pytorch

1pytorch 学习

在学习深度学习框架之前，个人认为了解各个框架的输入才是一切的开始。因此，今天讨论下pytorch的数据输入。各种数据之间的互换、常用代码块

1PIL

好多pytorch的测试代码都是用PIL读取图片而本人则比较喜欢opencv读取，那么先讨论PIL，cv2和torchvision.transforms的区别。

from PIL import Image
import numpy as np
image = Image.open('test.jpg') 
print type(image) # 输出是PIL类型
print image.size  
print image.mode # out: 'RGB'
print image.getpixel((0,0)) # out: (143, 198, 201)
# resize w*h
plt.figure(1)
plt.imshow(img)#可以正常的输出
plt.show()
image = image.resize((200,100)，Image.NEAREST)
print image.size # out: (200,100)
image = np.array(image,dtype=np.float32) # 输出是numpy
print image.shape
plt.figure(1)
plt.imshow(img)#不能正常的输出
plt.show()

1.2 opencv

import cv2
import numpy as np
image = cv2.imread('test.jpg')
print type(image) # 输出是numpy数组
print image.dtype # 图片读入的类型都是uint8
print image.shape # 
print image # BGR
'''
array([
        [ [143, 198, 201 (dim=3)],[143, 198, 201],... (w=200)],
        [ [143, 198, 201],[143, 198, 201],... ],
        ...(h=100)
      ], dtype=uint8)

'''
# w*h
image = cv2.resize(image,(100,200),interpolation=cv2.INTER_LINEAR)
print image.dtype # out: dtype('uint8')
print image.shape # out: (200, 100, 3) 
plt.figure(1)
plt.imshow(img)#可以正常输出
plt.show()

1.3 torchvision.transforms.ToTensor()

关键看一下其中的toTensor函数

def to_tensor(pic):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
	转变PIL和numpy数据，输入是pil数据或者numpy图片，输出是转变后的数据
    """
    if not(_is_pil_image(pic) or _is_numpy_image(pic)):
        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
    if isinstance(pic, np.ndarray):
        img = torch.from_numpy(pic.transpose((2, 0, 1)))
        if isinstance(img, torch.ByteTensor):
            return img.float().div(255)
        else:
            return img
    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.from_numpy(nppic)

    # handle PIL Image
    if pic.mode == 'I':
        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
    elif pic.mode == 'I;16':
        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
    elif pic.mode == 'F':
        img = torch.from_numpy(np.array(pic, np.float32, copy=False))
    elif pic.mode == '1':
        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
    else:
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
    # PIL image mode: L, P, I, F, RGB, YCbCr, RGBA, CMYK
    if pic.mode == 'YCbCr':
        nchannel = 3
    elif pic.mode == 'I;16':
        nchannel = 1
    else:
        nchannel = len(pic.mode)
    img = img.view(pic.size[1], pic.size[0], nchannel)
    # put it from HWC to CHW format
    # yikes, this transpose takes 80% of the loading time/CPU
    img = img.transpose(0, 1).transpose(0, 2).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img

总结

PIL图片转换成numpy后，格式为(h,w,c),像素顺序为RGB；
opencv在读取后就是numpy，格式为(h,w,c),像素顺序为BGR；
torchvison.transforms函数会自己判断图片是PIL格式还是numpy格式，并变成torch所需的格式。
PIL和cv2的互相转换代码如下：

import cv2
from PIL import Image
import numpy as np
def pil_cv2(img_path):    
    image = Image.open(img_path)    
    img = cv2.cvtColor(np.asarray(image),cv2.COLOR_RGB2BGR)    
    return img  

import cv2
from PIL import Image
def cv2_pil(img_path):    
    image = cv2.imread(img_path)    
    image = Image.fromarray(cv2.cvtColor(image,cv2.COLOR_BGR2RGB))    
    return image

