"""Standalone HuggingFace model for RepConTUR. Composes an InternVL backbone (hub remote code) whose 2D vision pipeline is replaced by a 3D CT-ViT encoder + attentional pooler, with a VoxTell 3D text-prompted grounding decoder. Loadable via ``AutoModel.from_pretrained(..., trust_remote_code=True)``; all weights ship in the model's safetensors. """ import logging import os import types from typing import List, Optional import numpy as np import torch import torch.nn as nn from transformers import ( AutoConfig, AutoModel, AutoTokenizer, GenerationConfig, PreTrainedModel, ) from .configuration_repcontur import RepConTURConfig from .ct_attentional_pooler import CTAttentionalPoolProjector from .ct_volume_encoder import CTVolumeEncoder from .voxtell_wrapper import VoxTellGroundingEncoder from ._utils import get_stop_criteria # HF's trust_remote_code loader copies only the direct `from .x import y` imports # of this file into its module cache (one level, this syntax), so name every # vendored leaf here to force the copy. from ._compat import print_log # noqa: F401 from .attention import Attention # noqa: F401 from .ctvit import CTViT # noqa: F401 from .nnunet import NNUNet3DEncoder # noqa: F401 from .transformer import TransformerDecoder # noqa: F401 from .voxtell_model import VoxTellModel # noqa: F401 from .preprocessing import VoxTellPreprocessor # noqa: F401 logger = logging.getLogger("repcontur") _DTYPE_MAP = { 'bfloat16': torch.bfloat16, 'float16': torch.float16, 'float32': torch.float32, } def _ct_extract_feature(self, pixel_values): """CT replacement for InternVL.extract_feature (no CLS/pixel-shuffle).""" vit_embeds = self.vision_model(pixel_values) vit_embeds = self.mlp1(vit_embeds) return vit_embeds class _MllmWrapper(nn.Module): """Holds the InternVL backbone under ``.model`` to mirror checkpoint keys.""" def __init__(self, model): super().__init__() self.model = model class RepConTURModel(PreTrainedModel): config_class = RepConTURConfig _no_split_modules = [] def __init__(self, config: RepConTURConfig): super().__init__(config) self.torch_dtype = _DTYPE_MAP.get(config.torch_dtype, torch.bfloat16) # ── InternVL backbone (architecture only; weights load from safetensors) internvl_config = AutoConfig.from_pretrained( config.internvl_model_path, trust_remote_code=True, ) internvl = AutoModel.from_config(internvl_config, trust_remote_code=True) internvl = internvl.to(self.torch_dtype) # Resize to the RepConTUR vocab (base + special tokens). mean_resizing # would call .item() on meta tensors during from_pretrained; the added # rows are overwritten by the loaded weights, so plain resizing is fine. internvl.language_model.resize_token_embeddings( config.vocab_size, mean_resizing=False, ) internvl.img_context_token_id = config.img_context_token_id # ── Replace the 2D vision pipeline with CT equivalents ────────── del internvl.vision_model del internvl.mlp1 ct_encoder_config = { k: v for k, v in config.ct_encoder.items() if k not in ('checkpoint_path', 'ct_clip_source_path') } internvl.vision_model = CTVolumeEncoder( checkpoint_path=None, **ct_encoder_config, ).to(self.torch_dtype) llm_hidden = internvl.config.llm_config.hidden_size if config.llm_hidden_size is not None and config.llm_hidden_size != llm_hidden: raise ValueError( f'config.llm_hidden_size={config.llm_hidden_size} does not match the ' f'InternVL backbone hidden size {llm_hidden}') internvl.mlp1 = CTAttentionalPoolProjector( context_dim=internvl.vision_model.dim, output_dim=llm_hidden, **config.ct_pooler, ) internvl.extract_feature = types.MethodType(_ct_extract_feature, internvl) self.mllm = _MllmWrapper(internvl) # ── VoxTell 3D grounding encoder ──────────────────────────────── self.grounding_encoder = VoxTellGroundingEncoder( arch_kwargs=config.voxtell_arch_kwargs, kw_requires_import=config.voxtell_kw_requires_import, decoder_layer=config.voxtell_decoder_layer, text_embedding_dim=config.voxtell_text_embedding_dim, num_maskformer_stages=config.voxtell_num_maskformer_stages, num_heads=config.voxtell_num_heads, query_dim=config.voxtell_query_dim, ) # ── Cross-modal bridge: LLM hidden → VoxTell query_dim ────────── self.text_hidden_fcs = nn.Sequential( nn.Linear(llm_hidden, llm_hidden), nn.ReLU(inplace=True), nn.Linear(llm_hidden, config.voxtell_query_dim), nn.Dropout(0.0), ) self.seg_token_idx = config.seg_token_idx self.tokenizer = None self.gen_config = None self.stop_criteria = None self.template = config.template # ----------------------------------------------------------------- def preparing_for_generation(self, tokenizer=None, generation_kwargs=None): """Set