add comments: networks.py

Author: junyanz

models/base_model.py

@@ -16,7 +16,7 @@ class BaseModel(ABC):
     """
 
     def __init__(self, opt):
-        """Initailize the BaseModel class.
+        """Initialize the BaseModel class.
 
         Parameters:
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

models/colorization_model.py

@@ -30,7 +30,7 @@ def modify_commandline_options(parser, is_train=True):
         return parser
 
     def __init__(self, opt):
-        """Initailize the class.
+        """Initialize the class.
 
         Parameters:
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

models/cycle_gan_model.py

@@ -45,7 +45,7 @@ def modify_commandline_options(parser, is_train=True):
         return parser
 
     def __init__(self, opt):
-        """Initailize the CycleGAN class.
+        """Initialize the CycleGAN class.
 
         Parameters:
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

models/networks.py

@@ -38,7 +38,7 @@ def modify_commandline_options(parser, is_train=True):
         return parser
 
     def __init__(self, opt):
-        """Initailize the pix2pix class.
+        """Initialize the pix2pix class.
 
         Parameters:
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
@@ -57,7 +57,7 @@ def __init__(self, opt):
         self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
 
-        if self.isTrain:  # define a discriminator
+        if self.isTrain:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
             self.netD = networks.define_D(opt.input_nc + opt.output_nc, opt.ndf, opt.netD,
                                           opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
 

models/pix2pix_model.py

@@ -29,7 +29,7 @@ def modify_commandline_options(parser, is_train=True):
         return parser
 
     def __init__(self, opt):
-        """Initailize the pix2pix class.
+        """Initialize the pix2pix class.
 
         Parameters:
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

models/test_model.py

@@ -28,8 +28,8 @@ def initialize(self, parser):
         parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
         parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
         parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
-        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
-        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
+        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
+        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
         parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
         parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
         parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')

options/base_options.py

@@ -33,7 +33,7 @@ def initialize(self, parser):
         parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
         parser.add_argument('--gan_mode', type=str, default='lsgan', help='the type of GAN objective. [vanilla| lsgan ｜ wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
         parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
-        parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy. [lambda | step | plateau | cosine]')
+        parser.add_argument('--lr_policy', type=str, default='linear', help='learning rate policy. [linear | step | plateau | cosine]')
         parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')
 
         self.isTrain = True