Bnb/train only features hot fix

sup3r/models/abstract.py

@@ -327,7 +327,7 @@ def _combine_loss_input(self, high_res_true, high_res_gen):
         """
         if high_res_true.shape[-1] > high_res_gen.shape[-1]:
             for feature in self.exogenous_features:
-                f_idx = self.training_features.index(feature)
+                f_idx = self.hr_features.index(feature)
                 exo_data = high_res_true[..., f_idx: f_idx + 1]
                 high_res_gen = tf.concat((high_res_gen, exo_data), axis=-1)
         return high_res_gen
@@ -360,6 +360,23 @@ def training_features(self):
         trained on."""
         return self.meta.get('training_features', None)
 
+    @property
+    def train_only_features(self):
+        """Get the list of feature names used only for training (expected as
+        input but not included in output)."""
+        return self.meta.get('train_only_features', None)
+
+    @property
+    def hr_features(self):
+        """Get the list of features stored in batch.high_res. This is the same
+        as training_features but without train_only_features. This is used to
+        select the correct high res exogenous data."""
+        hr_features = self.training_features
+        if self.train_only_features is not None:
+            hr_features = [f for f in self.training_features
+                           if f not in self.train_only_features]
+        return hr_features
+
     @property
     def output_features(self):
         """Get the list of output feature names that the generative model
@@ -445,7 +462,8 @@ def set_model_params(self, **kwargs):
         kwargs = self._check_exo_features(**kwargs)
 
         keys = ('input_resolution', 'training_features', 'output_features',
-                'smoothed_features', 's_enhance', 't_enhance', 'smoothing')
+                'train_only_features', 'smoothed_features', 's_enhance',
+                't_enhance', 'smoothing')
         keys = [k for k in keys if k in kwargs]
 
         for var in keys:
@@ -660,7 +678,6 @@ def norm_input(self, low_res):
                 warn(msg)
             else:
                 stdevs = self._stdevs
-
             low_res = (low_res.copy() - self._means) / stdevs
 
         return low_res
@@ -819,7 +836,7 @@ def get_high_res_exo_input(self, high_res):
         """
         exo_data = {}
         for feature in self.exogenous_features:
-            f_idx = self.training_features.index(feature)
+            f_idx = self.hr_features.index(feature)
             exo_fdata = high_res[..., f_idx: f_idx + 1]
             exo_data[feature] = exo_fdata
         return exo_data

sup3r/models/base.py

@@ -855,6 +855,7 @@ def train(self,
             t_enhance=batch_handler.t_enhance,
             smoothing=batch_handler.smoothing,
             training_features=batch_handler.training_features,
+            train_only_features=batch_handler.train_only_features,
             output_features=batch_handler.output_features,
             smoothed_features=batch_handler.smoothed_features)
 

sup3r/models/conditional_moments.py

@@ -395,6 +395,7 @@ def train(self, batch_handler,
             s_enhance=batch_handler.s_enhance,
             t_enhance=batch_handler.t_enhance,
             smoothing=batch_handler.smoothing,
+            train_only_features=batch_handler.train_only_features,
             training_features=batch_handler.training_features,
             output_features=batch_handler.output_features,
             smoothed_features=batch_handler.smoothed_features)

sup3r/preprocessing/batch_handling.py

@@ -13,11 +13,19 @@
 from scipy.ndimage.filters import gaussian_filter
 
 from sup3r.preprocessing.data_handling.h5_data_handling import (
-    DataHandlerDCforH5, )
+    DataHandlerDCforH5,
+)
 from sup3r.utilities.utilities import (
-    estimate_max_workers, nn_fill_array, nsrdb_reduce_daily_data, smooth_data,
-    spatial_coarsening, temporal_coarsening, uniform_box_sampler,
-    uniform_time_sampler, weighted_box_sampler, weighted_time_sampler,
+    estimate_max_workers,
+    nn_fill_array,
+    nsrdb_reduce_daily_data,
+    smooth_data,
+    spatial_coarsening,
+    temporal_coarsening,
+    uniform_box_sampler,
+    uniform_time_sampler,
+    weighted_box_sampler,
+    weighted_time_sampler,
 )
 
 np.random.seed(42)
@@ -577,6 +585,12 @@ def training_features(self):
         data handlers"""
         return self.data_handlers[0].features
 
