class RTDETRDecoder(nn.Module):"""Real-Time Deformable Transformer Decoder (RTDETRDecoder) module for object detection.This decoder module utilizes Transformer architecture along with deformable convolutions to predict bounding boxesand class labels for objects in an image. It integrates features from multiple layers and runs through a series ofTransformer decoder layers to output the final predictions."""export = False  # export modedef __init__(self,nc=80,ch=(512, 1024, 2048),hd=256,  # hidden dimnq=300,  # num queriesndp=4,  # num decoder pointsnh=8,  # num headndl=6,  # num decoder layersd_ffn=1024,  # dim of feedforwarddropout=0.,act=nn.ReLU(),eval_idx=-1,# Training argsnd=100,  # num denoisinglabel_noise_ratio=0.5,box_noise_scale=1.0,learnt_init_query=False):"""Initializes the RTDETRDecoder module with the given parameters.Args:nc (int): Number of classes. Default is 80.ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).hd (int): Dimension of hidden layers. Default is 256.nq (int): Number of query points. Default is 300.ndp (int): Number of decoder points. Default is 4.nh (int): Number of heads in multi-head attention. Default is 8.ndl (int): Number of decoder layers. Default is 6.d_ffn (int): Dimension of the feed-forward networks. Default is 1024.dropout (float): Dropout rate. Default is 0.act (nn.Module): Activation function. Default is nn.ReLU.eval_idx (int): Evaluation index. Default is -1.nd (int): Number of denoising. Default is 100.label_noise_ratio (float): Label noise ratio. Default is 0.5.box_noise_scale (float): Box noise scale. Default is 1.0.learnt_init_query (bool): Whether to learn initial query embeddings. Default is False."""super().__init__()self.hidden_dim = hdself.nhead = nhself.nl = len(ch)  # num levelself.nc = ncself.num_queries = nqself.num_decoder_layers = ndl# Backbone feature projectionself.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)# NOTE: simplified version but it's not consistent with .pt weights.# self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)# Transformer moduledecoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)# Denoising partself.denoising_class_embed = nn.Embedding(nc, hd)self.num_denoising = ndself.label_noise_ratio = label_noise_ratioself.box_noise_scale = box_noise_scale# Decoder embeddingself.learnt_init_query = learnt_init_queryif learnt_init_query:self.tgt_embed = nn.Embedding(nq, hd)self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)# Encoder headself.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))self.enc_score_head = nn.Linear(hd, nc)self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)# Decoder headself.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])self._reset_parameters()def forward(self, x, batch=None):"""Runs the forward pass of the module, returning bounding box and classification scores for the input."""from ultralytics.models.utils.ops import get_cdn_group# Input projection and embeddingfeats, shapes = self._get_encoder_input(x)# Prepare denoising trainingdn_embed, dn_bbox, attn_mask, dn_meta = \get_cdn_group(batch,self.nc,self.num_queries,self.denoising_class_embed.weight,self.num_denoising,self.label_noise_ratio,self.box_noise_scale,self.training)embed, refer_bbox, enc_bboxes, enc_scores = \self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)# Decoderdec_bboxes, dec_scores = self.decoder(embed,refer_bbox,feats,shapes,self.dec_bbox_head,self.dec_score_head,self.query_pos_head,attn_mask=attn_mask)x = dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_metaif self.training:return x# (bs, 300, 4+nc)y = torch.cat((dec_bboxes.squeeze(0), dec_scores.squeeze(0).sigmoid()), -1)return y if self.export else (y, x)def _generate_anchors(self, shapes, grid_size=0.05, dtype=torch.float32, device='cpu', eps=1e-2):"""Generates anchor bounding boxes for given shapes with specific grid size and validates them."""anchors = []for i, (h, w) in enumerate(shapes):sy = torch.arange(end=h, dtype=dtype, device=device)sx = torch.arange(end=w, dtype=dtype, device=device)grid_y, grid_x = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)grid_xy = torch.stack([grid_x, grid_y], -1)  # (h, w, 2)valid_WH = torch.tensor([h, w], dtype=dtype, device=device)grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH  # (1, h, w, 2)wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0 ** i)anchors.append(torch.cat([grid_xy, wh], -1).view(-1, h * w, 4))  # (1, h*w, 4)anchors = torch.cat(anchors, 1)  # (1, h*w*nl, 4)valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)  # 1, h*w*nl, 1anchors = torch.log(anchors / (1 - anchors))anchors = anchors.masked_fill(~valid_mask, float('inf'))return anchors, valid_maskdef _get_encoder_input(self, x):"""Processes and returns encoder inputs by getting projection features from input and concatenating them."""# Get projection featuresx = [self.input_proj[i](feat) for i, feat in enumerate(x)]# Get encoder inputsfeats = []shapes = []for feat in x:h, w = feat.shape[2:]# [b, c, h, w] -> [b, h*w, c]feats.append(feat.flatten(2).permute(0, 2, 1))# [nl, 2]shapes.append([h, w])# [b, h*w, c]feats = torch.cat(feats, 1)return feats, shapesdef _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):"""Generates and prepares the input required for the decoder from the provided features and shapes."""bs = len(feats)# Prepare input for decoderanchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)features = self.enc_output(valid_mask * feats)  # bs, h*w, 256enc_outputs_scores = self.enc_score_head(features)  # (bs, h*w, nc)# Query selection# (bs, num_queries)topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)# (bs, num_queries)batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)# (bs, num_queries, 256)top_k_features = features[batch_ind, topk_ind].view(bs, self.num_queries, -1)# (bs, num_queries, 4)top_k_anchors = anchors[:, topk_ind].view(bs, self.num_queries, -1)# Dynamic anchors + static contentrefer_bbox = self.enc_bbox_head(top_k_features) + top_k_anchorsenc_bboxes = refer_bbox.sigmoid()if dn_bbox is not None:refer_bbox = torch.cat([dn_bbox, refer_bbox], 1)enc_scores = enc_outputs_scores[batch_ind, topk_ind].view(bs, self.num_queries, -1)embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1) if self.learnt_init_query else top_k_featuresif self.training:refer_bbox = refer_bbox.detach()if not self.learnt_init_query:embeddings = embeddings.detach()if dn_embed is not None:embeddings = torch.cat([dn_embed, embeddings], 1)return embeddings, refer_bbox, enc_bboxes, enc_scores# TODOdef _reset_parameters(self):"""Initializes or resets the parameters of the model's various components with predefined weights and biases."""# Class and bbox head initbias_cls = bias_init_with_prob(0.01) / 80 * self.nc# NOTE: the weight initialization in `linear_init_` would cause NaN when training with custom datasets.# linear_init_(self.enc_score_head)constant_(self.enc_score_head.bias, bias_cls)constant_(self.enc_bbox_head.layers[-1].weight, 0.)constant_(self.enc_bbox_head.layers[-1].bias, 0.)for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):# linear_init_(cls_)constant_(cls_.bias, bias_cls)constant_(reg_.layers[-1].weight, 0.)constant_(reg_.layers[-1].bias, 0.)linear_init_(self.enc_output[0])xavier_uniform_(self.enc_output[0].weight)if self.learnt_init_query:xavier_uniform_(self.tgt_embed.weight)xavier_uniform_(self.query_pos_head.layers[0].weight)xavier_uniform_(self.query_pos_head.layers[1].weight)for layer in self.input_proj:xavier_uniform_(layer[0].weight)

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 21 (P5/32-large)- [[15, 18, 21], 1, RTDETRDecoder, [nc]]  # Detect(P3, P4, P5)

import warnings
warnings.filterwarnings('ignore')
from ultralytics import RTDETRif __name__ == '__main__':model = RTDETR('你替换了RT-DETR检测头的yaml文件地址')model.train(data='替换你数据集的yaml文件地址',imgsz=640,epochs=200,batch=16,workers=0,device=0,optimizer='SGD', # 这里可以使用两个优化器SGD 和AdamW其它的可能会导致模型无法收敛)