a  d;@sddlZddlZddlZddlmZddlmZddlm Z m Z ddl m Z m Z mZmZddlmZddlmZeGdd d ejZeGd d d ejZdS) N)Path)data)augmentpaired_random_crop) FileClientget_root_logger imfrombytes img2tensor)dequantize_flow)DATASET_REGISTRYcs0eZdZdZfddZddZddZZS) REDSDatasetaREDS dataset for training. The keys are generated from a meta info txt file. basicsr/data/meta_info/meta_info_REDS_GT.txt Each line contains: 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by a white space. Examples: 000 100 (720,1280,3) 001 100 (720,1280,3) ... Key examples: "000/00000000" GT (gt): Ground-Truth; LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. Args: opt (dict): Config for train dataset. It contains the following keys: dataroot_gt (str): Data root path for gt. dataroot_lq (str): Data root path for lq. dataroot_flow (str, optional): Data root path for flow. meta_info_file (str): Path for meta information file. val_partition (str): Validation partition types. 'REDS4' or 'official'. io_backend (dict): IO backend type and other kwarg. num_frame (int): Window size for input frames. gt_size (int): Cropped patched size for gt patches. interval_list (list): Interval list for temporal augmentation. random_reverse (bool): Random reverse input frames. use_hflip (bool): Use horizontal flips. use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). scale (bool): Scale, which will be added automatically. cs.tt|||_t|dt|d|_|_|ddurJt|dnd|_|dddksrJd|d|d|_|dd|_ g|_ t |dd L}|D]6}| d \}}|j fd d tt|DqWdn1s0Y|d dkrgdn8|d dkr6dd tddDntd|d dfdd |j D|_ d|_|d|_d|_|jddkrd|_|jdur|j|j|jg|jd<gd|jd<n |j|jg|jd<dd g|jd<|d!|_|d"|_d#d$d%|d!D}t}|d&|d'|jd(dS))N dataroot_gt dataroot_lqZ dataroot_flow num_framez(num_frame should be odd number, but got meta_info_filer csg|]}d|dqS/08d.0ifolderrb/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/data/reds_dataset.py Bz(REDSDataset.__init__.. val_partitionREDS4Z000Z011Z015Z020officialcSsg|] }|dqSZ03drrvrrrrHr Wrong validation partition *.Supported ones are ['official', 'REDS4'].cs"g|]}|ddvr|qSrrsplitr&r!rrrLr io_backendFtypelmdbTdb_pathslqgtflow client_keysr5r6 interval_listrandom_reverse,css|]}t|VqdSNstrrxrrr ^r z'REDSDataset.__init__..&Temporal augmentation interval list: []; random reverse is .)superr __init__optrgt_rootlq_root flow_rootrnum_half_frameskeysopenr.extendrangeint ValueError file_clientio_backend_optis_lmdbr9r:joinrinfoselfrGZfinlineZ frame_num_Z interval_strlogger __class__rr!rrF5sF" B      zREDSDataset.__init__cCs |jdur&t|jdfi|j|_|jd}|jd}|j|}|d\}}t|}t |j }||j |} ||j |} | dks| dkrt dd}||j |} ||j |} q|d}t t| | d|} |jrtd kr| t| |jksJd t| |jr,|d|} n|j||d } |j| d } t| d d}g}| D]Z}|jr|d|d}n|j||dd }|j|d} t| d d}||qb|jdur g}t|j ddD]}|jr|d|d|}n|j||d|d }|j|d} t| ddd}tj|ddd\}}t||ddd}||qtd|j dD]}|jr|d|d|}n|j||d|d }|j|d} t| ddd}tj|ddd\}}t||ddd}||qv||t||||| \}}|jdurH|d|j||jd}}|||jdur~t||jd|jd|\}}nt||jd|jd}t|}t j!|dddd}|d}|jdurt|}|"|j t #|dt j!