snapshot...

modules/deeplearning/icing_fcn.py

@@ -76,19 +76,29 @@ DO_ZERO_OUT = False
 lunar_map = {'cld_reff_dcomp': 'cld_reff_nlcomp', 'cld_opd_dcomp': 'cld_opd_nlcomp', 'iwc_dcomp': None, 'lwc_dcomp': None}
 
 
-# def build_residual_block_conv2d(x_in, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,
-#                                 do_drop_out=True, do_batch_norm=True):
-#     conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(x_in)
-#     conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
-#     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)
-#     skip = tf.keras.layers.BatchNormalization()(skip)
-#
-#     conv = conv + skip
-#     conv = tf.keras.layers.LeakyReLU()(conv)
-#     print(conv.shape)
+def build_residual_block_conv2d(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=5, 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)
+
+    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 build_residual_block_1x1(input_layer, num_filters, activation, block_name, padding='SAME', drop_rate=0.5,