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mmdet.core.bbox.iou_calculators.iou2d_calculator 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch

from .builder import IOU_CALCULATORS


def cast_tensor_type(x, scale=1., dtype=None):
    if dtype == 'fp16':
        # scale is for preventing overflows
        x = (x / scale).half()
    return x


def fp16_clamp(x, min=None, max=None):
    if not x.is_cuda and x.dtype == torch.float16:
        # clamp for cpu float16, tensor fp16 has no clamp implementation
        return x.float().clamp(min, max).half()

    return x.clamp(min, max)


[文档]@IOU_CALCULATORS.register_module() class BboxOverlaps2D: """2D Overlaps (e.g. IoUs, GIoUs) Calculator.""" def __init__(self, scale=1., dtype=None): self.scale = scale self.dtype = dtype def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): """Calculate IoU between 2D bboxes. Args: bboxes1 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2, y2, score> format. bboxes2 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2, score> format, or be empty. If ``is_aligned `` is ``True``, then m and n must be equal. mode (str): "iou" (intersection over union), "iof" (intersection over foreground), or "giou" (generalized intersection over union). is_aligned (bool, optional): If True, then m and n must be equal. Default False. Returns: Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) """ assert bboxes1.size(-1) in [0, 4, 5] assert bboxes2.size(-1) in [0, 4, 5] if bboxes2.size(-1) == 5: bboxes2 = bboxes2[..., :4] if bboxes1.size(-1) == 5: bboxes1 = bboxes1[..., :4] if self.dtype == 'fp16': # change tensor type to save cpu and cuda memory and keep speed bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype) bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype) overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) if not overlaps.is_cuda and overlaps.dtype == torch.float16: # resume cpu float32 overlaps = overlaps.float() return overlaps return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) def __repr__(self): """str: a string describing the module""" repr_str = self.__class__.__name__ + f'(' \ f'scale={self.scale}, dtype={self.dtype})' return repr_str
[文档]def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6): """Calculate overlap between two set of bboxes. FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889 Note: Assume bboxes1 is M x 4, bboxes2 is N x 4, when mode is 'iou', there are some new generated variable when calculating IOU using bbox_overlaps function: 1) is_aligned is False area1: M x 1 area2: N x 1 lt: M x N x 2 rb: M x N x 2 wh: M x N x 2 overlap: M x N x 1 union: M x N x 1 ious: M x N x 1 Total memory: S = (9 x N x M + N + M) * 4 Byte, When using FP16, we can reduce: R = (9 x N x M + N + M) * 4 / 2 Byte R large than (N + M) * 4 * 2 is always true when N and M >= 1. Obviously, N + M <= N * M < 3 * N * M, when N >=2 and M >=2, N + 1 < 3 * N, when N or M is 1. Given M = 40 (ground truth), N = 400000 (three anchor boxes in per grid, FPN, R-CNNs), R = 275 MB (one times) A special case (dense detection), M = 512 (ground truth), R = 3516 MB = 3.43 GB When the batch size is B, reduce: B x R Therefore, CUDA memory runs out frequently. Experiments on GeForce RTX 2080Ti (11019 MiB): | dtype | M | N | Use | Real | Ideal | |:----:|:----:|:----:|:----:|:----:|:----:| | FP32 | 512 | 400000 | 8020 MiB | -- | -- | | FP16 | 512 | 400000 | 4504 MiB | 3516 MiB | 3516 MiB | | FP32 | 40 | 400000 | 1540 MiB | -- | -- | | FP16 | 40 | 400000 | 1264 MiB | 276MiB | 275 MiB | 2) is_aligned is True area1: N x 1 area2: N x 1 lt: N x 2 rb: N x 2 wh: N x 2 overlap: N x 1 union: N x 1 ious: N x 1 Total memory: S = 11 x N * 4 Byte When using FP16, we can reduce: R = 11 x N * 4 / 2 Byte So do the 'giou' (large than 'iou'). Time-wise, FP16 is generally faster than FP32. When gpu_assign_thr is not -1, it takes more time on cpu but not reduce memory. There, we can reduce half the memory and keep the speed. If ``is_aligned`` is ``False``, then calculate the overlaps between each bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned pair of bboxes1 and bboxes2. Args: bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty. bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty. B indicates the batch dim, in shape (B1, B2, ..., Bn). If ``is_aligned`` is ``True``, then m and n must be equal. mode (str): "iou" (intersection over union), "iof" (intersection over foreground) or "giou" (generalized intersection over union). Default "iou". is_aligned (bool, optional): If True, then m and n must be equal. Default False. eps (float, optional): A value added to the denominator for numerical stability. Default 1e-6. Returns: Tensor: shape (m, n) if ``is_aligned`` is False else shape (m,) Example: >>> bboxes1 = torch.FloatTensor([ >>> [0, 0, 10, 10], >>> [10, 10, 20, 20], >>> [32, 32, 38, 42], >>> ]) >>> bboxes2 = torch.FloatTensor([ >>> [0, 0, 10, 20], >>> [0, 10, 10, 19], >>> [10, 10, 20, 20], >>> ]) >>> overlaps = bbox_overlaps(bboxes1, bboxes2) >>> assert overlaps.shape == (3, 3) >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True) >>> assert overlaps.shape == (3, ) Example: >>> empty = torch.empty(0, 4) >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]]) >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) """ assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}' # Either the boxes are empty or the length of boxes' last dimension is 4 assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) # Batch dim must be the same # Batch dim: (B1, B2, ... Bn) assert bboxes1.shape[:-2] == bboxes2.shape[:-2] batch_shape = bboxes1.shape[:-2] rows = bboxes1.size(-2) cols = bboxes2.size(-2) if is_aligned: assert rows == cols if rows * cols == 0: if is_aligned: return bboxes1.new(batch_shape + (rows, )) else: return bboxes1.new(batch_shape + (rows, cols)) area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * ( bboxes1[..., 3] - bboxes1[..., 1]) area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * ( bboxes2[..., 3] - bboxes2[..., 1]) if is_aligned: lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2] rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] if mode in ['iou', 'giou']: union = area1 + area2 - overlap else: union = area1 if mode == 'giou': enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2]) enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:]) else: lt = torch.max(bboxes1[..., :, None, :2], bboxes2[..., None, :, :2]) # [B, rows, cols, 2] rb = torch.min(bboxes1[..., :, None, 2:], bboxes2[..., None, :, 2:]) # [B, rows, cols, 2] wh = fp16_clamp(rb - lt, min=0) overlap = wh[..., 0] * wh[..., 1] if mode in ['iou', 'giou']: union = area1[..., None] + area2[..., None, :] - overlap else: union = area1[..., None] if mode == 'giou': enclosed_lt = torch.min(bboxes1[..., :, None, :2], bboxes2[..., None, :, :2]) enclosed_rb = torch.max(bboxes1[..., :, None, 2:], bboxes2[..., None, :, 2:]) eps = union.new_tensor([eps]) union = torch.max(union, eps) ious = overlap / union if mode in ['iou', 'iof']: return ious # calculate gious enclose_wh = fp16_clamp(enclosed_rb - enclosed_lt, min=0) enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1] enclose_area = torch.max(enclose_area, eps) gious = ious - (enclose_area - union) / enclose_area return gious
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