add upsample resnet model

model_resnet_up.py

@@ -0,0 +1,32 @@
+import tensorflow as tf
+import util
+
+upsample = True
+
+def build_model(x, scale, training):
+    hidden_size = 128
+    bottleneck_size = 32
+    x = tf.layers.conv2d(x, hidden_size, 3, activation=None, name='in')
+    for i in range(5):
+        x = util.crop_by_pixel(x, 1) + conv(x, hidden_size, bottleneck_size, training, 'lr_conv'+str(i), False)
+    x = tf.layers.conv2d_transpose(x, hidden_size, scale, strides=scale, activation=None, name='up')
+    for i in range(5):
+        x = util.crop_by_pixel(x, 1) + conv(x, hidden_size, bottleneck_size, training, 'hr_conv'+str(i), False)
+    x = tf.layers.conv2d(x, 3, 1, activation=None, name='out')
+    return x
+
+def conv(x, hidden_size, bottleneck_size, training, name, reuse):
+    x = tf.layers.batch_normalization(x, training=training)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, bottleneck_size, 1, activation=None, name=name+'_proj', reuse=reuse)
+
+    x = tf.layers.batch_normalization(x, training=training)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, bottleneck_size, 3, activation=None, name=name+'_filt', reuse=reuse)
+
+    x = tf.layers.batch_normalization(x, training=training)
+    x = tf.nn.relu(x)
+    x = tf.layers.conv2d(x, hidden_size, 1, activation=None, name=name+'_recv', reuse=reuse)
+    return x
+
+