在做测试的时候代码如下，可以看出torch的像素顺序为RGB，这就是torch使用PIL多的原因大概。

def totorch(img_path,mode):
    if mode=="PIL":
        img = Image.open(img_path)
        if img.mode == 'L':
            img = img.convert('RGB')
        img = np.array(img)
    else:
        img=cv2.imread(img_path)
    height, width, _ = img.shape
    max_im_shrink = np.sqrt(
        1700 * 1200 / (img.shape[0] * img.shape[1]))
    image = cv2.resize(img, None, None, fx=max_im_shrink,
                       fy=max_im_shrink, interpolation=cv2.INTER_LINEAR)
    # image = cv2.resize(img, (640, 640))
    x = to_chw_bgr(image)
    x = x.astype('float32')
    x -= cfg.img_mean
    if mode=="PIL":
        x = x[[2, 1, 0], :, :]
    x = Variable(torch.from_numpy(x).unsqueeze(0))
    if use_cuda:
        x = x.cuda()
    t1 = time.time()
    y = net(x)

2torch保存模型

首先了解下什么是字典状态
在Pytorch中，torch.nn.Module 模型的可学习参数(即权重和偏差)包含在模型的 parameters 中，(使用model.parameters()可以进行访问)。 state_dict 仅仅是python字典对象，它将每一层映射到其参数张量。注意，只有具有可学习参数的层(如卷积层、线性层等)的模型才具有 state_dict 这一项。优化目标 torch.optim 也有 state_dict 属性，它包含有关优化器的状态信息，以及使用的超参数

2.1保存/加载 state_dict (推荐使用)

保存：

torch.save(model.state_dict(), PATH)

加载：

model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()

2.2保存/加载完整模型

保存：

torch.save(model, PATH)

加载

# Model class must be defined somewhere
model = torch.load(PATH)
model.eval()

2.3保存 和 加载 Checkpoint 用于推理/继续训练

保存：

torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'loss': loss,
            ...
            }, PATH)

加载：

model = TheModelClass(*args, **kwargs)
optimizer = TheOptimizerClass(*args, **kwargs)

checkpoint = torch.load(PATH)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']

model.eval()
# - or -
model.train()

2.4 保存多个模型

保存：

torch.save({
            'modelA_state_dict': modelA.state_dict(),
            'modelB_state_dict': modelB.state_dict(),
            'optimizerA_state_dict': optimizerA.state_dict(),
            'optimizerB_state_dict': optimizerB.state_dict(),
            ...
            }, PATH)

加载：

modelA = TheModelAClass(*args, **kwargs)
modelB = TheModelBClass(*args, **kwargs)
optimizerA = TheOptimizerAClass(*args, **kwargs)
optimizerB = TheOptimizerBClass(*args, **kwargs)

checkpoint = torch.load(PATH)
modelA.load_state_dict(checkpoint['modelA_state_dict'])
modelB.load_state_dict(checkpoint['modelB_state_dict'])
optimizerA.load_state_dict(checkpoint['optimizerA_state_dict'])
optimizerB.load_state_dict(checkpoint['optimizerB_state_dict'])

modelA.eval()
modelB.eval()
# - or -
modelA.train()
modelB.train()

2.5保存多gpu模型

保存：

torch.save(model.module.state_dict(), PATH)

加载可以用任何方式加载，但要注意使用关键字 map_location

3 网络模型的构造

3.1 nn.Sequential()

对于cnn前馈神经网络如果前馈一次写一个forward函数会有些麻烦，在此就有两种简化方式，ModuleList和Sequential。建立nn.Sequential()对象，必须小心确保一个块的输出大小与下一个块的输入大小匹配。
第一种写法：

net1=nn.Sequential()
net1.add_module('conv', nn.Conv2d(3,3,3))
net1.add_module('batchnorm', nn.BatchNorm2d(3))
net1.add_module('activation_layer', nn.ReLU())

第二种写法：

net2 = nn.Sequential(
        nn.Conv2d(3, 3, 3),
        nn.BatchNorm2d(3),
        nn.ReLU())

