formatting changes from black 22.3.0

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

d2go/data/disk_cache.py

@@ -63,7 +63,7 @@ class DiskCachedDatasetFromList(data.Dataset):
         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)
+            "Serialized dataset takes {:.2f} MiB".format(total_size / 1024**2)
         )
         self._initialize_diskcache()
 
@@ -128,7 +128,7 @@ class DiskCachedDatasetFromList(data.Dataset):
         comm.synchronize()
         logger.info(
             "Finished writing to local disk, db size: {:.2f} MiB".format(
-                self._cache.cache.volume() / 1024 ** 2
+                self._cache.cache.volume() / 1024**2
             )
         )
         # Optional sync for some strategies
@@ -158,7 +158,7 @@ class DiskCachedDatasetFromList(data.Dataset):
             "({:.2f}%) Wrote {} elements to local disk cache, db size: {:.2f} MiB".format(
                 percentage,
                 len(self._cache.cache),
-                self._cache.cache.volume() / 1024 ** 2,
+                self._cache.cache.volume() / 1024**2,
             ),
             n=10,
         )

d2go/utils/testing/lightning_train_helper.py

@@ -8,7 +8,7 @@ from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 def get_lt_trainer(output_dir: str, cfg):
     checkpoint_callback = ModelCheckpoint(dirpath=output_dir, save_last=True)
     return pl.Trainer(
-        max_epochs=10 ** 8,
+        max_epochs=10**8,
         max_steps=cfg.SOLVER.MAX_ITER,
         val_check_interval=cfg.TEST.EVAL_PERIOD
         if cfg.TEST.EVAL_PERIOD > 0

projects_oss/detr/detr/models/deformable_transformer.py

@@ -159,7 +159,7 @@ class DeformableTransformer(nn.Module):
             # grid shape (bs, H_l, W_l, 2). Value could be > 1
             grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
             # wh shape (bs, H_l, W_l, 2)
-            wh = torch.ones_like(grid) * base_object_scale * (2.0 ** lvl)
+            wh = torch.ones_like(grid) * base_object_scale * (2.0**lvl)
             # proposal shape (bs, H_l * W_l, 4)
             proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
             proposals.append(proposal)

projects_oss/detr/detr/models/matcher.py

@@ -84,7 +84,7 @@ class HungarianMatcher(nn.Module):
             alpha = 0.25
             gamma = 2.0
             neg_cost_class = (
-                (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())
+                (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
             )
             pos_cost_class = (
                 alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())

tests/runner/test_runner_lightning_quantization.py

@@ -204,7 +204,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
         test_out = model.eval()(test_in)
         self.assertGreater(
-            (test_out ** 2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
+            (test_out**2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
         )
 
         base_out = qat.quantized.eval()(test_in)
@@ -330,7 +330,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
         test_out = model.eval()(test_in)
         self.assertGreater(
-            (test_out ** 2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
+            (test_out**2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
         )
 
         base_out = qat.quantized.eval()(test_in)
@@ -369,7 +369,7 @@ class TestQuantizationAwareTraining(unittest.TestCase):
 
         test_out = model.eval()(test_in)
         self.assertGreater(
-            (test_out ** 2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
+            (test_out**2).sum(), 0.03, "With the given seend, L2^2 should be > 0.03."
         )
 
         base_out = qat.quantized.eval()(test_in)