- Resnet 50
# -*- coding: utf-8 -*-
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
class residual_block(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample = None):
super(residual_block, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, bias=False, kernel_size=1)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, stride = stride, kernel_size=3 , padding=1, bias = False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1,bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
conv1 = self.conv1(x)
bn1 = self.bn1(conv1)
relu1 = self.relu(bn1)
conv2 = self.conv2(relu1)
bn2 = self.bn2(conv2)
relu2 = self.relu(bn2)
conv3 = self.conv3(relu2)
bn3 = self.bn3(conv3)
if self.downsample is not None:
residual = self.downsample(x)
bn3 += residual
out = self.relu(bn3)
return out
class Resnet(nn.Module):
def __init__(self, s, numclass):
self.inplanes = 64
super(Resnet, self).__init__() ## super函数是用于调用父类(超类)的一个方法
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2,
padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(num_features=64)
self.relu = nn.ReLU(inplace=True) ##inplace为True，将会改变输入的数据 ，否则不会改变原输入，只会产生新的输出
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self. 1 = self._make_ (residual_block, 64, blocks = s[0], stride=1)
self. 2 = self._make_ (residual_block, 128, blocks = s[1], stride=2)
self. 3 = self._make_ (residual_block, 256, blocks = s[2], stride=2)
self. 4 = self._make_ (residual_block, 512, blocks = s[3], stride=2)
self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
self.fc = nn.Linear(512* residual_block.expansion, numclass)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_ (self, block, planes, blocks, stride=1):
downsample = None
if stride !=1 or self.inplanes != block.expansion * planes :
print (planes, blocks)
## torch.nn.Sequential是一个Sequential容器，模块将按照构造函数中传递的顺序添加到模块中。
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes*block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes*block.expansion)
)
s = []
s.append(block(self.inplanes, planes, stride, downsample)) ###该部分是将每个blocks的第一个residual结构保存在 s列表中,这个地方是用来进行下采样的
self.inplanes = planes * block.expansion
for i in range(1, blocks):
s.append(block(self.inplanes, planes))##该部分是将每个blocks的剩下residual 结构保存在 s列表中，这样就完成了一个blocks的构造。
return nn.Sequential(* s)
def forward(self, x):
x = self.conv1(x)
bn1 = self.bn1(x)
relu = self.relu(bn1)
maxpool = self.maxpool(relu)
1 = self. 1(maxpool)
2 = self. 2( 1)
3 = self. 3( 2)
4 = self. 4( 3)
avgpool = self.avgpool( 4)
x = avgpool.view(avgpool.size(0),-1)
x = self.fc(x)
return x
def resnet50(pretrained=False, **kwargs):
\"\"\"Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
\"\"\"
model = Resnet([3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls[\'resnet50\']))
return model
- 利用Resnet50， 在场景数据集MIT67上进行Finetune，并进行训练
-
# -*- coding: utf-8 -*-
\"\"\"
Auther : Haitao Zeng
date : 2018.12.18
Function: finetune the pre-trained model on MIT67 $sun397
\"\"\"
from __future__ import print_function, division, absolute_import
from PIL import Image
import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
from torchvision import datasets, models, transforms
import os
import time
import copy
import torch.utils.data as data
from Rsenet50 import Resnet
import torch.utils.model_zoo as model_zoo
def pil_loader(path):
with open(path, \'rb\') as f:
with Image.open(f) as img:
return img.convert(\'RGB\')
def accimage_loader(path):
import accimage
try:
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def default_loader(path):
from torchvision import get_image_backend
if get_image_backend() == \'accimage\':
return accimage_loader(path)
else:
return pil_loader(path)
class CustomImageLoader(data.Dataset):
##自定义类型数据输入
def __init__(self, img_path, txt_path, dataset = \'\', data_transforms=None, loader = default_loader):
im_list = []
im_labels = []
with open(txt_path, \'r\') as files:
for line in files:
items = line.split()
