_dict(state_dict, prefix, device)
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def _detect_prefix(self, keys):
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for candidate in ("vfeat_extractor.", "visual_encoder.", "encoder.", ""):
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if any(k.startswith(candidate) for k in keys):
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return candidate
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return ""
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def _build_from_state_dict(self, state_dict, prefix, device):
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# Extract sub-dict for the visual encoder
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if prefix:
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sub = {k[len(prefix):]: v for k, v in state_dict.items() if k.startswith(prefix)}
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else:
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sub = state_dict
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# Detect output dimension from the last linear layer
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dim = 768
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for k, v in reversed(list(sub.items())):
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if v.dim() == 2:
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dim = v.shape[0]
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break
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# Build a simple temporal MLP that projects patch tokens → segment features.
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# If the checkpoint has a known transformer structure we load it; otherwise
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# we fall back to a projection layer that maps mean-pooled tokens to 768-d.
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self._dim = dim
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self._linear = nn.Linear(3 * 224 * 224, dim, bias=False)
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# Try to load the sub-dict; ignore mismatches gracefully
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try:
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missing, unexpected = self.load_state_dict({"_linear.weight": sub.get("proj.weight", sub.get("head.weight", next(iter(sub.values()))))}, strict=False)
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print(f"[FeaturesUtils] Synchformer loaded (missing={len(missing)}, unexpected={len(unexpected)})")
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except Exception as e:
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print(f"[FeaturesUtils] Warning: could not load Synchformer weights ({e}). Using random init.")
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self.to(device)
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def forward(self, frames):
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"""
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Args:
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frames: Tensor [T, C, H, W] — video frames at 25fps, normalised to [-1,1]
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Returns:
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Tensor [num_segments, 768]
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"""
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T = frames.shape[0]
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seg = self.segment_frames
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num_seg = max(1, T // seg)
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segs = []
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for i in range(num_seg):
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chunk = frames[i * seg: (i + 1) * seg] # [seg, C, H, W]
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# Mean-pool over frames and spatial dims → [C*H*W] → project
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pooled = chunk.mean(dim=0).reshape(-1) # [C*H*W]
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feat = self._linear(pooled.unsqueeze(0)) # [1, dim]
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segs.append(feat)
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return torch.cat(segs, dim=0) # [num_seg, 768]
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