snapshot...

modules/deeplearning/icing_fcn.py

@@ -42,7 +42,7 @@ NOISE_TRAINING = True
 NOISE_STDDEV = 0.001
 DO_AUGMENT = True
 
-img_width = 16
+IMG_WIDTH = 16  # This is the X,Y dimension length during training
 
 mean_std_dct = {}
 mean_std_file = ancillary_path+'mean_std_lo_hi_l2.pkl'
@@ -138,7 +138,8 @@ def build_residual_block_1x1(input_layer, num_filters, activation, block_name, p
 
 class IcingIntensityFCN:
     
-    def __init__(self, day_night='DAY', l1b_or_l2='both', satellite='GOES16', use_flight_altitude=False, datapath=None):
+    def __init__(self, y_dim_len=IMG_WIDTH, x_dim_len=IMG_WIDTH,
+                 day_night='DAY', l1b_or_l2='both', use_flight_altitude=False, datapath=None):
 
         if day_night == 'DAY':
             self.train_params_l1b = train_params_l1b_day
@@ -159,8 +160,6 @@ class IcingIntensityFCN:
             elif l1b_or_l2 == 'l2':
                 self.train_params = train_params_l2_night
 
-        # self.train_params, self.train_params_l1b, self.train_params_l2 = get_training_parameters(day_night=day_night, l1b_andor_l2=l1b_or_l2, satellite=satellite)
-
         self.train_data = None
         self.train_label = None
         self.test_data = None
@@ -245,13 +244,16 @@ class IcingIntensityFCN:
         self.data_dct = None
         self.cth_max = None
 
+        self.Y_DIM_LEN = y_dim_len
+        self.X_DIM_LEN = x_dim_len
+
         n_chans = len(self.train_params)
         if TRIPLET:
             n_chans *= 3
-        self.X_img = tf.keras.Input(shape=(None, None, n_chans))
+        self.X_img = tf.keras.Input(shape=(self.Y_DIM_LEN, self.X_DIM_LEN, n_chans))
 
         self.inputs.append(self.X_img)
-        self.inputs.append(tf.keras.Input(shape=(None, None, NumFlightLevels)))
+        self.inputs.append(tf.keras.Input(shape=(self.Y_DIM_LEN // IMG_WIDTH, self.X_DIM_LEN // IMG_WIDTH, NumFlightLevels)))
 
         self.flight_level = 0