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mmcv.ops.nms 源代码

from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from mmengine.utils import deprecated_api_warning
from torch import Tensor

from ..utils import ext_loader

ext_module = ext_loader.load_ext(
    '_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated', 'nms_quadri'])


# This function is modified from: https://github.com/pytorch/vision/
class NMSop(torch.autograd.Function):

    @staticmethod
    def forward(ctx: Any, bboxes: Tensor, scores: Tensor, iou_threshold: float,
                offset: int, score_threshold: float, max_num: int) -> Tensor:
        is_filtering_by_score = score_threshold > 0
        if is_filtering_by_score:
            valid_mask = scores > score_threshold
            bboxes, scores = bboxes[valid_mask], scores[valid_mask]
            valid_inds = torch.nonzero(
                valid_mask, as_tuple=False).squeeze(dim=1)

        inds = ext_module.nms(
            bboxes, scores, iou_threshold=float(iou_threshold), offset=offset)

        if max_num > 0:
            inds = inds[:max_num]
        if is_filtering_by_score:
            inds = valid_inds[inds]
        return inds


class SoftNMSop(torch.autograd.Function):

    @staticmethod
    def forward(ctx: Any, boxes: Tensor, scores: Tensor, iou_threshold: float,
                sigma: float, min_score: float, method: int,
                offset: int) -> Tuple[Tensor, Tensor]:
        dets = boxes.new_empty((boxes.size(0), 5), device='cpu')
        inds = ext_module.softnms(
            boxes.cpu(),
            scores.cpu(),
            dets.cpu(),
            iou_threshold=float(iou_threshold),
            sigma=float(sigma),
            min_score=float(min_score),
            method=int(method),
            offset=int(offset))
        return dets, inds

    @staticmethod
    def symbolic(g, boxes, scores, iou_threshold, sigma, min_score, method,
                 offset):
        from packaging import version
        assert version.parse(torch.__version__) >= version.parse('1.7.0')
        nms_out = g.op(
            'mmcv::SoftNonMaxSuppression',
            boxes,
            scores,
            iou_threshold_f=float(iou_threshold),
            sigma_f=float(sigma),
            min_score_f=float(min_score),
            method_i=int(method),
            offset_i=int(offset),
            outputs=2)
        return nms_out


array_like_type = Union[Tensor, np.ndarray]


