change deta_modeling_test of configuration 'two_stage' default to TRU…

…E and minor change of dist checking

src/transformers/models/deta/modeling_deta.py

@@ -2220,7 +2220,7 @@ def forward(self, outputs, targets):
         num_boxes = sum(len(t["class_labels"]) for t in targets)
         num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
         # (Niels): comment out function below, distributed training to be added
-        if dist.is_available() or dist.is_initialized():
+        if dist.is_available() and dist.is_initialized():
             torch.distributed.all_reduce(num_boxes)
             world_size = dist.get_world_size()
         else:
@@ -2665,6 +2665,7 @@ def forward(self, outputs, targets, return_cost_matrix=False):
         bs = len(targets)
         indices = []
         ious = []
+        print(outputs["init_reference"][0].shape)
         for b in range(bs):
             iou, _ = box_iou(
                 center_to_corners_format(targets[b]["boxes"]),

tests/models/deta/test_modeling_deta.py

@@ -64,7 +64,7 @@ def __init__(
         num_feature_levels=4,
         encoder_n_points=2,
         decoder_n_points=6,
-        two_stage=False,
+        two_stage=True,
     ):
         self.parent = parent
         self.batch_size = batch_size