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#Copyright (C) 2023. Huawei Technologies Co., Ltd. All rights reserved.#This program is free software; you can redistribute it and/or modify it under the terms of the MIT License.#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License for more details.import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import weight_init, DropPath
import numpy as np__all__ = ['vanillanet_5', 'vanillanet_6', 'vanillanet_7', 'vanillanet_8', 'vanillanet_9', 'vanillanet_10', 'vanillanet_11', 'vanillanet_12', 'vanillanet_13', 'vanillanet_13_x1_5', 'vanillanet_13_x1_5_ada_pool']class activation(nn.ReLU):def __init__(self, dim, act_num=3, deploy=False):super(activation, self).__init__()self.deploy = deployself.weight = torch.nn.Parameter(torch.randn(dim, 1, act_num*2 + 1, act_num*2 + 1))self.bias = Noneself.bn = nn.BatchNorm2d(dim, eps=1e-6)self.dim = dimself.act_num = act_numweight_init.trunc_normal_(self.weight, std=.02)def forward(self, x):if self.deploy:return torch.nn.functional.conv2d(super(activation, self).forward(x), self.weight, self.bias, padding=(self.act_num*2 + 1)//2, groups=self.dim)else:return self.bn(torch.nn.functional.conv2d(super(activation, self).forward(x),self.weight, padding=self.act_num, groups=self.dim))def _fuse_bn_tensor(self, weight, bn):kernel = weightrunning_mean = bn.running_meanrunning_var = bn.running_vargamma = bn.weightbeta = bn.biaseps = bn.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta + (0 - running_mean) * gamma / stddef switch_to_deploy(self):if not self.deploy:kernel, bias = self._fuse_bn_tensor(self.weight, self.bn)self.weight.data = kernelself.bias = torch.nn.Parameter(torch.zeros(self.dim))self.bias.data = biasself.__delattr__('bn')self.deploy = Trueclass VanillaBlock(nn.Module):def __init__(self, dim, dim_out, act_num=3, stride=2, deploy=False, ada_pool=None):super().__init__()self.act_learn = 1self.deploy = deployif self.deploy:self.conv = nn.Conv2d(dim, dim_out, kernel_size=1)else:self.conv1 = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=1),nn.BatchNorm2d(dim, eps=1e-6),)self.conv2 = nn.Sequential(nn.Conv2d(dim, dim_out, kernel_size=1),nn.BatchNorm2d(dim_out, eps=1e-6))if not ada_pool:self.pool = nn.Identity() if stride == 1 else nn.MaxPool2d(stride)else:self.pool = nn.Identity() if stride == 1 else nn.AdaptiveMaxPool2d((ada_pool, ada_pool))self.act = activation(dim_out, act_num)def forward(self, x):if self.deploy:x = self.conv(x)else:x = self.conv1(x)x = torch.nn.functional.leaky_relu(x,self.act_learn)x = self.conv2(x)x = self.pool(x)x = self.act(x)return xdef _fuse_bn_tensor(self, conv, bn):kernel = conv.weightbias = conv.biasrunning_mean = bn.running_meanrunning_var = bn.running_vargamma = bn.weightbeta = bn.biaseps = bn.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta + (bias - running_mean) * gamma / stddef switch_to_deploy(self):if not self.deploy:kernel, bias = self._fuse_bn_tensor(self.conv1[0], self.conv1[1])self.conv1[0].weight.data = kernelself.conv1[0].bias.data = bias# kernel, bias = self.conv2[0].weight.data, self.conv2[0].bias.datakernel, bias = self._fuse_bn_tensor(self.conv2[0], self.conv2[1])self.conv = self.conv2[0]self.conv.weight.data = torch.matmul(kernel.transpose(1,3), self.conv1[0].weight.data.squeeze(3).squeeze(2)).transpose(1,3)self.conv.bias.data = bias + (self.conv1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)self.__delattr__('conv1')self.__delattr__('conv2')self.act.switch_to_deploy()self.deploy = Trueclass VanillaNet(nn.Module):def __init__(self, in_chans=3, num_classes=1000, dims=[96, 192, 384, 768], drop_rate=0, act_num=3, strides=[2,2,2,1], deploy=False, ada_pool=None, **kwargs):super().__init__()self.deploy = deployif self.deploy:self.stem = nn.Sequential(nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),activation(dims[0], act_num))else:self.stem1 = nn.Sequential(nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),nn.BatchNorm2d(dims[0], eps=1e-6),)self.stem2 = nn.Sequential(nn.Conv2d(dims[0], dims[0], kernel_size=1, stride=1),nn.BatchNorm2d(dims[0], eps=1e-6),activation(dims[0], act_num))self.act_learn = 1self.stages = nn.ModuleList()for i in range(len(strides)):if not ada_pool:stage = VanillaBlock(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy)else:stage = VanillaBlock(dim=dims[i], dim_out=dims[i+1], act_num=act_num, stride=strides[i], deploy=deploy, ada_pool=ada_pool[i])self.stages.append(stage)self.depth = len(strides)self.apply(self._init_weights)self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def _init_weights(self, m):if isinstance(m, (nn.Conv2d, nn.Linear)):weight_init.trunc_normal_(m.weight, std=.02)nn.init.constant_(m.bias, 0)def change_act(self, m):for i in range(self.depth):self.stages[i].act_learn = mself.act_learn = mdef forward(self, x):res = []if self.deploy:x = self.stem(x)else:x = self.stem1(x)x = torch.nn.functional.leaky_relu(x,self.act_learn)x = self.stem2(x)res.append(x)for i in range(self.depth):x = self.stages[i](x)res.append(x)return resdef _fuse_bn_tensor(self, conv, bn):kernel = conv.weightbias = conv.biasrunning_mean = bn.running_meanrunning_var = bn.running_vargamma = bn.weightbeta = bn.biaseps = bn.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta + (bias - running_mean) * gamma / stddef switch_to_deploy(self):if not self.deploy:self.stem2[2].switch_to_deploy()kernel, bias = self._fuse_bn_tensor(self.stem1[0], self.stem1[1])self.stem1[0].weight.data = kernelself.stem1[0].bias.data = biaskernel, bias = self._fuse_bn_tensor(self.stem2[0], self.stem2[1])self.stem1[0].weight.data = torch.einsum('oi,icjk->ocjk', kernel.squeeze(3).squeeze(2), self.stem1[0].weight.data)self.stem1[0].bias.data = bias + (self.stem1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)self.stem = torch.nn.Sequential(*[self.stem1[0], self.stem2[2]])self.__delattr__('stem1')self.__delattr__('stem2')for i in range(self.depth):self.stages[i].switch_to_deploy()self.deploy = Truedef update_weight(model_dict, weight_dict):idx, temp_dict = 0, {}for k, v in weight_dict.items():if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):temp_dict[k] = vidx += 1model_dict.update(temp_dict)print(f'loading weights... {idx}/{len(model_dict)} items')return model_dictdef vanillanet_5(pretrained='',in_22k=False, **kwargs):model = VanillaNet(dims=[128//2, 256//2, 512//2, 1024//2], strides=[2,2,2], **kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_6(pretrained='',in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[2,2,2,1], **kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_7(pretrained='',in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,2,1], **kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_8(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,2,1], **kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_9(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 1024*4, 1024*4], strides=[1,2,2,1,1,2,1], **kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_10(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],strides=[1,2,2,1,1,1,2,1],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_11(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],strides=[1,2,2,1,1,1,1,2,1],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_12(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],strides=[1,2,2,1,1,1,1,1,2,1],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_13(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*4, 128*4, 256*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 512*4, 1024*4, 1024*4],strides=[1,2,2,1,1,1,1,1,1,2,1],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_13_x1_5(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],strides=[1,2,2,1,1,1,1,1,1,2,1],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return modeldef vanillanet_13_x1_5_ada_pool(pretrained='', in_22k=False, **kwargs):model = VanillaNet(dims=[128*6, 128*6, 256*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 512*6, 1024*6, 1024*6],strides=[1,2,2,1,1,1,1,1,1,2,1],ada_pool=[0,40,20,0,0,0,0,0,0,10,0],**kwargs)if pretrained:weights = torch.load(pretrained)['model_ema']model.load_state_dict(update_weight(model.state_dict(), weights))return model

