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# Copyright (c) Lin Song. All rights reserved.
import math
from typing import List, Optional, Tuple, Union, Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.batchnorm import _BatchNorm
from mmcv.cnn import ConvModule
from mmengine.config import ConfigDict
from mmengine.dist import get_dist_info
from mmengine.structures import InstanceData
from mmdet.structures import SampleList
from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
OptMultiConfig, InstanceList)
from mmdet.models.utils import multi_apply, unpack_gt_instances
from mmyolo.models.dense_heads import YOLOv8HeadModule
from mmyolo.models.utils import gt_instances_preprocess
from mmyolo.registry import MODELS, TASK_UTILS
from mmyolo.models.dense_heads.yolov5_ins_head import (
ProtoModule, YOLOv5InsHead
)
from .yolo_world_head import ContrastiveHead, BNContrastiveHead
@MODELS.register_module()
class YOLOWorldSegHeadModule(YOLOv8HeadModule):
def __init__(self,
*args,
embed_dims: int,
proto_channels: int,
mask_channels: int,
freeze_bbox: bool = False,
freeze_all: bool = False,
use_bn_head: bool = False,
**kwargs) -> None:
self.embed_dims = embed_dims
self.proto_channels = proto_channels
self.mask_channels = mask_channels
self.freeze_bbox = freeze_bbox
self.freeze_all = freeze_all
self.use_bn_head = use_bn_head
super().__init__(*args, **kwargs)
def init_weights(self, prior_prob=0.01):
"""Initialize the weight and bias of PPYOLOE head."""
super().init_weights()
for cls_pred, cls_contrast, stride in zip(self.cls_preds,
self.cls_contrasts,
self.featmap_strides):
cls_pred[-1].bias.data[:] = 0.0 # reset bias
if hasattr(cls_contrast, 'bias'):
nn.init.constant_(
cls_contrast.bias.data,
math.log(5 / self.num_classes / (640 / stride)**2))
def _init_layers(self) -> None:
"""initialize conv layers in YOLOv8 head."""
# Init decouple head
self.cls_preds = nn.ModuleList()
self.reg_preds = nn.ModuleList()
self.seg_preds = nn.ModuleList()
self.cls_contrasts = nn.ModuleList()
reg_out_channels = max(
(16, self.in_channels[0] // 4, self.reg_max * 4))
seg_out_channels = max(self.in_channels[0] // 4, self.mask_channels)
cls_out_channels = max(self.in_channels[0], self.num_classes)
bbox_norm_cfg = self.norm_cfg
bbox_norm_cfg['requires_grad'] = not self.freeze_bbox
if self.freeze_all:
self.norm_cfg['requires_grad'] = False
bbox_norm_cfg['requires_grad'] = False
for i in range(self.num_levels):
self.reg_preds.append(
nn.Sequential(
ConvModule(in_channels=self.in_channels[i],
out_channels=reg_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=bbox_norm_cfg,
act_cfg=self.act_cfg),
ConvModule(in_channels=reg_out_channels,
out_channels=reg_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=bbox_norm_cfg,
act_cfg=self.act_cfg),
nn.Conv2d(in_channels=reg_out_channels,
out_channels=4 * self.reg_max,
kernel_size=1)))
self.cls_preds.append(
nn.Sequential(
ConvModule(in_channels=self.in_channels[i],
out_channels=cls_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=bbox_norm_cfg,
act_cfg=self.act_cfg),
ConvModule(in_channels=cls_out_channels,
out_channels=cls_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=bbox_norm_cfg,
act_cfg=self.act_cfg),
nn.Conv2d(in_channels=cls_out_channels,
out_channels=self.embed_dims,
kernel_size=1)))
self.seg_preds.append(
nn.Sequential(
ConvModule(in_channels=self.in_channels[i],
out_channels=seg_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
ConvModule(in_channels=seg_out_channels,
out_channels=seg_out_channels,
kernel_size=3,
stride=1,
padding=1,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg),
nn.Conv2d(in_channels=seg_out_channels,
out_channels=self.mask_channels,
kernel_size=1)))
if self.use_bn_head:
self.cls_contrasts.append(
BNContrastiveHead(self.embed_dims, self.norm_cfg))
else:
self.cls_contrasts.append(ContrastiveHead(self.embed_dims))
proj = torch.arange(self.reg_max, dtype=torch.float)
self.register_buffer('proj', proj, persistent=False)
self.proto_pred = ProtoModule(in_channels=self.in_channels[0],
middle_channels=self.proto_channels,
mask_channels=self.mask_channels,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
if self.freeze_bbox or self.freeze_bbox:
self._freeze_all()
def _freeze_all(self):
frozen_list = [self.cls_preds, self.reg_preds, self.cls_contrasts]
if self.freeze_all:
frozen_list.extend([self.proto_pred, self.seg_preds])
for module in frozen_list:
for m in module.modules():
if isinstance(m, _BatchNorm):
m.eval()
for param in m.parameters():
param.requires_grad = False
def train(self, mode: bool = True):
"""Convert the model into training mode while keep normalization layer
frozen."""
