我是将代码从Keras/Tensorflow转移到Pytorch的新手，我正在尝试在Pytorch中重新训练我的TF模型，然而，我的数据集有一些特殊性，这使我很难在Pytork中运行它。
为了理解我的问题，请记住，我有一个自定义数据集是这样初始化的：
class MyDataSet(torch.utils.data.Dataset):
def __init__(self, x, y, transform=None):
super(MyDataSet, self).__init__()
# store the raw tensors
self._x = np.load(x)
self._y = np.load(y)
self._x=np.swapaxes(self._x,3,2)
self._x=np.swapaxes(self._x,2,1)
self.transform = transform
def __len__(self):
# a DataSet must know it size
return self._x.shape[0]
def __getitem__(self, index):
x = self._x[index, :]
y = self._y[index]
return x, y
_self的形状_x是(12000，3224224(和self的形状_y是(12000，(
我正在这个数据中微调一个预先训练的RESNET-50，训练按照以下方式进行：
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision.models import resnet50
import time
import copy
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
#Transform dataset
print("Loading Data")
transform = transforms.Compose([transforms.ToTensor()])
dataset = MyDataSet("me/train1-features.npy","/me/train1-classes.npy",transform=transform)
dataloader = DataLoader(dataset, batch_size=4)
print("Configuring network")
feature_extract = True
num_epochs = 15
num_classes=12
model_ft = resnet50(pretrained=True)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, num_classes)
if torch.cuda.is_available():
model_ft.cuda()
params_to_update = model_ft.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("t",name)
else:
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
print("t",name)
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)
# Setup the loss fxn
criterion = nn.CrossEntropyLoss()
#Train (how to validate?)
for epoch in range(num_epochs): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(dataloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
#transfer labels and inputs to cuda()
inputs,labels=inputs.cuda(), labels.cuda()
# zero the parameter gradients
optimizer_ft.zero_grad()
# forward + backward + optimize
outputs = model_ft(inputs)
loss = loss_func(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
然而，每当我运行此代码时，我都会收到以下错误
Traceback (most recent call last):
File "train_my_data_example.py", line 114, in <module>
outputs = model_ft(inputs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torchvision/models/resnet.py", line 249, in forward
return self._forward_impl(x)
File "/usr/local/lib/python3.8/dist-packages/torchvision/models/resnet.py", line 232, in _forward_impl
x = self.conv1(x)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 399, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 395, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
我也可以在TF/Kras上正常完成训练和验证过程，但我不知道如何在Pytorch的自定义数据集中完成。
如何解决我的问题，并在我的自定义数据中使用Pytorch运行train/val循环？
似乎np.load正在将二进制数据加载到X，因此ToTensor()试图通过将其强制为ByteTensor来保留数据类型。您可以通过在__getitem__:中进行小的更改来解决此问题
def __getitem__(self, index):
x = self._x[index, :]
y = self._y[index]
return x.astype(np.float32), y