apply Black 2024 style in fbcode (7/16)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: aleivag

Differential Revision: D54447732

fbshipit-source-id: e21fbbe27882c8af183d021f4ac27029cbe93e8e

d2go/data/build.py

@@ -98,12 +98,16 @@ def build_weighted_detection_train_loader(
             name: get_detection_dataset_dicts(
                 [name],
                 filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-                min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-                if cfg.MODEL.KEYPOINT_ON
-                else 0,
-                proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
-                if cfg.MODEL.LOAD_PROPOSALS
-                else None,
+                min_keypoints=(
+                    cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
+                    if cfg.MODEL.KEYPOINT_ON
+                    else 0
+                ),
+                proposal_files=(
+                    cfg.DATASETS.PROPOSAL_FILES_TRAIN
+                    if cfg.MODEL.LOAD_PROPOSALS
+                    else None
+                ),
             )
             for name in cfg.DATASETS.TRAIN
         }

d2go/data/disk_cache.py

@@ -67,9 +67,7 @@ class DiskCachedList(object):
         self._lst = [_serialize(x) for x in self._lst]
         total_size = sum(len(x) for x in self._lst)
         # TODO: only enabling DiskCachedDataset for large enough dataset
-        logger.info(
-            "Serialized dataset takes {:.2f} MiB".format(total_size / 1024**2)
-        )
+        logger.info("Serialized dataset takes {:.2f} MiB".format(total_size / 1024**2))
         self._initialize_diskcache()
 
     def _initialize_diskcache(self):

d2go/data/extended_coco.py

@@ -363,7 +363,7 @@ def convert_to_dict_list(
     default_record = {"dataset_name": dataset_name} if dataset_name else {}
 
     converted_dict_list = []
-    for (img_dict, anno_dict_list) in zip(imgs, anns):
+    for img_dict, anno_dict_list in zip(imgs, anns):
         record = copy.deepcopy(default_record)
 
         # NOTE: besides using (relative path) in the "file_name" filed to represent

d2go/data/extended_lvis.py

@@ -89,7 +89,7 @@ def extended_lvis_load(json_file, image_root, dataset_name=None):
     dataset_dicts = []
 
     count_ignore_image_root_warning = 0
-    for (img_dict, anno_dict_list) in imgs_anns:
+    for img_dict, anno_dict_list in imgs_anns:
         record = {}
         if "://" not in img_dict["file_name"]:
             file_name = img_dict["file_name"]

d2go/modeling/ema.py

@@ -198,7 +198,7 @@ class EMAUpdater(object):
                 averaged_model_parameters, model_parameters, 1.0 - decay
             )
             if self.debug_lerp:
-                for (orig_val, lerp_val) in zip(
+                for orig_val, lerp_val in zip(
                     orig_averaged_model_parameters, averaged_model_parameters
                 ):
                     assert torch.allclose(orig_val, lerp_val, rtol=1e-4, atol=1e-3)

d2go/quantization/modeling.py

@@ -282,7 +282,6 @@ def mock_quantization_type(quant_func):
 
 
 def default_prepare_for_quant(cfg, model):
-
     """
     Default implementation of preparing a model for quantization. This function will
     be called to before training if QAT is enabled, or before calibration during PTQ if

d2go/runner/callbacks/quantization.py

@@ -418,9 +418,11 @@ class QuantizationAwareTraining(Callback, QuantizationMixin):
     def from_config(cls, cfg: CfgNode):
         qat = cfg.QUANTIZATION.QAT
         callback = cls(
-            qconfig_dicts={submodule: None for submodule in cfg.QUANTIZATION.MODULES}
-            if cfg.QUANTIZATION.MODULES
-            else None,
+            qconfig_dicts=(
+                {submodule: None for submodule in cfg.QUANTIZATION.MODULES}
+                if cfg.QUANTIZATION.MODULES
+                else None
+            ),
             # We explicitly pass this to maintain properties for now.
             preserved_attrs=["model.backbone.size_divisibility"],
             start_step=qat.START_ITER,
@@ -576,9 +578,11 @@ class PostTrainingQuantization(Callback, QuantizationMixin):
     @classmethod
     def from_config(cls, cfg: CfgNode):
         return cls(
-            qconfig_dicts={submodule: None for submodule in cfg.QUANTIZATION.MODULES}
-            if cfg.QUANTIZATION.MODULES
-            else None,
+            qconfig_dicts=(
+                {submodule: None for submodule in cfg.QUANTIZATION.MODULES}
+                if cfg.QUANTIZATION.MODULES
+                else None
+            ),
             # We explicitly pass this to maintain properties for now.
             preserved_attrs=["model.backbone.size_divisibility"],
         )

