extend hccl time out and modify lr schedule

model_zoo/official/cv/inceptionv3/README.md

@@ -133,6 +133,7 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 ```
 > Notes: 
     RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html)  , and the device_ip can be got as [Link]https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
+	For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 

model_zoo/official/cv/inceptionv3/scripts/run_distribute_train.sh

@@ -17,7 +17,7 @@
 DATA_DIR=$2
 export RANK_TABLE_FILE=$1
 export RANK_SIZE=8
-
+export HCCL_CONNECT_TIMEOUT=600
 
 cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
 echo "the number of logical core" $cores

model_zoo/official/cv/inceptionv3/src/lr_generator.py

@@ -45,7 +45,7 @@ def _generate_steps_lr(lr_init, lr_max, total_steps, warmup_steps, global_step=0
             else:
                 lr = lr_max * 0.001
         lr_each_step.append(lr)
-        lr_each_step = np.array(lr_each_step).astype(np.float32)[global_step:]
+    lr_each_step = np.array(lr_each_step).astype(np.float32)[global_step:]
     return lr_each_step
 
 
@@ -81,7 +81,7 @@ def _generate_exponential_lr(lr_init, lr_max, total_steps, warmup_steps, steps_p
     return lr_each_step
 
 
-def _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
+def _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps, global_step=0):
     """
     Applies cosine decay to generate learning rate array.
 
@@ -105,6 +105,7 @@ def _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
             cosine_decay = 0.5 * (1 + math.cos(math.pi * (i-warmup_steps) / decay_steps))
             lr = (lr_max-lr_end)*cosine_decay + lr_end
         lr_each_step.append(lr)
+    lr_each_step = np.array(lr_each_step).astype(np.float32)[global_step:]
     return lr_each_step
 
 
@@ -155,7 +156,7 @@ def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch
     elif lr_decay_mode == 'steps_decay':
         lr_each_step = _generate_exponential_lr(lr_init, lr_max, total_steps, warmup_steps, steps_per_epoch)
     elif lr_decay_mode == 'cosine':
-        lr_each_step = _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
+        lr_each_step = _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps, global_step)
     else:
         lr_each_step = _generate_liner_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
     learning_rate = np.array(lr_each_step).astype(np.float32)