fixing name position_embeddings to object_queries

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -555,16 +555,16 @@ def __init__(
     def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
         return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
 
-    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
-        return tensor if position_embeddings is None else tensor + position_embeddings
+    def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
+        return tensor if object_queries is None else tensor + object_queries
 
     def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
-        position_embeddings: Optional[torch.Tensor] = None,
+        object_queries: Optional[torch.Tensor] = None,
         key_value_states: Optional[torch.Tensor] = None,
-        key_value_position_embeddings: Optional[torch.Tensor] = None,
+        spatial_position_embeddings: Optional[torch.Tensor] = None,
         output_attentions: bool = False,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         """Input shape: Batch x Time x Channel"""
@@ -575,14 +575,14 @@ def forward(
         batch_size, target_len, embed_dim = hidden_states.size()
 
         # add position embeddings to the hidden states before projecting to queries and keys
-        if position_embeddings is not None:
+        if object_queries is not None:
             hidden_states_original = hidden_states
-            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+            hidden_states = self.with_pos_embed(hidden_states, object_queries)
 
         # add key-value position embeddings to the key value states
-        if key_value_position_embeddings is not None:
+        if spatial_position_embeddings is not None:
             key_value_states_original = key_value_states
-            key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)
+            key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings)
 
         # get query proj
         query_states = self.q_proj(hidden_states) * self.scaling
@@ -790,7 +790,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: torch.Tensor,
-        position_embeddings: torch.Tensor = None,
+        object_queries: torch.Tensor = None,
         output_attentions: bool = False,
     ):
         """
@@ -799,7 +799,8 @@ def forward(
             attention_mask (`torch.FloatTensor`): attention mask of size
                 `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                 values.
-            position_embeddings (`torch.FloatTensor`, *optional*): position embeddings, to be added to hidden_states.
+            object_queries (`torch.FloatTensor`, *optional*):
+                Object queries (also called content embeddings), to be added to the hidden states.
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                 returned tensors for more detail.
@@ -808,7 +809,7 @@ def forward(
         hidden_states, attn_weights = self.self_attn(
             hidden_states=hidden_states,
             attention_mask=attention_mask,
-            position_embeddings=position_embeddings,
+            object_queries=object_queries,
             output_attentions=output_attentions,
         )
 
@@ -885,7 +886,7 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
-        position_embeddings: Optional[torch.Tensor] = None,
+        object_queries: Optional[torch.Tensor] = None,
         query_position_embeddings: Optional[torch.Tensor] = None,
         query_sine_embed: Optional[torch.Tensor] = None,
         encoder_hidden_states: Optional[torch.Tensor] = None,
@@ -899,11 +900,11 @@ def forward(
             attention_mask (`torch.FloatTensor`): attention mask of size
                 `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                 values.
-            position_embeddings (`torch.FloatTensor`, *optional*):
-                position embeddings that are added to the queries and keys
+            object_queries (`torch.FloatTensor`, *optional*):
+                object_queries that are added to the queries and keys
             in the cross-attention layer.
             query_position_embeddings (`torch.FloatTensor`, *optional*):
-                position embeddings that are added to the queries and keys
+                object_queries that are added to the queries and keys
             in the self-attention layer.
             encoder_hidden_states (`torch.FloatTensor`):
                 cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
@@ -954,7 +955,7 @@ def forward(
         batch_size, num_queries, n_model = q_content.shape
         _, source_len, _ = k_content.shape
 
-        k_pos = self.ca_kpos_proj(position_embeddings)
+        k_pos = self.ca_kpos_proj(object_queries)
 
         # For the first decoder layer, we concatenate the positional embedding predicted from
         # the object query (the positional embedding) into the original query (key) in DETR.
@@ -1150,7 +1151,7 @@ class ConditionalDetrEncoder(ConditionalDetrPreTrainedModel):
 
     Small tweak for ConditionalDETR:
 
-    - position_embeddings are added to the forward pass.
+    - object_queries are added to the forward pass.
 
     Args:
         config: ConditionalDetrConfig
@@ -1173,7 +1174,7 @@ def forward(
         self,
         inputs_embeds=None,
         attention_mask=None,
-        position_embeddings=None,
+        object_queries=None,
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
@@ -1191,8 +1192,8 @@ def forward(
 
                 [What are attention masks?](../glossary#attention-mask)
 
-            position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Position embeddings that are added to the queries and keys in each self-attention layer.
+            object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Object queries that are added to the queries in each self-attention layer.
 
             output_attentions (`bool`, *optional*):
                 Whether or not to return the attentions tensors of all attention layers. See `attentions` under
@@ -1232,11 +1233,11 @@ def forward(
             if to_drop:
                 layer_outputs = (None, None)
             else:
-                # we add position_embeddings as extra input to the encoder_layer
+                # we add object_queries as extra input to the encoder_layer
                 layer_outputs = encoder_layer(
                     hidden_states,
                     attention_mask,
-                    position_embeddings=position_embeddings,
+                    object_queries=object_queries,
                     output_attentions=output_attentions,
                 )
 
@@ -1263,7 +1264,7 @@ class ConditionalDetrDecoder(ConditionalDetrPreTrainedModel):
 
     Some small tweaks for Conditional DETR:
 
-    - position_embeddings and query_position_embeddings are added to the forward pass.
+    - object_queries and query_position_embeddings are added to the forward pass.
     - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers.
 
     Args:
@@ -1296,7 +1297,7 @@ def forward(
         attention_mask=None,
         encoder_hidden_states=None,
         encoder_attention_mask=None,
-        position_embeddings=None,
+        object_queries=None,
         query_position_embeddings=None,
         output_attentions=None,
         output_hidden_states=None,
@@ -1324,7 +1325,7 @@ def forward(
                 - 1 for pixels that are real (i.e. **not masked**),
                 - 0 for pixels that are padding (i.e. **masked**).
 
