RSNA with fast ai error

Hi I m trying to run fastai with https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data

I m facing problem in while running the kernel.


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-20-d053c1db58c5> in <module>()
----> 1 lrf=learn.lr_find(1e-5,100)

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/learner.py in lr_find(self, start_lr, end_lr, wds, linear)
    255         layer_opt = self.get_layer_opt(start_lr, wds)
    256         self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr, linear=linear)
--> 257         self.fit_gen(self.model, self.data, layer_opt, 1)
    258         self.load('tmp')
    259 

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, **kwargs)
    159         n_epoch = sum_geom(cycle_len if cycle_len else 1, cycle_mult, n_cycle)
    160         return fit(model, data, n_epoch, layer_opt.opt, self.crit,
--> 161             metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)
    162 
    163     def get_layer_groups(self): return self.models.get_layer_groups()

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/model.py in fit(model, data, epochs, opt, crit, metrics, callbacks, stepper, **kwargs)
    104             i += 1
    105 
--> 106         vals = validate(stepper, data.val_dl, metrics)
    107         if epoch == 0: print(layout.format(*names))
    108         print_stats(epoch, [debias_loss] + vals)

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/model.py in validate(stepper, dl, metrics)
    126         preds,l = stepper.evaluate(VV(x), VV(y))
    127         loss.append(to_np(l))
--> 128         res.append([f(preds.data,y) for f in metrics])
    129     return [np.mean(loss)] + list(np.mean(np.stack(res),0))
    130 

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/model.py in <listcomp>(.0)
    126         preds,l = stepper.evaluate(VV(x), VV(y))
    127         loss.append(to_np(l))
--> 128         res.append([f(preds.data,y) for f in metrics])
    129     return [np.mean(loss)] + list(np.mean(np.stack(res),0))
    130 

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/metrics.py in accuracy(preds, targs)
      8 def accuracy(preds, targs):
      9     preds = torch.max(preds, dim=1)[1]
---> 10     return (preds==targs).float().mean()
     11 
     12 def accuracy_thresh(thresh):

/opt/conda/lib/python3.6/site-packages/torch/tensor.py in __eq__(self, other)
    358 
    359     def __eq__(self, other):
--> 360         return self.eq(other)
    361 
    362     def __ne__(self, other):

TypeError: eq received an invalid combination of arguments - got (torch.cuda.FloatTensor), but expected one of:
 * (int value)
      didn't match because some of the arguments have invalid types: (!torch.cuda.FloatTensor!)
 * (torch.cuda.LongTensor other)
      didn't match because some of the arguments have invalid types: (!torch.cuda.FloatTensor!)

I m not sure where I have gone wrong in reading the image with custom open_image

def open_image(loc):
    if isinstance(loc, str):
        loc = loc + '.dcm'
    else: # posix path
        loc = loc.as_posix()
    img_arr = pydicom.read_file(loc).pixel_array
    img_arr = img_arr/img_arr.max()
    img_arr = (255*img_arr).clip(0, 255)#.astype(np.int32)
    img_arr = Image.fromarray(img_arr).convert('RGB') # model expects 3 channel image
    return np.array(img_arr)

It seems to be issue with accuracy method.

pred=
0.0320  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0166  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.7434  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0001  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0000  0.0000  1.0000  1.0000  1.0000  0.0000
 0.0016  0.0000  1.0000  1.0000  1.0000  0.0000
[torch.cuda.FloatTensor of size 20x6 (GPU 0)]
targs=
 0     0     0     1     0     0
    0     1     0     0     0     1
    1     1     1     1     1     1
    0     1     0     0     0     1
    1     1     1     1     1     1
    0     1     0     0     0     1
    1     1     1     1     1     1
    0     0     0     1     0     0
    0     0     0     1     0     0
    0     0     0     1     0     0
    0     1     0     0     0     1
    0     0     0     1     0     0
    0     0     0     1     0     0
    0     1     0     0     0     1
    0     1     0     0     0     1
    0     0     0     1     0     0
    0     0     0     1     0     0
    1     1     1     1     1     1
    0     0     0     1     0     0
    1     1     1     1     1     1
[torch.LongTensor of size 20x6]

the accuracy func is,

/opt/conda/lib/python3.6/site-packages/fastai-0.6-py3.6.egg/fastai/metrics.py in accuracy(preds, targs)
      8 def accuracy(preds, targs):
      9     preds = torch.max(preds, dim=1)[1]
---> 10     return (preds==targs).float().mean()

You can see my kernel at https://www.kaggle.com/nikhilikhar/rsna-with-fastai

it seems the issue was with library version. In kaggle kernel pytorch is available at ver 0.3 & fastai at 0.6.

I ran the notebook on cloud server. It ran smoothly.

I am practicing Lesson 8 on RSNA dataset, i have converted all the files into ‘jpg’ format, i am receving the same error which you have mentioned. Can you please elaborate what the issue is and you mentioned you ran your notebook on cloud, which cloud services are you using? i am using Google CoLab for my practice.

I m using google cloud. If you haven’t used it earlier then you can avail 300$ discount. You can use this guide https://medium.com/@howkhang/ultimate-guide-to-setting-up-a-google-cloud-machine-for-fast-ai-version-2-f374208be43

You will need credit card to start using cloud.
Also it might take more than a day for GPU allocation (a step mentioned in guide.)

-Nikhil