所以我想对一些(3, 50, 50)
图片进行分类。首先,我在没有数据加载器或批处理的情况下从文件中加载了数据集,它起作用了。现在,在添加这两个东西之后我得到了那个错误:
RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15
我在互联网上找到了很多答案,主要是使用 target.squeeze(1)
但它对我不起作用。
我的目标批处理如下所示:
tensor([[1, 0],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[1, 0],
[1, 0]], device='cuda:0')
这样应该没问题吧?
这里是完整的代码(请注意,我只是创建模型的结构,之后我将在其上应用完整和正确的数据集,因为我还没有完整的数据,只有 32 张图片,没有标签,这就是为什么我添加了 torch.tensor([1, 0])
作为所有标签的占位符:
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
from torch.autograd import Variable
import numpy as np
from PIL import Image
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
# model structur:
self.conv1 = nn.Conv2d(3, 10, kernel_size=(5,5), stride=(1,1))
self.conv2 = nn.Conv2d(10, 20, kernel_size=(5,5), stride=(1,1)) # with mapool: output = 20 * (9,9) feature-maps -> flatten
self.fc1 = nn.Linear(20*9*9, 250)
self.fc2 = nn.Linear(250, 100)
self.fc3 = nn.Linear(100, 2)
def forward(self, x):
# conv layers
x = F.relu(self.conv1(x)) # shape: 1, 10, 46, 46
x = F.max_pool2d(x, 2, 2) # shape: 1, 10, 23, 23
x = F.relu(self.conv2(x)) # shape: 1, 20, 19, 19
x = F.max_pool2d(x, 2, 2) # shape: 1, 20, 9, 9
# flatten to dense layer:
x = x.view(-1, 20*9*9)
# dense layers
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
output = F.log_softmax(self.fc3(x), dim=1)
return output
class Run:
def __init__(self, epochs, learning_rate, dropout, momentum):
# load model
self.model = Model().cuda()
# hyperparameters:
self.epochs = epochs
self.learning_rate = learning_rate
self.dropout = dropout
def preporcessing(self):
dataset_folder = "/media/theodor/hdd/Programming/BWKI/dataset/bilder/"
dataset = []
for i in range(0, 35):
sample_image = Image.open(dataset_folder + str(i) + ".png")
data = torch.from_numpy(np.array(sample_image)).type("torch.Tensor").reshape(3, 50, 50)
target = torch.tensor([[1, 0]])
sample = (data, target)
dataset.append(sample)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=8)
return train_loader
def train(self):
train_set = self.preporcessing()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
for epoch in range(self.epochs):
epoch_loss = 0
for i, data in enumerate(train_set, 0):
sample, target = data
# set data as cuda varibale
sample = Variable(sample.float().cuda())
target = Variable(target.cuda())
# initialize optimizer
optimizer.zero_grad()
# predict
output = self.model(sample)
# backpropagation
print(output, target.squeeze(1))
loss = criterion(output, target.squeeze(1)) # ERROR MESSAGE: RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15
loss.backward()
optimizer.step()
epoch_loss += loss.item()
print("loss after epoch [", epoch, "|", self.epochs, "] :", epoch_loss)
run = Run(10, 0.001, 0.5, 0.9)
run.train()
所以我希望它开始训练(当然不会学习任何东西,因为标签是错误的)。
最佳答案
对于 nn.CrossEntropyLoss
,目标必须是区间 [0, #classes] 中的单个数字,而不是单热编码目标向量。您的目标是 [1, 0],因此 PyTorch 认为您希望每个输入有多个标签,这是不支持的。
替换您的单热编码目标:
[1, 0] --> 0
[0, 1] --> 1
关于python - torch : "multi-target not supported"错误消息,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57325844/