mmyolo.models.dense_heads.yolox_head 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.task_modules.samplers import PseudoSampler
from mmdet.models.utils import multi_apply
from mmdet.structures.bbox import bbox_xyxy_to_cxcywh
from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
OptMultiConfig, reduce_mean)
from mmengine.model import BaseModule, bias_init_with_prob
from mmengine.structures import InstanceData
from torch import Tensor
from mmyolo.registry import MODELS, TASK_UTILS
from .yolov5_head import YOLOv5Head
[文档]@MODELS.register_module()
class YOLOXHeadModule(BaseModule):
"""YOLOXHead head module used in `YOLOX.
`<https://arxiv.org/abs/2107.08430>`_
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (Union[int, Sequence]): Number of channels in the input
feature map.
widen_factor (float): Width multiplier, multiply number of
channels in each layer by this amount. Defaults to 1.0.
num_base_priors (int): The number of priors (points) at a point
on the feature grid
stacked_convs (int): Number of stacking convs of the head.
Defaults to 2.
featmap_strides (Sequence[int]): Downsample factor of each feature map.
Defaults to [8, 16, 32].
use_depthwise (bool): Whether to depthwise separable convolution in
blocks. Defaults to False.
dcn_on_last_conv (bool): If true, use dcn in the last layer of
towers. Defaults to False.
conv_bias (bool or str): If specified as `auto`, it will be decided by
the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
None, otherwise False. Defaults to "auto".
conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
convolution layer. Defaults to None.
norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
layer. Defaults to dict(type='BN', momentum=0.03, eps=0.001).
act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
Defaults to None.
init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(
self,
num_classes: int,
in_channels: Union[int, Sequence],
widen_factor: float = 1.0,
num_base_priors: int = 1,
feat_channels: int = 256,
stacked_convs: int = 2,
featmap_strides: Sequence[int] = [8, 16, 32],
use_depthwise: bool = False,
dcn_on_last_conv: bool = False,
conv_bias: Union[bool, str] = 'auto',
conv_cfg: OptConfigType = None,
norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
act_cfg: ConfigType = dict(type='SiLU', inplace=True),
init_cfg: OptMultiConfig = None,
):
super().__init__(init_cfg=init_cfg)
self.num_classes = num_classes
self.feat_channels = int(feat_channels * widen_factor)
self.stacked_convs = stacked_convs
self.use_depthwise = use_depthwise
self.dcn_on_last_conv = dcn_on_last_conv
assert conv_bias == 'auto' or isinstance(conv_bias, bool)
self.conv_bias = conv_bias
self.num_base_priors = num_base_priors
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.featmap_strides = featmap_strides
if isinstance(in_channels, int):
in_channels = int(in_channels * widen_factor)
self.in_channels = in_channels
self._init_layers()
def _init_layers(self):
"""Initialize heads for all level feature maps."""
self.multi_level_cls_convs = nn.ModuleList()
self.multi_level_reg_convs = nn.ModuleList()
self.multi_level_conv_cls = nn.ModuleList()
self.multi_level_conv_reg = nn.ModuleList()
self.multi_level_conv_obj = nn.ModuleList()
for _ in self.featmap_strides:
self.multi_level_cls_convs.append(self._build_stacked_convs())
self.multi_level_reg_convs.append(self._build_stacked_convs())
conv_cls, conv_reg, conv_obj = self._build_predictor()
self.multi_level_conv_cls.append(conv_cls)
self.multi_level_conv_reg.append(conv_reg)
self.multi_level_conv_obj.append(conv_obj)
def _build_stacked_convs(self) -> nn.Sequential:
"""Initialize conv layers of a single level head."""
conv = DepthwiseSeparableConvModule \
if self.use_depthwise else ConvModule
stacked_convs = []
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
if self.dcn_on_last_conv and i == self.stacked_convs - 1:
conv_cfg = dict(type='DCNv2')
else:
conv_cfg = self.conv_cfg
stacked_convs.append(
conv(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
bias=self.conv_bias))
return nn.Sequential(*stacked_convs)
def _build_predictor(self) -> Tuple[nn.Module, nn.Module, nn.Module]:
"""Initialize predictor layers of a single level head."""
conv_cls = nn.Conv2d(self.feat_channels, self.num_classes, 1)
conv_reg = nn.Conv2d(self.feat_channels, 4, 1)
conv_obj = nn.Conv2d(self.feat_channels, 1, 1)
return conv_cls, conv_reg, conv_obj
[文档] def init_weights(self):
"""Initialize weights of the head."""