if items[0][0] == \'/\':
# fnewname = \'_\'.join(items[0][1:-4].split(\'/\'))
imname = line.split()[0][1:]
else:
# fnewname = \'_\'.join(items[0][:-4].split(\'/\'))
imname = line.split()[0]
im_list.append(os.path.join(img_path, imname))
im_labels.append(int(items[1]))
self.imgs = im_list
self.labels = im_labels
self.data_tranforms = data_transforms
self.loader = loader
self.dataset = dataset
def __len__(self):
return len(self.imgs)
def __getitem__(self, item):
img_name = self.imgs[item]
label = self.labels[item]
img = self.loader(img_name)
if self.data_tranforms is not None:
try:
img = self.data_tranforms[self.dataset](img)
except:
print(\"Cannot transform image: {}\".format(img_name))
return img, label
NUM_EPOCH=20
batch_size = 32
device = torch.device(\'cuda:0\')
##对数据进行预处理，训练部分包括随机裁剪和水平变换
##测试部分包括中心裁剪， 两个部分都包含了数据的正则化的过程
data_tranforms={
\'Train\':transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])
]),
\'Test\':transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])
])
}
image_dir = \'/media/haitaizeng/00038FCE000387A5/cgw/Datasets/MIT67/Images\'
image_datasets = {x : CustomImageLoader(image_dir, txt_path=(\'/home/haitaizeng/stanforf/alex_mit/data_image/\'+x+\'Images.label\'),
data_transforms=data_tranforms,
dataset=x) for x in [\'Train\', \'Test\']
}
dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size,
shuffle=True) for x in [\'Train\', \'Test\']}
dataset_sizes = {x: len(image_datasets[x]) for x in [\'Train\', \'Test\']}
Train_nums= 5360
Test_nums = 1340
def train_model(model, crtiation, optimizer,schedular, num_epochs=NUM_EPOCH):
begin_time = time.time()
best_weights = copy.deepcopy(model.state_dict())#copy the weights from the model
best_acc = 0.0
for epoch in range(num_epochs):
print(\"-*-\" * 20)
for phase in [\'Train\', \'Test\']:
if phase==\'Train\':
schedular.step()
model.train()
else:
model.eval()
running_loss = 0.0
running_acc = 0.0
for images, labels in dataloders[phase]:
images.to(device)
labels.to(device)
optimizer.zero_grad()
with torch.set_grad_enabled(phase==\'Train\'):
opt = model(images.cuda())
# opt = model(images)
_,pred = torch.max(opt,1)
labels = labels.cuda()
loss = crtiation(opt, labels)
if phase==\'Train\':
loss.backward()
optimizer.step()
running_loss += loss.item()*images.size(0)
running_acc += torch.sum(pred==labels)
if phase == \'Train\':
nums = Train_nums
elif phase==\'Test\':
nums == Test_nums
epoch_loss = running_loss/dataset_sizes[phase]
epoch_acc = running_acc.double()/dataset_sizes[phase]
print(\'epoch={}, Phase={}, Loss={:.4f}, ACC:{:.4f}\'.format(epoch, phase, epoch_loss, epoch_acc))
if phase == \'Test\' and epoch_acc>best_acc:
# Upgrade the weights
best_acc=epoch_acc
best_weights = copy.deepcopy(model.state_dict())
time_elapes = time.time() - begin_time
print(\'Training Complete in{:.0f}m {:0f}s\'.format(
time_elapes // 60, time_elapes % 60
))
print(\'Best Val ACC: {:}\'.format(best_acc))
model.load_state_dict(best_weights)
return models
if __name__ == \'__main__\':
NUMCLASS = 67
pthpath = \'/home/haitaizeng/cgw/for_zht/CVPR19/resnet50_places365.pth.tar\'
# model_ft = models.__ dict__[\'resnet50\'](num_classes=365) ## 加载的是pytorch库中预先写好的Resnet50的网络结构
model_ft = Resnet([3, 4, 6, 3], 365)##这是自行编写的Resnet50，用于后面的特征提取的操作
##load pre-trained model for funetuning
ckpt = torch.load(pthpath, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k, \'module.\', \'\'): v for k, v in ckpt[\'state_dict\'].items()}
model_ft.load_state_dict(state_dict)
## change the last fully connedected
num_fits = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_fits, NUMCLASS)
## run the model on the cuda---Nvidia -1080ti
model_ft = model_ft.to(device)
model_ft.cuda()
## cacluate the cross entropu loss
criterion = nn.CrossEntropyLoss()
## using SGD to optimize
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
## learning rate decay ,when epoch ==10
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