-            position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                 Position embeddings that are added to the queries and keys in each cross-attention layer.
             query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
                 , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
@@ -1404,7 +1405,7 @@ def custom_forward(*inputs):
                     create_custom_forward(decoder_layer),
                     hidden_states,
                     combined_attention_mask,
-                    position_embeddings,
+                    object_queries,
                     query_position_embeddings,
                     query_sine_embed,
                     encoder_hidden_states,
@@ -1416,7 +1417,7 @@ def custom_forward(*inputs):
                 layer_outputs = decoder_layer(
                     hidden_states,
                     attention_mask=combined_attention_mask,
-                    position_embeddings=position_embeddings,
+                    object_queries=object_queries,
                     query_position_embeddings=query_position_embeddings,
                     query_sine_embed=query_sine_embed,
                     encoder_hidden_states=encoder_hidden_states,
@@ -1484,8 +1485,8 @@ def __init__(self, config: ConditionalDetrConfig):
 
         # Create backbone + positional encoding
         backbone = ConditionalDetrConvEncoder(config)
-        position_embeddings = build_position_encoding(config)
-        self.backbone = ConditionalDetrConvModel(backbone, position_embeddings)
+        object_queries = build_position_encoding(config)
+        self.backbone = ConditionalDetrConvModel(backbone, object_queries)
 
         # Create projection layer
         self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1)
@@ -1569,7 +1570,7 @@ def forward(
         # First, sent pixel_values + pixel_mask through Backbone to obtain the features
         # pixel_values should be of shape (batch_size, num_channels, height, width)
         # pixel_mask should be of shape (batch_size, height, width)
-        features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)
+        features, object_queries_list = self.backbone(pixel_values, pixel_mask)
 
         # get final feature map and downsampled mask
         feature_map, mask = features[-1]
@@ -1580,21 +1581,21 @@ def forward(
         # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
         projected_feature_map = self.input_projection(feature_map)
 
-        # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
+        # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
         # In other words, turn their shape into (batch_size, sequence_length, hidden_size)
         flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
-        position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1)
+        object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
 
         flattened_mask = mask.flatten(1)
 
-        # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder
+        # Fourth, sent flattened_features + flattened_mask + object_queries through encoder
         # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
         # flattened_mask is a Tensor of shape (batch_size, heigth*width)
         if encoder_outputs is None:
             encoder_outputs = self.encoder(
                 inputs_embeds=flattened_features,
                 attention_mask=flattened_mask,
-                position_embeddings=position_embeddings,
+                object_queries=object_queries,
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
@@ -1607,15 +1608,15 @@ def forward(
                 attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
             )
 
-        # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output)
+        # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
         query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1)
         queries = torch.zeros_like(query_position_embeddings)
 
         # decoder outputs consists of (dec_features, dec_hidden, dec_attn)
         decoder_outputs = self.decoder(
             inputs_embeds=queries,
             attention_mask=None,
-            position_embeddings=position_embeddings,
+            object_queries=object_queries,
             query_position_embeddings=query_position_embeddings,
             encoder_hidden_states=encoder_outputs[0],
             encoder_attention_mask=flattened_mask,
@@ -1931,29 +1932,29 @@ def forward(
         if pixel_mask is None:
             pixel_mask = torch.ones((batch_size, height, width), device=device)
 
-        # First, get list of feature maps and position embeddings
-        features, position_embeddings_list = self.conditional_detr.model.backbone(pixel_values, pixel_mask=pixel_mask)
+        # First, get list of feature maps and object_queries
+        features, object_queries_list = self.conditional_detr.model.backbone(pixel_values, pixel_mask=pixel_mask)
 
         # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
         feature_map, mask = features[-1]
         batch_size, num_channels, height, width = feature_map.shape
         projected_feature_map = self.conditional_detr.model.input_projection(feature_map)
 
-        # Third, flatten the feature map + position embeddings of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
+        # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC
         # In other words, turn their shape into (batch_size, sequence_length, hidden_size)
         flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1)
-        position_embeddings = position_embeddings_list[-1].flatten(2).permute(0, 2, 1)
+        object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1)
 
         flattened_mask = mask.flatten(1)
 
-        # Fourth, sent flattened_features + flattened_mask + position embeddings through encoder
+        # Fourth, sent flattened_features + flattened_mask + object_queries through encoder
         # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size)
         # flattened_mask is a Tensor of shape (batch_size, heigth*width)
         if encoder_outputs is None:
             encoder_outputs = self.conditional_detr.model.encoder(
                 inputs_embeds=flattened_features,
                 attention_mask=flattened_mask,
-                position_embeddings=position_embeddings,
+                object_queries=object_queries,
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
@@ -1966,7 +1967,7 @@ def forward(
                 attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
             )
 
-        # Fifth, sent query embeddings + position embeddings through the decoder (which is conditioned on the encoder output)
+        # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output)
         query_position_embeddings = self.conditional_detr.model.query_position_embeddings.weight.unsqueeze(0).repeat(
             batch_size, 1, 1
         )
@@ -1976,7 +1977,7 @@ def forward(
         decoder_outputs = self.conditional_detr.model.decoder(
             inputs_embeds=queries,
             attention_mask=None,
-            position_embeddings=position_embeddings,
+            object_queries=object_queries,
             query_position_embeddings=query_position_embeddings,
             encoder_hidden_states=encoder_outputs[0],
             encoder_attention_mask=flattened_mask,