test...

modules/deeplearning/espcn.py

 import glob
 import tensorflow as tf
 from util.setup import logdir, modeldir, cachepath, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize
+from util.util import EarlyStop, normalize, denormalize, resample
 import os, datetime
 import numpy as np
 import pickle
@@ -59,6 +59,11 @@ data_idx, label_idx = 1, 1
 data_param = data_params[data_idx]
 label_param = label_params[label_idx]
 
+x_70 = np.arange(70)
+y_70 = np.arange(70)
+x_70_2 = x_70[3:67:2]
+y_70_2 = y_70[3:67:2]
+
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.leaky_relu, padding='SAME'):
 
@@ -194,18 +199,15 @@ class ESPCN:
         data = np.concatenate(label_s)
         label = np.concatenate(label_s)
 
-        # label = label[:, label_idx, :, :]
-        #label = label[:, label_idx, 3:67, 3:67]
-        label = label[:, label_idx, 0:32, 0:32]
-        label = np.expand_dims(label, axis=3)
-        #label = tf.image.resize(label, (32, 32), method='nearest').numpy()
-
-        # data = data[:, data_idx, :, :]
+        data = data[:, data_idx, :, :]
         #data = data[:, data_idx, 3:67, 3:67]
-        data = data[:, data_idx, 0:32, 0:32]
+        data = resample(x_70, y_70, data, x_70_2, y_70_2)
         data = np.expand_dims(data, axis=3)
-        #data = tf.image.resize(data, (32, 32), method='nearest').numpy()
-        # data = tf.image.resize(data, (36, 36)).numpy()
+        # data = tf.image.resize(data, (32, 32)).numpy()
+
+        # label = label[:, label_idx, :, :]
+        label = label[:, label_idx, 3:67:2, 3:67:2]
+        label = np.expand_dims(label, axis=3)
 
         data = data.astype(np.float32)
         label = label.astype(np.float32)
@@ -357,11 +359,11 @@ class ESPCN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_1')
 
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2')
-        #
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3')
-        #
-        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4')
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_2')
+
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3')
+
+        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4')
 
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding)(conv_b)