update augmentation preimitives

autovideo/augmentation/arithmetic/AddElementwise_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,14 +27,14 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    value = hyperparams.Set[float](
-        default=(-40, 40),
+    value = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=(-20, 20),
         description='Value to add to all pixels.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
 
-    per_channel = hyperparams.Constant[bool](
-        default=True,
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
         description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )

autovideo/augmentation/arithmetic/Add_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,14 +27,14 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    value = hyperparams.Set[float](
-        default=(-40, 40),
+    value = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=(-20, 20),
         description='Value to add to all pixels.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
 
-    per_channel = hyperparams.Constant[bool](
-        default=True,
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
         description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )

autovideo/augmentation/arithmetic/AdditiveGaussianNoise_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,14 +27,20 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    scale = hyperparams.Hyperparameter[tuple](
+    loc = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=0,
+        description='Mean of the normal distribution from which the noise is sampled.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    scale = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
         default=(0, 15),
         description='Standard deviation of the normal distribution that generates the noise. Must be >=0. If 0 then loc will simply be added to all pixels.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
 
-    per_channel = hyperparams.Hyperparameter[bool](
-        default=True,
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
         description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
@@ -59,6 +65,7 @@ def _get_function(self):
         """
 
         scale = self.hyperparams["scale"]
+        loc = self.hyperparams['loc']
         per_channel = self.hyperparams["per_channel"]
         seed = self.hyperparams["seed"]
-        return iaa.AdditiveGaussianNoise(scale=scale, per_channel=per_channel, seed=seed)
+        return iaa.AdditiveGaussianNoise(scale=scale, loc=loc,per_channel=per_channel, seed=seed)

autovideo/augmentation/arithmetic/AdditiveLaplaceNoise_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,14 +27,20 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    scale = hyperparams.Hyperparameter[tuple](
+    loc = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=0,
+        description='Mean of the normal distribution from which the noise is sampled.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    scale = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
         default=(0, 15),
         description='Standard deviation of the normal distribution that generates the noise. Must be >=0. If 0 then loc will simply be added to all pixels.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
 
-    per_channel = hyperparams.Hyperparameter[bool](
-        default=True,
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
         description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
@@ -60,5 +66,6 @@ def _get_function(self):
 
         scale = self.hyperparams["scale"]
         per_channel = self.hyperparams["per_channel"]
+        loc = self.hyperparams['loc']
         seed = self.hyperparams["seed"]
-        return iaa.AdditiveLaplaceNoise(scale=scale, per_channel=per_channel, seed=seed)
+        return iaa.AdditiveLaplaceNoise(scale=scale, loc=loc,per_channel=per_channel, seed=seed)

autovideo/augmentation/arithmetic/AdditivePoissonNoise_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,14 +27,14 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    scale = hyperparams.Set[float](
-        default=(0.0, 15.0),
-        description='Standard deviation of the normal distribution that generates the noise. Must be >=0. If 0 then loc will simply be added to all pixels.',
+    lam = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=(0.0,15.0),
+        description='Lambda parameter of the poisson distribution.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
 
-    per_channel = hyperparams.Constant[bool](
-        default=True,
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
         description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
@@ -58,7 +58,7 @@ def _get_function(self):
         set up function and parameter of functions
         """
 
-        scale = self.hyperparams["scale"]
+        lam = self.hyperparams["lam"]
         per_channel = self.hyperparams["per_channel"]
         seed = self.hyperparams["seed"]
-        return iaa.AdditivePoissonNoise(scale=scale, per_channel=per_channel, seed=seed)
+        return iaa.AdditivePoissonNoise(lam=lam, per_channel=per_channel, seed=seed)

autovideo/augmentation/arithmetic/CoarseDropout_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,21 +27,36 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    size_percent = hyperparams.Constant[float](
+    p = hyperparams.Hyperparameter[typing.Union[float,tuple]](
+        default=(0.02,0.1),
+        description=' The probability of any pixel being dropped.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+    size_px = hyperparams.Hyperparameter[typing.Union[int,tuple,None]](
+        default=None,
+        description=' The size of the lower resolution image from which to sample the dropout mask in absolute pixel dimensions..',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+    size_percent = hyperparams.Hyperparameter[typing.Union[float,tuple,None]](
         default=0.5,
         description=' The size of the lower resolution image from which to sample the replacement mask in percent of the input image. ',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
+        description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+    min_size = hyperparams.Hyperparameter[int](
+        default=3,
+        description='Minimum height and width of the low resolution mask.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
     seed = hyperparams.Constant[int](
         default=0,
         description='Minimum workers to extract frames simultaneously',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
-    p = hyperparams.Constant[float](
-        default=0.02,
-        description='The probability of an image to be inverted.',
-        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
-    )
 
