pytorch官网教程+注释

Classifier

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=3,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=3,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

Files already downloaded and verified
Files already downloaded and verified

import matplotlib.pyplot as plt
import numpy as np
def imshow(img):
    img = img/2 + 0.5 # 因为之前标准化的时候除以0.5就是乘以2，还减了0.5，所以回复原来的亮度值
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg,(1,2,0))) # 转置根本的是改变索引顺序，即bgr转rgb
    plt.show()
dataiter = iter(trainloader)
images,labels = dataiter.next()
print(images.shape) #torch.Size([4, 3, 32, 32]) bchw
print(torchvision.utils.make_grid(images).shape) #torch.Size([3, 36, 138])
#imshow(torchvision.utils.make_grid(images)) # 以格子形式显示多张图片
#print(" ".join("%5s"% classes[labels[j]] for j in range(4)))

torch.Size([3, 3, 32, 32])
torch.Size([3, 36, 104])

import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = nn.Conv2d(3,6,5)
        self.pool = nn.MaxPool2d(2,2)
        self.conv2 = nn.Conv2d(6,16,5)
        self.fc1 = nn.Linear(16*5*5,120)
        self.fc2 = nn.Linear(120,84)
        self.fc3 = nn.Linear(84,10)
    def forward(self,x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1,16*5*5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
net = Net()

import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr = 0.001,momentum = 0.9)

for epoch in range(2):
    running_loss = 0.0
    for i,data in enumerate(trainloader):
        inputs,labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs,labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i%2000 ==1999:
            print('[%d,%5d] loss:%.3f'%(epoch+1,i+1,running_loss/2000))
            running_loss = 0.0
print("finished training")

[1, 2000] loss:1.468
[1, 4000] loss:1.410
[1, 6000] loss:1.378
[1, 8000] loss:1.363
[1,10000] loss:1.330
[1,12000] loss:1.299
[2, 2000] loss:1.245
[2, 4000] loss:1.217
[2, 6000] loss:1.237
[2, 8000] loss:1.197
[2,10000] loss:1.193
[2,12000] loss:1.196
finished training

dataiter = iter(testloader)
images,labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))

outputs = net(images)
_,predicted = torch.max(outputs,1)
print("predicted"," ".join([classes[predicted[j]] for j in range(4)]))

predicted cat ship ship ship

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images,labels = data
        outputs = net(images)
        _,predicted = torch.max(outputs.data,1)
        total += labels.size(0) # 等价于labels.size()[0]
        correct+= (predicted==labels).sum().item()
print("acc:{}%%".format(100*correct/total))

acc:56.96%%

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

print(device)

cpu

net.to(device)

Net(
  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (fc1): Linear(in_features=400, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

DataLoading And Processing

from __future__ import print_function,division
import os
import torch
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,utils
import warnings
from skimage import io,transform
warnings.filterwarnings("ignore")
plt.ion()

landmarks_frame = pd.read_csv("data/faces/face_landmarks.csv") # name x y x y ...
#print(landmarks_frame.columns.tolist())
n = 65 # 第65个样本
img_name = landmarks_frame.iloc[n,0]# 第65个样本的文件名
#print(img_name)
#print(landmarks_frame.iloc[n,1:])
landmarks = landmarks_frame.iloc[n,1:].as_matrix()# 第65个样本的样本值向量
#如果不加上as_matrix的结果就是feature name + feature val，加了之后只有feature val
#print(landmarks)
landmarks = landmarks.astype('float').reshape(-1,2) # 两个一组，组成两列的矩阵

def show_landmarks(image,landmarks):
    plt.imshow(image)
    plt.scatter(landmarks[:,0],landmarks[:,1],s=10,marker='.',c='r')
    #plt.pause(0.001) # python 窗口用得着

plt.figure()
show_landmarks(io.imread(os.path.join("data/faces/",img_name)),landmarks)

# torch.utils.data.Dataset 是一个抽象类，我们的dataset需要继承这个类，才能对其进行操作
class FaceLandmarksDataset(Dataset):
    def __init__(self,csv_file,root_dir,transform=None):
        self.landmarks_frame = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform
    def __len__(self):
        return len(self.landmarks_frame)
    def __getitem__(self,idx):
        img_name = os.path.join(
            self.root_dir,
            self.landmarks_frame.iloc[idx,0]
        )
        image = io.imread(img_name)
        landmarks = self.landmarks_frame.iloc[idx,1:].as_matrix()
        landmarks = landmarks.astype('float').reshape([-1,2])
        sample = {"image":image,"landmarks":landmarks}
        if self.transform:
            sample = self.transform(sample)
        return sample

face_dataset = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv',root_dir='data/faces/')
fig = plt.figure()
for i in range(len(face_dataset)):
    sample = face_dataset[i]
    print(i,sample['image'].shape,sample['landmarks'].shape)
    ax = plt.subplot(1,4,i+1)
    #plt.tight_layout()
    ax.set_title("sample #{}".format(i))
    ax.axis("off")
    show_landmarks(**sample) #**为python 拆包，将dict拆解为x=a,y=b的格式
    if i==3:
        plt.show()
        break

