From cc67961c29e805a23ed93efedfb167dd717848c7 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 24 Jan 2023 15:53:23 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_cld_frac.py | 33 +++++++++++++-------------
1 file changed, 17 insertions(+), 16 deletions(-)
diff --git a/modules/deeplearning/srcnn_cld_frac.py b/modules/deeplearning/srcnn_cld_frac.py
index b3b7ae3f..4819d938 100644
--- a/modules/deeplearning/srcnn_cld_frac.py
+++ b/modules/deeplearning/srcnn_cld_frac.py
@@ -184,25 +184,21 @@ def upsample_nearest(tmp):
def get_label_data(grd_k):
- num, leny, lenx = grd_k.shape
grd_k = np.where(np.isnan(grd_k), 0, grd_k)
grd_k = np.where(grd_k < 0.5, 0, 1)
- grd_down_2x = []
- for t in range(num):
- a = grd_k[t, 0::2, 0::2]
- b = grd_k[t, 1::2, 0::2]
- c = grd_k[t, 0::2, 1::2]
- d = grd_k[t, 1::2, 1::2]
- s_t = a + b + c + d
- s_t = np.where(s_t == 0, 0, s_t)
- s_t = np.where(s_t == 1, 1, s_t)
- s_t = np.where(s_t == 2, 1, s_t)
- s_t = np.where(s_t == 3, 1, s_t)
- s_t = np.where(s_t == 4, 2, s_t)
- grd_down_2x.append(s_t)
+ a = grd_k[:, 0::2, 0::2]
+ b = grd_k[:, 1::2, 0::2]
+ c = grd_k[:, 0::2, 1::2]
+ d = grd_k[:, 1::2, 1::2]
+ s_t = a + b + c + d
+ s_t = np.where(s_t == 0, 0, s_t)
+ s_t = np.where(s_t == 1, 1, s_t)
+ s_t = np.where(s_t == 2, 1, s_t)
+ s_t = np.where(s_t == 3, 1, s_t)
+ s_t = np.where(s_t == 4, 2, s_t)
- return np.stack(grd_down_2x)
+ return s_t
class SRCNN:
@@ -519,9 +515,14 @@ class SRCNN:
conv = conv_b
print(conv.shape)
+ if NumClasses == 2:
+ final_activation = tf.nn.sigmoid # For binary
+ else:
+ final_activation = tf.nn.softmax # For multi-class
+
if not DO_ESPCN:
# This is effectively a Dense layer
- self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
+ self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, activation=final_activation)(conv)
else:
conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding=padding, activation=activation)(conv)
print(conv.shape)
--
GitLab