第三种写法：

from collections import OrderedDict
net3=nn.Sequential(OrderedDict([
    ("conv1",nn.Conv2d(3,3,3)),
    ("batch",nn.BatchNorm2d(3)),
    ("activat",nn.ReLU())
]))

打印出三种写法的网络输出：

print(net1)
'''Sequential(
  (conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  (batchnorm): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (activation_layer): ReLU()
)'''
print(net2)
'''Sequential(
  (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (2): ReLU())'''
print(net3)
'''Sequential(
  (conv1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
  (batch): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (activat): ReLU())'''

so,可根据名字或序号取出子module

x= torch.randn(3,3,320,320)
y=net1(x)
net1.conv, net2[0], net3.conv1
(Conv2d (3, 3, kernel_size=(3, 3), stride=(1, 1)),
 Conv2d (3, 3, kernel_size=(3, 3), stride=(1, 1)),
 Conv2d (3, 3, kernel_size=(3, 3), stride=(1, 1)))

3.2nn.ModuleList

vgg_cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',512, 512, 512, 'M']
def vgg(cfg, i, batch_norm=False):
    layers = []
    in_channels = i
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        elif v == 'C':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
    conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
    layers += [conv6,
               nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
    return layers
vgg=vgg(vgg_cfg,3)
vgg=nn.ModuleList(vgg)
x= torch.randn(3,3,320,320)
for i in range(len(vgg)):
    x=vgg[i](x)

3.3 torch.nn的某些函数

print(vgg)
ModuleList(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace=True)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )

获取网络的层名和每层的结构

for name,module in vgg.named_children():
    print("name is {}".format(name))
    print(module)

输出

name is 0
Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
name is 1
ReLU(inplace=True)
name is 2
Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

named_modules()和named_children()区别在于一个返回的是子模块的迭代器，另一个返回的是所有模块的迭代器。
import torch
import torch.nn as nn
 
class TestModule(nn.Module):
    def __init__(self):
        super(TestModule,self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(16,32,3,1),
            nn.ReLU(inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.Linear(32,10)
        )
 
    def forward(self,x):
        x = self.layer1(x)
        x = self.layer2(x)
 
model = TestModule()
 
for name, module in model.named_children():
    print('children module:', name)
 
for name, module in model.named_modules():
    print('modules:', name)

输出

>>out:
children module: layer1
children module: layer2
modules: 
modules: layer1
modules: layer1.0
modules: layer1.1
modules: layer2
modules: layer2.0

获取参数名和其值

for name,param in vgg.named_parameters():
    print("name is {}".format(name))
    print(param)

输出

name is 0.weight
Parameter containing:
tensor([[[[-3.6296e-02,  1.0836e-01,  1.3510e-01],
          [-1.3361e-01,  5.9745e-02,  2.4438e-02],
          [-1.7095e-01, -9.9576e-02,  1.1297e-01]],

         [[-8.1718e-02,  6.0913e-02, -3.0109e-02],
          [-1.5120e-01,  8.5647e-02,  1.4474e-01],
          [-6.8853e-02, -8.2803e-02, -1.8270e-01]]]）
name is 0.bias
Parameter containing:
tensor([ 1.8735e-01,  3.1941e-03, -1.8747e-01, -3.1531e-02,  3.9880e-04,
        -8.7427e-02,  1.9193e-01, -7.6810e-03, -5.3820e-02, -5.4126e-02,
         1.5074e-01, -6.0450e-02, -7.7125e-02,  8.1087e-02,  1.8392e-01,
         7.3845e-02,  1.4453e-01, -1.0507e-01, -5.9577e-02,  2.3714e-02],
       requires_grad=True)
name is 2.weight
Parameter containing:
tensor([[[[-2.3275e-02, -3.5920e-02, -3.5464e-02],
          [-1.2356e-02, -1.4983e-02, -2.8448e-02],
          [ 7.7514e-03,  3.7017e-02, -3.8343e-02]],

         [[-2.7261e-02,  2.7608e-03,  3.8855e-02],
          [ 3.4545e-02, -2.2671e-02, -1.8810e-02]])

