!15515 modify model_zoo textcnn net for clould

From: @Somnus2020
Reviewed-by: @c_34,@oacjiewen
Signed-off-by: @c_34

model_zoo/official/nlp/textcnn/README.md

@@ -65,25 +65,59 @@ After installing MindSpore via the official website, you can start training and
   sh scripts/run_eval.sh ckpt_path
   ```
 
+If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
+
+```python
+# run distributed training on modelarts example
+# (1) First, Perform a or b.
+#       a. Set "enable_modelarts=True" on yaml file.
+#          Set other parameters on yaml file you need.
+#       b. Add "enable_modelarts=True" on the website UI interface.
+#          Add other parameters on the website UI interface.
+# (2) Set the code directory to "/path/textcnn" on the website UI interface.
+# (3) Set the startup file to "train.py" on the website UI interface.
+# (4) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+# (5) Create your job.
+
+# run evaluation on modelarts example
+# (1) Copy or upload your trained model to S3 bucket.
+# (2) Perform a or b.
+#       a. Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
+#          Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
+#       b. Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+# (3) Set the code directory to "/path/textcnn" on the website UI interface.
+# (4) Set the startup file to "eval.py" on the website UI interface.
+# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+# (6) Create your job.
+```
+
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)
 
 ```bash
 ├── model_zoo
-    ├── README.md                          // descriptions about all the models
+    ├── README.md                            // descriptions about all the models
     ├── textcnn
-        ├── README.md                    // descriptions about textcnn
+        ├── README.md                        // descriptions about textcnn
         ├──scripts
-        │   ├── run_train.sh             // shell script for distributed on Ascend
-        │   ├── run_eval.sh              // shell script for evaluation on Ascend
+        │   ├── run_train.sh                 // shell script for distributed on Ascend
+        │   ├── run_eval.sh                  // shell script for evaluation on Ascend
         ├── src
-        │   ├── dataset.py             // Processing dataset
-        │   ├── textcnn.py          // textcnn architecture
-        │   ├── config.py            // parameter configuration
-        ├── train.py               // training script
-        ├── eval.py               //  evaluation script
-        ├── export.py               //  export checkpoint to other format file
+        │   ├── dataset.py                     // Processing dataset
+        │   ├── textcnn.py                     // textcnn architecture
+        ├── utils
+        │   ├──device_adapter.py      // device adapter
+        │   ├──local_adapter.py         // local adapter
+        │   ├──moxing_adapter.py   // moxing adapter
+        │   ├── config.py                       // parameter analysis
+        ├── mr_config.yaml                 // parameter configuration
+        ├── sst2_config.yaml              // parameter configuration
+        ├── subj_config.yaml             // parameter configuration
+        ├── train.py                                // training script
+        ├── eval.py                                 //  evaluation script
+        ├── export.py                            //  export checkpoint to other format file
 ```
 
 ## [Script Parameters](#contents)

model_zoo/official/nlp/textcnn/eval.py

@@ -16,50 +16,40 @@
 ##############test textcnn example on movie review#################
 python eval.py
 """
-import argparse
-
 import mindspore.nn as nn
 from mindspore.nn.metrics import Accuracy
 from mindspore import context
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from src.config import cfg_mr, cfg_subj, cfg_sst2
+from utils.moxing_adapter import moxing_wrapper
+from utils.device_adapter import get_device_id
+from utils.config import config
 from src.textcnn import TextCNN
 from src.dataset import MovieReview, SST2, Subjectivity
 
-parser = argparse.ArgumentParser(description='TextCNN')
-parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
-parser.add_argument('--dataset', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
-args_opt = parser.parse_args()
-
-if __name__ == '__main__':
-    if args_opt.dataset == 'MR':
-        cfg = cfg_mr
-        instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args_opt.dataset == 'SUBJ':
-        cfg = cfg_subj
+@moxing_wrapper()
+def eval_net():
+    '''eval net'''
+    if config.dataset == 'MR':
+        instance = MovieReview(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    elif config.dataset == 'SUBJ':
         instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args_opt.dataset == 'SST2':
-        cfg = cfg_sst2
-        instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    device_target = cfg.device_target
-    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
+    elif config.dataset == 'SST2':
+        instance = SST2(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    device_target = config.device_target
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
     if device_target == "Ascend":
-        context.set_context(device_id=cfg.device_id)
