o )i*@sddlmZmZddlZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z mZddlmZd d lmZmZd d Zd dZejjddZejj   ddedeeeefde ededededefddZGdddejZ dS) )ListUnionN)nnTensor)BroadcastingList2)_pair)_assert_has_ops_has_ops)_log_api_usage_once)check_roi_boxes_shapeconvert_boxes_to_roi_formatcs\}}}|jdd}|jdd}|} |} t| |dk|d| d} t| |dk|d| } t| |dk|j|}t| |dk|d| d} t| |dk|d| } t| |dk|j|}|| } || }d| }d|}fdd}|| | }|| | }|| | }|| | }dd}|||}|||}|| |}|| |}||||||||}|S) Nrminr ?c sdur*dus Jtdddddf|d}tdddddf|d}dddddddftjjddddddddf|dddddddddf|dddddddddffS)Nrdevice)torchwherearangeryxZchannelsinput roi_batch_indxmaskymaskh/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/torchvision/ops/roi_align.py masked_index/s   "z+_bilinear_interpolate..masked_indexc Ss@|dddddddddf|dddddddddfSNrrrrr outer_prodDs@z)_bilinear_interpolate..outer_prod)sizeclampintrrtodtype)rrrrrr_heightwidthZy_lowZx_lowZy_highZx_highZlylxhyhxr!Zv1Zv2Zv3Zv4r#Zw1Zw2Zw3Zw4valrrr _bilinear_interpolates6           r0cCs&tr|jr|jtjkr|S|Sr")rZis_autocast_enabledis_cudar(doublefloat)Ztensorrrr maybe_castRsr4c$ Cs6|j}t|}t|}|\}}} } tj||jd} tj||jd} |dddf} |r1dnd}|dddf||}|dddf||}|dddf||}|dddf||}||}||}|s{tj|d d }tj|d d }||}||}|dk}|r|nt||}|r|nt||} |rt ||d}tj||jd}tj||jd}d}d}n9tj||dd }tj| |jd}tj| |jd}|dddf|dddfk}|dddf|dddfk}d d }||| ddddf|||ddddfd |j|||} ||| ddddf|||ddddfd |j|||}!t || | |!||}"|swt |ddddddddf|"d}"t |ddddddddf|"d}"|" d }#t|tjr|#|dddddf}#n|#|}#|# |}#|#S)Nrrg?gr r rrcSs|ddddfSr"r)trrr from_Ksz_roi_align..from_K))r(r4r$rrrr&r%ceilmaxr'r0rsum isinstancer)$rrois spatial_scaleZ pooled_heightZ pooled_widthsampling_ratioalignedZ orig_dtyper)r*r+phpwroffsetZ roi_start_wZ roi_start_hZ roi_end_wZ roi_end_hZ roi_widthZ roi_heightZ bin_size_hZ bin_size_wZexact_samplingZroi_bin_grid_hZroi_bin_grid_wcountZiyZixrrr8rrr/outputrrr _roi_alignasr   ((&&  rHrr9Frboxes output_sizer@rArBreturnc Cstjstjsttt||}t|}t|tj s"t |}tjsBt r4t rB|j s4|jrBt||||d|d||Sttjj||||d|d||S)aj Performs Region of Interest (RoI) Align operator with average pooling, as described in Mask R-CNN. Args: input (Tensor[N, C, H, W]): The input tensor, i.e. a batch with ``N`` elements. Each element contains ``C`` feature maps of dimensions ``H x W``. If the tensor is quantized, we expect a batch size of ``N == 1``. boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2) format where the regions will be taken from. The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. If a single Tensor is passed, then the first column should contain the index of the corresponding element in the batch, i.e. a number in ``[0, N - 1]``. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in the batch. output_size (int or Tuple[int, int]): the size of the output (in bins or pixels) after the pooling is performed, as (height, width). spatial_scale (float): a scaling factor that maps the box coordinates to the input coordinates. For example, if your boxes are defined on the scale of a 224x224 image and your input is a 112x112 feature map (resulting from a 0.5x scaling of the original image), you'll want to set this to 0.5. Default: 1.0 sampling_ratio (int): number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling points per bin are used. If <= 0, then an adaptive number of grid points are used (computed as ``ceil(roi_width / output_width)``, and likewise for height). Default: -1 aligned (bool): If False, use the legacy implementation. If True, pixel shift the box coordinates it by -0.5 for a better alignment with the two neighboring pixel indices. This version is used in Detectron2 Returns: Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. rr )rZjitZ is_scripting is_tracingr roi_alignr rr>rrr Z$are_deterministic_algorithms_enabledr1Zis_mpsrHropsZ torchvision)rrIrJr@rArBr?rrr rMs)  rMc sjeZdZdZ ddeedededeffdd Zd e d e e e e fd e fd d Z d e fddZZS)RoIAlignz See :func:`roi_align`. FrJr@rArBcs.tt|||_||_||_||_dSr")super__init__r rJr@rArB)selfrJr@rArB __class__rr rQs  zRoIAlign.__init__rr?rKcCst|||j|j|j|jSr")rMrJr@rArB)rRrr?rrr forwardszRoIAlign.forwardc Cs2|jjd|jd|jd|jd|jd }|S)Nz (output_size=z, spatial_scale=z, sampling_ratio=z , aligned=))rT__name__rJr@rArB)rRsrrr __repr__ s zRoIAlign.__repr__)F)rW __module__ __qualname____doc__rr&r3boolrQrrrrUstrrY __classcell__rrrSr rOs "rO)rr9F)!typingrrrZ torch._dynamoZtorch.fxrrZtorch.jit.annotationsrZtorch.nn.modules.utilsrZtorchvision.extensionrr utilsr _utilsr rr0r4Z_dynamoZallow_in_graphrHZfxwrapr&r3r]rMModulerOrrrr sD   B X 8