d2go/runner/default_runner.py

@@ -464,9 +464,11 @@ class Detectron2GoRunner(D2GoDataAPIMixIn, BaseRunner):
                 output_folder,
                 train_iter,
                 model_tag,
-                model.module
-                if isinstance(model, nn.parallel.DistributedDataParallel)
-                else model,
+                (
+                    model.module
+                    if isinstance(model, nn.parallel.DistributedDataParallel)
+                    else model
+                ),
             )
 
             inference_callbacks = self._get_inference_callbacks()

d2go/trainer/activation_checkpointing.py

@@ -35,9 +35,11 @@ class ActivationCheckpointModelingHook(mh.ModelingHook):
         logger.info("Activation Checkpointing is used")
         wrapper_fn = partial(
             checkpoint_wrapper,
-            checkpoint_impl=CheckpointImpl.NO_REENTRANT
-            if not self.cfg.ACTIVATION_CHECKPOINT.REENTRANT
-            else CheckpointImpl.REENTRANT,
+            checkpoint_impl=(
+                CheckpointImpl.NO_REENTRANT
+                if not self.cfg.ACTIVATION_CHECKPOINT.REENTRANT
+                else CheckpointImpl.REENTRANT
+            ),
         )
         policy_name = self.cfg.ACTIVATION_CHECKPOINT.AUTO_WRAP_POLICY
         assert (

d2go/utils/helper.py

@@ -56,7 +56,7 @@ def run_once(
     `MultipleFunctionCallError`.
     """
 
-    def decorator(func: Callable[..., T]) -> (Callable[..., T]):
+    def decorator(func: Callable[..., T]) -> Callable[..., T]:
         signal: List[T] = []
 
         @wraps(func)

d2go/utils/testing/lightning_train_helper.py

@@ -10,9 +10,9 @@ def get_lt_trainer(output_dir: str, cfg):
     return pl.Trainer(
         max_epochs=10**8,
         max_steps=cfg.SOLVER.MAX_ITER,
-        val_check_interval=cfg.TEST.EVAL_PERIOD
-        if cfg.TEST.EVAL_PERIOD > 0
-        else cfg.SOLVER.MAX_ITER,
+        val_check_interval=(
+            cfg.TEST.EVAL_PERIOD if cfg.TEST.EVAL_PERIOD > 0 else cfg.SOLVER.MAX_ITER
+        ),
         callbacks=[checkpoint_callback],
         logger=False,
     )

demo/demo.py

@@ -90,9 +90,11 @@ def main():
             logger.info(
                 "{}: {} in {:.2f}s".format(
                     path,
-                    "detected {} instances".format(len(predictions["instances"]))
-                    if "instances" in predictions
-                    else "finished",
+                    (
+                        "detected {} instances".format(len(predictions["instances"]))
+                        if "instances" in predictions
+                        else "finished"
+                    ),
                     time.time() - start_time,
                 )
             )

tools/lightning_train_net.py

@@ -67,9 +67,9 @@ def get_trainer_params(cfg: CfgNode) -> Dict[str, Any]:
     params = {
         "max_epochs": -1,
         "max_steps": cfg.SOLVER.MAX_ITER,
-        "val_check_interval": cfg.TEST.EVAL_PERIOD
-        if cfg.TEST.EVAL_PERIOD > 0
-        else cfg.SOLVER.MAX_ITER,
+        "val_check_interval": (
+            cfg.TEST.EVAL_PERIOD if cfg.TEST.EVAL_PERIOD > 0 else cfg.SOLVER.MAX_ITER
+        ),
         "num_nodes": comm.get_num_nodes(),
         "devices": comm.get_local_size(),
         "strategy": strategy,
@@ -78,11 +78,11 @@ def get_trainer_params(cfg: CfgNode) -> Dict[str, Any]:
         "logger": TensorBoardLogger(save_dir=cfg.OUTPUT_DIR),
         "num_sanity_val_steps": 0,
         "replace_sampler_ddp": False,
-        "precision": parse_precision_from_string(
-            cfg.SOLVER.AMP.PRECISION, lightning=True
-        )
-        if cfg.SOLVER.AMP.ENABLED
-        else 32,
+        "precision": (
+            parse_precision_from_string(cfg.SOLVER.AMP.PRECISION, lightning=True)
+            if cfg.SOLVER.AMP.ENABLED
+            else 32
+        ),
     }
     if cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
         if (