forked from mrq/DL-Art-School
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import argparse
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import logging
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import os
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import os.path as osp
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import random
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import time
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from collections import OrderedDict
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import numpy
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import numpy as np
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import torch
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from PIL import Image
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from torchvision.transforms import ToTensor
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from tqdm import tqdm
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import dlas.utils
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import dlas.utils.options as option
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import dlas.utils.util as util
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from dlas.data import create_dataloader, create_dataset
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from dlas.trainer.ExtensibleTrainer import ExtensibleTrainer
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# A rough copy of test.py that "surfs" along a spectrum of correction factors for a single image.
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def forward_pass(model, data, output_dir, jc, bc):
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with torch.no_grad():
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model.feed_data(data, 0)
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model.test()
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visuals = model.get_current_visuals()['rlt'].cpu()
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img_path = data['GT_path'][0]
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img_name = osp.splitext(osp.basename(img_path))[0]
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sr_img = util.tensor2img(visuals[0]) # uint8
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# save images
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suffixes = [f'_blur_{int(bc*1000)}_{int(jc*1000)}',
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f'_jpeg_{int(jc*1000)}_{int(bc*1000)}']
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for suffix in suffixes:
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save_img_path = osp.join(output_dir, img_name + suffix + '.png')
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util.save_img(sr_img, save_img_path)
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if __name__ == "__main__":
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# Set seeds
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torch.manual_seed(5555)
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random.seed(5555)
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np.random.seed(5555)
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# options
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.',
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default='../options/test_diffusion_unet.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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util.mkdirs(
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(path for key, path in opt['path'].items()
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if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
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util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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# Load test image
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im = ToTensor()(Image.open(opt['image'])) * 2 - 1
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_, h, w = im.shape
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if h % 2 == 1:
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im = im[:, 1:, :]
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h = h-1
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if w % 2 == 1:
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im = im[:, :, 1:]
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w = w-1
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dh, dw = (h - 32 * (h // 32)) // 2, (w - 32 * (w // 32)) // 2
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if dh > 0:
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im = im[:, dh:-dh]
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if dw > 0:
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im = im[:, :, dw:-dw]
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im = im[:3].unsqueeze(0)
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# Build the corruption indexes we are going to use.
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jpegs = list(numpy.arange(opt['min_jpeg_correction'],
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opt['max_jpeg_correction'], opt['jpeg_correction_step_size']))
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deblurs = list(numpy.arange(
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opt['min_blur_correction'], opt['max_blur_correction'], opt['blur_correction_step_size']))
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model = ExtensibleTrainer(opt)
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results_dir = osp.join(opt['path']['results_root'],
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os.path.basename(opt['image']))
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util.mkdir(results_dir)
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for jpeg_correction in jpegs:
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for blur_correction in deblurs:
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data = {
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'hq': im.to('cuda'),
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'lq': im.to('cuda'),
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'corruption_entropy': torch.tensor([[jpeg_correction, blur_correction]], device='cuda',
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dtype=torch.float),
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'GT_path': opt['image']
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}
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forward_pass(model, data, results_dir,
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jpeg_correction, blur_correction)
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