lint

Author: shaikh58

dreem/models/transformer.py

@@ -211,9 +211,11 @@ def forward(
 
         # apply fourier embeddings if using fourier rope, OR if using descriptor (compact) visual encoder
         if (
-            self.embedding_meta and "use_fourier" in self.embedding_meta and self.embedding_meta["use_fourier"]
+            self.embedding_meta
+            and "use_fourier" in self.embedding_meta
+            and self.embedding_meta["use_fourier"]
         ) or (
-            self.encoder_cfg 
+            self.encoder_cfg
             and "encoder_type" in self.encoder_cfg
             and self.encoder_cfg["encoder_type"] == "descriptor"
         ):
@@ -269,9 +271,11 @@ def forward(
 
         # apply fourier embeddings if using fourier rope, OR if using descriptor (compact) visual encoder
         if (
-            self.embedding_meta and "use_fourier" in self.embedding_meta and self.embedding_meta["use_fourier"]
+            self.embedding_meta
+            and "use_fourier" in self.embedding_meta
+            and self.embedding_meta["use_fourier"]
         ) or (
-            self.encoder_cfg 
+            self.encoder_cfg
             and "encoder_type" in self.encoder_cfg
             and self.encoder_cfg["encoder_type"] == "descriptor"
         ):

dreem/models/visual_encoder.py

@@ -234,7 +234,7 @@ def create_visual_encoder(d_model: int, **encoder_cfg) -> torch.nn.Module:
     # register any custom encoders here
 
     # compatibility with configs that don't specify encoder_type; default to resnet
-    if not encoder_cfg or"encoder_type" not in encoder_cfg:
+    if not encoder_cfg or "encoder_type" not in encoder_cfg:
         encoder_type = "resnet"
         return ENCODER_REGISTRY[encoder_type](d_model=d_model, **encoder_cfg)
     else: