!5717 Modelzoo network seed interface update.

Merge pull request !5717 from linqingke/fasterrcnn

model_zoo/official/cv/alexnet/train.py

@@ -30,7 +30,9 @@ from mindspore import Tensor
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.common import set_seed
 
+set_seed(1)
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='MindSpore AlexNet Example')

model_zoo/official/cv/deeplabv3/train.py

@@ -21,11 +21,14 @@ from mindspore import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.callback import Callback, CheckpointConfig, ModelCheckpoint, TimeMonitor
+from mindspore.common import set_seed
 from src.md_dataset import create_dataset
 from src.losses import OhemLoss
 from src.deeplabv3 import deeplabv3_resnet50
 from src.config import config
 
+set_seed(1)
+
 parser = argparse.ArgumentParser(description="Deeplabv3 training")
 parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
 parser.add_argument('--data_url', required=True, default=None, help='Train data url')

model_zoo/official/cv/faster_rcnn/README.md

@@ -87,7 +87,9 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
 After installing MindSpore via the official website, you can start training and evaluation as follows: 
 
-Note: 1.the first run will generate the mindeocrd file, which will take a long time. 2. pretrained model is a resnet50 checkpoint that trained over ImageNet2012. 3. VALIDATION_JSON_FILE is label file. CHECKPOINT_PATH is a checkpoint file after trained. 
+Note: 1.the first run will generate the mindeocrd file, which will take a long time.
+      2.pretrained model is a resnet50 checkpoint that trained over ImageNet2012.
+      3.VALIDATION_JSON_FILE is label file. CHECKPOINT_PATH is a checkpoint file after trained. 
 
 ```
 # standalone training
@@ -106,7 +108,7 @@ sh run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
 
 ```shell
 .
-└─FasterRcnn      
+└─faster_rcnn      
   ├─README.md    // descriptions about fasterrcnn
   ├─scripts
     ├─run_standalone_train_ascend.sh    // shell script for standalone on ascend
@@ -148,6 +150,7 @@ sh run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
  
 > Rank_table.json which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 > As for PRETRAINED_MODEL，it should be a ResNet50 checkpoint that trained over ImageNet2012. Ready-made pretrained_models are not available now. Stay tuned.
+> The original dataset path needs to be in the config.py,you can select "coco_root" or "image_dir".
 
 ### Result
  
@@ -205,10 +208,10 @@ Eval result will be stored in the example path, whose folder name is "eval". Und
 | -------------------------- | ----------------------------------------------------------- |
 | Model Version              | V1                                                |
 | Resource                   | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G             |
-| uploaded Date              | 06/01/2020 (month/day/year)                                 |
-| MindSpore Version          | 0.3.0-alpha                                                       |
+| uploaded Date              | 08/31/2020 (month/day/year)                                 |
+| MindSpore Version          | 0.7.0-beta                                                       |
 | Dataset                    | COCO2017                                                   |
-| Training Parameters        | epoch=12,  batch_size = 2          |
+| Training Parameters        | epoch=12,  batch_size=2          |
 | Optimizer                  | SGD                                                         |
 | Loss Function              | Softmax Cross Entropy ,Sigmoid Cross Entropy,SmoothL1Loss                                      |
 | Speed                      | 1pc: 190 ms/step;  8pcs: 200 ms/step                          |
@@ -223,12 +226,12 @@ Eval result will be stored in the example path, whose folder name is "eval". Und
 | ------------------- | --------------------------- |
 | Model Version       | V1                |
 | Resource            | Ascend 910                  |
-| Uploaded Date       | 06/01/2020 (month/day/year) |
-| MindSpore Version   | 0.3.0-alpha                       |
+| Uploaded Date       | 08/31/2020 (month/day/year) |
+| MindSpore Version   | 0.7.0-beta                       |
 | Dataset             | COCO2017    |
 | batch_size          | 2                         |
 | outputs             | mAP                 |
-| Accuracy            |  IoU=0.50: 58.6%  |
+| Accuracy            |  IoU=0.50: 57.6%  |
 | Model for inference | 250M (.ckpt file)         |
 
