U <þjgã@s¶ddlmZmZddlZddlZddlmZmZddlmZddl m Z ddlmZddl mZd d lmZmZejjdeeeeefeeeedœd d„ƒZGdd„dejƒZdS)é)ÚListÚUnionN)ÚnnÚTensor)ÚBroadcastingList2)Ú_pair)Ú_assert_has_opsé)Ú_log_api_usage_onceé)Úcheck_roi_boxes_shapeÚconvert_boxes_to_roi_formatçð?)ÚinputÚboxesÚoutput_sizeÚ spatial_scaleÚreturncCsptj ¡stj ¡sttƒtƒt|ƒ|}t|ƒ}t |tj ƒsJt|ƒ}tjj ||||d|d¡\}}|S)aU Performs Region of Interest (RoI) Pool operator described in Fast 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``. 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 after the cropping 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 Returns: Tensor[K, C, output_size[0], output_size[1]]: The pooled RoIs. rr)ÚtorchZjitZis_scriptingÚ is_tracingr Úroi_poolrrrÚ isinstancerr ÚopsZtorchvision)rrrrÚroisÚoutputÚ_©rús$üû)