+    @property
+    def train_only_features(self):
+        """Get the ordered list of feature names used only for training which
+        will not be stored in batch.high_res"""
+        return self.data_handlers[0].train_only_features
+
     @property
     def output_features(self):
         """Get the ordered list of feature names held in this object's

sup3r/preprocessing/data_handling/base.py

@@ -1656,7 +1656,7 @@ def _check_grid_extent(cls, target, grid_shape, lat_lon):
         min_lon = np.min(lat_lon[..., 1])
         max_lat = np.max(lat_lon[..., 0])
         max_lon = np.max(lat_lon[..., 1])
-        logger.debug('Calculating raster index from WRF file '
+        logger.debug('Calculating raster index from NETCDF file '
                      f'for shape {grid_shape} and target {target}')
         logger.debug(f'lat/lon (min, max): {min_lat}/{min_lon}, '
                      f'{max_lat}/{max_lon}')

sup3r/preprocessing/data_handling/dual_data_handling.py

@@ -181,6 +181,11 @@ def output_features(self):
         GAN"""
         return self.hr_dh.output_features
 
+    @property
+    def train_only_features(self):
+        """Features to use for training only and not output"""
+        return self.lr_dh.train_only_features
+
     def _shape_check(self):
         """Check if hr_handler.shape is divisible by s_enhance. If not take
         the largest shape that can be."""

sup3r/preprocessing/data_handling/nc_data_handling.py

@@ -19,13 +19,34 @@
 
 from sup3r.preprocessing.data_handling.base import DataHandler, DataHandlerDC
 from sup3r.preprocessing.feature_handling import (
-    BVFreqMon, BVFreqSquaredNC, ClearSkyRatioCC, Feature, InverseMonNC,
-    LatLonNC, PotentialTempNC, PressureNC, Rews, Shear, Tas, TasMax, TasMin,
-    TempNC, TempNCforCC, UWind, UWindPowerLaw, VWind, VWindPowerLaw,
-    WinddirectionNC, WindspeedNC)
+    BVFreqMon,
+    BVFreqSquaredNC,
+    ClearSkyRatioCC,
+    Feature,
+    InverseMonNC,
+    LatLonNC,
+    PotentialTempNC,
+    PressureNC,
+    Rews,
+    Shear,
+    Tas,
+    TasMax,
+    TasMin,
+    TempNC,
+    TempNCforCC,
+    UWind,
+    UWindPowerLaw,
+    VWind,
+    VWindPowerLaw,
+    WinddirectionNC,
+    WindspeedNC,
+)
 from sup3r.utilities.interpolation import Interpolator
-from sup3r.utilities.utilities import (estimate_max_workers, get_time_dim_name,
-                                       np_to_pd_times)
+from sup3r.utilities.utilities import (
+    estimate_max_workers,
+    get_time_dim_name,
+    np_to_pd_times,
+)
 
 np.random.seed(42)
 
@@ -385,7 +406,7 @@ def _check_grid_extent(cls, target, grid_shape, lat_lon):
         min_lon = np.min(lat_lon[..., 1])
         max_lat = np.max(lat_lon[..., 0])
         max_lon = np.max(lat_lon[..., 1])
-        logger.debug('Calculating raster index from WRF file '
+        logger.debug('Calculating raster index from NETCDF file '
                      f'for shape {grid_shape} and target {target}')
         logger.debug(f'lat/lon (min, max): {min_lat}/{min_lon}, '
                      f'{max_lat}/{max_lon}')

tests/training/test_train_gan_exo.py

@@ -28,13 +28,107 @@
 TARGET_S = (39.01, -105.13)
 
 INPUT_FILE_W = os.path.join(TEST_DATA_DIR, 'test_wtk_co_2012.h5')
-FEATURES_W = ['U_100m', 'V_100m', 'temperature_100m', 'topography']
+FEATURES_W = ['temperature_100m', 'U_100m', 'V_100m', 'topography']
 TARGET_W = (39.01, -105.15)
 
 FP_WTK = os.path.join(TEST_DATA_DIR, 'test_wtk_co_2012.h5')
 TARGET_COORD = (39.01, -105.15)
 