-    dataset = instance.create_test_dataset(batch_size=cfg.batch_size)
+        context.set_context(device_id=get_device_id())
+    dataset = instance.create_test_dataset(batch_size=config.batch_size)
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
-    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
-                  num_classes=cfg.num_classes, vec_length=cfg.vec_length)
+    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=config.word_len,
+                  num_classes=config.num_classes, vec_length=config.vec_length)
     opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=0.001,
-                  weight_decay=cfg.weight_decay)
+                  weight_decay=float(config.weight_decay))
 
-    if args_opt.checkpoint_path is not None:
-        param_dict = load_checkpoint(args_opt.checkpoint_path)
-        print("load checkpoint from [{}].".format(args_opt.checkpoint_path))
-    else:
-        param_dict = load_checkpoint(cfg.checkpoint_path)
-        print("load checkpoint from [{}].".format(cfg.checkpoint_path))
+    param_dict = load_checkpoint(config.checkpoint_file_path)
+    print("load checkpoint from [{}].".format(config.checkpoint_file_path))
 
     load_param_into_net(net, param_dict)
     net.set_train(False)
@@ -67,3 +57,6 @@ if __name__ == '__main__':
 
     acc = model.eval(dataset)
     print("accuracy: ", acc)
+
+if __name__ == '__main__':
+    eval_net()

model_zoo/official/nlp/textcnn/export.py

@@ -16,50 +16,34 @@
 ##############export checkpoint file into air, onnx, mindir models#################
 python export.py
 """
-import argparse
 import numpy as np
 
 from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
 
-from src.config import cfg_mr, cfg_subj, cfg_sst2
+from utils.config import config
 from src.textcnn import TextCNN
 from src.dataset import MovieReview, SST2, Subjectivity
 
-parser = argparse.ArgumentParser(description='TextCNN export')
-parser.add_argument("--device_id", type=int, default=0, help="device id")
-parser.add_argument("--ckpt_file", type=str, required=True, help="checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="textcnn", help="output file name.")
-parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
-parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
-                    help="device target")
-parser.add_argument('--dataset', type=str, default='MR', choices=['MR', 'SUBJ', 'SST2'],
-                    help='dataset name.')
-
-args = parser.parse_args()
-
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-if args.device_target == "Ascend":
-    context.set_context(device_id=args.device_id)
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+if config.device_target == "Ascend":
+    context.set_context(device_id=config.device_id)
 
 if __name__ == '__main__':
 
-    if args.dataset == 'MR':
-        cfg = cfg_mr
-        instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args.dataset == 'SUBJ':
-        cfg = cfg_subj
-        instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args.dataset == 'SST2':
-        cfg = cfg_sst2
-        instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
+    if config.dataset == 'MR':
+        instance = MovieReview(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    elif config.dataset == 'SUBJ':
+        instance = Subjectivity(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    elif config.dataset == 'SST2':
+        instance = SST2(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
     else:
         raise ValueError("dataset is not support.")
 
-    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
-                  num_classes=cfg.num_classes, vec_length=cfg.vec_length)
+    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=config.word_len,
+                  num_classes=config.num_classes, vec_length=config.vec_length)
 
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(net, param_dict)
 
-    input_arr = Tensor(np.ones([cfg.batch_size, cfg.word_len], np.int32))
-    export(net, input_arr, file_name=args.file_name, file_format=args.file_format)
+    input_arr = Tensor(np.ones([config.batch_size, config.word_len], np.int32))
+    export(net, input_arr, file_name=config.file_name, file_format=config.file_format)

model_zoo/official/nlp/textcnn/mr_config.yaml

@@ -0,0 +1,57 @@
+# 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
+
+# ==============================================================================
+# Training options
+dataset: 'MR'
+pre_trained: False
+num_classes: 2
+batch_size: 64
+epoch_size: 4
+weight_decay: 3e-5
+keep_checkpoint_max: 1
+checkpoint_path: './checkpoint/'
+checkpoint_file_path: 'train_textcnn-4_149.ckpt'
+word_len: 51
+vec_length: 40
+base_lr: 1e-3
+
+# Export options
+device_id: 0
+ckpt_file: ""
+file_name: ""
+file_format: ""
+
+---
+# Help description for each configuration
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+checkpoint_url: 'The location of checkpoint for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+load_path: 'The location of checkpoint for obs'
+device_target: 'Target device type, available: [Ascend, GPU, CPU]'
+enable_profiling: 'Whether enable profiling while training, default: False'
+dataset: "Dataset to be trained and evaluated, choice: ['MR, SUBJ, SST2']"
+train_epochs: "The number of epochs used to train."