 # [ModelZoo Homepage](#contents)  

model_zoo/official/cv/faster_rcnn/eval.py

@@ -17,21 +17,18 @@
 import os
 import argparse
 import time
-import random
 import numpy as np
 from pycocotools.coco import COCO
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.FasterRcnn.faster_rcnn_r50 import Faster_Rcnn_Resnet50
 from src.config import config
 from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset
 from src.util import coco_eval, bbox2result_1image, results2json
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="FasterRcnn evaluation")
 parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")

model_zoo/official/cv/faster_rcnn/train.py

@@ -19,8 +19,6 @@ import os
 import time
 import argparse
 import ast
-import random
-import numpy as np
 
 import mindspore.common.dtype as mstype
 from mindspore import context, Tensor
@@ -30,7 +28,7 @@ from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.nn import SGD
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.FasterRcnn.faster_rcnn_r50 import Faster_Rcnn_Resnet50
 from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
@@ -38,9 +36,7 @@ from src.config import config
 from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset
 from src.lr_schedule import dynamic_lr
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="FasterRcnn training")
 parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default: false.")
@@ -78,18 +74,24 @@ if __name__ == '__main__':
             os.makedirs(mindrecord_dir)
         if args_opt.dataset == "coco":
             if os.path.isdir(config.coco_root):
+                if not os.path.exists(config.coco_root):
+                    print("Please make sure config:coco_root is valid.")
+                    raise ValueError(config.coco_root)
                 print("Create Mindrecord. It may take some time.")
                 data_to_mindrecord_byte_image("coco", True, prefix)
                 print("Create Mindrecord Done, at {}".format(mindrecord_dir))
             else:
                 print("coco_root not exits.")
         else:
-            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
+            if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path):
+                if not os.path.exists(config.image_dir):
+                    print("Please make sure config:image_dir is valid.")
+                    raise ValueError(config.image_dir)
                 print("Create Mindrecord. It may take some time.")
                 data_to_mindrecord_byte_image("other", True, prefix)
                 print("Create Mindrecord Done, at {}".format(mindrecord_dir))
             else:
-                print("IMAGE_DIR or ANNO_PATH not exits.")
+                print("image_dir or anno_path not exits.")
 
     while not os.path.exists(mindrecord_file + ".db"):
         time.sleep(5)

model_zoo/official/cv/googlenet/eval.py

@@ -23,11 +23,14 @@ from mindspore import context
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
 
 from src.config import cifar_cfg as cfg
 from src.dataset import create_dataset
 from src.googlenet import GoogleNet
 
+set_seed(1)
+
 parser = argparse.ArgumentParser(description='googlenet')
 parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
 args_opt = parser.parse_args()

model_zoo/official/cv/googlenet/train.py

@@ -18,7 +18,6 @@ python train.py
 """
 import argparse
 import os
-import random
 
 import numpy as np
 
@@ -31,13 +30,13 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMoni
 from mindspore.train.model import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
 
 from src.config import cifar_cfg as cfg
 from src.dataset import create_dataset
 from src.googlenet import GoogleNet
 
-random.seed(1)
-np.random.seed(1)
+set_seed(1)
 
 def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
     """Set learning rate."""

model_zoo/official/cv/inceptionv3/train.py

@@ -15,8 +15,6 @@
 """train_imagenet."""
 import argparse
 import os
-import random
-import numpy as np
 
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -27,9 +25,9 @@ from mindspore.nn.optim.rmsprop import RMSProp
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore import dataset as de
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.common.initializer import XavierUniform, initializer
+from mindspore.common import set_seed
 
 from src.config import config_gpu, config_ascend
 from src.dataset import create_dataset
@@ -37,9 +35,7 @@ from src.inception_v3 import InceptionV3
 from src.lr_generator import get_lr
 from src.loss import CrossEntropy
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 
 if __name__ == '__main__':
@@ -94,7 +90,6 @@ if __name__ == '__main__':
     if args_opt.platform == "Ascend":
         for param in net.trainable_params():
             if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
-                np.random.seed(seed=1)
                 param.set_parameter_data(initializer(XavierUniform(), param.data.shape, param.data.dtype))
     group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
                     {'params': no_decayed_params},

model_zoo/official/cv/lenet/train.py

@@ -29,7 +29,9 @@ from mindspore import context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
+from mindspore.common import set_seed
 