[文档]@deprecated_api_warning({'iou_thr': 'iou_threshold'}) def nms(boxes: array_like_type, scores: array_like_type, iou_threshold: float, offset: int = 0, score_threshold: float = 0, max_num: int = -1) -> Tuple[array_like_type, array_like_type]: """Dispatch to either CPU or GPU NMS implementations. The input can be either torch tensor or numpy array. GPU NMS will be used if the input is gpu tensor, otherwise CPU NMS will be used. The returned type will always be the same as inputs. Arguments: boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). scores (torch.Tensor or np.ndarray): scores in shape (N, ). iou_threshold (float): IoU threshold for NMS. offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). score_threshold (float): score threshold for NMS. max_num (int): maximum number of boxes after NMS. Returns: tuple: kept dets (boxes and scores) and indice, which always have the same data type as the input. Example: >>> boxes = np.array([[49.1, 32.4, 51.0, 35.9], >>> [49.3, 32.9, 51.0, 35.3], >>> [49.2, 31.8, 51.0, 35.4], >>> [35.1, 11.5, 39.1, 15.7], >>> [35.6, 11.8, 39.3, 14.2], >>> [35.3, 11.5, 39.9, 14.5], >>> [35.2, 11.7, 39.7, 15.7]], dtype=np.float32) >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.5, 0.4, 0.3],\ dtype=np.float32) >>> iou_threshold = 0.6 >>> dets, inds = nms(boxes, scores, iou_threshold) >>> assert len(inds) == len(dets) == 3 """ assert isinstance(boxes, (Tensor, np.ndarray)) assert isinstance(scores, (Tensor, np.ndarray)) is_numpy = False if isinstance(boxes, np.ndarray): is_numpy = True boxes = torch.from_numpy(boxes) if isinstance(scores, np.ndarray): scores = torch.from_numpy(scores) assert boxes.size(1) == 4 assert boxes.size(0) == scores.size(0) assert offset in (0, 1) inds = NMSop.apply(boxes, scores, iou_threshold, offset, score_threshold, max_num) dets = torch.cat((boxes[inds], scores[inds].reshape(-1, 1)), dim=1) if is_numpy: dets = dets.cpu().numpy() inds = inds.cpu().numpy() return dets, inds
[文档]@deprecated_api_warning({'iou_thr': 'iou_threshold'}) def soft_nms(boxes: array_like_type, scores: array_like_type, iou_threshold: float = 0.3, sigma: float = 0.5, min_score: float = 1e-3, method: str = 'linear', offset: int = 0) -> Tuple[array_like_type, array_like_type]: """Dispatch to only CPU Soft NMS implementations. The input can be either a torch tensor or numpy array. The returned type will always be the same as inputs. Args: boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). scores (torch.Tensor or np.ndarray): scores in shape (N, ). iou_threshold (float): IoU threshold for NMS. sigma (float): hyperparameter for gaussian method min_score (float): score filter threshold method (str): either 'linear' or 'gaussian' offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). Returns: tuple: kept dets (boxes and scores) and indice, which always have the same data type as the input. Example: >>> boxes = np.array([[4., 3., 5., 3.], >>> [4., 3., 5., 4.], >>> [3., 1., 3., 1.], >>> [3., 1., 3., 1.], >>> [3., 1., 3., 1.], >>> [3., 1., 3., 1.]], dtype=np.float32) >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.4, 0.0], dtype=np.float32) >>> iou_threshold = 0.6 >>> dets, inds = soft_nms(boxes, scores, iou_threshold, sigma=0.5) >>> assert len(inds) == len(dets) == 5 """ assert isinstance(boxes, (Tensor, np.ndarray)) assert isinstance(scores, (Tensor, np.ndarray)) is_numpy = False if isinstance(boxes, np.ndarray): is_numpy = True boxes = torch.from_numpy(boxes) if isinstance(scores, np.ndarray): scores = torch.from_numpy(scores) assert boxes.size(1) == 4 assert boxes.size(0) == scores.size(0) assert offset in (0, 1) method_dict = {'naive': 0, 'linear': 1, 'gaussian': 2} assert method in method_dict.keys() if torch.__version__ == 'parrots': dets = boxes.new_empty((boxes.size(0), 5), device='cpu') indata_list = [boxes.cpu(), scores.cpu(), dets.cpu()] indata_dict = { 'iou_threshold': float(iou_threshold), 'sigma': float(sigma), 'min_score': min_score, 'method': method_dict[method], 'offset': int(offset) } inds = ext_module.softnms(*indata_list, **indata_dict) else: dets, inds = SoftNMSop.apply(boxes.cpu(), scores.cpu(), float(iou_threshold), float(sigma), float(min_score), method_dict[method], int(offset)) dets = dets[:inds.size(0)] if is_numpy: dets = dets.cpu().numpy() inds = inds.cpu().numpy() return dets, inds else: return dets.to(device=boxes.device), inds.to(device=boxes.device)