+pre_trained: 'If need load pre_trained checkpoint, default: False'
+num_classes: 'Class for dataset'
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+weight_decay: "Weight decay."
+keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
+num_factors: "The Embedding size of MF model."
+checkpoint_path: "The location of the checkpoint file."
+eval_file_name: "Eval output file."
+checkpoint_file_path: "The location of the checkpoint file."

model_zoo/official/nlp/textcnn/scripts/run_eval.sh

@@ -13,14 +13,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+BASE_PATH=$(cd ./ "`dirname $0`" || exit; pwd)
 dataset_type='MR'
+CONFIG_FILE="${BASE_PATH}/mr_config.yaml"
 if [ $# == 2 ]
 then
-    if [ $2 != "MR" ] && [ $2 != "SUBJ" ] && [ $2 != "SST2" ]
-    then
+    if [ $2 == "MR" ]; then
+        CONFIG_FILE="${BASE_PATH}/mr_config.yaml"
+    elif [ $2 == "SUBJ" ]; then
+        CONFIG_FILE="${BASE_PATH}/subj_config.yaml"
+    elif [ $2 == "SST2" ]; then
+        CONFIG_FILE="${BASE_PATH}/sst2_config.yaml"
+    else
         echo "error: the selected dataset is not in supported set{MR, SUBJ, SST2}"
     exit 1
     fi
     dataset_type=$2
 fi
-python eval.py --checkpoint_path="$1" --dataset=$dataset_type > eval.log 2>&1 &
+python eval.py --checkpoint_file_path="$1" --dataset=$dataset_type --config_path=$CONFIG_FILE > eval.log 2>&1 &

model_zoo/official/nlp/textcnn/scripts/run_train.sh

@@ -13,15 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+BASE_PATH=$(cd ./ "`dirname $0`" || exit; pwd)
 dataset_type='MR'
+CONFIG_FILE="${BASE_PATH}/mr_config.yaml"
 if [ $# == 1 ]
 then
-    if [ $1 != "MR" ] && [ $1 != "SUBJ" ] && [ $1 != "SST2" ]
-    then
+    if [ $1 == "MR" ]; then
+        CONFIG_FILE="${BASE_PATH}/mr_config.yaml"
+    elif [ $1 == "SUBJ" ]; then
+        CONFIG_FILE="${BASE_PATH}/subj_config.yaml"
+    elif [ $1 == "SST2" ]; then
+        CONFIG_FILE="${BASE_PATH}/sst2_config.yaml"
+    else
         echo "error: the selected dataset is not in supported set{MR, SUBJ, SST2}"
     exit 1
     fi
     dataset_type=$1
 fi
 rm ./ckpt_0 -rf
-python train.py --dataset=$dataset_type > train.log 2>&1 &
+python train.py --dataset=$dataset_type --config_path=$CONFIG_FILE --output_path './output' > train.log 2>&1 &

model_zoo/official/nlp/textcnn/src/config.py

@@ -1,69 +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 setting, will be used in main.py
-"""
-from easydict import EasyDict as edict
-
-cfg_mr = edict({
-    'name': 'movie review',
-    'pre_trained': False,
-    'num_classes': 2,
-    'batch_size': 64,
-    'epoch_size': 4,
-    'weight_decay': 3e-5,
-    'data_path': './data/',
-    'device_target': 'Ascend',
-    'device_id': 7,
-    'keep_checkpoint_max': 1,
-    'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
-    'word_len': 51,
-    'vec_length': 40,
-    'base_lr': 1e-3
-})
-
-cfg_subj = edict({
-    'name': 'subjectivity',
-    'pre_trained': False,
-    'num_classes': 2,
-    'batch_size': 64,
-    'epoch_size': 5,
-    'weight_decay': 3e-5,
-    'data_path': './Subj/',
-    'device_target': 'Ascend',
-    'device_id': 7,
-    'keep_checkpoint_max': 1,
-    'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
-    'word_len': 51,
-    'vec_length': 40,
-    'base_lr': 8e-4
-})
-
-cfg_sst2 = edict({
-    'name': 'SST2',
-    'pre_trained': False,
-    'num_classes': 2,
-    'batch_size': 64,
-    'epoch_size': 4,
-    'weight_decay': 3e-5,
-    'data_path': './SST-2/',
-    'device_target': 'Ascend',
-    'device_id': 7,
-    'keep_checkpoint_max': 1,
-    'checkpoint_path': './ckpt/train_textcnn-4_149.ckpt',
-    'word_len': 51,
-    'vec_length': 40,
-    'base_lr': 5e-3
-})

model_zoo/official/nlp/textcnn/sst2_config.yaml

@@ -0,0 +1,57 @@
+# 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
+
+# ==============================================================================
+# Training options
+dataset: 'SST2'
+pre_trained: False
+num_classes: 2
+batch_size: 64
+epoch_size: 4
+weight_decay: 3e-5
+keep_checkpoint_max: 1
+checkpoint_path: './checkpoint/'
+checkpoint_file_path: 'train_textcnn-4_149.ckpt'
+word_len: 51
+vec_length: 40
+base_lr: 1e-3
+
+# Export options
+device_id: 0
+ckpt_file: ""
+file_name: ""
+file_format: ""
+
+---
+# Help description for each configuration
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+checkpoint_url: 'The location of checkpoint for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+load_path: 'The location of checkpoint for obs'
+device_target: 'Target device type, available: [Ascend, GPU, CPU]'
+enable_profiling: 'Whether enable profiling while training, default: False'
+dataset: "Dataset to be trained and evaluated, choice: ['MR, SUBJ, SST2']"
+train_epochs: "The number of epochs used to train."
