annotator/uniformer_base/mmcv/ops/nms.py

import os

import numpy as np
import torch

from annotator.uniformer.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader

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


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

    @staticmethod
    def forward(ctx, bboxes, scores, iou_threshold, offset, score_threshold,
                max_num):
        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

    @staticmethod
    def symbolic(g, bboxes, scores, iou_threshold, offset, score_threshold,
                 max_num):
        from ..onnx import is_custom_op_loaded
        has_custom_op = is_custom_op_loaded()
        # TensorRT nms plugin is aligned with original nms in ONNXRuntime
        is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
        if has_custom_op and (not is_trt_backend):
            return g.op(
                'mmcv::NonMaxSuppression',
                bboxes,
                scores,
                iou_threshold_f=float(iou_threshold),
                offset_i=int(offset))
        else:
            from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze
            from ..onnx.onnx_utils.symbolic_helper import _size_helper

            boxes = unsqueeze(g, bboxes, 0)
            scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)

            if max_num > 0:
                max_num = g.op(
                    'Constant',
                    value_t=torch.tensor(max_num, dtype=torch.long))
            else:
                dim = g.op('Constant', value_t=torch.tensor(0))
                max_num = _size_helper(g, bboxes, dim)
            max_output_per_class = max_num
            iou_threshold = g.op(
                'Constant',
                value_t=torch.tensor([iou_threshold], dtype=torch.float))
            score_threshold = g.op(
                'Constant',
                value_t=torch.tensor([score_threshold], dtype=torch.float))
            nms_out = g.op('NonMaxSuppression', boxes, scores,
                           max_output_per_class, iou_threshold,
                           score_threshold)
            return squeeze(
                g,
                select(
                    g, nms_out, 1,
                    g.op(
                        'Constant',
                        value_t=torch.tensor([2], dtype=torch.long))), 1)


class SoftNMSop(torch.autograd.Function):

    @staticmethod
    def forward(ctx, boxes, scores, iou_threshold, sigma, min_score, method,
                offset):
        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


@deprecated_api_warning({'iou_thr': 'iou_threshold'})
def nms(boxes, scores, iou_threshold, offset=0, score_threshold=0, max_num=-1):
    """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 is always 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, (torch.Tensor, np.ndarray))
    assert isinstance(scores, (torch.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)

    if torch.__version__ == 'parrots':
        indata_list = [boxes, scores]
        indata_dict = {
            'iou_threshold': float(iou_threshold),
            'offset': int(offset)
        }
        inds = ext_module.nms(*indata_list, **indata_dict)
    else:
        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


@deprecated_api_warning({'iou_thr': 'iou_threshold'})
def soft_nms(boxes,
             scores,
             iou_threshold=0.3,
             sigma=0.5,
             min_score=1e-3,
             method='linear',
             offset=0):
    """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.

    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.
        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 is always 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, (torch.Tensor, np.ndarray))
    assert isinstance(scores, (torch.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)


def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
    """Performs non-maximum suppression in a batched fashion.

    Modified from 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.

    Arguments:
        boxes (torch.Tensor): boxes in shape (N, 4).
        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): specify nms type and other parameters like iou_thr.
            Possible keys includes the following.

            - iou_thr (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.

    Returns:
        tuple: kept dets and indice.
    """
    nms_cfg_ = nms_cfg.copy()
    class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic)
    if class_agnostic:
        boxes_for_nms = boxes
    else:
        max_coordinate = boxes.max()
        offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
        boxes_for_nms = boxes + offsets[:, None]

    nms_type = nms_cfg_.pop('type', 'nms')
    nms_op = eval(nms_type)

    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 or torch.onnx.is_in_onnx_export():
        dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_)
        boxes = boxes[keep]
        # -1 indexing works abnormal in TensorRT
        # This assumes `dets` has 5 dimensions where
        # the last dimension is score.
        # TODO: more elegant way to handle the dimension issue.
        # Some type of nms would reweight the score, such as SoftNMS
        scores = dets[:, 4]
    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]

    return torch.cat([boxes, scores[:, None]], -1), keep


def nms_match(dets, iou_threshold):
    """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.

    Arguments:
        dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5).
        iou_thr (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, torch.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()

    if isinstance(dets, torch.Tensor):
        return [dets.new_tensor(m, dtype=torch.long) for m in matched]
    else:
        return [np.array(m, dtype=np.int) for m in matched]


def nms_rotated(dets, scores, iou_threshold, labels=None):
    """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:
        boxes (Tensor):  Rotated boxes in shape (N, 5). They are expected to \
            be in (x_ctr, y_ctr, width, height, angle_radian) format.
        scores (Tensor): scores in shape (N, ).
        iou_threshold (float): IoU thresh for NMS.
        labels (Tensor): 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_wl = torch.cat((dets, labels.unsqueeze(1)), 1)
    else:
        dets_wl = dets
    _, 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,
            iou_threshold=iou_threshold,
            multi_label=multi_label)
    else:
        keep_inds = ext_module.nms_rotated(dets_wl, 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