SparseRCNN 模型，用于目标检测任务

SparseRCNN 模型，用于目标检测任务
import logging
import math
from typing import List

import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn
#项目完整代码下载链接：https://download.csdn.net/download/huanghm88/89909179

#from detectron2.layers import ShapeSpec
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, detector_postprocess
from detectron2.modeling.roi_heads import build_roi_heads

from detectron2.structures import Boxes, ImageList, Instances
from detectron2.utils.logger import log_first_n
from fvcore.nn import giou_loss, smooth_l1_loss

from .loss import SetCriterion, HungarianMatcher
from .head import DynamicHead
from .util.box_ops import box_cxcywh_to_xyxy, box_xyxy_to_cxcywh
from .util.misc import (NestedTensor, nested_tensor_from_tensor_list,
                        accuracy, get_world_size, interpolate,
                        is_dist_avail_and_initialized)

__all__ = ["SparseRCNN"]

import numpy as np
import torch
from torch import nn
from torch.nn import init


class SEAttention(nn.Module):
    def __init__(self, channel=512, reduction=16):
        super().__init__()
        self.ave_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.weight, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.ave_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x