+set_seed(1)
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='MindSpore Lenet Example')

model_zoo/official/cv/lenet_quant/train_quant.py

@@ -28,11 +28,14 @@ from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.quant import quant
 from mindspore.train.quant.quant_utils import load_nonquant_param_into_quant_net
+from mindspore.common import set_seed
 from src.dataset import create_dataset
 from src.config import mnist_cfg as cfg
 from src.lenet_fusion import LeNet5 as LeNet5Fusion
 from src.loss_monitor import LossMonitor
 
+set_seed(1)
+
 parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
 parser.add_argument('--device_target', type=str, default="Ascend",
                     choices=['Ascend', 'GPU'],

model_zoo/official/cv/maskrcnn/eval.py

@@ -17,21 +17,18 @@
 import os
 import argparse
 import time
-import random
 import numpy as np
 from pycocotools.coco import COCO
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
 from src.config import config
 from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
 from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="MaskRcnn evaluation")
 parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")

model_zoo/official/cv/maskrcnn/train.py

@@ -17,9 +17,7 @@
 
 import os
 import argparse
-import random
 import ast
-import numpy as np
 
 import mindspore.common.dtype as mstype
 from mindspore import context, Tensor
@@ -29,7 +27,7 @@ from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.nn import SGD
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
 from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
@@ -37,9 +35,7 @@ from src.config import config
 from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
 from src.lr_schedule import dynamic_lr
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="MaskRcnn training")
 parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "

model_zoo/official/cv/mobilenetv2/src/utils.py

@@ -13,16 +13,12 @@
 # limitations under the License.
 # ============================================================================
 
-import random
-import numpy as np
-
 from mindspore import context
 from mindspore import nn
 from mindspore.common import dtype as mstype
 from mindspore.train.model import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.communication.management import get_rank, init
-from mindspore.dataset import engine as de
 
 from src.models import Monitor
 
@@ -84,10 +80,3 @@ def config_ckpoint(config, lr, step_size):
             ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=ckpt_save_dir, config=config_ck)
             cb += [ckpt_cb]
     return cb
-
-
-
-def set_random_seed(seed=1):
-    random.seed(seed)
-    np.random.seed(seed)
-    de.config.set_seed(seed)

model_zoo/official/cv/mobilenetv2/train.py

@@ -27,16 +27,17 @@ from mindspore.common import dtype as mstype
 from mindspore.train.model import Model
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import _exec_save_checkpoint
+from mindspore.common import set_seed
 
 from src.dataset import create_dataset, extract_features
 from src.lr_generator import get_lr
 from src.config import set_config
 
 from src.args import train_parse_args
-from src.utils import set_random_seed, context_device_init, switch_precision, config_ckpoint
+from src.utils import context_device_init, switch_precision, config_ckpoint
 from src.models import CrossEntropyWithLabelSmooth, define_net
 
-set_random_seed(1)
+set_seed(1)
 
 if __name__ == '__main__':
     args_opt = train_parse_args()

model_zoo/official/cv/mobilenetv2_quant/train.py

@@ -16,8 +16,6 @@
 
 import os
 import argparse
-import random
-import numpy as np
 
 from mindspore import context
 from mindspore import Tensor
@@ -30,7 +28,7 @@ from mindspore.train.serialization import load_checkpoint
 from mindspore.communication.management import init, get_group_size, get_rank
 from mindspore.train.quant import quant
 from mindspore.train.quant.quant_utils import load_nonquant_param_into_quant_net
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
@@ -38,9 +36,7 @@ from src.utils import Monitor, CrossEntropyWithLabelSmooth
 from src.config import config_ascend_quant, config_gpu_quant
 from src.mobilenetV2 import mobilenetV2
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')

model_zoo/official/cv/mobilenetv3/train.py

@@ -16,7 +16,6 @@
 
 import time
 import argparse
-import random
 import numpy as np
 
 from mindspore import context
@@ -33,7 +32,7 @@ from mindspore.context import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 from mindspore.communication.management import init, get_group_size, get_rank
 
 from src.dataset import create_dataset
@@ -41,9 +40,7 @@ from src.lr_generator import get_lr
 from src.config import config_gpu
 from src.mobilenetV3 import mobilenet_v3_large
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')

model_zoo/official/cv/nasnet/train.py

@@ -15,8 +15,6 @@
 """train imagenet."""
 import argparse
 import os
-import random
-import numpy as np
 
 from mindspore import Tensor
 from mindspore import context
@@ -26,7 +24,7 @@ from mindspore.nn.optim.rmsprop import RMSProp
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore import dataset as de
+from mindspore.common import set_seed
 
 from src.config import nasnet_a_mobile_config_gpu as cfg
 from src.dataset import create_dataset
@@ -34,9 +32,7 @@ from src.nasnet_a_mobile import NASNetAMobileWithLoss, NASNetAMobileTrainOneStep
 from src.lr_generator import get_lr
 
