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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.dataset_for_clean_descrip import PoseHICODetDataset
from data.convsersation import Conversation_For_Action_Pharse as Conversation
import re
from dataclasses import dataclass
from tools.vlm_backend import build_batch_tensors, decode_generated_text, load_model_and_processor
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
class StreamingJsonArrayWriter:
def __init__(self, output_path):
self.output_path = output_path
self.file = None
self.is_first = True
def __enter__(self):
self.file = open(self.output_path, "w", encoding="utf-8")
self.file.write("[\n")
self.file.flush()
return self
def write(self, item):
if not self.is_first:
self.file.write(",\n")
json.dump(item, self.file, ensure_ascii=False, indent=2)
self.file.flush()
self.is_first = False
def __exit__(self, exc_type, exc_val, exc_tb):
if self.file is not None:
self.file.write("\n]\n")
self.file.close()
@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},]},
])
batch_tensors = build_batch_tensors(
processor=self.processor,
prompts=batch_prompts,
images=batch_images,
system_prompt=self.conv.system,
)
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 = 16
data_loader = DataLoader(sub_dataset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=DataCollatorForSupervisedDataset(processor, args.data_path))
output_path = os.path.join(args.output_dir, f'labels_{rank}.json')
with StreamingJsonArrayWriter(output_path) as writer:
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 = decode_generated_text(processor, output_id, input_id)
meta['action_description'] = output
writer.write(meta)
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()
backend_name, model, processor = load_model_and_processor(
model_path=args.model_path,
backend=args.model_backend,
torch_dtype=args.torch_dtype,
trust_remote_code=True,
)
print(f'Using model backend: {backend_name}')
model = model.cuda()
model.eval()
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="")
parser.add_argument("--model-backend", type=str, default="auto")
parser.add_argument("--torch-dtype", type=str, default="bfloat16")
args = parser.parse_args()
eval_model(args)
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