我正在尝试使用 pytorch 构建语义分割模型。但是,我遇到了这个错误,不知道如何解决。
这是模型:
class SegmentationNN(pl.LightningModule):
def __init__(self, num_classes=23, hparams=None):
super().__init__()
self.hparams = hparams
self.model=models.alexnet(pretrained=True).features
self.conv=nn.Conv2d(256, 3, kernel_size=1)
self.upsample = nn.Upsample(size=(240,240))
def forward(self, x):
print('Input:', x.shape)
x = self.model(x)
print('After Alexnet convs:', x.shape)
x = self.conv(x)
print('After 1-conv:', x.shape)
x = self.upsample(x)
print('After upsampling:', x.shape)
return x
def training_step(self, batch, batch_idx):
images, targets = batch
# targets = targets.view(targets.size(0), -1)
out = self.forward(images)
loss_func = nn.CrossEntropyLoss(ignore_index=-1, reduction='mean')
loss = loss_func(out, targets.unsqueeze(0))
tensorboard_logs = {'loss': loss}
return {'loss': loss, 'log':tensorboard_logs}
def validation_step(self, batch, batch_idx):
images, targets = batch
# targets = targets.view(targets.size(0), -1)
out = self.forward(images)
loss_func = nn.CrossEntropyLoss(ignore_index=-1, reduction='mean')
loss = loss_func(out, targets.unsqueeze(0))
tensorboard_logs = {'loss': loss}
return {'loss': loss, 'log':tensorboard_logs}
def configure_optimizers(self):
optim = torch.optim.Adam(self.parameters(), lr=self.hparams['learning_rate'])
return optim
这是训练和健身:
train_dataloader = DataLoader(train_data, batch_size=hparams['batch_size'])
val_dataloader = DataLoader(val_data, batch_size=hparams['batch_size'])
trainer = pl.Trainer(
max_epochs=50,
gpus=1 if torch.cuda.is_available() else None
)
pass
trainer.fit(model, train_dataloader, val_dataloader)
这些是每一层后张量的大小:
Input: torch.Size([59, 3, 240, 240])
After Alexnet convs: torch.Size([59, 256, 6, 6])
After 1-conv: torch.Size([59, 3, 6, 6])
After upsampling: torch.Size([59, 3, 240, 240])
我是 Pytorch 和 Pytorch Lightning 的初学者,所以我会很感激每一个建议!
答案 0 :(得分:0)
你能在这里删除 unsqueeze(0) 部分吗:loss = loss_func(out, targets.unsqueeze(0))