[文档]def batched_nms(boxes: Tensor, scores: Tensor, idxs: Tensor, nms_cfg: Optional[Dict], class_agnostic: bool = False) -> Tuple[Tensor, Tensor]: r"""Performs non-maximum suppression in a batched fashion. Modified from `torchvision/ops/boxes.py#L39 <https://github.com/pytorch/vision/blob/ 505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39>`_. In order to perform NMS independently per class, we add an offset to all the boxes. The offset is dependent only on the class idx, and is large enough so that boxes from different classes do not overlap. Note: In v1.4.1 and later, ``batched_nms`` supports skipping the NMS and returns sorted raw results when `nms_cfg` is None. Args: boxes (torch.Tensor): boxes in shape (N, 4) or (N, 5). scores (torch.Tensor): scores in shape (N, ). idxs (torch.Tensor): each index value correspond to a bbox cluster, and NMS will not be applied between elements of different idxs, shape (N, ). nms_cfg (dict | optional): Supports skipping the nms when `nms_cfg` is None, otherwise it should specify nms type and other parameters like `iou_thr`. Possible keys includes the following. - iou_threshold (float): IoU threshold used for NMS. - split_thr (float): threshold number of boxes. In some cases the number of boxes is large (e.g., 200k). To avoid OOM during training, the users could set `split_thr` to a small value. If the number of boxes is greater than the threshold, it will perform NMS on each group of boxes separately and sequentially. Defaults to 10000. class_agnostic (bool): if true, nms is class agnostic, i.e. IoU thresholding happens over all boxes, regardless of the predicted class. Defaults to False. Returns: tuple: kept dets and indice. - boxes (Tensor): Bboxes with score after nms, has shape (num_bboxes, 5). last dimension 5 arrange as (x1, y1, x2, y2, score) - keep (Tensor): The indices of remaining boxes in input boxes. """ # skip nms when nms_cfg is None if nms_cfg is None: scores, inds = scores.sort(descending=True) boxes = boxes[inds] return torch.cat([boxes, scores[:, None]], -1), inds nms_cfg_ = nms_cfg.copy() class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic) if class_agnostic: boxes_for_nms = boxes else: # When using rotated boxes, only apply offsets on center. if boxes.size(-1) == 5: # Strictly, the maximum coordinates of the rotating box # (x,y,w,h,a) should be calculated by polygon coordinates. # But the conversion from rotated box to polygon will # slow down the speed. # So we use max(x,y) + max(w,h) as max coordinate # which is larger than polygon max coordinate # max(x1, y1, x2, y2,x3, y3, x4, y4) max_coordinate = boxes[..., :2].max() + boxes[..., 2:4].max() offsets = idxs.to(boxes) * ( max_coordinate + torch.tensor(1).to(boxes)) boxes_ctr_for_nms = boxes[..., :2] + offsets[:, None] boxes_for_nms = torch.cat([boxes_ctr_for_nms, boxes[..., 2:5]], dim=-1) else: max_coordinate = boxes.max() offsets = idxs.to(boxes) * ( max_coordinate + torch.tensor(1).to(boxes)) boxes_for_nms = boxes + offsets[:, None] nms_op = nms_cfg_.pop('type', 'nms') if isinstance(nms_op, str): nms_op = eval(nms_op) split_thr = nms_cfg_.pop('split_thr', 10000) # Won't split to multiple nms nodes when exporting to onnx if boxes_for_nms.shape[0] < split_thr: dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_) boxes = boxes[keep] # This assumes `dets` has arbitrary dimensions where # the last dimension is score. # Currently it supports bounding boxes [x1, y1, x2, y2, score] or # rotated boxes [cx, cy, w, h, angle_radian, score]. scores = dets[:, -1] else: max_num = nms_cfg_.pop('max_num', -1) total_mask = scores.new_zeros(scores.size(), dtype=torch.bool) # Some type of nms would reweight the score, such as SoftNMS scores_after_nms = scores.new_zeros(scores.size()) for id in torch.unique(idxs): mask = (idxs == id).nonzero(as_tuple=False).view(-1) dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_) total_mask[mask[keep]] = True scores_after_nms[mask[keep]] = dets[:, -1] keep = total_mask.nonzero(as_tuple=False).view(-1) scores, inds = scores_after_nms[keep].sort(descending=True) keep = keep[inds] boxes = boxes[keep] if max_num > 0: keep = keep[:max_num] boxes = boxes[:max_num] scores = scores[:max_num] boxes = torch.cat([boxes, scores[:, None]], -1) return boxes, keep
