Merge pull request #13 from remicres/model_fixes_2

FIX: normalization of input for image summary

code/train.py

@@ -5,7 +5,7 @@
 from functools import partial
 import otbtf
 import logging
-from ops import blur2d, downscale2d
+from ops import downscale2d
 from vgg import compute_vgg_loss
 import network
 import constants
@@ -89,24 +89,23 @@ def main(unused_argv):
         iterator_init = iterator.make_initializer(tf_ds)
         dataset_inputs = iterator.get_next()
 
-        # placeholders with normalization
-        def _get_normalized_input(key, scale, name):
+        # model inputs
+        def _get_input(key, name):
             default_input = dataset_inputs[key]
             shape = (None, None, None, ds.output_shapes[key][-1])
-            ph = tf.compat.v1.placeholder_with_default(default_input, shape=shape, name=name)
-            return scale * ph
+            return tf.compat.v1.placeholder_with_default(default_input, shape=shape, name=name)
 
-        lr_image = _get_normalized_input(constants.lr_key, params.lr_scale, constants.lr_input_name)
-        hr_image = _get_normalized_input(constants.hr_key, params.hr_scale, constants.hr_input_name)
+        lr_image = _get_input(constants.lr_key, constants.lr_input_name)
+        hr_image = _get_input(constants.hr_key, constants.hr_input_name)
 
         # model
         hr_nch = ds.output_shapes[constants.hr_key][-1]
         generator = partial(network.generator, scope=constants.gen_scope, nchannels=hr_nch,
                             nresblocks=params.nresblocks, dim=params.depth)
         discriminator = partial(network.discriminator, scope=constants.dis_scope, dim=params.depth)
 
-        hr_images_real = {factor: downscale2d(hr_image, factor=factor) for factor in constants.factors}
-        hr_images_fake = generator(lr_image)
+        hr_images_real = {factor: params.hr_scale * downscale2d(hr_image, factor=factor) for factor in constants.factors}
+        hr_images_fake = generator(params.lr_scale * lr_image)
 
         # model outputs
         gen = {factor: (1.0 / params.hr_scale) * hr_images_fake[factor] for factor in constants.factors}