I’m wondering if anybody knows of any sample code/examples of building a Conv VAE using the fastai library?
1 Like
Bump…
Okay, I’ve adapted a PyTorch VAE and the Learner seems happy enough loading it. However, I’m trying to run lr_find() on it and it’s complaining about the data. I just have two folders of images, in train and test, but I’m really not sure how I’m supposed to provide the same image as input and output (i.e., for the autoencoder)??
I’m getting the following error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-42-f01cf5c6afa7> in <module>
----> 1 learner.lr_find()
~/src/fastai/fastai/train.py in lr_find(learn, start_lr, end_lr, num_it, stop_div, wd)
30 cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
31 a = int(np.ceil(num_it/len(learn.data.train_dl)))
---> 32 learn.fit(a, start_lr, callbacks=[cb], wd=wd)
33
34 def to_fp16(learn:Learner, loss_scale:float=512., flat_master:bool=False)->Learner:
~/src/fastai/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
176 callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
177 fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,
--> 178 callbacks=self.callbacks+callbacks)
179
180 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
~/src/fastai/fastai/utils/mem.py in wrapper(*args, **kwargs)
101
102 try:
--> 103 return func(*args, **kwargs)
104 except Exception as e:
105 if ("CUDA out of memory" in str(e) or
~/src/fastai/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
88 for xb,yb in progress_bar(data.train_dl, parent=pbar):
89 xb, yb = cb_handler.on_batch_begin(xb, yb)
---> 90 loss = loss_batch(model, xb, yb, loss_func, opt, cb_handler)
91 if cb_handler.on_batch_end(loss): break
92
~/src/fastai/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
22
23 if not loss_func: return to_detach(out), yb[0].detach()
---> 24 loss = loss_func(out, *yb)
25
26 if opt is not None:
~/src/fastai/fastai/layers.py in __call__(self, input, target, **kwargs)
237
238 def __call__(self, input:Tensor, target:Tensor, **kwargs)->Rank0Tensor:
--> 239 input = input.transpose(self.axis,-1).contiguous()
240 target = target.transpose(self.axis,-1).contiguous()
241 if self.floatify: target = target.float()
AttributeError: 'tuple' object has no attribute 'transpose'
Presumably this is because it’s not getting what its expecting from the dataBunch… but I’ve no idea where to start with fixing it.
I’ve done nothing special with the data, just:
PATH = "/home/jbmaxwell/src/data/image_data/"
data = ImageDataBunch.from_folder(PATH, ds_tfms=None, size=64)
J.
On the whimsical notion that perhaps train and valid need to be the same thing (i.e., since it’s auto-encoding), I tried:
data = ImageDataBunch.from_folder(PATH, train='train', valid='train', ds_tfms=None, size=64, bs=32)
But I get the same error.
I just need to know how to provide the same image as both the input and the target for the optimizer… ugh…
(It would be really, really, extremely helpful if there were some examples that were less black-box-ish and magical.)
Hi James,
Did you figure it out? I am trying to implement a VAE myself using FastAI with a bit of PyTorch.