From 92bddedbce468382620a4d47af0d36f4a9287622 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 4 Mar 2023 11:31:49 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac_mod_res.py | 14 +++++---------
1 file changed, 5 insertions(+), 9 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac_mod_res.py b/modules/deeplearning/cnn_cld_frac_mod_res.py
index 6ed2173a..8cd62688 100644
--- a/modules/deeplearning/cnn_cld_frac_mod_res.py
+++ b/modules/deeplearning/cnn_cld_frac_mod_res.py
@@ -602,10 +602,10 @@ class SRCNN:
self.test_loss(t_loss)
self.test_accuracy(labels, pred)
- def predict(self, mini_batch):
- inputs = [mini_batch[0]]
- labels = mini_batch[1]
- pred = self.model(inputs, training=False)
+ @tf.function(input_signature=[tf.TensorSpec(None, tf.float32), tf.TensorSpec(None, tf.float32)])
+ def predict(self, inputs, labels):
+ labels = tf.squeeze(labels)
+ pred = self.model([inputs], training=False)
t_loss = self.loss(labels, pred)
self.test_labels.append(labels)
@@ -746,7 +746,7 @@ class SRCNN:
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)
+ self.predict(mini_batch_test[0], mini_batch_test[1])
print('loss, acc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy())
@@ -754,10 +754,6 @@ class SRCNN:
preds = np.concatenate(self.test_preds)
print(labels.shape, preds.shape)
- # if label_param != 'cloud_probability':
- # labels_denorm = denormalize(labels, label_param, mean_std_dct)
- # preds_denorm = denormalize(preds, label_param, mean_std_dct)
-
return labels, preds
def do_evaluate(self, data, ckpt_dir):
--
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