From d96a7edfb140e75d7857b3c0326d7bd15bf2b445 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 17 Oct 2022 14:32:53 -0500
Subject: [PATCH] normalize before upsampling (nans)
---
modules/deeplearning/srcnn_l1b_l2.py | 18 ++++++++++--------
1 file changed, 10 insertions(+), 8 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 1d9fe809..899539f6 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -225,22 +225,23 @@ class SRCNN:
for param in data_params:
idx = params.index(param)
tmp = input_data[:, idx, 3:131:2, 3:131:2]
- tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
+ tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
data_norm.append(tmp)
# --------
idx = params.index('refl_0_65um_nom')
tmp = input_data[:, idx, 3:131, 3:131]
+ tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
# tmp = input_data[:, idx, 3:131:2, 3:131:2]
# tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
- tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
data_norm.append(tmp)
# --------
tmp = input_data[:, label_idx, 3:131:2, 3:131:2]
- tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
if label_param != 'cloud_fraction':
tmp = normalize(tmp, 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_64, y_64, tmp, t, s)
data_norm.append(tmp)
# ---------
data = np.stack(data_norm, axis=3)
@@ -248,10 +249,11 @@ class SRCNN:
# label = input_data[:, label_idx, 3:131:2, 3:131:2]
label = input_data[:, label_idx, 3:131, 3:131]
- label = np.where(np.isnan(label), 0, label)
- label = np.expand_dims(label, axis=3)
if label_param != 'cloud_fraction':
label = normalize(label, label_param, mean_std_dct)
+ else:
+ label = np.where(np.isnan(label), 0, label)
+ label = np.expand_dims(label, axis=3)
data = data.astype(np.float32)
label = label.astype(np.float32)
@@ -692,16 +694,16 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
x_up = np.arange(0, lenx, 0.5)
y_up = np.arange(0, leny, 0.5)
- grd_a = resample_2d_linear_one(x, y, grd_a, x_up, y_up)
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
+ grd_a = resample_2d_linear_one(x, y, grd_a, x_up, y_up)
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
- grd_c = resample_2d_linear_one(x, y, grd_c, x_up, y_up)
if label_param == 'cloud_fraction':
grd_c = np.where(np.isnan(grd_c), 0, grd_c)
else:
grd_c = normalize(grd_c, label_param, mean_std_dct)
+ grd_c = resample_2d_linear_one(x, y, grd_c, x_up, y_up)
data = np.stack([grd_a, grd_b, grd_c], axis=2)
data = np.expand_dims(data, axis=0)
--
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