 
-random.seed(cfg.random_seed)
-np.random.seed(cfg.random_seed)
-de.config.set_seed(cfg.random_seed)
+set_seed(cfg.random_seed)
 
 
 if __name__ == '__main__':

model_zoo/official/cv/resnet/eval.py

@@ -14,11 +14,9 @@
 # ============================================================================
 """train resnet."""
 import os
-import random
 import argparse
-import numpy as np
 from mindspore import context
-from mindspore import dataset as de
+from mindspore.common import set_seed
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -33,9 +31,7 @@ parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path
 parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
 args_opt = parser.parse_args()
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 if args_opt.net == "resnet50":
     from src.resnet import resnet50 as resnet

model_zoo/official/cv/resnet/train.py

@@ -14,13 +14,10 @@
 # ============================================================================
 """train resnet."""
 import os
-import random
 import argparse
 import ast
-import numpy as np
 from mindspore import context
 from mindspore import Tensor
-from mindspore import dataset as de
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.model import Model
@@ -30,6 +27,7 @@ from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.common import set_seed
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 from src.lr_generator import get_lr, warmup_cosine_annealing_lr
@@ -47,9 +45,7 @@ parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained ch
 parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
 args_opt = parser.parse_args()
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 if args_opt.net == "resnet50":
     from src.resnet import resnet50 as resnet

model_zoo/official/cv/resnet50_quant/train.py

@@ -31,6 +31,7 @@ from mindspore.train.quant.quant_utils import load_nonquant_param_into_quant_net
 from mindspore.communication.management import init
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
+from mindspore.common import set_seed
 
 #from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
 from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
@@ -39,6 +40,8 @@ from src.lr_generator import get_lr
 from src.config import config_quant
 from src.crossentropy import CrossEntropy
 
+set_seed(1)
+
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
 parser.add_argument('--device_num', type=int, default=1, help='Device num.')

model_zoo/official/cv/resnet_thor/eval.py

@@ -14,11 +14,9 @@
 # ============================================================================
 """train resnet."""
 import os
-import random
 import argparse
-import numpy as np
 from mindspore import context
-from mindspore import dataset as de
+from mindspore.common import set_seed
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.crossentropy import CrossEntropy
@@ -32,9 +30,7 @@ parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path
 parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
 args_opt = parser.parse_args()
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 if __name__ == '__main__':
     target = args_opt.device_target

model_zoo/official/cv/resnet_thor/train.py

@@ -14,13 +14,12 @@
 # ============================================================================
 """train resnet."""
 import os
-import random
 import argparse
 import numpy as np
 
 from mindspore import context
 from mindspore import Tensor
-from mindspore import dataset as de
+from mindspore.common import set_seed
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.context import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor
@@ -46,9 +45,7 @@ else:
     from src.thor import THOR_GPU as THOR
     from src.config import config_gpu as config
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 
 def get_model_lr(global_step, lr_init, decay, total_epochs, steps_per_epoch, decay_epochs=100):

model_zoo/official/cv/resnext50/src/utils/var_init.py

@@ -151,7 +151,6 @@ class KaimingUniform(KaimingInit):
     def _initialize(self, arr):
         fan = _select_fan(arr, self.mode)
         bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
-        np.random.seed(0)
         data = np.random.uniform(-bound, bound, arr.shape)
 
         _assignment(arr, data)
@@ -179,7 +178,6 @@ class KaimingNormal(KaimingInit):
     def _initialize(self, arr):
         fan = _select_fan(arr, self.mode)
         std = self.gain / math.sqrt(fan)
-        np.random.seed(0)
         data = np.random.normal(0, std, arr.shape)
 
