3 Nb&@sddlZddlZddlmZddlZddlZddljZddl Z ddl m Z ddl m Z ejeZGdddZddd Zdd d ZGd d d ejZdS)N)Dict) DataLoader)move_to_devicec@s(eZdZd ddZdd Zd d d ZdS)InpaintingEvaluatorT cudaNc CsL||_||_||_||_tj||_t|jd|d|_||_||_ | |_ dS)a  :param dataset: torch.utils.data.Dataset which contains images and masks :param scores: dict {score_name: EvaluatorScore object} :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples which are defined by share of area occluded by mask :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) :param batch_size: batch_size for the dataloader :param device: device to use F)shuffle batch_sizeN) scoresdataset area_groupingbinstorchdevicer dataloader integral_funcintegral_titleclamp_image_range) selfr r r rr rrrrrD/home/ec2-user/SageMaker/lama/saicinpainting/evaluation/evaluator.py__init__s  zInpaintingEvaluator.__init__c Cs2tjdd|jd}tdtjtj|jd}g}xjt|jD]\}td|||td||d|}}dj ||d}dj ||d}|j dj ||q@Wg}xz|j D]p}|d} | j d} | j |jj| d jd d} tj|| jjjd d d} |jd| | |jk<|j | qWtj|}||fS) Nrdz{:.{n}f})nz{0}-{1}%mask)dimright)sider )nplinspacermaxmathceillog10rangeroundformatappendrshapetorreshapemean searchsorteddetachcpunumpyhstack) r bin_edges num_digitsinterval_namesidx_bin start_percent end_percentgroupsbatchrr area bin_indicesrrr_get_bin_edges*s&    z"InpaintingEvaluator._get_bin_edgescCst}|jr|j\}}nd}x6tjj|jjddD]\}}|j|jt j |j xtjj|j |ddD]}t ||j}|d|d}} |jdk rt j||jd|jd d }|dkrd |kstd |d } n ||| } || || qvW|j|d \} } WdQRX| ||df<|dk r8x*| jD]\} }|| }||||f<q2Wq8W|jdk r~t|j|d||jdf<|S)ae :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or name of the particular group arranged by area of mask (e.g. '10-20%') and score statistics for the group as values. Nr )descF)r?leaveimagerrr)minr# inpaintedzOModel is None, so we expected precomputed inpainting results at key "inpainted")r:total)r.)dictr r>tqdmautor itemsr,rrno_gradresetrrrclampAssertionError get_valuerr)rmodelresultsr:r6 score_namescorer; image_batch mask_batchinpainted_batch total_results group_results group_index group_values group_namerrrevaluateCs<$         zInpaintingEvaluator.evaluate)TrrrNNN)N)__name__ __module__ __qualname__rr>rZrrrrrs rrcCsF|dd}|d d}td|||}d||||d}|S) NssimrDr.fidrgMbP?)r^rD)r_rD)r#)metrics fid_scaler^r_fid_relf1rrrssim_fid100_f1ps   recCsJd|d d}|d d}td|||}d||||d}|S) NrlpipsrDr.r_rr`gMbP?)rfrD)r_rD)r#)rarbZ neg_lpipsr_rcrdrrrlpips_fid100_f1xs  rgcs`eZdZdfdd ZddZeeejfd d d Z eeejfd d d Z dddZ Z S)InpaintingEvaluatorOnlinerrArCNc stjtjt|dtj||_||_||_ ||_ t j dd|j d|_ tdtjtj|j d}g|_xpt|j D]b} td|j | |td|j | d|} } dj| |d} dj| |d} |jjdj| | qWg|_||_||_||_tjt|dd S) z :param scores: dict {score_name: EvaluatorScore object} :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1) :param device: device to use z init calledrrrz{:.{n}f})rz{0}-{1}%z init doneN)superrLOGGERinfotypenn ModuleDictr image_key inpainted_keybins_numr!r"r4r#r$r%r&r6r'r(r)r*r:rrr) rr rrorprrrr5r7r8r9) __class__rrrs(  z"InpaintingEvaluatorOnline.__init__cCsP|jd}|j|djddjjj}tjtj|j |dd|j d}|S)Nrr)rr r ) r+viewr.r0r1r2r!clipr/r4rq)rrSr r<r=rrr _get_binss  "z#InpaintingEvaluatorOnline._get_bins)r;c Csi}tj||j|d||j}}}|jdk rTtj||jd|jdd}|jj|j|x&|j j D]\}}||||||<qrWWdQRX|S)z Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key) rNrr)rBr#) rrIrorprrKr:extendrur rH)rr;resultrRrSrTrPrQrrrforwards   z!InpaintingEvaluatorOnline.forwardcCs||S)Nr)rr;rrr process_batchsz'InpaintingEvaluatorOnline.process_batchc sRtjt|dtj|j|_i}x|jjD]\}tjd|dk rdfdd|Dnd}|j|j|d\}}tjdd||df<x*|jD]\}}|j |} ||| f<qWq2W|j dk rt |j |d ||j df<tjt|d g|_x|jj D]} | jqWtjt|d tjt|d |S) z:return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or name of the particular group arranged by area of mask (e.g. '10-20%') and score statistics for the group as values. z: evaluation_end calledzGetting value of Ncsg|] }|qSrr).0s)rPrr sz.)r:statesz donerD)r.z: reset scoresz: reset scores donez: evaluation_end done)rjrkrlr!arrayr:r rHrMr6rrErvaluesrJ) rr}rOrQZ cur_statesrUrVrWrXrYscr)rPrevaluation_ends*   z(InpaintingEvaluatorOnline.evaluation_end)rrArCNNN)N) r[r\r]rrurstrrTensorrxryr __classcell__rr)rrrrhs rh)r)r)loggingr$typingrr2r!rtorch.nnrmrFtorch.utils.datarsaicinpainting.evaluation.utilsr getLoggerr[rjrrergModulerhrrrrs     `