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test.py
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test.py
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"""General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from --checkpoints_dir and save the results to --results_dir.
It first creates model and dataset given the option. It will hard-code some parameters.
It then runs inference for --num_test images and save results to an HTML file.
Example (You need to train models first or download pre-trained models from our website):
Test a CycleGAN model (both sides):
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Test a CycleGAN model (one side only):
python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
The option '--model test' is used for generating CycleGAN results only for one side.
This option will automatically set '--dataset_mode single', which only loads the images from one set.
On the contrary, using '--model cycle_gan' requires loading and generating results in both directions,
which is sometimes unnecessary. The results will be saved at ./results/.
Use '--results_dir <directory_path_to_save_result>' to specify the results directory.
Test a pix2pix model:
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/test_options.py for more test options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.visualizer import save_images
from util import html
import util.util as util
import torch
import numpy as np
import ntpath
import torch.distributed as dist
import torch
from cal_fid import get_fid
import torchvision
from evaluation.evaluate_maps import eval_maps
from evaluation.evaluate_city2parsing import eval_city2parsing
from evaluation.parsing2city.evaluate import eval_parsing2city
def cleanup():
dist.destroy_process_group()
if __name__ == '__main__':
opt = TestOptions().parse() # get test options
opt.name = os.path.join(opt.dataroot.strip('../data/'), opt.model,
str(opt.batch_size) + '_' + str(opt.crop_size)
+ '_' + opt.direction
+ ('_' + opt.netG + '_' + opt.netD))
# hard-code some parameters for test
opt.num_threads = 10 # test code only supports num_threads = 1
opt.batch_size = 20 # test code only supports batch_size = 1
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
opt.load_size = opt.crop_size
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
model = create_model(opt) # create a model given opt.model and other options
opt.name = model.name
model = create_model(opt)
model.parallelize()
model.setup(opt)
if opt.eval:
model.eval()
# create a webpage for viewing the results
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)
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
with torch.no_grad():
for i, data in enumerate(dataset):
model.set_input(data) # unpack data from data loader
model.test() # run inference
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
if i % 5 == 0: # save images to an HTML file
print('processing (%04d)-th image... %s' % (i, img_path))
text_path = os.path.join(save_images(webpage, visuals, img_path, width=opt.display_winsize))
if opt.model =='cycle_gan':
save_path_B = os.path.join(text_path, 'fake_B')
save_path_A = os.path.join(text_path, 'fake_A')
else:
save_path = os.path.join(text_path,'fake_B')
# print(img_path[0])
for k, path in enumerate(img_path):
short_path = ntpath.basename(path)
name = os.path.splitext(short_path)[0]
if 'T1' in opt.dataroot:
np.save(os.path.join(save_path, name),
visuals['fake_B'].cpu().numpy()[k])
else:
if opt.model == 'cycle_gan':
saved_img = ((((visuals['fake_B'].cpu()[k]) + 1.0) / 2.0))
torchvision.utils.save_image(saved_img, os.path.join(save_path_B, name + '.png'))
saved_img = ((((visuals['fake_A'].cpu()[k]) + 1.0) / 2.0))
torchvision.utils.save_image(saved_img, os.path.join(save_path_A, name + '.png'))
else:
saved_img = ((((visuals['fake_B'].cpu()[k]) + 1.0) / 2.0))
torchvision.utils.save_image(saved_img, os.path.join(save_path, name + '.png'))
# util.save_image(saved_img, os.path.join(save_path, name, 'png'))
webpage.save() # save the HTML
if opt.dataroot.strip('../data/') == 'cityscapes':
if opt.model == 'cycle_gan':
real_root = os.path.join(opt.dataroot, 'testB')
metric = eval_city2parsing(real_root, save_path_B)
real_root = os.path.join(opt.dataroot, 'testB')
metric += '\n' + eval_parsing2city(real_root, save_path_A)
else:
if opt.direction == 'AtoB':
real_root = os.path.join(opt.dataroot, 'testB')
metric = eval_city2parsing(real_root, save_path)
elif opt.direction == 'BtoA':
real_root = os.path.join(opt.dataroot, 'testB')
metric = eval_parsing2city(real_root, save_path)
elif opt.dataroot.strip('../data/') == 'maps':
if opt.model == 'cycle_gan':
real_root = os.path.join(opt.dataroot, 'testB')
metric = eval_maps(real_root, save_path_B)
real_root = os.path.join(opt.dataroot, 'testA')
metric += '\n' + eval_maps(real_root, save_path_A)
else:
real_root = os.path.join(opt.dataroot, ('testB' if opt.direction == 'AtoB' else 'testA'))
metric = eval_maps(real_root, save_path)
else:
if opt.phase == 'test':
if opt.model == 'cycle_gan':
real_root_A = os.path.join(opt.dataroot, 'testA')
real_root_B = os.path.join(opt.dataroot, 'testB')
else:
real_root = os.path.join(opt.dataroot, ('testB' if opt.direction == 'AtoB' else 'testA'))
elif opt.phase == 'train':
if opt.model == 'cycle_gan':
real_root_A = os.path.join(opt.dataroot, 'trainA')
real_root_B = os.path.join(opt.dataroot, 'trainB')
else:
real_root = os.path.join(opt.dataroot, ('trainB' if opt.direction == 'AtoB' else 'trainA'))
if opt.model == 'cycle_gan':
fid = get_fid([real_root_A, save_path_A], 50, 2048, 8)
metric = 'fid: ' + str(fid)
fid = get_fid([real_root_B, save_path_B], 50, 2048, 8)
metric += '\n' + 'fid: ' + str(fid)
else:
fid = get_fid([real_root, save_path], 50, 2048, 8)
metric = 'fid: ' + str(fid)
file = open(os.path.join(text_path, 'eval_result.txt'), 'w')
file.writelines(metric)