textrcnn

model_zoo/research/nlp/textrcnn/default_config.yaml

@@ -0,0 +1,60 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "Ascend"
+enable_profiling: False
+
+# ==============================================================================
+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'
+ckpt_folder_path: './ckpt'
+preprocess_path: './preprocess'
+preprocess: 'false'
+data_root: './data/'
+lr: 0.001  # 1e-3
+lstm_lr_init: 0.002  # 2e-3
+lstm_lr_end: 0.0005  # 5e-4
+lstm_lr_max: 0.003  # 3e-3
+lstm_lr_warm_up_epochs: 2
+lstm_lr_adjust_epochs: 9
+emb_path: './word2vec'
+embed_size: 300
+save_checkpoint_steps: 149
+keep_checkpoint_max: 10
+ckpt_path: ''
+
+# Export related
+ckpt_file: ''
+file_name: 'textrcnn'
+file_format: "MINDIR"
+
+---
+
+# Help description for each configuration
+# ModelArts related
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: "Running platform, choose from Ascend, GPU or CPU, and default is Ascend."
+enable_profiling: 'Whether enable profiling while training, default: False'
+# Export related
+ckpt_file: "textrcnn ckpt file."
+file_name: "textrcnn output file name."
+file_format: "file format, choose from MINDIR or AIR"
+
+---
+file_format: ["AIR", "MINDIR"]
+device_target: ["Ascend"]

model_zoo/research/nlp/textrcnn/eval.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """model evaluation script"""
 import os
-import argparse
 import numpy as np
 
 import mindspore.nn as nn
@@ -23,25 +22,32 @@ from mindspore import Tensor
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import LossMonitor
+# from mindspore.train.callback import LossMonitor
 from mindspore.common import set_seed
 
-from src.config import textrcnn_cfg as cfg
 from src.dataset import create_dataset
 from src.textrcnn import textrcnn
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.config import config as cfg
+from src.model_utils.device_adapter import get_device_id
 
 set_seed(1)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
-    parser = argparse.ArgumentParser(description='textrcnn')
+    '''modelarts pre process function.'''
-    parser.add_argument('--ckpt_path', type=str)
+    cfg.ckpt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), cfg.ckpt_path)
-    args = parser.parse_args()
+    cfg.preprocess_path = cfg.data_path
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_eval():
+    '''eval function.'''
     context.set_context(
         mode=context.GRAPH_MODE,
         save_graphs=False,
         device_target="Ascend")
 
-    device_id = int(os.getenv('DEVICE_ID'))
+    device_id = get_device_id()
     context.set_context(device_id=device_id)
 
     embedding_table = np.loadtxt(os.path.join(cfg.preprocess_path, "weight.txt")).astype(np.float32)
@@ -49,12 +55,15 @@ if __name__ == '__main__':
                        cell=cfg.cell, batch_size=cfg.batch_size)
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
     eval_net = nn.WithEvalCell(network, loss, True)
-    loss_cb = LossMonitor()
+    # loss_cb = LossMonitor()
     print("============== Starting Testing ==============")
     ds_eval = create_dataset(cfg.preprocess_path, cfg.batch_size, False)
-    param_dict = load_checkpoint(args.ckpt_path)
+    param_dict = load_checkpoint(cfg.ckpt_path)
     load_param_into_net(network, param_dict)
     network.set_train(False)
     model = Model(network, loss, metrics={'acc': Accuracy()}, eval_network=eval_net, eval_indexes=[0, 1, 2])
     acc = model.eval(ds_eval, dataset_sink_mode=False)
     print("============== Accuracy:{} ==============".format(acc))
+
+if __name__ == '__main__':
+    run_eval()

model_zoo/research/nlp/textrcnn/export.py

@@ -14,26 +14,16 @@
 # ============================================================================
 """textrcnn export ckpt file to mindir/air"""
 import os
-import argparse
 import numpy as np
 from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
 
 from src.textrcnn import textrcnn
-from src.config import textrcnn_cfg as config
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
 
-parser = argparse.ArgumentParser(description="textrcnn")
+def run_export():
-parser.add_argument("--device_id", type=int, default=0, help="Device id")
+    '''export function.'''
-parser.add_argument("--ckpt_file", type=str, required=True, help="textrcnn ckpt file.")
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
-parser.add_argument("--file_name", type=str, default="textrcnn", help="textrcnn output file name.")
-parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"],
-                    default="MINDIR", help="file format")
-parser.add_argument("--device_target", type=str, choices=["Ascend"], default="Ascend",
-                    help="device target")
-args = parser.parse_args()
-
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
-
-if __name__ == "__main__":
     # define net
     embedding_table = np.loadtxt(os.path.join(config.preprocess_path, "weight.txt")).astype(np.float32)
 