|dd}|jdur||||dS|||dSdS) Nr1scalegt_sizerrcrr?zWrong length of neighbor list: .pngr6Tfloat32r5_pr7Z grayscaleF)flagrer)Zaxis)Zmax_valZdenormZ_n use_hflipuse_rotZdim)r5r7r6keyr5r6rm)$rRrrSpoprGrLr.rPrandomchoicer9rKrandintlistrOr:reverselenrrTrHgetrrIappendrJnpr rNrrr torchstackinsertZ zeros_like)rXindexr_r`rm clip_name frame_nameZcenter_frame_idxintervalstart_frame_idx end_frame_idx neighbor_list img_gt_path img_bytesimg_gtimg_lqsneighbor img_lq_pathimg_lqZ img_flowsrZ flow_pathZcat_flowZdxZdyr7 img_resultsrrr __getitem__cs      "          zREDSDataset.__getitem__cCs t|jSr<rurLrXrrr__len__szREDSDataset.__len____name__ __module__ __qualname____doc__rFrr __classcell__rrr\rr s& .mr cs0eZdZdZfddZddZddZZS)REDSRecurrentDatasetaREDS dataset for training recurrent networks. The keys are generated from a meta info txt file. basicsr/data/meta_info/meta_info_REDS_GT.txt Each line contains: 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by a white space. Examples: 000 100 (720,1280,3) 001 100 (720,1280,3) ... Key examples: "000/00000000" GT (gt): Ground-Truth; LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. Args: opt (dict): Config for train dataset. It contains the following keys: dataroot_gt (str): Data root path for gt. dataroot_lq (str): Data root path for lq. dataroot_flow (str, optional): Data root path for flow. meta_info_file (str): Path for meta information file. val_partition (str): Validation partition types. 'REDS4' or 'official'. io_backend (dict): IO backend type and other kwarg. num_frame (int): Window size for input frames. gt_size (int): Cropped patched size for gt patches. interval_list (list): Interval list for temporal augmentation. random_reverse (bool): Random reverse input frames. use_hflip (bool): Use horizontal flips. use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). scale (bool): Scale, which will be added automatically. cstt|||_t|dt|d|_|_|d|_g|_t |ddL}|D]6}| d\}}|j fddt t |DqVWdn1s0Y|d d krgd n6|d d krd dt ddDntd|d d|drfdd|jD|_nfdd|jD|_d|_|d|_d|_|jddkrd|_t|dr|jdur|j|j|jg|jd<gd|jd<n |j|jg|jd<ddg|jd<|d d!g|_|d"d|_d#d$d%|jD}t}|d&|d'|jd(dS))Nr rrrrrcsg|]}d|dqSrrrrrrrr z1REDSRecurrentDataset.__init__..r!r"r#r$cSsg|] }|dqSr%rr&rrrr r r(r)r*r+Z test_modecs"g|]}|ddvr|qSr,r-r&r/rrrr cs"g|]}|ddvr|qSr,r-r&r/rrrr r0Fr1r2TrJr3r4r8r5r6r9rr:r;css|]}t|VqdSr<r=r?rrrrA%r z0REDSRecurrentDataset.__init__..rBrCrD)rErrFrGrrHrIrrLrMr.rNrOrPrQrRrSrThasattrrJrvr9r:rUrrVrWr\r^rrFsD B      zREDSRecurrentDataset.__init__cCs|jdur&t|jdfi|j|_|jd}|jd}|j|}|d\}}t|j }t |}|d|j |krt dd|j |}||j |} t t|| |} |jrtdkr| g} g} | D]} |jr|d| d}|d| d}n,|j|| dd }|j|| dd }|j|d }t|d d }| ||j|d }t|d d }| |qt| | |||\} } | | t| |jd|jd}t|}tj|t| dddd} tj|dt| ddd} | | |dS)Nr1r_r`rdrrbrrcr5Trdr6rjrkrrlrn)rRrrSrorGrLr.rprqr9rPrrrrsrOr:rtrTrIrHrvrrwrrNrr ryrzru)rXr|r_r`rmr}r~rrrrrZimg_gtsrrrrrrrrrrr*sF          z REDSRecurrentDataset.__getitem__cCs t|jSr<rrrrrrgszREDSRecurrentDataset.__len__rrrr\rrs& .=r)numpyrxrprypathlibrZ torch.utilsrZbasicsr.data.transformsrrZ basicsr.utilsrrrr Zbasicsr.utils.flow_utilr Zbasicsr.utils.registryr registerZDatasetr rrrrrs    G