 
 class CoarseDropoutPrimitive(AugmentationPrimitiveBase[Inputs, Hyperparams]):
@@ -57,5 +72,8 @@ def _get_function(self):
         """
         seed = self.hyperparams["seed"]
         p = self.hyperparams["p"]
+        size_px = self.hyperparams['size_px']
         size_percent = self.hyperparams["size_percent"]
-        return iaa.CoarseDropout(p=p,size_percent=size_percent,seed=seed)
+        per_channel = self.hyperparams['per_channel']
+        min_size = self.hyperparams['min_size']
+        return iaa.CoarseDropout(p=p,size_percent=size_percent,size_px=size_px,per_channel=per_channel,min_size=min_size,seed=seed)

autovideo/augmentation/arithmetic/CoarsePepper_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,21 +27,38 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    size_percent = hyperparams.Set[float](
-        default=(0.01,0.1),
+    p = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=(0.02,0.1),
+        description='Probability of changing a pixel to pepper noise.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    size_px = hyperparams.Hyperparameter[typing.Union[int,tuple,None]](
+        default=None,
+        description=' The size of the lower resolution image from which to sample the dropout mask in absolute pixel dimensions..',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    size_percent = hyperparams.Hyperparameter[typing.Union[float,tuple,None]](
+        default=0.5,
         description=' The size of the lower resolution image from which to sample the replacement mask in percent of the input image. ',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
+        description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+    min_size = hyperparams.Hyperparameter[int](
+        default=3,
+        description='Minimum height and width of the low resolution mask.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
     seed = hyperparams.Constant[int](
         default=0,
         description='Minimum workers to extract frames simultaneously',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
-    p = hyperparams.Constant[float](
-        default=0.05,
-        description='The probability of an image to be inverted.',
-        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
-    )
 
 
 class CoarsePepperPrimitive(AugmentationPrimitiveBase[Inputs, Hyperparams]):
@@ -57,5 +74,8 @@ def _get_function(self):
         """
         seed = self.hyperparams["seed"]
         p = self.hyperparams["p"]
+        size_px = self.hyperparams['size_px']
         size_percent = self.hyperparams["size_percent"]
-        return iaa.CoarsePepper(p=p,size_percent=size_percent,seed=seed)
+        per_channel = self.hyperparams['per_channel']
+        min_size = self.hyperparams['min_size']
+        return iaa.CoarsePepper(p=p,size_percent=size_percent,size_px=size_px,per_channel=per_channel,min_size=min_size,seed=seed)

autovideo/augmentation/arithmetic/CoarseSaltAndPepper_primitive.py

@@ -18,7 +18,7 @@
 from d3m import container
 from d3m.metadata import hyperparams
 import imgaug.augmenters as iaa
-
+import typing
 from autovideo.utils import construct_primitive_metadata
 from autovideo.base.augmentation_base import AugmentationPrimitiveBase
 
@@ -27,21 +27,40 @@
 Inputs = container.DataFrame
 
 class Hyperparams(hyperparams.Hyperparams):
-    size_percent = hyperparams.Set[float](
-        default=(0.01,0.1),
+    p = hyperparams.Hyperparameter[typing.Union[float,tuple,list]](
+        default=(0.02,0.1),
+        description='Probability of changing a pixel to salt/pepper noise.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    size_px = hyperparams.Hyperparameter[typing.Union[int,tuple,None]](
+        default=None,
+        description=' The size of the lower resolution image from which to sample the dropout mask in absolute pixel dimensions..',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
+    size_percent = hyperparams.Hyperparameter[typing.Union[float,tuple,None]](
+        default=0.5,
         description=' The size of the lower resolution image from which to sample the replacement mask in percent of the input image. ',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
+    per_channel = hyperparams.Hyperparameter[typing.Union[bool,float]](
+        default=False,
+        description='Whether to use (imagewise) the same sample(s) for all channels (False) or to sample value(s) for each channel (True). Setting this to True will therefore lead to different transformations per image and channel, otherwise only per image.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+    min_size = hyperparams.Hyperparameter[int](
+        default=3,
+        description='Minimum height and width of the low resolution mask.',
+        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
+    )
+
     seed = hyperparams.Constant[int](
         default=0,
         description='Minimum workers to extract frames simultaneously',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
     )
-    p = hyperparams.Constant[float](
-        default=0.05,
-        description='The probability of an image to be inverted.',
-        semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
-    )
+
 
 
 class CoarseSaltAndPepperPrimitive(AugmentationPrimitiveBase[Inputs, Hyperparams]):
@@ -57,5 +76,8 @@ def _get_function(self):
         """
         seed = self.hyperparams["seed"]
         p = self.hyperparams["p"]
+        size_px = self.hyperparams['size_px']
         size_percent = self.hyperparams["size_percent"]
-        return iaa.CoarseSaltAndPepper(p=p,size_percent=size_percent,seed=seed)
+        per_channel = self.hyperparams['per_channel']
+        min_size = self.hyperparams['min_size']
+        return iaa.CoarseSaltAndPepper(p=p,size_percent=size_percent,size_px=size_px,per_channel=per_channel,min_size=min_size,seed=seed)