from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
if __name__ == '__main__':
plt.ion() # interactive mode
#加载数据我们将使用torchvision和torch.utils.data包来加载数据。我们今天要解决的问题是
# 训练一个模型来对蚂蚁和蜜蜂进行分类。我们有大约120个蚂蚁和蜜蜂的训练图像。
# 每个类有75个验证图像。通常，这是一个非常小的数据集，如果从抓痕中训练的话。
# 因为我们使用的是传输。学习，我们应该能够合理地概括。这个数据集是ImageNet的一个很小的子集。
# 数据增强和培训规范化
# 只是验证的规范化
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=4)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#可视化一些图像，让我们可视化一些培训图像，以便理解数据增强。
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # 暂停一下，以便更新情节。
# 获取一批培训数据
inputs, classes = next(iter(dataloaders['train']))
# 从批处理中生成网格
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
plt.waitforbuttonpress(0)
#现在训练模型，让我们编写一个通用的函数来训练模型。在这里，我们将说明：
# 调度学习率保存了下面的最佳模型，参数调度器是torch.Optim.lr_调度器中的LR调度器对象。
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 每个时代都有一个培训和验证阶段。
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train() # 将模型设置为训练模式
else:
model.eval() # 设定评估模式
running_loss = 0.0
running_corrects = 0
# 迭代数据。
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# 参数梯度为零
optimizer.zero_grad()
# forward
# 如果只在火车上跟踪历史
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# 只有在训练阶段才能向后优化
if phase == 'train':
loss.backward()
optimizer.step()
# 统计
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
# 可视化模型预测泛型函数以显示一些图像的预测
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images // 2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
#FinetuningConvNet加载一个预先训练的模型并重置最终完全连接的层。
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# 注意到所有参数都在优化。
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# 每7个周期衰减0.1倍
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
#在GPU上训练和评估它应该需要15-25分钟，但是它只需要不到一分钟
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
visualize_model(model_ft)
#ConvNet作为固定的特征提取器，我们需要冻结除最后一层之外的所有网络。
# 我们需要设置Required_grad=false来冻结参数，这样梯度就不会用反向()计算。
# 您可以在这里的文档中读到更多关于这一点的内容。
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
model_conv = model_conv.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that only parameters of final layer are being optimized as
# opoosed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
#在CPU上进行训练和评估将花费大约一半的时间，这是因为对于大多数网络来说，不需要计算渐变。然而，前进确实需要计算。
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
visualize_model(model_conv)
plt.ioff()
plt.show()
plt.waitforbuttonpress(0)
最新发布
扫一扫
1万+
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
