Merge pull request #141 from NREL/bnb/dev

Bnb/dev

sup3r/models/abstract.py

@@ -909,9 +909,9 @@ class AbstractWindInterface(ABC):
  Abstract class to define the required training interface
  for Sup3r wind model subclasses
  """
-
+ # pylint: disable=E0211
  @staticmethod
- def set_model_params_wind(**kwargs):
+ def set_model_params(**kwargs):
  """Set parameters used for training the model
 
  Parameters
@@ -1002,8 +1002,8 @@ def _reshape_norm_topo(self, hi_res, hi_res_topo, norm_in=True):
 
  return hi_res_topo
 
- def generate_wind(self, low_res, norm_in=True, un_norm_out=True,
-  exogenous_data=None):
+ def generate(self, low_res, norm_in=True, un_norm_out=True,
+ exogenous_data=None):
  """Use the generator model to generate high res data from low res
  input. This is the public generate function.
 
@@ -1076,7 +1076,7 @@ def generate_wind(self, low_res, norm_in=True, un_norm_out=True,
  return hi_res
 
  @tf.function
- def _tf_generate_wind(self, low_res, hi_res_topo):
+ def _tf_generate(self, low_res, hi_res_topo):
  """Use the generator model to generate high res data from los res input
 
  Parameters
@@ -1115,8 +1115,8 @@ def _tf_generate_wind(self, low_res, hi_res_topo):
  return hi_res
 
  @tf.function()
- def get_single_grad_wind(self, low_res, hi_res_true, training_weights,
-  device_name=None, **calc_loss_kwargs):
+ def get_single_grad(self, low_res, hi_res_true, training_weights,
+ device_name=None, **calc_loss_kwargs):
  """Run gradient descent for one mini-batch of (low_res, hi_res_true),
  do not update weights, just return gradient details.
 

sup3r/models/wind.py

@@ -12,7 +12,7 @@
 logger = logging.getLogger(__name__)
 
 
-class WindGan(Sup3rGan, AbstractWindInterface):
+class WindGan(AbstractWindInterface, Sup3rGan):
  """Wind super resolution GAN with handling of low and high res topography
  inputs.
 
@@ -69,110 +69,8 @@ def set_model_params(self, **kwargs):
  Keyword arguments including 'training_features', 'output_features',
  'smoothed_features', 's_enhance', 't_enhance', 'smoothing'
  """
- kwargs = self.set_model_params_wind(**kwargs)
- super().set_model_params(**kwargs)
-
- def generate(self, low_res, norm_in=True, un_norm_out=True,
- exogenous_data=None):
- """Use the generator model to generate high res data from low res
- input. This is the public generate function.
-
- Parameters
- ----------
- low_res : np.ndarray
- Low-resolution input data, usually a 4D or 5D array of shape:
- (n_obs, spatial_1, spatial_2, n_features)
- (n_obs, spatial_1, spatial_2, n_temporal, n_features)
- norm_in : bool
- Flag to normalize low_res input data if the self._means,
- self._stdevs attributes are available. The generator should always
- received normalized data with mean=0 stdev=1. This also normalizes
- hi_res_topo.
- un_norm_out : bool
- Flag to un-normalize synthetically generated output data to physical
- units
- exogenous_data : ndarray | list | None
- Exogenous data for topography inputs. The first entry in this list
- (or only entry) is a low-resolution topography array that can be
- concatenated to the low_res input array. The second entry is
- high-resolution topography (either 2D or 4D/5D depending on if
- spatial or spatiotemporal super res).
-
- Returns
- -------
- hi_res : ndarray
- Synthetically generated high-resolution data, usually a 4D or 5D
- array with shape:
- (n_obs, spatial_1, spatial_2, n_features)
- (n_obs, spatial_1, spatial_2, n_temporal, n_features)
- """
- return self.generate_wind(low_res, norm_in,
- un_norm_out, exogenous_data)
-
- @tf.function
- def _tf_generate(self, low_res, hi_res_topo):
- """Use the generator model to generate high res data from los res input
-
- Parameters
- ----------
- low_res : np.ndarray
- Real low-resolution data. The generator should always
- received normalized data with mean=0 stdev=1.
- hi_res_topo : np.ndarray
- This should be a 4D array for spatial enhancement model or 5D array
- for a spatiotemporal enhancement model (obs, spatial_1, spatial_2,
- (temporal), features) corresponding to the high-resolution
- spatial_1 and spatial_2. This data will be input to the custom
- phygnn Sup3rAdder or Sup3rConcat layer if found in the generative
- network. This differs from the exogenous_data input in that
- exogenous_data always matches the low-res input.
-
- Returns
- -------
- hi_res : tf.Tensor
- Synthetically generated high-resolution data
- """
- return self._tf_generate_wind(low_res, hi_res_topo)
-
- @tf.function()
- def get_single_grad(self, low_res, hi_res_true, training_weights,
- device_name=None, **calc_loss_kwargs):
- """Run gradient descent for one mini-batch of (low_res, hi_res_true),
- do not update weights, just return gradient details.
-
- Parameters
- ----------
- low_res : np.ndarray
- Real low-resolution data in a 4D or 5D array:
- (n_observations, spatial_1, spatial_2, features)
- (n_observations, spatial_1, spatial_2, temporal, features)
- hi_res_true : np.ndarray
- Real high-resolution data in a 4D or 5D array:
- (n_observations, spatial_1, spatial_2, features)
- (n_observations, spatial_1, spatial_2, temporal, features)
- training_weights : list
- A list of layer weights that are to-be-trained based on the
- current loss weight values.
- device_name : None | str
- Optional tensorflow device name for GPU placement. Note that if a
- GPU is available, variables will be placed on that GPU even if
- device_name=None.
- calc_loss_kwargs : dict
- Kwargs to pass to the self.calc_loss() method
-
- Returns
- -------
- grad : list
- a list or nested structure of Tensors (or IndexedSlices, or None,
- or CompositeTensor) representing the gradients for the
- training_weights
- loss_details : dict
- Namespace of the breakdown of loss components
- """
- return self.get_single_grad_wind(low_res, hi_res_true,
- training_weights,
- device_name=device_name,
- **calc_loss_kwargs)
+ AbstractWindInterface.set_model_params(**kwargs)
+ Sup3rGan.set_model_params(self, **kwargs)
 
