From ab0dd90a5b64c6643b500581ea320a9e79221275 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 22 Apr 2023 11:57:58 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_viirs.py | 47 ++++++++++---------
1 file changed, 25 insertions(+), 22 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_viirs.py b/modules/deeplearning/cloud_opd_srcnn_viirs.py
index 506a24c6..c08f1671 100644
--- a/modules/deeplearning/cloud_opd_srcnn_viirs.py
+++ b/modules/deeplearning/cloud_opd_srcnn_viirs.py
@@ -70,18 +70,20 @@ N_X = N_Y = 1
LEN_X = LEN_Y = 128
if KERNEL_SIZE == 3:
- slc_x = slice(2, N_X*LEN_X + 4)
- slc_y = slice(2, N_Y*LEN_Y + 4)
- slc_x_2 = slice(1, N_X*LEN_X + 6, 2)
- slc_y_2 = slice(1, N_Y*LEN_Y + 6, 2)
+ slc_x_m = slice(1, int((N_X*LEN_X)/2) + 4)
+ slc_y_m = slice(1, int((N_Y*LEN_Y)/2) + 4)
+ slc_x = slice(3, N_X*LEN_X + 5)
+ slc_y = slice(3, N_Y*LEN_Y + 5)
+ slc_x_2 = slice(2, N_X*LEN_X + 7, 2)
+ slc_y_2 = slice(2, N_Y*LEN_Y + 7, 2)
x_2 = np.arange(int((N_X*LEN_X)/2) + 3)
y_2 = np.arange(int((N_Y*LEN_Y)/2) + 3)
t = np.arange(0, int((N_X*LEN_X)/2) + 3, 0.5)
s = np.arange(0, int((N_Y*LEN_Y)/2) + 3, 0.5)
x_k = slice(1, N_X*LEN_X + 3)
y_k = slice(1, N_Y*LEN_Y + 3)
- x_128 = slice(3, N_X*LEN_X + 3)
- y_128 = slice(3, N_Y*LEN_Y + 3)
+ x_128 = slice(4, N_X*LEN_X + 4)
+ y_128 = slice(4, N_Y*LEN_Y + 4)
elif KERNEL_SIZE == 5:
slc_x = slice(3, 135)
slc_y = slice(3, 135)
@@ -120,7 +122,6 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
def upsample(tmp):
- tmp = tmp[:, slc_y_2, slc_x_2]
tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
tmp = tmp[:, y_k, x_k]
return tmp
@@ -290,9 +291,10 @@ class SRCNN:
data_norm = []
for param in data_params_half:
- idx = params_i.index(param)
- tmp = input_label[:, idx, :, :]
+ idx = params.index(param)
+ tmp = input_data[:, idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
+ tmp = tmp[:, slc_y_m, slc_x_m]
tmp = upsample(tmp)
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
@@ -302,23 +304,24 @@ class SRCNN:
tmp = input_label[:, idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
- lo, hi, std, avg = get_min_max_std(tmp)
- lo = upsample_nearest(lo)
- hi = upsample_nearest(hi)
- avg = upsample_nearest(avg)
- lo = normalize(lo, param, mean_std_dct)
- hi = normalize(hi, param, mean_std_dct)
- avg = normalize(avg, param, mean_std_dct)
+ # lo, hi, std, avg = get_min_max_std(tmp)
+ # lo = upsample_nearest(lo)
+ # hi = upsample_nearest(hi)
+ # avg = upsample_nearest(avg)
+ # lo = normalize(lo, param, mean_std_dct)
+ # hi = normalize(hi, param, mean_std_dct)
+ # avg = normalize(avg, param, mean_std_dct)
+ #
+ # data_norm.append(lo[:, slc_y, slc_x])
+ # data_norm.append(hi[:, slc_y, slc_x])
+ # data_norm.append(avg[:, slc_y, slc_x])
- data_norm.append(lo[:, slc_y, slc_x])
- data_norm.append(hi[:, slc_y, slc_x])
- data_norm.append(avg[:, slc_y, slc_x])
-
- # tmp = normalize(tmp, param, mean_std_dct)
- # data_norm.append(tmp[:, slc_y, slc_x])
+ tmp = normalize(tmp, param, mean_std_dct)
+ data_norm.append(tmp[:, slc_y, slc_x])
# ---------------------------------------------------
tmp = input_label[:, label_idx_i, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
+ tmp = tmp[:, slc_y_2, slc_x_2]
tmp = upsample(tmp)
data_norm.append(tmp)
# ---------
--
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