snapshot...

modules/deeplearning/cloud_fraction_fcn.py

@@ -76,18 +76,18 @@ KERNEL_SIZE = 3  # target size: (128, 128)
 N = 1
 
 if KERNEL_SIZE == 3:
-    # slc_x = slice(2, N*128 + 4)
-    # slc_y = slice(2, N*128 + 4)
-    slc_x_2 = slice(1, N*128 + 6, 2)
-    slc_y_2 = slice(1, N*128 + 6, 2)
-    x_2 = np.arange(int((N*128)/2) + 3)
-    y_2 = np.arange(int((N*128)/2) + 3)
-    t = np.arange(0, int((N*128)/2) + 3, 0.5)
-    s = np.arange(0, int((N*128)/2) + 3, 0.5)
-    x_k = slice(1, N*128 + 3)
-    y_k = slice(1, N*128 + 3)
-    slc_x = slice(1, 67)
-    slc_y = slice(1, 67)
+    # # slc_x = slice(2, N*128 + 4)
+    # # slc_y = slice(2, N*128 + 4)
+    # slc_x_2 = slice(1, N*128 + 6, 2)
+    # slc_y_2 = slice(1, N*128 + 6, 2)
+    # x_2 = np.arange(int((N*128)/2) + 3)
+    # y_2 = np.arange(int((N*128)/2) + 3)
+    # t = np.arange(0, int((N*128)/2) + 3, 0.5)
+    # s = np.arange(0, int((N*128)/2) + 3, 0.5)
+    # x_k = slice(1, N*128 + 3)
+    # y_k = slice(1, N*128 + 3)
+    slc_x = slice(1, int((N*128)/2) + 3)
+    slc_y = slice(1, int((N*128)/2) + 3)
     x_128 = slice(4, N*128 + 4)
     y_128 = slice(4, N*128 + 4)
 elif KERNEL_SIZE == 5:
@@ -127,11 +127,11 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
     return conv
 
 
-def upsample(tmp):
-    tmp = tmp[:, slc_y_2, slc_x_2]
-    tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
-    tmp = tmp[:, y_k, x_k]
-    return tmp
+# def upsample(tmp):
+#     tmp = tmp[:, slc_y_2, slc_x_2]
+#     tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
+#     tmp = tmp[:, y_k, x_k]
+#     return tmp
 
 
 def upsample_mean(grd):