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import os,time
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from options.test_options import TestOptions
from dataloader.data_loader import dataloader
from model import create_model
from itertools import islice
from util.visualizer import save_images
from util import html
if __name__=='__main__':
opt = TestOptions().parse() # get test options
opt.name = 'imagenet'
opt.img_file='../../tmp/img/'
opt.mask_file='../../tmp/mask/'
opt.results_dir='../../results'
opt.model='tc'
opt.coarse_or_refine='refine'
opt.gpu_id=0
opt.no_shuffle=True
opt.batch_size=1
opt.preprocess='scale_shortside'
opt.mask_type=3
opt.load_size=512
opt.attn_G=True
opt.add_noise=True
dataset = dataloader(opt) # create a dataset
dataset_size = len(dataset) * opt.batch_size
print('testing images = %d' % dataset_size)
model = create_model(opt) # create a model
# create a website
opt.epoch = '%d' % opt.which_iter if opt.which_iter > 0 else opt.epoch
web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory
print('creating web directory', web_dir)
opt.save_dir = web_dir
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
opt.how_many = dataset_size if opt.how_many == float("inf") else opt.how_many
iter_data_time = time.time()
for i, data in enumerate(islice(dataset, opt.how_many)):
if i == 0:
model.setup(opt)
model.parallelize()
model.eval()
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
save_images(webpage, visuals, img_path, width=opt.display_winsize)
if i % 5 == 0:
print('processing (%04d)-th image... %s' % (i, img_path))
total_time = time.time() - iter_data_time
print('the total evaluation time %f' % (total_time))
webpage.save()
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