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import os
import json
import argparse
import numpy as np
from tqdm import tqdm
import torch
import torch.distributed as dist
from torch.utils.data import DataLoader
from torchvision import transforms as T
from data.pose_hicodet import PoseHICODetDataset
from data.convsersation import Conversation
import re
from dataclasses import dataclass
from transformers import Qwen3VLForConditionalGeneration
from transformers import AutoTokenizer, AutoConfig, AutoProcessor
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
import os, json
import torch
import torch.distributed as dist
def gather_labels_and_save(labels, output_path):
# Make sure dist is initialized (torchrun / deepspeed / accelerate usually does this)
world_size = dist.get_world_size()
rank = dist.get_rank()
gathered = [None for _ in range(world_size)]
dist.all_gather_object(gathered, labels) # gathered[i] is labels from rank i
if rank == 0:
merged = []
for part in gathered:
merged.extend(part)
with open(output_path, "w", encoding="utf-8") as f:
json.dump(merged, f, ensure_ascii=False, indent=2)
dist.barrier() # optional: ensure rank0 finished writing before others exit
@dataclass
class DataCollatorForSupervisedDataset(object):
def __init__(self, processor, data_path):
self.processor = processor
self.conv = Conversation(
system='',
data_path=data_path
)
def __call__(self, data_dicts):
"""Collate examples for supervised fine-tuning."""
batch_prompts = []
batch_images = []
result_meta = []
for i, data_dict in enumerate(data_dicts):
batch_images.append(data_dict['image'])
batch_prompts.append(self.conv.get_prompt(data_dict['meta']))
result_meta.append(data_dict['meta'])
messages = []
for prompt in zip(batch_prompts):
messages.append([
{"role": "system",
"content":[
{"type": "text",
"text": self.conv.system},]},
{"role": "user",
"content":[
{"type": "image"},
{"type": "text",
"text": prompt},]},
])
prompts = [self.processor.apply_chat_template(m,
tokenize=False,
add_generation_prompt=True)
for m in messages]
batch_tensors = self.processor(
text=prompts,
images=batch_images,
return_tensors="pt",
padding=True
)
return batch_tensors, result_meta
@torch.no_grad()
def worker(model, processor, dataset, args, output_dir):
rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
indices = list(range(rank, len(dataset), world_size))
print("==>" + " Worker {} Started, responsible for {} images".format(rank, len(indices)))
sub_dataset = torch.utils.data.Subset(dataset, indices)
batch_size = 1
data_loader = DataLoader(sub_dataset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=DataCollatorForSupervisedDataset(processor, args.data_path))
labels = []
for batch_tensors, result_meta in tqdm(data_loader):
input_ids = batch_tensors['input_ids'].cuda()
batch_tensors = {k: v.cuda() for k, v in batch_tensors.items() if isinstance(v, torch.Tensor)}
with torch.inference_mode():
output_dict = model.generate(do_sample=False,
output_scores=True,
return_dict_in_generate=True,
max_new_tokens=1600,
output_logits=True,
**batch_tensors,)
output_ids = output_dict['sequences']
for input_id, output_id, meta in zip(input_ids, output_ids, result_meta):
input_token_len = input_id.shape[0]
n_diff_input_output = (input_id != output_id[:input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] Sample: {n_diff_input_output} output_ids are not the same as the input_ids')
output = processor.tokenizer.batch_decode(output_id[input_token_len:].unsqueeze(0), skip_special_tokens=True)[0]
labels.append({
'file_name': meta['file_name'],
'image_id': meta['image_id'],
'instance_id': meta['instance_id'],
'keypoints': meta['joints_3d'].reshape(-1).tolist(),
'vis': meta['joints_3d_vis'].reshape(-1).tolist(),
'im_height': meta['hoi_obj']['height'],
'im_width': meta['hoi_obj']['width'],
'human_bbox': meta['hoi_obj']['human_bbox'],
'object_bbox': meta['hoi_obj']['object_bbox'],
'action_labels': meta['hoi_obj']['action_labels'],
'description': output,
})
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
output_path = os.path.join(args.output_dir, f'labels_{local_rank}.json')
with open(output_path, "w", encoding="utf-8") as f:
json.dump(labels, f, ensure_ascii=False, indent=2)
def eval_model(args):
torch.distributed.init_process_group(backend='nccl')
rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
print('Init process group: world_size: {}, rank: {}'.format(world_size, rank))
torch.cuda.set_device(rank)
disable_torch_init()
model = Qwen3VLForConditionalGeneration.from_pretrained(
args.model_path,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
model = model.cuda()
model.eval()
processor = AutoProcessor.from_pretrained(
args.model_path,
trust_remote_code=True)
processor.tokenizer.padding_side = "left"
processor.tokenizer.pad_token = processor.tokenizer.eos_token
dataset = PoseHICODetDataset(
data_path=args.data_path,
multimodal_cfg=dict(image_folder=os.path.join(args.data_path, 'Images/images/train2015'),
data_augmentation=False,
image_size=336,),)
worker(model, processor, dataset, args, args.output_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--data-path", type=str, default="")
parser.add_argument("--output-dir", type=str, default="")
args = parser.parse_args()
eval_model(args)
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