run formatter and linter

.github/workflows/publish.yml

@@ -26,4 +26,4 @@ jobs:
         TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
       run: |
         python -m build
-        python -m twine upload -r testpypi dist/*
+        python -m twine upload dist/*

DeepSaki/__init__.py

@@ -6,9 +6,10 @@
 from DeepSaki import losses
 from DeepSaki import models
 from DeepSaki import optimizers
+from DeepSaki import types
 from DeepSaki import utils
 
-__version__ = "1.0.2"
+__version__ = "1.0.0"
 
 __author__ = "Sascha Kirch"
 
@@ -21,5 +22,6 @@
     "losses",
     "models",
     "optimizers",
+    "types",
     "utils",
 ]

DeepSaki/activations/__init__.py

@@ -1 +1,5 @@
 from DeepSaki.activations.complex_valued_activations import ComplexActivation
+
+__all__ = [
+    "ComplexActivation",
+]

DeepSaki/activations/complex_valued_activations.py

@@ -1,6 +1,5 @@
 from typing import Any
 from typing import Dict
-from typing import Union
 
 import tensorflow as tf
 

DeepSaki/augmentations/__init__.py

@@ -1,2 +1,7 @@
 from DeepSaki.augmentations.grid_cutting import cut_mix
 from DeepSaki.augmentations.grid_cutting import cut_out
+
+__all__ = [
+    "cut_mix",
+    "cut_out",
+]

DeepSaki/constraints/__init__.py

@@ -1 +1,5 @@
 from DeepSaki.constraints.constraints import NonNegative
+
+__all__ = [
+    "NonNegative",
+]

DeepSaki/initializers/__init__.py

@@ -1,6 +1,14 @@
 # he_alpha.py
+from DeepSaki.initializers.he_alpha import HeAlpha
 from DeepSaki.initializers.he_alpha import HeAlphaUniform
 from DeepSaki.initializers.he_alpha import HeAlphaNormal
 
 # initializer_helper.py
 from DeepSaki.initializers.complex_initializer import ComplexInitializer
+
+__all__ = [
+    "HeAlpha",
+    "HeAlphaUniform",
+    "HeAlphaNormal",
+    "ComplexInitializer",
+]

DeepSaki/layers/__init__.py

@@ -7,8 +7,8 @@
 from DeepSaki.layers.fourier_layer import FourierFilter2D
 from DeepSaki.layers.fourier_layer import FFT2D
 from DeepSaki.layers.fourier_layer import iFFT2D
-from DeepSaki.layers.fourier_layer import FFT2D
 from DeepSaki.layers.fourier_layer import FFT3D
+from DeepSaki.layers.fourier_layer import iFFT3D
 from DeepSaki.layers.fourier_layer import FourierPooling2D
 from DeepSaki.layers.fourier_layer import rFFT2DFilter
 
@@ -38,3 +38,34 @@
 from DeepSaki.layers.layer_helper import get_initializer
 from DeepSaki.layers.layer_helper import pad_func
 from DeepSaki.layers.layer_helper import dropout_func
+
+__all__ = [
+    "GlobalSumPooling2D",
+    "LearnedPooling",
+    "FourierFilter2D",
+    "FFT2D",
+    "iFFT2D",
+    "FFT3D",
+    "iFFT3D",
+    "FourierConvolution2D",
+    "FourierPooling2D",
+    "rFFT2DFilter",
+    "ReflectionPadding2D",
+    "Conv2DSplitted",
+    "Conv2DBlock",
+    "DenseBlock",
+    "DownSampleBlock",
+    "ResBlockDown",
+    "ResBlockUp",
+    "UpSampleBlock",
+    "ScaleLayer",
+    "ScalarGatedSelfAttention",
+    "Encoder",
+    "ResidualBlock",
+    "Bottleneck",
+    "Decoder",
+    "get_initializer",
+    "plot_layer",
+    "pad_func",
+    "dropout_func",
+]

