Merge pull request #167 from WuHaobo/dynamic

Polish Dynamic Graph 

ppcls/utils/misc.py

@@ -20,7 +20,7 @@ class AverageMeter(object):
     Computes and stores the average and current value
     """
 
-    def __init__(self, name='', fmt='f', need_avg=False):
+    def __init__(self, name='', fmt='f', need_avg=True):
         self.name = name
         self.fmt = fmt
         self.need_avg = need_avg

ppcls/utils/save_load.py

@@ -74,7 +74,9 @@ def load_params(exe, prog, path, ignore_params=None):
         raise ValueError("Model pretrain path {} does not "
                          "exists.".format(path))
 
-    logger.info(logger.coloring('Loading parameters from {}...'.format(path), 'HEADER'))
+    logger.info(
+        logger.coloring('Loading parameters from {}...'.format(path),
+                        'HEADER'))
 
     ignore_set = set()
     state = _load_state(path)
@@ -100,37 +102,54 @@ def load_params(exe, prog, path, ignore_params=None):
     if len(ignore_set) > 0:
         for k in ignore_set:
             if k in state:
-                logger.warning('variable {} is already excluded automatically'.format(k))
+                logger.warning(
+                    'variable {} is already excluded automatically'.format(k))
                 del state[k]
 
     fluid.io.set_program_state(prog, state)
 
 
-def init_model(config, program, exe):
+def init_model(config, net, optimizer):
     """
     load model from checkpoint or pretrained_model
     """
     checkpoints = config.get('checkpoints')
     if checkpoints:
-        fluid.load(program, checkpoints, exe)
-        logger.info(logger.coloring("Finish initing model from {}".format(checkpoints),"HEADER"))
+        assert os.path.exists(checkpoints + ".pdparams"), \
+            "Given dir {}.pdparams not exist.".format(checkpoints)
+        assert os.path.exists(checkpoints + ".pdopt"), \
+            "Given dir {}.pdopt not exist.".format(checkpoints)
+        para_dict, opti_dict = fluid.dygraph.load_dygraph(checkpoints)
+        net.set_dict(para_dict)
+        optimizer.set_dict(opti_dict)
+        logger.info(
+            logger.coloring("Finish initing model from {}".format(checkpoints),
+                            "HEADER"))
         return
 
     pretrained_model = config.get('pretrained_model')
     if pretrained_model:
         if not isinstance(pretrained_model, list):
             pretrained_model = [pretrained_model]
+        # TODO: load pretrained_model
+        raise NotImplementedError
         for pretrain in pretrained_model:
             load_params(exe, program, pretrain)
-        logger.info(logger.coloring("Finish initing model from {}".format(pretrained_model),"HEADER"))
+        logger.info(
+            logger.coloring("Finish initing model from {}".format(
+                pretrained_model), "HEADER"))
 
 
-def save_model(program, model_path, epoch_id, prefix='ppcls'):
+def save_model(net, optimizer, model_path, epoch_id, prefix='ppcls'):
     """
     save model to the target path
     """
     model_path = os.path.join(model_path, str(epoch_id))
     _mkdir_if_not_exist(model_path)
     model_prefix = os.path.join(model_path, prefix)
-    fluid.save(program, model_prefix)
-    logger.info(logger.coloring("Already save model in {}".format(model_path),"HEADER"))
+
+    fluid.dygraph.save_dygraph(net.state_dict(), model_prefix)
+    fluid.dygraph.save_dygraph(optimizer.state_dict(), model_prefix)
+    logger.info(
+        logger.coloring("Already save model in {}".format(model_path),
+                        "HEADER"))