snapshot...

modules/deeplearning/espcn_l1b_l2.py

@@ -55,8 +55,7 @@ label_params = ['refl_0_65um_nom', 'temp_11_0um_nom', 'cld_temp_acha', 'cld_pres
 
 DO_ZERO_OUT = False
 
-data_idx, label_idx = 1, 1
-data_param = data_params[data_idx]
+label_idx = 1
 label_param = label_params[label_idx]
 
 x_134 = np.arange(134)
@@ -211,11 +210,6 @@ class ESPCN:
         label = np.concatenate(label_s)
         data = np.concatenate(data_s)
 
-        # data = data[:, data_idx, :, :]
-        # data = resample(x_134, y_134, data, x_134_2, y_134_2)
-        # data = np.expand_dims(data, axis=3)
-        # data = data.astype(np.float32)
-
         add_noise = None
         noise_scale = None
         if is_training:
@@ -231,13 +225,6 @@ class ESPCN:
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
 
-        add_noise = None
-        noise_scale = None
-        if is_training:
-            add_noise = True
-            noise_scale = 0.005
-        data = normalize(data, data_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
-
         # label = label[:, label_idx, :, :]
         label = label[:, label_idx, 3:131:2, 3:131:2]
         # label = label[:, label_idx, 3:131, 3:131]
@@ -684,8 +671,7 @@ def prepare(param_idx=1, filename='/Users/tomrink/data_valid_40.npy'):
 
 def run_evaluate_static(in_file, out_file, param='temp_11_0um_nom', ckpt_dir='/Users/tomrink/tf_model_sres/run-20220805173619/'):
     nda = np.load(in_file)
-    nda = nda[:, data_idx, 2:133:2, 2:133:2]
-    nda = np.expand_dims(nda, axis=3)
+    nda = np.transpose(nda[0, 2, 3, 1])
 
     nn = ESPCN()
     out_sr = nn.run_evaluate(nda, param, ckpt_dir)