+pre_trained: 'If need load pre_trained checkpoint, default: False'
+num_classes: 'Class for dataset'
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+weight_decay: "Weight decay."
+keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
+num_factors: "The Embedding size of MF model."
+checkpoint_path: "The location of the checkpoint file."
+eval_file_name: "Eval output file."
+checkpoint_file_path: "The location of the checkpoint file."

model_zoo/official/nlp/textcnn/subj_config.yaml

@@ -0,0 +1,58 @@
+# 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
+
+# ==============================================================================
+# Training options
+dataset: 'SUBJ'
+pre_trained: False
+num_classes: 2
+batch_size: 64
+epoch_size: 4
+weight_decay: 3e-5
+keep_checkpoint_max: 1
+checkpoint_path: './checkpoint/'
+checkpoint_file_path: 'train_textcnn-4_149.ckpt'
+word_len: 51
+vec_length: 40
+base_lr: 1e-3
+
+# Export options
+device_id: 0
+ckpt_file: ""
+file_name: ""
+file_format: ""
+
+---
+# Help description for each configuration
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+checkpoint_url: 'The location of checkpoint for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+load_path: 'The location of checkpoint for obs'
+device_target: 'Target device type, available: [Ascend, GPU, CPU]'
+enable_profiling: 'Whether enable profiling while training, default: False'
+dataset: "Dataset to be trained and evaluated, choice: ['MR, SUBJ, SST2']"
+train_epochs: "The number of epochs used to train."
+pre_trained: 'If need load pre_trained checkpoint, default: False'
+num_classes: 'Class for dataset'
+batch_size: "Batch size for training and evaluation"
+epoch_size: "Total training epochs."
+weight_decay: "Weight decay."
+keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
+num_factors: "The Embedding size of MF model."
+checkpoint_path: "The location of the checkpoint file."
+eval_file_name: "Eval output file."
+checkpoint_file_path: "The location of the checkpoint file."
+

model_zoo/official/nlp/textcnn/train.py

@@ -16,7 +16,7 @@
 #################train textcnn example on movie review########################
 python train.py
 """
-import argparse
+import os
 import math
 
 import mindspore.nn as nn
@@ -26,62 +26,62 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMoni
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from src.config import cfg_mr, cfg_subj, cfg_sst2
+from utils.moxing_adapter import moxing_wrapper
+from utils.device_adapter import get_device_id, get_rank_id
+from utils.config import config
 from src.textcnn import TextCNN
 from src.textcnn import SoftmaxCrossEntropyExpand
 from src.dataset import MovieReview, SST2, Subjectivity
 
-parser = argparse.ArgumentParser(description='TextCNN')
-parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
-                    help='device where the code will be implemented (default: Ascend)')
-parser.add_argument('--device_id', type=int, default=5, help='device id of GPU or Ascend.')
