From 2cc9d96964ed3a813c632a83b49caaeee3ee32b9 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 20 Jul 2023 14:10:14 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 16 +++++++++-------
1 file changed, 9 insertions(+), 7 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index baf49e8c..ed2ee135 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -31,7 +31,7 @@ NOISE_TRAINING = False
NOISE_STDDEV = 0.01
DO_AUGMENT = True
-DO_SMOOTH = False
+DO_SMOOTH = True
SIGMA = 1.0
DO_ZERO_OUT = False
@@ -268,7 +268,8 @@ class SRCNN:
tmp = np.where(np.isnan(tmp), 0.0, tmp)
tmp = tmp[:, self.slc_y_m, self.slc_x_m]
tmp = self.upsample(tmp)
- if DO_SMOOTH:
+ # if DO_SMOOTH:
+ if False:
tmp = smooth_2d(tmp)
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
@@ -456,17 +457,18 @@ class SRCNN:
conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
+ # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
+ # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
+ # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', kernel_size=KERNEL_SIZE, scale=scale)
- conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', kernel_size=KERNEL_SIZE, scale=scale)
+ # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_6', kernel_size=KERNEL_SIZE, scale=scale)
conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, activation=activation, kernel_initializer='he_uniform', padding=padding)(conv_b)
- conv = conv + conv_b
+ # conv = conv + conv_b
+ conv = conv_b
print(conv.shape)
# This is effectively a Dense layer
--
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