snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -59,7 +59,7 @@ label_param = 'cld_opd_dcomp'
 # label_param = 'cloud_probability'
 
 params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
-data_params = ['temp_11_0um_nom']
+data_params = ['temp_11_0um_nom', 'refl_0_65um_nom']
 # data_params = []
 
 label_idx = params.index(label_param)
@@ -730,23 +730,25 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     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]
-    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 = grd_a[slc_y, slc_x]
+    # 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 = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
-    #
-    # 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[y_130, x_130]
-    # refl = grd_b
-    # grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+    # ------------------------------------------------------
+    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]
+    grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
 
     grd_c = get_grid_values_all(h5f, label_param)
     grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
-
     hr_grd_c = grd_c.copy()
     hr_grd_c = hr_grd_c[y_128, x_128]
-
     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)
@@ -756,9 +758,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     if label_param != 'cloud_probability':
         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_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_c], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()
@@ -766,9 +768,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)
     if out_file is not None:
-        np.save(out_file, (out_sr[0, :, :, 0], hr_grd_a, hr_grd_c))
+        np.save(out_file, (out_sr[0, :, :, 0], hr_grd_a, hr_grd_b, hr_grd_c))
     else:
-        return out_sr, hr_grd_a, hr_grd_c
+        return out_sr, hr_grd_a, hr_grd_b, hr_grd_c
 
 
 def analyze(file='/Users/tomrink/cld_opd_out.npy'):