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save_syn.py
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save_syn.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
import sys
import h5py
import numpy as np
from PIL import Image
from data import create_dataset
from models import create_model
from options.test_options import TestOptions
from util import html
from util.visualizer import save_images
def save_data(gt, visuals, index, model, file):
if np.sum(gt[index].numpy() + 1) < 10:
print("skip this seg")
return
real_img = visuals['real_B'][index]
syn_img = visuals['fake_B'][index]
label = visuals['real_A'][index]
img_path = model.get_image_paths() # get image paths
gt = gt[index].cpu().squeeze().numpy()
syn_img = syn_img.cpu().numpy()
# modify to match segmentation task
# syn_img = (syn_img - syn_img.min()) * (255 / (syn_img- syn_img.min()).max())
syn_img = (syn_img + 1) * (255 / 2)
syn_img = syn_img.astype("uint8")
syn_img = np.moveaxis(syn_img, 0, -1)
gt = (gt+1)
gt[gt>0]=255
gt = gt.astype("uint8")
syn_img = Image.fromarray(syn_img)
syn_img = np.array(syn_img.resize((240,240)))
gt = Image.fromarray(gt)
gt = np.array(gt.resize((240,240), resample=Image.BILINEAR))
file.create_dataset(f"images/{img_path[0]}_{index}", data=syn_img)
file.create_dataset(f"labels/{img_path[0]}_{index}", data=gt)
file.create_dataset(f"labels_with_skull/{img_path[0]}_{index}", data=label.cpu().numpy())
file.create_dataset(f"reference_real_image_please_dont_use/{img_path[0]}_{index}", data=real_img.cpu().numpy())
# misc.imsave(f"imgs/test/{img_path[0]}_{index}_img.png",
# np.moveaxis(syn_img.cpu().squeeze().numpy(), 0, -1))
# misc.imsave(f"imgs/test/{img_path[0]}_{index}_seg.png",gt)
# misc.imsave(f"imgs/test/{img_path[0]}_{index}_label.png",
# np.moveaxis(label.cpu().squeeze().numpy(), 0, -1))
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, '%s_%s' % (opt.phase, opt.epoch)) # define the website directory
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()
# db_name = "whole_syn_db_uint8.h5"
# f = h5py.File(os.path.join(web_dir, db_name), 'w')
folder_name = f"brats_multilabel_perceptionloss100_hgglgg_{opt.netG}_epoch{opt.epoch}"
# root_path = os.path.join(web_dir, folder_name)
file = h5py.File(os.path.join(web_dir, f"{folder_name}.h5"), 'w')
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
#
gt = data['Seg']
for j in range(gt.shape[0]):
sys.stdout.flush()
save_data(gt, visuals, j, model, file)
# misc.imsave(os.path.join(root_path, "images", f"{img_path[0]}.png"),
# np.moveaxis(syn_img.cpu().squeeze().numpy(), 0, -1))
# misc.imsave(os.path.join(root_path, "labels", f"{img_path[0]}.png"),
# np.moveaxis(gt.cpu().squeeze().numpy(), 0, -1))
# 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)
# f.create_dataset(f"{i}/real_A",data=label.cpu())
# f.create_dataset(f"{i}/real_B", data=real_img.cpu())
# f.create_dataset(f"{i}/fake_B", data=syn_img.cpu())
# f.create_dataset(f"{i}/seg", data=gt.cpu())
print(f"{i} processing")
file.close()
# 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)
# webpage.save() # save the HTML