0 (324, 215, 3) (68, 2)
1 (500, 333, 3) (68, 2)
2 (250, 258, 3) (68, 2)
3 (434, 290, 3) (68, 2)

class Rescale():
    def __init__(self,output_size):
        assert isinstance(output_size,(int,tuple)) # 必须是int或tuple类型，否则报错，要习惯用assert isinstance
        self.output_size = output_size
    def __call__(self,sample): # 这个类的对象是一个函数，所以定义call
        image,landmarks = sample['image'],sample['landmarks']
        h,w = image.shape[:2]
        if isinstance(self.output_size,int): # 如果只输入了一个值，则以较短边为基准保持长宽比率不变变换
            if h>w:
                new_h,new_w = self.output_size*h/w,self.output_size
            else:
                new_h,new_w = self.output_size,self.output_size*w/h
        else: #给定一个size那就直接变成这个size
            new_h,new_w = output_size
        new_h,new_w = int(new_h),int(new_w)
        img = transform.resize(image,(new_h,new_w)) # 进行resize操作，调用的是skimage的resize，提供(h,w)，跟opencv相反
        landmarks = landmarks * [new_w/w,new_h/h]# 相应的，landmark也要做转换
        return {"image":img,"landmarks":landmarks} # 返回dict
class RandomCrop():
    def __init__(self,output_size):
        assert isinstance(output_size,(int,tuple))
        if isinstance(output_size,int):
            self.output_size = (output_size,output_size)
        else:
            assert len(output_size) ==2
            self.output_size = output_size
    def __call__(self,sample):
        image,landmarks = sample['image'],sample['landmarks']
        h,w = image.shape[:2]# 原图宽高
        new_h,new_w = self.output_size # 裁剪的宽高
        top = np.random.randint(0,h-new_h) # 裁剪输出图像最上端
        left = np.random.randint(0,w-new_w) # 最左端，保证取的时候不越界
        image = image[top:top+new_h,left:left+new_w] # 随机裁剪
        landmarks = landmarks - [left,top] # 这里landmarks同样也需要做变换,之所以减去[left,top]是因为存储的是x,y对应的轴是w轴和h轴
        return {"image":image,"landmarks":landmarks}
class ToTensor():
    def __call__(self,sample):
        image,landmarks = sample['image'],sample['landmarks']
        image = image.transpose((2,0,1)) # transpose实质是reshape
        # skimage的shape [h,w,c]
        # torch的shape [c,h,w]
        return {"image":torch.from_numpy(image),"landmarks":torch.from_numpy(landmarks)}

scale = Rescale(256)
crop = RandomCrop(128)
composed = transforms.Compose([Rescale(256),RandomCrop(224)]) # 组合变换
fig = plt.figure()
sample = face_dataset[65]
for i,tsfrm in enumerate([scale,crop,composed]): # 三种变换
    transformed_sample = tsfrm(sample) # 应用其中之一
    ax = plt.subplot(1,3,i+1)
    plt.tight_layout()
    show_landmarks(**transformed_sample)
plt.show()

transformed_dataset = FaceLandmarksDataset(csv_file = 'data/faces/face_landmarks.csv',
                                           root_dir="data/faces/",
                                           transform=transforms.Compose(
                                               [
                                                   Rescale(256),
                                                   RandomCrop(224),
                                                   ToTensor()
                                               ])
                                          )
for i in range(len(transformed_dataset)):
    sample = transformed_dataset[i]
    print(i,sample['image'].size(),sample['landmarks'].size())
    if i==3:
        break

0 torch.Size([3, 224, 224]) torch.Size([68, 2])
1 torch.Size([3, 224, 224]) torch.Size([68, 2])
2 torch.Size([3, 224, 224]) torch.Size([68, 2])
3 torch.Size([3, 224, 224]) torch.Size([68, 2])

dataloader = DataLoader(transformed_dataset,batch_size=4,shuffle=True,num_workers=4) # 调用dataloader
def show_landmarks_batch(sample_batched):
    images_batch,landmarks_batch = sample_batched['image'],sample_batched['landmarks']
    batch_size = len(images_batch)
    im_size = images_batch.size(2) #这里的size是shape
    grid = utils.make_grid(images_batch) # 多张图变成一张图
    plt.imshow(grid.numpy().transpose(1,2,0)) # reshape到能用plt显示
    for i in range(batch_size):
        # 第i张图片的所有点的x，所有点的y，后面 + i*im_size是由于所有图像水平显示，所以需要水平有个偏移
        # 转numpy是因为torch类型的数据没办法scatter
        plt.scatter(landmarks_batch[i,:,0].numpy() + i*im_size,
                   landmarks_batch[i,:,1].numpy(),
                   s=10,marker='.',c='r')
        plt.title("batch from dataloader")
for i_batch,sample_batched in enumerate(dataloader):
    if i_batch ==3:
        plt.figure()
        show_landmarks_batch(sample_batched)
        plt.axis("off") # 关闭坐标系
        plt.ioff()
        plt.show()
        break

<built-in method size of Tensor object at 0x7f273f3bad38>

原文地址：https://www.cnblogs.com/aoru45/p/10668250.html