123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
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# Code based on https://github.com/carpedm20/DCGAN-tensorflow/blob/master/download.py
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from __future__ import print_function
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import os
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import sys
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import gzip
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import json
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import tarfile
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import zipfile
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import argparse
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import requests
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from tqdm import tqdm
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from six.moves import urllib
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from utils import query_yes_no
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parser = argparse.ArgumentParser(description='Download model checkpoints.')
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parser.add_argument('checkpoints', metavar='N', type=str, nargs='+', choices=['son', 'park'],
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help='name of checkpoints to download [son, park]')
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def download(url, dirpath):
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filename = url.split('/')[-1]
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filepath = os.path.join(dirpath, filename)
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u = urllib.request.urlopen(url)
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f = open(filepath, 'wb')
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filesize = int(u.headers["Content-Length"])
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print("Downloading: %s Bytes: %s" % (filename, filesize))
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downloaded = 0
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block_sz = 8192
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status_width = 70
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while True:
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buf = u.read(block_sz)
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if not buf:
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print('')
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break
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else:
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print('', end='\r')
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downloaded += len(buf)
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f.write(buf)
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status = (("[%-" + str(status_width + 1) + "s] %3.2f%%") %
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('=' * int(float(downloaded) / filesize * status_width) + '>', downloaded * 100. / filesize))
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print(status, end='')
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sys.stdout.flush()
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f.close()
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return filepath
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def download_file_from_google_drive(id, destination):
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URL = "https://docs.google.com/uc?export=download"
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session = requests.Session()
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response = session.get(URL, params={ 'id': id }, stream=True)
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token = get_confirm_token(response)
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if token:
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params = { 'id' : id, 'confirm' : token }
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response = session.get(URL, params=params, stream=True)
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save_response_content(response, destination)
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def get_confirm_token(response):
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for key, value in response.cookies.items():
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if key.startswith('download_warning'):
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return value
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return None
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def save_response_content(response, destination, chunk_size=32*1024):
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total_size = int(response.headers.get('content-length', 0))
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with open(destination, "wb") as f:
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for chunk in tqdm(response.iter_content(chunk_size), total=total_size,
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unit='B', unit_scale=True, desc=destination):
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if chunk: # filter out keep-alive new chunks
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f.write(chunk)
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def unzip(filepath):
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print("Extracting: " + filepath)
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dirpath = os.path.dirname(filepath)
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with zipfile.ZipFile(filepath) as zf:
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zf.extractall(dirpath)
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os.remove(filepath)
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def download_checkpoint(checkpoint):
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if checkpoint == "son":
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save_path, drive_id = "son-20171015.tar.gz", "0B_7wC-DuR6ORcmpaY1A5V1AzZUU"
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elif checkpoint == "park":
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save_path, drive_id = "park-20171015.tar.gz", "0B_7wC-DuR6ORYjhlekl5bVlkQ2c"
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else:
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raise Exception(" [!] Unknown checkpoint: {}".format(checkpoint))
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if os.path.exists(save_path):
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print('[*] {} already exists'.format(save_path))
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else:
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download_file_from_google_drive(drive_id, save_path)
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if save_path.endswith(".zip"):
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zip_dir = ''
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with zipfile.ZipFile(save_path) as zf:
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zip_dir = zf.namelist()[0]
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zf.extractall(dirpath)
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os.remove(save_path)
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os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, data_dir))
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elif save_path.endswith("tar.gz"):
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tar = tarfile.open(save_path, "r:gz")
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tar.extractall()
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tar.close()
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elif save_path.endswith("tar"):
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tar = tarfile.open(save_path, "r:")
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tar.extractall()
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tar.close()
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if __name__ == '__main__':
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args = parser.parse_args()
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print(" [!] The pre-trained models are being made available for research purpose only")
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print(" [!] 학습된 모델을 연구 이외의 목적으로 사용하는 것을 금지합니다.")
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print()
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if query_yes_no(" [?] Are you agree on this? 이에 동의하십니까?"):
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if 'park' in args.checkpoints:
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download_checkpoint('park')
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if 'son' in args.checkpoints:
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download_checkpoint('son')
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