detr和Dynamic Convolution结合的python代码

以下是 DETR 模型结合 Dynamic Convolution 的 Python 代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class DynamicConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(DynamicConv, self).__init__()
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        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, weight=None):
        if weight is None:
            weight = self.weight
        weight = weight + 1e-6
        weight = weight / weight.sum(dim=[1, 2], keepdim=True)
        weight = weight.unsqueeze(1)

        return F.conv1d(x, weight, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups)

class DynamicConvLayer(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(DynamicConvLayer, self).__init__()
        self.conv = DynamicConv(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)

    def forward(self, x, weight=None):
        return self.conv(x, weight)

class TransformerEncoderLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu"):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.activation = nn.ReLU()

    def forward(self, src, src_mask=None, src_weight=None):
        src2 = self.self_attn(src, src, src, attn_mask=src_mask)[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src

class DETR(nn.Module):
    def __init__(self, num_classes):
        super(DETR, self).__init__()

        # Backbone
        self.backbone = nn.Sequential(
            DynamicConvLayer(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm1d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2, padding=1),

            DynamicConvLayer(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(64),
            nn.ReLU(inplace=True),
            DynamicConvLayer(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2, padding=1),

            DynamicConvLayer(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(128),
            nn.ReLU(inplace=True),
            DynamicConvLayer(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2, padding=1),

            DynamicConvLayer(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            DynamicConvLayer(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2, padding=1),

            DynamicConvLayer(256, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(512),
            nn.ReLU(inplace=True),
            DynamicConvLayer(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool1d(kernel_size=3, stride=2, padding=1),
        )

        # Transformer Encoder
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=512, nhead=8),
            num_layers=6
        )

        # Final layers
        self.fc_bbox = nn.Linear(512, 4)
        self.fc_class = nn.Linear(512, num_classes + 1)

    def forward(self, x):
        x = self.backbone(x)
        x = self.transformer_encoder(x)
        bbox = self.fc_bbox(x)
        class_logits = self.fc_class(x)
        return bbox, class_logits

以上代码是 DETR 模型结合 Dynamic Convolution 的一个简单实现，其中 `DynamicConv` 类和 `DynamicConvLayer` 类分别实现了动态卷积和动态卷积层的功能，`TransformerEncoderLayer` 类实现了 Transformer 编码器层的功能，`DETR` 类则集成了以上各个组件，实现了整个 DETR 模型。注意，在实际使用中，建议根据具体需求对模型进行调整和优化。