detr算法和Convolutional Feature Masking相结合的python代码

以下是DETR算法和Convolutional Feature Masking相结合的Python代码：

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

class ConvFeatureMasking(nn.Module):
    def __init__(self, in_channels=2048, kernel_size=3, stride=1, padding=1):
        super(ConvFeatureMasking, self).__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=stride, padding=padding)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x, mask):
        x = self.conv(x)
        mask = self.sigmoid(mask)
        x = x * mask
        return x

class DETR(nn.Module):
    def __init__(self, num_classes, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6):
        super(DETR, self).__init__()
        self.num_classes = num_classes
        self.bbox_attention = nn.MultiheadAttention(hidden_dim, nheads)
        self.query_pos = nn.Parameter(torch.rand(100, hidden_dim))
        self.input_proj = nn.Conv2d(2048, hidden_dim, kernel_size=1)
        self.pos_encoding = PositionalEncoding(hidden_dim, 100)
        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(hidden_dim, nheads), num_encoder_layers)
        self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(hidden_dim, nheads), num_decoder_layers)
        self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
        self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
        self.mask_embed = nn.Sequential(nn.Conv2d(1, hidden_dim, kernel_size=1), nn.ReLU(inplace=True))
        self.cnn = resnet50(pretrained=True)

    def forward(self, x, mask):
        x = self.cnn(x)
        mask = self.mask_embed(mask)
        x = self.input_proj(x)
        x = x.flatten(2).permute(2, 0, 1)
        mask = mask.flatten(2).permute(2, 0, 1)
        pos_encoding = self.pos_encoding(self.query_pos).permute(1, 0, 2)
        x = self.encoder(x, mask, pos_encoding)
        query_embed = torch.zeros(100, 1, self.decoder.d_model)
        if x.is_cuda:
            query_embed = query_embed.cuda()
        hs = self.decoder(query_embed, x, pos_encoding)
        outputs_class = self.class_embed(hs)
        outputs_coord = self.bbox_embed(hs).sigmoid()
        outputs_coord = outputs_coord * 200 - 100
        outputs_coord = outputs_coord.permute(1, 0, 2)
        outputs_class = outputs_class.permute(1, 0, 2)
        out_boxes = []
        for i in range(outputs_coord.shape[0]):
            qk = self.bbox_attention(queries=outputs_coord[i:i+1], keys=outputs_coord, values=hs)[0]
            qk = qk.squeeze(0)
            out_boxes.append(qk)
        out_boxes = torch.stack(out_boxes, dim=0)
        out_boxes = out_boxes.permute(1, 0, 2)
        return {'pred_logits': outputs_class[:-1], 'pred_boxes': out_boxes[:-1]}

该代码包含了DETR算法和Convolutional Feature Masking的实现。其中，DETR算法用于目标检测，Convolutional Feature Masking用于在卷积特征层上对掩码进行处理。具体来说，ConvFeatureMasking模块实现了卷积特征层上的掩码处理，DETR模块实现了目标检测的前向传递。这些模块组合在一起，可以实现基于DETR算法和Convolutional Feature Masking的目标检测任务。