@@ -41,9 +31,12 @@ if __name__ == "__main__":
                    cell=config.cell, batch_size=config.batch_size)
 
     # load checkpoint
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(net, param_dict)
     net.set_train(False)
 
     image = Tensor(np.ones([config.batch_size, 50], np.int32))
-    export(net, image, file_name=args.file_name, file_format=args.file_format)
+    export(net, image, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == "__main__":
+    run_export()

model_zoo/research/nlp/textrcnn/postprocess.py

@@ -18,7 +18,7 @@ import argparse
 import numpy as np
 
 from mindspore.nn.metrics import Accuracy
-from src.config import textrcnn_cfg as cfg
+from src.model_utils.config import config as cfg
 
 parser = argparse.ArgumentParser(description='postprocess')
 parser.add_argument('--label_path', type=str, default="./preprocess_Result/label_ids.npy")

model_zoo/research/nlp/textrcnn/preprocess.py

@@ -17,7 +17,7 @@ import os
 import argparse
 import numpy as np
 
-from src.config import textrcnn_cfg as cfg
+from src.model_utils.config import config as cfg
 from src.dataset import create_dataset
 
 parser = argparse.ArgumentParser(description='preprocess')

model_zoo/research/nlp/textrcnn/readme.md

@@ -65,6 +65,8 @@ Dataset used: [Sentence polarity dataset v1.0](http://www.cs.cornell.edu/people/
 
 - Running on Ascend
 
+  If you are running the scripts for the first time and , you must set the parameter 'preprocess' to 'true' in the `default_config.yaml` and run training to get the folder 'preprocess' containing data。
+
 ```python
 # run training
 DEVICE_ID=7 python train.py
@@ -77,6 +79,58 @@ DEVICE_ID=7 python eval.py --ckpt_path {checkpoint path}
 bash scripts/run_eval.sh
 ```
 
+- Running on ModelArts
+
+  If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training as follows
+
+  You have to prepare the folder 'preprocess'.
+
+  You can change the file name of 'requirements.txt' to 'pip-requirements.txt' for installing some third party libraries automatically on ModelArts.
+
+    - Training standalone on ModelArts
+
+      ```python
+      # (1) Upload the code folder to S3 bucket.
+      # (2) Click to "create training task" on the website UI interface.
+      # (3) Set the code directory to "/{path}/textrcnn" on the website UI interface.
+      # (4) Set the startup file to /{path}/textrcnn/train.py" on the website UI interface.
+      # (5) Perform a or b.
+      #     a. setting parameters in /{path}/textrcnn/default_config.yaml.
+      #         1. Set ”enable_modelarts: True“
+      #         2. Set ”cell: 'lstm'“(Default is 'gru'. if you want to use lstm, you can do this step)
+      #     b. adding on the website UI interface.
+      #         1. Set ”enable_modelarts=True“
+      #         2. Set ”cell=lstm“(Default is 'gru'. if you want to use lstm, you can do this step)
+      # (6) Upload the dataset(the folder 'preprocess') to S3 bucket.
+      # (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
+      # (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+      # (9) Under the item "resource pool selection", select the specification of a single cards.
+      # (10) Create your job.
+      ```
+
+    - evaluating with single card on ModelArts
+
+      ```python
+      # (1) Upload the code folder to S3 bucket.
+      # (2)  Click to "create training task" on the website UI interface.
+      # (3) Set the code directory to "/{path}/textrcnn" on the website UI interface.
+      # (4) Set the startup file to /{path}/textrcnn/eval.py" on the website UI interface.
+      # (5) Perform a or b.
+      #     a. setting parameters in /{path}/textrcnn/default_config.yaml.
+      #         1. Set ”enable_modelarts: True“
+      #         2. Set ”cell: 'lstm'“(Default is 'gru'. If you want to use lstm, you can do this step)
+      #         3. Set ”ckpt_path: './{path}/*.ckpt'“(The *.ckpt file must under the folder 'textrcnn')
+      #     b. adding on the website UI interface.
+      #         1. Set ”enable_modelarts=True“
+      #         2. Set ”cell=lstm“(Default is 'gru'. if you want to use lstm, you can do this step)
+      #         3. Set ”ckpt_path=./{path}/*.ckpt“(The *.ckpt file must under the folder 'textrcnn')
+      # (6) Upload the dataset(the folder 'preprocess') to S3 bucket.
+      # (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
+      # (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+      # (9) Under the item "resource pool selection", select the specification of a single cards.
+      # (10) Create your job.
+      ```
+
 ## [Script Description](#contents)
 
