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# Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from functools import partial
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from mmdet.models.backbones.csp_darknet import Focus
from mmdet.models.layers import ChannelAttention
from mmengine.config import ConfigDict
from torch import Tensor
from mmyolo.models import RepVGGBlock
from mmyolo.models.dense_heads import (PPYOLOEHead, RTMDetHead, YOLOv5Head,
YOLOv7Head, YOLOv8Head, YOLOXHead)
from mmyolo.models.layers import ImplicitA, ImplicitM
from ..backbone import DeployFocus, GConvFocus, NcnnFocus
from ..bbox_code import (rtmdet_bbox_decoder, yolov5_bbox_decoder,
yolox_bbox_decoder)
from ..nms import batched_nms, efficient_nms, onnx_nms
from .backend import MMYOLOBackend
class DeployModel(nn.Module):
transpose = False
def __init__(self,
baseModel: nn.Module,
backend: MMYOLOBackend,
postprocess_cfg: Optional[ConfigDict] = None):
super().__init__()
self.baseModel = baseModel
self.baseHead = baseModel.bbox_head
self.backend = backend
if postprocess_cfg is None:
self.with_postprocess = False
else:
self.with_postprocess = True
self.__init_sub_attributes()
self.detector_type = type(self.baseHead)
self.pre_top_k = postprocess_cfg.get('pre_top_k', 1000)
self.keep_top_k = postprocess_cfg.get('keep_top_k', 100)
self.iou_threshold = postprocess_cfg.get('iou_threshold', 0.65)
self.score_threshold = postprocess_cfg.get('score_threshold', 0.25)
self.__switch_deploy()
def __init_sub_attributes(self):
self.bbox_decoder = self.baseHead.bbox_coder.decode
self.prior_generate = self.baseHead.prior_generator.grid_priors
self.num_base_priors = self.baseHead.num_base_priors
self.featmap_strides = self.baseHead.featmap_strides
self.num_classes = self.baseHead.num_classes
def __switch_deploy(self):
headType = type(self.baseHead)
if not self.with_postprocess:
if headType in (YOLOv5Head, YOLOv7Head):
self.baseHead.head_module.forward_single = self.forward_single
elif headType in (PPYOLOEHead, YOLOv8Head):
self.baseHead.head_module.reg_max = 0
if self.backend in (MMYOLOBackend.HORIZONX3, MMYOLOBackend.NCNN,
MMYOLOBackend.TORCHSCRIPT):
self.transpose = True
for layer in self.baseModel.modules():
if isinstance(layer, RepVGGBlock):
layer.switch_to_deploy()
elif isinstance(layer, ChannelAttention):
layer.global_avgpool.forward = self.forward_gvp
elif isinstance(layer, Focus):
# onnxruntime openvino tensorrt8 tensorrt7
if self.backend in (MMYOLOBackend.ONNXRUNTIME,
MMYOLOBackend.OPENVINO,
MMYOLOBackend.TENSORRT8,
MMYOLOBackend.TENSORRT7):
self.baseModel.backbone.stem = DeployFocus(layer)
# ncnn
elif self.backend == MMYOLOBackend.NCNN:
self.baseModel.backbone.stem = NcnnFocus(layer)
# switch focus to group conv
else:
self.baseModel.backbone.stem = GConvFocus(layer)
def pred_by_feat(self,
cls_scores: List[Tensor],
bbox_preds: List[Tensor],
objectnesses: Optional[List[Tensor]] = None,
**kwargs):
assert len(cls_scores) == len(bbox_preds)
dtype = cls_scores[0].dtype
device = cls_scores[0].device
nms_func = self.select_nms()
if self.detector_type in (YOLOv5Head, YOLOv7Head):
bbox_decoder = yolov5_bbox_decoder
elif self.detector_type is RTMDetHead:
bbox_decoder = rtmdet_bbox_decoder
elif self.detector_type is YOLOXHead:
bbox_decoder = yolox_bbox_decoder
else:
bbox_decoder = self.bbox_decoder
num_imgs = cls_scores[0].shape[0]
featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
mlvl_priors = self.prior_generate(
featmap_sizes, dtype=dtype, device=device)
flatten_priors = torch.cat(mlvl_priors)
mlvl_strides = [
flatten_priors.new_full(
(featmap_size[0] * featmap_size[1] * self.num_base_priors, ),
stride) for featmap_size, stride in zip(
featmap_sizes, self.featmap_strides)
]
flatten_stride = torch.cat(mlvl_strides)
# flatten cls_scores, bbox_preds and objectness
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
self.num_classes)
for cls_score in cls_scores
]
cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
for bbox_pred in bbox_preds
]
flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
if objectnesses is not None:
flatten_objectness = [
objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
for objectness in objectnesses
]
flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
cls_scores = cls_scores * (flatten_objectness.unsqueeze(-1))
scores = cls_scores
bboxes = bbox_decoder(flatten_priors[None], flatten_bbox_preds,
flatten_stride)
return nms_func(bboxes, scores, self.keep_top_k, self.iou_threshold,
self.score_threshold, self.pre_top_k, self.keep_top_k)
def select_nms(self):
if self.backend in (MMYOLOBackend.ONNXRUNTIME, MMYOLOBackend.OPENVINO):
nms_func = onnx_nms
elif self.backend == MMYOLOBackend.TENSORRT8:
nms_func = efficient_nms
elif self.backend == MMYOLOBackend.TENSORRT7:
nms_func = batched_nms
else:
raise NotImplementedError
if type(self.baseHead) in (YOLOv5Head, YOLOv7Head, YOLOXHead):
nms_func = partial(nms_func, box_coding=1)
return nms_func
def forward(self, inputs: Tensor):
neck_outputs = self.baseModel(inputs)
if self.with_postprocess:
return self.pred_by_feat(*neck_outputs)
else:
outputs = []
if self.transpose:
for feats in zip(*neck_outputs):
if self.backend in (MMYOLOBackend.NCNN,
MMYOLOBackend.TORCHSCRIPT):
outputs.append(
torch.cat(
[feat.permute(0, 2, 3, 1) for feat in feats],
-1))
else:
outputs.append(torch.cat(feats, 1).permute(0, 2, 3, 1))
else:
for feats in zip(*neck_outputs):
outputs.append(torch.cat(feats, 1))
return tuple(outputs)
@staticmethod
def forward_single(x: Tensor, convs: nn.Module) -> Tuple[Tensor]:
if isinstance(convs, nn.Sequential) and any(
type(m) in (ImplicitA, ImplicitM) for m in convs):
a, c, m = convs
aw = a.implicit.clone()
mw = m.implicit.clone()
c = deepcopy(c)
nw, cw, _, _ = c.weight.shape
na, ca, _, _ = aw.shape
nm, cm, _, _ = mw.shape
c.bias = nn.Parameter(c.bias + (
c.weight.reshape(nw, cw) @ aw.reshape(ca, na)).squeeze(1))
c.bias = nn.Parameter(c.bias * mw.reshape(cm))
c.weight = nn.Parameter(c.weight * mw.transpose(0, 1))
convs = c
feat = convs(x)
return (feat, )
@staticmethod
def forward_gvp(x: Tensor) -> Tensor:
return torch.mean(x, [2, 3], keepdim=True)
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