I’m trying to implement ‘BBox only’ part of lesson 8 from 2018 course using fastai2. I’m not sure if it’s relevant but posting it here just because I’m getting the exact same error:
My implementation:
def get_bbox_dls(df,sz=128,bs=128):
getters = [lambda o: path/'train'/o,\
lambda o: img2bbox[o],
lambda o: ['']]
dblock = DataBlock(
blocks=(ImageBlock,BBoxBlock, BBoxLblBlock),
get_items=get_train_imgs,
getters=getters,n_inp=1,
splitter=RandomSplitter(seed=47),
item_tfms=Resize(sz,method='squish'),
batch_tfms=[*aug_transforms(),Normalize.from_stats(*imagenet_stats)])
return dblock.dataloaders(df,bs=bs)
img2bbox has the mapping of img_path to bbox of largest object. Using BBoxLblBox to adapt underlying bb_pad implementation but model isn’t supposed to predict any label yet. I’ve modified L1Loss accordingly to work with ys:
class CustomL1Loss(nn.L1Loss):
def forward(self, input, bbox_targets, lbl_targets):
return F.l1_loss(input, bbox_targets, reduction=self.reduction)
Some debugging done.
The bbox_targets coming out of dataloaders are of shape (bs,1,4)
_,x,_ = dls.one_batch(); x.shape
Working with random tensors cause no issues with CustomL1Loss
cust_l1 = CustomL1Loss()
inp = torch.randn(8,1,4)
op = torch.randn(8,1,4)
op2 = torch.randn(8,1)
cust_l1(inp,op,op2)
EDIT: Here is the whole stack trace of error
RuntimeError Traceback (most recent call last)
<ipython-input-52-bb1de44e7349> in <module>()
----> 1 learn.fit_one_cycle(1,lr_max=1e-4)
7 frames
/usr/local/lib/python3.6/dist-packages/fastai2/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
110 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
111 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 112 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
113
114 # Cell
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
188 try:
189 self.epoch=epoch; self('begin_epoch')
--> 190 self._do_epoch_train()
191 self._do_epoch_validate()
192 except CancelEpochException: self('after_cancel_epoch')
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in _do_epoch_train(self)
161 try:
162 self.dl = self.dls.train; self('begin_train')
--> 163 self.all_batches()
164 except CancelTrainException: self('after_cancel_train')
165 finally: self('after_train')
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in all_batches(self)
139 def all_batches(self):
140 self.n_iter = len(self.dl)
--> 141 for o in enumerate(self.dl): self.one_batch(*o)
142
143 def one_batch(self, i, b):
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in one_batch(self, i, b)
147 self.pred = self.model(*self.xb); self('after_pred')
148 if len(self.yb) == 0: return
--> 149 self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')
150 if not self.training: return
151 self.loss.backward(); self('after_backward')
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in __init__(self, size_average, reduce, reduction)
83
84 def __init__(self, size_average=None, reduce=None, reduction='mean'):
---> 85 super(L1Loss, self).__init__(size_average, reduce, reduction)
86
87 def forward(self, input, target):
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in __init__(self, size_average, reduce, reduction)
10 super(_Loss, self).__init__()
11 if size_average is not None or reduce is not None:
---> 12 self.reduction = _Reduction.legacy_get_string(size_average, reduce)
13 else:
14 self.reduction = reduction
/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py in legacy_get_string(size_average, reduce, emit_warning)
34 reduce = True
35
---> 36 if size_average and reduce:
37 ret = 'mean'
38 elif reduce:
RuntimeError: bool value of Tensor with more than one value is ambiguous