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Author: PangzeCheung

options/base_options.py

@@ -0,0 +1,116 @@
+import argparse
+import os
+from util import util
+import torch
+import data
+import models
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):    
+        # experiment specifics
+        self.parser.add_argument('--name', type=str, default='label2city', 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('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use')
+        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('--fp16', action='store_true', default=False, help='train with AMP')
+        self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+
+        # input/output sizes
+        self.parser.add_argument('--image_nc', type=int, default=3)
+        self.parser.add_argument('--pose_nc', type=int, default=18)
+        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
+        self.parser.add_argument('--old_size', type=int, default=(256, 176), help='Scale images to this size. The final image will be cropped to --crop_size.')
+        self.parser.add_argument('--loadSize', type=int, default=256, 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('--label_nc', type=int, default=35, help='# of input label channels')
+        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')
+
+        # for setting inputs
+        self.parser.add_argument('--dataset_mode', type=str, default='fashion')
+        self.parser.add_argument('--dataroot', type=str, default='/media/data2/zhangpz/DataSet/Fashion')
+        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=2, 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.')
+
+        # for displays
+        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.parser.add_argument('--display_id', type=int, default=0, help='display id of the web')  # 1
+        self.parser.add_argument('--display_port', type=int, default=8096, help='visidom port of the web display')
+        self.parser.add_argument('--display_single_pane_ncols', type=int, default=0,
+                            help='if positive, display all images in a single visidom web panel')
+        self.parser.add_argument('--display_env', type=str, default=self.parser.parse_known_args()[0].name.replace('_', ''),
+                            help='the environment of visidom display')
+        # for instance-wise features
+        self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')        
+        self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
+        self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')        
+        self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')        
+        self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
+        self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') 
+        self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')        
+        self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')
+
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        opt, _ = self.parser.parse_known_args()
+        # modify the options for different models
+        model_option_set = models.get_option_setter(opt.model)
+        self.parser = model_option_set(self.parser, self.isTrain)
+
+        data_option_set = data.get_option_setter(opt.dataset_mode)
+        self.parser = data_option_set(self.parser, self.isTrain)
+
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        if torch.cuda.is_available():
+            self.opt.device = torch.device("cuda")
+            torch.backends.cudnn.benchmark = True  # cudnn auto-tuner
+        else:
+            self.opt.device = torch.device("cpu")
+
+        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)
+        
+        # set gpu ids
+        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 ----------------')
+
+        # save to the disk        
+        expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+        util.mkdirs(expr_dir)
+        if save and not (self.isTrain and self.opt.continue_train):
+            name = 'train' if self.isTrain else 'test'
+            file_name = os.path.join(expr_dir, name+'_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