From 0f57179c8dd04681bd8109ef6580bdf11904348b Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 10 Jul 2023 14:25:58 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 33 +++++++++++----------
1 file changed, 17 insertions(+), 16 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 6d328b52..167580c9 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -210,7 +210,7 @@ class SRCNN:
self.test_label_files = None
# self.n_chans = len(data_params_half) + len(data_params_full) + 1
- self.n_chans = 3
+ self.n_chans = 5
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
@@ -270,10 +270,11 @@ class SRCNN:
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
- tmp = input_label[:, label_idx_i, :, :]
- 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])
+ # High res refectance ----------
+ # tmp = input_label[:, label_idx_i, :, :]
+ # 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])
tmp = input_label[:, label_idx_i, :, :]
tmp = tmp.copy()
@@ -297,16 +298,16 @@ class SRCNN:
# tmp = np.where(np.isnan(tmp), 0.0, tmp)
# data_norm.append(tmp)
- # for param in sub_fields:
- # idx = params.index(param)
- # tmp = input_data[:, idx, :, :]
- # tmp = upsample_nearest(tmp)
- # tmp = tmp[:, self.slc_y, self.slc_x]
- # if param != 'refl_substddev_ch01':
- # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
- # else:
- # tmp = np.where(np.isnan(tmp), 0, tmp)
- # data_norm.append(tmp)
+ for param in sub_fields:
+ idx = params.index(param)
+ tmp = input_data[:, idx, :, :]
+ tmp = upsample_nearest(tmp)
+ tmp = tmp[:, self.slc_y, self.slc_x]
+ if param != 'refl_substddev_ch01':
+ tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+ else:
+ tmp = np.where(np.isnan(tmp), 0, tmp)
+ data_norm.append(tmp)
# ---------------------------------------------------
data = np.stack(data_norm, axis=3)
@@ -454,7 +455,7 @@ class SRCNN:
self.loss = tf.keras.losses.MeanSquaredError() # Regression
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
- initial_learning_rate = 0.002
+ initial_learning_rate = 0.001
decay_rate = 0.95
steps_per_epoch = int(self.num_data_samples/BATCH_SIZE) # one epoch
decay_steps = int(steps_per_epoch) * 4
--
GitLab