小编给大家分享一下pytorch如何构建多模型实例,相信大部分人都还不怎么了解,因此分享这篇文章给大家参考一下,希望大家阅读完这篇文章后大有收获,下面让我们一起去了解一下吧!
pytorch构建双模型
第一部分:构建"se_resnet152","DPN92()"双模型
import numpy as np from functools import partial import torch from torch import nn import torch.nn.functional as F from torch.optim import SGD,Adam from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader from torch.optim.optimizer import Optimizer import torchvision from torchvision import models import pretrainedmodels from pretrainedmodels.models import * from torch import nn from torchvision import transforms as T import random random.seed(2050) np.random.seed(2050) torch.manual_seed(2050) torch.cuda.manual_seed_all(2050) class FCViewer(nn.Module): def forward(self, x): return x.view(x.size(0), -1) '''Dual Path Networks in PyTorch.''' class Bottleneck(nn.Module): def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).__init__() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def __init__(self, cfg): super(DPN, self).__init__() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(7, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], 64) self.bn2 = nn.BatchNorm1d(64) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + [1]*(num_blocks-1) layers = [] for i,stride in enumerate(strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) out= F.relu(self.bn2(out)) return out def DPN26(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (2,2,2,2), 'dense_depth': (16,32,24,128) } return DPN(cfg) def DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg) class MultiModalNet(nn.Module): def __init__(self, backbone1, backbone2, drop, pretrained=True): super().__init__() if pretrained: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained='imagenet') #seresnext101 else: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained=None) self.visit_model=DPN26() self.img_encoder = list(img_model.children())[:-2] self.img_encoder.append(nn.AdaptiveAvgPool2d(1)) self.img_encoder = nn.Sequential(*self.img_encoder) if drop > 0: self.img_fc = nn.Sequential(FCViewer(), nn.Dropout(drop), nn.Linear(img_model.last_linear.in_features, 512), nn.BatchNorm1d(512)) else: self.img_fc = nn.Sequential( FCViewer(), nn.BatchNorm1d(img_model.last_linear.in_features), nn.Linear(img_model.last_linear.in_features, 512)) self.bn=nn.BatchNorm1d(576) self.cls = nn.Linear(576,9) def forward(self, x_img,x_vis): x_img = self.img_encoder(x_img) x_img = self.img_fc(x_img) x_vis=self.visit_model(x_vis) x_cat = torch.cat((x_img,x_vis),1) x_cat = F.relu(self.bn(x_cat)) x_cat = self.cls(x_cat) return x_cat test_x = Variable(torch.zeros(64, 7,26,24)) test_x1 = Variable(torch.zeros(64, 3,224,224)) model=MultiModalNet("se_resnet152","DPN92()",0.1) out=model(test_x1,test_x) print(model._modules.keys()) print(model) print(out.shape)
第二部分构建densenet201单模型
#encoding:utf-8 import torchvision.models as models import torch import pretrainedmodels from torch import nn from torch.autograd import Variable #model = models.resnet18(pretrained=True) #print(model) #print(model._modules.keys()) #feature = torch.nn.Sequential(*list(model.children())[:-2])#模型的结构 #print(feature) ''' class FCViewer(nn.Module): def forward(self, x): return x.view(x.size(0), -1) class M(nn.Module): def __init__(self, backbone1, drop, pretrained=True): super(M,self).__init__() if pretrained: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained='imagenet') else: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained=None) self.img_encoder = list(img_model.children())[:-1] self.img_encoder.append(nn.AdaptiveAvgPool2d(1)) self.img_encoder = nn.Sequential(*self.img_encoder) if drop > 0: self.img_fc = nn.Sequential(FCViewer(), nn.Dropout(drop), nn.Linear(img_model.last_linear.in_features, 236)) else: self.img_fc = nn.Sequential( FCViewer(), nn.Linear(img_model.last_linear.in_features, 236) ) self.cls = nn.Linear(236,9) def forward(self, x_img): x_img = self.img_encoder(x_img) x_img = self.img_fc(x_img) return x_img model1=M('densenet201',0,pretrained=True) print(model1) print(model1._modules.keys()) feature = torch.nn.Sequential(*list(model1.children())[:-2])#模型的结构 feature1 = torch.nn.Sequential(*list(model1.children())[:]) #print(feature) #print(feature1) test_x = Variable(torch.zeros(1, 3, 100, 100)) out=feature(test_x) print(out.shape) ''' ''' import torch.nn.functional as F class LenetNet(nn.Module): def __init__(self): super(LenetNet, self).__init__() self.conv1 = nn.Conv2d(7, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(144, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = x.view(x.size()[0], -1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x model1=LenetNet() #print(model1) #print(model1._modules.keys()) feature = torch.nn.Sequential(*list(model1.children())[:-3])#模型的结构 #feature1 = torch.nn.Sequential(*list(model1.children())[:]) print(feature) #print(feature1) test_x = Variable(torch.zeros(1, 7, 27, 24)) out=model1(test_x) print(out.shape) class FCViewer(nn.Module): def forward(self, x): return x.view(x.size(0), -1) class M(nn.Module): def __init__(self): super(M,self).__init__() img_model =model1 self.img_encoder = list(img_model.children())[:-3] self.img_encoder.append(nn.AdaptiveAvgPool2d(1)) self.img_encoder = nn.Sequential(*self.img_encoder) self.img_fc = nn.Sequential(FCViewer(), nn.Linear(16, 236)) self.cls = nn.Linear(236,9) def forward(self, x_img): x_img = self.img_encoder(x_img) x_img = self.img_fc(x_img) return x_img model2=M() test_x = Variable(torch.zeros(1, 7, 27, 24)) out=model2(test_x) print(out.shape) '''
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