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run.py

@@ -6,6 +6,15 @@
 from importlib import import_module
 import mindspore.ops as ops 
 import argparse
+import mindspore.nn as nn
+
+class MyNet(nn.Cell):
+  def __init__(self):
+    super(MyNet, self).__init__()
+
+# The following implements mindspore.nn.Cell.get_parameters() with MindSpore.
+net = MyNet()
+
 
 parser = argparse.ArgumentParser(description='Chinese Text Classification')
 parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer')
@@ -43,8 +52,9 @@
 
     # train
     config.n_vocab = len(vocab)
-    model = x.Model(config).to(config.device)
+    model = x.Model(config)
     if model_name != 'Transformer':
         init_network(model)
-    print(model.parameters)
+        for params in  net.get_parameters():
+           print("params:", params)
     train(config, model, train_iter, dev_iter, test_iter)

train_eval.py

@@ -9,11 +9,16 @@
 from mindspore.common.initializer import initializer, XavierNormal, Normal, HeNormal, Constant
 from mindspore.train import Model, CheckpointConfig, ModelCheckpoint, LossMonitor
 
+class MyNet(nn.Cell):
+  def __init__(self):
+    super(MyNet, self).__init__()
 
+# The following implements mindspore.nn.Cell.get_parameters() with MindSpore.
+net = MyNet()
 
 # 权重初始化，默认xavier
 def init_network(model, method='xavier', exclude='embedding', seed=123):
-    for name, w in model.named_parameters():
+    for name, w in net.trainable_params():
         if exclude not in name:
             if 'weight' in name:
                 if method == 'xavier':
@@ -30,8 +35,8 @@ def init_network(model, method='xavier', exclude='embedding', seed=123):
 
 def train(config, model, train_iter, dev_iter, test_iter):
     start_time = time.time()
-    model.train()
-    optimizer = nn.Adam(model.parameters(), lr=config.learning_rate)
+    model = Model(net)
+    optimizer = nn.Adam(net.trainable_params(), learning_rate=0.001)
 
     # 学习率指数衰减，每次epoch：学习率 = gamma * 学习率
     # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)

utils.py

@@ -71,22 +71,21 @@ def load_dataset(path, pad_size=32):
     return vocab, train, dev, test
 
 class DatasetIterater(object):
-    def __init__(self, batches, batch_size, device):
+    def __init__(self, batches, batch_size):
         self.batch_size = batch_size
         self.batches = batches
         self.n_batches = len(batches) // batch_size
         self.residue = False  # 记录batch数量是否为整数
         if len(batches) % self.n_batches != 0:
             self.residue = True
         self.index = 0
-        self.device = device
 
     def _to_tensor(self, datas):
-        x = Tensor.long([_[0] for _ in datas]).to(self.device)
-        y = Tensor.long([_[1] for _ in datas]).to(self.device)
+        x = Tensor.long([_[0] for _ in datas])
+        y = Tensor.long([_[1] for _ in datas])
 
         # pad前的长度(超过pad_size的设为pad_size)
-        seq_len = Tensor.long([_[2] for _ in datas]).to(self.device)
+        seq_len = Tensor.long([_[2] for _ in datas])
         return (x, seq_len), y
 
     def __next__(self):
@@ -115,7 +114,7 @@ def __len__(self):
             return self.n_batches
 
 def build_iterator(dataset, config):
-    iter = DatasetIterater(dataset, config.batch_size, config.device)
+    iter = DatasetIterater(dataset, config.batch_size)
     return iter