Modify absolute imports to relative ones

tests/tf_keras_vis/activation_maximization/activation_maximization_test.py

@@ -5,8 +5,8 @@
 from tensorflow.keras.models import load_model
 
 from tf_keras_vis.activation_maximization import ActivationMaximization
-from tf_keras_vis.utils.input_modifiers import Jitter, Rotate2D
-from tf_keras_vis.utils.regularizers import Norm, TotalVariation2D
+from tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D
+from tf_keras_vis.activation_maximization.regularizers import Norm, TotalVariation2D
 from tf_keras_vis.utils.test import (MockCallback, MockListOfScore, MockScore, MockTupleOfScore,
                                      does_not_raise, dummy_sample, mock_conv_model,
                                      mock_conv_model_with_float32_output, mock_multiple_io_model)

tf_keras_vis/__init__.py

@@ -2,7 +2,7 @@
 
 import tensorflow as tf
 
-from tf_keras_vis.utils import listify
+from .utils import listify
 
 
 class ModelVisualization(ABC):

tf_keras_vis/activation_maximization/__init__.py

@@ -7,11 +7,10 @@
 import tensorflow.keras.backend as K
 from packaging.version import parse as version
 
-from tf_keras_vis import ModelVisualization
-from tf_keras_vis.utils import (get_num_of_steps_allowed, is_mixed_precision, listify,
-                                lower_precision_dtype)
-from tf_keras_vis.utils.input_modifiers import Jitter, Rotate2D
-from tf_keras_vis.utils.regularizers import Norm, TotalVariation2D
+from .. import ModelVisualization
+from ..utils import get_num_of_steps_allowed, is_mixed_precision, listify
+from .input_modifiers import Jitter, Rotate2D
+from .regularizers import Norm, TotalVariation2D
 
 if version(tf.version.VERSION) >= version("2.4.0"):
     from tensorflow.keras.mixed_precision import LossScaleOptimizer
@@ -23,20 +22,21 @@ class ActivationMaximization(ModelVisualization):
     Todo:
         * Write examples
     """
-    def __call__(self,
-                 score,
-                 seed_input=None,
-                 input_range=(0, 255),
-                 input_modifiers=[Jitter(jitter=8), Rotate2D(degree=3)],
-                 regularizers=[TotalVariation2D(weight=1.0),
-                               Norm(weight=1.0, p=2)],
-                 steps=200,
-                 optimizer=None,  # When None, the default is tf.optimizers.RMSprop(1.0, 0.999)
-                 normalize_gradient=None,  # Disabled option.
-                 gradient_modifier=None,
-                 callbacks=None,
-                 training=False,
-                 unconnected_gradients=tf.UnconnectedGradients.NONE) -> Union[np.array, list]:
+    def __call__(
+            self,
+            score,
+            seed_input=None,
+            input_range=(0, 255),
+            input_modifiers=[Jitter(jitter=8), Rotate2D(degree=3)],
+            regularizers=[TotalVariation2D(weight=1.0),
+                          Norm(weight=1.0, p=2)],
+            steps=200,
+            optimizer=None,  # When None, the default is tf.optimizers.RMSprop(1.0, 0.999)
+            normalize_gradient=None,  # Disabled option.
+            gradient_modifier=None,
+            callbacks=None,
+            training=False,
+            unconnected_gradients=tf.UnconnectedGradients.NONE) -> Union[np.array, list]:
         """Generate the model inputs that maximize the output of the given `score` functions.
 
         Args:

tf_keras_vis/gradcam.py

@@ -8,8 +8,8 @@
 from scipy.ndimage.interpolation import zoom
 from tensorflow.python.keras.layers.convolutional import Conv
 
-from tf_keras_vis import ModelVisualization
-from tf_keras_vis.utils import find_layer, is_mixed_precision, standardize, zoom_factor
+from . import ModelVisualization
+from .utils import find_layer, is_mixed_precision, standardize, zoom_factor
 
 if version(tf.version.VERSION) >= version("2.4.0"):
     from tensorflow.keras.mixed_precision import LossScaleOptimizer

tf_keras_vis/gradcam_plus_plus.py

@@ -6,8 +6,8 @@
 from packaging.version import parse as version
 from scipy.ndimage.interpolation import zoom
 
