嗨，我已经使用原始和目标是图像的数据集类创建了一个自定义数据集，例如语义分割和pix2pix，我正在使用Imagefolder和一个自定义整理功能加载数据集，并且尝试使用dataloader加载我的自定义数据集进行训练神经网络，但发生错误，提示输入应为张量而不是列表：
我的整理功能
def my_collate(batch):
data = [item[0] for item in batch]
target = [item[1] for item in batch]
return [data, target]
数据集类：
class bsds_dataset(Dataset):
def __init__(self, ds_main, ds_energy):
self.dataset1 = ds_main
self.dataset2 = ds_energy
def __getitem__(self, index):
x1 = self.dataset1[index]
x2 = self.dataset2[index]
return x1, x2
def __len__(self):
return len(self.dataset1)
加载数据集：
generic_transform = transforms.Compose([
transforms.ToTensor(),
transforms.ToPILImage(),
#transforms.CenterCrop(size=128),
#transforms.Lambda(lambda x: myimresize(x, (128, 128))),
transforms.ToTensor(),
#transforms.Normalize((0., 0., 0.), (6, 6, 6))
])
original_imagefolder = './images/whole'
target_imagefolder = './results/whole'
original_ds = ImageFolder(original_imagefolder,
transform=generic_transform)
energy_ds = ImageFolder(target_imagefolder, transform=generic_transform)
dataset = bsds_dataset(original_ds, energy_ds)
loader = DataLoader(dataset, batch_size=16, collate_fn=my_collate)
epochs = 2
model = UNet(1, depth=5, merge_mode='concat')
model.cuda()
loss = torch.nn.MSELoss()
criterion_pixelwise = torch.nn.L1Loss()
loss.cuda()
criterion_pixelwise.cuda()
optimizer = optim.SGD(model.parameters(), lr=0.001)
Tensor = torch.cuda.FloatTensor
#main loop
for epoch in range(epochs):
for i, batch in enumerate(loader):
original, target = batch
out = model(original)
发生此错误：
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not
list
我正在尝试将批处理的每个实例转发到模型中。迭代中的批处理是列表，但是应该是张量，我不知道如何将其转换为张量或加载每个批处理实例。请帮忙。非常感谢。
完整追溯：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-147-d1dea1bc00f8> in <module>
15 for i, batch in enumerate(loader):
16 original, target = batch
---> 17 out = model(original)
C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
<ipython-input-7-5f743c3455c4> in forward(self, x)
89 # encoder pathway, save outputs for merging
90 for i, module in enumerate(self.down_convs):
---> 91 x, before_pool = module(x)
92 encoder_outs.append(before_pool)
93
C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
<ipython-input-5-26a0f7e21ea6> in forward(self, x)
14
15 def forward(self, x):
---> 16 x = F.relu(self.conv1(x))
17 x = F.relu(self.conv2(x))
18 before_pool = x
C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
C:\Anaconda3\envs\torchgpu\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
336 _pair(0), self.dilation, self.groups)
337 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 338 self.padding, self.dilation, self.groups)
339
340
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not list
答案 0 :(得分：0)
您的整理功能是问题所在。即使单个数据集样本是张量，批次也是一个包含两个列表元素的列表。
至少，data 和 target 都需要是张量。有关详细信息，请参阅 here。
也许这样的事情会奏效：
def my_collate(batch):
data = [item[0] for item in batch]
target = [item[1] for item in batch]
return torch.Tensor(data), torch.Tensor(target)