Multi-threaded VisDrone and VOC downloads (#7108)
* Multi-threaded VOC download * Update VOC.yaml * Update * Update general.py * Update general.pypull/6979/head
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@ -34,6 +34,7 @@ names: ['wheat_head'] # class names
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download: |
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from utils.general import download, Path
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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@ -65,6 +65,7 @@ download: |
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from utils.general import Path, download, np, xyxy2xywhn
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# Make Directories
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dir = Path(yaml['path']) # dataset root dir
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for p in 'images', 'labels':
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@ -24,6 +24,7 @@ download: |
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from tqdm import tqdm
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from utils.general import np, pd, Path, download, xyxy2xywh
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# Download
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dir = Path(yaml['path']) # dataset root dir
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parent = Path(dir.parent) # download dir
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@ -62,7 +62,7 @@ download: |
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urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
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url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
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url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
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download(urls, dir=dir / 'images', delete=False)
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download(urls, dir=dir / 'images', delete=False, threads=3)
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# Convert
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path = dir / f'images/VOCdevkit'
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@ -54,7 +54,7 @@ download: |
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
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download(urls, dir=dir)
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download(urls, dir=dir, threads=4)
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# Convert
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for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
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@ -30,6 +30,7 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
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download: |
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from utils.general import download, Path
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# Download labels
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segments = False # segment or box labels
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dir = Path(yaml['path']) # dataset root dir
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@ -449,8 +449,9 @@ def check_dataset(data, autodownload=True):
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if val:
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val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
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if not all(x.exists() for x in val):
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LOGGER.info('\nDataset not found, missing paths: %s' % [str(x) for x in val if not x.exists()])
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LOGGER.info(emojis('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]))
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if s and autodownload: # download script
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t = time.time()
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root = path.parent if 'path' in data else '..' # unzip directory i.e. '../'
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if s.startswith('http') and s.endswith('.zip'): # URL
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f = Path(s).name # filename
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@ -465,9 +466,11 @@ def check_dataset(data, autodownload=True):
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r = os.system(s)
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else: # python script
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r = exec(s, {'yaml': data}) # return None
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LOGGER.info(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
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dt = f'({round(time.time() - t, 1)}s)'
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s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
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LOGGER.info(emojis(f"Dataset download {s}"))
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else:
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raise Exception('Dataset not found.')
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raise Exception(emojis('Dataset not found ❌'))
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return data # dictionary
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@ -491,7 +494,7 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
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if curl:
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os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail
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else:
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torch.hub.download_url_to_file(url, f, progress=True) # torch download
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torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download
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if unzip and f.suffix in ('.zip', '.gz'):
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LOGGER.info(f'Unzipping {f}...')
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if f.suffix == '.zip':
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