test.py

### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
import time
import os
import numpy as np
from collections import OrderedDict
from torch.autograd import Variable
from options.test_options import TestOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import util.util as util
from util.visualizer import Visualizer
from util import html

opt = TestOptions().parse(save=False)
opt.nThreads = 1   # test code only supports nThreads = 1
opt.batchSize = 1  # test code only supports batchSize = 1
opt.serial_batches = True  # no shuffle
opt.no_flip = True	# no flip
if opt.dataset_mode == 'temporal':
	opt.dataset_mode = 'test'

data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
input_nc = 1 if opt.label_nc != 0 else opt.input_nc

save_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
print('Doing %d frames' % len(dataset))
for i, data in enumerate(dataset):
	if i >= opt.how_many:
		break	 
	if data['change_seq']:
		model.fake_B_prev = None

	_, _, height, width = data['A'].size()
	A = Variable(data['A']).view(1, -1, input_nc, height, width)
	B = Variable(data['B']).view(1, -1, opt.output_nc, height, width) if len(data['B'].size()) > 2 else None
	inst = Variable(data['inst']).view(1, -1, 1, height, width) if len(data['inst'].size()) > 2 else None
	generated = model.inference(A, B, inst)
	
	if opt.label_nc != 0:
		real_A = util.tensor2label(generated[1], opt.label_nc)
	else:
		c = 3 if opt.input_nc == 3 else 1
		real_A = util.tensor2im(generated[1][:c], normalize=True)	 
	
	
	visual_list = [('real_A', real_A), 
				   ('fake_B', util.tensor2im(generated[0].data[0]))]
	visuals = OrderedDict(visual_list) 
	img_path = data['A_path']
	print('process image... %s' % img_path)
	visualizer.save_images(save_dir, visuals, img_path)