def __init__(self, opt):
self.opt = opt
dataset = 'cityscapes_seq_full'
self.workspace = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
self.jobname = dataset + '_gpu8_refine_genmask_linklink_256_1node'
self.modeldir = self.jobname + 'model'
self.sampledir = os.path.join(self.workspace, self.jobname)
self.parameterdir = self.sampledir + '/params'
self.useHallucination = False
if not os.path.exists(self.parameterdir):
os.makedirs(self.parameterdir)
# whether to start training from an existing snapshot
self.load = False
self.iter_to_load = 62000
# Write parameters setting file
if os.path.exists(self.parameterdir):
utils.save_parameters(self)
''' Cityscapes'''
train_Dataset = get_training_set(opt)
test_Dataset = get_test_set(opt)
self.trainloader = DataLoader(train_Dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers,
pin_memory=True, drop_last=True)
self.testloader = DataLoader(test_Dataset, batch_size=2, shuffle=False, num_workers=opt.workers,
pin_memory=True, drop_last=True)
def __init__(self, opt):
self.opt = opt
dataset = 'MUSIC21'
self.workspace = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..')
self.job_name = dataset + '_gpu8_estimate_mask_'
self.model_dir = self.job_name + 'model'
self.sample_dir = os.path.join(self.workspace, self.job_name)
self.parameter_dir = self.sample_dir + '/params'
if not os.path.exists(self.parameter_dir):
os.makedirs(self.parameter_dir)
# whether to start training from an existing snapshot
self.load = False
self.iter_to_load = 62000
# Write parameters setting file
if os.path.exists(self.parameter_dir):
utils.save_parameters(self)
'''MUSIC21'''
self.trainloader, self.valloader, self.n_training_samples = ds.get_dataloader(
root=opt.root, tag_dir=opt.train_tag_json_path, is_training=True)
self.testloader, self.n_test_samples = ds.get_dataloader(
root=opt.root, tag_dir=opt.test_tag_json_path, is_training=False)
# visualization
self.visualizer = Visualizer(opt)