up tokenizer, generation config, and stop criteria for inference.""" if tokenizer is None: tokenizer = AutoTokenizer.from_pretrained( self.config._name_or_path, trust_remote_code=True, ) self.tokenizer = tokenizer stop_words = list(self.template.get('STOP_WORDS', [])) self.stop_criteria = get_stop_criteria(tokenizer=tokenizer, stop_words=stop_words) default_gen_kwargs = dict( max_new_tokens=512, do_sample=False, eos_token_id=tokenizer.eos_token_id, pad_token_id=( tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id ), ) default_gen_kwargs.update(self.config.generation_kwargs) if generation_kwargs: default_gen_kwargs.update(generation_kwargs) self.gen_config = GenerationConfig(**default_gen_kwargs) # Cast the whole model (incl. grounding_encoder), matching the source # eval harness, so VoxTell does not stay fp32 at inference. self.to(self.torch_dtype) return self # ----------------------------------------------------------------- @torch.inference_mode() def predict_volume( self, pixel_values: torch.Tensor, input_ids: list, g_pixel_values: torch.Tensor = None, ) -> dict: """Autoregressive generation + VoxTell sliding-window segmentation. Args: pixel_values: (N, 1, D, H, W) CT volume for the vision encoder. input_ids: prompt token IDs ending at the assistant-turn opener. g_pixel_values: (1, D', H', W') preprocessed full volume for VoxTell. Returns: dict with ``prediction`` (str) and ``prediction_mask`` ((D',H',W') uint8 or None). """ assert self.gen_config is not None, "call preparing_for_generation() first" language_model = self.mllm.model.language_model input_ids_t = torch.tensor(input_ids, device=pixel_values.device).unsqueeze(0) attention_mask = torch.ones_like(input_ids_t, dtype=torch.bool) input_embeds = language_model.get_input_embeddings()(input_ids_t).clone() pixel_values = pixel_values.to(dtype=self.torch_dtype) vit_embeds = self.mllm.model.extract_feature(pixel_values) vit_embeds = vit_embeds.to(input_embeds.dtype) image_flags = torch.sum(pixel_values, dim=tuple(range(1, pixel_values.ndim))) != 0 vit_embeds = vit_embeds[image_flags.long() == 1] batch, seq_len, channels = input_embeds.shape input_embeds_flat = input_embeds.reshape(batch * seq_len, channels) img_token_mask = (input_ids_t.reshape(-1) == self.mllm.model.img_context_token_id) if img_token_mask.sum() > 0 and vit_embeds.numel() > 0: visual_flat = vit_embeds.reshape(-1, channels) n_vis = min(img_token_mask.sum().item(), visual_flat.shape[0]) mask_indices = img_token_mask.nonzero(as_tuple=True)[0] input_embeds_flat[mask_indices[:n_vis]] = visual_flat[:n_vis] input_embeds = input_embeds_flat.reshape(batch, seq_len, channels) generate_output = language_model.generate( inputs_embeds=input_embeds, attention_mask=attention_mask, generation_config=self.gen_config, stopping_criteria=self.stop_criteria, bos_token_id=self.tokenizer.bos_token_id, output_hidden_states=True, return_dict_in_generate=True, ) pred_text = self.tokenizer.decode( generate_output.sequences[0], skip_special_tokens=False, ).strip() hidden_states = generate_output.hidden_states sequences = generate_output.sequences[0] prompt_len = input_embeds.shape[1] gen_start = prompt_len if len(sequences) >= prompt_len + 2 else 0 generated_ids = sequences[gen_start:] seg_hidden = self._collect_seg_hidden(hidden_states, generated_ids) pred_mask = None if len(seg_hidden) > 0 and g_pixel_values is not None: if len(seg_hidden) > 1: logger.warning("predict_volume: %d [SEG] tokens, using first", len(seg_hidden)) seg_hidden = seg_hidden[:1] seg_embd = self.text_hidden_fcs(seg_hidden).unsqueeze(1) # (1, 1, 2048) vol = g_pixel_values.unsqueeze(0).to( dtype=torch.bfloat16, device=seg_embd.device, ) spatial = g_pixel_values.shape[1:] if any(s > 192 for s in spatial): logits = self.grounding_encoder.predict_sliding_window(vol, seg_embd, num_objs=1) else: logits = self.grounding_encoder.forward_with_language(vol, seg_embd, num_objs=1) pred_mask = (logits.squeeze(0).float().sigmoid() > 0.5).cpu().numpy().astype(np.uint8) return {'prediction': pred_text, 'prediction_mask': pred_mask} def _collect_seg_hidden(self, hidden_states, generated_ids): """Gather last-layer hidden states at generated [SEG] tokens → (n_seg, H).""" n_steps = len(hidden_states) seg_list = [] for gen_idx, tok_id in enumerate(generated_ids): if tok_id == self.seg_token_idx: step = gen_idx + 1 if step < n_steps: seg_list.append(hidden_states[step][-1][0]) ref = hidden_states[0][-1] if seg_list: return torch.cat(seg_list, dim=0) return torch.empty(0, ref.shape[-1], device=ref.device, dtype=ref.dtype)