DL-Art-School/codes/scripts/diffusion/diffusion_noise_surfer.py

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import os
import os.path as osp
import logging
import random
import time
import argparse
from collections import OrderedDict
import numpy
from PIL import Image
from torchvision.transforms import ToTensor
import utils
import utils.options as option
import utils.util as util
from trainer.ExtensibleTrainer import ExtensibleTrainer
from data import create_dataset, create_dataloader
from tqdm import tqdm
import torch
import numpy as np
# A rough copy of test.py that "surfs" along a set of random noise priors to show the affect of gaussian noise on the results.
def forward_pass(model, data, output_dir, spacing):
with torch.no_grad():
model.feed_data(data, 0)
model.test()
visuals = model.get_current_visuals()['rlt'].cpu()
img_path = data['GT_path'][0]
img_name = osp.splitext(osp.basename(img_path))[0]
sr_img = util.tensor2img(visuals[0]) # uint8
# save images
suffixes = [f'_{int(spacing)}']
for suffix in suffixes:
save_img_path = osp.join(output_dir, img_name + suffix + '.png')
util.save_img(sr_img, save_img_path)
if __name__ == "__main__":
# Set seeds
torch.manual_seed(5555)
random.seed(5555)
np.random.seed(5555)
#### options
torch.backends.cudnn.benchmark = True
want_metrics = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_unet.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt
util.mkdirs(
(path for key, path in opt['path'].items()
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
# Load test image
im = ToTensor()(Image.open(opt['image'])) * 2 - 1
_, h, w = im.shape
if h % 2 == 1:
im = im[:,1:,:]
h = h-1
if w % 2 == 1:
im = im[:,:,1:]
w = w-1
dh, dw = (h - 32 * (h // 32)) // 2, (w - 32 * (w // 32)) // 2
if dh > 0:
im = im[:,dh:-dh]
if dw > 0:
im = im[:,:,dw:-dw]
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im = im[:3].unsqueeze(0)
# Build the corruption indexes we are going to use.
correction_factors = opt['correction_factor']
opt['steps']['generator']['injectors']['visual_debug']['zero_noise'] = False
model = ExtensibleTrainer(opt)
results_dir = osp.join(opt['path']['results_root'], os.path.basename(opt['image']))
util.mkdir(results_dir)
for i in range(10):
data = {
'hq': im.to('cuda'),
'corruption_entropy': torch.tensor([correction_factors], device='cuda',
dtype=torch.float),
'GT_path': opt['image']
}
forward_pass(model, data, results_dir, i)