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import pandas as pd | |
from twstock import Stock | |
import argparse | |
def parse(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--etf_code", type=str, default="00733", | |
) |
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from backtesting import Backtest, Strategy | |
from backtesting.lib import crossover | |
from FinMind.data import DataLoader | |
import pandas as pd | |
import talib | |
from talib import abstract | |
## 取得資料 |
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import whisper | |
import openai | |
import os | |
openai.api_key = "sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" | |
# input audio file name | |
audio_file = r"C:\Users\User\Desktop\input.mp3" | |
# load the model and transcribe the audio |
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import os | |
import cv2 | |
import numpy as np | |
from tqdm import tqdm | |
import argparse | |
from facenet_pytorch import MTCNN | |
mtcnn = MTCNN(select_largest=True, min_face_size=64, post_process=False, device='cuda:0') |
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import re | |
import os | |
urls = "https://drive.google.com/file/d/FILEID_1/view?usp=drive_link, https://drive.google.com/file/d/FILEID_2/view?usp=drive_link, https://drive.google.com/file/d/FILEID_3/view?usp=drive_link" | |
url_list = urls.split(', ') | |
pat = re.compile('https://drive.google.com/file/d/(.*)/view\?usp=drive_link') | |
for idx, url in enumerate(url_list): | |
g = pat.match(url) | |
id = g.group(1) | |
down_url = f'https://drive.google.com/uc?id={id}' |
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""" | |
https://github.com/d246810g2000/YOLOX/blob/main/datasets/train_val_data_split_coco.py | |
""" | |
import os | |
import cv2 | |
import json | |
import random | |
import shutil | |
import xml.etree.ElementTree as ET | |
from tqdm import tqdm |
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def resume_train(self, model): | |
if self.args.resume: | |
logger.info("resume training") | |
if self.args.ckpt is None: | |
ckpt_file = os.path.join(self.file_name, "latest" + "_ckpt.pth") | |
else: | |
ckpt_file = self.args.ckpt | |
ckpt = torch.load(ckpt_file, map_location=self.device) | |
# resume the model/optimizer state dict |
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def _cache_images(self): | |
logger.warning("\n********************************************************************************\n" | |
"You are using cached images in RAM to accelerate training.\n" | |
"This requires large system RAM.\n" | |
"Make sure you have 200G+ RAM and 136G available disk space for training COCO.\n" | |
"********************************************************************************\n") | |
max_h = self.img_size[0] | |
max_w = self.img_size[1] | |
cache_file = self.data_dir + "/img_resized_cache_" + self.name + ".array" | |
if not os.path.exists(cache_file): |
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""" | |
https://github.com/z-bingo/FastDVDNet/tree/master/arch | |
Reimplementation of 4 channel FastDVDNet in PyTorch | |
""" | |
import torch | |
import torch.nn as nn | |
import numpy as np | |
from thop import profile |
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