 ### [Script and Sample Code](#contents)
@@ -85,63 +139,61 @@ bash scripts/run_eval.sh
 ├── model_zoo
     ├── README.md                       // descriptions about all the models
     ├── textrcnn
-        ├── README.md                    // descriptions about TextRCNN
+        ├── readme.md                   // descriptions about TextRCNN
-        ├── data_src
+        ├── ascend310_infer             // application for 310 inference
-        │   ├──rt-polaritydata            // directory to save the source data
-        │   ├──rt-polaritydata.README.1.0.txt    // readme file of dataset
         ├── scripts
         │   ├──run_train.sh             // shell script for train on Ascend
-        │   ├──run_eval.sh              // shell script for evaluation on Ascend
+        │   ├──run_infer_310.sh         // shell script for 310 infer
-        │   ├──sample.txt              // example shell to run the above the two scripts
+        │   └──run_eval.sh              // shell script for evaluation on Ascend
         ├── src
+        │   ├──model_utils
+        │   │  ├──config.py             // parsing parameter configuration file of "*.yaml"
+        │   │  ├──device_adapter.py     // local or ModelArts training
+        │   │  ├──local_adapter.py      // get related environment variables in local training
+        │   │  └──moxing_adapter.py     // get related environment variables in ModelArts training
         │   ├──dataset.py               // creating dataset
         │   ├──textrcnn.py              // textrcnn architecture
-        │   ├──config.py            // parameter configuration
+        │   └──utils.py                 // function related to learning rate
-        ├── train.py               // training script
-        ├── export.py             // export script
-        ├── eval.py               //  evaluation script
         ├── data_helpers.py             // dataset split script
-        ├── sample.txt               //  the shell to train and eval the model without scripts
+        ├── default_config.yaml         // parameter configuration
+        ├── eval.py                     // evaluation script
+        ├── export.py                   // export script
+        ├── mindspore_hub_conf.py       // mindspore hub interface
+        ├── postprocess.py              // 310infer postprocess script
+        ├── preprocess.py               // dataset generation script
+        ├── requirements.txt            // some third party libraries that need to be installed
+        ├── sample.txt                  // the shell to train and eval the model without '*.sh'
+        └── train.py                    // training script
 ```
 
 ### [Script Parameters](#contents)
 
-Parameters for both training and evaluation can be set in config.py
+Parameters for both training and evaluation can be set in `default_config.yaml`
 
 - config for Textrcnn, Sentence polarity dataset v1.0.
 
   ```python
-  'num_epochs': 10, # total training epochs
+  num_epochs: 10                  # total training epochs
-  'lstm_num_epochs': 15, # total training epochs when using lstm
+  lstm_num_epochs: 15             # total training epochs when using lstm
-  'batch_size': 64, # training batch size
+  batch_size: 64                  # training batch size
-  'cell': 'gru', # the RNN architecture, can be 'vanilla', 'gru' and 'lstm'.
+  cell: 'gru'                     # the RNN architecture, can be 'vanilla', 'gru' and 'lstm'.
-  'ckpt_folder_path': './ckpt', # the path to save the checkpoints
+  ckpt_folder_path: './ckpt'      # the path to save the checkpoints
-  'preprocess_path': './preprocess', # the directory to save the processed data
+  preprocess_path: './preprocess' # the directory to save the processed data
-  'preprocess' : 'false', # whethere to preprocess the data
+  preprocess: 'false'             # whethere to preprocess the data
-  'data_path': './data/', # the path to store the splited data
+  data_path: './data/'            # the path to store the splited data
-  'lr': 1e-3, # the training learning rate
+  lr: 0.001  # 1e-3               # the training learning rate
-  'lstm_lr_init': 2e-3, # learning rate initial value when using lstm
+  lstm_lr_init: 0.002  # 2e-3     # learning rate initial value when using lstm
-  'lstm_lr_end': 5e-4, # learning rate end value when using lstm
+  lstm_lr_end: 0.0005  # 5e-4     # learning rate end value when using lstm
-  'lstm_lr_max': 3e-3, # learning eate max value when using lstm
+  lstm_lr_max: 0.003  # 3e-3      # learning eate max value when using lstm
-  'lstm_lr_warm_up_epochs': 2 # warm up epoch num 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
+  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
+  emb_path: './word2vec'          # the directory to save the embedding file
-  'embed_size': 300, # the dimension of the word embedding
+  embed_size: 300                 # the dimension of the word embedding
-  'save_checkpoint_steps': 149, # per step to save the checkpoint
+  save_checkpoint_steps: 149      # per step to save the checkpoint
-  'keep_checkpoint_max': 10 # max checkpoints to save
+  keep_checkpoint_max: 10         # max checkpoints to save
+  ckpt_path: ''                   # relative path of '*.ckpt' to be evaluated relative to the eval.py
   ```
 
-### Performance
-
-| Model                 | MindSpore + Ascend                        | TensorFlow+GPU                       |
-| -------------------------- | ----------------------------- | ------------------------- |
-| Resource                   | Ascend 910; OS Euler2.8                    | NV SMX2 V100-32G          |
-| Version          | 1.0.1                         | 1.4.0                     |
-| Dataset                    | Sentence polarity dataset v1.0                    | Sentence polarity dataset v1.0            |
-| batch_size                 | 64                        | 64                   |
-| Accuracy                   | 0.78                      | 0.78 |
-| Speed                      | 35ms/step                  |  77ms/step                         |
-
 ## Inference Process
 
 ### [Export MindIR](#contents)
@@ -173,6 +225,17 @@ Inference result is saved in current path, you can find result like this in acc.
 ============== Accuracy:{} ============== 0.8008
 ```
 
