丢失函数时出现语义分割运行时错误

Question

我正在使用服装模型进行分割(SETRModel(。模型输出形状为(nBatch256256(，下面的代码对此进行了确认(请注意，通道被挤出(。目标形状是相同的(它是一个PILMask(。

当我开始训练时，我会得到一个与loss函数相关的运行时错误(见下文(。我做错了什么？

```
size = 480
half= (256, 256) 
splitter = FuncSplitter(lambda o: Path(o).parent.name == 'validation')
dblock = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),
get_items=get_relevant_images,
splitter=splitter,
get_y=get_mask, 
item_tfms=Resize((size,size)),
batch_tfms=[*aug_transforms(size=half), Normalize.from_stats(*imagenet_stats)])
dls = dblock.dataloaders(path/'images', bs=4)
model = SETRModel(patch_size=(32, 32), 
in_channels=3, 
out_channels=1, 
hidden_size=1024, 
num_hidden_layers=8, 
num_attention_heads=16, 
decode_features=[512, 256, 128, 64])

# Create a Learner using a custom model
loss = nn.BCEWithLogitsLoss()
learn = Learner(dls, model, loss_func=loss, lr=1.0e-4, cbs=callbacks, metrics=[Dice()])

# Let's test and make sure the loss function is happy with its inputs
learn.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
t1 = torch.rand(4, 3, 256, 256).to(device)
print("input: " + str(t1.shape))
pred = learn.model(t1).to(device)
print("output: " + str(pred.shape))
# prints this:
# input: torch.Size([4, 3, 256, 256])
# output: torch.Size([4, 256, 256])
target = next(iter(learn.dls.train))[1]
target = target.type(torch.float32).to(device)
target.size(), pred.size()
# prints this:
# (torch.Size([4, 256, 256]), torch.Size([4, 256, 256]))
loss(pred, target)
# prints this:
# TensorMask(0.6844, device='cuda:0', grad_fn=<AliasBackward>)
# so, the loss function is happy with its inputs
learn.fine_tune(50)
# prints this:
# ---------------------------------------------------------------------------
# RuntimeError                              Traceback (most recent call last)
# <ipython-input-114-0e514c73651a> in <module>()
# ----> 1 learn.fine_tune(50)
# 19 frames
# /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in         binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction,     pos_weight)
#    2827 pixel_shuffle = _add_docstr(torch.pixel_shuffle, r"""
#    2828 Rearranges elements in a tensor of shape :math:`(*, C times r^2, H, W)` to a
# -> 2829 tensor of shape :math:`(*, C, H times r, W times r)`.
#    2830 
#    2831 See :class:`~torch.nn.PixelShuffle` for details.
# RuntimeError: result type Float can't be cast to the desired output type Long

Answer 1

当您在fastai中使用PyTorch时会发生这种情况(我认为这应该得到修复(。

只需创建自定义CCD_2。例如：

dblock = DataBlock(... , loss_func=loss_func, ...)

并在创建DataBlock:时传递

PD_4