o ߥi&4@sdZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddlm ZddlmZmZddlmZd d lmZd d Zd dZddZd?dejdefddZ    d@dejdedededef ddZ  dAdejdejdefddZ dBd ejd!ejd"ejdejd#ejd$ejd%efd&d'Z ( )dCd$ejdejd*ej d+ej d,ejd-ed.e!d/e"d0e"d1efd2d3Z#d4e$d5e"d6e"d7e"fd8d9Z%d4e$d:ej d;e&dZ'dS)Dzx Part of the implementation is borrowed and modified from LaMa, publicly available at https://github.com/saic-mdal/lama N)gaussian_blur2d)resizeerosion) functional)SGDAdam)tqdm)FFCResnetBlockcs|t|tjr |St|r|St|ttfr%fdd|DSt|tr5fdd| DSt dt |)Ncsg|]}t|qSmove_to_device).0eldevicer d/home/ubuntu/.local/lib/python3.10/site-packages/modelscope/models/cv/image_inpainting/refinement.py z"move_to_device..csi|] \}}|t|qSr r )rnamevalrr r sz"move_to_device..zUnexpected type ) isinstancennModuletotorch is_tensortuplelistdictitems ValueErrortype)objrr rrrs     rcCs ||dkr|S||d|S)Nrr r )xmodr r r ceil_modulos r(cCsB|j\}}}}t||}t||}tj|d||d||fddS)Nrreflect)padmode)shaper(Fr*)imgr' batch_sizechannelsheightwidth out_height out_widthr r rpad_tensor_to_modulo%s  r5imdownsizecCs^|dur|jdd|jddf}|jddksJdt|ddd}tj||d d d }|S) zdownscale the imageNr z5Expected shape for the input to be (n,3,height,width)r;?r= kernel_sizesigmabilinearFsizer+ align_cornersr,rr- interpolate)r6r7r r r_pyrdown/srG:0yE>Tmaskeps blur_maskround_upcCs|dur|jdd|jddf}|jddksJd|dur2t|ddd }tj||d d d }n tj||d d d }|rKd|||k<d |||k<|Sd||d|k<d ||d|k<|S)avdownscale the mask tensor Parameters ---------- mask : torch.Tensor mask of size (B, 1, H, W) downsize : tuple, optional size to downscale to. If None, image is downscaled to half, by default None eps : float, optional threshold value for binarizing the mask, by default 1e-8 blur_mask : bool, optional if True, apply gaussian filter before downscaling, by default True round_up : bool, optional if True, values above eps are marked 1, else, values below 1-eps are marked 0, by default True Returns ------- torch.Tensor downscaled mask Nr8r9r z5Expected shape for the input to be (n,1,height,width)Tr:r<r>rAFrBrr=rE)rIr7rJrKrLr r r _pyrdown_mask:s0  rMekernelcCs6|durt||}d||d|k<d||d|k<|S)z(erode the mask, and set gray pixels to 0Nr r=rr)rIrNrJr r r _erode_maskhs  rOpredpred_downscaledrefmask_downscaledimageon_predc CsTtt||dk||dk}|r(|tt||dk||dk7}|S)zAl1 loss on src pixels, and downscaled predictions if on_pred=TruerH)rmeanabs)rPrQrRrIrSrTrUlossr r r_l1_lossss$ rYMb`? forward_front forward_rears ref_lower_res orig_shapedevices scale_indn_iterslrc  Cs|d|} tj| |gdd} |dddd}|dur|}t|| \} } Wdn1s4wY||d}tttj d t  } | |d} ||d}| |d| |d} } d\| _ | _ t| | g| d }tt|d d }|D]}|| | f}t|D].\}}||}|t|dkr|\}}|||d|||d}}||f}q|}q|durnsi}t|ddddd|dd|df}t|ddddd|dd|dfd d d }t|| d }|dddd}t||||||dd|d<t|}|d|d||||dkr=||~~~q||d||}|}|S)a{Performs inference with refinement at a given scale. Parameters ---------- image : torch.Tensor input image to be inpainted, of size (1,3,H,W) mask : torch.Tensor input inpainting mask, of size (1,1,H,W) forward_front : nn.Module the front part of the inpainting network forward_rears : nn.Module the rear part of the inpainting network ref_lower_res : torch.Tensor the inpainting at previous scale, used as reference image orig_shape : tuple shape of the original input image before padding devices : list list of available devices scale_ind : int the scale index n_iters : int, optional number of iterations of refinement, by default 15 lr : float, optional learning rate, by default 0.002 Returns ------- torch.Tensor inpainted image r )dimr9N)rZrZr)TT)rcF)leave)rKrL)rNT)rUms_l1z8Refining scale {} using scale {} ...current loss: {:.4f}) rcatrepeatdetachno_gradr from_numpycv2getStructuringElement MORPH_ELLIPSEastypeboolfloat requires_gradrr range zero_grad enumeratelenrGrMrOrYsumvaluesset_descriptionformatitembackwardstepcpu)rTrIr\r]r^r_r`rarbrc masked_imagez1z2rN optimizerpbaridi input_featidd forward_rear output_featmidz1midz2rPlossesrQrSrX inpaintedr r r_infers ( &   ,&   rbatchmin_side max_scales px_budgetc Cs|djddks Jd|d\}}|d|d}}|ddd|d|f}|ddd|d|f}|||krt|t||}||} } t||t||}}td | | fd ||fd t|||fd d d}t|||fd d d}d||dk<t||} tdtt t dt | ||} g} g}| || |t | dD]}t| d}t|d}| || |q| ddd|dddfS)aBuild the image mask pyramid Parameters ---------- batch : dict batch containing image, mask, etc min_side : int minimum side length to limit the number of scales of the pyramid max_scales : int maximum number of scales allowed px_budget : int the product H*W cannot exceed this budget, because of resource constraints Returns ------- tuple image-mask pyramid in the form of list of images and list of masks rTrr z(refiner works on only batches of size 1! unpad_to_size.NrIz2Original image too large for refinement! Resizing z to z...rAF) interpolationrDrHre)r,r|npsqrtrrintprintrminroundmaxlog2appendrtrGrM)rrrrhwrTrIratioh_origw_origbreadthn_scales ls_imagesls_masks_image_pmask_pr r r_get_image_mask_pyramidsN              r inpaintergpu_idsmoduloc  CsF|j}|jrJ|jr J|jsJdd|dddD}d} d} d} tt|jjD]} t |jj| t r@| d7} d } q.| sF| d7} q.| t|} d d|D}|jjd| }| |dg}tt|D]8}|t|dkr| |jj| | || | |dn| |jj| | |d || ||qkt ||||\}}d }tt||D]j\}\}}|jd d }t||}t||}d ||dk<d||dk<t||dt||d}}|d urt||d}t|||||||||| }|d d d d d |dd |df}|}|}q|S)afRefines the inpainting of the network Parameters ---------- batch : dict image-mask batch, currently we assume the batchsize to be 1 inpainter : nn.Module the inpainting neural network gpu_ids : str the GPU ids of the machine to use. If only single GPU, use: "0," modulo : int pad the image to ensure dimension % modulo == 0 n_iters : int number of iterations of refinement for each scale lr : float learning rate min_side : int all sides of image on all scales should be >= min_side / sqrt(2) max_scales : int max number of downscaling scales for the image-mask pyramid px_budget : int pixels budget. Any image will be resized to satisfy height*width <= px_budget Returns ------- torch.Tensor inpainted image of size (1,3,H,W) cSsg|] }|rd|qS)zcuda:)isdigit)rgpuidr r rrQs z"refine_predict.. ,rFr TcSsg|]}t|qSr )rr)rgpu_idr r rr`rNr8r=rHgre)modeltrainingadd_noise_kwargs concat_maskreplacesplitrtrw generatorrr rrrrvzipr,r5rrrjr)rrrrrbrcrrrn_resnet_blocksfirst_resblock_indfound_first_resblockidlresblocks_per_gpur`r\r]rrrimage_inpaintedidsrTrIr_r r rrefine_predict-s          ( r)N)NrHTT)NrH)T)rZr[)(__doc__rmnumpyrrtorch.nnrkornia.filtersrkornia.geometry.transformrkornia.morphologyrrr- torch.optimrrr modules.ffcr rr(r5TensorrrGrrrqrMrOrYrr rrr!rstrrr r r rs            /      n <