modify network RCAN scripts and resnet readme

official/cv/resnet/README_CN.md

@@ -453,16 +453,20 @@ bash run_parameter_server_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [PRETRAINED_
 
 ```bash
 # Ascend 分布式训练时推理示例:
+cd scripts/
 bash run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # Ascend 单机训练时推理示例:
-bash run_standalone_train.sh [RANK_TABLE_FILE] [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+cd scripts/
+bash run_standalone_train.sh [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # GPU 分布式训练时推理示例:
-bash run_distribute_train_gpu.sh [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+cd scripts/
+bash run_distribute_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 
 # GPU 单机训练时推理示例:
-bash run_standalone_train_gpu.sh [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
+cd scripts/
+bash run_standalone_train_gpu.sh [DATASET_PATH] [CONFIG_PATH] [RUN_EVAL](optional) [EVAL_DATASET_PATH](optional)
 ```
 
 训练时推理需要在设置`RUN_EVAL`为True，与此同时还需要设置`EVAL_DATASET_PATH`。此外，当设置`RUN_EVAL`为True时还可为python脚本设置`save_best_ckpt`, `eval_start_epoch`, `eval_interval`等参数。

research/cv/RCAN/src/rcan_model.py

@@ -86,11 +86,12 @@ class Upsampler(nn.Cell):
         """rcan"""
         super(Upsampler, self).__init__()
         m = []
-        if (scale & (scale - 1)) == 0:
-            for _ in range(int(math.log(scale, 2))):
-                m.append(SmallUpSampler(conv, 2, n_feats, has_bias=has_bias))
-        elif scale == 3:
-            m.append(SmallUpSampler(conv, 3, n_feats, has_bias=has_bias))
+        for s in scale:
+            if (s & (s - 1)) == 0:
+                for _ in range(int(math.log(s, 2))):
+                    m.append(SmallUpSampler(conv, 2, n_feats, has_bias=has_bias))
+            elif s == 3:
+                m.append(SmallUpSampler(conv, 3, n_feats, has_bias=has_bias))
         self.net = nn.SequentialCell(m)
 
     def construct(self, x):