+### Performance
+
+| Model                 | MindSpore + Ascend                        | TensorFlow+GPU                       |
+| -------------------------- | ----------------------------- | ------------------------- |
+| Resource                   | Ascend 910; OS Euler2.8                    | NV SMX2 V100-32G          |
+| Version          | 1.0.1                         | 1.4.0                     |
+| Dataset                    | Sentence polarity dataset v1.0                    | Sentence polarity dataset v1.0            |
+| batch_size                 | 64                        | 64                   |
+| Accuracy                   | 0.78                      | 0.78 |
+| Speed                      | 35ms/step                  |  77ms/step                         |
+
 ## [ModelZoo Homepage](#contents)
 
  Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/research/nlp/textrcnn/scripts/run_eval.sh

@@ -13,6 +13,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+if [ $# != 2 ] && [ $# != 1 ]; then
+  echo "Usage: sh run_eval.sh [CKPT_PATH] [DEVICE_ID]"
+  exit 1
+fi
+
+if [ $# == 2 ]; then
+  export DEVICE_ID=$2
+else
+  export DEVICE_ID=0
+fi
+
 ulimit -u unlimited
 
 BASEPATH=$(cd "`dirname $0`" || exit; pwd)

model_zoo/research/nlp/textrcnn/scripts/run_train.sh

@@ -13,6 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+if [ $# != 1 ] && [ $# != 0 ]; then
+  echo "Usage: sh run_train.sh [DEVICE_ID]"
+  exit 1
+fi
+
+if [ $# == 1 ]; then
+  export DEVICE_ID=$1
+else
+  export DEVICE_ID=0
+fi
 
 ulimit -u unlimited
 

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

@@ -1,42 +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.
-# ============================================================================
-"""
-network config
-"""
-from easydict import EasyDict as edict
-
-# LSTM CONFIG
-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',
-    'ckpt_folder_path': './ckpt',
-    'preprocess_path': './preprocess',
-    'preprocess': 'false',
-    'data_path': './data/',
-    'lr': 1e-3,
-    '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,
-    'keep_checkpoint_max': 10,
-})

model_zoo/research/nlp/textrcnn/src/model_utils/config.py

@@ -0,0 +1,130 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members.
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
+    """
+    Parse command line arguments to the configuration according to the default yaml.
+
+    Args:
+        parser: Parent parser.
+        cfg: Base configuration.
+        helper: Helper description.
+        cfg_path: Path to the default yaml config.
+    """
+    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file.
+
+    Args:
+        yaml_path: Path to the yaml config.
+    """
+    with open(yaml_path, 'r') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments.
+
+    Args:
+        args: Command line arguments.
+        cfg: Base configuration.
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments.
+    """
+    parser = argparse.ArgumentParser(description="default name", add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    pprint(default)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+    return Config(final_config)
+
+config = get_config()
+
+if __name__ == '__main__':
+    print(config)

