move set_context to run.py

README.md

@@ -20,7 +20,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
 | Audio | Speech Synthesis | [lpcnet](https://gitee.com/mindspore/models/tree/master/official/audio/lpcnet) |✅| ✅ |   |
 | Audio | Speech Synthesis | [melgan](https://gitee.com/mindspore/models/tree/master/official/audio/melgan) |✅| ✅ |   |
 | Audio | Speech Synthesis | [tacotron2](https://gitee.com/mindspore/models/tree/master/research/audio/tacotron2) |✅|   |   |
-| Graph Neural Network | Recommender System | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
+| Graph Neural Network | Text Classification | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
 | Graph Neural Network | Text Classification | [gat](https://gitee.com/mindspore/models/tree/master/official/gnn/gat) |✅| ✅ |   |
 | Graph Neural Network | Text Classification | [gcn](https://gitee.com/mindspore/models/tree/master/official/gnn/gcn) |✅| ✅ |   |
 | Recommendation | Recommender System | [naml](https://gitee.com/mindspore/models/tree/master/official/recommend/naml) |✅| ✅ |   |

README_CN.md

@@ -20,7 +20,7 @@
 | 语音 | 语音合成 | [lpcnet](https://gitee.com/mindspore/models/tree/master/official/audio/lpcnet) |✅| ✅ |   |
 | 语音 | 语音合成 | [melgan](https://gitee.com/mindspore/models/tree/master/official/audio/melgan) |✅| ✅ |   |
 | 语音 | 语音合成 | [tacotron2](https://gitee.com/mindspore/models/tree/master/research/audio/tacotron2) |✅|   |   |
-| 推荐 | 推荐系统 | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
+| 图神经网络 | 文本分类 | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
 | 图神经网络 | 文本分类 | [gat](https://gitee.com/mindspore/models/tree/master/official/gnn/gat) |✅| ✅ |   |
 | 图神经网络 | 文本分类 | [gcn](https://gitee.com/mindspore/models/tree/master/official/gnn/gcn) |✅| ✅ |   |
 | 推荐 | 推荐系统 | [naml](https://gitee.com/mindspore/models/tree/master/official/recommend/naml) |✅| ✅ |   |

official/cv/mobilenetv2/eval.py

@@ -20,7 +20,6 @@ import mindspore as ms
 import mindspore.nn as nn
 from src.dataset import create_dataset
 from src.models import define_net, load_ckpt
-from src.utils import context_device_init
 from src.model_utils.config import config
 from src.model_utils.moxing_adapter import moxing_wrapper, modelarts_process
 from src.model_utils.device_adapter import get_device_id
@@ -30,11 +29,11 @@ config.is_training = config.is_training_eval
 
 @moxing_wrapper(pre_process=modelarts_process)
 def eval_mobilenetv2():
+    ms.set_context(mode=ms.GRAPH_MODE, device_target=config.platform, save_graphs=False)
     config.dataset_path = os.path.join(config.dataset_path, 'validation_preprocess')
     print('\nconfig: \n', config)
     if not config.device_id:
         config.device_id = get_device_id()
-    context_device_init(config)
     _, _, net = define_net(config, config.is_training)
 
     load_ckpt(net, config.pretrain_ckpt)

official/cv/mobilenetv2/export.py

@@ -18,9 +18,7 @@ mobilenetv2 export file.
 import numpy as np
 import mindspore as ms
 from src.models import define_net, load_ckpt
-from src.utils import context_device_init
 from src.model_utils.config import config
-from src.model_utils.device_adapter import get_device_id
 from src.model_utils.moxing_adapter import moxing_wrapper
 
 config.batch_size = config.batch_size_export
@@ -31,9 +29,7 @@ config.is_training = config.is_training_export
 def export_mobilenetv2():
     """ export_mobilenetv2 """
     print('\nconfig: \n', config)
-    if not config.device_id:
-        config.device_id = get_device_id()
-    context_device_init(config)
+    ms.set_context(mode=ms.GRAPH_MODE, device_target=config.platform, save_graphs=False)
     _, _, net = define_net(config, config.is_training)
 
     load_ckpt(net, config.ckpt_file)

official/cv/mobilenetv2/scripts/run_train.sh

@@ -68,7 +68,7 @@ run_ascend_or_gpu()
     fi
 
     export PYTHONPATH=${BASEPATH}:$PYTHONPATH
-    if [ $1 = "Ascend" ]; then
+    if [ $1 = "Ascend" ] && [ $3 -eq 1 ]; then
         export DEVICE_ID=${CANDIDATE_DEVICE[0]}
         export RANK_ID=0
     elif [ $1 = "GPU" ]; then

official/cv/mobilenetv2/src/utils.py

@@ -13,34 +13,9 @@
 # limitations under the License.
 # ============================================================================
 
