Add new model and change the clip size

config.py

@@ -1,16 +1,18 @@
 # parameters for model and training
-INPUT_SIZE = 28
+INPUT_SIZE = 36
 NUM_CHANNELS = 3
 PATCH_SIZE = 80
 SCALE_FACTOR = 2
 LABEL_SIZE = SCALE_FACTOR * INPUT_SIZE
 JPEG_NOISE_LEVEL = 1
 GAUSSIAN_NOISE_STD = 0.01
 BATCH_SIZE = 16
-NUM_EPOCHS = 100000
+NUM_EPOCHS = 500000
+MODEL_NAME = 'vgg7'
 # train dataset path and etc
-TRAIN_PATH = 'E:/image_data/data_train/'
-TEST_PATH = 'E:/image_data/data_test/'
-TRAINING_SUMMARY_PATH = 'E:/image_data/Training_summary/'
-CHECKPOINT_PATH = 'E:/image_data/checkpoint/'
-INFERENCE_SAVE_PATH = 'C:/Users/kyloris/Desktop/inference'
+TRAIN_PATH = '/Users/kyloris/Projects/image_data/data_train/'
+TEST_PATH = '/Users/kyloris/Projects/image_data/data_test/'
+TRAINING_SUMMARY_PATH = '/Users/kyloris/Projects/image_data/Training_summary/'
+CHECKPOINT_PATH = '/Users/kyloris/Projects/image_data/checkpoint/'
+INFERENCE_SAVE_PATH = '/Users/kyloris/Projects/image_data/inference/'
+OUTPUT_SAVE_PATH = './save/'

model.py

@@ -3,7 +3,14 @@
 from config import *
 
 
-def create_model(patches):
+def create_model(patches, model_name=MODEL_NAME):
+    if model_name == 'vgg7':
+        return vgg7(patches)
+    elif model_name == 'vgg12':
+        return vgg12(patches)
+
+
+def vgg7(patches):
     with tf.variable_scope('vgg7'):
         net = tl.layers.InputLayer(patches, name='input_layer')
         net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
@@ -44,6 +51,67 @@ def create_model(patches):
         return net.outputs
 
 
+def vgg12(patches):
+    with tf.variable_scope('vgg12'):
+        net = tl.layers.InputLayer(patches, name='input_layer')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 3, 16),
+                                    padding='VALID',
+                                    name='Conv1')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 16, 16),
+                                    padding='VALID',
+                                    name='Conv2')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 16, 16),
+                                    padding='VALID',
+                                    name='Conv3')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 16, 32),
+                                    padding='VALID',
+                                    name='Conv4')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 32, 64),
+                                    padding='VALID',
+                                    name='Conv5')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 64, 64),
+                                    padding='VALID',
+                                    name='Conv6')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 64, 64),
+                                    padding='VALID',
+                                    name='Conv7')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 64, 128),
+                                    padding='VALID',
+                                    name='Conv8')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 128, 128),
+                                    padding='VALID',
+                                    name='Conv9')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 128, 256),
+                                    padding='VALID',
+                                    name='Conv10')
+        net = tl.layers.Conv2dLayer(net, act=tf.nn.leaky_relu,
+                                    shape=(3, 3, 256, 256),
+                                    padding='VALID',
+                                    name='Conv11')
+
+        batch_size = int(net.outputs.shape[0])
+        rows = int(net.outputs.shape[1])
+        cows = int(net.outputs.shape[2])
+        channels = int(patches.get_shape()[3])
+
+        net = tl.layers.DeConv2dLayer(net,
+                                      shape=(4, 4, 3, 256),
+                                      output_shape=(batch_size, rows * 2, cows * 2, channels),
+                                      strides=(1, 2, 2, 1),
+                                      name='Deconv')
+        return net.outputs
+
+
 def s_mse_loss(inference, ground_truth, name='mse_loss'):
     with tf.name_scope(name):
         slice_begin = (int(ground_truth.get_shape()[1]) - int(inference.get_shape()[1])) // 2

sk-2x.py

@@ -6,6 +6,9 @@
 
 
 def main():
+    temp = input('Enter filename: ')
+    f_name = './' + temp + '.png'
+    f2x_name = OUTPUT_SAVE_PATH + temp + '_2x.png'
     ckpt_state = tf.train.get_checkpoint_state(CHECKPOINT_PATH)
     if not ckpt_state or not ckpt_state.model_checkpoint_path:
         print('No check point files are found!')
@@ -28,7 +31,7 @@ def main():
     saver = tf.train.Saver(tf.global_variables())
     saver.restore(sess, ckpt_files[-1])  # load the lateast model
 
-    low_res_img = cv.imread('./005.png')
+    low_res_img = cv.imread(f_name)
     output_size = int(inferences.get_shape()[1])
     input_size = INPUT_SIZE
     available_size = output_size // SCALE_FACTOR
@@ -63,7 +66,7 @@ def main():
     high_res_img = tf.image.convert_image_dtype(high_res_img, tf.uint8, True)
 
     high_res_img = high_res_img[:SCALE_FACTOR * img_rows, :SCALE_FACTOR * img_cols, ...]
-    cv.imwrite('./005-2x.png', high_res_img.eval(session=sess))
+    cv.imwrite(f2x_name, high_res_img.eval(session=sess))
 
     print('Enhance Finished!')