let me try this
thanks @muellerzr
downloading and uploading will take forever i guess haha
Hi!
Trying to follow apply 03_Cross_validation in SIIM pneumotorax kaggle dataset for classification (not segmentation). Following the tutorial for medical imaging from fastai2 nbs I generate a dataframe like this:
However I am stucked at the generation of Datasets, I do not know how to add a tranform to get the labels using df. My code so far:
items = get_dicom_files(pneumothorax_source/f"dicom-images-train/")
start_val = len(items) - int(len(items)*.2)
idxs = list(range(start_val, len(items)))
splits = IndexSplitter(idxs)
split = splits(items)
split_list = [split[0], split[1]]
dsrc = Datasets(items, tfms=[[PILDicom.create][???, Categorize]],
splits = split_list)
Any ideas?
Hi Joan!
Why are you using Datasets instead of DataBlock?
I think if you use DataBlock with Getters get_x and get_y you can read the labels from a df.
2 Likes
Also, Cross Validation can be done in the DataBlock like so:
dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
get_y=parent_label,
splitter=IndexSplitter(val_idx),
(if looking at the CV code)
2 Likes
Hi all,
I was following the excellent notebook from Zachary:
And wanted to give it a try with my own data. Unfortunately, despite matching the format of the bboxes, I think exactly, I run into misalignment issues: I try to replicate the format displayed below:
So the coordinate system seems to be [Upper Left (x,y), Lower Right (x,y) ], but when I put any other data into this format, the
show_batch method puts the bounding boxes all over the place. I have tried number of different ways and even wrote my own plotting function, but was unable to get the same results as in the notebook. Any ideas on how this might be fixed?
I am using fastai 0.0.12.
Also when I run learn.show_results() on Zacharyâs original notebook, I get the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
216 # in this case we return a big list
--> 217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
TypeError: expected Tensor as element 0 in argument 0, but got int
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-38-c3b657dcc9ae> in <module>()
----> 1 learn.show_results()
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in show_results(self, ds_idx, dl, max_n, shuffle, **kwargs)
224 if dl is None: dl = self.dls[ds_idx].new(shuffle=shuffle)
225 b = dl.one_batch()
--> 226 _,_,preds = self.get_preds(dl=[b], with_decoded=True)
227 self.dls.show_results(b, preds, max_n=max_n, **kwargs)
228
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, **kwargs)
202 self(event.begin_epoch if inner else _before_epoch)
203 self._do_epoch_validate(dl=dl)
--> 204 self(event.after_epoch if inner else _after_epoch)
205 if act is None: act = getattr(self.loss_func, 'activation', noop)
206 res = cb.all_tensors()
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in __call__(self, event_name)
106 def ordered_cbs(self, cb_func): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, cb_func)]
107
--> 108 def __call__(self, event_name): L(event_name).map(self._call_one)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
360 else f.format if isinstance(f,str)
361 else f.__getitem__)
--> 362 return self._new(map(g, self))
363
364 def filter(self, f, negate=False, **kwargs):
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
313 @property
314 def _xtra(self): return None
--> 315 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
316 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
317 def copy(self): return self._new(self.items.copy())
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
39 return x
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
43 return res
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
304 if items is None: items = []
305 if (use_list is not None) or not _is_array(items):
--> 306 items = list(items) if use_list else _listify(items)
307 if match is not None:
308 if is_coll(match): match = len(match)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in _listify(o)
240 if isinstance(o, list): return o
241 if isinstance(o, str) or _is_array(o): return [o]
--> 242 if is_iter(o): return list(o)
243 return [o]
244
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
206 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
207 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 208 return self.fn(*fargs, **kwargs)
209
210 # Cell
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in _call_one(self, event_name)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
113 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in <listcomp>(.0)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
113 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/callback/core.py in __call__(self, event_name)
21 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
22 (self.run_valid and not getattr(self, 'training', False)))
---> 23 if self.run and _run: getattr(self, event_name, noop)()
24 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
25
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/callback/core.py in after_fit(self)
93 "Concatenate all recorded tensors"
94 if self.with_input: self.inputs = detuplify(to_concat(self.inputs, dim=self.concat_dim))
---> 95 if not self.save_preds: self.preds = detuplify(to_concat(self.preds, dim=self.concat_dim))
96 if not self.save_targs: self.targets = detuplify(to_concat(self.targets, dim=self.concat_dim))
97 if self.with_loss: self.losses = to_concat(self.losses)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in <listcomp>(.0)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in <listcomp>(.0)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in <listcomp>(.0)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs.keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
--> 219 for i in range_of(o_)) for o_ in xs], L())
220
221 # Cell
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in <listcomp>(.0)
217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
--> 219 for i in range_of(o_)) for o_ in xs], L())
220
221 # Cell
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/utils.py in range_of(x)
162 def range_of(x):
163 "All indices of collection `x` (i.e. `list(range(len(x)))`)"
--> 164 return list(range(len(x)))
165
166 # Cell
TypeError: object of type 'int' has no len()
If you notice I never called show_results. Object detection and all its bugs is still in the works. Earlier in the thread I posted a link to where more people are working on it but show results, predict, etc wonât work OOTB
1 Like
Siamese twins notebook
Hi Zachary according to the siamese twins notebook I would like to ask, how I could use the dataloader?
