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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import Tensor
_XYWH2XYXY = torch.tensor([[1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0],
[-0.5, 0.0, 0.5, 0.0], [0.0, -0.5, 0.0, 0.5]],
dtype=torch.float32)
def select_nms_index(scores: Tensor,
boxes: Tensor,
nms_index: Tensor,
batch_size: int,
keep_top_k: int = -1):
batch_inds, cls_inds = nms_index[:, 0], nms_index[:, 1]
box_inds = nms_index[:, 2]
scores = scores[batch_inds, cls_inds, box_inds].unsqueeze(1)
boxes = boxes[batch_inds, box_inds, ...]
dets = torch.cat([boxes, scores], dim=1)
batched_dets = dets.unsqueeze(0).repeat(batch_size, 1, 1)
batch_template = torch.arange(
0, batch_size, dtype=batch_inds.dtype, device=batch_inds.device)
batched_dets = batched_dets.where(
(batch_inds == batch_template.unsqueeze(1)).unsqueeze(-1),
batched_dets.new_zeros(1))
batched_labels = cls_inds.unsqueeze(0).repeat(batch_size, 1)
batched_labels = batched_labels.where(
(batch_inds == batch_template.unsqueeze(1)),
batched_labels.new_ones(1) * -1)
N = batched_dets.shape[0]
batched_dets = torch.cat((batched_dets, batched_dets.new_zeros((N, 1, 5))),
1)
batched_labels = torch.cat((batched_labels, -batched_labels.new_ones(
(N, 1))), 1)
_, topk_inds = batched_dets[:, :, -1].sort(dim=1, descending=True)
topk_batch_inds = torch.arange(
batch_size, dtype=topk_inds.dtype,
device=topk_inds.device).view(-1, 1)
batched_dets = batched_dets[topk_batch_inds, topk_inds, ...]
batched_labels = batched_labels[topk_batch_inds, topk_inds, ...]
batched_dets, batched_scores = batched_dets.split([4, 1], 2)
batched_scores = batched_scores.squeeze(-1)
num_dets = (batched_scores > 0).sum(1, keepdim=True)
return num_dets, batched_dets, batched_scores, batched_labels
class ONNXNMSop(torch.autograd.Function):
@staticmethod
def forward(
ctx,
boxes: Tensor,
scores: Tensor,
max_output_boxes_per_class: Tensor = torch.tensor([100]),
iou_threshold: Tensor = torch.tensor([0.5]),
score_threshold: Tensor = torch.tensor([0.05])
) -> Tensor:
device = boxes.device
batch = scores.shape[0]
num_det = 20
batches = torch.randint(0, batch, (num_det, )).sort()[0].to(device)
idxs = torch.arange(100, 100 + num_det).to(device)
zeros = torch.zeros((num_det, ), dtype=torch.int64).to(device)
selected_indices = torch.cat([batches[None], zeros[None], idxs[None]],
0).T.contiguous()
selected_indices = selected_indices.to(torch.int64)
return selected_indices
@staticmethod
def symbolic(
g,
boxes: Tensor,
scores: Tensor,
max_output_boxes_per_class: Tensor = torch.tensor([100]),
iou_threshold: Tensor = torch.tensor([0.5]),
score_threshold: Tensor = torch.tensor([0.05]),
):
return g.op(
'NonMaxSuppression',
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
outputs=1)
def onnx_nms(
boxes: torch.Tensor,
scores: torch.Tensor,
max_output_boxes_per_class: int = 100,
iou_threshold: float = 0.5,
score_threshold: float = 0.05,
pre_top_k: int = -1,
keep_top_k: int = 100,
box_coding: int = 0,
):
max_output_boxes_per_class = torch.tensor([max_output_boxes_per_class])
iou_threshold = torch.tensor([iou_threshold])
score_threshold = torch.tensor([score_threshold])
batch_size, _, _ = scores.shape
if box_coding == 1:
boxes = boxes @ (_XYWH2XYXY.to(boxes.device))
scores = scores.transpose(1, 2).contiguous()
selected_indices = ONNXNMSop.apply(boxes, scores,
max_output_boxes_per_class,
iou_threshold, score_threshold)
num_dets, batched_dets, batched_scores, batched_labels = select_nms_index(
scores, boxes, selected_indices, batch_size, keep_top_k=keep_top_k)
return num_dets, batched_dets, batched_scores, batched_labels.to(
torch.int32)
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