27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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#
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import sys, time, torch, random, argparse
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from utils import get_model_infos
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torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)
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for model_name, xshape in [('resnest50', (1,3,224,224)),
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('resnest101', (1,3,256,256)),
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('resnest200', (1,3,320,320)),
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('resnest269', (1,3,416,416))]:
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# net = torch.hub.load('zhanghang1989/ResNeSt', model_name, pretrained=True)
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net = torch.hub.load('zhanghang1989/ResNeSt', model_name, pretrained=False)
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print('Model : {:}, input shape : {:}'.format(model_name, xshape))
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flops, param = get_model_infos(net, xshape)
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print('flops : {:.3f}M'.format(flops))
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print('params : {:.3f}M'.format(param))
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