From 08dd38e36039430aaf824929a6b7854c802d680b Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 11 Apr 2022 11:39:50 -0500
Subject: [PATCH] minor
---
modules/deeplearning/unet.py | 19 -------------------
1 file changed, 19 deletions(-)
diff --git a/modules/deeplearning/unet.py b/modules/deeplearning/unet.py
index 53cfff79..15a89cf2 100644
--- a/modules/deeplearning/unet.py
+++ b/modules/deeplearning/unet.py
@@ -11,9 +11,6 @@ import h5py
LOG_DEVICE_PLACEMENT = False
-# Manual (data, label) caching, but has been replaced with tf.data.dataset.cache()
-CACHE_DATA_IN_MEM = False
-
PROC_BATCH_SIZE = 4096
PROC_BATCH_BUFFER_SIZE = 50000
@@ -241,16 +238,6 @@ class UNET:
def get_in_mem_data_batch(self, idxs, is_training):
- # Pretty much dead, but left in here for reference (See note above)
- if CACHE_DATA_IN_MEM:
- key = frozenset(idxs)
- if is_training:
- tup = self.in_mem_data_cache.get(key)
- else:
- tup = self.in_mem_data_cache_test(key)
- if tup is not None:
- return tup[0], tup[1], tup[2]
-
# sort these to use as numpy indexing arrays
nd_idxs = np.array(idxs)
nd_idxs = np.sort(nd_idxs)
@@ -289,12 +276,6 @@ class UNET:
label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
label = label.reshape((label.shape[0], 1))
- if CACHE_DATA_IN_MEM:
- if is_training:
- self.in_mem_data_cache[key] = (data, data_alt, label)
- else:
- self.in_mem_data_cache_test[key] = (data, data_alt, label)
-
if is_training and DO_AUGMENT:
data_ud = np.flip(data, axis=1)
data_alt_ud = np.copy(data_alt)
--
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