[文档]def nms_match(dets: array_like_type, iou_threshold: float) -> List[array_like_type]: """Matched dets into different groups by NMS. NMS match is Similar to NMS but when a bbox is suppressed, nms match will record the indice of suppressed bbox and form a group with the indice of kept bbox. In each group, indice is sorted as score order. Args: dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5). iou_threshold (float): IoU thresh for NMS. Returns: list[torch.Tensor | np.ndarray]: The outer list corresponds different matched group, the inner Tensor corresponds the indices for a group in score order. """ if dets.shape[0] == 0: matched = [] else: assert dets.shape[-1] == 5, 'inputs dets.shape should be (N, 5), ' \ f'but get {dets.shape}' if isinstance(dets, Tensor): dets_t = dets.detach().cpu() else: dets_t = torch.from_numpy(dets) indata_list = [dets_t] indata_dict = {'iou_threshold': float(iou_threshold)} matched = ext_module.nms_match(*indata_list, **indata_dict) if torch.__version__ == 'parrots': matched = matched.tolist() # type: ignore if isinstance(dets, Tensor): return [dets.new_tensor(m, dtype=torch.long) for m in matched] else: return [np.array(m, dtype=int) for m in matched]
[文档]def nms_rotated(dets: Tensor, scores: Tensor, iou_threshold: float, labels: Optional[Tensor] = None, clockwise: bool = True) -> Tuple[Tensor, Tensor]: """Performs non-maximum suppression (NMS) on the rotated boxes according to their intersection-over-union (IoU). Rotated NMS iteratively removes lower scoring rotated boxes which have an IoU greater than iou_threshold with another (higher scoring) rotated box. Args: dets (torch.Tensor): Rotated boxes in shape (N, 5). They are expected to be in (x_ctr, y_ctr, width, height, angle_radian) format. scores (torch.Tensor): scores in shape (N, ). iou_threshold (float): IoU thresh for NMS. labels (torch.Tensor, optional): boxes' label in shape (N,). clockwise (bool): flag indicating whether the positive angular orientation is clockwise. default True. `New in version 1.4.3.` Returns: tuple: kept dets(boxes and scores) and indice, which is always the same data type as the input. """ if dets.shape[0] == 0: return dets, None if not clockwise: flip_mat = dets.new_ones(dets.shape[-1]) flip_mat[-1] = -1 dets_cw = dets * flip_mat else: dets_cw = dets multi_label = labels is not None if labels is None: input_labels = scores.new_empty(0, dtype=torch.int) else: input_labels = labels if dets.device.type in ('npu', 'mlu'): order = scores.new_empty(0, dtype=torch.long) if dets.device.type == 'npu': coefficient = 57.29578 # 180 / PI for i in range(dets.size()[0]): dets_cw[i][4] *= coefficient # radians to angle keep_inds = ext_module.nms_rotated(dets_cw, scores, order, dets_cw, input_labels, iou_threshold, multi_label) dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)), dim=1) return dets, keep_inds if multi_label: dets_wl = torch.cat((dets_cw, labels.unsqueeze(1)), 1) # type: ignore else: dets_wl = dets_cw _, order = scores.sort(0, descending=True) dets_sorted = dets_wl.index_select(0, order) if torch.__version__ == 'parrots': keep_inds = ext_module.nms_rotated( dets_wl, scores, order, dets_sorted, input_labels, iou_threshold=iou_threshold, multi_label=multi_label) else: keep_inds = ext_module.nms_rotated(dets_wl, scores, order, dets_sorted, input_labels, iou_threshold, multi_label) dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)), dim=1) return dets, keep_inds
def nms_quadri(dets: Tensor, scores: Tensor, iou_threshold: float, labels: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]: """Performs non-maximum suppression (NMS) on the quadrilateral boxes according to their intersection-over-union (IoU). Quadri NMS iteratively removes lower scoring quadrilateral boxes which have an IoU greater than iou_threshold with another (higher scoring) quadrilateral box. Args: dets (torch.Tensor): Quadri boxes in shape (N, 8). They are expected to be in (x1, y1, ..., x4, y4) format. scores (torch.Tensor): scores in shape (N, ). iou_threshold (float): IoU thresh for NMS. labels (torch.Tensor, optional): boxes' label in shape (N,). Returns: tuple: kept dets(boxes and scores) and indice, which is always the same data type as the input. """ if dets.shape[0] == 0: return dets, None multi_label = labels is not None if multi_label: dets_with_lables = \ torch.cat((dets, labels.unsqueeze(1)), 1) # type: ignore else: dets_with_lables = dets _, order = scores.sort(0, descending=True) dets_sorted = dets_with_lables.index_select(0, order) keep_inds = ext_module.nms_quadri(dets_with_lables, scores, order, dets_sorted, iou_threshold, multi_label) dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)), dim=1) return dets, keep_inds
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