From 3544a0df08ab048aed9c66b63e80bc760f550cf4 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 26 Dec 2022 15:09:23 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 12 ------------
1 file changed, 12 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 49c0d7ad..9b89fc7e 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -514,11 +514,7 @@ class SRCNN:
def test_step(self, mini_batch):
inputs = [mini_batch[0]]
labels = mini_batch[1]
- in_nd = tf.make_ndarray(mini_batch[0])
- print('****: ', in_nd.shape, in_nd.min(), in_nd.max())
pred = self.model(inputs, training=False)
- in_nd = tf.make_ndarray(pred)
- print('****: ', in_nd.shape, in_nd.min(), in_nd.max())
t_loss = self.loss(labels, pred)
self.test_loss(t_loss)
@@ -780,27 +776,19 @@ 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)
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
--
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