# Use prior in model initialization to improve stability
super().init_weights()
bias_init = bias_init_with_prob(0.01)
for conv_cls, conv_obj in zip(self.multi_level_conv_cls,
self.multi_level_conv_obj):
conv_cls.bias.data.fill_(bias_init)
conv_obj.bias.data.fill_(bias_init)
[文档] def forward(self, x: Tuple[Tensor]) -> Tuple[List]:
"""Forward features from the upstream network.
Args:
x (Tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
Tuple[List]: A tuple of multi-level classification scores, bbox
predictions, and objectnesses.
"""
return multi_apply(self.forward_single, x, self.multi_level_cls_convs,
self.multi_level_reg_convs,
self.multi_level_conv_cls,
self.multi_level_conv_reg,
self.multi_level_conv_obj)
[文档] def forward_single(self, x: Tensor, cls_convs: nn.Module,
reg_convs: nn.Module, conv_cls: nn.Module,
conv_reg: nn.Module,
conv_obj: nn.Module) -> Tuple[Tensor, Tensor, Tensor]:
"""Forward feature of a single scale level."""
cls_feat = cls_convs(x)
reg_feat = reg_convs(x)
cls_score = conv_cls(cls_feat)
bbox_pred = conv_reg(reg_feat)
objectness = conv_obj(reg_feat)
return cls_score, bbox_pred, objectness
[文档]@MODELS.register_module()
class YOLOXHead(YOLOv5Head):
"""YOLOXHead head used in `YOLOX <https://arxiv.org/abs/2107.08430>`_.
Args:
head_module(ConfigType): Base module used for YOLOXHead
prior_generator: Points generator feature maps in
2D points-based detectors.
loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
loss_obj (:obj:`ConfigDict` or dict): Config of objectness loss.
loss_bbox_aux (:obj:`ConfigDict` or dict): Config of bbox aux loss.
train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
anchor head. Defaults to None.
test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
anchor head. Defaults to None.
init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
head_module: ConfigType,
prior_generator: ConfigType = dict(
type='mmdet.MlvlPointGenerator',
offset=0,
strides=[8, 16, 32]),
bbox_coder: ConfigType = dict(type='YOLOXBBoxCoder'),
loss_cls: ConfigType = dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_bbox: ConfigType = dict(
type='mmdet.IoULoss',
mode='square',
eps=1e-16,
reduction='sum',
loss_weight=5.0),
loss_obj: ConfigType = dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_bbox_aux: ConfigType = dict(
type='mmdet.L1Loss', reduction='sum', loss_weight=1.0),
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None):
self.use_bbox_aux = False
self.loss_bbox_aux = loss_bbox_aux
super().__init__(
head_module=head_module,
prior_generator=prior_generator,
bbox_coder=bbox_coder,
loss_cls=loss_cls,
loss_bbox=loss_bbox,
loss_obj=loss_obj,
train_cfg=train_cfg,
test_cfg=test_cfg,
init_cfg=init_cfg)
[文档] def special_init(self):
"""Since YOLO series algorithms will inherit from YOLOv5Head, but
different algorithms have special initialization process.
The special_init function is designed to deal with this situation.
"""
self.loss_bbox_aux: nn.Module = MODELS.build(self.loss_bbox_aux)
if self.train_cfg:
self.assigner = TASK_UTILS.build(self.train_cfg.assigner)
# YOLOX does not support sampling
self.sampler = PseudoSampler()
[文档] def loss_by_feat(
self,
cls_scores: Sequence[Tensor],
bbox_preds: Sequence[Tensor],
objectnesses: Sequence[Tensor],
batch_gt_instances: Tensor,
batch_img_metas: Sequence[dict],
batch_gt_instances_ignore: OptInstanceList = None) -> dict:
"""Calculate the loss based on the features extracted by the detection
head.
Args:
cls_scores (Sequence[Tensor]): Box scores for each scale level,
each is a 4D-tensor, the channel number is
num_priors * num_classes.
bbox_preds (Sequence[Tensor]): Box energies / deltas for each scale
level, each is a 4D-tensor, the channel number is
num_priors * 4.
objectnesses (Sequence[Tensor]): Score factor for
all scale level, each is a 4D-tensor, has shape
(batch_size, 1, H, W).
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes`` and ``labels``
attributes.
batch_img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
Batch of gt_instances_ignore. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
dict[str, Tensor]: A dictionary of losses.
