From 578ac0242a16827bd3432fda275d8b3120ebca37 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 10 Aug 2022 12:55:31 -0500
Subject: [PATCH] minor
---
modules/deeplearning/espcn.py | 41 +----------------------------------
1 file changed, 1 insertion(+), 40 deletions(-)
diff --git a/modules/deeplearning/espcn.py b/modules/deeplearning/espcn.py
index e2137478..40dce3c3 100644
--- a/modules/deeplearning/espcn.py
+++ b/modules/deeplearning/espcn.py
@@ -72,38 +72,6 @@ def build_conv2d_block(conv, num_filters, block_name, activation=tf.nn.leaky_rel
return conv
-# def build_residual_block_1x1(input_layer, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
-# do_drop_out=True, do_batch_norm=True):
-#
-# with tf.name_scope(block_name):
-# skip = input_layer
-# if do_drop_out:
-# input_layer = tf.keras.layers.Dropout(drop_rate)(input_layer)
-# if do_batch_norm:
-# input_layer = tf.keras.layers.BatchNormalization()(input_layer)
-# conv = tf.keras.layers.Conv2D(num_filters, kernel_size=1, strides=1, padding=padding, activation=activation)(input_layer)
-# print(conv.shape)
-#
-# # 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=1, strides=1, padding=padding, activation=activation)(conv)
-# # print(conv.shape)
-#
-# 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=1, strides=1, padding=padding, activation=None)(conv)
-#
-# conv = conv + skip
-# conv = tf.keras.layers.LeakyReLU()(conv)
-# print(conv.shape)
-#
-# return conv
-
-
class ESPCN:
def __init__(self):
@@ -204,12 +172,10 @@ class ESPCN:
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
- self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
+ # self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
self.inputs.append(self.X_img)
- self.DISK_CACHE = False
-
tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
def get_in_mem_data_batch(self, idxs, is_training):
@@ -652,11 +618,6 @@ class ESPCN:
ckpt_manager.save()
- if self.DISK_CACHE and epoch == 0:
- f = open(cachepath, 'wb')
- pickle.dump(self.in_mem_data_cache, f)
- f.close()
-
if EARLY_STOP and es.check_stop(tst_loss):
break
--
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