From adca25e22a609f1522b83f680870f61437ca75f0 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 3 Aug 2022 16:24:40 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn.py | 30 ++++++++----------------------
1 file changed, 8 insertions(+), 22 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index f665a5cf..be6d471e 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -210,14 +210,14 @@ class ESPCN:
self.n_chans = 1
- #self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
+ self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
- self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
+ # self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
self.inputs.append(self.X_img)
- #self.inputs.append(tf.keras.Input(shape=(None, None, self.n_chans)))
+ self.inputs.append(tf.keras.Input(shape=(None, None, self.n_chans)))
# self.inputs.append(tf.keras.Input(shape=(36, 36, self.n_chans)))
- self.inputs.append(tf.keras.Input(shape=(32, 32, self.n_chans)))
+ # self.inputs.append(tf.keras.Input(shape=(32, 32, self.n_chans)))
self.DISK_CACHE = False
@@ -225,24 +225,16 @@ class ESPCN:
def get_in_mem_data_batch(self, idxs, is_training):
if is_training:
- data_files = self.train_data_files
- label_files = self.train_label_files
+ label_files = self.train_data_files
else:
- data_files = self.test_data_files
- label_files = self.test_label_files
+ label_files = self.test_data_files
- data_s = []
label_s = []
for k in idxs:
- f = data_files[k]
- nda = np.load(f)
- data_s.append(nda)
-
f = label_files[k]
nda = np.load(f)
label_s.append(nda)
- # data = np.concatenate(data_s)
data = np.concatenate(label_s)
label = np.concatenate(label_s)
@@ -350,12 +342,10 @@ class ESPCN:
dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
self.eval_dataset = dataset
- def setup_pipeline(self, train_data_files, train_label_files, test_data_files, test_label_files, num_train_samples):
+ def setup_pipeline(self, train_data_files, test_data_files, num_train_samples):
self.train_data_files = train_data_files
- self.train_label_files = train_label_files
self.test_data_files = test_data_files
- self.test_label_files = test_label_files
trn_idxs = np.arange(len(train_data_files))
np.random.shuffle(trn_idxs)
@@ -807,15 +797,11 @@ class ESPCN:
def run(self, directory):
train_data_files = glob.glob(directory+'data_train*.npy')
valid_data_files = glob.glob(directory+'data_valid*.npy')
- train_label_files = glob.glob(directory+'label_train*.npy')
- valid_label_files = glob.glob(directory+'label_valid*.npy')
train_data_files.sort()
valid_data_files.sort()
- train_label_files.sort()
- valid_label_files.sort()
- self.setup_pipeline(train_data_files, train_label_files, valid_data_files, valid_label_files, 200000)
+ self.setup_pipeline(train_data_files, valid_data_files, 200000)
self.build_model()
self.build_training()
self.build_evaluation()
--
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