Demo (open-mmlab#20)

* fix

* fix 1

* add notebook

autogluon/network/nets.py

@@ -3,6 +3,8 @@
 from ..space import *
 from .utils import Net
 
+from ..task.image_classification import get_model
+
 __all__ = ['Nets']
 
 
@@ -25,6 +27,8 @@ def add_search_space(self):
         cs.add_hyperparameter(net_list_hyper_param)
         for net in self.net_list:
             #TODO(cgraywang): distinguish between different nets, only support resnet for now
+            if isinstance(net, str):
+                net = get_model(net)
             net_hyper_params = net.get_hyper_params()
             conds = []
             for net_hyper_param in net_hyper_params:
@@ -45,6 +49,10 @@ def get_net_strs(self):
         for net in self.net_list:
             if isinstance(net, Net):
                 net_strs.append(net.name)
+            elif isinstance(net, str):
+                net_strs.append(net)
+            else:
+                pass
         return net_strs
 
     def __repr__(self):

autogluon/optim/optims.py

@@ -23,6 +23,8 @@ def add_search_space(self):
         cs.add_hyperparameter(optim_list_hyper_param)
         for optim in self.optim_list:
             # TODO: add more optims
+            if isinstance(optim, str):
+                optim = get_optim(optim)
             optim_hyper_params = optim.get_hyper_params()
             conds = []
             for optim_hyper_param in optim_hyper_params:
@@ -42,6 +44,10 @@ def get_optim_strs(self):
         for optim in self.optim_list:
             if isinstance(optim, Optimizer):
                 optim_strs.append(optim.name)
+            elif isinstance(optim, str):
+                optim_strs.append(optim)
+            else:
+                pass
         return optim_strs
 
     def __repr__(self):

autogluon/task/image_classification/core.py

@@ -18,6 +18,12 @@
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.DEBUG)
 
+class Results(object):
+    def __init__(self, model, accuracy, config):
+        self.model = model
+        self.accuracy = accuracy
+        self.config =config
+
 
 def fit(data,
         nets=Nets([
@@ -160,8 +166,9 @@ def _run_ray_backend(searcher, trial_scheduler):
         best_result = max([trial.best_result for trial in trials])
         # TODO (cgraywang)
         best_config = None
+        results = Results(None, best_result, best_config)
         logger.debug('Finished.')
-        return trials, best_result, cs
+        return results
 
     def _run_backend(searcher, trial_scheduler):
         logger.debug('Start using default backend.')
@@ -209,12 +216,13 @@ def _run_backend(searcher, trial_scheduler):
         trials = None
         best_result = trial_scheduler.get_best_reward()
         best_config = trial_scheduler.get_best_config()
+        results = Results(None, best_result, best_config)
         logger.debug('Finished.')
-        return trials, best_result, best_config
+        return results
 
     if backend == 'ray':
-        result = _run_ray_backend(searcher, trial_scheduler)
+        results = _run_ray_backend(searcher, trial_scheduler)
     else:
-        result = _run_backend(searcher, trial_scheduler)
+        results = _run_backend(searcher, trial_scheduler)
     logger.debug('Finished.')
-    return result
+    return results

autogluon/task/image_classification/dataset.py

@@ -66,3 +66,8 @@ def _read_dataset(self):
         self.train_data = train_data
         self.val_data = test_data
 
+    def __repr__(self):
+        return "AutoGluon Dataset %s" % self.__str__()
+
+    # def __str__(self):
+    #     return "AutoGluon List Space %s: %s" % (self.name, str(self.choices))