snapshot...

modules/deeplearning/cnn_cld_frac_mod_res.py

@@ -315,23 +315,6 @@ class SRCNN:
         tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
 
     def get_in_mem_data_batch(self, idxs, is_training):
-        # if is_training:
-        #     files = self.train_data_files
-        # else:
-        #     files = self.test_data_files
-        #
-        # data_s = []
-        # for k in idxs:
-        #     f = files[k]
-        #     try:
-        #         nda = np.load(f)
-        #     except Exception:
-        #         print(f)
-        #         continue
-        #     data_s.append(nda)
-        # input_data = np.concatenate(data_s)
-        # # input_label = input_data[:, label_idx, :, :]
-
         if is_training:
             data_files = self.train_data_files
             label_files = self.train_label_files
@@ -359,14 +342,12 @@ class SRCNN:
             tmp = tmp.copy()
             if DO_ESPCN:
                 tmp = tmp[:, slc_y_2, slc_x_2]
-            else:  # Half res upsampled to full res:
+            else:
                 tmp = tmp[:, slc_y, slc_x]
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
         for param in data_params_full:
-            # idx = params.index(param)
-            # tmp = input_data[:, idx, :, :]
             idx = params_i.index(param)
             tmp = input_label[:, idx, :, :]
             tmp = tmp.copy()
@@ -386,7 +367,7 @@ class SRCNN:
         tmp = tmp.copy()
         if DO_ESPCN:
             tmp = tmp[:, slc_y_2, slc_x_2]
-        else:  # Half res upsampled to full res:
+        else:
             tmp = tmp[:, slc_y, slc_x]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
@@ -396,7 +377,6 @@ class SRCNN:
         data = data.astype(np.float32)
         # -----------------------------------------------------
         # -----------------------------------------------------
-        # label = input_data[:, label_idx, :, :]
         label = input_label[:, label_idx_i, :, :]
         label = label.copy()
         label = label[:, y_128, x_128]
@@ -469,9 +449,6 @@ class SRCNN:
         self.test_data_files = test_data_files
         self.test_label_files = test_label_files
 
-        # self.train_data_files = train_data_files
-        # self.test_data_files = test_data_files
-
         trn_idxs = np.arange(len(train_data_files))
         np.random.shuffle(trn_idxs)
 
@@ -572,7 +549,7 @@ class SRCNN:
         optimizer = tf.keras.optimizers.Adam(learning_rate=self.learningRateSchedule)
 
         if TRACK_MOVING_AVERAGE:
-            # Not really sure this works properly (from tfa)
+            # Not sure that this works properly (from tfa)
             # optimizer = tfa.optimizers.MovingAverage(optimizer)
             self.ema = tf.train.ExponentialMovingAverage(decay=0.9999)
 
@@ -804,10 +781,6 @@ class SRCNN:
         valid_label_files = glob.glob(directory+'valid*ires*.npy')
         self.setup_pipeline(train_data_files, train_label_files, valid_data_files, valid_label_files, num_data_samples)
 
-        # train_data_files = glob.glob(directory+'data_train_*.npy')
-        # valid_data_files = glob.glob(directory+'data_valid_*.npy')
-        # self.setup_pipeline(train_data_files, None, valid_data_files, None, num_data_samples)
-
         self.build_model()
         self.build_training()
         self.build_evaluation()
@@ -815,8 +788,6 @@ class SRCNN:
 
     def run_restore(self, directory, ckpt_dir):
         self.num_data_samples = 1000
-        # valid_data_files = glob.glob(directory + 'data_valid*.npy')
-        # self.setup_test_pipeline(valid_data_files, None)
 
         valid_data_files = glob.glob(directory + 'valid*mres*.npy')
         valid_label_files = glob.glob(directory + 'valid*ires*.npy')