Fix stash

src/test.py

@@ -20,11 +20,7 @@ def test(data, model, checkpoint_info, dataset_id):
  generatedB = os.path.join(path_to_dataset, 'generatedB' + os.sep)
  genA2B = model['genA2B']
  genB2A = model['genB2A']
-<<<<<<< Updated upstream
 
-=======
-
->>>>>>> Stashed changes
  checkpoint, checkpoint_dir = checkpoint_info
  restore_from_checkpoint(checkpoint, checkpoint_dir)
  test_datasetA, test_datasetB, testA_size, testB_size = data
@@ -61,10 +57,6 @@ def test(data, model, checkpoint_info, dataset_id):
  with tf.device("/cpu:0"): # Preprocess data on CPU for significant performance gains.
  data = load_test_data(dataset_id, project_dir)
  with tf.device("/gpu:0"):
-<<<<<<< Updated upstream
  model = define_model(training=False)
-=======
- model = define_model(initial_learning_rate, training=False)
->>>>>>> Stashed changes
  checkpoint_info = initialize_checkpoint(checkpoint_dir, model, training=False)
  test(data, model, checkpoint_info, dataset_id)

src/train.py

@@ -25,13 +25,7 @@
 batch_size = 1 # Set batch size to 4 or 16 if training multigpu
 img_size = 256
 cyc_lambda = 10
-<<<<<<< Updated upstream
 identity_lambda = 0.5
-=======
-identity_lambda = 0
-if dataset_id == 'facades':
- identity_lambda = 0.5
->>>>>>> Stashed changes
 epochs = 15
 save_epoch_freq = 5
 batches_per_epoch = models.get_batches_per_epoch(dataset_id, project_dir)
@@ -143,32 +137,23 @@ def train(data, model, checkpoint_info, epochs):
  discA_real = discA(trainA)
  discB_real = discB(trainB)
 
- discA_fake_refined = discA(genB2A_output)
- discB_fake_refined = discB(genA2B_output)
+ discA_fake = discA(genB2A_output)
+ discB_fake = discB(genA2B_output)
  # Sample from history buffer of 50 images:
- #discA_fake_refined = discA_buffer.query(discA_fake)
- #discB_fake_refined = discB_buffer.query(discB_fake)
+ discA_fake_refined = discA_buffer.query(discA_fake)
+ discB_fake_refined = discB_buffer.query(discB_fake)
 
-<<<<<<< Updated upstream
  identityA = genB2A(trainA)
  identityB = genA2B(trainB)
-=======
- reconstructedA = genB2A(genA2B_output)
- reconstructedB = genA2B(genB2A_output)
- identityA = genB2A(trainA)
- identityB = genA2B(trainB)
-
- cyc_loss = cyc_lambda * cycle_consistency_loss(trainA, trainB, reconstructedA, reconstructedB)
->>>>>>> Stashed changes
  id_loss = identity_lambda * cyc_lambda * identity_loss(trainA, trainB, identityA, identityB)
 
  genA2B_loss_basic = generator_loss(discB_fake_refined)
  genB2A_loss_basic = generator_loss(discA_fake_refined)
- cyc_loss_A = cyc_lambda * cycle_consistency_loss(trainA, reconstructedA)
- cyc_loss_B = cyc_lambda * cycle_consistency_loss(trainB, reconstructedB)
+ cyc_lossA = cyc_lambda * cycle_consistency_loss(trainA, reconstructedA)
+ cyc_lossB = cyc_lambda * cycle_consistency_loss(trainB, reconstructedB)
 
- genA2B_loss = genA2B_loss_basic + cyc_loss_A + cyc_loss_B + id_loss
- genB2A_loss = genB2A_loss_basic + cyc_loss_B + cyc_loss_B + id_loss
+ genA2B_loss = genA2B_loss_basic + cyc_lossA + cyc_lossB + id_loss
+ genB2A_loss = genB2A_loss_basic + cyc_lossB + cyc_lossB + id_loss
 
  discA_loss = discriminator_loss(discA_real, discA_fake_refined)
  discB_loss = discriminator_loss(discB_real, discB_fake_refined)