diff options
Diffstat (limited to 'tensorflow/contrib/gan/python/losses/python/losses_impl.py')
| -rw-r--r-- | tensorflow/contrib/gan/python/losses/python/losses_impl.py | 16 |
1 files changed, 7 insertions, 9 deletions
diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl.py b/tensorflow/contrib/gan/python/losses/python/losses_impl.py index 1ba3a64167..d389748374 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl.py @@ -949,6 +949,11 @@ def cycle_consistency_loss(data_x, * loss = (loss_x2x + loss_y2y) / 2 where `loss` is the final result. + For the L1-norm, we follow the original implementation: + https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua + we use L1-norm of pixel-wise error normalized by data size such that + `cycle_loss_weight` can be specified independent of image size. + See https://arxiv.org/abs/1703.10593 for more details. Args: @@ -965,19 +970,12 @@ def cycle_consistency_loss(data_x, A scalar `Tensor` of cycle consistency loss. """ - def _partial_cycle_consistency_loss(data, reconstructed_data): - # Following the original implementation - # https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua - # use L1-norm of pixel-wise error normalized by data size so that - # `cycle_loss_weight` can be specified independent of image size. - return math_ops.reduce_mean(math_ops.abs(data - reconstructed_data)) - with ops.name_scope( scope, 'cycle_consistency_loss', values=[data_x, reconstructed_data_x, data_y, reconstructed_data_y]): - loss_x2x = _partial_cycle_consistency_loss(data_x, reconstructed_data_x) - loss_y2y = _partial_cycle_consistency_loss(data_y, reconstructed_data_y) + loss_x2x = losses.absolute_difference(data_x, reconstructed_data_x) + loss_y2y = losses.absolute_difference(data_y, reconstructed_data_y) loss = (loss_x2x + loss_y2y) / 2.0 if add_summaries: summary.scalar('cycle_consistency_loss_x2x', loss_x2x) |