-
Notifications
You must be signed in to change notification settings - Fork 66
/
test.py
129 lines (108 loc) · 6.87 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
"""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
import time
from deepliif.options.test_options import TestOptions
from deepliif.options import read_model_params, Options, print_options
from deepliif.data import create_dataset
from deepliif.models import create_model
from deepliif.util.visualizer import save_images
from deepliif.util import html
import torch
import click
@click.command()
#@click.option('--input-dir', default='./Sample_Large_Tissues/', help='reads images from here')
#@click.option('--output-dir', help='saves results here.')
#@click.option('--tile-size', default=None, help='tile size')
#@click.option('--model-dir', default='./model-server/DeepLIIF_Latest_Model/', help='load models from here.')
@click.option('--dataroot', required=True, help='reads images from here; expected to have a subfolder')
@click.option('--results_dir', required=True, help='saves results here.')
@click.option('--name', default='.', help='name of the experiment, used as a subfolder under results_dir')
@click.option('--checkpoints_dir', required=True, help='load models from here.')
@click.option('--gpu_ids', type=int, multiple=True)
@click.option('--num_test', default=10000, help='only run test for num_test images')
def test(dataroot, results_dir, name, checkpoints_dir, gpu_ids, num_test):
# retrieve options used in training setting, similar to cli.py test
model_dir = os.path.join(checkpoints_dir, name)
opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
# overwrite/supply unseen options using the values from the options provided in the command
setattr(opt,'checkpoints_dir',checkpoints_dir)
setattr(opt,'dataroot',dataroot)
setattr(opt,'name',name)
setattr(opt,'results_dir',results_dir)
setattr(opt,'num_test',num_test)
if not hasattr(opt,'seg_gen'): # old settings for DeepLIIF models
opt.seg_gen = True
number_of_gpus_all = torch.cuda.device_count()
if number_of_gpus_all < len(gpu_ids) and -1 not in gpu_ids:
number_of_gpus = 0
gpu_ids = [-1]
print(f'Specified to use GPU {opt.gpu_ids} for inference, but there are only {number_of_gpus_all} GPU devices. Switched to CPU inference.')
if len(gpu_ids) > 0 and gpu_ids[0] == -1:
gpu_ids = []
elif len(gpu_ids) == 0:
gpu_ids = list(range(number_of_gpus_all))
opt.gpu_ids = gpu_ids # overwrite gpu_ids; for test command, default gpu_ids at first is [] which will be translated to a list of all gpus
# hard-code some parameters for test.py
opt.aspect_ratio = 1.0 # from previous default setting
opt.display_winsize = 512 # from previous default setting
opt.use_dp = True # whether to initialize model in DataParallel setting (all models to one gpu, then pytorch controls the usage of specified set of GPUs for inference)
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.
print_options(opt)
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
torch.backends.cudnn.benchmark = False
# 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.
model.eval()
# if opt.eval:
# model.eval()
_start_time = time.time()
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
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)
t_sec = round(time.time() - _start_time)
(t_min, t_sec) = divmod(t_sec, 60)
(t_hour, t_min) = divmod(t_min, 60)
print('Time passed: {}hour:{}min:{}sec'.format(t_hour, t_min, t_sec))
webpage.save() # save the HTML
if __name__ == '__main__':
test()