VGGNet
VGGNet
- 感受野
- 网络结构
- 代码
-
- model.py
- train.py
- predict.py
- 其他版本
-
- model.py
- train.py
- predict.py
感受野
网络结构
代码
model.py
import torch
import torch.nn as nn
class VGG16(nn.Module):
def __init__(self, num__class):
super(VGG16, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(64, 128, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(128, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(256, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, 1, 1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2)
)
self.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(512 * 7 * 7, 2048),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(2048, 2048),
nn.ReLU(inplace=True),
nn.Linear(2048, num__class)
)
def forward(self, x):
x = self.features(x)
x = torch.flatten(x, start_dim=1)
x = self.classifier(x)
return x
train.py
from model import VGG16
import torch
import torchvision as tv
import torchvision.transforms as transforms
import json
data_transform = {
"train":
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]),
"val":
transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
}
train_set = tv.datasets.ImageFolder(root="C:/Users/14251/Desktop/workspace/VGGNet/flower_data/train",
transform=data_transform["train"])
val_set = tv.datasets.ImageFolder(root="C:/Users/14251/Desktop/workspace/VGGNet/flower_data/train",
transform=data_transform["val"])
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=32,
shuffle=True,
num_workers=0)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=32,
shuffle=True,
num_workers=0)
flower_list = train_set.class_to_idx
flower_dict = dict((val, key) for key, val in flower_list.items())
json_str = json.dumps(flower_dict, indent=4)
with open("class_indices.json", "w") as json_file:
json_file.write(json_str)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = VGG16(num__class=5).to(device)
loss_fun = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.0002)
best_accurate = 0.0
for epoch in range(10):
net.train()
running_loss = 0.0
for step, train_data in enumerate(train_loader, start=0):
train_images, train_labels = train_data
optimizer.zero_grad()
outputs = net(train_images.to(device))
loss = loss_fun(outputs, train_labels.to(device))
loss.backward()
optimizer.step()
running_loss += loss.item()
# print train process
rate = (step + 1) / len(train_loader)
a = "*" * int(rate * 50)
b = "." * int((1 - rate) * 50)
print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(
int(rate * 100), a, b, loss),
end="")
print()
net.eval()
accurate = 0.0
with torch.no_grad():
for val_data in val_loader:
val_images, val_labels = val_data
outputs = net(val_images.to(device))
pred = torch.max(outputs, dim=1)[1]
accurate += (pred == val_labels.to(device)).sum().item()
val_accurate = accurate / len(val_set)
if (val_accurate > best_accurate):
best_accurate = val_accurate
torch.save(net.state_dict(),
"C:/Users/14251/Desktop/workspace/VGGNet/VGG_dict.pth")
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' %
(epoch + 1, running_loss / step, val_accurate))
print("Finished Training")
predict.py
import torch
import json
import torchvision.transforms as transforms
from model import VGG16
from PIL import Image
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
img = Image.open("C:/Users/14251/Desktop/workspace/VGGNet/test.jpg")
img = transform(img)
img = img.unsqueeze(dim=0)
try:
json_file = open(
"C:/Users/14251/Desktop/workspace/VGGNet/class_indices.json", "r")
class_indices = json.load(json_file)
except Exception as e:
print(e)
exit(-1)
net = VGG16(num__class=5)
net.load_state_dict(
torch.load("C:/Users/14251/Desktop/workspace/VGGNet/VGG_dict.pth"))
with torch.no_grad():
output = net(img).squeeze()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
print(class_indices[str(predict_cla)], predict[predict_cla].item())
其他版本
model.py
import torch.nn as nn
import torch
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=False):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(512*7*7, 2048),
nn.ReLU(True),
nn.Dropout(p=0.5),
nn.Linear(2048, 2048),
nn.ReLU(True),
nn.Linear(2048, num_classes)
)
if init_weights:
self._initialize_weights()
def forward(self, x):
# N x 3 x 224 x 224
x = self.features(x)
# N x 512 x 7 x 7
x = torch.flatten(x, start_dim=1)
# N x 512*7*7
x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
# nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def make_features(cfg: list):
layers = []
in_channels = 3
for v in cfg:
if v == "M":
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
layers += [conv2d, nn.ReLU(True)]
in_channels = v
return nn.Sequential(*layers)
cfgs = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
def vgg(model_name="vgg16", **kwargs):
try:
cfg = cfgs[model_name]
except:
print("Warning: model number {} not in cfgs dict!".format(model_name))
exit(-1)
model = VGG(make_features(cfg), **kwargs)
return model
train.py
import torch.nn as nn
from torchvision import transforms, datasets
import json
import os
import torch.optim as optim
from model import vgg
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
data_transform = {
"train": transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
"val": transforms.Compose([transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}
data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root path
image_path = data_root + "/data_set/flower_data/" # flower data set path
train_dataset = datasets.ImageFolder(root=image_path+"train",
transform=data_transform["train"])
train_num = len(train_dataset)
# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
batch_size = 32
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size, shuffle=True,
num_workers=0)
validate_dataset = datasets.ImageFolder(root=image_path + "val",
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=batch_size, shuffle=False,
num_workers=0)
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
model_name = "vgg16"
net = vgg(model_name=model_name, num_classes=5, init_weights=True)
net.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
best_acc = 0.0
save_path = './{}Net.pth'.format(model_name)
for epoch in range(30):
# train
net.train()
running_loss = 0.0
for step, data in enumerate(train_loader, start=0):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(device))
loss = loss_function(outputs, labels.to(device))
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
# print train process
rate = (step + 1) / len(train_loader)
a = "*" * int(rate * 50)
b = "." * int((1 - rate) * 50)
print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")
print()
# validate
net.eval()
acc = 0.0 # accumulate accurate number / epoch
with torch.no_grad():
for val_data in validate_loader:
val_images, val_labels = val_data
optimizer.zero_grad()
outputs = net(val_images.to(device))
predict_y = torch.max(outputs, dim=1)[1]
acc += (predict_y == val_labels.to(device)).sum().item()
val_accurate = acc / val_num
if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)
print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' %
(epoch + 1, running_loss / step, val_accurate))
print('Finished Training')
predict.py
import torch
from model import vgg
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json
data_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# load image
img = Image.open("../tulip.jpg")
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
# read class_indict
try:
json_file = open('./class_indices.json', 'r')
class_indict = json.load(json_file)
except Exception as e:
print(e)
exit(-1)
# create model
model = vgg(model_name="vgg16", num_classes=5)
# load model weights
model_weight_path = "./vgg16Net.pth"
model.load_state_dict(torch.load(model_weight_path))
model.eval()
with torch.no_grad():
# predict class
output = torch.squeeze(model(img))
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
print(class_indict[str(predict_cla)])
plt.show()