  @tf.function
  def calc_loss(self, hi_res_true, hi_res_gen, **kwargs):

sup3r/models/wind_conditional_moments.py

@@ -11,7 +11,7 @@
 logger = logging.getLogger(__name__)
 
 
-class WindCondMom(Sup3rCondMom, AbstractWindInterface):
+class WindCondMom(AbstractWindInterface, Sup3rCondMom):
  """Wind conditional moment estimator with handling of low and
  high res topography inputs.
 
@@ -33,110 +33,8 @@ def set_model_params(self, **kwargs):
  Keyword arguments including 'training_features', 'output_features',
  'smoothed_features', 's_enhance', 't_enhance', 'smoothing'
  """
- kwargs = self.set_model_params_wind(**kwargs)
- super().set_model_params(**kwargs)
-
- def generate(self, low_res, norm_in=True, un_norm_out=True,
- exogenous_data=None):
- """Use the generator model to generate high res data from low res
- input. This is the public generate function.
-
- Parameters
- ----------
- low_res : np.ndarray
- Low-resolution input data, usually a 4D or 5D array of shape:
- (n_obs, spatial_1, spatial_2, n_features)
- (n_obs, spatial_1, spatial_2, n_temporal, n_features)
- norm_in : bool
- Flag to normalize low_res input data if the self._means,
- self._stdevs attributes are available. The generator should always
- received normalized data with mean=0 stdev=1. This also normalizes
- hi_res_topo.
- un_norm_out : bool
- Flag to un-normalize synthetically generated output data to physical
- units
- exogenous_data : ndarray | list | None
- Exogenous data for topography inputs. The first entry in this list
- (or only entry) is a low-resolution topography array that can be
- concatenated to the low_res input array. The second entry is
- high-resolution topography (either 2D or 4D/5D depending on if
- spatial or spatiotemporal super res).
-
- Returns
- -------
- hi_res : ndarray
- Synthetically generated high-resolution data, usually a 4D or 5D
- array with shape:
- (n_obs, spatial_1, spatial_2, n_features)
- (n_obs, spatial_1, spatial_2, n_temporal, n_features)
- """
- return self.generate_wind(low_res, norm_in,
- un_norm_out, exogenous_data)
-
- @tf.function
- def _tf_generate(self, low_res, hi_res_topo):
- """Use the generator model to generate high res data from los res input
-
- Parameters
- ----------
- low_res : np.ndarray
- Real low-resolution data. The generator should always
- received normalized data with mean=0 stdev=1.
- hi_res_topo : np.ndarray
- This should be a 4D array for spatial enhancement model or 5D array
- for a spatiotemporal enhancement model (obs, spatial_1, spatial_2,
- (temporal), features) corresponding to the high-resolution
- spatial_1 and spatial_2. This data will be input to the custom
- phygnn Sup3rAdder or Sup3rConcat layer if found in the generative
- network. This differs from the exogenous_data input in that
- exogenous_data always matches the low-res input.
-
- Returns
- -------
- hi_res : tf.Tensor
- Synthetically generated high-resolution data
- """
- return self._tf_generate_wind(low_res, hi_res_topo)
-
- @tf.function()
- def get_single_grad(self, low_res, hi_res_true, training_weights,
- device_name=None, **calc_loss_kwargs):
- """Run gradient descent for one mini-batch of (low_res, hi_res_true),
- do not update weights, just return gradient details.
-
- Parameters
- ----------
- low_res : np.ndarray
- Real low-resolution data in a 4D or 5D array:
- (n_observations, spatial_1, spatial_2, features)
- (n_observations, spatial_1, spatial_2, temporal, features)
- hi_res_true : np.ndarray
- Real high-resolution data in a 4D or 5D array:
- (n_observations, spatial_1, spatial_2, features)
- (n_observations, spatial_1, spatial_2, temporal, features)
- training_weights : list
- A list of layer weights that are to-be-trained based on the
- current loss weight values.
- device_name : None | str
- Optional tensorflow device name for GPU placement. Note that if a
- GPU is available, variables will be placed on that GPU even if
- device_name=None.
- calc_loss_kwargs : dict
- Kwargs to pass to the self.calc_loss() method
-
- Returns
- -------
- grad : list
- a list or nested structure of Tensors (or IndexedSlices, or None,
- or CompositeTensor) representing the gradients for the
- training_weights
- loss_details : dict
- Namespace of the breakdown of loss components
- """
- return self.get_single_grad_wind(low_res, hi_res_true,
- training_weights,
- device_name=device_name,
- **calc_loss_kwargs)
+ AbstractWindInterface.set_model_params(**kwargs)
+ Sup3rCondMom.set_model_params(self, **kwargs)
 
  @tf.function
  def calc_loss(self, hi_res_true, hi_res_gen, mask, **kwargs):