-import mindspore as ms
-import mindspore.communication as comm
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-
 from src.models import Monitor
 
-def context_device_init(config):
-    if config.platform == "GPU" and config.run_distribute:
-        config.device_id = 0
-    config.rank_id = 0
-    config.rank_size = 1
-    if config.platform == "CPU":
-        ms.set_context(mode=ms.GRAPH_MODE, device_target=config.platform, save_graphs=False)
-
-    elif config.platform in ["Ascend", "GPU"]:
-        ms.set_context(mode=ms.GRAPH_MODE, device_target=config.platform, device_id=config.device_id,
-                       save_graphs=False)
-        if config.run_distribute:
-            comm.init()
-            config.rank_id = comm.get_rank()
-            config.rank_size = comm.get_group_size()
-            ms.set_auto_parallel_context(device_num=config.rank_size,
-                                         parallel_mode=ms.ParallelMode.DATA_PARALLEL,
-                                         gradients_mean=True)
-    else:
-        raise ValueError("Only support CPU, GPU and Ascend.")
-
-
 def config_ckpoint(config, lr, step_size, model=None, eval_dataset=None):
     cb = [Monitor(lr_init=lr.asnumpy(), model=model, eval_dataset=eval_dataset)]
     if config.save_checkpoint and config.rank_id == 0:

official/cv/mobilenetv2/train.py

@@ -20,11 +20,12 @@ import random
 import numpy as np
 
 import mindspore as ms
+import mindspore.communication as comm
 import mindspore.nn as nn
 
 from src.dataset import create_dataset, extract_features
 from src.lr_generator import get_lr
-from src.utils import context_device_init, config_ckpoint
+from src.utils import config_ckpoint
 from src.models import CrossEntropyWithLabelSmooth, define_net, load_ckpt, build_params_groups
 from src.metric import DistAccuracy, ClassifyCorrectCell
 from src.model_utils.config import config
@@ -38,13 +39,21 @@ ms.set_seed(1)
 @moxing_wrapper(pre_process=modelarts_process)
 def train_mobilenetv2():
     """ train_mobilenetv2 """
+    if config.platform == "CPU":
+        config.run_distribute = False
+    ms.set_context(mode=ms.GRAPH_MODE, device_target=config.platform, save_graphs=False)
+    if config.run_distribute:
+        comm.init()
+        config.rank_id = comm.get_rank()
+        config.rank_size = comm.get_group_size()
+        ms.set_auto_parallel_context(parallel_mode=ms.ParallelMode.DATA_PARALLEL,
+                                     gradients_mean=True)
     config.train_dataset_path = os.path.join(config.dataset_path, 'train')
     config.eval_dataset_path = os.path.join(config.dataset_path, 'validation_preprocess')
     if not config.device_id:
         config.device_id = get_device_id()
     start = time.time()
     # set context and device init
-    context_device_init(config)
     print('\nconfig: {} \n'.format(config))
     # define network
     backbone_net, head_net, net = define_net(config, config.is_training)

research/cv/CycleGAN/eval.py

@@ -16,7 +16,7 @@
 """Cycle GAN test."""
 
 import os
-from mindspore import Tensor
+import mindspore as ms
 from src.models.cycle_gan import get_generator
 from src.utils.args import get_args
 from src.dataset.cyclegan_dataset import create_dataset
@@ -27,6 +27,12 @@ from src.utils.tools import save_image, load_ckpt
 def predict():
     """Predict function."""
     args = get_args("predict")
+    ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform,
+                   save_graphs=args.save_graphs, device_id=args.device_id)
+    args.rank = 0
+    args.device_num = 1
+    if args.platform == "GPU":
+        ms.set_context(enable_graph_kernel=True)
     G_A = get_generator(args)
     G_B = get_generator(args)
     G_A.set_train(True)
@@ -44,7 +50,7 @@ def predict():
     reporter = Reporter(args)
     reporter.start_predict("A to B")
     for data in ds.create_dict_iterator(output_numpy=True):
-        img_A = Tensor(data["image"])
+        img_A = ms.Tensor(data["image"])
         path_A = data["image_name"][0]
         path_B = path_A[0:-4] + "_fake_B.jpg"
         fake_B = G_A(img_A)
@@ -57,7 +63,7 @@ def predict():
     reporter.dataset_size = args.dataset_size
     reporter.start_predict("B to A")
     for data in ds.create_dict_iterator(output_numpy=True):
-        img_B = Tensor(data["image"])
+        img_B = ms.Tensor(data["image"])
         path_B = data["image_name"][0]
         path_A = path_B[0:-4] + "_fake_A.jpg"
         fake_A = G_B(img_B)