-from tf_keras_vis.gradcam import Gradcam
-from tf_keras_vis.utils import is_mixed_precision, standardize, zoom_factor
+from .gradcam import Gradcam
+from .utils import is_mixed_precision, standardize, zoom_factor
 
 if version(tf.version.VERSION) >= version("2.4.0"):
     from tensorflow.keras.mixed_precision import LossScaleOptimizer

tf_keras_vis/saliency.py

@@ -5,8 +5,8 @@
 import tensorflow.keras.backend as K
 from packaging.version import parse as version
 
-from tf_keras_vis import ModelVisualization
-from tf_keras_vis.utils import get_num_of_steps_allowed, is_mixed_precision, listify, standardize
+from . import ModelVisualization
+from .utils import get_num_of_steps_allowed, is_mixed_precision, listify, standardize
 
 if version(tf.version.VERSION) >= version("2.4.0"):
     from tensorflow.keras.mixed_precision import LossScaleOptimizer

tf_keras_vis/scorecam.py

@@ -5,9 +5,8 @@
 import tensorflow.keras.backend as K
 from scipy.ndimage.interpolation import zoom
 
-from tf_keras_vis.gradcam import Gradcam
-from tf_keras_vis.utils import (get_num_of_steps_allowed, is_mixed_precision, listify, standardize,
-                                zoom_factor)
+from .gradcam import Gradcam
+from .utils import get_num_of_steps_allowed, is_mixed_precision, listify, standardize, zoom_factor
 
 
 class Scorecam(Gradcam):

tf_keras_vis/utils/callbacks.py

@@ -4,9 +4,7 @@
                'Please use `tf_keras_vis.activation_maximization.callbacks` instead.'),
               DeprecationWarning)
 
-from tf_keras_vis.activation_maximization.callbacks import \
-    Callback as OptimizerCallback  # noqa: F401 E402 E501
-from tf_keras_vis.activation_maximization.callbacks import GifGenerator2D  # noqa: F401 E402
-from tf_keras_vis.activation_maximization.callbacks import \
-    GifGenerator2D as GifGenerator  # noqa: F401 E402
-from tf_keras_vis.activation_maximization.callbacks import PrintLogger as Print  # noqa: F401 E402
+from ..activation_maximization.callbacks import Callback as OptimizerCallback  # noqa: F401 E402
+from ..activation_maximization.callbacks import GifGenerator2D  # noqa: F401 E402
+from ..activation_maximization.callbacks import GifGenerator2D as GifGenerator  # noqa: F401 E402
+from ..activation_maximization.callbacks import PrintLogger as Print  # noqa: F401 E402

tf_keras_vis/utils/losses.py

@@ -3,7 +3,7 @@
 warnings.warn(('`tf_keras_vis.utils.losses` module is deprecated. '
                'Please use `tf_keras_vis.utils.scores` instead.'), DeprecationWarning)
 
-from tf_keras_vis.utils.scores import BinaryScore as BinaryLoss  # noqa: F401 E402
-from tf_keras_vis.utils.scores import CategoricalScore as CategoricalLoss  # noqa: F401 E402
-from tf_keras_vis.utils.scores import InactiveScore as InactiveLoss  # noqa: F401 E402
-from tf_keras_vis.utils.scores import Score as Loss  # noqa: F401 E402
+from .scores import BinaryScore as BinaryLoss  # noqa: F401 E402
+from .scores import CategoricalScore as CategoricalLoss  # noqa: F401 E402
+from .scores import InactiveScore as InactiveLoss  # noqa: F401 E402
+from .scores import Score as Loss  # noqa: F401 E402

tf_keras_vis/utils/scores.py

@@ -2,7 +2,7 @@
 
 import tensorflow as tf
 
-from tf_keras_vis.utils import listify
+from . import listify
 
 
 class Score(ABC):

tf_keras_vis/utils/test.py

@@ -6,10 +6,10 @@
 from tensorflow.keras.layers import Conv2D, Dense, GlobalAveragePooling2D, Input
 from tensorflow.keras.models import Model
 
-from tf_keras_vis.activation_maximization.callbacks import Callback
-from tf_keras_vis.utils.input_modifiers import InputModifier
-from tf_keras_vis.utils.regularizers import Regularizer
-from tf_keras_vis.utils.scores import Score
+from ..activation_maximization.callbacks import Callback
+from ..activation_maximization.input_modifiers import InputModifier
+from ..activation_maximization.regularizers import Regularizer
+from .scores import Score
 
 
 def mock_dense_model():