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'''
python test.py recipes/tinyllama_mole/sft/config_routeraux_ep3.yaml
'''

import logging
import random
import sys

import datasets
import torch

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import cv2
import numpy as np
import trimesh
import os
import glob
from PIL import Image
from colmap_utils import qvec2rotmat, rotmat2qvec, read_images_binary, read_cameras_text, read_images_text


def load_K_Rt_from_P(P):

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This is the detailed profiling:
```
Execute Stats: 

Execute Stat 1

Number of HVX threads used : 4  count
RPC (execute) time : 2717302  us
QNN accelerator (execute) time : 2714698  us
Num times yield occured : 0  count

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@inproceedings{karras2020analyzing,
  title={Analyzing and improving the image quality of stylegan},
  author={Karras, Tero and Laine, Samuli and Aittala, Miika and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={8110--8119},
  year={2020}
}

@article{radford2019language,
  title={Language models are unsupervised multitask learners},

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import torch

from srt.encoder import SRTEncoder
from srt.decoder import SRTDecoder, NerfDecoder

import yaml

batch_size = 1
num_views = 2
device = torch.device("cuda:0")

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# https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py

import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from torch.nn.modules.utils import _pair

MIN_NUM_PATCHES = 16

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import os
import torch
import h5py as h5
from torchvision import datasets
from tqdm import tqdm

"""
Usage: this script creates `imagenet_train.h5` in `data_path`. 
If you need to create `imagenet_val.h5`, set `is_train = False` at L26.

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import copy
from typing import Optional, List
from dotmap import DotMap

import torch
import torch.nn.functional as F
from torch import nn, Tensor

from asteroid_filterbanks import make_enc_dec

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#!/usr/bin/env python
# coding: utf-8
import numpy as np
import random
from PIL import Image
import matplotlib.pyplot as plt

import torch#!/usr/bin/env python
# coding: utf-8
import numpy as np

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import os
import time
import math
import itertools
import numpy as np
from PIL import Image
from tqdm import tqdm
from copy import deepcopy

import torch