first commit

4 files changed, 107 insertions, 0 deletions

diff --git a/options/__init__.py b/options/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/options/__init__.py
diff --git a/options/base_options.py b/options/base_options.py
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index 0000000..c569306
--- /dev/null
+++ b/options/base_options.py
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+import argparse
+import os
+from util import util
+from pdb import set_trace as st
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
+        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
+        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+        self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
+        self.parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD')
+        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')
+        self.parser.add_argument('--model', type=str, default='cycle_gan',
+                                 help='chooses which model to use. cycle_gan, one_direction_test, pix2pix, ...')
+        self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
+        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        self.parser.add_argument('--norm', type=str, default='batch', help='batch normalization or instance normalization')
+        self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
+        self.parser.add_argument('--display_winsize', type=int, default=256,  help='display window size')
+        self.parser.add_argument('--display_id', type=int, default=0, help='window id of the web display')
+        self.parser.add_argument('--identity', type=float, default=0.0, help='use identity mapping. Setting identity other than 1 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set optidentity = 0.1')
+
+        self.initialized = True
+
+    def parse(self):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+
+        args = dict((name, getattr(self.opt, name)) for name in dir(self.opt)
+                    if not name.startswith('_'))
+
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk
+        expr_dir =  os.path.join(self.opt.checkpoints_dir, self.opt.name)
+        util.mkdirs(expr_dir)
+        file_name = os.path.join(expr_dir, 'opt.txt')
+        with open(file_name, 'wt') as opt_file:
+            opt_file.write('------------ Options -------------\n')
+            for k, v in sorted(args.items()):
+                opt_file.write('%s: %s\n' % (str(k), str(v)))
+            opt_file.write('-------------- End ----------------\n')
+        return self.opt
diff --git a/options/test_options.py b/options/test_options.py
new file mode 100644
index 0000000..c4ecff6
--- /dev/null
+++ b/options/test_options.py
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+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', 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('--how_many', type=int, default=50, help='how many test images to run')
+        self.isTrain = False
diff --git a/options/train_options.py b/options/train_options.py
new file mode 100644
index 0000000..e7d4b3a
--- /dev/null
+++ b/options/train_options.py
@@ -0,0 +1,24 @@
+from .base_options import BaseOptions
+
+class TrainOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen')
+        self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
+        self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
+        self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')
+        self.parser.add_argument('--save_display_freq', type=int, default=2500, help='save the current display of results every save_display_freq_iterations')
+        self.parser.add_argument('--continue_train', action='store_true', help='if 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=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('--ntrain', type=int, default=float("inf"), help='# of examples per epoch.')
+        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='if true, 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('--preprocessing', type=str, default='resize_and_crop', help='resizing/cropping strategy')
+        self.isTrain = True