snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -51,6 +51,7 @@ f.close()
 mean_std_dct.update(mean_std_dct_l1b)
 mean_std_dct.update(mean_std_dct_l2)
 
+IMG_DEPTH = 1
 # label_param = 'cloud_fraction'
 label_param = 'cld_opd_dcomp'
 # label_param = 'cloud_probability'
@@ -92,6 +93,11 @@ elif KERNEL_SIZE == 5:
     x_2 = np.arange(68)
     y_2 = np.arange(68)
 # ----------------------------------------
+# Exp for ESPCN version
+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',
@@ -411,7 +417,6 @@ class SRCNN:
 
         conv = conv + conv_b
         print(conv.shape)
-
         # This is effectively a Dense layer
         self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='regression')(conv)
 
@@ -747,8 +752,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
         grd_c = normalize(grd_c, label_param, mean_std_dct)
 
     # data = np.stack([grd_a, grd_b, grd_c], axis=2)
-    data = np.stack([grd_a, grd_c], axis=2)
-    # data = np.stack([grd_c], axis=2)
+    # data = np.stack([grd_a, grd_c], axis=2)
+    data = np.stack([grd_c], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()