         _assignment(arr, data)
@@ -195,7 +193,6 @@ def default_recurisive_init(custom_cell):
             if cell.bias is not None:
                 fan_in, _ = _calculate_in_and_out(cell.weight)
                 bound = 1 / math.sqrt(fan_in)
-                np.random.seed(0)
                 cell.bias.default_input = init.initializer(init.Uniform(bound),
                                                            cell.bias.shape,
                                                            cell.bias.dtype)
@@ -206,7 +203,6 @@ def default_recurisive_init(custom_cell):
             if cell.bias is not None:
                 fan_in, _ = _calculate_in_and_out(cell.weight)
                 bound = 1 / math.sqrt(fan_in)
-                np.random.seed(0)
                 cell.bias.default_input = init.initializer(init.Uniform(bound),
                                                            cell.bias.shape,
                                                            cell.bias.dtype)

model_zoo/official/cv/resnext50/train.py

@@ -28,6 +28,7 @@ from mindspore.train.callback import CheckpointConfig, Callback
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.model import Model
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
+from mindspore.common import set_seed
 
 from src.dataset import classification_dataset
 from src.crossentropy import CrossEntropy
@@ -38,6 +39,7 @@ from src.utils.optimizers__init__ import get_param_groups
 from src.image_classification import get_network
 from src.config import config
 
+set_seed(1)
 
 class BuildTrainNetwork(nn.Cell):
     """build training network"""

model_zoo/official/cv/shufflenetv2/train.py

@@ -16,14 +16,11 @@
 import argparse
 import ast
 import os
-import random
-import numpy as np
 
 from network import ShuffleNetV2
 
 import mindspore.nn as nn
 from mindspore import context
-from mindspore import dataset as de
 from mindspore.context import ParallelMode
 from mindspore import Tensor
 from mindspore.communication.management import init, get_rank, get_group_size
@@ -31,14 +28,13 @@ from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
 
 from src.config import config_gpu as cfg
 from src.dataset import create_dataset
 from src.lr_generator import get_lr_basic
 
-random.seed(cfg.random_seed)
-np.random.seed(cfg.random_seed)
-de.config.set_seed(cfg.random_seed)
+set_seed(cfg.random_seed)
 
 
 if __name__ == '__main__':

model_zoo/official/cv/ssd/src/init_params.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """Parameters utils"""
 
-import numpy as np
 from mindspore.common.initializer import initializer, TruncatedNormal
 
 def init_net_param(network, initialize_mode='TruncatedNormal'):
@@ -22,7 +21,6 @@ def init_net_param(network, initialize_mode='TruncatedNormal'):
     params = network.trainable_params()
     for p in params:
         if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
-            np.random.seed(seed=1)
             if initialize_mode == 'TruncatedNormal':
                 p.set_parameter_data(initializer(TruncatedNormal(), p.data.shape, p.data.dtype))
             else:

model_zoo/official/cv/ssd/train.py

@@ -25,12 +25,14 @@ from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMoni
 from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
 from src.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2
 from src.config import config
 from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
 from src.lr_schedule import get_lr
 from src.init_params import init_net_param, filter_checkpoint_parameter
 
+set_seed(1)
 
 def main():
     parser = argparse.ArgumentParser(description="SSD training")

model_zoo/official/cv/vgg16/src/utils/var_init.py

@@ -151,7 +151,6 @@ class KaimingUniform(KaimingInit):
     def _initialize(self, arr):
         fan = _select_fan(arr, self.mode)
         bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
-        np.random.seed(0)
         data = np.random.uniform(-bound, bound, arr.shape)
 
         _assignment(arr, data)
@@ -179,7 +178,6 @@ class KaimingNormal(KaimingInit):
     def _initialize(self, arr):
         fan = _select_fan(arr, self.mode)
         std = self.gain / math.sqrt(fan)
-        np.random.seed(0)
         data = np.random.normal(0, std, arr.shape)
 
         _assignment(arr, data)
@@ -195,7 +193,6 @@ def default_recurisive_init(custom_cell):
             if cell.bias is not None:
                 fan_in, _ = _calculate_in_and_out(cell.weight)
                 bound = 1 / math.sqrt(fan_in)
-                np.random.seed(0)
                 cell.bias.default_input = init.initializer(init.Uniform(bound),
                                                            cell.bias.shape,
                                                            cell.bias.dtype)
@@ -206,7 +203,6 @@ def default_recurisive_init(custom_cell):
             if cell.bias is not None:
                 fan_in, _ = _calculate_in_and_out(cell.weight)
                 bound = 1 / math.sqrt(fan_in)
-                np.random.seed(0)
                 cell.bias.default_input = init.initializer(init.Uniform(bound),
                                                            cell.bias.shape,
                                                            cell.bias.dtype)

model_zoo/official/cv/vgg16/train.py

@@ -19,9 +19,6 @@ python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
 import argparse
 import datetime
 import os
-import random
-
-import numpy as np
 