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# YOLO11n
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.25  # layer channel multiple
max_channel：1024# YOLO11s
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.50  # layer channel multiple
max_channel：1024# YOLO11m
depth_multiple: 0.50  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512# YOLO11l 
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512 # YOLO11x
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel：512

def _predict_once(self, x, profile=False, visualize=False, embed=None):"""Perform a forward pass through the network.Args:x (torch.Tensor): The input tensor to the model.profile (bool):  Print the computation time of each layer if True, defaults to False.visualize (bool): Save the feature maps of the model if True, defaults to False.embed (list, optional): A list of feature vectors/embeddings to return.Returns:(torch.Tensor): The last output of the model."""y, dt, embeddings = [], [], []  # outputsfor m in self.model:if m.f != -1:  # if not from previous layerx = (y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f])  # from earlier layersif profile:self._profile_one_layer(m, x, dt)if hasattr(m, "backbone"):x = m(x)for _ in range(5 - len(x)):x.insert(0, None)for i_idx, i in enumerate(x):if i_idx in self.save:y.append(i)else:y.append(None)# for i in x:#     if i is not None:#         print(i.size())x = x[-1]else:x = m(x)  # runy.append(x if m.i in self.save else None)  # save outputif visualize:feature_visualization(x, m.type, m.i, save_dir=visualize)if embed and m.i in embed:embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flattenif m.i == max(embed):return torch.unbind(torch.cat(embeddings, 1), dim=0)return x

from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Pathif __name__ == '__main__':# 加载模型model = YOLO("ultralytics/cfg/11/yolo11.yaml")  # 你要选择的模型yaml文件地址# Use the modelresults = model.train(data=r"你的数据集的yaml文件地址",epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem)  # 训练模型

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