Fix tests

src/transformers/models/deta/convert_deta_resnet_to_pytorch.py

@@ -268,11 +268,17 @@ def convert_deta_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
     outputs = model(pixel_values.to(device))
 
     # verify logits
-    print("Logits:", outputs.logits[0, :3, :3])
-    expected_logits = torch.tensor(
-        [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]
-    )
-    expected_boxes = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]])
+    if model_name == "deta-resnet-50":
+        expected_logits = torch.tensor(
+            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]
+        )
+        expected_boxes = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]])
+    elif model_name == "deta-resnet-50-24-epochs":
+        expected_logits = torch.tensor(
+            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]
+        )
+        expected_boxes = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]])
+
     assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
     print("Everything ok!")

tests/models/deta/test_modeling_deta.py

@@ -63,6 +63,7 @@ def __init__(
         num_feature_levels=4,
         encoder_n_points=2,
         decoder_n_points=6,
+        two_stage=False,
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -83,6 +84,7 @@ def __init__(
         self.num_feature_levels = num_feature_levels
         self.encoder_n_points = encoder_n_points
         self.decoder_n_points = decoder_n_points
+        self.two_stage = two_stage
 
         # we also set the expected seq length for both encoder and decoder
         self.encoder_seq_length = (
@@ -130,6 +132,7 @@ def get_config(self):
             num_feature_levels=self.num_feature_levels,
             encoder_n_points=self.encoder_n_points,
             decoder_n_points=self.decoder_n_points,
+            two_stage=self.two_stage,
         )
 
     def prepare_config_and_inputs_for_common(self):
@@ -397,12 +400,15 @@ def test_forward_signature(self):
                 expected_arg_names = ["pixel_values", "pixel_mask"]
                 self.assertListEqual(arg_names[:1], expected_arg_names)
 
+    @unittest.skip(reason="Model doesn't use tied weights")
+    def test_tied_model_weights_key_ignore(self):
+        pass
+
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         configs_no_init = _config_zero_init(config)
         for model_class in self.all_model_classes:
-            print("Model class:", model_class)
             model = model_class(config=configs_no_init)
             for name, param in model.named_parameters():
                 if param.requires_grad: