Did you check the order of your transforms? Pretty sure Resize has an order of 10, and in the code you show HeatMapScaler had an order of 1.
1 Like
Ahhhh that would do it! Thank you. That makes more sense. 
will check later but that is definitely one thing I missed!
Hi @sguggerābut is there a way to specify it?
Well even if I follow the MNIST example, and just re-run it on my own instance, this is what I get on the last line learn.fit_one_cycle(epochs, lr)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-7c21d2a641cb> in <module>
----> 1 learn.fit_one_cycle(epochs, lr)
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
88 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
89 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
---> 90 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
91
92 # Cell
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
287 try:
288 self.epoch=epoch; self('begin_epoch')
--> 289 self._do_epoch_train()
290 self._do_epoch_validate()
291 except CancelEpochException: self('after_cancel_epoch')
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in _do_epoch_train(self)
262 try:
263 self.dl = self.dbunch.train_dl; self('begin_train')
--> 264 self.all_batches()
265 except CancelTrainException: self('after_cancel_train')
266 finally: self('after_train')
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in all_batches(self)
240 def all_batches(self):
241 self.n_iter = len(self.dl)
--> 242 for o in enumerate(self.dl): self.one_batch(*o)
243
244 def one_batch(self, i, b):
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/data/load.py in __iter__(self)
95 self.randomize()
96 self.before_iter()
---> 97 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
98 if self.device is not None: b = to_device(b, self.device)
99 yield self.after_batch(b)
~/daisy-gan/venv/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
671
672 def __init__(self, loader):
--> 673 super(_MultiProcessingDataLoaderIter, self).__init__(loader)
674
675 assert self._num_workers > 0
~/daisy-gan/venv/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
321 self._dataset = loader.dataset
322 self._dataset_kind = loader._dataset_kind
--> 323 self._IterableDataset_len_called = loader._IterableDataset_len_called
324 self._auto_collation = loader._auto_collation
325 self._drop_last = loader.drop_last
~/daisy-gan/venv/lib/python3.6/site-packages/fastcore/foundation.py in __getattr__(self, k)
221 attr = getattr(self,self._default,None)
222 if attr is not None: return getattr(attr, k)
--> 223 raise AttributeError(k)
224 def __dir__(self): return custom_dir(self, self._dir() if self._xtra is None else self._dir())
225 # def __getstate__(self): return self.__dict__
AttributeError: _IterableDataset_len_called
1 Like
This error means you have PyTorch v1.4 or torchvision 0.5.0. As specified in the deps, you need to downgrade to <=1.3 and <=0.4.2 (they just broke fastai v2 with their new release and we havenāt had time to work on a fix yet).
Oh yes, that worked (I got a different error on my actual problem after the pytorch upgrade so I thought I was making progress).
So I rescoped, but then I got a different error on MNIST, when I e.g. tried to use the example like so:
learn = cnn_learner(data, resnet50, loss_func=F.nll_loss)
learn.fit_one_cycle(epochs, lr)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-15-7c21d2a641cb> in <module>
----> 1 learn.fit_one_cycle(epochs, lr)
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
88 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
89 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
---> 90 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
91
92 # Cell
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
287 try:
288 self.epoch=epoch; self('begin_epoch')
--> 289 self._do_epoch_train()
290 self._do_epoch_validate()
291 except CancelEpochException: self('after_cancel_epoch')
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in _do_epoch_train(self)
262 try:
263 self.dl = self.dbunch.train_dl; self('begin_train')
--> 264 self.all_batches()
265 except CancelTrainException: self('after_cancel_train')
266 finally: self('after_train')
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in all_batches(self)
240 def all_batches(self):
241 self.n_iter = len(self.dl)
--> 242 for o in enumerate(self.dl): self.one_batch(*o)
243
244 def one_batch(self, i, b):
~/daisy-gan/venv/lib/python3.6/site-packages/fastai2/learner.py in one_batch(self, i, b)
246 try:
247 self._split(b); self('begin_batch')
--> 248 self.pred = self.model(*self.xb); self('after_pred')
249 if len(self.yb) == 0: return
250 self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
343
344 def forward(self, input):
--> 345 return self.conv2d_forward(input, self.weight)
346
347 class Conv3d(_ConvNd):
~/daisy-gan/venv/lib/python3.6/site-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight)
340 _pair(0), self.dilation, self.groups)
341 return F.conv2d(input, weight, self.bias, self.stride,
--> 342 self.padding, self.dilation, self.groups)
343
344 def forward(self, input):
RuntimeError: Given groups=1, weight of size 64 3 7 7, expected input[256, 1, 28, 28] to have 3 channels, but got 1 channels instead
So maybe these two worlds do not yet mesh well?
You need to have three channels to use a pretrained model that expects three-channels input, this is nothing to do with fastai. An error you will have afterward is that the cnn_learner function expects a data object built with fastai to infer the number of classes (you can set that by doing data.c = 10 if you really want to use cnn_learner, this will be documented when we are at the release stage of fastai v2).
When we say you can use the training loop with PyTorch-built databunches, we only guarantee the training loop, not the rest. So you should use your own model and use the init of Learner like in this example.
@sgugger there is an issue when trying to do a show_batch on the PETs notebook. It seems when calling, it does not call the item transforms first as it should.
batch_tfms = [*aug_transforms(size=224, max_warp=0), Normalize.from_stats(*imagenet_stats)]
item_tfms = RandomResizedCrop(460, min_scale=0.75, ratio=(1.,1.))
bs=64
data = ImageDataLoaders.from_name_re(path, fnames, pat, batch_tfms=batch_tfms,
item_tfms=item_tfms, bs=bs)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-8-08d0dd5c9f7c> in <module>()
----> 1 data.show_batch()
12 frames
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in show_batch(self, b, max_n, ctxs, show, **kwargs)
85
86 def show_batch(self, b=None, max_n=9, ctxs=None, show=True, **kwargs):
---> 87 if b is None: b = self.one_batch()
88 if not show: return self._pre_show_batch(b, max_n=max_n)
89 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in one_batch(self)
128 def one_batch(self):
129 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 130 with self.fake_l.no_multiproc(): res = first(self)
131 if hasattr(self, 'it'): delattr(self, 'it')
132 return res
/usr/local/lib/python3.6/dist-packages/fastcore/utils.py in first(x)
172 def first(x):
173 "First element of `x`, or None if missing"
--> 174 try: return next(iter(x))
175 except StopIteration: return None
176
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in __iter__(self)
95 self.randomize()
96 self.before_iter()
---> 97 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
98 if self.device is not None: b = to_device(b, self.device)
99 yield self.after_batch(b)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
344 def __next__(self):
345 index = self._next_index() # may raise StopIteration
--> 346 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
347 if self._pin_memory:
348 data = _utils.pin_memory.pin_memory(data)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
32 raise StopIteration
33 else:
---> 34 data = next(self.dataset_iter)
35 return self.collate_fn(data)
36
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in create_batches(self, samps)
104 self.it = iter(self.dataset) if self.dataset is not None else None
105 res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 106 yield from map(self.do_batch, self.chunkify(res))
107
108 def new(self, dataset=None, cls=None, **kwargs):
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in do_batch(self, b)
125 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
126 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
--> 127 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
128 def one_batch(self):
129 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in create_batch(self, b)
124 def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)
125 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
--> 126 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
127 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
128 def one_batch(self):
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in fa_collate(t)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in <listcomp>(.0)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in fa_collate(t)
43 def fa_collate(t):
44 b = t[0]
---> 45 return (default_collate(t) if isinstance(b, _collate_types)
46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
53 storage = elem.storage()._new_shared(numel)
54 out = elem.new(storage)
---> 55 return torch.stack(batch, 0, out=out)
56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
57 and elem_type.__name__ != 'string_':
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 333 and 225 in dimension 2 at /pytorch/aten/src/TH/generic/THTensor.cpp:689
This is running dev as the most recent pip doesnāt include ImageDataLoaders
Edit: Also does not work when calling one_batch
You mean someone forgot to adapt the factory methods to the last version of DataBlock? Nah, thatās impossible. 
