import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets,transforms
from torch.optim import lr_scheduler
import torch.optim as optim
class VGG(nn.Module):
def __init__(self):
super(VGG,self).__init__()
self.conv1 = nn.Conv2d(3,64,3,padding=(1,1))
self.bn1 = nn.BatchNorm2d(64)
self.maxpool1 = nn.MaxPool2d((2,2))
self.conv2 = nn.Conv2d(64,128,3,padding=(1,1))
# self.bn2 = nn.BatchNorm2d(128)
self.maxpool2 = nn.MaxPool2d((2,2))
self.conv3 = nn.Conv2d(128,256,3,padding=(1,1))
# self.bn3 = nn.BatchNorm2d(256)
self.maxpool3 = nn.MaxPool2d((2,2))
self.fc1 = nn.Linear(256*16*8,4096)
self.fc2 = nn.Linear(4096,1000)
self.fc3 = nn.Linear(1000,10)
def forward(self,x):
in_size = x.size(0)
out = self.conv1(x)
out = self.bn1(out)
out = F.relu(out)
out = self.maxpool1(out)
out = self.conv2(out)
out = F.relu(out)
out = self.maxpool2(out)
out = self.conv3(out)
out = F.relu(out)
out = self.maxpool3(out)
out = out.view(out.size(0),-1)
out = self.fc1(out)
out = F.relu(out)
out = self.fc2(out)
out = F.relu(out)
out = self.fc3(out)
return out
transform_train_list = transforms.Compose([
transforms.Resize( (256,128),interpolation=3 ),
transforms.RandomCrop((128,64)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder('./train',transform_train_list)
dataloaders = torch.utils.data.DataLoader(train_dataset,batch_size=2,num_workers=0)
pre_model_dict = torch.load("first.pth")
print(pre_model_dict['conv1.bias'])
for k,v in pre_model_dict.items():
print(k)
model1 = VGG()
model1_dict = model1.state_dict()
model1_dict['conv1.bias'] = pre_model_dict['conv1.bias']
model1.load_state_dict(model1_dict)
print(model1_dict['conv1.bias'])
for k,v in model1_dict.items():
print(k)
print()
##
