fix cnn efficientnet bugs

model_zoo/official/cv/cnn_direction_model/scripts/run_distribute_train_ascend.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 if [ $# != 2 ] && [ $# != 3 ]
 then 
-	echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+    echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
 exit 1
 fi
 
@@ -76,13 +76,13 @@ do
     
     if [ $# == 2 ]
     then
-        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 > train.log 2>&1 &
     fi
     
     if [ $# == 3 ]
     then
-        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
+        python train.py --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 > train.log 2>&1 &
     fi
 
     cd ..
-done
+done

model_zoo/official/cv/cnn_direction_model/scripts/run_standalone_eval_ascend.sh

@@ -57,6 +57,6 @@ cd ./eval || exit
 echo "start evaluation for device $DEVICE_ID"
 env > env.log
 
-python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 #&> log &
+python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 > eval.log 2>&1 &
 
-cd ..
+cd ..

model_zoo/official/cv/cnn_direction_model/scripts/run_standalone_train_ascend.sh

@@ -66,7 +66,7 @@ fi
 
 if [ $# == 2 ]
 then
-    python train.py --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+    python train.py --dataset_path=$PATH1 --pre_trained=$PATH2 > train.log 2>&1 &
 fi
 
-cd ..
+cd ..

model_zoo/official/cv/cnn_direction_model/train.py

@@ -41,6 +41,7 @@ parser.add_argument('--device_num', type=int, default=1, help='Device num.')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
 parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
 parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
+parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
 
 args_opt = parser.parse_args()
 
@@ -70,6 +71,13 @@ if __name__ == '__main__':
         context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL)
         init()
 
+    args_opt.rank_save_ckpt_flag = 0
+    if args_opt.is_save_on_master:
+        if rank_id == 0:
+            args_opt.rank_save_ckpt_flag = 1
+    else:
+        args_opt.rank_save_ckpt_flag = 1
+
     # create dataset
     dataset_name = config.dataset_name
     dataset = create_dataset_train(args_opt.dataset_path +  "/" + dataset_name +
@@ -100,10 +108,11 @@ if __name__ == '__main__':
     loss_cb = LossMonitor()
     cb = [time_cb, loss_cb]
     if config.save_checkpoint:
-        config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
+        if args_opt.rank_save_ckpt_flag == 1:
-                                     keep_checkpoint_max=config.keep_checkpoint_max)
+            config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
-        ckpt_cb = ModelCheckpoint(prefix="cnn_direction_model", directory=ckpt_save_dir, config=config_ck)
+                                         keep_checkpoint_max=config.keep_checkpoint_max)
-        cb += [ckpt_cb]
+            ckpt_cb = ModelCheckpoint(prefix="cnn_direction_model", directory=ckpt_save_dir, config=config_ck)
+            cb += [ckpt_cb]
 
     # train model
     model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=False)

model_zoo/official/cv/efficientnet/README.md

@@ -18,7 +18,6 @@
 
 # [EfficientNet-B0 Description](#contents)
 
-
 [Paper](https://arxiv.org/abs/1905.11946): Mingxing Tan, Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 2019.
 
 # [Model architecture](#contents)
@@ -27,27 +26,25 @@ The overall network architecture of EfficientNet-B0 is show below:
 
 [Link](https://arxiv.org/abs/1905.11946)
 
-
 # [Dataset](#contents)
 
 Dataset used: [imagenet](http://www.image-net.org/)
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
-  - Train: 120G, 1.2W images
+    - Train: 120G, 1.2W images
-  - Test: 5G, 50000 images
+    - Test: 5G, 50000 images
 - Data format: RGB images.
-  - Note: Data will be processed in src/dataset.py
+    - Note: Data will be processed in src/dataset.py
-
 
 # [Environment Requirements](#contents)
 
 - Hardware GPU
-  - Prepare hardware environment with GPU processor.
+    - Prepare hardware environment with GPU processor.
 - Framework
-  - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
-  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # [Script description](#contents)
 
@@ -77,7 +74,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 Parameters for both training and evaluating can be set in config.py.
 
-```
+```python
 'random_seed': 1,                # fix random seed
 'model': 'efficientnet_b0',      # model name
 'drop': 0.2,                     # dropout rate
@@ -106,17 +103,17 @@ Parameters for both training and evaluating can be set in config.py.
 
 ## [Training Process](#contents)
 
-#### Usage
+### Usage
 
-```
+```python
 GPU:
     # distribute training example(8p)
-    sh run_distribute_train_for_gpu.sh 
+    sh run_distribute_train_for_gpu.sh
     # standalone training
     sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
 ```
 
-#### Launch
+### Launch
 
 ```bash
 # distributed training example(8p) for GPU
@@ -133,7 +130,7 @@ You can find checkpoint file together with result in log.
 
 ### Usage
 
-```
+```bash
 # Evaluation
 sh run_eval_for_gpu.sh DATA_DIR DEVICE_ID PATH_CHECKPOINT
 ```
@@ -148,9 +145,9 @@ sh run_eval_for_gpu.sh /dataset/eval ./checkpoint/efficientnet_b0-600_1251.ckpt
 
 #### Result
 
-Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
+Evaluation result will be stored in the scripts path. Under this, you can find result like the following in log.
 
-```
+```python
 acc=76.96%(TOP1)
 ```
 
@@ -186,7 +183,6 @@ acc=76.96%(TOP1)
 | outputs                    | probability               |
 | Accuracy                   | acc=76.96%(TOP1)          |
 
-
 # [ModelZoo Homepage](#contents)
- 
+
 Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/cv/efficientnet/src/transform.py

@@ -31,7 +31,7 @@ class RandAugment:
         self.hparams = hparams
 
     def __call__(self, imgs, labels, batchInfo):
-        # assert the imgs objetc are pil_images
+        # assert the imgs object are pil_images
         ret_imgs = []
         ret_labels = []
         py_to_pil_op = P.ToPIL()