From cab215cf33f63374ec7179c56b93db85efe9d279 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 21 Mar 2023 12:04:37 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac_mod_res.py | 36 ++++----------------
1 file changed, 6 insertions(+), 30 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac_mod_res.py b/modules/deeplearning/cnn_cld_frac_mod_res.py
index 39ab18a2..a17e2cdd 100644
--- a/modules/deeplearning/cnn_cld_frac_mod_res.py
+++ b/modules/deeplearning/cnn_cld_frac_mod_res.py
@@ -40,7 +40,6 @@ DO_AUGMENT = 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)
# setup scaling parameters dictionary
mean_std_dct = {}
@@ -103,12 +102,6 @@ elif KERNEL_SIZE == 5:
x_2 = np.arange(68)
y_2 = np.arange(68)
# ----------------------------------------
-# Exp for ESPCN version
-if DO_ESPCN:
- slc_x_2 = slice(0, 132, 2)
- slc_y_2 = slice(0, 132, 2)
- x_128 = slice(2, 130)
- y_128 = slice(2, 130)
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
@@ -340,10 +333,7 @@ class SRCNN:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
tmp = tmp.copy()
- if DO_ESPCN:
- tmp = tmp[:, slc_y_2, slc_x_2]
- else:
- tmp = tmp[:, slc_y, slc_x]
+ tmp = tmp[:, slc_y, slc_x]
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
@@ -365,16 +355,12 @@ class SRCNN:
# ---------------------------------------------------
tmp = input_data[:, label_idx, :, :]
tmp = tmp.copy()
- if DO_ESPCN:
- tmp = tmp[:, slc_y_2, slc_x_2]
- else:
- tmp = tmp[:, slc_y, slc_x]
- if label_param != 'cloud_probability':
- tmp = normalize(tmp, label_param, mean_std_dct)
+ tmp = tmp[:, slc_y, slc_x]
data_norm.append(tmp)
# ---------
data = np.stack(data_norm, axis=3)
data = data.astype(np.float32)
+
# -----------------------------------------------------
# -----------------------------------------------------
label = input_label[:, label_idx_i, :, :]
@@ -385,10 +371,7 @@ class SRCNN:
else:
label = get_label_data(label)
- if label_param != 'cloud_probability':
- label = normalize(label, label_param, mean_std_dct)
- else:
- label = np.where(np.isnan(label), 0, label)
+ label = np.where(np.isnan(label), 0, label)
label = np.expand_dims(label, axis=3)
data = data.astype(np.float32)
@@ -519,15 +502,8 @@ class SRCNN:
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, activation=final_activation)(conv)
- else:
- conv = tf.keras.layers.Conv2D(num_filters * (factor ** 2), 3, padding=padding, activation=activation)(conv)
- print(conv.shape)
- conv = tf.nn.depth_to_space(conv, factor)
- print(conv.shape)
- self.logits = tf.keras.layers.Conv2D(IMG_DEPTH, kernel_size=3, strides=1, padding=padding, activation=final_activation)(conv)
+ # This is effectively a Dense layer
+ self.logits = tf.keras.layers.Conv2D(NumLogits, kernel_size=1, strides=1, padding=padding, activation=final_activation)(conv)
print(self.logits.shape)
def build_training(self):
--
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