detr算法和Dynamic Convolution结合的python代码

下面是使用detr算法和Dynamic Convolution结合的Python代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet50

# Dynamic Convolution
class DynamicConvolution(nn.Module):
    def __init__(self, in_channels, out_channels, k=3, stride=1, padding=1, dilation=1, groups=1, bias=True, eps=1e-5, momentum=0.1):
        super(DynamicConvolution, selfinit__()

        self.k = k
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.bias = bias
        self.eps = eps
        self.momentum = momentum

        # define weight and bias
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, k * k))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)

        # define batch normalization
        self.bn = nn.BatchNorm2d(in_channels, eps=eps, momentum=momentum)

        # initialize weight and bias
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, x):
        # apply batch normalization
        x = self.bn(x)

        # reshape input tensor
        b, c, h, w = x.shape
        x = x.view(b * self.groups, -1, h, w)

        # pad input tensor
        x = F.pad(x, [self.padding, self.padding, self.padding, self.padding])

        # reshape weight tensor
        weight = self.weight.view(self.groups, -1, self.k * self.k)

        # perform dynamic convolution
        out = F.conv2d(x, weight, bias=self.bias, stride=self.stride, padding=0, dilation=self.dilation, groups=self.groups * b)

        # reshape output tensor
        _, _, h, w = out.shape
        out = out.view(b, -1, h, w)

        return out

# DETR with Dynamic Convolution
class DETR(nn.Module):
    def __init__(self, num_classes):
        super(DETR, self).__init__()

        # define resnet50 backbone
        self.backbone = nn.Sequential(*list(resnet50(pretrained=True).children())[:-2])

        # define dynamic convolutional layers
        self.conv1 = DynamicConvolution(2048, 256, k=1)
        self.conv2 = DynamicConvolution(256, 256, k=3, padding=1, groups=4)
        self.conv3 = DynamicConvolution(256, 256, k=3, padding=1, groups=4)
        self.conv4 = DynamicConvolution(256, 256, k=3, padding=1, groups=4)
        self.conv5 = DynamicConvolution(256, 256, k=3, padding=1, groups=4)

        # define classification and regression heads
        self.classification_head = nn.Linear(256, num_classes)
        self.regression_head = nn.Linear(256, 4)

    def forward(self, x):
        # extract features from backbone
        x = self.backbone(x)

        # apply dynamic convolutional layers
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))

        # average pooling
        x = F.adaptive_avg_pool2d(x, (1, 1))

        # flatten
        x = x.view(x.size(0), -1)

        # classification and regression
        classification = self.classification_head(x)
        regression = self.regression_head(x)

        return classification, regression

这段代码定义了一个DETR模型，其中使用了Dynamic Convolution，可以通过修改k、stride、padding等参数进行调整。在这个模型中，我们还定义了分类和回归头，用于预测物体的类别和位置。