clould

model_zoo/research/audio/fcn-4/README.md

@@ -97,12 +97,18 @@ SLOG_PRINT_TO_STDOUT=1 python eval.py --device_id 0
         │   ├──dataset.py                     // creating dataset
         │   ├──pre_process_data.py            // pre-process dataset
         │   ├──musictagger.py                 // googlenet architecture
-        │   ├──config.py                      // parameter configuration
         │   ├──loss.py                        // loss function
         │   ├──tag.txt                        // tag for each number
+        |   └─model_utils
+        |     ├─config.py          // Processing configuration parameters
+        |     ├─device_adapter.py  // Get cloud ID
+        |     ├─local_adapter.py   // Get local ID
+        |     └─moxing_adapter.py  // Parameter processing
         ├── train.py               // training script
         ├── eval.py                //  evaluation script
         ├── export.py              //  export model in air format
+        ├─default_config.yaml      // Training parameter profile
+        └─train.py                 // Train net
 ```
 
 ### [Script Parameters](#contents)

model_zoo/research/audio/fcn-4/default_config.yaml

@@ -0,0 +1,58 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+# config of data
+num_classes: 50
+num_consumer: 4
+get_npy: 1
+get_mindrecord: 1
+audio_path: "/cache/data"
+npy_path: "/cache/data"
+info_path: "/cache/data"
+info_name: 'annotations_final.csv'
+device_target: 'Ascend'
+device_id: 0
+mr_path: "/cache/data"
+mr_name: ['train', 'val']
+
+# config of music
+pre_trained: False
+lr: 0.0005
+batch_size: 32
+epoch_size: 10
+loss_scale: 1024.0
+mixed_precision: False
+train_filename: 'train.mindrecord0'
+val_filename: 'val.mindrecord0'
+data_dir: "/cache/data"
+keep_checkpoint_max: 10
+save_step: 2000
+checkpoint_path: "/cache/data/musicTagger"
+prefix: 'MusicTagger'
+model_name: 'MusicTagger-10_543.ckpt'
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+device_target: 'Target device type' 
+enable_profiling: 'Whether enable profiling while training, default: False'
+file_name: 'output file name.'
+file_format: 'file format'
+
+---
+device_target: ['Ascend', 'GPU', 'CPU']
+file_format: ['AIR', 'ONNX', 'MINDIR']

model_zoo/research/audio/fcn-4/default_config_music.yaml

@@ -0,0 +1,47 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+# What is the meaning of separating the two dictionaries in the original config file？
+pre_trained: False
+lr: 0.0005
+batch_size: 32
+epoch_size: 10
+loss_scale: 1024.0
+num_consumer: 4
+mixed_precision: False
+train_filename: 'train.mindrecord0'
+val_filename: 'val.mindrecord0'
+data_dir: "/cache/data"
+device_target: 'Ascend'
+device_id: 0
+keep_checkpoint_max: 10
+save_step: 2000
+checkpoint_path: "/cache/data/musicTagger"
+prefix: 'MusicTagger'
+model_name: 'MusicTagger-10_543.ckpt'
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+device_target: 'Target device type' 
+enable_profiling: 'Whether enable profiling while training, default: False'
+file_name: 'output file name.'
+file_format: 'file format'
+
+---
+device_target: ['Ascend', 'GPU', 'CPU']
+file_format: ['AIR', 'ONNX', 'MINDIR']

model_zoo/research/audio/fcn-4/eval.py

@@ -17,15 +17,18 @@
 python eval.py
 '''
 
-import argparse
 import numpy as np
+
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id
+from src.musictagger import MusicTaggerCNN
+from src.dataset import create_dataset
+
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore import Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.musictagger import MusicTaggerCNN
-from src.config import music_cfg as cfg
-from src.dataset import create_dataset
 
 
 def calculate_auc(labels_list, preds_list):
@@ -107,22 +110,15 @@ def validation(net, model_path, data_dir, filename, num_consumer, batch):
     return auc
 
 
-if __name__ == "__main__":
+def modelarts_process():
-    parser = argparse.ArgumentParser(description='Evaluate model')
+    pass
-    parser.add_argument('--device_id',
-                        type=int,
-                        help='device ID',
-                        default=None)
-    args = parser.parse_args()
 
-    if args.device_id is not None:
+@moxing_wrapper(pre_process=modelarts_process)
-        context.set_context(device_target=cfg.device_target,
+def fcn4_eval():
-                            mode=context.GRAPH_MODE,
+    """
-                            device_id=args.device_id)
+    eval network
-    else:
+    """
-        context.set_context(device_target=cfg.device_target,
+    context.set_context(device_target=config.device_target, mode=context.GRAPH_MODE, device_id=get_device_id())
-                            mode=context.GRAPH_MODE,
-                            device_id=cfg.device_id)
 
     network = MusicTaggerCNN(in_classes=[1, 128, 384, 768, 2048],
                              kernel_size=[3, 3, 3, 3, 3],