-parser.add_argument('--dataset', type=str, default="MR", choices=['MR', 'SUBJ', 'SST2'])
-args_opt = parser.parse_args()
+def modelarts_pre_process():
+    config.checkpoint_path = os.path.join(config.output_path, str(get_rank_id()), config.checkpoint_path)
 
-if __name__ == '__main__':
-    rank = 0
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def train_net():
+    '''train net'''
     # set context
-    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
-    context.set_context(device_id=args_opt.device_id)
-    if args_opt.dataset == 'MR':
-        cfg = cfg_mr
-        instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args_opt.dataset == 'SUBJ':
-        cfg = cfg_subj
-        instance = Subjectivity(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
-    elif args_opt.dataset == 'SST2':
-        cfg = cfg_sst2
-        instance = SST2(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+    context.set_context(device_id=get_device_id())
+    if config.dataset == 'MR':
+        instance = MovieReview(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    elif config.dataset == 'SUBJ':
+        instance = Subjectivity(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
+    elif config.dataset == 'SST2':
+        instance = SST2(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
 
-    dataset = instance.create_train_dataset(batch_size=cfg.batch_size, epoch_size=cfg.epoch_size)
+    dataset = instance.create_train_dataset(batch_size=config.batch_size, epoch_size=config.epoch_size)
     batch_num = dataset.get_dataset_size()
 
-    base_lr = cfg.base_lr
+    base_lr = float(config.base_lr)
     learning_rate = []
-    warm_up = [base_lr / math.floor(cfg.epoch_size / 5) * (i + 1) for _ in range(batch_num) for i in
-               range(math.floor(cfg.epoch_size / 5))]
-    shrink = [base_lr / (16 * (i + 1)) for _ in range(batch_num) for i in range(math.floor(cfg.epoch_size * 3 / 5))]
+    warm_up = [base_lr / math.floor(config.epoch_size / 5) * (i + 1) for _ in range(batch_num) for i in
+               range(math.floor(config.epoch_size / 5))]
+    shrink = [base_lr / (16 * (i + 1)) for _ in range(batch_num) for i in range(math.floor(config.epoch_size * 3 / 5))]
     normal_run = [base_lr for _ in range(batch_num) for i in
-                  range(cfg.epoch_size - math.floor(cfg.epoch_size / 5) - math.floor(cfg.epoch_size * 2 / 5))]
+                  range(config.epoch_size - math.floor(config.epoch_size / 5) - math.floor(config.epoch_size * 2 / 5))]
     learning_rate = learning_rate + warm_up + normal_run + shrink
 
-    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
-                  num_classes=cfg.num_classes, vec_length=cfg.vec_length)
+    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=config.word_len,
+                  num_classes=config.num_classes, vec_length=config.vec_length)
     # Continue training if set pre_trained to be True
-    if cfg.pre_trained:
-        param_dict = load_checkpoint(cfg.checkpoint_path)
+    if config.pre_trained:
+        param_dict = load_checkpoint(config.checkpoint_path)
         load_param_into_net(net, param_dict)
 
-    opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=learning_rate, weight_decay=cfg.weight_decay)
+    opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), \
+                                    learning_rate=learning_rate, weight_decay=float(config.weight_decay))
     loss = SoftmaxCrossEntropyExpand(sparse=True)
 
     model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc': Accuracy()})
 
-    config_ck = CheckpointConfig(save_checkpoint_steps=int(cfg.epoch_size*batch_num/2),
-                                 keep_checkpoint_max=cfg.keep_checkpoint_max)
+    config_ck = CheckpointConfig(save_checkpoint_steps=int(config.epoch_size*batch_num/2),
+                                 keep_checkpoint_max=config.keep_checkpoint_max)
     time_cb = TimeMonitor(data_size=batch_num)
-    ckpt_save_dir = "./ckpt_" + str(rank) + "/"
+    ckpt_save_dir = os.path.join(config.output_path, config.checkpoint_path)
     ckpoint_cb = ModelCheckpoint(prefix="train_textcnn", directory=ckpt_save_dir, config=config_ck)
     loss_cb = LossMonitor()
-    model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
+    model.train(config.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
     print("train success")
+
+if __name__ == '__main__':
+    train_net()

model_zoo/official/nlp/textcnn/utils/config.py

@@ -0,0 +1,125 @@
+# 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
+
+_config_path = "./mr_config.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]
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+            else:
+                raise ValueError("At most 2 docs (config and help description for help) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper
+
+
+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, "../mr_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper = parse_yaml(path_args.config_path)
+    pprint(default)
+    args = parse_cli_to_yaml(parser, default, helper, path_args.config_path)
+    final_config = merge(args, default)
+    return Config(final_config)
+
+config = get_config()

model_zoo/official/nlp/textcnn/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 utils.config import config
+
+if config.enable_modelarts:
+    from utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from 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/official/nlp/textcnn/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/official/nlp/textcnn/utils/moxing_adapter.py

@@ -0,0 +1,115 @@
+# 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 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)
+        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()
+
+            run_func(*args, **kwargs)
+
+            # 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