问题描述
我必须添加到原始 CIFAR 数据集，对于每个图像，对应的图像，旋转 90 度。这个想法是创建一个 RotationDateset，一个扩展 datasets.VisionDataset 的类，它采用 CIFAR 并执行上述操作。
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//org_dataset 是 CIFAR //num_rots 为 4 //转换是transforms.Compose([transforms.ToTensor(),transforms.normalize((0.5,0.5,0.5),(0.5,0.5))])
from __future__ import print_function,division
import skimage.io
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
from torchvision.datasets import ImageFolder
import torchvision
from torchvision import datasets,models,transforms
import matplotlib.pyplot as plt
import time
import os
from sklearn.model_selection import train_test_split
import copy
import cv2
from torchvision.models.resnet import BasicBlock
from torchvision.models.resnet import resnet
from PIL import Image
import xml.etree.ElementTree as ET
from torch.utils.model_zoo import load_url as load_state_dict_from_url
from torchvision.models.resnet import model_urls
这是我最初导入和转换 CIFAR 的方法：
class RotDataset(datasets.VisionDataset):
def __init__(self,org_dataset,transforms,num_rots):
self.samples = org_dataset.data
self.targets = []
self.num_rots = num_rots
self.transforms = transforms
for k in self.samples:
self.targets.append(k)
for i in range(0,self.num_rots):
tr = torchvision.transforms.Compose([torchvision.transforms.Randomrotation(degrees=90*i),torchvision.transforms.ToTensor(),torchvision.transforms.normalize((0.5,0.5))])
# from PIL import Image
p_i = Image.fromarray(k)
te = tr(p_i)
r_im = torch.reshape(te,(k.shape))
r_im = np.array(r_im)
self.targets.append(r_im)
def __len__(self):
return len(self.samples)
def __getitem__(self,index):
imgs = self.targets[index:index + self.num_rots]
labels = list(range(0,self.num_rots))
return imgs,labels
这是我创建 CIFAR 增强的方法：
transform = transforms.Compose([transforms.ToTensor(),0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data',train=False,transform=transform)
classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
和模型训练
cifar_rot = RotDataset(trainset,trainset.transforms,4)
rot_train,rot_val= train_test_split(
np.arange(len(cifar_rot.targets)),test_size=0.2,shuffle=True,)
train_sampler_rot = torch.utils.data.SubsetRandomSampler(rot_train)
val_sampler_rot = torch.utils.data.SubsetRandomSampler(rot_val)
DataLoaders_rot = {'train': torch.utils.data.DataLoader(cifar_rot,batch_size=8,sampler=train_sampler_rot),'val':torch.utils.data.DataLoader(cifar_rot,sampler=val_sampler_rot)}
sizes_rot = {'train':len(rot_train)*4,'val':len(rot_val)*4}
//问题是我启动模型的时候pythorch抛出这个错误：
model_rot = torchvision.models.resnet34(pretrained=False)
num_ftrs = model_rot.fc.in_features
output_dim_rot = 4 # since are 4 rotations
model_rot.fc = nn.Linear(num_ftrs,output_dim_rot)
model_rot = model_rot.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_conv = optim.SGD(model_rot.parameters(),lr=0.001,momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv,step_size=7,gamma=0.1)
model_rot = train_model(model_rot,criterion,optimizer_conv,exp_lr_scheduler,DataLoaders_rot,sizes_rot,num_epochs=10)
torch.save(model_rot.state_dict(),'rotation_resnet34_10_epochs.pt')
谁能帮帮我？提前致谢
解决方法
问题来自于您对 org_dataset.data 的依赖，这是一个形状为 (N,32,3) 的 numpy 数组（您希望它是 (N,3,32)）
因此，使用 self.targets.append(k) 行，您在目标列表中放置了不正确的形状。然后，张量 te 具有正确的形状（感谢 ToTensor），但是您在
我还想指出，诸如 RandomRotation 之类的随机变换通常应用于 __getitem__ 方法中，而不是 __init__ 中。由于在这些变换中生成随机数，因此您希望每个时期都生成新样本，以便拥有几乎无限的数据集和样本。我实际上不确定您是否理解 RandomRotation 的作用：它使用 随机旋转 旋转输入张量，您只需指定可能的角度范围。因此，完全有可能应用参数 180 (i=2) 的“旋转”将产生几乎不变的张量。我看到您之后试图预测 i 的值，它很可能不起作用。您可能想改用 torch.rot90。
除此之外，由于您已经在 ToTensor 中应用了 Normalize 和 RotDataset，因此您当然不需要在 CIFAR10 中使用它们。
最后评论：我真的不明白你为什么要 __getitem 返回张量（和标签）列表。我会在下面的代码中保持这种方式，但看起来它最终会破坏某些东西。
因此，您可以通过以下方式更正代码：
class RotDataset(datasets.VisionDataset):
def __init__(self,org_dataset,transforms,num_rots):
# Let's buffer the underlying dataset,we will sample
# from it on the fly
self.dataset = org_dataset
self.num_rots = num_rots
# You did not use this attribute previously,probably a mistake
# It will now be applied in the __getitem__
self.transforms = transforms
def __len__(self):
# Typical front dataset : size is the same as the
# underlying dataset size
return len(self.dataset)
def __getitem__(self,index):
# sampling from CIFAR10
sample = self.dataset[index]
# Because you want to return a list
imgs = []
for i in range(0,self.num_rots):
# Creating the corresponding rotation
rotation = torchvision.transforms.RandomRotation(degrees=90*i)
# Applying rotation,followed by other transforms (toTensor,Normalize...)
transformed = self.transform(rotation(sample))
imgs.append(transformed)
# Cleaner way to generate your range :
labels = np.arange(self.num_rots)
return imgs,labels
# transform=None,since we will apply them in RotDataset
trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=None)
# The transforms to call in RotDataset
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5))])
cifar_rot = RotDataset(trainset,transform,4)
# using torch's random split to remove dependency on sklearn
from torch.utils.data import random_split
test_size = 0.2*len(cifar_rot)
rot_train,rot_val= random_split(cifar_rot,[len(cifar_rot)-test_size,test_size])