o h}.@sddlmZmZddlZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z ej jd ed eed efd d Z   d2dededededef ddZGdddZdeed efddZdedeed efdd Zejjd!eed"eeeefdeded eeeeff d#d$Zejjd%eeefd&eed eefd'd(Zejjd)eedeed*eed+ed,eeed-eed efd.d/ZGd0d1d1ejZ dS)3)OptionalUnionN)nnTensorbox_area)_log_api_usage_once) roi_alignlevelsunmerged_resultsreturnc Cs|d}|j|j}}tj|d|d|d|df||d}tt|D]4}t||kddddd}| |d||d||d||d}| d|||}q)|S)Nrr rdtypedevice) rrtorchzerossizerangelenwhereviewexpandZscatter)r r Z first_resultrrreslevelindexr]/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torchvision/ops/poolers.py_onnx_merge_levelss&   r!ư>k_mink_maxcanonical_scalecanonical_levelepscCst|||||SN) LevelMapper)r%r&r'r(r)rrr initLevelMapper%sr,c @sLeZdZdZ   ddedededed ef d d Zd eed efddZ dS)r+zDetermine which FPN level each RoI in a set of RoIs should map to based on the heuristic in the FPN paper. 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The coordinate must satisfy ``0 <= x1 < x2`` and ``0 <= y1 < y2``. output_size (Union[List[Tuple[int, int]], List[int]]): size of the output sampling_ratio (int): sampling ratio for ROIAlign scales (Optional[List[float]]): If None, scales will be automatically inferred. Default value is None. mapper (Optional[LevelMapper]): If none, mapper will be automatically inferred. Default value is None. Returns: result (Tensor) Nz$scales and mapper should not be Noner r)rkZ spatial_scalerlr)r_rrOr rSrrrrrMrTr torchvisionZ _is_tracingrVr=r!)rgrHrkrlrarmZ num_levelsrNr Znum_roisZ num_channelsrrresultZtracing_resultsrZper_level_featurerYZ idx_in_levelZrois_per_levelZresult_idx_in_levelrrr _multiscale_roi_alignsT   rpc seZdZdZeeeeedZddddee de e e e ee fde d e d e f fd d Z d ee efdeedee e e fdefddZde fddZZS)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]) )rarbr"r#r^rerkrlr'r(csVtt|t|tr||f}||_||_t||_d|_ d|_ ||_ ||_ dSr*) superr0r isinstancerErerltuplerkrarbr'r()r/rerkrlr'r( __class__rr r0 s    zMultiScaleRoIAlign.__init__rdrHr\rcCsTt||j}|jdus|jdurt|||j|j\|_|_t|||j|j |j|jS)a Args: x (OrderedDict[Tensor]): feature maps for each level. 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