Should be fixed now.
1 Like
Awesome! Did the trick Also saw inklings of something interestingā¦ a DataBlock.summary?..
DataBlock.summary. Not ready yet but hopefully will be great for debugging/seeing what happens behind the scenes.
2 Likes
Canāt wait!
Hi I was just trying the new release version 0.0.6 of fastai last couple of days and I noticed this behavior. Not actually a problem for me, (I am stuck on other issues), but I thought itād point it out in case it is helpful:
dir(DataLoaders) has device listed twice and dir(DataLoader) has device and dataset listed twice.
I am investigating this, because my cnn_learner errors out with AttributeError: 'DataLoader' object has no attribute 'after_item', but that happens here: if getattr(dls.train_dl.after_item, 'c', False): return dls.train_dl.after_item.c so they only may be related.
Will add more safeguard here, thanks for reporting. Itās going to error anyway because it canāt find the number of output channels in your data, but with a useful error message (pass it with n_out=...).
Iām using U-net with different number of input channels (current model only accepts 3).
Here is my change:
- added arg
new_arch_in_channelstounet_learner(could find a better name) - added function
change_body_in_channelsand call it inunet_learner - rest is the same
Let me know if you are interested in a PR (or even require any change before)?
Here is the change in vision/learner.py
def change_body_in_channels(body, in_channels, pretrained):
"Change first layer to match `in_channels`"
assert not(pretrained), 'Change of input channels does not support pretrained models'
assert body[0].__class__.__name__ == 'Conv2d', f'Change of input channels only supported with Conv2d, found {body[0].__class__.__name__}'
prev_layer = body[0]
# get init parameters
params = {attr:getattr(prev_layer, attr) for attr in 'out_channels,kernel_size,stride,padding,dilation,groups,padding_mode'.split(',')}
params['bias'] = getattr(prev_layer, 'bias') is not None
# set number of input channels
params['in_channels'] = in_channels
body[0] = nn.Conv2d(**params)
return(body)
@delegates(Learner.__init__)
def unet_learner(dls, arch, new_arch_in_channels=None, loss_func=None, pretrained=True, cut=None, splitter=None, config=None, **kwargs):
"Build a unet learner from `dls` and `arch`"
if config is None: config = unet_config()
meta = model_meta.get(arch, _default_meta)
body = create_body(arch, pretrained, ifnone(cut, meta['cut']))
if new_arch_in_channels:
change_body_in_channels(body, new_arch_in_channels, pretrained)
size = dls.one_batch()[0].shape[-2:]
model = models.unet.DynamicUnet(body, get_c(dls), size, **config)
learn = Learner(dls, model, loss_func=loss_func, splitter=ifnone(splitter, meta['split']), **kwargs)
if pretrained: learn.freeze()
return learn
2 Likes
This could be useful for cnn_learner as well. Iād make the first function private (it can also have a shorter name ) and use ch_in for the argument instead of new_arch_in_channels.
The only problem is that this new conv will be frozen if you use pretrained=True so we should not freeze the Learner in that case.
Sounds good. Yes I have some assert statements at the start of the function.
Iāll implement the changes and will do some tests, probably taking only one dimension from MNIST.
