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a95e79a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | """EC-SimToken: SimToken + Existence Head for null detection.
Architecture additions over Simtoken_ForCausalLM:
- existence_head: Linear(out_dim, 1) → sigmoid → p(object exists)
- BCE existence loss on synthetic null samples (audio-swapped during training)
- Mask loss gated: null-augmented samples skip mask loss
Null augmentation is done in the training script (audio swap), not here.
This module just accepts an optional `is_null` bool tensor per batch.
"""
from __future__ import annotations
from typing import List
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.avs_model import (
Simtoken_ForCausalLM,
dice_loss,
sigmoid_ce_loss,
compute_alignment_loss,
)
class ECSimtoken_ForCausalLM(Simtoken_ForCausalLM):
"""SimToken with an existence head for null-sample detection.
Extra kwargs (consumed here, not passed to parent):
exist_loss_weight: float BCE existence loss weight (default 1.0)
"""
def __init__(self, config, **kwargs):
self.exist_loss_weight = kwargs.pop("exist_loss_weight", 1.0)
super().__init__(config, **kwargs)
out_dim = config.out_dim
self.existence_head = nn.Linear(out_dim, 1)
# ------------------------------------------------------------------
# Forward
# ------------------------------------------------------------------
def model_forward(
self,
images: torch.FloatTensor,
images_clip: torch.FloatTensor,
audio_features: torch.FloatTensor,
image_features: torch.FloatTensor,
input_ids: torch.LongTensor,
labels: torch.LongTensor,
attention_masks: torch.LongTensor,
masks_list: List[torch.FloatTensor],
resize_list: List[tuple],
orgsize_list: List[tuple],
conversation_list: List[str],
ref_ids: List[torch.LongTensor],
refs_num: List[int],
vids,
fids,
epoch: int = 0,
inference: bool = False,
num_frames: int = 10,
contrast: float = 0.0,
is_null: torch.BoolTensor = None, # [B] True = synthetic null sample
**kwargs,
):
batch_size = len(images)
image_embeddings = torch.cat(image_features, dim=0) # [BT, 256, 64, 64]
audio_embeddings = self.audio_feature_layer(
torch.stack(audio_features, dim=0)
) # [B, T, 4096]
target_frame = 5 # fixed as in original
(
input_ids_mm,
attention_masks_mm,
past_key_values,
inputs_embeds,
labels_mm,
) = super(Simtoken_ForCausalLM, self).prepare_inputs_labels_for_multimodal(
input_ids,
attention_masks,
past_key_values=None,
labels=labels,
images=images_clip,
audio_features=audio_embeddings,
target_frame=target_frame,
ref_ids=ref_ids,
)
output = super(Simtoken_ForCausalLM, self).forward(
input_ids=input_ids_mm,
attention_mask=attention_masks_mm,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels_mm,
output_hidden_states=True,
)
output_hidden_states = output.hidden_states
seg_token_mask = output.labels[..., 1:] == self.seg_token_idx
seg_token_mask = torch.cat(
[
seg_token_mask,
torch.zeros(
(seg_token_mask.shape[0], 1),
device=output.labels.device,
dtype=torch.bool,
),
],
dim=1,
) # [B, seq_len]
seg_embeddings = self.model.text_hidden_fcs[0](
output_hidden_states[-1][seg_token_mask]
) # [seg_num, 256] (seg_num == B when refs_num == [1]*B)
# ── Existence head ────────────────────────────────────────────────
exist_logit = self.existence_head(seg_embeddings) # [seg_num, 1]
# ── Memory / contrastive (optional, gated by contrast weight) ────
fis_flat = [fid[0] for fid in fids]
ct_loss = torch.tensor(0.0, device=seg_embeddings.device)
if not inference and contrast > 0.0:
pos_feats = self.memory.get_positive_features(vids, fis_flat)
neg_feats = self.memory.get_negative_features_same_vid(vids, fis_flat)
for i in range(len(neg_feats)):
for j in range(len(seg_embeddings)):
if j != i:
neg_feats[i].append(seg_embeddings[j].detach().cpu())