research/cv/CycleGAN/export.py

@@ -16,16 +16,15 @@
 """export file."""
 
 import numpy as np
-from mindspore import context, Tensor
-from mindspore.train.serialization import export
+import mindspore as ms
 from src.models.cycle_gan import get_generator
 from src.utils.args import get_args
 from src.utils.tools import load_ckpt, enable_batch_statistics
 
-args = get_args("export")
-context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
 
 if __name__ == '__main__':
+    args = get_args("export")
+    ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform)
     G_A = get_generator(args)
     G_B = get_generator(args)
     # Use BatchNorm2d with batchsize=1, affine=False, use_batch_statistics=True instead of InstanceNorm2d
@@ -35,8 +34,8 @@ if __name__ == '__main__':
     load_ckpt(args, G_A, G_B)
 
     input_shp = [args.export_batch_size, 3, args.image_size, args.image_size]
-    input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
+    input_array = ms.Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
     G_A_file = f"{args.export_file_name}_AtoB"
-    export(G_A, input_array, file_name=G_A_file, file_format=args.export_file_format)
+    ms.export(G_A, input_array, file_name=G_A_file, file_format=args.export_file_format)
     G_B_file = f"{args.export_file_name}_BtoA"
-    export(G_B, input_array, file_name=G_B_file, file_format=args.export_file_format)
+    ms.export(G_B, input_array, file_name=G_B_file, file_format=args.export_file_format)

research/cv/CycleGAN/src/utils/args.py

@@ -17,10 +17,6 @@
 
 import argparse
 import ast
-from mindspore import context
-from mindspore.context import ParallelMode
-from mindspore.communication.management import init, get_rank, get_group_size
-
 
 parser = argparse.ArgumentParser(description='Cycle GAN')
 # basic parameters
@@ -118,24 +114,6 @@ args = parser.parse_args()
 
 def get_args(phase):
     """Define the common options that are used in both training and test."""
-    if args.device_num > 1:
-
-        context.set_context(mode=context.GRAPH_MODE, device_target=args.platform, save_graphs=args.save_graphs)
-        context.reset_auto_parallel_context()
-        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
-                                          device_num=args.device_num)
-        init()
-        args.rank = get_rank()
-        args.group_size = get_group_size()
-    else:
-        context.set_context(mode=context.GRAPH_MODE, device_target=args.platform,
-                            save_graphs=args.save_graphs, device_id=args.device_id)
-        args.rank = 0
-        args.device_num = 1
-
-    if args.platform == "GPU":
-        context.set_context(enable_graph_kernel=True)
-
     if args.platform == "Ascend":
         args.pad_mode = "CONSTANT"
 

research/cv/CycleGAN/train.py

@@ -22,6 +22,7 @@ Example:
 
 import mindspore as ms
 import mindspore.nn as nn
+from mindspore.communication.management import init, get_rank, get_group_size
 from src.utils.args import get_args
 from src.utils.reporter import Reporter
 from src.utils.tools import get_lr, ImagePool, load_ckpt
@@ -34,6 +35,21 @@ ms.set_seed(1)
 def train():
     """Train function."""
     args = get_args("train")
+    if args.device_num > 1:
+        ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform, save_graphs=args.save_graphs)
+        init()
+        ms.reset_auto_parallel_context()
+        ms.set_auto_parallel_context(parallel_mode=ms.ParallelMode.DATA_PARALLEL, gradients_mean=True)
+        args.rank = get_rank()
+        args.group_size = get_group_size()
+    else:
+        ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform,
+                       save_graphs=args.save_graphs, device_id=args.device_id)
+        args.rank = 0
+        args.device_num = 1
+
+    if args.platform == "GPU":
+        ms.set_context(enable_graph_kernel=True)
     if args.need_profiler:
         from mindspore.profiler.profiling import Profiler
         profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)