minor

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