def init_data_loaders(self, num_of_workers):
"""
create torch data loaders for train and validation data
Args:
num_of_workers ():
Returns:
train , validation data loaders
"""
train_dataset = ClassificationLoader(self.args.train_path,
window_size=self.args.window_size,
window_stride=self.args.window_stride,
window_type=self.args.window_type,
normalize=self.args.normalize,
max_len=self.args.max_len)
sampler_train = Datasets.ImbalancedDatasetSampler(train_dataset)
train_loader = \
torch.utils.data.DataLoader(train_dataset,
batch_size=self.args.batch_size, shuffle=None,
num_workers=num_of_workers, pin_memory=self.args.cuda,
sampler=sampler_train)
valid_dataset = ClassificationLoader(self.args.valid_path,
window_size=self.args.window_size,
window_stride=self.args.window_stride,
window_type=self.args.window_type,
normalize=self.args.normalize,
max_len=self.args.max_len)
valid_loader = \
torch.utils.data.DataLoader(valid_dataset, batch_size=self.args.batch_size,
shuffle=None, num_workers=num_of_workers,
pin_memory=self.args.cuda, sampler=None)
return train_loader, valid_loader,
lr=args.lr,
momentum=args.momentum)
else:
optimizer = optim.SGD(speech_net.parameters(),
lr=args.lr,
momentum=args.momentum)
train_dataset = Datasets.SpeechYoloDataSet(classes_root_dir=args.train_data,
this_root_dir=args.train_data,
yolo_config=config_dict,
augment=args.augment_data)
val_dataset = Datasets.SpeechYoloDataSet(classes_root_dir=args.train_data,
this_root_dir=args.val_data,
yolo_config=config_dict)
sampler_train = Datasets.ImbalancedDatasetSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=20,
pin_memory=args.cuda,
sampler=sampler_train)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=None,
num_workers=20,
pin_memory=args.cuda,
sampler=None)
if os.path.isfile(args.trained_yolo_model): # model exists
