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import os
import random
from functools import partial
import numpy as np
import torch
import transformers
from peft import LoraConfig, get_peft_model
from torch.utils.data import DataLoader
from transformers import AutoConfig
from configs import args
from datasets import REFAVS
from load_model import collate_fn, dict_to_cuda
from models.avs_model import Simtoken_ForCausalLM
def set_seed(seed=42):
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def find_lora_target_modules(model, target_modules=("q_proj", "v_proj")):
modules = set()
excluded = [
"visual_model",
"vision_tower",
"mm_projector",
"text_hidden_fcs",
"audio_feature_layer",
]
for name, module in model.named_modules():
if not isinstance(module, torch.nn.Linear):
continue
if any(x in name for x in excluded):
continue
if any(x in name for x in target_modules):
modules.add(name)
return sorted(modules)
def build_model(tokenizer, seg_token_idx):
model_args = {
"train_mask_decoder": True,
"out_dim": 256,
"ce_loss_weight": 1.0,
"dice_loss_weight": 0.5,
"bce_loss_weight": 2.0,
"seg_token_idx": seg_token_idx,
"vision_pretrained": args.vision_pretrained,
"vision_tower": args.vision_tower,
"use_im_start_end": False,
"compress": args.compress,
"start": args.start,
}
model = Simtoken_ForCausalLM.from_pretrained(
args.mllm,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
**model_args,
)
model.config.eos_token_id = tokenizer.eos_token_id
model.config.bos_token_id = tokenizer.bos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
model.get_model().initialize_vision_modules(model.get_model().config)
vision_tower = model.get_model().get_vision_tower()
vision_tower.to(dtype=torch.float32, device="cuda")
model_args_from_pt = AutoConfig.from_pretrained(args.mllm)
model_args_from_pt.use_cluster = True
model_args_from_pt.freeze = False
model_args_from_pt.mm_tune = True
model_args_from_pt.spatial_cluster_rate0 = 64
model_args_from_pt.spatial_cluster_rate1 = 32
model_args_from_pt.spatial_cluster_rate2 = 16
model_args_from_pt.temporal_cluster_rate = 0.0625
model_args_from_pt.vision_tune = False
model.get_model().initialize_cluster_modules(model_args_from_pt)
model.get_model().initialize_lisa_modules(model.get_model().config)
lora_config = LoraConfig(
r=8,
lora_alpha=16,
target_modules=find_lora_target_modules(model),
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
model = model.to("cuda")
model.resize_token_embeddings(len(tokenizer))
state = torch.load(args.saved_model, map_location="cpu")
missing, unexpected = model.load_state_dict(state, strict=False)
print(f"Loaded checkpoint: {args.saved_model}")
print(f"Missing keys: {len(missing)} | Unexpected keys: {len(unexpected)}")
model.eval()
return model
def get_seg_embedding(model, batch):
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
output = model.forward(
images=batch["images"],
images_clip=batch["images_clip"],
audio_features=batch["audio_feats"],
image_features=batch["image_feats"],
input_ids=batch["input_ids"],
labels=batch["labels"],
attention_masks=batch["attention_masks"],
masks_list=batch["masks"],
resize_list=batch["resizes"],
orgsize_list=batch["orgsizes"],
conversation_list=batch["convs"],
refs_num=batch["refs_num"],
fids=batch["fids"],
vids=batch["vids"],
contrast=args.ct_weight,
ref_ids=batch["ref_ids"],
inference=True,
)
return output["seg_embeddings"][0][0:1]
def check_one_sample(model, batch):
q = get_seg_embedding(model, batch)
image_embeddings = batch["image_feats"][0]
visual_model = model.get_model().visual_model
sparse, dense = visual_model.prompt_encoder(
points=None,
boxes=None,
masks=None,
text_embeds=q.unsqueeze(1),
)
sparse = sparse.to(q.dtype)
dense = dense.to(q.dtype)
decoder = visual_model.mask_decoder
image_pe = visual_model.prompt_encoder.get_dense_pe()
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
full_masks, full_iou = decoder(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse,
dense_prompt_embeddings=dense,
multimask_output=False,
)
rows = []
for t in range(image_embeddings.shape[0]):
single_masks, single_iou = decoder(
image_embeddings=image_embeddings[t : t + 1],
image_pe=image_pe,
sparse_prompt_embeddings=sparse,
dense_prompt_embeddings=dense,
multimask_output=False,
)
diff = (full_masks[t : t + 1] - single_masks).float().abs()
iou_diff = (full_iou[t : t + 1] - single_iou).float().abs()
rows.append(
{
"vid": batch["vids"][0],
"ref": batch["refs"][0][0],
"frame": t,
"max_abs_diff": diff.max().item(),
"mean_abs_diff": diff.mean().item(),
"iou_pred_diff": iou_diff.max().item(),
}
)
return rows
def main():
set_seed(42)
torch.set_grad_enabled(False)
tokenizer = transformers.AutoTokenizer.from_pretrained(
args.mllm,
cache_dir=None,
model_max_length=2048,
padding_side="right",
use_fast=False,
)
tokenizer.pad_token = tokenizer.unk_token
tokenizer.add_tokens("[SEG]")
seg_token_idx = tokenizer("[SEG]", add_special_tokens=False).input_ids[0]
dataset = REFAVS(args.eval_split, args, tokenizer, input_type="refer")
loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=partial(collate_fn, tokenizer=tokenizer),
)
limit = args.max_eval_rows if args.max_eval_rows > 0 else 1
print(f"Split: {args.eval_split} | samples to check: {limit}")
model = build_model(tokenizer, seg_token_idx)
all_rows = []
for sample_idx, batch in enumerate(loader):
if sample_idx >= limit:
break
batch = dict_to_cuda(batch)
rows = check_one_sample(model, batch)
all_rows.extend(rows)
print(f"\nSample {sample_idx}: vid={rows[0]['vid']} ref={rows[0]['ref']}")
print("frame | max_abs_diff | mean_abs_diff | iou_pred_diff")
for row in rows:
print(
f"{row['frame']:02d} | "
f"{row['max_abs_diff']:.8e} | "
f"{row['mean_abs_diff']:.8e} | "
f"{row['iou_pred_diff']:.8e}"
)
if not all_rows:
raise RuntimeError("No rows were checked. Is the selected split empty?")
max_diff = max(row["max_abs_diff"] for row in all_rows)
mean_diff = sum(row["mean_abs_diff"] for row in all_rows) / len(all_rows)
max_iou_diff = max(row["iou_pred_diff"] for row in all_rows)
print("\nSummary")
print(f"checked frames: {len(all_rows)}")
print(f"global max_abs_diff: {max_diff:.8e}")
print(f"average mean_abs_diff: {mean_diff:.8e}")
print(f"global max_iou_pred_diff: {max_iou_diff:.8e}")
csv_path = os.environ.get("DECODER_INVARIANCE_CSV")
if csv_path:
os.makedirs(os.path.dirname(os.path.abspath(csv_path)), exist_ok=True)
with open(csv_path, "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=list(all_rows[0].keys()))
writer.writeheader()
writer.writerows(all_rows)
print(f"Saved CSV: {csv_path}")
if __name__ == "__main__":
main()
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