Remove unused module DETR based models (#30823)

* removing heads for classification from DETR models.

* quality fix

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -1091,25 +1091,6 @@ class ConditionalDetrDecoderLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->ConditionalDetr
-class ConditionalDetrClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
 # Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with DetrMLPPredictionHead->MLP
 class MLP(nn.Module):
     """

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -1066,25 +1066,6 @@ class DeformableDetrDecoderLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead
-class DeformableDetrClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
 class DeformableDetrPreTrainedModel(PreTrainedModel):
     config_class = DeformableDetrConfig
     base_model_prefix = "model"

src/transformers/models/deta/modeling_deta.py

@@ -1032,25 +1032,6 @@ class DetaDecoderLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead
-class DetaClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
 class DetaPreTrainedModel(PreTrainedModel):
     config_class = DetaConfig
     base_model_prefix = "model"

src/transformers/models/detr/modeling_detr.py

@@ -875,24 +875,6 @@ class DetrDecoderLayer(nn.Module):
         return outputs
 
 
-class DetrClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
 class DetrPreTrainedModel(PreTrainedModel):
     config_class = DetrConfig
     base_model_prefix = "model"

src/transformers/models/table_transformer/modeling_table_transformer.py

@@ -782,25 +782,6 @@ class TableTransformerDecoderLayer(nn.Module):
         return outputs
 
 
-# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->TableTransformer
-class TableTransformerClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, hidden_states: torch.Tensor):
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.dense(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.out_proj(hidden_states)
-        return hidden_states
-
-
 class TableTransformerPreTrainedModel(PreTrainedModel):
     config_class = TableTransformerConfig
     base_model_prefix = "model"