From 412b207c768d543d91286db85a608aedda30c6a4 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 22 Aug 2022 09:51:18 -0500
Subject: [PATCH] snapshot..
---
modules/deeplearning/srcnn.py | 30 ++++++++++--------------------
1 file changed, 10 insertions(+), 20 deletions(-)
diff --git a/modules/deeplearning/srcnn.py b/modules/deeplearning/srcnn.py
index 30f95980..8e10dad5 100644
--- a/modules/deeplearning/srcnn.py
+++ b/modules/deeplearning/srcnn.py
@@ -327,9 +327,10 @@ class SRCNN:
print('num test samples: ', tst_idxs.shape[0])
print('setup_pipeline: Done')
- def setup_test_pipeline(self, filename):
- self.test_data_files = [filename]
- self.get_test_dataset([0])
+ def setup_test_pipeline(self, test_data_files):
+ self.test_data_files = test_data_files
+ tst_idxs = np.arange(len(test_data_files))
+ self.get_test_dataset(tst_idxs)
print('setup_test_pipeline: Done')
def setup_eval_pipeline(self, filename):
@@ -602,27 +603,14 @@ class SRCNN:
self.reset_test_metrics()
- for data0, data1, label in self.test_dataset:
- ds = tf.data.Dataset.from_tensor_slices((data0, data1, label))
+ for data, label in self.test_dataset:
+ ds = tf.data.Dataset.from_tensor_slices((data, label))
ds = ds.batch(BATCH_SIZE)
for mini_batch_test in ds:
self.predict(mini_batch_test)
print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
- labels = np.concatenate(self.test_labels)
- self.test_labels = labels
-
- preds = np.concatenate(self.test_preds)
- self.test_probs = preds
-
- if NumClasses == 2:
- preds = np.where(preds > 0.5, 1, 0)
- else:
- preds = np.argmax(preds, axis=1)
-
- self.test_preds = preds
-
def do_evaluate(self, nda_lr, param, ckpt_dir):
ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
@@ -655,8 +643,10 @@ class SRCNN:
self.build_evaluation()
self.do_training()
- def run_restore(self, filename, ckpt_dir):
- self.setup_test_pipeline(filename)
+ def run_restore(self, directory, ckpt_dir):
+ valid_data_files = glob.glob(directory + 'data_valid*.npy')
+ self.num_data_samples = 1000
+ self.setup_test_pipeline(valid_data_files)
self.build_model()
self.build_training()
self.build_evaluation()
--
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