super().train(mode)
if self.freeze_bbox or self.freeze_all:
self._freeze_all()
def forward(self, img_feats: Tuple[Tensor],
txt_feats: Tensor) -> Tuple[List]:
"""Forward features from the upstream network."""
assert len(img_feats) == self.num_levels
txt_feats = [txt_feats for _ in range(self.num_levels)]
mask_protos = self.proto_pred(img_feats[0])
cls_logit, bbox_preds, bbox_dist_preds, coeff_preds = multi_apply(
self.forward_single, img_feats, txt_feats, self.cls_preds,
self.reg_preds, self.cls_contrasts, self.seg_preds)
if self.training:
return cls_logit, bbox_preds, bbox_dist_preds, coeff_preds, mask_protos
else:
return cls_logit, bbox_preds, None, coeff_preds, mask_protos
def forward_single(self, img_feat: Tensor, txt_feat: Tensor,
cls_pred: nn.ModuleList, reg_pred: nn.ModuleList,
cls_contrast: nn.ModuleList,
seg_pred: nn.ModuleList) -> Tuple:
"""Forward feature of a single scale level."""
b, _, h, w = img_feat.shape
cls_embed = cls_pred(img_feat)
cls_logit = cls_contrast(cls_embed, txt_feat)
bbox_dist_preds = reg_pred(img_feat)
coeff_pred = seg_pred(img_feat)
if self.reg_max > 1:
bbox_dist_preds = bbox_dist_preds.reshape(
[-1, 4, self.reg_max, h * w]).permute(0, 3, 1, 2)
# TODO: The get_flops script cannot handle the situation of
# matmul, and needs to be fixed later
# bbox_preds = bbox_dist_preds.softmax(3).matmul(self.proj)
bbox_preds = bbox_dist_preds.softmax(3).matmul(
self.proj.view([-1, 1])).squeeze(-1)
bbox_preds = bbox_preds.transpose(1, 2).reshape(b, -1, h, w)
else:
bbox_preds = bbox_dist_preds
if self.training:
return cls_logit, bbox_preds, bbox_dist_preds, coeff_pred
else:
return cls_logit, bbox_preds, None, coeff_pred
@MODELS.register_module()
class YOLOWorldSegHead(YOLOv5InsHead):
def __init__(self,
head_module: ConfigType,
prior_generator: ConfigType = dict(
type='mmdet.MlvlPointGenerator',
offset=0.5,
strides=[8, 16, 32]),
bbox_coder: ConfigType = dict(type='DistancePointBBoxCoder'),
loss_cls: ConfigType = dict(type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='none',
loss_weight=0.5),
loss_bbox: ConfigType = dict(type='IoULoss',
iou_mode='ciou',
bbox_format='xyxy',
reduction='sum',
loss_weight=7.5,
return_iou=False),
loss_dfl=dict(type='mmdet.DistributionFocalLoss',
reduction='mean',
loss_weight=1.5 / 4),
mask_overlap: bool = True,
loss_mask: ConfigType = dict(type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='none'),
loss_mask_weight=0.05,
train_cfg: OptConfigType = None,
test_cfg: OptConfigType = None,
init_cfg: OptMultiConfig = None):
super().__init__(head_module=head_module,
prior_generator=prior_generator,
bbox_coder=bbox_coder,
loss_cls=loss_cls,
loss_bbox=loss_bbox,
train_cfg=train_cfg,
test_cfg=test_cfg,
init_cfg=init_cfg)
self.loss_dfl = MODELS.build(loss_dfl)
self.loss_obj = None
self.mask_overlap = mask_overlap
self.loss_mask: nn.Module = MODELS.build(loss_mask)
self.loss_mask_weight = loss_mask_weight
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.
"""
if self.train_cfg:
self.assigner = TASK_UTILS.build(self.train_cfg.assigner)
# Add common attributes to reduce calculation
self.featmap_sizes_train = None
self.num_level_priors = None
self.flatten_priors_train = None
self.stride_tensor = None
"""YOLO World head."""
def loss(self, img_feats: Tuple[Tensor], txt_feats: Tensor,
batch_data_samples: Union[list, dict]) -> dict:
"""Perform forward propagation and loss calculation of the detection
head on the features of the upstream network."""
outs = self(img_feats, txt_feats)
# Fast version
loss_inputs = outs + (batch_data_samples['bboxes_labels'],
batch_data_samples['masks'],
batch_data_samples['img_metas'])
losses = self.loss_by_feat(*loss_inputs)
return losses
def loss_and_predict(
self,
img_feats: Tuple[Tensor],
txt_feats: Tensor,
batch_data_samples: SampleList,
proposal_cfg: Optional[ConfigDict] = None
) -> Tuple[dict, InstanceList]:
"""Perform forward propagation of the head, then calculate loss and
predictions from the features and data samples.
