Check models used for common tests are small (#24824)

* First models

* Conditional DETR

* Treat DETR models, skip others

* Skip LayoutLMv2 as well

* Fix last tests

tests/models/deformable_detr/test_modeling_deformable_detr.py

@@ -20,9 +20,16 @@ import math
 import unittest
 from typing import Dict, List, Tuple
 
-from transformers import DeformableDetrConfig, is_timm_available, is_vision_available
+from transformers import DeformableDetrConfig, ResNetConfig, is_torch_available, is_vision_available
 from transformers.file_utils import cached_property
-from transformers.testing_utils import require_timm, require_torch_gpu, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    require_timm,
+    require_torch,
+    require_torch_gpu,
+    require_vision,
+    slow,
+    torch_device,
+)
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -30,10 +37,10 @@ from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_
 from ...test_pipeline_mixin import PipelineTesterMixin
 
 
-if is_timm_available():
+if is_torch_available():
     import torch
 
-    from transformers import DeformableDetrForObjectDetection, DeformableDetrModel, ResNetConfig
+    from transformers import DeformableDetrForObjectDetection, DeformableDetrModel
 
 
 if is_vision_available():
@@ -49,7 +56,7 @@ class DeformableDetrModelTester:
         batch_size=8,
         is_training=True,
         use_labels=True,
-        hidden_size=256,
+        hidden_size=32,
         num_hidden_layers=2,
         num_attention_heads=8,
         intermediate_size=4,
@@ -116,6 +123,16 @@ class DeformableDetrModelTester:
         return config, pixel_values, pixel_mask, labels
 
     def get_config(self):
+        resnet_config = ResNetConfig(
+            num_channels=3,
+            embeddings_size=10,
+            hidden_sizes=[10, 20, 30, 40],
+            depths=[1, 1, 2, 1],
+            hidden_act="relu",
+            num_labels=3,
+            out_features=["stage2", "stage3", "stage4"],
+            out_indices=[2, 3, 4],
+        )
         return DeformableDetrConfig(
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -131,6 +148,8 @@ class DeformableDetrModelTester:
             num_feature_levels=self.num_feature_levels,
             encoder_n_points=self.encoder_n_points,
             decoder_n_points=self.decoder_n_points,
+            use_timm_backbone=False,
+            backbone_config=resnet_config,
         )
 
     def prepare_config_and_inputs_for_common(self):
@@ -165,32 +184,13 @@ class DeformableDetrModelTester:
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
         self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
 
-    def create_and_check_no_timm_backbone(self, config, pixel_values, pixel_mask, labels):
-        config.use_timm_backbone = False
-        config.backbone_config = ResNetConfig()
-        model = DeformableDetrForObjectDetection(config=config)
-        model.to(torch_device)
-        model.eval()
 
-        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
-        result = model(pixel_values)
-
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
-        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
-
-        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
-
-        self.parent.assertEqual(result.loss.shape, ())
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
-        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
-
-
-@require_timm
+@require_torch
 class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_timm_available() else ()
+    all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else ()
     pipeline_model_mapping = (
         {"feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
-        if is_timm_available()
+        if is_torch_available()
         else {}
     )
     is_encoder_decoder = True
@@ -246,10 +246,6 @@ class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deformable_detr_object_detection_head_model(*config_and_inputs)
 
-    def test_deformable_detr_no_timm_backbone(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_no_timm_backbone(*config_and_inputs)
-
     @unittest.skip(reason="Deformable DETR does not use inputs_embeds")
     def test_inputs_embeds(self):
         pass