README and is_train -> train

README.md

@@ -16,7 +16,7 @@ Can specify epochs, learning rate, data directory, etc:
 `python main.py --epochs 10 --learning_rate 0.0001 --data_dir Train`
 <br>
 <br>
-For testing: `python main.py --is_train False`
+For testing: `python main.py --train False`
 
 To use FSCRNN-s instead of FSCRNN: `python main.py --fast True`
 

main.py

@@ -18,7 +18,7 @@ flags.DEFINE_integer("stride", 4, "The size of stride to apply to input image [4
 flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")
 flags.DEFINE_string("output_dir", "result", "Name of test output directory [result]")
 flags.DEFINE_string("data_dir", "FastTrain", "Name of data directory to train on [FastTrain]")
-flags.DEFINE_boolean("is_train", True, "True for training, False for testing [True]")
+flags.DEFINE_boolean("train", True, "True for training, false for testing [True]")
 flags.DEFINE_integer("threads", 1, "Number of processes to pre-process data with [1]")
 flags.DEFINE_boolean("params", False, "Save weight and bias parameters [False]")
 

model.py

@@ -23,7 +23,7 @@ class FSRCNN(object):
   def __init__(self, sess, config):
     self.sess = sess
     self.fast = config.fast
-    self.is_train = config.is_train
+    self.train = config.train
     self.c_dim = config.c_dim
     self.is_grayscale = (self.c_dim == 1)
     self.epoch = config.epoch
@@ -38,7 +38,7 @@ class FSRCNN(object):
     # Different image/label sub-sizes for different scaling factors x2, x3, x4
     scale_factors = [[10, 20], [11, 21], [6, 24]]
     self.image_size, self.label_size = scale_factors[self.scale - 2]
-    if not self.is_train:
+    if not self.train:
       self.stride = [10, 7, 6][self.scale - 2]
 
     # Different model layer counts/filter sizes for FSRCNN vs FSRCNN-s (fast)
@@ -102,7 +102,7 @@ class FSRCNN(object):
 
     if self.params:
       save_params(self.sess, self.weights, self.biases)
-    elif self.is_train:
+    elif self.train:
       self.train()
     else:
       self.test()

utils.py

@@ -65,7 +65,7 @@ def prepare_data(sess, dataset):
     
     For train dataset, output data would be ['.../t1.bmp', '.../t2.bmp', ..., '.../t99.bmp']
   """
-  if FLAGS.is_train:
+  if FLAGS.train:
     filenames = os.listdir(dataset)
     data_dir = os.path.join(os.getcwd(), dataset)
   else:
@@ -77,9 +77,9 @@ def prepare_data(sess, dataset):
 def make_data(sess, checkpoint_dir, data, label):
   """
   Make input data as h5 file format
-  Depending on 'is_train' (flag value), savepath would be changed.
+  Depending on 'train' (flag value), savepath would be changed.
   """
-  if FLAGS.is_train:
+  if FLAGS.train:
     savepath = os.path.join(os.getcwd(), '{}/train.h5'.format(checkpoint_dir))
   else:
     savepath = os.path.join(os.getcwd(), '{}/test.h5'.format(checkpoint_dir))