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Pass LOCAL_RANK to torch_distributed_zero_first() (#5114)

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Co-authored-by: qiningonline <qiningonline@gmail.com>
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Nan 2021-10-09 20:41:50 -05:00 committed by GitHub
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1 changed files with 3 additions and 3 deletions
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train.py
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@ -99,7 +99,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
plots = not evolve # create plots plots = not evolve # create plots
cuda = device.type != 'cpu' cuda = device.type != 'cpu'
init_seeds(1 + RANK) init_seeds(1 + RANK)
with torch_distributed_zero_first(RANK): with torch_distributed_zero_first(LOCAL_RANK):
data_dict = data_dict or check_dataset(data) # check if None data_dict = data_dict or check_dataset(data) # check if None
train_path, val_path = data_dict['train'], data_dict['val'] train_path, val_path = data_dict['train'], data_dict['val']
nc = 1 if single_cls else int(data_dict['nc']) # number of classes nc = 1 if single_cls else int(data_dict['nc']) # number of classes
@ -111,7 +111,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
check_suffix(weights, '.pt') # check weights check_suffix(weights, '.pt') # check weights
pretrained = weights.endswith('.pt') pretrained = weights.endswith('.pt')
if pretrained: if pretrained:
with torch_distributed_zero_first(RANK): with torch_distributed_zero_first(LOCAL_RANK):
weights = attempt_download(weights) # download if not found locally weights = attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
@ -208,7 +208,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# Trainloader # Trainloader
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=RANK, hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
workers=workers, image_weights=opt.image_weights, quad=opt.quad, workers=workers, image_weights=opt.image_weights, quad=opt.quad,
prefix=colorstr('train: ')) prefix=colorstr('train: '))
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class