def load(cls, checkpoint):
"""Load trained Image Classifier from directory specified by `checkpoint`.
"""
state_dict = load(checkpoint)
args = state_dict['args']
results = pkl.loads(state_dict['results'])
eval_func = state_dict['eval_func']
scheduler_checkpoint = state_dict['scheduler_checkpoint']
model_params = state_dict['model_params']
ensemble = state_dict['ensemble']
if ensemble <= 1:
model_args = copy.deepcopy(args)
model_args.update(results['best_config'])
model = get_network(args.net,
num_classes=results['num_classes'],
ctx=mx.cpu(0))
update_params(model, model_params)
else:
raise NotImplemented
return cls(model,
results,
eval_func,
scheduler_checkpoint,
args,
ensemble,
format_results=False)
def load(cls, checkpoint):
""" load trained object detector from the file specified by 'checkpoint'
"""
state_dict = load(checkpoint)
args = state_dict['args']
results = pkl.loads(state_dict['results'])
scheduler_checkpoint = state_dict['scheduler_checkpoint']
model_params = state_dict['model_params']
classes = state_dict['classes']
model = get_network(args.meta_arch,
args.net,
classes,
ctx=mx.cpu(0),
syncbn=args.syncbn)
update_params(model, model_params)
return cls(model,
results,
scheduler_checkpoint,
args,
format_results=False)
def load(self, checkname=None):
checkname = checkname if checkname else self.checkname
state_dict = load(checkname)
self.load_state_dict(state_dict)