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# Copyright (c) OpenMMLab. All rights reserved.
import random
import torch
import torch.distributed as dist
import torch.nn.functional as F
from mmcv.runner import get_dist_info
from ...utils import log_img_scale
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLOX(SingleStageDetector):
r"""Implementation of `YOLOX: Exceeding YOLO Series in 2021
<https://arxiv.org/abs/2107.08430>`_
Note: Considering the trade-off between training speed and accuracy,
multi-scale training is temporarily kept. More elegant implementation
will be adopted in the future.
Args:
backbone (nn.Module): The backbone module.
neck (nn.Module): The neck module.
bbox_head (nn.Module): The bbox head module.
train_cfg (obj:`ConfigDict`, optional): The training config
of YOLOX. Default: None.
test_cfg (obj:`ConfigDict`, optional): The testing config
of YOLOX. Default: None.
pretrained (str, optional): model pretrained path.
Default: None.
input_size (tuple): The model default input image size. The shape
order should be (height, width). Default: (640, 640).
size_multiplier (int): Image size multiplication factor.
Default: 32.
random_size_range (tuple): The multi-scale random range during
multi-scale training. The real training image size will
be multiplied by size_multiplier. Default: (15, 25).
random_size_interval (int): The iter interval of change
image size. Default: 10.
init_cfg (dict, optional): Initialization config dict.
Default: None.
"""
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=None,
test_cfg=None,
pretrained=None,
input_size=(640, 640),
size_multiplier=32,
random_size_range=(15, 25),
random_size_interval=10,
init_cfg=None):
super(YOLOX, self).__init__(backbone, neck, bbox_head, train_cfg,
test_cfg, pretrained, init_cfg)
log_img_scale(input_size, skip_square=True)
self.rank, self.world_size = get_dist_info()
self._default_input_size = input_size
self._input_size = input_size
self._random_size_range = random_size_range
self._random_size_interval = random_size_interval
self._size_multiplier = size_multiplier
self._progress_in_iter = 0
def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None):
"""
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
img_metas (list[dict]): A List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
:class:`mmdet.datasets.pipelines.Collect`.
gt_bboxes (list[Tensor]): Each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): Class indices corresponding to each box
gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
boxes can be ignored when computing the loss.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
# Multi-scale training
img, gt_bboxes = self._preprocess(img, gt_bboxes)
losses = super(YOLOX, self).forward_train(img, img_metas, gt_bboxes,
gt_labels, gt_bboxes_ignore)
# random resizing
if (self._progress_in_iter + 1) % self._random_size_interval == 0:
self._input_size = self._random_resize(device=img.device)
self._progress_in_iter += 1
return losses
def _preprocess(self, img, gt_bboxes):
scale_y = self._input_size[0] / self._default_input_size[0]
scale_x = self._input_size[1] / self._default_input_size[1]
if scale_x != 1 or scale_y != 1:
img = F.interpolate(
img,
size=self._input_size,
mode='bilinear',
align_corners=False)
for gt_bbox in gt_bboxes:
gt_bbox[..., 0::2] = gt_bbox[..., 0::2] * scale_x
gt_bbox[..., 1::2] = gt_bbox[..., 1::2] * scale_y
return img, gt_bboxes
def _random_resize(self, device):
tensor = torch.LongTensor(2).to(device)
if self.rank == 0:
size = random.randint(*self._random_size_range)
aspect_ratio = float(
self._default_input_size[1]) / self._default_input_size[0]
size = (self._size_multiplier * size,
self._size_multiplier * int(aspect_ratio * size))
tensor[0] = size[0]
tensor[1] = size[1]
if self.world_size > 1:
dist.barrier()
dist.broadcast(tensor, 0)
input_size = (tensor[0].item(), tensor[1].item())
return input_size
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