 
+@pytest.mark.parametrize('custom_layer', ['Sup3rAdder', 'Sup3rConcat'])
+def test_wind_hi_res_topo_with_train_only(custom_layer, log=False):
+    """Test a special wind cc model with the custom Sup3rAdder or Sup3rConcat
+    layer that adds/concatenates hi-res topography in the middle of the
+    network. This also includes a train only feature"""
+
+    handler = DataHandlerH5WindCC(INPUT_FILE_W,
+                                  FEATURES_W,
+                                  target=TARGET_W, shape=SHAPE,
+                                  temporal_slice=slice(None, None, 2),
+                                  time_roll=-7,
+                                  val_split=0.1,
+                                  sample_shape=(20, 20),
+                                  worker_kwargs=dict(max_workers=1),
+                                  train_only_features=['temperature_100m'])
+    batcher = SpatialBatchHandlerCC([handler], batch_size=2, n_batches=2,
+                                    s_enhance=2)
+
+    if log:
+        init_logger('sup3r', log_level='DEBUG')
+
+    gen_model = [{"class": "FlexiblePadding",
+                  "paddings": [[0, 0], [3, 3], [3, 3], [0, 0]],
+                  "mode": "REFLECT"},
+                 {"class": "Conv2DTranspose", "filters": 64, "kernel_size": 3,
+                  "strides": 1, "activation": "relu"},
+                 {"class": "Cropping2D", "cropping": 4},
+
+                 {"class": "FlexiblePadding",
+                  "paddings": [[0, 0], [3, 3], [3, 3], [0, 0]],
+                  "mode": "REFLECT"},
+                 {"class": "Conv2DTranspose", "filters": 64,
+                  "kernel_size": 3, "strides": 1, "activation": "relu"},
+                 {"class": "Cropping2D", "cropping": 4},
+
+                 {"class": "FlexiblePadding",
+                  "paddings": [[0, 0], [3, 3], [3, 3], [0, 0]],
+                  "mode": "REFLECT"},
+                 {"class": "Conv2DTranspose", "filters": 64,
+                  "kernel_size": 3, "strides": 1, "activation": "relu"},
+                 {"class": "Cropping2D", "cropping": 4},
+                 {"class": "SpatialExpansion", "spatial_mult": 2},
+                 {"class": "Activation", "activation": "relu"},
+
+                 {"class": custom_layer, "name": "topography"},
+
+                 {"class": "FlexiblePadding",
+                  "paddings": [[0, 0], [3, 3], [3, 3], [0, 0]],
+                  "mode": "REFLECT"},
+                 {"class": "Conv2DTranspose", "filters": 2,
+                  "kernel_size": 3, "strides": 1, "activation": "relu"},
+                 {"class": "Cropping2D", "cropping": 4}]
+
+    fp_disc = os.path.join(CONFIG_DIR, 'spatial/disc.json')
+
+    Sup3rGan.seed()
+    model = Sup3rGan(gen_model, fp_disc, learning_rate=1e-4)
+
+    with tempfile.TemporaryDirectory() as td:
+        model.train(batcher,
+                    input_resolution={'spatial': '16km',
+                                      'temporal': '3600min'},
+                    n_epoch=1,
+                    weight_gen_advers=0.0,
+                    train_gen=True, train_disc=False,
+                    checkpoint_int=None,
+                    out_dir=os.path.join(td, 'test_{epoch}'))
+
+        assert model.train_only_features == ['temperature_100m']
+        assert model.hr_features == ['U_100m', 'V_100m', 'topography']
+        assert 'test_0' in os.listdir(td)
+        assert model.meta['output_features'] == ['U_100m', 'V_100m']
+        assert model.meta['class'] == 'Sup3rGan'
+        assert 'topography' in batcher.output_features
+        assert 'topography' not in model.output_features
+
+    x = np.random.uniform(0, 1, (4, 30, 30, 4))
+    hi_res_topo = np.random.uniform(0, 1, (4, 60, 60, 1))
+
+    with pytest.raises(RuntimeError):
+        y = model.generate(x, exogenous_data=None)
+
+    exo_tmp = {
+        'topography': {
+            'steps': [
+                {'model': 0, 'combine_type': 'layer', 'data': hi_res_topo}]}}
+    y = model.generate(x, exogenous_data=exo_tmp)
+
+    assert y.shape[0] == x.shape[0]
+    assert y.shape[1] == x.shape[1] * 2
+    assert y.shape[2] == x.shape[2] * 2
+    assert y.shape[3] == x.shape[3] - 2
+
+
 @pytest.mark.parametrize('custom_layer', ['Sup3rAdder', 'Sup3rConcat'])
 def test_wind_hi_res_topo(custom_layer, log=False):
     """Test a special wind cc model with the custom Sup3rAdder or Sup3rConcat