Pytorch 丢失时语义分段运行时错误函数

我正在使用服装模型进行分割（SETRModel）。模型输出形状为（nBatch，256，256），下面的代码证实了这一点（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

---

这是在fastai中使用Pytork时发生的事情（我认为这应该得到修复）

只需创建自定义

loss\u func

。例如：

def loss_func(output, target): return CrossEntropyLossFlat()(out, targ.long())

并在创建数据块时传递它：

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

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