"""
outputs = unpack_gt_instances(batch_data_samples)
(batch_gt_instances, batch_gt_instances_ignore,
batch_img_metas) = outputs
outs = self(img_feats, txt_feats)
loss_inputs = outs + (batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore)
losses = self.loss_by_feat(*loss_inputs)
predictions = self.predict_by_feat(*outs,
batch_img_metas=batch_img_metas,
cfg=proposal_cfg)
return losses, predictions
def forward(self, img_feats: Tuple[Tensor],
txt_feats: Tensor) -> Tuple[List]:
"""Forward features from the upstream network."""
return self.head_module(img_feats, txt_feats)
def predict(self,
img_feats: Tuple[Tensor],
txt_feats: Tensor,
batch_data_samples: SampleList,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the detection head and predict
detection results on the features of the upstream network.
"""
batch_img_metas = [
data_samples.metainfo for data_samples in batch_data_samples
]
outs = self(img_feats, txt_feats)
predictions = self.predict_by_feat(*outs,
batch_img_metas=batch_img_metas,
rescale=rescale)
return predictions
def aug_test(self,
aug_batch_feats,
aug_batch_img_metas,
rescale=False,
with_ori_nms=False,
**kwargs):
"""Test function with test time augmentation."""
raise NotImplementedError('aug_test is not implemented yet.')
def loss_by_feat(
self,
cls_scores: Sequence[Tensor],
bbox_preds: Sequence[Tensor],
bbox_dist_preds: Sequence[Tensor],
coeff_preds: Sequence[Tensor],
proto_preds: Tensor,
batch_gt_instances: Sequence[InstanceData],
batch_gt_masks: Sequence[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.
bbox_dist_preds (Sequence[Tensor]): Box distribution logits for
each scale level with shape (bs, reg_max + 1, H*W, 4).
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)
current_featmap_sizes = [
cls_score.shape[2:] for cls_score in cls_scores
]
# If the shape does not equal, generate new one
if current_featmap_sizes != self.featmap_sizes_train:
self.featmap_sizes_train = current_featmap_sizes
mlvl_priors_with_stride = self.prior_generator.grid_priors(
self.featmap_sizes_train,
dtype=cls_scores[0].dtype,
device=cls_scores[0].device,
with_stride=True)
self.num_level_priors = [len(n) for n in mlvl_priors_with_stride]
self.flatten_priors_train = torch.cat(mlvl_priors_with_stride,
dim=0)
self.stride_tensor = self.flatten_priors_train[..., [2]]
# gt info
gt_info = gt_instances_preprocess(batch_gt_instances, num_imgs)
gt_labels = gt_info[:, :, :1]
gt_bboxes = gt_info[:, :, 1:] # xyxy
pad_bbox_flag = (gt_bboxes.sum(-1, keepdim=True) > 0).float()
# pred info
flatten_cls_preds = [
cls_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.num_classes)
for cls_pred in cls_scores
]
flatten_pred_bboxes = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
for bbox_pred in bbox_preds
]
# (bs, n, 4 * reg_max)
flatten_pred_dists = [
bbox_pred_org.reshape(num_imgs, -1, self.head_module.reg_max * 4)
for bbox_pred_org in bbox_dist_preds
]
flatten_pred_coeffs = [
coeff_pred.permute(0, 2, 3,
1).reshape(num_imgs, -1,
self.head_module.mask_channels)
for coeff_pred in coeff_preds
]
flatten_dist_preds = torch.cat(flatten_pred_dists, dim=1)
flatten_cls_preds = torch.cat(flatten_cls_preds, dim=1)
flatten_pred_bboxes = torch.cat(flatten_pred_bboxes, dim=1)
flatten_pred_bboxes = self.bbox_coder.decode(
self.flatten_priors_train[..., :2], flatten_pred_bboxes,
self.stride_tensor[..., 0])
flatten_pred_coeffs = torch.cat(flatten_pred_coeffs, dim=1)
assigned_result = self.assigner(
(flatten_pred_bboxes.detach()).type(gt_bboxes.dtype),
flatten_cls_preds.detach().sigmoid(), self.flatten_priors_train,
gt_labels, gt_bboxes, pad_bbox_flag)
