diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 2dcb906ddbd5f017bccbbf1cbaae58a8dba20371..41289cae8c8084418e888796439edd23b26a2721 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -69,21 +69,21 @@ print('data_params_full: ', data_params_full)
print('label_param: ', label_param)
KERNEL_SIZE = 3 # target size: (128, 128)
-N = 1
+N_X = N_Y = 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)
- x_128 = slice(3, N*128 + 3)
- y_128 = slice(3, N*128 + 3)
+ slc_x = slice(2, N_X*128 + 4)
+ slc_y = slice(2, N_Y*128 + 4)
+ slc_x_2 = slice(1, N_X*128 + 6, 2)
+ slc_y_2 = slice(1, N_Y*128 + 6, 2)
+ x_2 = np.arange(int((N_X*128)/2) + 3)
+ y_2 = np.arange(int((N_Y*128)/2) + 3)
+ t = np.arange(0, int((N_X*128)/2) + 3, 0.5)
+ s = np.arange(0, int((N_Y*128)/2) + 3, 0.5)
+ x_k = slice(1, N_X*128 + 3)
+ y_k = slice(1, N_Y*128 + 3)
+ x_128 = slice(3, N_X*128 + 3)
+ y_128 = slice(3, N_Y*128 + 3)
elif KERNEL_SIZE == 5:
slc_x = slice(3, 135)
slc_y = slice(3, 135)
@@ -718,47 +718,42 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
def run_evaluate_static(in_file, out_file, ckpt_dir):
- N = 10
-
- 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)
- x_128 = slice(3, N*128 + 3)
- y_128 = slice(3, N*128 + 3)
-
- sub_y, sub_x = (N * 128) + 10, (N * 128) + 10
+ N_X = N_Y = 10
+
+ slc_x = slice(2, N_X*128 + 4)
+ slc_y = slice(2, N_Y*128 + 4)
+ slc_x_2 = slice(1, N_X*128 + 6, 2)
+ slc_y_2 = slice(1, N_Y*128 + 6, 2)
+ x_2 = np.arange(int((N_X*128)/2) + 3)
+ y_2 = np.arange(int((N_Y*128)/2) + 3)
+ t = np.arange(0, int((N_X*128)/2) + 3, 0.5)
+ s = np.arange(0, int((N_Y*128)/2) + 3, 0.5)
+ x_k = slice(1, N_X*128 + 3)
+ y_k = slice(1, N_Y*128 + 3)
+ x_128 = slice(3, N_X*128 + 3)
+ y_128 = slice(3, N_Y*128 + 3)
+
+ sub_y, sub_x = (N_Y * 128) + 10, (N_X * 128) + 10
y_0, x_0, = 3232 - int(sub_y/2), 3200 - int(sub_x/2)
h5f = h5py.File(in_file, 'r')
grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_a = grd_a.copy()
grd_a = np.where(np.isnan(grd_a), 0, grd_a)
hr_grd_a = grd_a.copy()
- hr_grd_a = hr_grd_a[y_128, x_128]
- # Full res:
- # grd_a = grd_a[slc_y, slc_x]
- # Half res:
- grd_a = grd_a[slc_y_2, slc_x_2]
- grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
- grd_a = grd_a[y_k, x_k]
+ grd_a = upsample(grd_a)
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
+ hr_grd_a = hr_grd_a[y_128, x_128]
+
# ------------------------------------------------------
grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_b = grd_b.copy()
grd_b = np.where(np.isnan(grd_b), 0, grd_b)
hr_grd_b = grd_b.copy()
hr_grd_b = hr_grd_b[y_128, x_128]
- grd_b = grd_b[slc_y, slc_x]
+ # Full res:
+ grd_b = grd_b[:, slc_y, slc_x]
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
grd_c = get_grid_values_all(h5f, label_param)
@@ -766,13 +761,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
hr_grd_c = grd_c.copy()
hr_grd_c = np.where(np.isnan(hr_grd_c), 0, grd_c)
hr_grd_c = hr_grd_c[y_128, x_128]
- # hr_grd_c = smooth_2d_single(hr_grd_c, sigma=1.0)
+
grd_c = np.where(np.isnan(grd_c), 0, grd_c)
- grd_c = grd_c.copy()
- # grd_c = smooth_2d_single(grd_c, sigma=1.0)
- grd_c = grd_c[slc_y_2, slc_x_2]
- grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
- grd_c = grd_c[y_k, x_k]
+ grd_c = upsample(grd_c)
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)