107 lines
5.0 KiB
Python
107 lines
5.0 KiB
Python
"""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 torch
|
|
import time
|
|
|
|
if __name__ == '__main__':
|
|
opt = TestOptions().parse() # get test options
|
|
# hard-code some parameters for test
|
|
opt.num_threads = 0 # test code only supports num_threads = 1
|
|
opt.batch_size = 1 # 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.
|
|
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
|
|
model.setup(opt) # regular setup: load and print networks; create schedulers
|
|
# create a website
|
|
web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory
|
|
if opt.load_iter > 0: # load_iter is 0 by default
|
|
web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
|
|
print('creating web directory', web_dir)
|
|
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
|
|
# test with eval mode. This only affects layers like batchnorm and dropout.
|
|
# For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
|
|
# For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
|
|
if opt.eval:
|
|
model.eval()
|
|
|
|
torch.manual_seed(0)
|
|
|
|
# Initialize Metrics
|
|
l1_error, l2_error, min_dist, l0_error = 0.0, 0.0, 0.0, 0.0
|
|
n_dist, n_samples = 0, 0
|
|
|
|
for i, data in enumerate(dataset):
|
|
if i >= opt.num_test: # only apply our model to opt.num_test images.
|
|
break
|
|
model.set_input(data) # unpack data from data loader
|
|
|
|
# Get ground-truth output
|
|
with torch.no_grad():
|
|
model.forward_noattack()
|
|
|
|
# Attack
|
|
input_adv, perturb = model.attack(target=model.fake_noattack)
|
|
|
|
# Get output from adversarial sample
|
|
with torch.no_grad():
|
|
model.forward_attack(perturb)
|
|
model.compute_visuals()
|
|
|
|
# Compute metrics
|
|
l1, l2, l0, d, above = model.compute_errors()
|
|
l1_error += l1
|
|
l2_error += l2
|
|
l0_error += l0
|
|
min_dist += d
|
|
n_dist += above
|
|
n_samples += 1
|
|
|
|
# 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))
|
|
save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
|
|
|
|
# Print metrics
|
|
print('{} images. L1 error: {}. L2 error: {}. prop_dist: {}. L0 error: {}. L_-inf error: {}.'.format(n_samples,
|
|
l1_error / n_samples, l2_error / n_samples, float(n_dist) / n_samples, l0_error / n_samples, min_dist / n_samples))
|
|
|
|
webpage.save() # save the HTML
|
|
|
|
|