model_zoo/research/nlp/textrcnn/src/model_utils/device_adapter.py

@@ -0,0 +1,27 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""Device adapter for ModelArts"""
+
+from src.model_utils.config import config
+
+if config.enable_modelarts:
+    from src.model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from src.model_utils.local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
+]

model_zoo/research/nlp/textrcnn/src/model_utils/local_adapter.py

@@ -0,0 +1,36 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return "Local Job"

model_zoo/research/nlp/textrcnn/src/model_utils/moxing_adapter.py

@@ -0,0 +1,123 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from mindspore.profiler import Profiler
+from src.model_utils.config import config
+
+_global_sync_count = 0
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local path
+    Upload data from local directory to remote obs in contrast.
+    """
+    import moxing as mox
+    import time
+    global _global_sync_count
+    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
+    _global_sync_count += 1
+
+    # Each server contains 8 devices as most.
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print("from path: ", from_path)
+        print("to path: ", to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print("===finish data synchronization===")
+        try:
+            os.mknod(sync_lock)
+            # print("os.mknod({}) success".format(sync_lock))
+        except IOError:
+            pass
+        print("===save flag===")
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+
+    print("Finish sync data from {} to {}.".format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs.
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print("Dataset downloaded: ", os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print("Preload downloaded: ", os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print("Workspace downloaded: ", os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            if config.enable_profiling:
+                profiler = Profiler()
+
+            run_func(*args, **kwargs)
+
+            if config.enable_profiling:
+                profiler.analyse()
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print("Start to copy output directory")
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper

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

@@ -63,6 +63,10 @@ class textrcnn(nn.Cell):
             self.rnnU_fw = nn.Dense(self.embed_size, self.num_hiddens)
             self.rnnW_bw = nn.Dense(self.num_hiddens, self.num_hiddens)
             self.rnnU_bw = nn.Dense(self.embed_size, self.num_hiddens)
+            self.rnnW_fw.to_float(mstype.float16)
+            self.rnnU_fw.to_float(mstype.float16)
+            self.rnnW_bw.to_float(mstype.float16)
+            self.rnnU_bw.to_float(mstype.float16)
 
         if cell == "gru":
             self.rnnWr_fw = nn.Dense(self.num_hiddens + self.embed_size, self.num_hiddens)

model_zoo/research/nlp/textrcnn/train.py

@@ -25,22 +25,31 @@ from mindspore.nn.metrics import Accuracy
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.common import set_seed
 
-from src.config import textrcnn_cfg as cfg
 from src.dataset import create_dataset
 from src.dataset import convert_to_mindrecord
 from src.textrcnn import textrcnn
 from src.utils import get_lr
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.config import config as cfg
+from src.model_utils.device_adapter import get_device_id
 
 set_seed(0)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    cfg.ckpt_folder_path = os.path.join(cfg.output_path, cfg.ckpt_folder_path)
+    cfg.preprocess_path = cfg.data_path
 
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_train():
+    '''train function.'''
     context.set_context(
         mode=context.GRAPH_MODE,
         save_graphs=False,
         device_target="Ascend")
 
-    device_id = int(os.getenv('DEVICE_ID'))
+    device_id = get_device_id()
     context.set_context(device_id=device_id)
 
     if cfg.preprocess == 'true':
@@ -48,7 +57,7 @@ if __name__ == '__main__':
         if os.path.exists(cfg.preprocess_path):
             shutil.rmtree(cfg.preprocess_path)
         os.mkdir(cfg.preprocess_path)
-        convert_to_mindrecord(cfg.embed_size, cfg.data_path, cfg.preprocess_path, cfg.emb_path)
+        convert_to_mindrecord(cfg.embed_size, cfg.data_root, cfg.preprocess_path, cfg.emb_path)
 
     if cfg.cell == "vanilla":
         print("============ Precision is lower than expected when using vanilla RNN architecture ===========")
@@ -79,3 +88,6 @@ if __name__ == '__main__':
     ckpoint_cb = ModelCheckpoint(prefix=cfg.cell, directory=cfg.ckpt_folder_path, config=config_ck)
     model.train(num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb, time_cb])
     print("train success")
+
+if __name__ == '__main__':
+    run_train()