From 35e254c3d11414e85e930e003fee14fb0bf55b71 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 4 Jun 2022 11:45:07 -0500
Subject: [PATCH] minor...
---
modules/deeplearning/unet.py | 60 ++++++++++++++++++------------------
1 file changed, 30 insertions(+), 30 deletions(-)
diff --git a/modules/deeplearning/unet.py b/modules/deeplearning/unet.py
index 39ac89b8..9e420efa 100644
--- a/modules/deeplearning/unet.py
+++ b/modules/deeplearning/unet.py
@@ -309,35 +309,35 @@ class UNET:
dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
self.eval_dataset = dataset
- def setup_pipeline(self, data_nda, label_nda, perc=0.20):
-
- num_samples = data_nda.shape[0]
- num_test = int(num_samples * perc)
- self.num_data_samples = num_samples - num_test
- num_train = self.num_data_samples
-
- self.train_data_nda = data_nda[0:num_train]
- self.train_label_nda = label_nda[0:num_train]
- self.test_data_nda = data_nda[num_train:]
- self.test_label_nda = label_nda[num_train:]
-
- trn_idxs = np.arange(self.train_data_nda.shape[0])
- tst_idxs = np.arange(self.test_data_nda.shape[0])
-
- np.random.shuffle(tst_idxs)
-
- self.get_train_dataset(trn_idxs)
- self.get_test_dataset(tst_idxs)
-
- print('datetime: ', now)
- print('training and test data: ')
- print('---------------------------')
- print('num train samples: ', self.num_data_samples)
- print('BATCH SIZE: ', BATCH_SIZE)
- print('num test samples: ', tst_idxs.shape[0])
- print('setup_pipeline: Done')
-
- def setup_pipeline_files(self, data_files, label_files, perc=0.20):
+ # def setup_pipeline(self, data_nda, label_nda, perc=0.20):
+ #
+ # num_samples = data_nda.shape[0]
+ # num_test = int(num_samples * perc)
+ # self.num_data_samples = num_samples - num_test
+ # num_train = self.num_data_samples
+ #
+ # self.train_data_nda = data_nda[0:num_train]
+ # self.train_label_nda = label_nda[0:num_train]
+ # self.test_data_nda = data_nda[num_train:]
+ # self.test_label_nda = label_nda[num_train:]
+ #
+ # trn_idxs = np.arange(self.train_data_nda.shape[0])
+ # tst_idxs = np.arange(self.test_data_nda.shape[0])
+ #
+ # np.random.shuffle(tst_idxs)
+ #
+ # self.get_train_dataset(trn_idxs)
+ # self.get_test_dataset(tst_idxs)
+ #
+ # print('datetime: ', now)
+ # print('training and test data: ')
+ # print('---------------------------')
+ # print('num train samples: ', self.num_data_samples)
+ # print('BATCH SIZE: ', BATCH_SIZE)
+ # print('num test samples: ', tst_idxs.shape[0])
+ # print('setup_pipeline: Done')
+
+ def setup_pipeline(self, data_files, label_files, perc=0.20):
num_files = len(data_files)
num_test_files = int(num_files * perc)
num_train_files = num_files - num_test_files
@@ -859,7 +859,7 @@ class UNET:
def run(self, directory):
data_files = glob.glob(directory+'mod_res*.npy')
label_files = [f.replace('mod', 'img') for f in data_files]
- self.setup_pipeline_files(data_files, label_files)
+ self.setup_pipeline(data_files, label_files)
self.build_model()
self.build_training()
self.build_evaluation()
--
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