Spaces:
Sleeping
Sleeping
File size: 8,378 Bytes
186701e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
# 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)
|