Merge pull request #234 from neuronets/pre-commit-ci-update-config

[pre-commit.ci] pre-commit autoupdate
neuronets · Feb 6, 2023 · affbd76 · affbd76
2 parents 18c2473 + 46d9e20
commit affbd76
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Showing 8 changed files with 2 additions and 14 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -12,15 +12,15 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
     -   id: black
 -   repo: https://github.com/PyCQA/flake8
     rev: 6.0.0
     hooks:
     - id: flake8
 -   repo: https://github.com/PyCQA/isort
-    rev: 5.11.4
+    rev: 5.12.0
     hooks:
     -   id: isort
         exclude: ^(nobrainer/_version\.py|versioneer\.py)$

diff --git a/nobrainer/losses.py b/nobrainer/losses.py
@@ -218,7 +218,6 @@ def __init__(self, reduction=ReductionV2.AUTO, name="wasserstein"):
 
 
 def gradient_penalty(gradients, real_pred, gp_weight=10, epsilon_weight=0.001):
-
     gradients_squared = tf.square(gradients)
     gradients_sqr_sum = tf.reduce_sum(
         gradients_squared, axis=tf.range(1, tf.rank(gradients_squared))

diff --git a/nobrainer/models/progressiveae.py b/nobrainer/models/progressiveae.py
@@ -206,7 +206,6 @@ def add_resolution(self):
         self.build([images_shape, alpha_shape])
 
     def call(self, inputs):
-
         images, alpha = inputs
 
         x = self.Head_Conv(images)
@@ -261,7 +260,6 @@ def __init__(
         self.build([(None, latent_size), (1,)])
 
     def update_res(self):
-
         self.current_resolution += 1
         self.current_width = 2**self.current_resolution
 
@@ -363,7 +361,6 @@ def add_resolution(self):
         self.build([(None, self.latent_size), (1,)])
 
     def call(self, inputs):
-
         latents, alpha = inputs
 
         x = self.make_Dbase(latents)

diff --git a/nobrainer/models/progressivegan.py b/nobrainer/models/progressivegan.py
@@ -159,7 +159,6 @@ def _make_generator_block(self, nf, kernel_size=4, name=""):
         return models.Sequential(block_layers, name=name)
 
     def generator_head(self, x, y, alpha):
-
         x = self.Upsampling()(x)
         x = self.HeadConv1(x)
 
@@ -171,7 +170,6 @@ def generator_head(self, x, y, alpha):
         return output
 
     def add_resolution(self):
-
         self.current_resolution += 1
         self.resolution_blocks.append(self.highest_resolution_block)
         self.highest_resolution_block = self._make_generator_block(
@@ -281,7 +279,6 @@ def _nf(self, stage):
         return min(int(self.fmap_base / (2.0 ** (stage))), self.fmap_max)
 
     def discriminator_base(self, x, y, alpha):
-
         x = self.AveragePooling()(x)
         x = self.BaseConv(x)
 
@@ -357,7 +354,6 @@ def add_resolution(self):
         self.build([images_shape, alpha_shape])
 
     def call(self, inputs):
-
         images, alpha = inputs
 
         # To bring to the right number of filters

diff --git a/nobrainer/prediction.py b/nobrainer/prediction.py
@@ -186,7 +186,6 @@ def predict_from_array(
         progbar = tf.keras.utils.Progbar(n_batches)
         progbar.update(0)
         for j in range(0, n_blocks, batch_size):
-
             this_x = features[j : j + batch_size]
             s = StreamingStats()
             for n in range(n_samples):

diff --git a/nobrainer/tests/prediction_test.py b/nobrainer/tests/prediction_test.py
@@ -12,7 +12,6 @@
 
 
 def test_predict(tmp_path):
-
     x = np.ones((4, 4, 4))
     img = nib.Nifti1Image(x, affine=np.eye(4))
     path = str(tmp_path / "features.nii.gz")

diff --git a/nobrainer/training.py b/nobrainer/training.py
@@ -206,7 +206,6 @@ def compile(self, optimizer, loss_fn):
         self.loss_fn = compile_utils.LossesContainer(loss_fn)
 
     def train_step(self, images):
-
         if isinstance(images, tuple):
             images = images[0]
 

diff --git a/nobrainer/validation.py b/nobrainer/validation.py
@@ -132,7 +132,6 @@ def validate_from_filepaths(
         None
     """
     for filepath in filepaths:
-
         outputs, dice = validate_from_filepath(
             filepath=filepath,
             predictor=predictor,