[DETR] Add num_channels attribute

src/transformers/models/detr/configuration_detr.py

@@ -42,8 +42,9 @@ class DetrConfig(PretrainedConfig):
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
 
-
     Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
         num_queries (`int`, *optional*, defaults to 100):
             Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetrModel`] can
             detect in a single image. For COCO, we recommend 100 queries.
@@ -132,6 +133,7 @@ class DetrConfig(PretrainedConfig):
 
     def __init__(
         self,
+        num_channels=3,
         num_queries=100,
         max_position_embeddings=1024,
         encoder_layers=6,
@@ -167,6 +169,7 @@ def __init__(
         eos_coefficient=0.1,
         **kwargs
     ):
+        self.num_channels = num_channels
         self.num_queries = num_queries
         self.max_position_embeddings = max_position_embeddings
         self.d_model = d_model

src/transformers/models/detr/modeling_detr.py

@@ -326,7 +326,7 @@ class DetrTimmConvEncoder(nn.Module):
 
     """
 
-    def __init__(self, name: str, dilation: bool, use_pretrained_backbone: bool):
+    def __init__(self, name: str, dilation: bool, use_pretrained_backbone: bool, num_channels: int = 3):
         super().__init__()
 
         kwargs = {}
@@ -336,7 +336,12 @@ def __init__(self, name: str, dilation: bool, use_pretrained_backbone: bool):
         requires_backends(self, ["timm"])
 
         backbone = create_model(
-            name, pretrained=use_pretrained_backbone, features_only=True, out_indices=(1, 2, 3, 4), **kwargs
+            name,
+            pretrained=use_pretrained_backbone,
+            features_only=True,
+            out_indices=(1, 2, 3, 4),
+            in_chans=num_channels,
+            **kwargs,
         )
         # replace batch norm by frozen batch norm
         with torch.no_grad():
@@ -1179,7 +1184,9 @@ def __init__(self, config: DetrConfig):
         super().__init__(config)
 
         # Create backbone + positional encoding
-        backbone = DetrTimmConvEncoder(config.backbone, config.dilation, config.use_pretrained_backbone)
+        backbone = DetrTimmConvEncoder(
+            config.backbone, config.dilation, config.use_pretrained_backbone, config.num_channels
+        )
         position_embeddings = build_position_encoding(config)
         self.backbone = DetrConvModel(backbone, position_embeddings)
 

tests/models/detr/test_modeling_detr.py

@@ -416,6 +416,26 @@ def test_different_timm_backbone(self):
 
             self.assertTrue(outputs)
 
+    def test_greyscale_images(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # use greyscale pixel values
+        inputs_dict["pixel_values"] = floats_tensor(
+            [self.model_tester.batch_size, 1, self.model_tester.min_size, self.model_tester.max_size]
+        )
+
+        # let's set num_channels to 1
+        config.num_channels = 1
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertTrue(outputs)
+
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()