1. Added flipping functionality

2. Changed the default options

4 files changed, 19 insertions, 12 deletions

diff --git a/data/aligned_data_loader.py b/data/aligned_data_loader.py
index a1efde8..039c113 100644
--- a/data/aligned_data_loader.py
+++ b/data/aligned_data_loader.py
@@ -43,12 +43,16 @@ class AlignedDataLoader(BaseDataLoader):
     def initialize(self, opt):
         BaseDataLoader.initialize(self, opt)
         self.fineSize = opt.fineSize
-        transform = transforms.Compose([
+
+        transformations = [
             # TODO: Scale
             transforms.Scale(opt.loadSize),
             transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5),
-                                 (0.5, 0.5, 0.5))])
+                                 (0.5, 0.5, 0.5))]
+        if opt.isTrain and not opt.no_flip:
+            transformations.insert(1, transforms.RandomHorizontalFlip())
+        transform = transforms.Compose(transformations)
 
         # Dataset A
         dataset = ImageFolder(root=opt.dataroot + '/' + opt.phase,
diff --git a/data/unaligned_data_loader.py b/data/unaligned_data_loader.py
index 77f9274..3deb55b 100644
--- a/data/unaligned_data_loader.py
+++ b/data/unaligned_data_loader.py
@@ -53,12 +53,14 @@ class PairedData(object):
 class UnalignedDataLoader(BaseDataLoader):
     def initialize(self, opt):
         BaseDataLoader.initialize(self, opt)
-        transform = transforms.Compose([
-                                       transforms.Scale(opt.loadSize),
-                                       transforms.RandomCrop(opt.fineSize),
-                                       transforms.ToTensor(),
-                                       transforms.Normalize((0.5, 0.5, 0.5),
-                                                            (0.5, 0.5, 0.5))])
+        transformations = [transforms.Scale(opt.loadSize),
+                           transforms.RandomCrop(opt.fineSize),
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.5, 0.5, 0.5),
+                                                (0.5, 0.5, 0.5))]
+        if opt.isTrain and not opt.no_flip:
+            transformations.insert(1, transforms.RandomHorizontalFlip())
+        transform = transforms.Compose(transformations)
 
         # Dataset A
         dataset_A = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'A',
diff --git a/options/base_options.py b/options/base_options.py
index 9ec7c9a..619ca60 100644
--- a/options/base_options.py
+++ b/options/base_options.py
@@ -20,7 +20,6 @@ class BaseOptions():
         self.parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG')
         self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
         self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2')
-        self.parser.add_argument('--flip'  , action='store_true', help='if flip the images for data argumentation')
         self.parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
         self.parser.add_argument('--align_data', action='store_true',
                                 help='if True, the datasets are loaded from "test" and "train" directories and the data pairs are aligned')
diff --git a/options/train_options.py b/options/train_options.py
index 4b4eac3..a1d347f 100644
--- a/options/train_options.py
+++ b/options/train_options.py
@@ -10,14 +10,16 @@ class TrainOptions(BaseOptions):
         self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
         self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
         self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        self.parser.add_argument('--niter', type=int, default=200, help='# of iter at starting learning rate')
-        self.parser.add_argument('--niter_decay', type=int, default=0, help='# of iter to linearly decay learning rate to zero')
+        self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
+        self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
         self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
         self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
         self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
         self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
         self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
-        self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
+        self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
         self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
+        self.parser.add_argument('--no_flip'  , action='store_true', help='if specified, do not flip the images for data argumentation')
+
         # NOT-IMPLEMENTED self.parser.add_argument('--preprocessing', type=str, default='resize_and_crop', help='resizing/cropping strategy')
         self.isTrain = True