I am trying to create an image classifier based on the lessons of part 1. I was successful using a cnn but I would like to see the results using an unet since the images are medical images.
My code:
from fastai import *
from fastai.vision import *
import osimage_path = Path(os.path.join(os.getcwd(),‘data’,‘images’))
data = (ImageList.from_folder(image_path)
.split_by_folder()
.label_from_folder()
.transform(size=256)
.databunch(bs=4))learn = unet_learner(data, models.resnet34, metrics=accuracy, wd=wd, self_attention=True, pretrained=False, bottle=True)
learn.lr_find()
Here the Error:
ValueError Traceback (most recent call last)
in ()
----> 1 learn.lr_find()
2 learn.recorder.plot()~\Anaconda3\envs\fastai\lib\site-packages\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 epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
—> 32 learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)
33
34 def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=False, clip:float=None,~\Anaconda3\envs\fastai\lib\site-packages\fastai\basic_train.py in fit(self, epochs, lr, wd, callbacks)
188 if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
189 fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,
–> 190 callbacks=self.callbacks+callbacks)
191
192 def create_opt(self, lr:Floats, wd:Floats=0.)->None:~\Anaconda3\envs\fastai\lib\site-packages\fastai\basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
91 for xb,yb in progress_bar(data.train_dl, parent=pbar):
92 xb, yb = cb_handler.on_batch_begin(xb, yb)
—> 93 loss = loss_batch(model, xb, yb, loss_func, opt, cb_handler)
94 if cb_handler.on_batch_end(loss): break
95~\Anaconda3\envs\fastai\lib\site-packages\fastai\basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
26
27 if not loss_func: return to_detach(out), yb[0].detach()
—> 28 loss = loss_func(out, *yb)
29
30 if opt is not None:~\Anaconda3\envs\fastai\lib\site-packages\fastai\layers.py in call(self, input, target, **kwargs)
242 if self.floatify: target = target.float()
243 input = input.view(-1,input.shape[-1]) if self.is_2d else input.view(-1)
–> 244 return self.func.call(input, target.view(-1), **kwargs)
245
246 def CrossEntropyFlat(*args, axis:int=-1, **kwargs):~\Anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py in call(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
–> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)~\Anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
902 def forward(self, input, target):
903 return F.cross_entropy(input, target, weight=self.weight,
–> 904 ignore_index=self.ignore_index, reduction=self.reduction)
905
906~\Anaconda3\envs\fastai\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
1968 if size_average is not None or reduce is not None:
1969 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 1970 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
1971
1972~\Anaconda3\envs\fastai\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1786 if input.size(0) != target.size(0):
1787 raise ValueError(‘Expected input batch_size ({}) to match target batch_size ({}).’
-> 1788 .format(input.size(0), target.size(0)))
1789 if dim == 2:
1790 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)ValueError: Expected input batch_size (3072) to match target batch_size (4).
If someone could shed some light on how to solve the error it would be of great help.
Thanks!