Format code with autopep8

flash/audio/speech_recognition/data.py

@@ -64,9 +64,9 @@ class BaseSpeechRecognition:
     def _load_sample(self, sample: Dict[str, Any]) -> Any:
         path = sample[DefaultDataKeys.INPUT]
         if (
-            not os.path.isabs(path)
-            and DefaultDataKeys.METADATA in sample
-            and "root" in sample[DefaultDataKeys.METADATA]
+            not os.path.isabs(path) and
+            DefaultDataKeys.METADATA in sample and
+            "root" in sample[DefaultDataKeys.METADATA]
         ):
             path = os.path.join(sample[DefaultDataKeys.METADATA]["root"], path)
         speech_array, sampling_rate = sf.read(path)

flash/core/finetuning.py

@@ -122,7 +122,7 @@ def finetune_function(
         if epoch == self.unfreeze_milestones[0]:
             # unfreeze num_layers last layers
             self.unfreeze_and_add_param_group(
-                modules=backbone_modules[-self.num_layers :],
+                modules=backbone_modules[-self.num_layers:],
                 optimizer=optimizer,
                 train_bn=self.train_bn,
             )

flash/core/model.py

@@ -402,9 +402,9 @@ def build_data_pipeline(
             deserializer = getattr(self.datamodule.data_pipeline, "_deserializer", None)
 
         elif (
-            self.trainer is not None
-            and hasattr(self.trainer, "datamodule")
-            and getattr(self.trainer.datamodule, "data_pipeline", None) is not None
+            self.trainer is not None and
+            hasattr(self.trainer, "datamodule") and
+            getattr(self.trainer.datamodule, "data_pipeline", None) is not None
         ):
             old_data_source = getattr(self.trainer.datamodule.data_pipeline, "data_source", None)
             preprocess = getattr(self.trainer.datamodule.data_pipeline, "_preprocess_pipeline", None)

flash/image/segmentation/data.py

@@ -129,8 +129,8 @@ def load_data(
 
         data = filter(
             lambda sample: (
-                has_file_allowed_extension(sample[0], self.extensions)
-                and has_file_allowed_extension(sample[1], self.extensions)
+                has_file_allowed_extension(sample[0], self.extensions) and
+                has_file_allowed_extension(sample[1], self.extensions)
             ),
             zip(input_data, target_data),
         )

flash/pointcloud/detection/model.py

@@ -120,9 +120,9 @@ def __init__(
     def compute_loss(self, losses: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
         losses = losses["loss"]
         return (
-            self.hparams.lambda_loss_cls * losses["loss_cls"]
-            + self.hparams.lambda_loss_bbox * losses["loss_bbox"]
-            + self.hparams.lambda_loss_dir * losses["loss_dir"]
+            self.hparams.lambda_loss_cls * losses["loss_cls"] +
+            self.hparams.lambda_loss_bbox * losses["loss_bbox"] +
+            self.hparams.lambda_loss_dir * losses["loss_dir"]
         )
 
     def compute_logs(self, logs: Dict[str, Any], losses: Dict[str, torch.Tensor]):

flash/pointcloud/segmentation/model.py

@@ -58,7 +58,7 @@ def finetune_function(
         if epoch != self.unfreeze_epoch:
             return
         self.unfreeze_and_add_param_group(
-            modules=list(pl_module.backbone.children())[-self.num_layers :],
+            modules=list(pl_module.backbone.children())[-self.num_layers:],
             optimizer=optimizer,
             train_bn=self.train_bn,
         )

flash/tabular/data.py

@@ -200,8 +200,8 @@ def __init__(
                 ),
             },
             default_data_source=DefaultDataSources.CSV,
-            deserializer=deserializer
-            or TabularDeserializer(
+            deserializer=deserializer or
+            TabularDeserializer(
                 cat_cols=cat_cols,
                 num_cols=num_cols,
                 target_col=target_col,

flash/text/seq2seq/core/metrics.py

@@ -49,7 +49,7 @@ def _count_ngram(ngram_input_list: List[str], n_gram: int) -> Counter:
 
     for i in range(1, n_gram + 1):
         for j in range(len(ngram_input_list) - i + 1):
-            ngram_key = tuple(ngram_input_list[j : (i + j)])
+            ngram_key = tuple(ngram_input_list[j: (i + j)])
             ngram_counter[ngram_key] += 1
 
     return ngram_counter

flash/video/classification/model.py

@@ -64,7 +64,7 @@ def finetune_function(
         if epoch != self.unfreeze_epoch:
             return
         self.unfreeze_and_add_param_group(
-            modules=list(pl_module.backbone.children())[-self.num_layers :],
+            modules=list(pl_module.backbone.children())[-self.num_layers:],
             optimizer=optimizer,
             train_bn=self.train_bn,
         )

tests/core/serve/test_dag/test_optimization.py

@@ -1223,8 +1223,8 @@ def test_fuse_subgraphs():
                             "inc-6",
                             inkeys,
                         ),
-                    )
-                    + inkeys,
+                    ) +
+                    inkeys,
                 }
             )
         )
@@ -1248,8 +1248,8 @@ def test_fuse_subgraphs():
                             "add-2",
                             inkeys,
                         ),
-                    )
-                    + inkeys,
+                    ) +
+                    inkeys,
                     "add-2": "inc-add-1",
                     "inc-6": (inc, (inc, "add-2")),
                 }

tests/image/segmentation/test_data.py

@@ -22,8 +22,8 @@
 
 def build_checkboard(n, m, k=8):
     x = np.zeros((n, m))
-    x[k :: k * 2, ::k] = 1
-    x[:: k * 2, k :: k * 2] = 1
+    x[k:: k * 2, ::k] = 1
+    x[:: k * 2, k:: k * 2] = 1
     return x