@@ -130,8 +126,12 @@ if __name__ == "__main__":
                              maxpool=[(2, 4), (4, 5), (3, 8), (4, 8)],
                              has_bias=True)
     network.set_train(False)
-    auc_val = validation(network, cfg.checkpoint_path + "/" + cfg.model_name, cfg.data_dir,
+    auc_val = validation(network, config.checkpoint_path + "/" + config.model_name, config.data_dir,
-                         cfg.val_filename, cfg.num_consumer, cfg.batch_size)
+                         config.val_filename, config.num_consumer, config.batch_size)
 
     print("=" * 10 + "Validation Performance" + "=" * 10)
     print("AUC: {:.5f}".format(auc_val))
+
+
+if __name__ == "__main__":
+    fcn4_eval()

model_zoo/research/audio/fcn-4/export.py

@@ -21,8 +21,10 @@ import numpy as np
 from mindspore.train.serialization import export
 from mindspore import Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from src.model_utils.config import config
 from src.musictagger import MusicTaggerCNN
-from src.config import music_cfg as cfg
+
 
 if __name__ == "__main__":
     network = MusicTaggerCNN(in_classes=[1, 128, 384, 768, 2048],
@@ -30,11 +32,11 @@ if __name__ == "__main__":
                              padding=[0] * 5,
                              maxpool=[(2, 4), (4, 5), (3, 8), (4, 8)],
                              has_bias=True)
-    param_dict = load_checkpoint(cfg.checkpoint_path + "/" + cfg.model_name)
+    param_dict = load_checkpoint(config.checkpoint_path + "/" + config.model_name)
     load_param_into_net(network, param_dict)
     input_data = np.random.uniform(0.0, 1.0, size=[1, 1, 96, 1366]).astype(np.float32)
     export(network,
            Tensor(input_data),
-           filename="{}/{}.air".format(cfg.checkpoint_path,
+           filename="{}/{}.air".format(config.checkpoint_path,
-                                       cfg.model_name[:-5]),
+                                       config.model_name[:-5]),
            file_format="AIR")

model_zoo/research/audio/fcn-4/scripts/run_eval.sh

@@ -14,5 +14,8 @@
 # limitations under the License.
 # ============================================================================
 
+export DATA_PATH=$1
+export CKPT_PATH=$2
+export DEVICE_ID=$3
 export SLOG_PRINT_TO_STDOUT=1
-python ../eval.py --device_id 0
+python ../eval.py --data_dir=$DATA_PATH --checkpoint_path=$CKPT_PATH --device_id=$DEVICE_ID > log_eval 2>&1 &

model_zoo/research/audio/fcn-4/scripts/run_train.sh

@@ -14,5 +14,8 @@
 # limitations under the License.
 # ============================================================================
 
+export DATA_PATH=$1
+export CKPT_PATH=$2
+export DEVICE_ID=$3
 export SLOG_PRINT_TO_STDOUT=1
-python ../train.py --device_id 0
+python ../train.py --data_dir=$DATA_PATH --checkpoint_path=$CKPT_PATH --device_id=$DEVICE_ID > log 2>&1 &

model_zoo/research/audio/fcn-4/src/config.py

@@ -1,53 +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 train.py, eval.py
-"""
-from easydict import EasyDict as edict
-
-data_cfg = edict({
-    'num_classes': 50,
-    'num_consumer': 4,
-    'get_npy': 1,
-    'get_mindrecord': 1,
-    'audio_path': "/dev/data/Music_Tagger_Data/fea/",
-    'npy_path': "/dev/data/Music_Tagger_Data/fea/",
-    'info_path': "/dev/data/Music_Tagger_Data/fea/",
-    'info_name': 'annotations_final.csv',
-    'device_target': 'Ascend',
-    'device_id': 0,
-    'mr_path': '/dev/data/Music_Tagger_Data/fea/',
-    'mr_name': ['train', 'val'],
-})
-
-music_cfg = edict({
-    'pre_trained': False,
-    'lr': 0.0005,
-    'batch_size': 32,
-    'epoch_size': 10,
-    'loss_scale': 1024.0,
-    'num_consumer': 4,
-    'mixed_precision': False,
-    'train_filename': 'train.mindrecord0',
-    'val_filename': 'val.mindrecord0',
-    'data_dir': '/dev/data/Music_Tagger_Data/fea/',
-    'device_target': 'Ascend',
-    'device_id': 0,
-    'keep_checkpoint_max': 10,
-    'save_step': 2000,
-    'checkpoint_path': '/dev/data/Music_Tagger_Data/model',
-    'prefix': 'MusicTagger',
-    'model_name': 'MusicTagger_3-50_543.ckpt',
-})

model_zoo/research/audio/fcn-4/src/model_utils/config.py

@@ -0,0 +1,127 @@
+# 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()

model_zoo/research/audio/fcn-4/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 .config import config
+
+if config.enable_modelarts:
+    from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from .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/audio/fcn-4/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/audio/fcn-4/src/model_utils/moxing_adapter.py

@@ -0,0 +1,122 @@
+# 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 .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()
+
+            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/audio/fcn-4/src/pre_process_data.py

@@ -21,7 +21,7 @@ import numpy as np
 import librosa
