updates
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5f00d7419e
commit
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2
test.py
2
test.py
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@ -20,7 +20,7 @@ def test(cfg,
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model=None):
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# Initialize/load model and set device
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if model is None:
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device = torch_utils.select_device(opt.device)
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device = torch_utils.select_device(opt.device, batch_size=batch_size)
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verbose = True
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# Initialize model
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2
train.py
2
train.py
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@ -423,7 +423,7 @@ if __name__ == '__main__':
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opt = parser.parse_args()
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opt.weights = last if opt.resume else opt.weights
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print(opt)
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device = torch_utils.select_device(opt.device, apex=mixed_precision)
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device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
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if device.type == 'cpu':
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mixed_precision = False
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@ -14,7 +14,7 @@ def init_seeds(seed=0):
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torch.backends.cudnn.benchmark = False
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def select_device(device='', apex=False):
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def select_device(device='', apex=False, batch_size=None):
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# device = 'cpu' or '0' or '0,1,2,3'
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cpu_request = device.lower() == 'cpu'
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if device and not cpu_request: # if device requested other than 'cpu'
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@ -25,11 +25,13 @@ def select_device(device='', apex=False):
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if cuda:
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c = 1024 ** 2 # bytes to MB
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ng = torch.cuda.device_count()
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if ng > 1 and batch_size: # check that batch_size is compatible with device_count
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assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
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x = [torch.cuda.get_device_properties(i) for i in range(ng)]
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cuda_str = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex
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s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex
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for i in range(0, ng):
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if i == 1:
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cuda_str = ' ' * len(cuda_str)
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s = ' ' * len(s)
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print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
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(cuda_str, i, x[i].name, x[i].total_memory / c))
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else:
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