3.4总结

两种方法的不同在于ModuleList传入的参数是一个列表，并且ModuleList不支持自动推导。

4数据集的定义

4.1 使用pytorch定义的Dataset类

data_transform= transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset=datasets.ImageFolder(root="/disk3/dataset/classfy/finaldataset/train",transform=data_transform)
train_loader=torch.utils.data.DataLoader(train_dataset,batch_size=4,shuffle=True)
val_dataset=datasets.ImageFolder(root="/disk3/dataset/classfy/finaldataset/val",transform=data_transform)
val_loader=torch.utils.data.DataLoader(val_dataset,batch_size=4,shuffle=True)

只需要数据按下图摆放即可

• train

  • 类别一
  • 类别二

• val

  • 类别一
  • 类别二

4.2编写自己的dataset类

编写自己的dataset类，需要继承Dataset类，并进行override，最重要的复写类中的几个函数如下：
(1) init : 读各种格式的数据集、路径等，控制传入参数
(2) getitem : 使dataset[i]能够获得第i个样本数据，即导入具体数据
(3) len : len(dataset) returns the size of the dataset

class mydataset(Dataset):
    def __init__(self,txt_path,transform=data_transform):
        self.image_path,self.label_name=self.get_image_path(txt_path)
        self.transform=data_transform
    def __getitem__(self,index):
        img_path=self.image_path[index]
        label=int(self.label_name[index])
        img=Image.open(img_path)
        
        if self.transform is not None:
            img=self.transform(img)
        
        return img,label
    def __len__(self):
        return len(self.label_name)
    def get_image_path(self,path):
        with open(path,"r") as fr:
            total_list=[i.replace("\n","") for i in fr.readlines()]
        img_list=[]
        label_list=[]
        for i in total_list:
            i=i.split(" ")
            img_list.append(i[0])
            label_list.append(i[1])
        
        return img_list,label_list
train1=mydataset("/disk3/dataset/classfy/finaldataset/train.txt",data_transform)
test1=mydataset("/disk3/dataset/classfy/finaldataset/test.txt",data_transform)
train1_loader=torch.utils.data.DataLoader(train1,batch_size=4,shuffle=True)
val1_loader=torch.utils.data.DataLoader(test1,batch_size=4,shuffle=True) 

其中__getitem__()函数主要关注下transform函数的输出和__getitem__()函数的输出，以后在探究。现在可行的是PIL读取的图片可直接送入transform(),getitem()输出的label不可以是string。

5训练时指标计算方法

def train():
    for i in range(epochs):
        runing_loss=0
        trian_correct=0
        train_total=0
        for j,(images,labels) in enumerate(train1_loader):
            images=Variable(images.cuda())
            labels=Variable(labels.cuda())
            optimizer.zero_grad()
            output=alexnet(images)
            _,pred=torch.max(output.data,1)
            trian_correct+=(pred==labels.data).sum()
            loss=criterion(output,labels)
            loss.backward()
            optimizer.step()
            runing_loss+=loss.item()
            train_total+=labels.size(0)
            if j%100==0:
                print("train epoch is{} ,loss is{},acc is{}".format(i+1,runing_loss/train_total,
                                                               100*trian_correct/train_total))
        print("train epoch is{} ,loss is{},acc is{}".format(i+1,runing_loss/train_total,
                                                               100*trian_correct/train_total))
        correct=0
        test_loss=0
        test_total=0
        alexnet.eval()
        for (test_images,test_labels) in val1_loader:
            test_images=Variable(test_images.cuda())
            test_labels=Variable(test_labels.cuda())
            test_output=alexnet(test_images)
            _,pred=torch.max(test_output.data,1)
            loss=criterion(test_output,test_labels)
            test_loss+=loss.item()
            test_total+=test_labels.size(0)
            correct+=(pred==test_labels.data).sum()
        print("test epoch is{},loss is{},acc is{}".format(i+1,test_loss/test_total,100*correct/test_total))

最后上传一个完整代码