snapshot...

modules/deeplearning/icing_fcn.py

@@ -2,7 +2,7 @@ import tensorflow as tf
 from util.setup import logdir, modeldir, cachepath, now, ancillary_path, home_dir
 from util.util import EarlyStop, normalize
 from util.geos_nav import get_navigation
-from util.augment import augment_image_3arg
+from util.augment import augment_icing
 
 import os, datetime
 import numpy as np
@@ -323,27 +323,6 @@ class IcingIntensityFCN:
             label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
             label = label.reshape((label.shape[0], 1))
 
-        # if is_training and DO_AUGMENT:
-        #     data_ud = np.flip(data, axis=1)
-        #     data_alt_ud = np.copy(data_alt)
-        #     label_ud = np.copy(label)
-        #
-        #     data_lr = np.flip(data, axis=2)
-        #     data_alt_lr = np.copy(data_alt)
-        #     label_lr = np.copy(label)
-        #
-        #     data_r1 = np.rot90(data, k=1, axes=(1, 2))
-        #     data_alt_r1 = np.copy(data_alt)
-        #     label_r1 = np.copy(label)
-        #
-        #     data_r2 = np.rot90(data, k=1, axes=(1, 2))
-        #     data_alt_r2 = np.copy(data_alt)
-        #     label_r2 = np.copy(label)
-        #
-        #     data = np.concatenate([data, data_ud, data_lr, data_r1, data_r2])
-        #     data_alt = np.concatenate([data_alt, data_alt_ud, data_alt_lr, data_alt_r1, data_alt_r2])
-        #     label = np.concatenate([label, label_ud, label_lr, label_r1, label_r2])
-
         return data, data_alt, label
 
     def get_parameter_data(self, param, nd_idxs, is_training):
@@ -469,7 +448,7 @@ class IcingIntensityFCN:
         dataset = dataset.cache()
         dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE, reshuffle_each_iteration=True)
         if DO_AUGMENT:
-            dataset = dataset.map(augment_image_3arg(), num_parallel_calls=8)
+            dataset = dataset.map(augment_icing(), num_parallel_calls=8)
         dataset = dataset.prefetch(buffer_size=1)
         self.train_dataset = dataset