snapshot...

modules/deeplearning/cnn_cld_frac_mod_res.py

@@ -110,19 +110,6 @@ if DO_ESPCN:
     y_128 = slice(2, 130)
 
 
-# gpus = tf.config.list_physical_devices('GPU')
-# if gpus:
-#   try:
-#     # Currently, memory growth needs to be the same across GPUs
-#     for gpu in gpus:
-#       tf.config.experimental.set_memory_growth(gpu, True)
-#     logical_gpus = tf.config.list_logical_devices('GPU')
-#     print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
-#   except RuntimeError as e:
-#     # Memory growth must be set before GPUs have been initialized
-#     print(e)
-
-
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
                                 kernel_initializer='he_uniform', scale=None, kernel_size=3,
                                 do_drop_out=True, drop_rate=0.5, do_batch_norm=True):
@@ -211,6 +198,30 @@ def get_label_data(grd_k):
     return s
 
 
+def get_label_data_5cat(grd_k):
+    grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+    grd_k = np.where(grd_k < 0.5, 0, 1)
+
+    a = grd_k[:, 0::2, 0::2]
+    b = grd_k[:, 1::2, 0::2]
+    c = grd_k[:, 0::2, 1::2]
+    d = grd_k[:, 1::2, 1::2]
+    s = a + b + c + d
+
+    cat_0 = (s == 0)
+    cat_1 = (s == 1)
+    cat_2 = (s == 2)
+    cat_3 = (s == 3)
+    cat_4 = (s == 4)
+
+    s[cat_0] = 0
+    s[cat_1] = 1
+    s[cat_2] = 2
+    s[cat_3] = 3
+    s[cat_4] = 4
+
+    return s
+
 class SRCNN:
     
     def __init__(self):