From 791a4c44c1478a334ded07169484bae090dd7b41 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 3 Feb 2023 15:38:54 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_cld_frac.py | 54 +++-----------------------
1 file changed, 5 insertions(+), 49 deletions(-)
diff --git a/modules/deeplearning/srcnn_cld_frac.py b/modules/deeplearning/srcnn_cld_frac.py
index cffa2343..e6811fcf 100644
--- a/modules/deeplearning/srcnn_cld_frac.py
+++ b/modules/deeplearning/srcnn_cld_frac.py
@@ -37,7 +37,7 @@ NOISE_TRAINING = False
NOISE_STDDEV = 0.01
DO_AUGMENT = True
-DO_SMOOTH = True
+DO_SMOOTH = False
SIGMA = 1.0
DO_ZERO_OUT = False
DO_ESPCN = False # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
@@ -62,7 +62,7 @@ IMG_DEPTH = 1
# label_param = 'cld_opd_dcomp'
label_param = 'cloud_probability'
-params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
+params = ['temp_11_0um_nom', 'refl_0_65um_nom', label_param]
data_params_half = ['temp_11_0um_nom']
data_params_full = ['refl_0_65um_nom']
@@ -131,43 +131,6 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
return conv
-def build_residual_block_conv2d_down2x(x_in, num_filters, activation, padding='SAME', drop_rate=0.5,
- do_drop_out=True, do_batch_norm=True):
- skip = x_in
-
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(x_in)
- conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(conv)
-
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(conv)
-
- conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
- if do_drop_out:
- conv = tf.keras.layers.Dropout(drop_rate)(conv)
- if do_batch_norm:
- conv = tf.keras.layers.BatchNormalization()(conv)
-
- skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
- skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
- if do_drop_out:
- skip = tf.keras.layers.Dropout(drop_rate)(skip)
- if do_batch_norm:
- skip = tf.keras.layers.BatchNormalization()(skip)
-
- conv = conv + skip
- conv = tf.keras.layers.LeakyReLU()(conv)
- print(conv.shape)
-
- return conv
-
-
def upsample(tmp):
tmp = tmp[:, slc_y_2, slc_x_2]
tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
@@ -395,10 +358,6 @@ class SRCNN:
# -----------------------------------------------------
label = input_data[:, label_idx, :, :]
label = label.copy()
- # if DO_SMOOTH:
- # label = np.where(np.isnan(label), 0, label)
- # label = smooth_2d(label, sigma=SIGMA)
- # # label = median_filter_2d(label)
label = label[:, y_128, x_128]
label = get_label_data(label)
@@ -518,16 +477,13 @@ class SRCNN:
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_6', 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_block_conv2d_down2x(conv_b, num_filters, activation)
+ 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_b
+ conv = conv + conv_b
print(conv.shape)
if NumClasses == 2:
--
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