yolo.py
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# Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) 2019 Western Digital Corporation or its affiliates.
import torch
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLOV3(SingleStageDetector):
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=None,
test_cfg=None,
pretrained=None,
init_cfg=None):
super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg,
test_cfg, pretrained, init_cfg)
def onnx_export(self, img, img_metas):
"""Test function for exporting to ONNX, without test time augmentation.
Args:
img (torch.Tensor): input images.
img_metas (list[dict]): List of image information.
Returns:
tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
and class labels of shape [N, num_det].
"""
x = self.extract_feat(img)
outs = self.bbox_head.forward(x)
# get shape as tensor
img_shape = torch._shape_as_tensor(img)[2:]
img_metas[0]['img_shape_for_onnx'] = img_shape
det_bboxes, det_labels = self.bbox_head.onnx_export(*outs, img_metas)
return det_bboxes, det_labels