move textrcnn from official to research, and raise acc when using lstm as RNN arch

model_zoo/official/nlp/textrcnn/src/utils.py

@@ -1,29 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""training utils"""
-from mindspore import dtype as mstype
-from mindspore.nn.dynamic_lr import exponential_decay_lr
-from mindspore import Tensor
-
-
-def get_lr(cfg, dataset_size):
-    if cfg.cell == "lstm":
-        lr = exponential_decay_lr(cfg.lstm_base_lr, cfg.lstm_decay_rate, dataset_size * cfg.num_epochs,
-                                  dataset_size,
-                                  cfg.lstm_decay_epoch)
-        lr_ret = Tensor(lr, mstype.float32)
-    else:
-        lr_ret = cfg.lr
-    return lr_ret

model_zoo/official/nlp/textrcnn/data_helpers.py → model_zoo/research/nlp/textrcnn/data_helpers.py

@@ -22,7 +22,7 @@ parser.add_argument('--data_dir', type=str, help='the source dataset directory.'
 parser.add_argument('--out_dir', type=str, help='the target dataset directory.', default='./data')
 
 args = parser.parse_args()
-
+np.random.seed(2)
 
 def dataset_split(label):
     """dataset_split api"""

model_zoo/official/nlp/textrcnn/readme.md → model_zoo/research/nlp/textrcnn/readme.md

@@ -113,17 +113,19 @@ Parameters for both training and evaluation can be set in config.py
 
   ```python
   'num_epochs': 10, # total training epochs
+  'lstm_num_epochs': 15, # total training epochs when using lstm
   'batch_size': 64, # training batch size
   'cell': 'gru', # the RNN architecture, can be 'vanilla', 'gru' and 'lstm'.
-  'opt': 'adam', # the optimizer strategy, can be 'adam' or 'momentum'
   'ckpt_folder_path': './ckpt', # the path to save the checkpoints
   'preprocess_path': './preprocess', # the directory to save the processed data
   'preprocess' : 'false', # whethere to preprocess the data
   'data_path': './data/', # the path to store the splited data
   'lr': 1e-3, # the training learning rate
-  'lstm_base_lr': 3e-3, # the training learning rate when using lstm as RNN cell
-  'lstm_decay_rate': 0.9, # lr decay rate when using lstm as RNN cell
-  'lstm_decay_epoch': 1, # lr decay epoch when using lstm as RNN cell
+  'lstm_lr_init': 2e-3, # learning rate initial value when using lstm
+  'lstm_lr_end': 5e-4, # learning rate end value when using lstm
+  'lstm_lr_max': 3e-3, # learning eate max value when using lstm
+  'lstm_lr_warm_up_epochs': 2 # warm up epoch num when using lstm
+  'lstm_lr_adjust_epochs': 9 # lr adjust in lr_adjust_epoch, after that, the lr is lr_end when using lstm
   'emb_path': './word2vec', # the directory to save the embedding file
   'embed_size': 300, # the dimension of the word embedding
   'save_checkpoint_steps': 149, # per step to save the checkpoint

model_zoo/official/nlp/textrcnn/src/config.py → model_zoo/research/nlp/textrcnn/src/config.py

@@ -22,17 +22,19 @@ textrcnn_cfg = edict({
     'pos_dir': 'data/rt-polaritydata/rt-polarity.pos',
     'neg_dir': 'data/rt-polaritydata/rt-polarity.neg',
     'num_epochs': 10,
+    'lstm_num_epochs': 15,
     'batch_size': 64,
     'cell': 'gru',
-    'opt': 'adam',
     'ckpt_folder_path': './ckpt',
     'preprocess_path': './preprocess',
     'preprocess': 'false',
     'data_path': './data/',
     'lr': 1e-3,
-    'lstm_base_lr': 3e-3,
-    'lstm_decay_rate': 0.9,
-    'lstm_decay_epoch': 1,
+    'lstm_lr_init': 2e-3,
+    'lstm_lr_end': 5e-4,
+    'lstm_lr_max': 3e-3,
+    'lstm_lr_warm_up_epochs': 2,
+    'lstm_lr_adjust_epochs': 9,
     'emb_path': './word2vec',
     'embed_size': 300,
     'save_checkpoint_steps': 149,

model_zoo/research/nlp/textrcnn/src/utils.py

@@ -0,0 +1,70 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""training utils"""
+import math
+import numpy as np
+from mindspore import dtype as mstype
+from mindspore import Tensor
+
+
+def get_lr(cfg, dataset_size):
+    if cfg.cell == "lstm":
+        lr = get_lr_lstm(0, cfg.lstm_lr_init, cfg.lstm_lr_end, cfg.lstm_lr_max, cfg.lstm_lr_warm_up_epochs,
+                         cfg.lstm_num_epochs, dataset_size, cfg.lstm_lr_adjust_epochs)
+        lr_ret = Tensor(lr, mstype.float32)
+    else:
+        lr_ret = cfg.lr
+    return lr_ret
+
+
+def get_lr_lstm(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_adjust_epoch):
+    """
+    generate learning rate array
+
+    Args:
+       global_step(int): total steps of the training
+       lr_init(float): init learning rate
+       lr_end(float): end learning rate
+       lr_max(float): max learning rate
+       warmup_epochs(float): number of warmup epochs
+       total_epochs(int): total epoch of training
+       steps_per_epoch(int): steps of one epoch
+       lr_adjust_epoch(int): lr adjust in lr_adjust_epoch, after that, the lr is lr_end
+
+    Returns:
+       np.array, learning rate array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    adjust_steps = lr_adjust_epoch * steps_per_epoch
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+        elif i < adjust_steps:
+            lr = lr_end + \
+                 (lr_max - lr_end) * \
+                 (1. + math.cos(math.pi * (i - warmup_steps) / (adjust_steps - warmup_steps))) / 2.
+        else:
+            lr = lr_end
+        if lr < 0.0:
+            lr = 0.0
+        lr_each_step.append(lr)
+
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate

model_zoo/official/nlp/textrcnn/train.py → model_zoo/research/nlp/textrcnn/train.py

@@ -32,7 +32,7 @@ from src.textrcnn import textrcnn
 from src.utils import get_lr
 
 
-set_seed(1)
+set_seed(2)
 
 if __name__ == '__main__':
 
@@ -64,11 +64,11 @@ if __name__ == '__main__':
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
     lr = get_lr(cfg, step_size)
+    num_epochs = cfg.num_epochs
+    if cfg.cell == "lstm":
+        num_epochs = cfg.lstm_num_epochs
 
-    if cfg.opt == "adam":
-        opt = nn.Adam(params=network.trainable_params(), learning_rate=lr)
-    elif cfg.opt == "momentum":
-        opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
+    opt = nn.Adam(params=network.trainable_params(), learning_rate=lr)
 
     loss_cb = LossMonitor()
     model = Model(network, loss, opt, {'acc': Accuracy()}, amp_level="O3")
@@ -77,5 +77,5 @@ if __name__ == '__main__':
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, \
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)
     ckpoint_cb = ModelCheckpoint(prefix=cfg.cell, directory=cfg.ckpt_folder_path, config=config_ck)
-    model.train(cfg.num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb])
+    model.train(num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb])
     print("train success")