Fix formatting and modue import sort order.

tensorflow_similarity/__init__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = "0.17.1"
+__version__ = "0.17.2"
 
 
 from . import algebra  # noqa

tensorflow_similarity/augmenters/barlow.py

@@ -132,7 +132,6 @@ def augment(
         num_augmentations_per_example: int = 2,
         is_warmup: bool = True,
     ) -> list[Any]:
-
         with tf.device("/cpu:0"):
             if y is None:
                 y = tf.constant([0])
@@ -162,7 +161,6 @@ def augment(
                 solarize_thresh=self.solarize_thresh,
             )
             for _ in range(num_augmentations_per_example):
-
                 view = tf.map_fn(
                     lambda img: augment_fn(image=img),
                     inputs,

tensorflow_similarity/augmenters/simclr.py

@@ -108,7 +108,6 @@ def __init__(
         version: str = "v2",
         num_cpu: int | None = os.cpu_count(),
     ):
-
         self.width = width
         self.height = height
         self.is_training = is_training
@@ -132,7 +131,6 @@ def __init__(
 
     @tf.function
     def augment(self, x: Tensor, y: Tensor, num_views: int, is_warmup: bool) -> list[Tensor]:
-
         with tf.device("/cpu:0"):
             inputs = tf.stack(x)
             inputs = tf.cast(inputs, dtype="float32") / 255.0

tensorflow_similarity/evaluators/memory_evaluator.py

@@ -317,7 +317,6 @@ def _optimal_cutpoint(
             "value": metrics[calibration_metric.name][idx].item(),
         }
         for metric_name in metrics.keys():
-
             optimal_cp[metric_name] = metrics[metric_name][idx].item()
 
         return optimal_cp

tensorflow_similarity/indexer.py

@@ -325,7 +325,6 @@ def single_lookup(self, prediction: FloatTensor, k: int = 5) -> list[Lookup]:
         return lookups
 
     def batch_lookup(self, predictions: FloatTensor, k: int = 5, verbose: int = 1) -> list[list[Lookup]]:
-
         """Find the k closest matches for a set of embeddings
 
         Args:

tensorflow_similarity/losses/__init__.py

@@ -17,6 +17,7 @@
 """
 from .barlow import Barlow  # noqa
 from .circle_loss import CircleLoss  # noqa
+from .lifted_structure_loss import LiftedStructLoss  # noqa
 from .metric_loss import MetricLoss  # noqa
 from .multinegrank_loss import MultiNegativesRankLoss  # noqa
 from .multisim_loss import MultiSimilarityLoss  # noqa
@@ -25,6 +26,5 @@
 from .simsiam import SimSiamLoss  # noqa
 from .softnn_loss import SoftNearestNeighborLoss  # noqa
 from .triplet_loss import TripletLoss  # noqa
-from .lifted_structure_loss import LiftedStructLoss  # noqa
 from .vicreg import VicReg  # noqa
 from .xbm_loss import XBM  # noqa

tensorflow_similarity/losses/lifted_structure_loss.py

@@ -17,11 +17,14 @@
     https://arxiv.org/abs/1511.06452
 """
 from __future__ import annotations
+
 import tensorflow as tf
+
+from tensorflow_similarity import losses as tfsim_losses
 from tensorflow_similarity.algebra import build_masks
 from tensorflow_similarity.distances import Distance, distance_canonicalizer
 from tensorflow_similarity.types import FloatTensor, IntTensor
-from tensorflow_similarity import losses as tfsim_losses
+
 from .metric_loss import MetricLoss
 from .utils import positive_distances
 

tensorflow_similarity/losses/simclr.py

@@ -39,7 +39,6 @@ def __init__(self, temperature: float = 0.05, **kwargs):
         self.temperature = temperature
 
     def contrast(self, hidden1: FloatTensor, hidden2: FloatTensor) -> FloatTensor:
-
         # local replica batch size
         batch_size = tf.shape(hidden1)[0]
 

tensorflow_similarity/matchers/match_majority_vote.py

@@ -24,7 +24,6 @@ class MatchMajorityVote(ClassificationMatch):
     """Match metrics for the most common label in a result set."""
 
     def __init__(self, name: str = "majority_vote", **kwargs) -> None:
-
         if "canonical_name" not in kwargs:
             kwargs["canonical_name"] = "match_majority_vote"
 

tensorflow_similarity/matchers/match_nearest.py

@@ -22,7 +22,6 @@ class MatchNearest(ClassificationMatch):
     """Match metrics for labels at k=1."""
 
     def __init__(self, name: str = "nearest", **kwargs) -> None:
-
         if "canonical_name" not in kwargs:
             kwargs["canonical_name"] = "match_nearest"
 

tensorflow_similarity/training_metrics/distance_metrics.py

@@ -36,7 +36,6 @@ def __init__(
         negative_mining_strategy: str = "hard",
         **kwargs,
     ):
-
         if not name:
             name = "%s_%s" % (aggregate, anchor[:3])
         super().__init__(name=name, **kwargs)
@@ -63,7 +62,6 @@ def __init__(
         self.aggregated_distances = tf.Variable(0, dtype=tf.keras.backend.floatx())
 
     def update_state(self, labels: IntTensor, embeddings: FloatTensor, sample_weight: FloatTensor) -> None:
-
         # [distances]
         pairwise_distances = self.distance(embeddings, embeddings)
 

tensorflow_similarity/visualization/vizualize_views.py

@@ -40,7 +40,6 @@ def visualize_views(
     # Plot the images
     fig, axes = plt.subplots(num_row, num_col, figsize=fig_size)
     for i in range(num_imgs):
-
         # If the number of rows is 1, the axes array is one-dimensional
         if num_row == 1:
             ax = axes[i % num_col]

tests/losses/test_lifted_structure_loss.py

@@ -1,8 +1,10 @@
 import tensorflow as tf
 from absl.testing import parameterized
-from tensorflow.python.framework import combinations
 from tensorflow.keras.losses import Reduction
+from tensorflow.python.framework import combinations
+
 from tensorflow_similarity import losses
+
 from . import utils