assigned_bboxes = assigned_result['assigned_bboxes']
assigned_scores = assigned_result['assigned_scores']
fg_mask_pre_prior = assigned_result['fg_mask_pre_prior']
assigned_gt_idxs = assigned_result['assigned_gt_idxs']
assigned_scores_sum = assigned_scores.sum().clamp(min=1)
loss_cls = self.loss_cls(flatten_cls_preds, assigned_scores).sum()
loss_cls /= assigned_scores_sum
# rescale bbox
assigned_bboxes /= self.stride_tensor
flatten_pred_bboxes /= self.stride_tensor
# select positive samples mask
num_pos = fg_mask_pre_prior.sum()
if num_pos > 0:
# when num_pos > 0, assigned_scores_sum will >0, so the loss_bbox
# will not report an error
# iou loss
prior_bbox_mask = fg_mask_pre_prior.unsqueeze(-1).repeat([1, 1, 4])
pred_bboxes_pos = torch.masked_select(
flatten_pred_bboxes, prior_bbox_mask).reshape([-1, 4])
assigned_bboxes_pos = torch.masked_select(
assigned_bboxes, prior_bbox_mask).reshape([-1, 4])
bbox_weight = torch.masked_select(assigned_scores.sum(-1),
fg_mask_pre_prior).unsqueeze(-1)
loss_bbox = self.loss_bbox(
pred_bboxes_pos, assigned_bboxes_pos,
weight=bbox_weight) / assigned_scores_sum
# dfl loss
pred_dist_pos = flatten_dist_preds[fg_mask_pre_prior]
assigned_ltrb = self.bbox_coder.encode(
self.flatten_priors_train[..., :2] / self.stride_tensor,
assigned_bboxes,
max_dis=self.head_module.reg_max - 1,
eps=0.01)
assigned_ltrb_pos = torch.masked_select(
assigned_ltrb, prior_bbox_mask).reshape([-1, 4])
loss_dfl = self.loss_dfl(pred_dist_pos.reshape(
-1, self.head_module.reg_max),
assigned_ltrb_pos.reshape(-1),
weight=bbox_weight.expand(-1,
4).reshape(-1),
avg_factor=assigned_scores_sum)
_, c, mask_h, mask_w = proto_preds.shape
if batch_gt_masks.shape[-2:] != (mask_h, mask_w):
batch_gt_masks = F.interpolate(batch_gt_masks[None],
(mask_h, mask_w),
mode='nearest')[0]
loss_mask = torch.zeros(1, device=loss_dfl.device)
box_sum_flag = pad_bbox_flag.long().sum(dim=1).squeeze(1)
batch_inds = torch.zeros(num_imgs,
dtype=torch.int64,
device=assigned_gt_idxs.device)[:, None]
batch_inds[1:] = box_sum_flag.cumsum(dim=0)[:-1][..., None]
_assigned_gt_idxs = assigned_gt_idxs + batch_inds
for bs in range(num_imgs):
# 8400
bbox_match_inds = assigned_gt_idxs[bs]
mask_match_inds = _assigned_gt_idxs[bs]
bbox_match_inds = torch.masked_select(bbox_match_inds,
fg_mask_pre_prior[bs])
mask_match_inds = torch.masked_select(mask_match_inds,
fg_mask_pre_prior[bs])
# mask
mask_dim = coeff_preds[0].shape[1]
prior_mask_mask = fg_mask_pre_prior[bs].unsqueeze(-1).repeat(
[1, mask_dim])
pred_coeffs_pos = torch.masked_select(flatten_pred_coeffs[bs],
prior_mask_mask).reshape(
[-1, mask_dim])
match_boxes = gt_bboxes[bs][bbox_match_inds] / 4
normed_boxes = gt_bboxes[bs][bbox_match_inds] / 640
bbox_area = (normed_boxes[:, 2:] -
normed_boxes[:, :2]).prod(dim=1)
if not mask_match_inds.any():
continue
assert not self.mask_overlap
mask_gti = batch_gt_masks[mask_match_inds]
mask_preds = (
pred_coeffs_pos @ proto_preds[bs].view(c, -1)).view(
-1, mask_h, mask_w)
loss_mask_full = self.loss_mask(mask_preds, mask_gti)
_loss_mask = (self.crop_mask(loss_mask_full[None],
match_boxes).mean(dim=(2, 3)) /
bbox_area)
loss_mask += _loss_mask.mean()
else:
loss_bbox = flatten_pred_bboxes.sum() * 0
loss_dfl = flatten_pred_bboxes.sum() * 0
loss_mask = flatten_pred_coeffs.sum() * 0
_, world_size = get_dist_info()
return dict(loss_cls=loss_cls * num_imgs * world_size,
loss_bbox=loss_bbox * num_imgs * world_size,
loss_dfl=loss_dfl * num_imgs * world_size,
loss_mask=loss_mask * self.loss_mask_weight * world_size)
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