"""
num_imgs = len(batch_img_metas)
if batch_gt_instances_ignore is None:
batch_gt_instances_ignore = [None] * num_imgs
batch_gt_instances = self.gt_instances_preprocess(
batch_gt_instances, len(batch_img_metas))
featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
mlvl_priors = self.prior_generator.grid_priors(
featmap_sizes,
dtype=cls_scores[0].dtype,
device=cls_scores[0].device,
with_stride=True)
flatten_cls_preds = [
cls_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.num_classes)
for cls_pred in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
for bbox_pred in bbox_preds
]
flatten_objectness = [
objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
for objectness in objectnesses
]
flatten_cls_preds = torch.cat(flatten_cls_preds, dim=1)
flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
flatten_objectness = torch.cat(flatten_objectness, dim=1)
flatten_priors = torch.cat(mlvl_priors)
flatten_bboxes = self.bbox_coder.decode(flatten_priors[..., :2],
flatten_bbox_preds,
flatten_priors[..., 2])
(pos_masks, cls_targets, obj_targets, bbox_targets, bbox_aux_target,
num_fg_imgs) = multi_apply(
self._get_targets_single,
flatten_priors.unsqueeze(0).repeat(num_imgs, 1, 1),
flatten_cls_preds.detach(), flatten_bboxes.detach(),
flatten_objectness.detach(), batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore)
# The experimental results show that 'reduce_mean' can improve
# performance on the COCO dataset.
num_pos = torch.tensor(
sum(num_fg_imgs),
dtype=torch.float,
device=flatten_cls_preds.device)
num_total_samples = max(reduce_mean(num_pos), 1.0)
pos_masks = torch.cat(pos_masks, 0)
cls_targets = torch.cat(cls_targets, 0)
obj_targets = torch.cat(obj_targets, 0)
bbox_targets = torch.cat(bbox_targets, 0)
if self.use_bbox_aux:
bbox_aux_target = torch.cat(bbox_aux_target, 0)
loss_obj = self.loss_obj(flatten_objectness.view(-1, 1),
obj_targets) / num_total_samples
if num_pos > 0:
loss_cls = self.loss_cls(
flatten_cls_preds.view(-1, self.num_classes)[pos_masks],
cls_targets) / num_total_samples
loss_bbox = self.loss_bbox(
flatten_bboxes.view(-1, 4)[pos_masks],
bbox_targets) / num_total_samples
else:
# Avoid cls and reg branch not participating in the gradient
# propagation when there is no ground-truth in the images.
# For more details, please refer to
# https://github.com/open-mmlab/mmdetection/issues/7298
loss_cls = flatten_cls_preds.sum() * 0
loss_bbox = flatten_bboxes.sum() * 0
loss_dict = dict(
loss_cls=loss_cls, loss_bbox=loss_bbox, loss_obj=loss_obj)
if self.use_bbox_aux:
if num_pos > 0:
loss_bbox_aux = self.loss_bbox_aux(
flatten_bbox_preds.view(-1, 4)[pos_masks],
bbox_aux_target) / num_total_samples
else:
# Avoid cls and reg branch not participating in the gradient
# propagation when there is no ground-truth in the images.
# For more details, please refer to
# https://github.com/open-mmlab/mmdetection/issues/7298
loss_bbox_aux = flatten_bbox_preds.sum() * 0
loss_dict.update(loss_bbox_aux=loss_bbox_aux)
return loss_dict
@torch.no_grad()
def _get_targets_single(
self,
priors: Tensor,
cls_preds: Tensor,
decoded_bboxes: Tensor,
objectness: Tensor,
gt_instances: InstanceData,
img_meta: dict,
gt_instances_ignore: Optional[InstanceData] = None) -> tuple:
"""Compute classification, regression, and objectness targets for
priors in a single image.
Args:
priors (Tensor): All priors of one image, a 2D-Tensor with shape
[num_priors, 4] in [cx, xy, stride_w, stride_y] format.
cls_preds (Tensor): Classification predictions of one image,
a 2D-Tensor with shape [num_priors, num_classes]
decoded_bboxes (Tensor): Decoded bboxes predictions of one image,
a 2D-Tensor with shape [num_priors, 4] in [tl_x, tl_y,
br_x, br_y] format.
objectness (Tensor): Objectness predictions of one image,
a 1D-Tensor with shape [num_priors]
gt_instances (:obj:`InstanceData`): Ground truth of instance
annotations. It should includes ``bboxes`` and ``labels``
attributes.
img_meta (dict): Meta information for current image.
gt_instances_ignore (:obj:`InstanceData`, optional): Instances
to be ignored during training. It includes ``bboxes`` attribute
data that is ignored during training and testing.
