infer/.ipynb_checkpoints/utils-checkpoint.py

# Open Source Model Licensed under the Apache License Version 2.0 
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited 
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.

# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
# The below software and/or models in this distribution may have been 
# modified by THL A29 Limited ("Tencent Modifications"). 
# All Tencent Modifications are Copyright (C) THL A29 Limited.

# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT 
# except for the third-party components listed below. 
# Hunyuan 3D does not impose any additional limitations beyond what is outlined 
# in the repsective licenses of these third-party components. 
# Users must comply with all terms and conditions of original licenses of these third-party 
# components and must ensure that the usage of the third party components adheres to 
# all relevant laws and regulations. 

# For avoidance of doubts, Hunyuan 3D means the large language models and 
# their software and algorithms, including trained model weights, parameters (including 
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, 
# fine-tuning enabling code and other elements of the foregoing made publicly available 
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

import os
import time
import random
import numpy as np
import torch
from torch.cuda.amp import autocast, GradScaler
from functools import wraps

def seed_everything(seed):
    '''
        seed everthing
    '''
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    os.environ["PL_GLOBAL_SEED"] = str(seed)

def timing_decorator(category: str):
    '''
        timing_decorator: record time
    '''
    def decorator(func):
        func.call_count = 0
        @wraps(func)
        def wrapper(*args, **kwargs):
            start_time = time.time()
            result = func(*args, **kwargs) 
            end_time = time.time()
            elapsed_time = end_time - start_time
            func.call_count += 1
            print(f"[HunYuan3D]-[{category}], cost time: {elapsed_time:.4f}s") # huiwen
            return result
        return wrapper
    return decorator

def auto_amp_inference(func):
    '''
        with torch.cuda.amp.autocast()"
            xxx
    '''
    @wraps(func)
    def wrapper(*args, **kwargs):
        with autocast(): 
            output = func(*args, **kwargs) 
        return output
    return wrapper

def get_parameter_number(model):
    total_num = sum(p.numel() for p in model.parameters())
    trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return {'Total': total_num, 'Trainable': trainable_num}

def set_parameter_grad_false(model):
    for p in model.parameters():
        p.requires_grad = False

def str_to_bool(s):
    if s.lower() in ['true', 't', 'yes', 'y', '1']: 
        return True
    elif s.lower() in ['false', 'f', 'no', 'n', '0']: 
        return False
    else: 
        raise f"bool arg must one of ['true', 't', 'yes', 'y', '1', 'false', 'f', 'no', 'n', '0']"