# Copyright (c) OpenMMLab. All rights reserved.
import os
import torch
def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape):
"""Clip boxes dynamically for onnx.
Since torch.clamp cannot have dynamic `min` and `max`, we scale the
boxes by 1/max_shape and clamp in the range [0, 1].
Args:
x1 (Tensor): The x1 for bounding boxes.
y1 (Tensor): The y1 for bounding boxes.
x2 (Tensor): The x2 for bounding boxes.
y2 (Tensor): The y2 for bounding boxes.
max_shape (Tensor or torch.Size): The (H,W) of original image.
Returns:
tuple(Tensor): The clipped x1, y1, x2, y2.
"""
assert isinstance(
max_shape,
torch.Tensor), '`max_shape` should be tensor of (h,w) for onnx'
# scale by 1/max_shape
x1 = x1 / max_shape[1]
y1 = y1 / max_shape[0]
x2 = x2 / max_shape[1]
y2 = y2 / max_shape[0]
# clamp [0, 1]
x1 = torch.clamp(x1, 0, 1)
y1 = torch.clamp(y1, 0, 1)
x2 = torch.clamp(x2, 0, 1)
y2 = torch.clamp(y2, 0, 1)
# scale back
x1 = x1 * max_shape[1]
y1 = y1 * max_shape[0]
x2 = x2 * max_shape[1]
y2 = y2 * max_shape[0]
return x1, y1, x2, y2
def get_k_for_topk(k, size):
"""Get k of TopK for onnx exporting.
The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime
it could be a Tensor.Due to dynamic shape feature, we have to decide
whether to do TopK and what K it should be while exporting to ONNX.
If returned K is less than zero, it means we do not have to do
TopK operation.
Args:
k (int or Tensor): The set k value for nms from config file.
size (Tensor or torch.Size): The number of elements of \
TopK's input tensor
Returns:
tuple: (int or Tensor): The final K for TopK.
"""
ret_k = -1
if k <= 0 or size <= 0:
return ret_k
if torch.onnx.is_in_onnx_export():
is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
if is_trt_backend:
# TensorRT does not support dynamic K with TopK op
if 0 < k < size:
ret_k = k
else:
# Always keep topk op for dynamic input in onnx for ONNX Runtime
ret_k = torch.where(k < size, k, size)
elif k < size:
ret_k = k
else:
# ret_k is -1
pass
return ret_k
def add_dummy_nms_for_onnx(boxes,
scores,
max_output_boxes_per_class=1000,
iou_threshold=0.5,
score_threshold=0.05,
pre_top_k=-1,
after_top_k=-1,
labels=None):
"""Create a dummy onnx::NonMaxSuppression op while exporting to ONNX.
This function helps exporting to onnx with batch and multiclass NMS op.
It only supports class-agnostic detection results. That is, the scores
is of shape (N, num_bboxes, num_classes) and the boxes is of shape
(N, num_boxes, 4).
Args:
boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4]
scores (Tensor): The detection scores of shape
[N, num_boxes, num_classes]
max_output_boxes_per_class (int): Maximum number of output
boxes per class of nms. Defaults to 1000.
iou_threshold (float): IOU threshold of nms. Defaults to 0.5
score_threshold (float): score threshold of nms.
Defaults to 0.05.
pre_top_k (bool): Number of top K boxes to keep before nms.
Defaults to -1.
after_top_k (int): Number of top K boxes to keep after nms.
Defaults to -1.
labels (Tensor, optional): It not None, explicit labels would be used.
Otherwise, labels would be automatically generated using
num_classed. Defaults to None.
Returns:
tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
and class labels of shape [N, num_det].
"""
max_output_boxes_per_class = torch.LongTensor([max_output_boxes_per_class])
iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32)
score_threshold = torch.tensor([score_threshold], dtype=torch.float32)
batch_size = scores.shape[0]
num_class = scores.shape[2]
nms_pre = torch.tensor(pre_top_k, device=scores.device, dtype=torch.long)
nms_pre = get_k_for_topk(nms_pre, boxes.shape[1])
if nms_pre > 0:
max_scores, _ = scores.max(-1)
_, topk_inds = max_scores.topk(nms_pre)
batch_inds = torch.arange(batch_size).view(
-1, 1).expand_as(topk_inds).long()
# Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
transformed_inds = boxes.shape[1] * batch_inds + topk_inds
boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape(
batch_size, -1, 4)
scores = scores.reshape(-1, num_class)[transformed_inds, :].reshape(
batch_size, -1, num_class)
if labels is not None:
labels = labels.reshape(-1, 1)[transformed_inds].reshape(
batch_size, -1)
scores = scores.permute(0, 2, 1)
num_box = boxes.shape[1]
# turn off tracing to create a dummy output of nms
state = torch._C._get_tracing_state()
# dummy indices of nms's output
num_fake_det = 2
batch_inds = torch.randint(batch_size, (num_fake_det, 1))
cls_inds = torch.randint(num_class, (num_fake_det, 1))
box_inds = torch.randint(num_box, (num_fake_det, 1))
indices = torch.cat([batch_inds, cls_inds, box_inds], dim=1)
output = indices
setattr(DummyONNXNMSop, 'output', output)
# open tracing
torch._C._set_tracing_state(state)
selected_indices = DummyONNXNMSop.apply(boxes, scores,
max_output_boxes_per_class,
iou_threshold, score_threshold)
batch_inds, cls_inds = selected_indices[:, 0], selected_indices[:, 1]
box_inds = selected_indices[:, 2]
if labels is None:
labels = torch.arange(num_class, dtype=torch.long).to(scores.device)
labels = labels.view(1, num_class, 1).expand_as(scores)
scores = scores.reshape(-1, 1)
boxes = boxes.reshape(batch_size, -1).repeat(1, num_class).reshape(-1, 4)
pos_inds = (num_class * batch_inds + cls_inds) * num_box + box_inds
mask = scores.new_zeros(scores.shape)
# Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
# PyTorch style code: mask[batch_inds, box_inds] += 1
mask[pos_inds, :] += 1
scores = scores * mask
boxes = boxes * mask
scores = scores.reshape(batch_size, -1)
boxes = boxes.reshape(batch_size, -1, 4)
labels = labels.reshape(batch_size, -1)
nms_after = torch.tensor(
after_top_k, device=scores.device, dtype=torch.long)
nms_after = get_k_for_topk(nms_after, num_box * num_class)
if nms_after > 0:
_, topk_inds = scores.topk(nms_after)
batch_inds = torch.arange(batch_size).view(-1, 1).expand_as(topk_inds)
# Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
transformed_inds = scores.shape[1] * batch_inds + topk_inds
scores = scores.reshape(-1, 1)[transformed_inds, :].reshape(
batch_size, -1)
boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape(
batch_size, -1, 4)
labels = labels.reshape(-1, 1)[transformed_inds, :].reshape(
batch_size, -1)
scores = scores.unsqueeze(2)
dets = torch.cat([boxes, scores], dim=2)
return dets, labels
class DummyONNXNMSop(torch.autograd.Function):
"""DummyONNXNMSop.
This class is only for creating onnx::NonMaxSuppression.
"""
@staticmethod
def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_threshold,
score_threshold):
return DummyONNXNMSop.output
@staticmethod
def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold,
score_threshold):
return g.op(
'NonMaxSuppression',
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
outputs=1)