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -33,6 +30,7 @@ from mindspore.train.model import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_param_into_net, load_checkpoint
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.common import set_seed
 from src.dataset import vgg_create_dataset
 from src.dataset import classification_dataset
 
@@ -45,8 +43,7 @@ from src.utils.util import get_param_groups
 from src.vgg import vgg16
 
 
-random.seed(1)
-np.random.seed(1)
+set_seed(1)
 
 
 def parse_args(cloud_args=None):

model_zoo/official/cv/warpctc/eval.py

@@ -15,11 +15,9 @@
 """Warpctc evaluation"""
 import os
 import math as m
-import random
 import argparse
-import numpy as np
 from mindspore import context
-from mindspore import dataset as de
+from mindspore.common import set_seed
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
@@ -29,9 +27,7 @@ from src.dataset import create_dataset
 from src.warpctc import StackedRNN, StackedRNNForGPU
 from src.metric import WarpCTCAccuracy
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="Warpctc training")
 parser.add_argument("--dataset_path", type=str, default=None, help="Dataset, default is None.")

model_zoo/official/cv/warpctc/train.py

@@ -15,12 +15,10 @@
 """Warpctc training"""
 import os
 import math as m
-import random
 import argparse
-import numpy as np
 import mindspore.nn as nn
 from mindspore import context
-from mindspore import dataset as de
+from mindspore.common import set_seed
 from mindspore.train.model import Model
 from mindspore.context import ParallelMode
 from mindspore.nn.wrap import WithLossCell
@@ -34,9 +32,7 @@ from src.warpctc import StackedRNN, StackedRNNForGPU
 from src.warpctc_for_train import TrainOneStepCellWithGradClip
 from src.lr_schedule import get_lr
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 parser = argparse.ArgumentParser(description="Warpctc training")
 parser.add_argument("--run_distribute", action='store_true', help="Run distribute, default is false.")

model_zoo/official/cv/yolov3_darknet53/src/initializer.py

@@ -21,9 +21,6 @@ from mindspore.common.initializer import Initializer as MeInitializer
 import mindspore.nn as nn
 
 
-np.random.seed(5)
-
-
 def calculate_gain(nonlinearity, param=None):
     r"""Return the recommended gain value for the given nonlinearity function.
     The values are as follows:

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -30,6 +30,7 @@ import mindspore as ms
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore import amp
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.common import set_seed
 
 from src.yolo import YOLOV3DarkNet53, YoloWithLossCell, TrainingWrapper
 from src.logger import get_logger
@@ -41,6 +42,7 @@ from src.initializer import default_recurisive_init
 from src.config import ConfigYOLOV3DarkNet53
 from src.util import keep_loss_fp32
 
+set_seed(1)
 
 class BuildTrainNetwork(nn.Cell):
     def __init__(self, network, criterion):

model_zoo/official/cv/yolov3_darknet53_quant/src/initializer.py

@@ -21,9 +21,6 @@ import mindspore.nn as nn
 from mindspore import Tensor
 
 
-np.random.seed(5)
-
-
 def calculate_gain(nonlinearity, param=None):
     r"""Return the recommended gain value for the given nonlinearity function.
     The values are as follows:

model_zoo/official/cv/yolov3_darknet53_quant/train.py

@@ -29,6 +29,7 @@ from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
 import mindspore as ms
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.quant import quant
+from mindspore.common import set_seed
 
 from src.yolo import YOLOV3DarkNet53, YoloWithLossCell, TrainingWrapper
 from src.logger import get_logger
@@ -41,6 +42,7 @@ from src.config import ConfigYOLOV3DarkNet53
 from src.transforms import batch_preprocess_true_box, batch_preprocess_true_box_single
 from src.util import ShapeRecord
 
