print(f'dataset size: {len(dataset)}')
model = model_fn(dataset)
if use_half:
model = model.half()
for partial_path in args.partial:
model.load_state_dict(torch.load(partial_path), strict=False)
optimiser = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
paths = env.Paths(model_name, data_path)
if args.scratch or args.load == None and not os.path.exists(
paths.model_path()):
# Start from scratch
step = 0
epoch = 0
else:
if args.load:
#remove .pyt extension and step number
prev_model_name = re.sub(
r'_[0-9]+$', '', re.sub(r'\.pyt$', '',
os.path.basename(args.load)))
prev_model_basename = prev_model_name.split('_')[0]
model_basename = model_name.split('_')[0]
if prev_model_basename != model_basename and not args.force:
else:
raise RuntimeError('bad dataset type')
print(f'dataset size: {len(dataset)}')
model = model_fn(dataset)
if use_half:
model = model.half()
for partial_path in args.partial:
model.load_state_dict(torch.load(partial_path), strict=False)
optimiser = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
paths = env.Paths(model_name, data_path, args.results_dir, args.exp_name)
if args.scratch or args.load == None and not os.path.exists(paths.model_path()):
# Start from scratch
step = 0
epoch = 0
print("Start from Scratch.")
else:
if args.load:
prev_model_name = re.sub(r'_[0-9]+$', '', re.sub(r'\.pyt$', '', os.path.basename(args.load)))
prev_model_basename = prev_model_name.split('_')[0]
model_basename = model_name.split('_')[0]
if prev_model_basename != model_basename and not args.force:
sys.exit(f'refusing to load {args.load} because its basename ({prev_model_basename}) is not {model_basename}')
if args.generate:
paths = env.Paths(prev_model_name, data_path, args.results_dir, args.exp_name)