snapshot

modules/deeplearning/espcn.py

@@ -211,7 +211,7 @@ class ESPCN:
         self.n_chans = 1
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
-        self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
+        # self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
         # self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
 
         self.inputs.append(self.X_img)
@@ -236,13 +236,14 @@ class ESPCN:
         label = np.concatenate(label_s)
 
         # label = label[:, label_idx, :, :]
-        label = label[:, label_idx, 4:68, 4:68]
+        label = label[:, label_idx, 3:67, 3:67]
         label = np.expand_dims(label, axis=3)
 
-        data = data[:, data_idx, :, :]
+        # data = data[:, data_idx, :, :]
+        data = data[:, data_idx, 3:67, 3:67]
         data = np.expand_dims(data, axis=3)
-        # data = tf.image.resize(data, (32, 32)).numpy()
-        data = tf.image.resize(data, (36, 36)).numpy()
+        data = tf.image.resize(data, (32, 32)).numpy()
+        # data = tf.image.resize(data, (36, 36)).numpy()
 
         data = data.astype(np.float32)
         label = label.astype(np.float32)
@@ -383,8 +384,8 @@ class ESPCN:
 
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding='VALID', activation=None)(input_2d)
-        # conv = input_2d
+        # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding='VALID', activation=None)(input_2d)
+        conv = input_2d
         print('input: ', conv.shape)
         skip = conv
 
@@ -818,6 +819,7 @@ def prepare(param_idx=1, filename='/Users/tomrink/data_valid_40.npy'):
     nda = np.load(filename)
     nda = nda[:, param_idx, :, :]
     nda = np.expand_dims(nda, axis=3)
+    # nda_lr = tf.image.resize(nda, (36, 36)).numpy()
     nda_lr = tf.image.resize(nda, (32, 32)).numpy()
     return nda_lr