From d0cf9ea52868ddf51138880d04fc6ee8d7208095 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 22 Dec 2022 11:45:22 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 39 +++++++++++++++-------------
1 file changed, 21 insertions(+), 18 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index deb39a13..0c162a63 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -30,7 +30,7 @@ NUM_EPOCHS = 60
TRACK_MOVING_AVERAGE = False
EARLY_STOP = True
-NOISE_TRAINING = True
+NOISE_TRAINING = False
NOISE_STDDEV = 0.01
DO_AUGMENT = True
@@ -246,15 +246,17 @@ class SRCNN:
DO_ADD_NOISE = True
data_norm = []
- # for param in data_params:
- # idx = params.index(param)
- # # tmp = input_data[:, idx, slc_y_2, slc_x_2]
- # tmp = input_data[:, idx, slc_y, slc_x]
- # tmp = normalize(tmp, param, mean_std_dct)
- # if DO_ADD_NOISE:
- # tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
- # # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
- # data_norm.append(tmp)
+ for param in data_params:
+ idx = params.index(param)
+ # tmp = input_data[:, idx, slc_y, slc_x]
+ tmp = input_data[:, idx, :, :]
+ tmp = smooth_2d(tmp, sigma=1.0)
+ tmp = tmp[:, slc_y_2, slc_x_2]
+ tmp = normalize(tmp, param, mean_std_dct)
+ if DO_ADD_NOISE:
+ tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
+ # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+ data_norm.append(tmp)
# # --------------------------
# param = 'refl_0_65um_nom'
# idx = params.index(param)
@@ -420,7 +422,7 @@ class SRCNN:
activation = tf.nn.relu
momentum = 0.99
- num_filters = 64
+ num_filters = 32
input_2d = self.inputs[0]
print('input: ', input_2d.shape)
@@ -437,7 +439,7 @@ 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)
@@ -749,11 +751,12 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
h5f = h5py.File(in_file, 'r')
- # grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
- # grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
- # grd_a = grd_a[y_130, x_130]
- # bt = grd_a
- # grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
+ grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
+ grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
+ hr_grd_a = grd_a.copy()
+ hr_grd_a = hr_grd_a[y_128, x_128]
+ grd_a = grd_a[slc_y_2, slc_x_2]
+ grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
#
# grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
# grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
@@ -773,7 +776,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
grd_c = grd_c[y_k, x_k]
# data = np.stack([grd_a, grd_b, grd_c], axis=2)
- data = np.stack([grd_c], axis=2)
+ data = np.stack([grd_a, grd_c], axis=2)
data = np.expand_dims(data, axis=0)
nn = SRCNN()
--
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