 from mindspore.mindrecord import FileWriter
 from mindspore import context
-from src.config import data_cfg as cfg
+from model_utils.config import config as cfg
 
 
 def compute_melgram(audio_path, save_path='', filename='', save_npy=True):

model_zoo/research/audio/fcn-4/train.py

@@ -16,18 +16,27 @@
 ##############train models#################
 python train.py
 '''
-import argparse
+
 from mindspore import context, nn
 from mindspore.train import Model
 from mindspore.common import set_seed
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+
+from src.model_utils.device_adapter import get_device_id
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 from src.dataset import create_dataset
 from src.musictagger import MusicTaggerCNN
 from src.loss import BCELoss
-from src.config import music_cfg as cfg
 
+
+def modelarts_pre_process():
+    pass
+    # config.ckpt_path = os.path.join(config.output_path, str(get_rank_id()), config.checkpoint_path)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
 def train(model, dataset_direct, filename, columns_list, num_consumer=4,
           batch=16, epoch=50, save_checkpoint_steps=2172, keep_checkpoint_max=50,
           prefix="model", directory='./'):
@@ -43,67 +52,46 @@ def train(model, dataset_direct, filename, columns_list, num_consumer=4,
                                 num_consumer)
 
 
-    model.train(epoch,
+    model.train(epoch, data_train, callbacks=[ckpoint_cb, \
-                data_train,
+        LossMonitor(per_print_times=181), TimeMonitor()], dataset_sink_mode=True)
-                callbacks=[
-                    ckpoint_cb,
-                    LossMonitor(per_print_times=181),
-                    TimeMonitor()
-                ],
-                dataset_sink_mode=True)
 
 
 if __name__ == "__main__":
     set_seed(1)
-    parser = argparse.ArgumentParser(description='Train model')
-    parser.add_argument('--device_id',
-                        type=int,
-                        help='device ID',
-                        default=None)
 
-    args = parser.parse_args()
+    context.set_context(device_target='Ascend', mode=context.GRAPH_MODE, device_id=get_device_id())
-
+    context.set_context(enable_auto_mixed_precision=config.mixed_precision)
-    if args.device_id is not None:
-        context.set_context(device_target='Ascend',
-                            mode=context.GRAPH_MODE,
-                            device_id=args.device_id)
-    else:
-        context.set_context(device_target='Ascend',
-                            mode=context.GRAPH_MODE,
-                            device_id=cfg.device_id)
-
-    context.set_context(enable_auto_mixed_precision=cfg.mixed_precision)
     network = MusicTaggerCNN(in_classes=[1, 128, 384, 768, 2048],
                              kernel_size=[3, 3, 3, 3, 3],
                              padding=[0] * 5,
                              maxpool=[(2, 4), (4, 5), (3, 8), (4, 8)],
                              has_bias=True)
 
-    if cfg.pre_trained:
+    if config.pre_trained:
-        param_dict = load_checkpoint(cfg.checkpoint_path + '/' +
+        param_dict = load_checkpoint(config.checkpoint_path + '/' +
-                                     cfg.model_name)
+                                     config.model_name)
         load_param_into_net(network, param_dict)
 
     net_loss = BCELoss()
 
     network.set_train(True)
     net_opt = nn.Adam(params=network.trainable_params(),
-                      learning_rate=cfg.lr,
+                      learning_rate=config.lr,
-                      loss_scale=cfg.loss_scale)
+                      loss_scale=config.loss_scale)
 
-    loss_scale_manager = FixedLossScaleManager(loss_scale=cfg.loss_scale,
+    loss_scale_manager = FixedLossScaleManager(loss_scale=config.loss_scale,
                                                drop_overflow_update=False)
     net_model = Model(network, net_loss, net_opt, loss_scale_manager=loss_scale_manager)
 
     train(model=net_model,
-          dataset_direct=cfg.data_dir,
+          dataset_direct=config.data_dir,
-          filename=cfg.train_filename,
+          filename=config.train_filename,
           columns_list=['feature', 'label'],
-          num_consumer=cfg.num_consumer,
+          num_consumer=config.num_consumer,
-          batch=cfg.batch_size,
+          batch=config.batch_size,
-          epoch=cfg.epoch_size,
+          epoch=config.epoch_size,
-          save_checkpoint_steps=cfg.save_step,
+          save_checkpoint_steps=config.save_step,
-          keep_checkpoint_max=cfg.keep_checkpoint_max,
+          keep_checkpoint_max=config.keep_checkpoint_max,
-          prefix=cfg.prefix,
+          prefix=config.prefix,
-          directory=cfg.checkpoint_path + "_{}".format(cfg.device_id))
+          directory=config.checkpoint_path + "_{}".format(get_device_id()))
     print("train success")