+set_seed(1)
 
 devid = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,

model_zoo/official/cv/yolov3_resnet18/train.py

@@ -34,11 +34,13 @@ from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common.initializer import initializer
+from mindspore.common import set_seed
 
 from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
 from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
 from src.config import ConfigYOLOV3ResNet18
 
+set_seed(1)
 
 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
     """Set learning rate."""
@@ -54,7 +56,7 @@ def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps
 
 
 def init_net_param(network, init_value='ones'):
-    """Init:wq the parameters in network."""
+    """Init the parameters in network."""
     params = network.trainable_params()
     for p in params:
         if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:

model_zoo/official/gnn/gat/train.py

@@ -19,12 +19,14 @@ import os
 import numpy as np
 import mindspore.context as context
 from mindspore.train.serialization import save_checkpoint, load_checkpoint
+from mindspore.common import set_seed
 
 from src.config import GatConfig
 from src.dataset import load_and_process
 from src.gat import GAT
 from src.utils import LossAccuracyWrapper, TrainGAT
 
+set_seed(1)
 
 def train():
     """Train GAT model."""

model_zoo/official/gnn/gcn/train.py

@@ -26,6 +26,7 @@ from matplotlib import pyplot as plt
 from matplotlib import animation
 from sklearn import manifold
 from mindspore import context
+from mindspore.common import set_seed
 
 from src.gcn import GCN
 from src.metrics import LossAccuracyWrapper, TrainNetWrapper
@@ -55,7 +56,7 @@ def train():
     parser.add_argument('--save_TSNE', type=ast.literal_eval, default=False, help='Whether to save t-SNE graph')
     args_opt = parser.parse_args()
 
-    np.random.seed(args_opt.seed)
+    set_seed(args_opt.seed)
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="Ascend", save_graphs=False)
     config = ConfigGCN()

model_zoo/official/nlp/bert/run_pretrain.py

@@ -19,7 +19,6 @@ python run_pretrain.py
 
 import os
 import argparse
-import numpy
 import mindspore.communication.management as D
 import mindspore.common.dtype as mstype
 from mindspore import context
@@ -30,6 +29,7 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMoni
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecay
 from mindspore import log as logger
+from mindspore.common import set_seed
 from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell, \
                 BertTrainAccumulateStepsWithLossScaleCell
 from src.dataset import create_bert_dataset
@@ -196,5 +196,5 @@ def run_pretrain():
 
 
 if __name__ == '__main__':
-    numpy.random.seed(0)
+    set_seed(0)
     run_pretrain()

model_zoo/official/nlp/bert_thor/run_pretrain.py

@@ -19,7 +19,6 @@ python run_pretrain.py
 
 import argparse
 import os
-import numpy
 from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from src.bert_net_config import bert_net_cfg
 from src.config import cfg
@@ -36,6 +35,7 @@ from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
 
 _current_dir = os.path.dirname(os.path.realpath(__file__))
 
@@ -197,5 +197,5 @@ def run_pretrain():
 
 
 if __name__ == '__main__':
-    numpy.random.seed(0)
+    set_seed(0)
     run_pretrain()

model_zoo/official/nlp/mass/train.py

@@ -30,6 +30,7 @@ from mindspore import context, Parameter
 from mindspore.context import ParallelMode
 from mindspore.communication import management as MultiAscend
 from mindspore.train.serialization import load_checkpoint
+from mindspore.common import set_seed
 
 from config import TransformerConfig
 from src.dataset import load_dataset
@@ -337,7 +338,7 @@ if __name__ == '__main__':
     _check_args(args.config)
     _config = get_config(args.config)
 
-    np.random.seed(_config.random_seed)
+    set_seed(_config.random_seed)
     context.set_context(save_graphs=_config.save_graphs)
 
     if _rank_size is not None and int(_rank_size) > 1:

model_zoo/official/nlp/tinybert/run_general_distill.py

@@ -18,7 +18,6 @@
 import os
 import argparse
 import datetime
-import numpy
 import mindspore.communication.management as D
 import mindspore.common.dtype as mstype
 from mindspore import context
@@ -28,6 +27,7 @@ from mindspore.context import ParallelMode
 from mindspore.nn.optim import AdamWeightDecay
 from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
 from mindspore import log as logger
+from mindspore.common import set_seed
 from src.dataset import create_tinybert_dataset, DataType
 from src.utils import LossCallBack, ModelSaveCkpt, BertLearningRate
 from src.gd_config import common_cfg, bert_teacher_net_cfg, bert_student_net_cfg
@@ -154,5 +154,5 @@ def run_general_distill():
                 sink_size=args_opt.data_sink_steps)
 