DeepSaki/layers/fourier_layer.py

@@ -69,7 +69,8 @@ def _elementwise_product(self, a: tf.Tensor, b: tf.Tensor) -> tf.Tensor:
         return tf.math.reduce_sum(c, axis=-1)
 
     def _get_multiplication_function(
-        self, multiplication_type: MultiplicationType
+        self,
+        multiplication_type: MultiplicationType,
     ) -> Callable[[tf.Tensor, tf.Tensor], tf.Tensor]:
         """Returns the corresponding elementwise multiplication function for a given type."""
         valid_multiplication_types = {

DeepSaki/losses/__init__.py

@@ -1,2 +1,9 @@
+from DeepSaki.losses.image_based_losses import ImageBasedLoss
 from DeepSaki.losses.image_based_losses import PixelDistanceLoss
 from DeepSaki.losses.image_based_losses import StructuralSimilarityLoss
+
+__all__ = [
+    "ImageBasedLoss",
+    "PixelDistanceLoss",
+    "StructuralSimilarityLoss",
+]

DeepSaki/models/__init__.py

@@ -4,3 +4,11 @@
 
 from DeepSaki.models.autoencoders import UNet
 from DeepSaki.models.autoencoders import ResNet
+
+__all__ = [
+    "LayoutContentDiscriminator",
+    "PatchDiscriminator",
+    "UNetDiscriminator",
+    "UNet",
+    "ResNet",
+]

DeepSaki/models/discriminators.py

@@ -125,7 +125,7 @@ def __init__(self,
         #To enable mixed precission support for matplotlib and distributed training and to increase training stability
         self.linear_dtype = tf.keras.layers.Activation("linear", dtype = tf.float32)
 
-    def build(self, input_shape):
+    def build(self, input_shape:tf.TensorShape)->None:
         _, height, width,_ = input_shape
         if height < 256 or width < 256:
             raise ValueError(f"Input requires a height and width of minimum 256, but got {height=} and {width=}")

DeepSaki/optimizers/swats.py

@@ -2,8 +2,6 @@
 from __future__ import division
 from __future__ import print_function
 
-from enum import Enum
-from enum import auto
 from typing import Any
 from typing import Dict
 from typing import List
@@ -21,8 +19,6 @@
 
 from DeepSaki.types.optimizers_enums import CurrentOptimizer
 
-
-
 class SwatsAdam(optimizer_v2.OptimizerV2):
     """Initializer that can switch from ADAM to SGD and vice versa.
 

DeepSaki/types/__init__.py

@@ -10,3 +10,19 @@
 # Losses Enums
 from DeepSaki.types.losses_enums import LossType
 from DeepSaki.types.losses_enums import LossCalcType
+
+# Optimizers Enums
+from DeepSaki.types.optimizers_enums import CurrentOptimizer
+
+__all__ = [
+    "PaddingType",
+    "InitializerFunc",
+    "MultiplicationType",
+    "FrequencyFilter",
+    "UpSampleType",
+    "LinearLayerType",
+    "DownSampleType",
+    "LossType",
+    "LossCalcType",
+    "CurrentOptimizer",
+]

DeepSaki/types/optimizers_enums.py

@@ -9,6 +9,7 @@ class CurrentOptimizer(Enum):
         ADAM: Indicates to switch to the ADAM optimizer.
         NADAM: Indicates to switch to the NADAM optimizer.
     """
+
     SGD = auto()
     ADAM = auto()
     NADAM = auto()

DeepSaki/utils/__init__.py

@@ -1,3 +1,9 @@
 from DeepSaki.utils.environment import detect_accelerator
 from DeepSaki.utils.environment import enable_xla_acceleration
 from DeepSaki.utils.environment import enable_mixed_precision
+
+__all__ = [
+    "detect_accelerator",
+    "enable_xla_acceleration",
+    "enable_mixed_precision",
+]

DeepSaki/utils/environment.py

@@ -83,4 +83,4 @@ def enable_mixed_precision() -> None:
     policy_config = "mixed_bfloat16" if tpu else "mixed_float16"
     policy = tf.keras.mixed_precision.Policy(policy_config)
     tf.keras.mixed_precision.set_global_policy(policy)
-    logging.info("Mixed precision enabled to {}".format(policy_config))
+    logging.info(f"Mixed precision enabled to {policy_config}")