Fix test.py bug

test.py

@@ -4,8 +4,8 @@
 """
 from __future__ import print_function
 from utils import get_config, get_data_loader_folder
-from trainer import MUNIT_Trainer
-from optparse import OptionParser
+from trainer import MUNIT_Trainer, UNIT_Trainer
+import argparse
 from torch.autograd import Variable
 import torchvision.utils as vutils
 import sys
@@ -14,50 +14,70 @@
 from torchvision import transforms
 from PIL import Image
 
-parser = OptionParser()
-parser.add_option('--config', type=str, help="net configuration")
-parser.add_option('--input', type=str, help="input image path")
-parser.add_option('--output_folder', type=str, help="output image path")
-parser.add_option('--checkpoint', type=str, help="checkpoint of autoencoders")
-parser.add_option('--style', type=str, default='', help="style image path")
-parser.add_option('--a2b', type=int, default=1, help="1 for a2b and others for b2a")
-parser.add_option('--seed', type=int, default=10, help="random seed")
-parser.add_option('--num_style',type=int, default=10, help="number of styles to sample")
-parser.add_option('--synchronized', action='store_true', help="whether use synchronized style code or not")
-parser.add_option('--output_only', action='store_true', help="whether use synchronized style code or not")
+parser = argparse.ArgumentParser()
+parser.add_argument('--config', type=str, help="net configuration")
+parser.add_argument('--input', type=str, help="input image path")
+parser.add_argument('--output_folder', type=str, help="output image path")
+parser.add_argument('--checkpoint', type=str, help="checkpoint of autoencoders")
+parser.add_argument('--style', type=str, default='', help="style image path")
+parser.add_argument('--a2b', type=int, default=1, help="1 for a2b and others for b2a")
+parser.add_argument('--seed', type=int, default=10, help="random seed")
+parser.add_argument('--num_style',type=int, default=10, help="number of styles to sample")
+parser.add_argument('--synchronized', action='store_true', help="whether use synchronized style code or not")
+parser.add_argument('--output_only', action='store_true', help="whether use synchronized style code or not")
+parser.add_argument('--output_path', type=str, default='.', help="path for logs, checkpoints, and VGG model weight")
+parser.add_argument('--trainer', type=str, default='MUNIT', help="MUNIT|UNIT")
 
-def main(argv):
-    (opts, args) = parser.parse_args(argv)
-    torch.manual_seed(opts.seed)
-    torch.cuda.manual_seed(opts.seed)
-    if not os.path.exists(opts.output_folder):
-        os.makedirs(opts.output_folder)
 
-    # Load experiment setting
-    config = get_config(opts.config)
-    style_dim = config['gen']['style_dim']
-    opts.num_style = 1 if opts.style != '' else opts.num_style
+opts = parser.parse_args()
+
+
+torch.manual_seed(opts.seed)
+torch.cuda.manual_seed(opts.seed)
+if not os.path.exists(opts.output_folder):
+    os.makedirs(opts.output_folder)
 
-    # Setup model and data loader
+# Load experiment setting
+config = get_config(opts.config)
+opts.num_style = 1 if opts.style != '' else opts.num_style
+
+# Setup model and data loader
+config['vgg_model_path'] = opts.output_path
+if opts.trainer == 'MUNIT':
+    style_dim = config['gen']['style_dim']
     trainer = MUNIT_Trainer(config)
-    state_dict = torch.load(opts.checkpoint)
-    trainer.gen_a.load_state_dict(state_dict['a'])
-    trainer.gen_b.load_state_dict(state_dict['b'])
-    trainer.cuda()
-    trainer.eval()
-    encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function
-    style_encode = trainer.gen_b.encode if opts.a2b else trainer.gen_a.encode # encode function
-    decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function
+elif opts.trainer == 'UNIT':
+    trainer = UNIT_Trainer(config)
+else:
+    sys.exit("Only support MUNIT|UNIT")
+state_dict = torch.load(opts.checkpoint)
+trainer.gen_a.load_state_dict(state_dict['a'])
+trainer.gen_b.load_state_dict(state_dict['b'])
+trainer.cuda()
+trainer.eval()
+encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function
+style_encode = trainer.gen_b.encode if opts.a2b else trainer.gen_a.encode # encode function
+decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function
 
-    transform = transforms.Compose([transforms.Resize(config['new_size']),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-    image = Variable(transform(Image.open(opts.input).convert('RGB')).unsqueeze(0).cuda(), volatile=True)
-    style_image = Variable(transform(Image.open(opts.style).convert('RGB')).unsqueeze(0).cuda(), volatile=True) if opts.style != '' else None
+if 'new_size' in config:
+    new_size = config['new_size']
+else:
+    if opts.a2b==1:
+        new_size = config['new_size_a']
+    else:
+        new_size = config['new_size_b']
+
+transform = transforms.Compose([transforms.Resize(new_size),
+                                transforms.ToTensor(),
+                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+image = Variable(transform(Image.open(opts.input).convert('RGB')).unsqueeze(0).cuda(), volatile=True)
+style_image = Variable(transform(Image.open(opts.style).convert('RGB')).unsqueeze(0).cuda(), volatile=True) if opts.style != '' else None
 
-    # Start testing
+# Start testing
+content, _ = encode(image)
+
+if opts.trainer == 'MUNIT':
     style_rand = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
-    content, _ = encode(image)
     if opts.style != '':
         _, style = style_encode(style_image)
     else:
@@ -68,9 +88,15 @@ def main(argv):
         outputs = (outputs + 1) / 2.
         path = os.path.join(opts.output_folder, 'output{:03d}.jpg'.format(j))
         vutils.save_image(outputs.data, path, padding=0, normalize=True)
-    if not opts.output_only:
-        # also save input images
-        vutils.save_image(image.data, os.path.join(opts.output_folder, 'input.jpg'), padding=0, normalize=True)
+elif opts.trainer == 'UNIT':
+    outputs = decode(content)
+    outputs = (outputs + 1) / 2.
+    path = os.path.join(opts.output_folder, 'output.jpg')
+    vutils.save_image(outputs.data, path, padding=0, normalize=True)
+else:
+    pass
+
+if not opts.output_only:
+    # also save input images
+    vutils.save_image(image.data, os.path.join(opts.output_folder, 'input.jpg'), padding=0, normalize=True)
 
-if __name__ == '__main__':
-    main(sys.argv)