赤峰网站建设red,帮助网站源码,在线直播免费服务器,做网站应该学什么深度学习网络模型——RepVGG网络详解0 前言1 RepVGG Block详解2 结构重参数化2.1 融合Conv2d和BN2.2 Conv2dBN融合实验(Pytorch)2.3 将1x1卷积转换成3x3卷积2.4 将BN转换成3x3卷积2.5 多分支融合2.6 结构重参数化实验(Pytorch)3 模型配置论文名称#xff1a;
RepVGG: Making V…
深度学习网络模型——RepVGG网络详解0 前言1 RepVGG Block详解2 结构重参数化2.1 融合Conv2d和BN2.2 Conv2dBN融合实验(Pytorch)2.3 将1x1卷积转换成3x3卷积2.4 将BN转换成3x3卷积2.5 多分支融合2.6 结构重参数化实验(Pytorch)3 模型配置论文名称
RepVGG: Making VGG-style ConvNets Great Again论文下载地址
https://arxiv.org/abs/2101.03697官方源码Pytorch实现
https://github.com/DingXiaoH/RepVGG0 前言 1 RepVGG Block详解 2 结构重参数化 2.1 融合Conv2d和BN 2.2 Conv2dBN融合实验(Pytorch) from collections import OrderedDictimport numpy as np
import torch
import torch.nn as nndef main():torch.random.manual_seed(0)f1 torch.randn(1, 2, 3, 3)module nn.Sequential(OrderedDict(convnn.Conv2d(in_channels2, out_channels2, kernel_size3, stride1, padding1, biasFalse),bnnn.BatchNorm2d(num_features2)))module.eval()with torch.no_grad():output1 module(f1)print(output1)# fuse conv bnkernel module.conv.weight running_mean module.bn.running_meanrunning_var module.bn.running_vargamma module.bn.weightbeta module.bn.biaseps module.bn.epsstd (running_var eps).sqrt()t (gamma / std).reshape(-1, 1, 1, 1) # [ch] - [ch, 1, 1, 1]kernel kernel * tbias beta - running_mean * gamma / stdfused_conv nn.Conv2d(in_channels2, out_channels2, kernel_size3, stride1, padding1, biasTrue)fused_conv.load_state_dict(OrderedDict(weightkernel, biasbias))with torch.no_grad():output2 fused_conv(f1)print(output2)np.testing.assert_allclose(output1.numpy(), output2.numpy(), rtol1e-03, atol1e-05)print(convert module has been tested, and the result looks good!)if __name__ __main__:main()
终端输出结果
2.3 将1x1卷积转换成3x3卷积 2.4 将BN转换成3x3卷积 代码截图如下所示
2.5 多分支融合 代码截图 图像演示
2.6 结构重参数化实验(Pytorch)
import time
import torch.nn as nn
import numpy as np
import torchdef conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups1):result nn.Sequential()result.add_module(conv, nn.Conv2d(in_channelsin_channels, out_channelsout_channels,kernel_sizekernel_size, stridestride, paddingpadding,groupsgroups, biasFalse))result.add_module(bn, nn.BatchNorm2d(num_featuresout_channels))return resultclass RepVGGBlock(nn.Module):def __init__(self, in_channels, out_channels, kernel_size3,stride1, padding1, dilation1, groups1, padding_modezeros, deployFalse):super(RepVGGBlock, self).__init__()self.deploy deployself.groups groupsself.in_channels in_channelsself.nonlinearity nn.ReLU()if deploy:self.rbr_reparam nn.Conv2d(in_channelsin_channels, out_channelsout_channels,kernel_sizekernel_size, stridestride,paddingpadding, dilationdilation, groupsgroups,biasTrue, padding_modepadding_mode)else:self.rbr_identity nn.BatchNorm2d(num_featuresin_channels) \if out_channels in_channels and stride 1 else Noneself.rbr_dense conv_bn(in_channelsin_channels, out_channelsout_channels, kernel_sizekernel_size,stridestride, paddingpadding, groupsgroups)self.rbr_1x1 conv_bn(in_channelsin_channels, out_channelsout_channels, kernel_size1,stridestride, padding0, groupsgroups)def forward(self, inputs):if hasattr(self, rbr_reparam):return self.nonlinearity(self.rbr_reparam(inputs))if self.rbr_identity is None:id_out 0else:id_out self.rbr_identity(inputs)return self.nonlinearity(self.rbr_dense(inputs) self.rbr_1x1(inputs) id_out)def get_equivalent_kernel_bias(self):kernel3x3, bias3x3 self._fuse_bn_tensor(self.rbr_dense)kernel1x1, bias1x1 self._fuse_bn_tensor(self.rbr_1x1)kernelid, biasid self._fuse_bn_tensor(self.rbr_identity)return kernel3x3 self._pad_1x1_to_3x3_tensor(kernel1x1) kernelid, bias3x3 bias1x1 biasiddef _pad_1x1_to_3x3_tensor(self, kernel1x1):if kernel1x1 is None:return 0else:return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])def _fuse_bn_tensor(self, branch):if branch is None:return 0, 0if isinstance(branch, nn.Sequential):kernel branch.conv.weightrunning_mean branch.bn.running_meanrunning_var branch.bn.running_vargamma branch.bn.weightbeta branch.bn.biaseps branch.bn.epselse:assert isinstance(branch, nn.BatchNorm2d)if not hasattr(self, id_tensor):input_dim self.in_channels // self.groupskernel_value np.zeros((self.in_channels, input_dim, 3, 3), dtypenp.float32)for i in range(self.in_channels):kernel_value[i, i % input_dim, 1, 1] 1self.id_tensor torch.from_numpy(kernel_value).to(branch.weight.device)kernel self.id_tensorrunning_mean branch.running_meanrunning_var branch.running_vargamma branch.weightbeta branch.biaseps branch.epsstd (running_var eps).sqrt()t (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta - running_mean * gamma / stddef switch_to_deploy(self):if hasattr(self, rbr_reparam):returnkernel, bias self.get_equivalent_kernel_bias()self.rbr_reparam nn.Conv2d(in_channelsself.rbr_dense.conv.in_channels,out_channelsself.rbr_dense.conv.out_channels,kernel_sizeself.rbr_dense.conv.kernel_size, strideself.rbr_dense.conv.stride,paddingself.rbr_dense.conv.padding, dilationself.rbr_dense.conv.dilation,groupsself.rbr_dense.conv.groups, biasTrue)self.rbr_reparam.weight.data kernelself.rbr_reparam.bias.data biasfor para in self.parameters():para.detach_()self.__delattr__(rbr_dense)self.__delattr__(rbr_1x1)if hasattr(self, rbr_identity):self.__delattr__(rbr_identity)if hasattr(self, id_tensor):self.__delattr__(id_tensor)self.deploy Truedef main():f1 torch.randn(1, 64, 64, 64)block RepVGGBlock(in_channels64, out_channels64)block.eval()with torch.no_grad():output1 block(f1)start_time time.time()for _ in range(100):block(f1)print(fconsume time: {time.time() - start_time})# re-parameterizationblock.switch_to_deploy()output2 block(f1)start_time time.time()for _ in range(100):block(f1)print(fconsume time: {time.time() - start_time})np.testing.assert_allclose(output1.numpy(), output2.numpy(), rtol1e-03, atol1e-05)print(convert module has been tested, and the result looks good!)if __name__ __main__:main()
终端输出结果如下 通过对比能够发现结构重参数化后推理速度翻倍了并且转换前后的输出保持一致。
3 模型配置
