kh.ddlmZmZmZmZmZddlZddlZddlZddlm Z m Z ddl m Z ddl mZddlmZej j"d e d ee d e fd Z d(d ededededef dZGddZdee d e fdZde deed efdZej2j4dee deeeefdeded eeeeff dZej2j4deee fdeed ee fdZej2j4d ee dee d!eed"ed#eeed$eed e fd%ZGd&d'e j>Z y)))DictListOptionalTupleUnionN)nnTensor)box_area)_log_api_usage_once) roi_alignlevelsunmerged_resultsreturnc Z|d}|j|j}}tj|j d|j d|j d|j df||}t t |D]}tj||k(djdddd}|j|j d||j d||j d||j d}|jd|||}|S)Nrr r dtypedevice) rrtorchzerossizerangelenwhereviewexpandscatter)rr first_resultrrreslevelindexs S/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/ops/poolers.py_onnx_merge_levelsr&s"#A&L && (;(;6E ++ Q**1-|/@/@/C\EVEVWXEYZbgpv Cs+,-= FeO,Q/44RAqA JJqM U # ( ( + U # ( ( + U # ( ( +   kk!U$4U$;<= Jk_mink_maxcanonical_scalecanonical_levelepsc t|||||SN) LevelMapper)r(r)r*r+r,s r%initLevelMapperr0%s ue_os KKr'c JeZdZdZ d dededededef dZdeed efd Z y ) r/zDetermine which FPN level each RoI in a set of RoIs should map to based on the heuristic in the FPN paper. Args: k_min (int) k_max (int) canonical_scale (int) canonical_level (int) eps (float) r(r)r*r+r,cJ||_||_||_||_||_yr.)r(r)s0lvl0r,)selfr(r)r*r+r,s r%__init__zLevelMapper.__init__;s'  !# r'boxlistsrc Ptjtj|Dcgc] }t|c}}tj|j tj ||jz ztj|j|jz}tj||j|j}|jtj|jz jtjScc}w)z< Args: boxlists (list[BoxList]) r)minmax)rsqrtcatr floorr4log2r3tensorr,rclampr(r)toint64)r5r7boxlists target_lvlss r%__call__zLevelMapper.__call__Is JJuyy8!L(7"3!LM Nkk$))ejjTWW.E"E UYU]U]efelelHm"mn kk+4::4::N u{{+djj8<) __name__ __module__ __qualname____doc__intfloatr6rr rGr'r%r/r//sa  #          Jf J& Jr'r/boxesc ftj|d}|j|j}}tjt |Dcgc]6\}}tj |ddddf||tj |8c}}d}tj||gd}|Scc}}w)Nr)dimr )rlayoutr)rr=rr enumerate full_likestrided)rR concat_boxesrribidsroiss r%_convert_to_roi_formatr^Ws99U*L ''););EF ))dmnsdtu\`\]_`1bqb51E%--PV Wu  C 99c<(a 0D K vs ;B- feature original_sizec |jdd}g}t||D]j\}}t|t|z }dttj|j j z}|j|l|dS)Nr r)shapeziprPrr@r?roundappend)r_r`rpossible_scaless1s2 approx_scalescales r% _infer_scalerlbs == D#%OdM*&BRy59, U5<< 5::<BBDEEu%& 1 r'features image_shapesc$|s tdd}d}|D] }t|d|}t|d|}"||f}|Dcgc]}t||} }tjtj | dtj j } tjtj | dtj j } tt| t| ||} | | fScc}w)Nzimages list should not be emptyrr r9rr*r+) ValueErrorr;rlrr?r@float32itemr0rO) rmrnr*r+max_xmax_yrcoriginal_input_shapefeatscaleslvl_minlvl_max map_levelss r% _setup_scalesr|ms :;; E E%E!He$E!He$%"5>CK L4l4!56 LF Lzz%,,vay FGLLNNGzz%,,vbzGHMMOOG  G  G '' J : MsD x featmap_namescfg}|jD]\}}||vs |j||Sr.)itemsrf)r}r~ x_filteredkvs r% _filter_inputrs>J !1    a ! r'r output_sizesampling_ratiorxmapperc|| tdt|}t|}|dk(rt|d|||d|S||}t|} |djd} |dj |dj } } tj| | f|z| | } g}tt||D]\}\}}tj||k(d}||}t|||||}tjr!|j|j| n|j| j | |<tjr t!||} | S)a Args: x_filtered (List[Tensor]): List of input tensors. boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in (x1, y1, x2, y2) format and in the image reference size, not the feature map reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. output_size (Union[List[Tuple[int, int]], List[int]]): size of the output sampling_ratio (int): sampling ratio for ROIAlign scales (Optional[List[float]]): If None, scales will be automatically inferred. Default value is None. mapper (Optional[LevelMapper]): If none, mapper will be automatically inferred. Default value is None. Returns: result (Tensor) z$scales and mapper should not be Noner r)r spatial_scalerr)rqrr^rrcrrrrrVrdr torchvision _is_tracingrfrBr&)rrRrrrxr num_levelsr]rnum_rois num_channelsrrresulttracing_resultsr#per_level_featurerk idx_in_levelrois_per_levelresult_idx_in_levels r%_multiscale_roi_alignrs,~?@@ZJ !% (DQ qM # ))   E]F4yHa=&&q)LqM''A)=)=6E [[       FO-6s:v7N-OH))!5{{6U?3A6 l+'  #)    " " $  " "#6#9#9%#@ A$7#9#9&,,#GF< -H0 #FO< Mr'c eZdZdZeeeeedZddddee de e e e ee fde d e d e f fd Z d ee efd eedee e e fdefdZde fdZxZS)MultiScaleRoIAligna{ Multi-scale RoIAlign pooling, which is useful for detection with or without FPN. It infers the scale of the pooling via the heuristics specified in eq. 1 of the `Feature Pyramid Network paper `_. They keyword-only parameters ``canonical_scale`` and ``canonical_level`` correspond respectively to ``224`` and ``k0=4`` in eq. 1, and have the following meaning: ``canonical_level`` is the target level of the pyramid from which to pool a region of interest with ``w x h = canonical_scale x canonical_scale``. Args: featmap_names (List[str]): the names of the feature maps that will be used for the pooling. output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region sampling_ratio (int): sampling ratio for ROIAlign canonical_scale (int, optional): canonical_scale for LevelMapper canonical_level (int, optional): canonical_level for LevelMapper Examples:: >>> m = torchvision.ops.MultiScaleRoIAlign(['feat1', 'feat3'], 3, 2) >>> i = OrderedDict() >>> i['feat1'] = torch.rand(1, 5, 64, 64) >>> i['feat2'] = torch.rand(1, 5, 32, 32) # this feature won't be used in the pooling >>> i['feat3'] = torch.rand(1, 5, 16, 16) >>> # create some random bounding boxes >>> boxes = torch.rand(6, 4) * 256; boxes[:, 2:] += boxes[:, :2] >>> # original image size, before computing the feature maps >>> image_sizes = [(512, 512)] >>> output = m(i, [boxes], image_sizes) >>> print(output.shape) >>> torch.Size([6, 5, 3, 3]) )rxr{rIrJrpr~rrr*r+ct|t|t|tr||f}||_||_t||_d|_ d|_ ||_ ||_ yr.) superr6r isinstancerOr~rtuplerrxr{r*r+)r5r~rrr*r+ __class__s r%r6zMultiScaleRoIAlign.__init__ sg D! k3 '& 4K*, - ..r'r}rRrnrc,t||j}|j |j/t |||j |j \|_|_t|||j|j|j|jS)a Args: x (OrderedDict[Tensor]): feature maps for each level. They are assumed to have all the same number of channels, but they can have different sizes. boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in (x1, y1, x2, y2) format and in the image reference size, not the feature map reference. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. image_shapes (List[Tuple[height, width]]): the sizes of each image before they have been fed to a CNN to obtain feature maps. This allows us to infer the scale factor for each one of the levels to be pooled. Returns: result (Tensor) ) rr~rxr{r|r*r+rrr)r5r}rRrnrs r%forwardzMultiScaleRoIAlign.forward!s&#1d&8&89 ;; $//"9+8L$*>*>@T@T, (DK%         KK OO   r'c|jjd|jd|jd|jdS)Nz(featmap_names=z, output_size=z, sampling_ratio=))rrKr~rr)r5s r%__repr__zMultiScaleRoIAlign.__repr__CsM~~&&'t7I7I6JK++,,=d>Q>Q=RRS U r')rKrLrMrNrrrPr/__annotations__strrrOrr6rr rr __classcell__)rs@r%rrs!F"*$u+!6h{F[\O # /Cy/3c DI56/ /  //*  V   F|  5c?+    D # r'rrH)!typingrrrrrrtorch.fxrrr torchvision.ops.boxesr utilsr rjitunusedr&rOrPr0r/r^rlfxwrapr|rrrModulerrQr'r%rs55 *' vf&, L L LL L  L%J%JP$v,6&cu6l*.uS#X*?RUhk 4; #$6T#v+&tCyT&\PV P <PcP P T%[ ! P [ ! P PPfa a r'