 if __name__ == '__main__':
-    numpy.random.seed(0)
+    set_seed(0)
     run_general_distill()

model_zoo/official/nlp/transformer/train.py

@@ -16,8 +16,6 @@
 
 import time
 import argparse
-import random
-import numpy as np
 
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
@@ -27,10 +25,10 @@ from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
 from mindspore.train.callback import Callback, TimeMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-import mindspore.dataset.engine as de
 import mindspore.communication.management as D
 from mindspore.context import ParallelMode
 from mindspore import context
+from mindspore.common import set_seed
 
 from src.transformer_for_train import TransformerTrainOneStepCell, TransformerNetworkWithLoss, \
                                       TransformerTrainOneStepWithLossScaleCell
@@ -38,10 +36,7 @@ from src.config import cfg, transformer_net_cfg
 from src.dataset import create_transformer_dataset
 from src.lr_schedule import create_dynamic_lr
 
-random_seed = 1
-random.seed(random_seed)
-np.random.seed(random_seed)
-de.config.set_seed(random_seed)
+set_seed(1)
 
 def get_ms_timestamp():
     t = time.time()

model_zoo/official/recommend/deepfm/train.py

@@ -16,15 +16,13 @@
 import os
 import sys
 import argparse
-import random
-import numpy as np
 
 from mindspore import context
 from mindspore.context import ParallelMode
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.model import Model
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
-import mindspore.dataset.engine as de
+from mindspore.common import set_seed
 
 from src.deepfm import ModelBuilder, AUCMetric
 from src.config import DataConfig, ModelConfig, TrainConfig
@@ -46,9 +44,7 @@ args_opt, _ = parser.parse_known_args()
 args_opt.do_eval = args_opt.do_eval == 'True'
 rank_size = int(os.environ.get("RANK_SIZE", 1))
 
-random.seed(1)
-np.random.seed(1)
-de.config.set_seed(1)
+set_seed(1)
 
 if __name__ == '__main__':
     data_config = DataConfig()

model_zoo/official/recommend/wide_and_deep/train_and_eval_distribute.py

@@ -17,11 +17,11 @@
 
 import os
 import sys
-import numpy as np
 from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.context import ParallelMode
 from mindspore.communication.management import get_rank, get_group_size, init
+from mindspore.common import set_seed
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
 from src.callbacks import LossCallBack, EvalCallBack
@@ -69,7 +69,7 @@ def train_and_eval(config):
     """
     test_train_eval
     """
-    np.random.seed(1000)
+    set_seed(1000)
     data_path = config.data_path
     batch_size = config.batch_size
     epochs = config.epochs

model_zoo/official/recommend/wide_and_deep/train_and_eval_parameter_server.py

@@ -17,11 +17,11 @@
 
 import os
 import sys
-import numpy as np
 from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
 from mindspore.context import ParallelMode
 from mindspore.communication.management import get_rank, get_group_size, init
+from mindspore.common import set_seed
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
 from src.callbacks import LossCallBack, EvalCallBack
@@ -70,7 +70,7 @@ def train_and_eval(config):
     """
     test_train_eval
     """
-    np.random.seed(1000)
+    set_seed(1000)
     data_path = config.data_path
     batch_size = config.batch_size
     epochs = config.epochs

model_zoo/official/recommend/wide_and_deep_multitable/train_and_eval_distribute.py

@@ -16,12 +16,12 @@
 
 import os
 import sys
-import numpy as np
 from mindspore import Model, context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.train.callback import TimeMonitor
 from mindspore.context import ParallelMode
 from mindspore.communication.management import get_rank, get_group_size, init
+from mindspore.common import set_seed
 
 from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
 from src.callbacks import LossCallBack, EvalCallBack
@@ -69,7 +69,7 @@ def train_and_eval(config):
     """
     train_and_eval
     """
-    np.random.seed(1000)
+    set_seed(1000)
     data_path = config.data_path
     epochs = config.epochs
     print("epochs is {}".format(epochs))