From f33d4d609a7701ac475cf6fa5e7ed982f61dd698 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 26 Dec 2022 13:26:18 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 11 +++++++++++
1 file changed, 11 insertions(+)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index f69728a5..09c8f640 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -693,6 +693,7 @@ class SRCNN:
pred = self.model([data], training=False)
self.test_probs = pred
pred = pred.numpy()
+ print('**: ', pred.shape, pred.min(), pred.max())
return pred
@@ -774,19 +775,29 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
# grd_c = gaussian_filter(grd_c, sigma=1.0)
grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_c = grd_c.copy()
+ print(grd_c.shape)
grd_c = np.where(np.isnan(grd_c), 0, grd_c)
hr_grd_c = grd_c.copy()
hr_grd_c = hr_grd_c[y_128, x_128]
+ print(hr_grd_c.shape)
grd_c = grd_c[slc_y_2, slc_x_2]
+ print(grd_c.shape)
grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
+ print(grd_c.shape)
grd_c = grd_c[y_k, x_k]
+ print(grd_c.shape)
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)
+ print(grd_c.shape)
# data = np.stack([grd_a, grd_b, grd_c], axis=2)
#data = np.stack([grd_a, grd_c], axis=2)
data = np.stack([grd_c], axis=2)
+ print(data.shape)
data = np.expand_dims(data, axis=0)
+ print(data.shape)
+ dn = denormalize(grd_c, label_param, mean_std_dct)
+ return hr_grd_c, grd_c, dn
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
--
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