ct_loss = compute_alignment_loss(seg_embeddings, pos_feats, neg_feats)
# Only add non-null samples to memory
valid_vids = [vids[i] for i in range(batch_size) if not (is_null is not None and is_null[i])]
valid_fids = [fis_flat[i] for i in range(batch_size) if not (is_null is not None and is_null[i])]
valid_embs = seg_embeddings[
[i for i in range(batch_size) if not (is_null is not None and is_null[i])]
] if valid_vids else seg_embeddings[:0]
if valid_vids:
self.memory.add_batch(valid_vids, valid_fids, valid_embs)
elif not inference:
self.memory.add_batch(vids, fis_flat, seg_embeddings)
# ── Reorganise seg embeddings per batch item ──────────────────────
pred_embeddings = []
pred_idx = 0
for ref_num in refs_num:
pred_embeddings.append(seg_embeddings[pred_idx : pred_idx + ref_num])
pred_idx += ref_num
# ── SAM mask decoder ──────────────────────────────────────────────
pred_masks = []
for i in range(batch_size):
sparse_embeddings, dense_embeddings = self.model.visual_model.prompt_encoder(
points=None,
boxes=None,
masks=None,
text_embeds=pred_embeddings[i].unsqueeze(1),
)
sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
dense_embeddings = dense_embeddings.to(pred_embeddings[i].dtype)
pred_masks_sample = []
for prompt_idx in range(len(sparse_embeddings)):
low_res_masks, _ = self.model.visual_model.mask_decoder(
image_embeddings=image_embeddings[i * num_frames : (i + 1) * num_frames],
image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings[prompt_idx : prompt_idx + 1],
dense_prompt_embeddings=dense_embeddings[prompt_idx : prompt_idx + 1],
multimask_output=False,
)
pred_mask = self.model.visual_model.postprocess_masks(
low_res_masks,
input_size=resize_list[i],
original_size=orgsize_list[i],
) # [T, 1, H, W]
pred_masks_sample.append(pred_mask.squeeze(1))
pred_masks.append(torch.stack(pred_masks_sample, dim=0)) # [num_seg, T, H, W]
gt_masks = masks_list
if inference:
return {
"pred_masks": pred_masks,
"gt_masks": gt_masks,
"exist_logit": exist_logit, # [seg_num, 1]
}
# ── Losses ────────────────────────────────────────────────────────
ce_loss = output.loss * self.ce_loss_weight
# Mask loss — skip null-augmented samples
mask_bce_loss = 0.0
mask_dice_loss = 0.0
num_masks = 0
for batch_idx in range(batch_size):
if is_null is not None and is_null[batch_idx]:
continue # null sample: no mask loss
gt_mask = gt_masks[batch_idx]
pred_mask = pred_masks[batch_idx]
a, b, c, d = gt_mask.shape
gt_flat = gt_mask.view(a * b, c, d)
pred_flat = pred_mask.view(a * b, c, d)
mask_bce_loss += (
sigmoid_ce_loss(pred_flat, gt_flat, num_masks=gt_flat.shape[0])
* gt_flat.shape[0]
)
mask_dice_loss += (
dice_loss(pred_flat, gt_flat, num_masks=gt_flat.shape[0])
* gt_flat.shape[0]
)
num_masks += gt_flat.shape[0]
mask_bce_loss = self.bce_loss_weight * mask_bce_loss / (num_masks + 1e-8)
mask_dice_loss = self.dice_loss_weight * mask_dice_loss / (num_masks + 1e-8)
mask_loss = mask_bce_loss + mask_dice_loss
# Existence loss (BCE)
if is_null is not None:
exist_target = (~is_null).float().to(exist_logit.device)
exist_loss = F.binary_cross_entropy_with_logits(
exist_logit.squeeze(-1), exist_target
)
else:
exist_loss = torch.tensor(0.0, device=exist_logit.device)
loss = (
ce_loss
+ mask_loss
+ self.exist_loss_weight * exist_loss
+ contrast * ct_loss
)
return {
"loss": loss,
"ce_loss": ce_loss,
"mask_bce_loss": mask_bce_loss if isinstance(mask_bce_loss, torch.Tensor) else torch.tensor(mask_bce_loss),
"mask_dice_loss": mask_dice_loss if isinstance(mask_dice_loss, torch.Tensor) else torch.tensor(mask_dice_loss),
"mask_loss": mask_loss if isinstance(mask_loss, torch.Tensor) else torch.tensor(mask_loss),
"exist_loss": exist_loss,
"ct_loss": ct_loss,
"pred_masks": pred_masks,
"gt_masks": gt_masks,
"exist_logit": exist_logit,
}
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