Defaults to None.
Returns:
tuple:
foreground_mask (list[Tensor]): Binary mask of foreground
targets.
cls_target (list[Tensor]): Classification targets of an image.
obj_target (list[Tensor]): Objectness targets of an image.
bbox_target (list[Tensor]): BBox targets of an image.
bbox_aux_target (int): BBox aux targets of an image.
num_pos_per_img (int): Number of positive samples in an image.
"""
num_priors = priors.size(0)
num_gts = len(gt_instances)
# No target
if num_gts == 0:
cls_target = cls_preds.new_zeros((0, self.num_classes))
bbox_target = cls_preds.new_zeros((0, 4))
bbox_aux_target = cls_preds.new_zeros((0, 4))
obj_target = cls_preds.new_zeros((num_priors, 1))
foreground_mask = cls_preds.new_zeros(num_priors).bool()
return (foreground_mask, cls_target, obj_target, bbox_target,
bbox_aux_target, 0)
# YOLOX uses center priors with 0.5 offset to assign targets,
# but use center priors without offset to regress bboxes.
offset_priors = torch.cat(
[priors[:, :2] + priors[:, 2:] * 0.5, priors[:, 2:]], dim=-1)
scores = cls_preds.sigmoid() * objectness.unsqueeze(1).sigmoid()
pred_instances = InstanceData(
bboxes=decoded_bboxes, scores=scores.sqrt_(), priors=offset_priors)
assign_result = self.assigner.assign(
pred_instances=pred_instances,
gt_instances=gt_instances,
gt_instances_ignore=gt_instances_ignore)
sampling_result = self.sampler.sample(assign_result, pred_instances,
gt_instances)
pos_inds = sampling_result.pos_inds
num_pos_per_img = pos_inds.size(0)
pos_ious = assign_result.max_overlaps[pos_inds]
# IOU aware classification score
cls_target = F.one_hot(sampling_result.pos_gt_labels,
self.num_classes) * pos_ious.unsqueeze(-1)
obj_target = torch.zeros_like(objectness).unsqueeze(-1)
obj_target[pos_inds] = 1
bbox_target = sampling_result.pos_gt_bboxes
bbox_aux_target = cls_preds.new_zeros((num_pos_per_img, 4))
if self.use_bbox_aux:
bbox_aux_target = self._get_bbox_aux_target(
bbox_aux_target, bbox_target, priors[pos_inds])
foreground_mask = torch.zeros_like(objectness).to(torch.bool)
foreground_mask[pos_inds] = 1
return (foreground_mask, cls_target, obj_target, bbox_target,
bbox_aux_target, num_pos_per_img)
def _get_bbox_aux_target(self,
bbox_aux_target: Tensor,
gt_bboxes: Tensor,
priors: Tensor,
eps: float = 1e-8) -> Tensor:
"""Convert gt bboxes to center offset and log width height."""
gt_cxcywh = bbox_xyxy_to_cxcywh(gt_bboxes)
bbox_aux_target[:, :2] = (gt_cxcywh[:, :2] -
priors[:, :2]) / priors[:, 2:]
bbox_aux_target[:,
2:] = torch.log(gt_cxcywh[:, 2:] / priors[:, 2:] + eps)
return bbox_aux_target
[文档] @staticmethod
def gt_instances_preprocess(batch_gt_instances: Tensor,
batch_size: int) -> List[InstanceData]:
"""Split batch_gt_instances with batch size.
Args:
batch_gt_instances (Tensor): Ground truth
a 2D-Tensor for whole batch, shape [all_gt_bboxes, 6]
batch_size (int): Batch size.
Returns:
List: batch gt instances data, shape [batch_size, InstanceData]
"""
# faster version
batch_instance_list = []
for i in range(batch_size):
batch_gt_instance_ = InstanceData()
single_batch_instance = \
batch_gt_instances[batch_gt_instances[:, 0] == i, :]
batch_gt_instance_.bboxes = single_batch_instance[:, 2:]
batch_gt_instance_.labels = single_batch_instance[:, 1]
batch_instance_list.append(batch_gt_instance_)
return batch_instance_list