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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 json
import os
from collections import OrderedDict
import torch
import evaluation.group_evaluator as group_evaluator
import util.util as util
from data import create_dataloader
from models import create_model
from options.test_options import TestOptions
from util import html
from util.visualizer import Visualizer
DATA_ROOT = os.getenv('DATA_ROOT', 'data')
DATAROOTS = {
'cityscapes': os.getenv('CITYSCAPES_DIR', os.path.join(DATA_ROOT, 'Cityscapes')),
'coco': os.getenv('COCO_DIR', os.path.join(DATA_ROOT, 'CocoStuff')),
'ade20k': os.getenv('ADE20K_DIR', os.path.join(DATA_ROOT, 'ADEChallengeData2016')),
'nyudepth': os.getenv('NYUDEPTH_DIR', os.path.join(DATA_ROOT, 'NYUDepth')),
'maps': os.getenv('MAPS_DIR', os.path.join(DATA_ROOT, 'pix2pix_maps')),
'gta': os.getenv('GTA_DIR', os.path.join(DATA_ROOT, 'GTA')),
}
def evaluate(model, opt, train_dataset, test_dataset, web_dir):
evaluator = group_evaluator.GroupEvaluator(opt)
metrics = evaluator.evaluate(
train_dataset=train_dataset,
test_dataset=test_dataset,
fn_model_forward=model.generate_visuals_for_evaluation,
)
mode = 'EVAL' if opt.use_eval_mode else 'TRAIN'
message = "MODE: {} | ".format(mode)
for k in sorted(list(metrics.keys())):
v = metrics[k]
message += "%s: %.3f " % (k, v)
print(message)
metric_log_path = os.path.join(web_dir, "metrics.txt")
with open(metric_log_path, "a") as f:
f.write(message + "\n")
metric_log_path = os.path.join(web_dir, "metrics.json")
with open(metric_log_path, 'w') as f:
json.dump(metrics, f)
metric_log_path = os.path.join(web_dir, "metrics_all.jsonl")
metrics['mode'] = mode.lower()
with open(metric_log_path, 'a') as f:
f.write(json.dumps(metrics, ensure_ascii=True) + '\n')
metrics.pop('mode')
return metrics
def test(
name,
opt=None,
which_epoch='latest',
num_test=float("inf"),
phase='test',
load_from_opt_file=True,
evaluation_metrics='fid',
results_dir='results',
gpu_ids=[0],
num_threads=1,
batch_size=1,
serial_batches=True,
no_flip=True,
no_flip_vert=True,
no_rotate=True,
fid_max_num_samples=50000,
use_wandb=True,
use_eval_mode=True,
inception_weights='fid_inception',
**kwargs,
):
opt.wandb_job_type = 'eval'
opt.phase = phase
opt.gpu_ids = gpu_ids
opt.num_test = num_test
opt.num_threads = num_threads
opt.use_eval_mode = use_eval_mode
opt.inception_weights = inception_weights
opt.batch_size = batch_size # test code only supports batch_size = 1
opt.load_from_opt_file = load_from_opt_file
opt.evaluation_metrics = evaluation_metrics
opt.serial_batches = serial_batches # disable data shuffling
opt.FID_max_num_samples = fid_max_num_samples
opt.cache_dir = 'evaluation/cache'
opt.cityscapes_FCN_dataroot = os.getenv(
'CITYSCAPES_DIR', '/data/datasets/Cityscapes/'
)
opt.results_dir = results_dir
opt.use_wandb = use_wandb
# no flip; comment this line if results on flipped images are needed.
opt.no_flip = no_flip
opt.no_flip_vert = no_flip_vert
opt.no_rotate = no_rotate
opt.which_epoch = which_epoch
# create a dataset given opt.dataset_mode and other options
dataset = create_dataloader(opt)
train_dataset = create_dataloader(util.copyconf(opt, phase="train"))
model = create_model(opt) # create a model given opt.model and other options
# create a website
web_dir = os.path.join(
opt.results_dir, opt.name, "{}_{}".format(opt.phase, opt.which_epoch)
) # define the website directory
print("creating web directory", web_dir)
webpage = html.HTML(
web_dir,
"Experiment = {}, Phase = {}, Epoch = {}".format(
opt.name, opt.phase, opt.which_epoch
),
)
if opt.use_eval_mode:
model.eval()
visualizer = Visualizer(opt)
with torch.no_grad():
metrics = evaluate(model, opt, train_dataset, dataset, web_dir)
if opt.use_wandb:
for name, value in metrics.items():
visualizer.wandb_run.summary[name] = value
model.eval()
for i, data_i in enumerate(dataset):
if i * opt.batch_size >= opt.num_test:
break
generated = model(data_i, mode='inference')
visuals = OrderedDict(
[
('input_label', data_i['label']),
('synthesized_image', generated),
('real_image', data_i['image']),
]
)
visualizer.display_current_results(visuals, 0, i)
img_path = data_i['path']
for b in range(generated.shape[0]):
print('process image... %s' % img_path[b])
visuals = OrderedDict(
[
('input_label', data_i['label'][b]),
('synthesized_image', generated[b]),
('real_image', data_i['image'][b]),
]
)
visualizer.save_images(webpage, visuals, img_path[b : b + 1])
webpage.save()
if opt.use_wandb:
visualizer.wandb_run.finish()
if __name__ == '__main__':
opt = TestOptions().parse()
test(opt.name, opt)