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options/__init__.py

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+# options_init

options/base_options.py

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+import argparse
+import torch
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):
+        self.parser.add_argument('--name', type=str, default='demo', help='name of the experiment. It decides where to store samples and models')
+        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')        
+        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
+        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
+        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
+
+        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=512, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=512, 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('--dataroot', type=str,
+                                 default='dataset/') 
+        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        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('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
+        self.parser.add_argument('--nThreads', default=1, type=int, help='# threads for loading data')                
+        self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+
+        self.parser.add_argument('--display_winsize', type=int, default=512,  help='display window size')
+        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
+
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt, unk = self.parser.parse_known_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)
+
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        return self.opt

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('--warp_checkpoint', type=str, default='checkpoints/PFAFN/warp_model_final.pth', help='load the pretrained model from the specified location')
+        self.parser.add_argument('--gen_checkpoint', type=str, default='checkpoints/PFAFN/gen_model_final.pth', help='load the pretrained model from the specified location')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+
+        self.isTrain = False