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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/vit_msn/modeling_vit_msn.py

@@ -75,10 +75,7 @@ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width:
         patch_window_width = width // self.config.patch_size
         # we add a small number to avoid floating point error in the interpolation
         # see discussion at https://github.com/facebookresearch/dino/issues/8
-        patch_window_height, patch_window_width = (
-            patch_window_height + 0.1,
-            patch_window_width + 0.1,
-        )
+        patch_window_height, patch_window_width = patch_window_height + 0.1, patch_window_width + 0.1
         patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
         patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
         patch_pos_embed = nn.functional.interpolate(
@@ -599,9 +596,7 @@ def forward(
         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
 
         embedding_output = self.embeddings(
-            pixel_values,
-            bool_masked_pos=bool_masked_pos,
-            interpolate_pos_encoding=interpolate_pos_encoding,
+            pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
         )
 
         encoder_outputs = self.encoder(

src/transformers/models/yolos/modeling_yolos.py

@@ -171,15 +171,9 @@ def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
         patch_pos_embed = patch_pos_embed.view(batch_size, hidden_size, patch_height, patch_width)
 
         height, width = img_size
-        new_patch_heigth, new_patch_width = (
-            height // self.config.patch_size,
-            width // self.config.patch_size,
-        )
+        new_patch_heigth, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
         patch_pos_embed = nn.functional.interpolate(
-            patch_pos_embed,
-            size=(new_patch_heigth, new_patch_width),
-            mode="bicubic",
-            align_corners=False,
+            patch_pos_embed, size=(new_patch_heigth, new_patch_width), mode="bicubic", align_corners=False
         )
         patch_pos_embed = patch_pos_embed.flatten(2).transpose(1, 2)
         scale_pos_embed = torch.cat((cls_pos_embed, patch_pos_embed, det_pos_embed), dim=1)
@@ -205,15 +199,9 @@ def forward(self, pos_embed, img_size=(800, 1344)) -> torch.Tensor:
         )
         patch_pos_embed = patch_pos_embed.view(depth * batch_size, hidden_size, patch_height, patch_width)
         height, width = img_size
-        new_patch_height, new_patch_width = (
-            height // self.config.patch_size,
-            width // self.config.patch_size,
-        )
+        new_patch_height, new_patch_width = height // self.config.patch_size, width // self.config.patch_size
         patch_pos_embed = nn.functional.interpolate(
-            patch_pos_embed,
-            size=(new_patch_height, new_patch_width),
-            mode="bicubic",
-            align_corners=False,
+            patch_pos_embed, size=(new_patch_height, new_patch_width), mode="bicubic", align_corners=False
         )
         patch_pos_embed = (
             patch_pos_embed.flatten(2)
@@ -756,16 +744,10 @@ def __init__(self, config: YolosConfig):
         # Object detection heads
         # We add one for the "no object" class
         self.class_labels_classifier = YolosMLPPredictionHead(
-            input_dim=config.hidden_size,
-            hidden_dim=config.hidden_size,
-            output_dim=config.num_labels + 1,
-            num_layers=3,
+            input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=config.num_labels + 1, num_layers=3
         )
         self.bbox_predictor = YolosMLPPredictionHead(
-            input_dim=config.hidden_size,
-            hidden_dim=config.hidden_size,
-            output_dim=4,
-            num_layers=3,
+            input_dim=config.hidden_size, hidden_dim=config.hidden_size, output_dim=4, num_layers=3
         )
 
         # Initialize weights and apply final processing
@@ -857,9 +839,7 @@ def forward(
         if labels is not None:
             # First: create the matcher
             matcher = YolosHungarianMatcher(
-                class_cost=self.config.class_cost,
-                bbox_cost=self.config.bbox_cost,
-                giou_cost=self.config.giou_cost,
+                class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost
             )
             # Second: create the criterion
             losses = ["labels", "boxes", "cardinality"]