* 将enas相关代码放到automl目录下

* 修复fast_param_mapping的一个bug
* Trainer添加自动创建save目录
* Vocabulary的打印，显示内容

fastNLP/models/enas_controller.py → fastNLP/automl/enas_controller.py

@@ -5,9 +5,9 @@ import os
 
 import torch
 import torch.nn.functional as F
-import fastNLP
-import fastNLP.models.enas_utils as utils
-from fastNLP.models.enas_utils import Node
+
+import fastNLP.automl.enas_utils as utils
+from fastNLP.automl.enas_utils import Node
 
 
 def _construct_dags(prev_nodes, activations, func_names, num_blocks):

fastNLP/models/enas_model.py → fastNLP/automl/enas_model.py

@@ -1,17 +1,17 @@
 # Code Modified from https://github.com/carpedm20/ENAS-pytorch
 
 """Module containing the shared RNN model."""
-import numpy as np
 import collections
 
+import numpy as np
 import torch
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 from torch.autograd import Variable
 
-import fastNLP.models.enas_utils as utils
+import fastNLP.automl.enas_utils as utils
 from fastNLP.models.base_model import BaseModel
-import fastNLP.modules.encoder as encoder
+
 
 def _get_dropped_weights(w_raw, dropout_p, is_training):
     """Drops out weights to implement DropConnect.

fastNLP/models/enas_trainer.py → fastNLP/automl/enas_trainer.py

@@ -1,14 +1,12 @@
 # Code Modified from https://github.com/carpedm20/ENAS-pytorch
 
-import os
+import math
 import time
 from datetime import datetime
 from datetime import timedelta
 
 import numpy as np
 import torch
-import math
-from torch import nn
 
 try:
     from tqdm.autonotebook import tqdm
@@ -16,12 +14,11 @@ except:
     from fastNLP.core.utils import pseudo_tqdm as tqdm
 
 from fastNLP.core.batch import Batch
-from fastNLP.core.callback import CallbackManager, CallbackException
+from fastNLP.core.callback import CallbackException
 from fastNLP.core.dataset import DataSet
-from fastNLP.core.utils import CheckError
 from fastNLP.core.utils import _move_dict_value_to_device
 import fastNLP
-import fastNLP.models.enas_utils as utils
+import fastNLP.automl.enas_utils as utils
 from fastNLP.core.utils import _build_args
 
 from torch.optim import Adam

fastNLP/models/enas_utils.py → fastNLP/automl/enas_utils.py

@@ -2,17 +2,14 @@
 
 from __future__ import print_function
 
-from collections import defaultdict
 import collections
-from datetime import datetime
-import os
-import json
+from collections import defaultdict
 
 import numpy as np
-
 import torch
 from torch.autograd import Variable
 
+
 def detach(h):
     if type(h) == Variable:
         return Variable(h.data)

fastNLP/core/metrics.py

@@ -157,7 +157,7 @@ class MetricBase(object):
         fast_param = {}
         if len(self.param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1:
             fast_param['pred'] = list(pred_dict.values())[0]
-            fast_param['target'] = list(pred_dict.values())[0]
+            fast_param['target'] = list(target_dict.values())[0]
             return fast_param
         return fast_param
 

fastNLP/core/trainer.py

@@ -367,6 +367,8 @@ class Trainer(object):
         """
         if self.save_path is not None:
             model_path = os.path.join(self.save_path, model_name)
+            if not os.path.exists(self.save_path):
+                os.makedirs(self.save_path, exist_ok=True)
             if only_param:
                 state_dict = model.state_dict()
                 for key in state_dict:

fastNLP/core/vocabulary.py

@@ -196,3 +196,6 @@ class Vocabulary(object):
         """
         self.__dict__.update(state)
         self.build_reverse_vocab()
+
+    def __repr__(self):
+        return "Vocabulary({}...)".format(list(self.word_count.keys())[:5])

fastNLP/modules/utils.py

@@ -60,7 +60,8 @@ def initial_parameter(net, initial_method=None):
                     init_method(w.data)  # weight
                 else:
                     init.normal_(w.data)  # bias
-        elif hasattr(m, 'weight') and m.weight.requires_grad:
+        elif m is not None and hasattr(m, 'weight') and \
+                hasattr(m.weight, "requires_grad"):
             init_method(m.weight.data)
         else:
             for w in m.parameters():

test/models/test_enas.py → test/automl/test_enas.py

@@ -69,13 +69,12 @@ class TestENAS(unittest.TestCase):
             print("batch_y has: ", batch_y)
             break
 
-        from fastNLP.models.enas_model import ENASModel
-        from fastNLP.models.enas_controller import Controller
+        from fastNLP.automl.enas_model import ENASModel
+        from fastNLP.automl.enas_controller import Controller
         model = ENASModel(embed_num=len(vocab), num_classes=5)
         controller = Controller()
 
-        from fastNLP.models.enas_trainer import ENASTrainer
-        from copy import deepcopy
+        from fastNLP.automl.enas_trainer import ENASTrainer
 
         # 更改DataSet中对应field的名称，要以模型的forward等参数名一致
         train_data.rename_field('words', 'word_seq')  # input field 与 forward 参数一致