From 60c682b2885f12e6fe9c23739f2c3847bee1035e Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 8 Nov 2022 15:43:59 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/espcn_l1b_l2.py | 15 +++++++++------
1 file changed, 9 insertions(+), 6 deletions(-)
diff --git a/modules/deeplearning/espcn_l1b_l2.py b/modules/deeplearning/espcn_l1b_l2.py
index 2b3ebc57..a9b3c391 100644
--- a/modules/deeplearning/espcn_l1b_l2.py
+++ b/modules/deeplearning/espcn_l1b_l2.py
@@ -72,6 +72,8 @@ y_134_2 = y_134[2:133:2]
slc_x = slice(3, 131)
slc_y = slice(3, 131)
+slc_x_2 = slice(3, 131, 2)
+slc_y_2 = slice(3, 131, 2)
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.leaky_relu, padding='SAME', scale=None):
@@ -214,17 +216,19 @@ class ESPCN:
data_norm = []
for k, param in enumerate(data_params):
- tmp = input_data[:, k, :, :]
+ # tmp = input_data[:, k, :, :]
+ tmp = input_data[:, k, slc_y_2, slc_x_2]
tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
- tmp = resample_2d_linear(x_134, y_134, tmp, x_134_2, y_134_2)
+ # tmp = resample_2d_linear(x_134, y_134, tmp, x_134_2, y_134_2)
data_norm.append(tmp)
- tmp = input_data[:, label_idx, :, ]
+ # tmp = input_data[:, label_idx, :, ]
+ tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
if label_param != 'cloud_fraction':
tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
else:
tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = resample_2d_linear(x_134, y_134, tmp, x_134_2, y_134_2)
+ # tmp = resample_2d_linear(x_134, y_134, tmp, x_134_2, y_134_2)
data_norm.append(tmp)
data = np.stack(data_norm, axis=3)
@@ -336,7 +340,6 @@ class ESPCN:
print('num test samples: ', tst_idxs.shape[0])
print('setup_pipeline: Done')
-
def setup_test_pipeline(self, test_data_files):
self.test_data_files = test_data_files
tst_idxs = np.arange(len(test_data_files))
@@ -389,7 +392,7 @@ class ESPCN:
conv = conv_b
print(conv.shape)
- conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding='same')(conv)
+ conv = tf.keras.layers.Conv2D(IMG_DEPTH * (factor ** 2), 3, padding='same', activation=activation)(conv)
print(conv.shape)
conv = tf.nn.depth_to_space(conv, factor)
--
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