Rebase #13757 to master (#15189)

* Update .gitmodules

* Set ImageNet data augmentation by default

https://github.com/apache/incubator-mxnet/blob/a38278ddebfcc9459d64237086cd7977ec20c70e/example/image-classification/train_imagenet.py#L42

When I try to train imagenet with this line commented, the train-accuracy reaches 99% while the validation-accuracy is only less than 50% (single machine, 8 GPUs, global batchsize=2048, Resnet50). Absolutely this is overfitting.

Then I uncomment this line and try again with the same experiment settings. This time both train and validation accuracy converge to about 70%. 

Thus, it seems that this data augmentation is pretty important for ImageNet training. Perhaps it will be better to uncomment this as default, so that future developers won't get confused by the over-fit issue.

* Add argument for imagenet data augmentation

* Enable data-aug with argument

* Update .gitmodules

example/image-classification/common/fit.py

@@ -142,6 +142,8 @@ def add_fit_args(parser):
     train.add_argument('--profile-server-suffix', type=str, default='',
                        help='profile server actions into a file with name like rank1_ followed by this suffix \
                              during distributed training')
+    train.add_argument('--use-imagenet-data-augmentation', type=int, default=0,
+                       help='enable data augmentation of ImageNet data, default disabled')
     return train
 
 
@@ -335,4 +337,4 @@ def fit(args, network, data_loader, **kwargs):
     if args.profile_server_suffix:
         mx.profiler.set_state(state='run', profile_process='server')
     if args.profile_worker_suffix:
-        mx.profiler.set_state(state='run', profile_process='worker')
+        mx.profiler.set_state(state='run', profile_process='worker')

example/image-classification/train_imagenet.py

@@ -38,8 +38,6 @@ def set_imagenet_aug(aug):
     fit.add_fit_args(parser)
     data.add_data_args(parser)
     data.add_data_aug_args(parser)
-    # uncomment to set standard augmentations for imagenet training
-    # set_imagenet_aug(parser)
     parser.set_defaults(
         # network
         network          = 'resnet',
@@ -56,6 +54,8 @@ def set_imagenet_aug(aug):
         dtype            = 'float32'
     )
     args = parser.parse_args()
+    if args.use_imagenet_data_augmentation:
+        set_imagenet_aug(parser)
 
     # load network
     from importlib import import_module