Pytorch中的数据转换Transforms与DataLoader方式
作者：Jeremy_lf
这篇文章主要介绍了Pytorch中的数据转换Transforms与DataLoader方式，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教
DataLoader
DataLoader是一个比较重要的类，它为我们提供的常用操作有：
batch_size(每个batch的大小)shuffle(是否进行shuffle操作)num_workers(加载数据的时候使用几个子进程)
import torch as t
import torch.nn as nn
import torch.nn.functional as F
import torch
'''
初始化网络
初始化Loss函数 & 优化器
进入step循环：
梯度清零
向前传播
计算本次Loss
向后传播
更新参数
'''
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return x
if __name__ == "__main__":
net = LeNet()
# #########训练网络#########
from torch import optim
# from torchvision.datasets import MNIST
import torchvision
import numpy
from torchvision import transforms
from torch.utils.data import DataLoader
# 初始化Loss函数 & 优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# transforms = transforms.Compose([])
DOWNLOAD = False
BATCH_SIZE = 32
transform = transforms.Compose([
transforms.ToTensor()
])
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 归一化
train_dataset = torchvision.datasets.MNIST(root='./', train=True, transform=transform, download=DOWNLOAD)
test_dataset = torchvision.datasets.MNIST(root='./data/mnist',
train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
train_loader = DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE)
for epoch in range(200):
running_loss = 0.0
for step, data in enumerate(train_loader):
inputs, labels = data
inputs, labels = t.autograd.Variable(inputs), t.autograd.Variable(labels)
# inputs = torch.from_numpy(inputs).unsqueeze(1)
# labels = torch.from_numpy(numpy.array(labels))
# 梯度清零
optimizer.zero_grad()
# forward
outputs = net(inputs)
# backward
loss = loss_fn(outputs, labels)
loss.backward()
# update
optimizer.step()
running_loss += loss.item()
if step % 10 == 9:
print("[{0:d}, {1:5d}] loss: {2:3f}".format(epoch + 1, step + 1, running_loss / 2000))
running_loss = 0.
print("Finished Training")
# save the trained net
torch.save(net, 'net.pkl')
# load the trained net
net1 = torch.load('net.pkl')
# test the trained net
correct = 0
total = 1
for images, labels in test_loader:
preds = net(images)
predicted = torch.argmax(preds, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print('accuracy of test data:{:.1%}'.format(accuracy))数据变换（Transform）
实例化数据库的时候，有一个可选的参数可以对数据进行转换，满足大多神经网络的要求输入固定尺寸的图片，因此要对原图进行Rescale或者Crop操作，然后返回的数据需要转换成Tensor。
数据转换（Transfrom）发生在数据库中的__getitem__操作中。
class Rescale(object):
"""Rescale the image in a sample to a given size.
Args:
output_size (tuple or int): Desired output size. If tuple, output is
matched to output_size. If int, smaller of image edges is matched
to output_size keeping aspect ratio the same.
"""
def __init__(self, output_size):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
h, w = image.shape[:2]
if isinstance(self.output_size, int):
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:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
img = transform.resize(image, (new_h, new_w))
# h and w are swapped for landmarks because for images,
# x and y axes are axis 1 and 0 respectively
landmarks = landmarks * [new_w / w, new_h / h]
return {'image': img, 'landmarks': landmarks}
class RandomCrop(object):
"""Crop randomly the image in a sample.
Args:
output_size (tuple or int): Desired output size. If int, square crop
is made.
"""
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]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[top: top + new_h,
left: left + new_w]
landmarks = landmarks - [left, top]
return {'image': image, 'landmarks': landmarks}
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((2, 0, 1))
return {'image': torch.from_numpy(image),
'landmarks': torch.from_numpy(landmarks)}torchvision 包的介绍
torchvision 是PyTorch中专门用来处理图像的库，这个包中有四个大类。
torchvision.datasets torchvision.models torchvision.transforms torchvision.utils
torchvision.datasets
torchvision.datasets 是用来进行数据加载的，PyTorch团队在这个包中帮我们提前处理好了很多很多图片数据集。
MNIST、COCO、Captions、Detection、LSUN、ImageFolder、Imagenet-12、CIFAR、STL10、SVHN、PhotoTour
import torchvision from torch.utils.data import DataLoader DOWNLOAD = False BATCH_SIZE = 32 transform = transforms.Compose([ transforms.ToTensor() ]) #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 归一化 train_dataset = torchvision.datasets.MNIST(root='./', train=True, transform=transform, download=DOWNLOAD) train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
torchvision.models
torchvision.models 中为我们提供了已经训练好的模型，加载之后，可以直接使用。包含以下模型结构。
AlexNet、VGG、ResNet、SqueezeNet、DenseNet、MobileNet
import torchvision.models as models resnet18 = models.resnet18(pretrained=True) alexnet = models.alexnet(pretrained=True)
torchvision.transforms
transforms提供了一般图像的转化操作类
# 图像预处理步骤 transform = transforms.Compose([ transforms.Resize(96), # 缩放到 96 * 96 大小 transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 归一化 ])
Transforms支持的变化
__all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale", "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop", "LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale", "RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode", "RandomInvert", "RandomPosterize", "RandomSolarize", "RandomAdjustSharpness", "RandomAutocontrast", "RandomEqualize"]
from PIL import Image
# from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from torch.autograd import Variable
from torchvision.transforms import functional as F
tensor数据类型
# 通过transforms.ToTensor去看两个问题
img_path = "./k.jpg"
img = Image.open(img_path)
# writer = SummaryWriter("logs")
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)
tensor_img1 = F.to_tensor(img)
print(tensor_img.type(),tensor_img1.type())
print(tensor_img.shape)
'''
transforms.Normalize使用如下公式进行归一化：
channel=（channel-mean）/std(因为transforms.ToTensor()已经把数据处理成[0,1],那么(x-0.5)/0.5就是[-1.0, 1.0])
'''
# writer.add_image("Tensor_img", tensor_img)
# writer.close()将输入的PIL.Image重新改变大小成给定的size，size是最小边的边长。
举个例子，如果原图的height>width,那么改变大小后的图片大小是(size*height/width, size)。
### class torchvision.transforms.Scale(size, interpolation=2)
```python
from torchvision import transforms
from PIL import Image
crop = transforms.Scale(12)
img = Image.open('test.jpg')
print(type(img))
print(img.size)
croped_img=crop(img)
print(type(croped_img))
print(croped_img.size)对PIL.Image进行变换
class torchvision.transforms.Compose(transforms)
将多个transform组合起来使用。
class torchvision.transforms.Normalize(mean, std)
给定均值：(R,G,B) 方差：（R，G，B），将会把Tensor正则化。即：Normalized_image=(image-mean)/std。
class torchvision.transforms.RandomSizedCrop(size, interpolation=2)
先将给定的PIL.Image随机切，然后再resize成给定的size大小。
class torchvision.transforms.RandomCrop(size, padding=0)
切割中心点的位置随机选取。size可以是tuple也可以是Integer。
class torchvision.transforms.CenterCrop(size)
将给定的PIL.Image进行中心切割，得到给定的size，size可以是tuple，(target_height, target_width)。size也可以是一个Integer，在这种情况下，切出来的图片的形状是正方形。
总结
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。