snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -67,11 +67,19 @@ x_64 = np.arange(64)
 y_64 = np.arange(64)
 x_134_2 = x_134[3:131:2]
 y_134_2 = y_134[3:131:2]
-# x_134_2 = x_134[2:133:2]
-# y_134_2 = y_134[2:133:2]
 t = np.arange(0, 64, 0.5)
 s = np.arange(0, 64, 0.5)
 
+x_128_2 = x_134[3:131:2]
+y_128_2 = y_134[3:131:2]
+x_128 = x_134[3:131]
+y_128 = y_134[3:131]
+
+#----------- New
+# x_134_2 = x_134[1:134:2]
+# t = np.arange(1, 66, 0.5)
+#--------------------------
+
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
                                 kernel_initializer='he_uniform', scale=None,
@@ -225,20 +233,19 @@ class SRCNN:
         data_norm = []
         for param in data_params:
             idx = params.index(param)
-            tmp = input_data[:, idx, 3:131:2, 3:131:2]
+            tmp = input_data[:, idx, y_128_2, x_128_2]
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
             data_norm.append(tmp)
-        # --------
+        # --------------------------
         param = 'refl_0_65um_nom'
         idx = params.index(param)
-        tmp = input_data[:, idx, 3:131:2, 3:131:2]
-        # tmp = input_data[:, idx, 3:131, 3:131]
+        tmp = input_data[:, idx, y_128_2, x_128_2]
         tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
         data_norm.append(tmp)
         # --------
-        tmp = input_data[:, label_idx, 3:131:2, 3:131:2]
+        tmp = input_data[:, label_idx, y_128_2, x_128_2]
         if label_param != 'cloud_fraction':
             tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         else:
@@ -249,8 +256,8 @@ class SRCNN:
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)
         # -----------------------------------------------------
-        # label = input_data[:, label_idx, 3:131:2, 3:131:2]
-        label = input_data[:, label_idx, 3:131, 3:131]
+        # -----------------------------------------------------
+        label = input_data[:, label_idx, y_128, x_128]
         if label_param != 'cloud_fraction':
             label = normalize(label, label_param, mean_std_dct)
         else: