From b98b04a39e3da82412cf0c87fda908f5bb0d16a3 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 12 Jul 2023 11:03:07 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 42 +++++++++++++++------
1 file changed, 30 insertions(+), 12 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 167580c9..8f277a61 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -29,7 +29,7 @@ EARLY_STOP = True
NOISE_TRAINING = False
NOISE_STDDEV = 0.01
-DO_AUGMENT = False
+DO_AUGMENT = True
DO_SMOOTH = False
SIGMA = 1.0
@@ -59,7 +59,8 @@ params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_s
# data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
data_params_half = ['temp_11_0um_nom']
data_params_full = ['refl_0_65um_nom']
-sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
+# sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
+sub_fields = ['refl_substddev_ch01']
# sub_fields = ['refl_stddev3x3_ch01']
label_idx_i = params_i.index(label_param)
@@ -210,7 +211,7 @@ class SRCNN:
self.test_label_files = None
# self.n_chans = len(data_params_half) + len(data_params_full) + 1
- self.n_chans = 5
+ self.n_chans = 4
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
@@ -271,11 +272,22 @@ class SRCNN:
data_norm.append(tmp)
# High res refectance ----------
- # tmp = input_label[:, label_idx_i, :, :]
+ # idx = params_i.index('refl_0_65um_nom')
+ # tmp = input_label[:, idx, :, :]
# tmp = np.where(np.isnan(tmp), 0, tmp)
# tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
# data_norm.append(tmp[:, self.slc_y, self.slc_x])
+ idx = params_i.index('refl_0_65um_nom')
+ tmp = input_label[:, idx, :, :]
+ tmp = tmp.copy()
+ tmp = np.where(np.isnan(tmp), 0.0, tmp)
+ tmp = tmp[:, self.slc_y_2, self.slc_x_2]
+ tmp = self.upsample(tmp)
+ tmp = smooth_2d(tmp)
+ tmp = normalize(tmp, label_param, mean_std_dct)
+ data_norm.append(tmp)
+
tmp = input_label[:, label_idx_i, :, :]
tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0.0, tmp)
@@ -328,14 +340,20 @@ class SRCNN:
label = label.astype(np.float32)
if is_training and DO_AUGMENT:
- data_ud = np.flip(data, axis=1)
- label_ud = np.flip(label, axis=1)
-
- data_lr = np.flip(data, axis=2)
- label_lr = np.flip(label, axis=2)
-
- data = np.concatenate([data, data_ud, data_lr])
- label = np.concatenate([label, label_ud, label_lr])
+ # data_ud = np.flip(data, axis=1)
+ # label_ud = np.flip(label, axis=1)
+ #
+ # data_lr = np.flip(data, axis=2)
+ # label_lr = np.flip(label, axis=2)
+ #
+ # data = np.concatenate([data, data_ud, data_lr])
+ # label = np.concatenate([label, label_ud, label_lr])
+
+ data_rot = np.rot90(data, axes=(1, 2))
+ label_rot = np.rot90(label, axes=(1, 2))
+
+ data = np.concatenate([data, data_rot])
+ label = np.concatenate([label, label_rot])
return data, label
--
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