Question

我正在尝试在训练神经网络的同时在Pytorch中调整图像大小以进行后续处理。但是，当我尝试在图像上调用transforms.Resize（）时，出现广播错误。这是我的代码段。

cuda:0
Classifier(
  (fc1): Linear(in_features=784, out_features=256, bias=True)
  (fc2): Linear(in_features=256, out_features=128, bias=True)
  (fc3): Linear(in_features=128, out_features=64, bias=True)
  (fc4): Linear(in_features=64, out_features=10, bias=True)
)
Traceback (most recent call last):
  File "netz.py", line 71, in <module>
    train()
  File "netz.py", line 46, in train
    outputs=model(inputs)
  File "/home/yyyy/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "netz.py", line 18, in forward
    x=F.relu(self.fc1(x))
  File "/home/yyy/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/yyy/anaconda3/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 55, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/yyy/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py", line 1024, in linear
    return torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [64 x 59536], m2: [784 x 256] at /opt/conda/conda-bld/pytorch_1532584813488/work/aten/src/THC/generic/THCTensorMathBlas.cu:249

---- Corresponding Code ---


import torch
from torch import nn,optim
import torch.nn.functional as F
from torchvision import datasets,transforms

NUM_EPOCH=700
class Classifier(nn.Module):

    def __init__(self):
        super().__init__()
        self.fc1=nn.Linear(784,256)
        self.fc2=nn.Linear(256,128)
        self.fc3=nn.Linear(128,64)
        self.fc4=nn.Linear(64,10)

    def forward(self,x):
        x=x.view(x.shape[0],-1)
        x=F.relu(self.fc1(x))
        x=F.relu(self.fc2(x))
        x=F.relu(self.fc3(x))
        x=F.log_softmax(self.fc4(x),dim=1)
        return x




def train():
    transform=transforms.Compose([
                    transforms.Resize(244),
                    transforms.ToTensor(),
                    transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
    trainset=datasets.FashionMNIST('./data',download=True,transform=transform)
    trainloader=torch.utils.data.DataLoader(trainset,batch_size=64,shuffle=True,num_workers=2)
    model=Classifier()
    model=model.to(device)
    criterion=nn.CrossEntropyLoss()
    optimizer=optim.Adam(model.parameters(),lr=0.001)

    for epoch in range(NUM_EPOCH):
        running_loss=0.0
        for i, data in enumerate(trainloader,0):
            inputs,labels=data
            inputs=inputs.to(device)
            labels=labels.to(device)
            optimizer.zero_grad()
            outputs=model(inputs)
            outputs.to(device)
            loss=criterion(outputs,labels)
            loss.backward()
            optimizer.step()
            running_loss+=loss.item()
            if(i%20 == 19):
                print("epoch: ",epoch+1)
                print("i + 1",i)
                print("loss: ",running_loss/20.0)
                #print('[%d, 5d] loss: %.3f' %(epoch+1,i+1,running_loss/20))
                running_loss=0.0




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

net=Classifier()
net.to(device)
print(net)
train()

所以我的问题是，什么时候我最适合调整图像大小的方法是为我的特定用例训练网络？

我正在Ubuntu 16.04 LTS系统上使用带有Pytorch版本0.4.1和Python3.7的Cuda8.0和CudaDNN7.1。

尝试应用transforms.Resize（）时出现广播错误

0 个答案: