snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -514,11 +514,7 @@ class SRCNN:
     def test_step(self, mini_batch):
         inputs = [mini_batch[0]]
         labels = mini_batch[1]
-        in_nd = tf.make_ndarray(mini_batch[0])
-        print('****: ', in_nd.shape, in_nd.min(), in_nd.max())
         pred = self.model(inputs, training=False)
-        in_nd = tf.make_ndarray(pred)
-        print('****: ', in_nd.shape, in_nd.min(), in_nd.max())
         t_loss = self.loss(labels, pred)
 
         self.test_loss(t_loss)
@@ -780,27 +776,19 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     # grd_c = gaussian_filter(grd_c, sigma=1.0)
     grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
     grd_c = grd_c.copy()
-    print(grd_c.shape)
     grd_c = np.where(np.isnan(grd_c), 0, grd_c)
     hr_grd_c = grd_c.copy()
     hr_grd_c = hr_grd_c[y_128, x_128]
-    print(hr_grd_c.shape)
     grd_c = grd_c[slc_y_2, slc_x_2]
-    print(grd_c.shape)
     grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
-    print(grd_c.shape)
     grd_c = grd_c[y_k, x_k]
-    print(grd_c.shape)
     if label_param != 'cloud_probability':
         grd_c = normalize(grd_c, label_param, mean_std_dct)
-    print(grd_c.shape)
 
     # 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)
-    print(data.shape)
     data = np.expand_dims(data, axis=0)
-    print(data.shape)
 
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)