def __init__(self, args):
self.opts = json.load(open(args.exp, 'r'))
self.image_dir = args.image_dir
self.data_loader = get_data_loaders(self.opts['dataset'],
cityscapes.DataProvider)
self.model = polyrnnpp.PolyRNNpp(self.opts).to(device)
self.model.reload(args.reload, strict=False)
def __init__(self, args):
self.global_step = 0
self.epoch = 0
self.opts = json.load(open(args.exp, 'r'))
utils.create_folder(os.path.join(self.opts['exp_dir'], 'checkpoints'))
# Copy experiment file
os.system('cp %s %s' % (args.exp, self.opts['exp_dir']))
# result of experiment
self.writer = SummaryWriter(
os.path.join(self.opts['exp_dir'], 'logs', 'train'))
self.val_writer = SummaryWriter(
os.path.join(self.opts['exp_dir'], 'logs', 'train_val'))
self.train_loader, self.val_loader = get_data_loaders(
self.opts['dataset'], cityscapes.DataProvider)
self.model = polyrnnpp.PolyRNNpp(self.opts).to(device)
self.grid_size = self.model.encoder.feat_size
if 'encoder_reload' in self.opts.keys():
self.model.encoder.reload(self.opts['encoder_reload'])
# OPTIMIZER
no_wd = []
wd = []
print 'Weight Decay applied to: '
for name, p in self.model.named_parameters():
if not p.requires_grad:
# No optimization for frozen params
continue
if 'bn' in name or 'conv_lstm' in name or 'bias' in name:
no_wd.append(p)
else:
wd.append(p)
print name,
# Allow individual options
self.optimizer = optim.Adam([{
'params': no_wd,
'weight_decay': 0.0
}, {
'params': wd
}],
lr=self.opts['lr'],
weight_decay=self.opts['weight_decay'],
amsgrad=False)
# TODO: Test how amsgrad works (On the convergence of Adam and Beyond)
self.lr_decay = optim.lr_scheduler.StepLR(
self.optimizer, step_size=self.opts['lr_decay'], gamma=0.1)
if args.resume is not None:
self.resume(args.resume)
def __init__(
self, args
): ## HTF the args given is defined here but not in the above object place
self.opts = json.load(open(args.exp, 'r'))
self.image_dir = args.image_dir
self.data_loader = get_data_loaders(self.opts['dataset'],
cityscapes.DataProvider)
self.model = polyrnnpp.PolyRNNpp(self.opts).to(
device) ## Network defenition
self.model.reload(
args.reload,
strict=False) ## loading of .pth from the saved models folder
def __init__(self, args):
self.opts = json.load(open(args.exp, 'r'))
self.output_dir = args.output_dir
self.fp_beam_size = args.fp_beam_size
self.lstm_beam_size = args.lstm_beam_size
if self.output_dir is None:
self.output_dir = os.path.join(self.opts['exp_dir'], 'preds')
utils.create_folder(self.output_dir)
self.opts = override_options(self.opts)
self.val_loader = get_data_loaders(self.opts['dataset'],
cityscapes.DataProvider)
self.model = polyrnnpp.PolyRNNpp(self.opts).to(device)
self.model.reload(args.reload, strict=False)
if self.opts['use_ggnn'] == True:
self.grid_size = self.model.ggnn.ggnn_grid_size
else:
self.grid_size = self.model.encoder.feat_size
def __init__(self, args):
self.global_step = 0
self.epoch = 0
self.opts = json.load(open(args.exp, 'r'))
utils.create_folder(os.path.join(self.opts['exp_dir'], 'checkpoints'))
# Copy experiment file
os.system('cp %s %s' % (args.exp, self.opts['exp_dir']))
self.writer = SummaryWriter(
os.path.join(self.opts['exp_dir'], 'logs', 'train'))
self.val_writer = SummaryWriter(
os.path.join(self.opts['exp_dir'], 'logs', 'train_val'))
self.train_loader, self.val_loader = get_data_loaders(
self.opts['dataset'], cityscapes.DataProvider)
self.model = polyrnnpp.PolyRNNpp(self.opts).to(device)
self.grid_size = self.model.encoder.feat_size
if 'xe_initializer' in self.opts.keys():
self.model.reload(self.opts['xe_initializer'])
elif 'encoder_reload' in self.opts.keys():
self.model.encoder.reload(self.opts['encoder_reload'])
self.model.encoder.eval()
print 'Setting encoder to eval'
print 'No weight decay in RL training'
train_params = [p for p in self.model.parameters() if p.requires_grad]
self.optimizer = optim.Adam(train_params,
lr=self.opts['lr'],
amsgrad=False)
# TODO: Test how amsgrad works (On the convergence of Adam and Beyond)
self.lr_decay = optim.lr_scheduler.StepLR(
self.optimizer, step_size=self.opts['lr_decay'], gamma=0.1)
if args.resume is not None:
self.resume(args.resume)