def init(config):
batchsize = config['train']['batchsize']
current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)
import ref as ds
ds.init()
train, valid = ds.setup_val_split() # 挑选有keypoints的样本作为数据集
dataset = { key: Dataset(config, ds, data) for key, data in zip( ['train', 'valid'], [train, valid] ) }
use_data_loader = config['train']['use_data_loader']
loaders = {}
for key in dataset:
loaders[key] = torch.utils.data.DataLoader(dataset[key], batch_size=batchsize, shuffle=True, num_workers=config['train']['num_workers'], pin_memory=False)
def gen(phase):
batchsize = config['train']['batchsize']
batchnum = config['train']['{}_iters'.format(phase)]
loader = loaders[phase].__iter__()
for i in range(batchnum):
imgs, masks, keypoints, heatmaps = next(loader)
yield {
'imgs': imgs,
'masks': masks,
'heatmaps': heatmaps,
'keypoints': keypoints
}
return lambda key: gen(key)
def init(config):
batchsize = config['train']['batchsize']
current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)
import ref as ds
ds.init()
train, valid = ds.setup_val_split()
dataset = { key: Dataset(config, ds, data) for key, data in zip( ['train', 'valid'], [train, valid] ) }
use_data_loader = config['train']['use_data_loader']
loaders = {}
for key in dataset:
loaders[key] = torch.utils.data.DataLoader(dataset[key], batch_size=batchsize, shuffle=True, num_workers=config['train']['num_workers'], pin_memory=False)
def gen(phase):
batchsize = config['train']['batchsize']
batchnum = config['train']['{}_iters'.format(phase)]
loader = loaders[phase].__iter__()
for i in range(batchnum):
imgs, masks, keypoints, heatmaps = next(loader)
yield {
'imgs': imgs,
'masks': masks,
'heatmaps': heatmaps,
'keypoints': keypoints
}
return lambda key: gen(key)
def init(config):
batchsize = config['train']['batchsize']
current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)
import ref as ds
ds.init()
train, valid = ds.setup_val_split()
dataset = {
key: Dataset(config, ds, data)
for key, data in zip(['train', 'valid'], [train, valid])
}
use_data_loader = config['train']['use_data_loader']
loaders = {}
for key in dataset:
loaders[key] = torch.utils.data.DataLoader(
dataset[key],
batch_size=batchsize,
shuffle=True,
num_workers=config['train']['num_workers'],
pin_memory=False)
def gen(phase):
batchsize = config['train']['batchsize']
batchnum = config['train']['{}_iters'.format(phase)]
loader = loaders[phase].__iter__()
for i in range(batchnum):
try:
imgs, heatmaps = next(loader)
except StopIteration:
# to avoid no data provided by dataloader
loader = loaders[phase].__iter__()
imgs, heatmaps = next(loader)
yield {
'imgs': imgs, #cropped and augmented
'heatmaps':
heatmaps, #based on keypoints. 0 if not in img for joint
}
return lambda key: gen(key)
def init(opts):
batchsize = opts.batchsize
current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)
import ref as ds
ds.init()
train, valid = ds.setup_val_split()
dataset = {
key: Dataset(opts, ds, data)
for key, data in zip(['train', 'valid'], [train, valid])
}
loaders = {}
for key in dataset:
loaders[key] = torch.utils.data.DataLoader(
dataset[key],
batch_size=batchsize,
shuffle=True,
num_workers=opts.num_workers,
pin_memory=False)
def gen(phase):
batchsize = opts.batchsize
if phase == 'train':
batchnum = opts.train_iters
else:
batchnum = opts.valid_iters
loader = loaders[phase].__iter__()
for i in range(batchnum):
imgs, masks, keypoints, heatmaps = next(loader)
yield {
'imgs': imgs,
'masks': masks,
'heatmaps': heatmaps,
'keypoints': keypoints
}
return lambda key: gen(key)