I am new to Fastai and deep Learning. I am trying image segmentation on 2 class Data. But i am having error as soon as i start training model!
About data being used - "The annotation images for segmentation task are binary images in which pixels are either 1 for the foreground or 0 for the background. The annotation images named as “xxx.png”. Where “xxx” presents patient ID (from 001 to 3644). "
path_img = '/content/00000TNSCUI2020_train/TNSCUI2020_train/image'
path_lbl = '/content/00000TNSCUI2020_train/TNSCUI2020_train/mask'
fnames = get_image_files(path_img)
fnames[:3]
lbl_names = get_image_files(path_lbl)
lbl_names[:3]
img_f = fnames[0]
img = open_image(img_f)
img.show(figsize=(5,5))
codes = ['others ,thyroid']
from pathlib import Path
import os
def get_y_fn(filename):
b = Path(path_lbl).joinpath(Path(filename).name)
return b
mask = open_mask(get_y_fn(img_f))
mask.show(figsize=(5,5), alpha=1)
src_size = np.array(mask.shape[1:])
src_size,mask.data
size = src_size//2
bs = 4
src = (SegmentationItemList.from_folder(path_img)
.split_by_fname_file('/content/valid.txt')
.label_from_func(get_y_fn, classes=codes))
data = (src.transform(get_transforms(), size=size, tfm_y=True)
.databunch(bs=bs)
.normalize(imagenet_stats))
metrics=accuracy
wd=1e-2
learn = unet_learner(data, models.resnet34, metrics=metrics, wd=wd)
lr=3e-3
learn.fit_one_cycle(10, slice(lr), pct_start=0.9)
RuntimeError: CUDA error: device-side assert triggered
Entire error was-
RuntimeError Traceback (most recent call last)
<ipython-input-24-c2c63f494cee> in <module>()
44 learn = unet_learner(data, models.resnet34, metrics=metrics, wd=wd)
45 lr=3e-3
---> 46 learn.fit_one_cycle(10, slice(lr), pct_start=0.9)
4 frames
/usr/local/lib/python3.6/dist-packages/fastai/train.py in fit_one_cycle(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)
21 callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,
22 final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))
---> 23 learn.fit(cyc_len, max_lr, wd=wd, callbacks=callbacks)
24
25 def fit_fc(learn:Learner, tot_epochs:int=1, lr:float=defaults.lr, moms:Tuple[float,float]=(0.95,0.85), start_pct:float=0.72,
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
198 else: self.opt.lr,self.opt.wd = lr,wd
199 callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
--> 200 fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
201
202 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, learn, callbacks, metrics)
99 for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
100 xb, yb = cb_handler.on_batch_begin(xb, yb)
--> 101 loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
102 if cb_handler.on_batch_end(loss): break
103
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
31
32 if opt is not None:
---> 33 loss,skip_bwd = cb_handler.on_backward_begin(loss)
34 if not skip_bwd: loss.backward()
35 if not cb_handler.on_backward_end(): opt.step()
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_backward_begin(self, loss)
288 def on_backward_begin(self, loss:Tensor)->Tuple[Any,Any]:
289 "Handle gradient calculation on `loss`."
--> 290 self.smoothener.add_value(loss.float().detach().cpu())
291 self.state_dict['last_loss'], self.state_dict['smooth_loss'] = loss, self.smoothener.smooth
292 self('backward_begin', call_mets=False)
RuntimeError: CUDA error: device-side assert triggered
Please help. Thanks in advance.
