fix some bugs in resnet, ssd and naml

model_zoo/official/cv/resnet/README.md

@@ -284,6 +284,8 @@ Please follow the instructions in the link [hccn_tools](https://gitee.com/mindsp
 
 Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the following in log.
 
+If you want to change device_id for standalone training, you can set environment variable `export DEVICE_ID=x` or set `device_id=x` in context.
+
 #### Running on GPU
 
 ```bash

model_zoo/official/cv/resnet/README_CN.md

@@ -268,6 +268,8 @@ bash run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH]
 
 训练结果保存在示例路径中，文件夹名称以“train”或“train_parallel”开头。您可在此路径下的日志中找到检查点文件以及结果，如下所示。
 
+运行单卡用例时如果想更换运行卡号，可以通过设置环境变量 `export DEVICE_ID=x` 或者在context中设置 `device_id=x`指定相应的卡号。
+
 #### GPU处理器环境运行
 
 ```text

model_zoo/official/cv/resnet/scripts/run_standalone_train.sh

@@ -73,7 +73,6 @@ fi
 
 ulimit -u unlimited
 export DEVICE_NUM=1
-export DEVICE_ID=0
 export RANK_ID=0
 export RANK_SIZE=1
 

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

@@ -391,7 +391,7 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="ssd.
 
 
 def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num=1, rank=0,
-                       is_training=True, num_parallel_workers=4, use_multiprocessing=True):
+                       is_training=True, num_parallel_workers=6, use_multiprocessing=True):
     """Create SSD dataset with MindDataset."""
     ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
                         shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)

model_zoo/official/recommend/naml/src/option.py

@@ -99,7 +99,7 @@ def get_args(phase):
     args.n_sub_categories = cfg.n_sub_categories
     args.n_words = cfg.n_words
     if phase == "train":
-        args.epochs = cfg.epochs if args.epochs is None else args.epochs * math.ceil(args.device_num ** 0.5)
+        args.epochs = cfg.epochs * math.ceil(args.device_num ** 0.5) if args.epochs is None else args.epochs
         args.lr = cfg.lr if args.lr is None else args.lr
         args.print_times = cfg.print_times if args.print_times is None else args.print_times
     args.embedding_file = cfg.embedding_file.format(args.dataset_path)