minor fixes

megatron/arguments.py

@@ -880,10 +880,9 @@ def _add_vision_args(parser):
     group.add_argument('--head-lr-mult', type=float, default=1.0,
                        help='learning rate multiplier for head during finetuning')
 
-
     # pretraining type and backbone selection`
     group.add_argument('--vision-pretraining-type', type=str, default='classify',
-                       choices=['classify', 'inpaint', 'contrast'],
+                       choices=['classify', 'inpaint', 'dino'],
                        help='pretraining objectives')
     group.add_argument('--vision-backbone-type', type=str, default='vit',
                        choices=['vit', 'mit', 'swin'],
@@ -898,7 +897,6 @@ def _add_vision_args(parser):
                        help='mask types')
     group.add_argument('--mask-factor', type=float, default=1.0,
                        help='mask size scaling parameter')
-
  
     # dino arguments
     group.add_argument('--iter-per-epoch', type=int, default=1250,

megatron/data/vit_dataset.py

@@ -251,7 +251,7 @@ def build_train_valid_datasets(data_path, image_size=224):
     val_transform = ClassificationTransform(image_size, train=False)
 
     # training dataset
-    train_data_path = data_path[0] if len(data_path) <= 2 else data_path[2] #TODO VIJAY
+    train_data_path = data_path[0] if len(data_path) <= 2 else data_path[2]
     train_data = ImageFolder(
         root=train_data_path,
         transform=train_transform,

megatron/model/vision/classification.py

@@ -68,7 +68,7 @@ class VitClassificationModel(MegatronModule):
 class MitClassificationModel(MegatronModule):
     """Mix vision Transformer Model."""
 
-    def __init__(self, num_classes
+    def __init__(self, num_classes,
                  pre_process=True, post_process=True):
         super(MitClassificationModel, self).__init__()
         args = get_args()

megatron/model/vision/mit_backbone.py

@@ -8,7 +8,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from functools import partial
-from megatron.model.vision.utils import DropPath, trunc_normal_
+from megatron.model.vision.utils import trunc_normal_
+from megatron.model.transformer import DropPath
 from megatron.model import LayerNorm
 
 

megatron/training.py

@@ -714,7 +714,7 @@ def train(forward_step_func, model, optimizer, opt_param_scheduler,
     report_memory_flag = True
     while iteration < args.train_iters:
         update_num_microbatches(args.consumed_train_samples)
-	args.curr_iteration = iteration
+        args.curr_iteration = iteration
         loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \
             train_step(forward_step_func,
                        train_data_iterator,
@@ -804,7 +804,7 @@ def evaluate(forward_step_func,
     """Evaluation."""
     args = get_args()
 
-    if args.vision_pretraining_type == "contrast":
+    if args.vision_pretraining_type == "dino":
         args.knn_features = compute_feature_bank(model)
 
     # Turn on evaluation mode which disables dropout.

pretrain_vision_classify.py

@@ -30,14 +30,15 @@ from megatron.utils import average_losses_across_data_parallel_group
 def model_provider(pre_process=True, post_process=True):
     """Build the model."""
 
-    print_rank_0("building VIT model ...")
     args = get_args()
 
     if args.vision_backbone_type == 'vit':
+        print_rank_0("building VIT model ...")
         model = VitClassificationModel(num_classes=args.num_classes,
                                        pre_process=pre_process,
                                        post_process=post_process)
     elif args.vision_backbone_type == 'mit':
+        print_rank_0("building MIT model ...")
         model = MitClassificationModel(num_classes=args.num_classes,
                                        pre_process=pre_process,
                                        post_process=post_process)