From b49a8160dfeadc39b5208211a383e37792cecabb Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 23 Oct 2023 14:36:51 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/icing_fcn.py | 44 +++++++++++++++----------------
1 file changed, 22 insertions(+), 22 deletions(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 015dd047..f70d69d7 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -316,26 +316,26 @@ 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])
+ # 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
@@ -477,8 +477,8 @@ class IcingIntensityFCN:
dataset = dataset.map(self.data_function, num_parallel_calls=8)
dataset = dataset.cache()
dataset = dataset.shuffle(PROC_BATCH_BUFFER_SIZE, reshuffle_each_iteration=True)
- # if DO_AUGMENT:
- # dataset = dataset.map(augment_image(), num_parallel_calls=8)
+ if DO_AUGMENT:
+ dataset = dataset.map(augment_image(), num_parallel_calls=8)
dataset = dataset.prefetch(buffer_size=1)
self.train_dataset = dataset
--
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