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Running
on
Zero
| from os import path as osp | |
| from torch.utils import data as data | |
| from torchvision.transforms.functional import normalize | |
| from basicsr.data.data_util import paths_from_lmdb | |
| from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir | |
| from basicsr.utils.registry import DATASET_REGISTRY | |
| from pathlib import Path | |
| import random | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| class SingleImageDataset(data.Dataset): | |
| """Read only lq images in the test phase. | |
| Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). | |
| There are two modes: | |
| 1. 'meta_info_file': Use meta information file to generate paths. | |
| 2. 'folder': Scan folders to generate paths. | |
| Args: | |
| opt (dict): Config for train datasets. It contains the following keys: | |
| dataroot_lq (str): Data root path for lq. | |
| meta_info_file (str): Path for meta information file. | |
| io_backend (dict): IO backend type and other kwarg. | |
| """ | |
| def __init__(self, opt): | |
| super(SingleImageDataset, self).__init__() | |
| self.opt = opt | |
| # file client (io backend) | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| self.mean = opt['mean'] if 'mean' in opt else None | |
| self.std = opt['std'] if 'std' in opt else None | |
| self.lq_folder = opt['dataroot_lq'] | |
| if self.io_backend_opt['type'] == 'lmdb': | |
| self.io_backend_opt['db_paths'] = [self.lq_folder] | |
| self.io_backend_opt['client_keys'] = ['lq'] | |
| self.paths = paths_from_lmdb(self.lq_folder) | |
| elif 'meta_info_file' in self.opt: | |
| with open(self.opt['meta_info_file'], 'r') as fin: | |
| self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] | |
| else: | |
| self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) | |
| def __getitem__(self, index): | |
| if self.file_client is None: | |
| self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) | |
| # load lq image | |
| lq_path = self.paths[index] | |
| img_bytes = self.file_client.get(lq_path, 'lq') | |
| img_lq = imfrombytes(img_bytes, float32=True) | |
| # color space transform | |
| if 'color' in self.opt and self.opt['color'] == 'y': | |
| img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] | |
| # BGR to RGB, HWC to CHW, numpy to tensor | |
| img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) | |
| # normalize | |
| if self.mean is not None or self.std is not None: | |
| normalize(img_lq, self.mean, self.std, inplace=True) | |
| return {'lq': img_lq, 'lq_path': lq_path} | |
| def __len__(self): | |
| return len(self.paths) | |
| class SingleImageNPDataset(data.Dataset): | |
| """Read only lq images in the test phase. | |
| Read diffusion generated data for training CFW. | |
| Args: | |
| opt (dict): Config for train datasets. It contains the following keys: | |
| gt_path: Data root path for training data. The path needs to contain the following folders: | |
| gts: Ground-truth images. | |
| inputs: Input LQ images. | |
| latents: The corresponding HQ latent code generated by diffusion model given the input LQ image. | |
| samples: The corresponding HQ image given the HQ latent code, just for verification. | |
| io_backend (dict): IO backend type and other kwarg. | |
| """ | |
| def __init__(self, opt): | |
| super(SingleImageNPDataset, self).__init__() | |
| self.opt = opt | |
| # file client (io backend) | |
| self.file_client = None | |
| self.io_backend_opt = opt['io_backend'] | |
| self.mean = opt['mean'] if 'mean' in opt else None | |
| self.std = opt['std'] if 'std' in opt else None | |
| if 'image_type' not in opt: | |
| opt['image_type'] = 'png' | |
| if isinstance(opt['gt_path'], str): | |
| self.gt_paths = sorted([str(x) for x in Path(opt['gt_path']+'/gts').glob('*.'+opt['image_type'])]) | |
| self.lq_paths = sorted([str(x) for x in Path(opt['gt_path']+'/inputs').glob('*.'+opt['image_type'])]) | |
| self.np_paths = sorted([str(x) for x in Path(opt['gt_path']+'/latents').glob('*.npy')]) | |
| self.sample_paths = sorted([str(x) for x in Path(opt['gt_path']+'/samples').glob('*.'+opt['image_type'])]) | |
| else: | |
| self.gt_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/gts').glob('*.'+opt['image_type'])]) | |
| self.lq_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/inputs').glob('*.'+opt['image_type'])]) | |
| self.np_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/latents').glob('*.npy')]) | |
| self.sample_paths = sorted([str(x) for x in Path(opt['gt_path'][0]+'/samples').glob('*.'+opt['image_type'])]) | |
| if len(opt['gt_path']) > 1: | |
| for i in range(len(opt['gt_path'])-1): | |
| self.gt_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/gts').glob('*.'+opt['image_type'])])) | |
| self.lq_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/inputs').glob('*.'+opt['image_type'])])) | |
| self.np_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/latents').glob('*.npy')])) | |
| self.sample_paths.extend(sorted([str(x) for x in Path(opt['gt_path'][i+1]+'/samples').glob('*.'+opt['image_type'])])) | |
| assert len(self.gt_paths) == len(self.lq_paths) | |
| assert len(self.gt_paths) == len(self.np_paths) | |
| assert len(self.gt_paths) == len(self.sample_paths) | |
| def __getitem__(self, index): | |
| if self.file_client is None: | |
| self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) | |
| # load lq image | |
| lq_path = self.lq_paths[index] | |
| gt_path = self.gt_paths[index] | |
| sample_path = self.sample_paths[index] | |
| np_path = self.np_paths[index] | |
| img_bytes = self.file_client.get(lq_path, 'lq') | |
| img_lq = imfrombytes(img_bytes, float32=True) | |
| img_bytes_gt = self.file_client.get(gt_path, 'gt') | |
| img_gt = imfrombytes(img_bytes_gt, float32=True) | |
| img_bytes_sample = self.file_client.get(sample_path, 'sample') | |
| img_sample = imfrombytes(img_bytes_sample, float32=True) | |
| latent_np = np.load(np_path) | |
| # color space transform | |
| if 'color' in self.opt and self.opt['color'] == 'y': | |
| img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] | |
| img_gt = rgb2ycbcr(img_gt, y_only=True)[..., None] | |
| img_sample = rgb2ycbcr(img_sample, y_only=True)[..., None] | |
| # BGR to RGB, HWC to CHW, numpy to tensor | |
| img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) | |
| img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) | |
| img_sample = img2tensor(img_sample, bgr2rgb=True, float32=True) | |
| latent_np = torch.from_numpy(latent_np).float() | |
| latent_np = latent_np.to(img_gt.device) | |
| # normalize | |
| if self.mean is not None or self.std is not None: | |
| normalize(img_lq, self.mean, self.std, inplace=True) | |
| normalize(img_gt, self.mean, self.std, inplace=True) | |
| normalize(img_sample, self.mean, self.std, inplace=True) | |
| return {'lq': img_lq, 'lq_path': lq_path, 'gt': img_gt, 'gt_path': gt_path, 'latent': latent_np[0], 'latent_path': np_path, 'sample': img_sample, 'sample_path': sample_path} | |
| def __len__(self): | |
| return len(self.gt_paths) | |