snapshot..

modules/deeplearning/srcnn_l1b_l2.py

@@ -724,36 +724,44 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
 def run_evaluate_static(in_file, out_file, ckpt_dir):
     N = 8
 
-    sub_y, sub_x = (N * 128) + 6, (N * 128) + 6
-    y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
-    x_130 = slice(2, (N * 128) + 4)
-    y_130 = slice(2, (N * 128) + 4)
+    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_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
-    y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
-    t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
+    sub_y, sub_x = (N * 128) + 10, (N * 128) + 10
+    y_0, x_0, = 2432 - int(sub_y/2), 2432 - 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[y_130, x_130]
-    bt = grd_a
-    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_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[y_130, x_130]
+    # bt = grd_a
+    # 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_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 = grd_c[slc_y_2, slc_x_2]
     if label_param != 'cloud_probability':
         grd_c = normalize(grd_c, label_param, mean_std_dct)
     grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
-    print(grd_a.shape, grd_b.shape, grd_c.shape)
+    grd_c = grd_c[y_k, x_k]
 
     # data = np.stack([grd_a, grd_b, grd_c], axis=2)
     data = np.stack([grd_c], axis=2)
@@ -766,40 +774,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     if out_file is not None:
         np.save(out_file, [out_sr, hr_grd_c])
     else:
-        return out_sr, bt, refl
-
-
-def run_evaluate_static_2(in_file, out_file, ckpt_dir):
-    nda = np.load(in_file)
-
-    grd_a = nda[:, 0, :, :]
-    grd_a = grd_a[:, slc_y_2, slc_x_2]
-    grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
-    grd_a = resample_2d_linear(x_2, y_2, grd_a, t, s)
-
-    grd_b = nda[:, 2, :, :]
-    grd_b = grd_b[:, slc_y_2, slc_x_2]
-    grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
-    grd_b = resample_2d_linear(x_2, y_2, grd_b, t, s)
-
-    grd_c = nda[:, 3, :, :]
-    grd_c = grd_c[:, slc_y_2, slc_x_2]
-    if label_param != 'cloud_probability':
-        grd_c = normalize(grd_c, label_param, mean_std_dct)
-    grd_c = resample_2d_linear(x_2, y_2, grd_c, t, s)
-
-    data = np.stack([grd_a, grd_b, grd_c], axis=3)
-    print(data.shape)
-
-    nn = SRCNN()
-    out_sr = nn.run_evaluate(data, ckpt_dir)
-    if label_param != 'cloud_probability':
-        out_sr = denormalize(out_sr, label_param, mean_std_dct)
-        pass
-    if out_file is not None:
-        np.save(out_file, out_sr)
-    else:
-        return out_sr
+        return out_sr, None, None
 
 
 def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_probability'):