@boris I suggest you use the param n_in for the number of input channels. I used to have some code to do this for pretrained models, but now embarassingly I canāt find it. Perhaps you can add this logic to your code - Iāve found it works well in practice:
- For n_in==1: take the sum of the pretrained weights to create the unit axis (e.g. going from color->b&w this works great)
- For n_in>3: add all-zero slices for the additional channels, and leave the existing weights as-is (e.g. adding an alpha channel to an RGB pretrained model; fine-tuning will only change the weights much from zero if itās actually useful)
- For n_in==2: Delete the 3rd channel and increase the weights of the other channels by 50%
5 Likes
Thanks, at the moment I was not loading any pretrained weights but your suggestion will make it more useful. Iāll do some tests and propose a change.
1 Like
@sgugger just to make you aware of a bug someone showed me, the resize bug seems to also be present on the DataBlock level too (same transforms as before):
pets = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(),
get_y=RegexLabeller(pat = r'/([^/]+)_\d+.*'))
dbunch = pets.dataloaders(path_im, item_tfms=item_tfms, batch_tfms=batch_tfms, bs=bs)
dbunch.show_batch(max_n=9, figsize=(6,7))
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 456 and 375 in dimension 2 at /pytorch/aten/src/TH/generic/THTensor.cpp:689
(Full trace if you need it):
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-12-5a9f6693bc49> in <module>()
----> 1 dbunch.show_batch(max_n=9, figsize=(6,7))
12 frames
/usr/local/lib/python3.6/dist-packages/fastai2/data/core.py in show_batch(self, b, max_n, ctxs, show, **kwargs)
87
88 def show_batch(self, b=None, max_n=9, ctxs=None, show=True, **kwargs):
---> 89 if b is None: b = self.one_batch()
90 if not show: return self._pre_show_batch(b, max_n=max_n)
91 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in one_batch(self)
128 def one_batch(self):
129 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 130 with self.fake_l.no_multiproc(): res = first(self)
131 if hasattr(self, 'it'): delattr(self, 'it')
132 return res
/usr/local/lib/python3.6/dist-packages/fastcore/utils.py in first(x)
172 def first(x):
173 "First element of `x`, or None if missing"
--> 174 try: return next(iter(x))
175 except StopIteration: return None
176
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in __iter__(self)
95 self.randomize()
96 self.before_iter()
---> 97 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
98 if self.device is not None: b = to_device(b, self.device)
99 yield self.after_batch(b)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
344 def __next__(self):
345 index = self._next_index() # may raise StopIteration
--> 346 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
347 if self._pin_memory:
348 data = _utils.pin_memory.pin_memory(data)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
32 raise StopIteration
33 else:
---> 34 data = next(self.dataset_iter)
35 return self.collate_fn(data)
36
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in create_batches(self, samps)
104 self.it = iter(self.dataset) if self.dataset is not None else None
105 res = filter(lambda o:o is not None, map(self.do_item, samps))
--> 106 yield from map(self.do_batch, self.chunkify(res))
107
108 def new(self, dataset=None, cls=None, **kwargs):
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in do_batch(self, b)
125 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
126 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
--> 127 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
128 def one_batch(self):
129 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in create_batch(self, b)
124 def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)
125 def create_item(self, s): return next(self.it) if s is None else self.dataset[s]
--> 126 def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)
127 def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
128 def one_batch(self):
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in fa_collate(t)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in <listcomp>(.0)
44 b = t[0]
45 return (default_collate(t) if isinstance(b, _collate_types)
---> 46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
48
/usr/local/lib/python3.6/dist-packages/fastai2/data/load.py in fa_collate(t)
43 def fa_collate(t):
44 b = t[0]
---> 45 return (default_collate(t) if isinstance(b, _collate_types)
46 else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)
47 else default_collate(t))
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
53 storage = elem.storage()._new_shared(numel)
54 out = elem.new(storage)
---> 55 return torch.stack(batch, 0, out=out)
56 elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
57 and elem_type.__name__ != 'string_':
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 456 and 375 in dimension 2 at /pytorch/aten/src/TH/generic/THTensor.cpp:689
It is also present on the most recent pip
Yes you pass the transforms to the DataBlock now, not your call to dataloaders.
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