pytorch构建多模型实例

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)

'''

来源：https://blog.csdn.net/weixin_40123108/article/details/90670584