Would I create a model that takes a tuple of two images as input and returns 0,1 whether they are the same or not?
Sorry for the basic question but I could follow your steps on creating all the stuff but now I feel kind of stuck.
Thereâs a fuller example here, it happened to come out a few weeks after the lesson:
1 Like
Fair enough.
But I even get an error when trying to predict one of the images:
test_img = plt.imread("/home/ubuntu/.fastai/data/pascal_2007/train/000012.jpg") and learn.predict(test_img) gives:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
174 self.dl = dl; self('begin_validate')
--> 175 with torch.no_grad(): self.all_batches()
176 except CancelValidException: self('after_cancel_validate')
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in all_batches(self)
142 self.n_iter = len(self.dl)
--> 143 for o in enumerate(self.dl): self.one_batch(*o)
144
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/data/load.py in __iter__(self)
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)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
344 def __next__(self):
--> 345 data = self._next_data()
346 self._num_yielded += 1
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)
855 del self._task_info[idx]
--> 856 return self._process_data(data)
857
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
880 if isinstance(data, ExceptionWrapper):
--> 881 data.reraise()
882 return data
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/_utils.py in reraise(self)
393 msg = KeyErrorMessage(msg)
--> 394 raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 34, in fetch
data = next(self.dataset_iter)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/data/load.py", line 106, in create_batches
yield from map(self.do_batch, self.chunkify(res))
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/data/load.py", line 127, in do_batch
def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/transform.py", line 185, in __call__
def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/transform.py", line 138, in compose_tfms
x = f(x, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/transform.py", line 72, in __call__
def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/transform.py", line 82, in _call
return self._do_call(getattr(self, fn), x, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/transform.py", line 86, in _do_call
return x if f is None else retain_type(f(x, **kwargs), x, f.returns_none(x))
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/dispatch.py", line 98, in __call__
return f(*args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/vision/data.py", line 33, in bb_pad
samples = [(s[0], *clip_remove_empty(*s[1:])) for s in samples]
File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/vision/data.py", line 33, in <listcomp>
samples = [(s[0], *clip_remove_empty(*s[1:])) for s in samples]
TypeError: clip_remove_empty() missing 2 required positional arguments: 'bbox' and 'label'
During handling of the above exception, another exception occurred:
IndexError Traceback (most recent call last)
<ipython-input-43-2a654fbc7b08> in <module>()
----> 1 learn.predict(test_img)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in predict(self, item, rm_type_tfms, with_input)
229 def predict(self, item, rm_type_tfms=None, with_input=False):
230 dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms)
--> 231 inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
232 dec = self.dls.decode_batch((*tuplify(inp),*tuplify(dec_preds)))[0]
233 i = getattr(self.dls, 'n_inp', -1)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, **kwargs)
217 for mgr in ctx_mgrs: stack.enter_context(mgr)
218 self(event.begin_epoch if inner else _before_epoch)
--> 219 self._do_epoch_validate(dl=dl)
220 self(event.after_epoch if inner else _after_epoch)
221 if act is None: act = getattr(self.loss_func, 'activation', noop)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
176 except CancelValidException: self('after_cancel_validate')
177 finally:
--> 178 dl,*_ = change_attrs(dl, names, old, has); self('after_validate')
179
180 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in __call__(self, event_name)
122 def ordered_cbs(self, cb_func): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, cb_func)]
123
--> 124 def __call__(self, event_name): L(event_name).map(self._call_one)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
370 else f.format if isinstance(f,str)
371 else f.__getitem__)
--> 372 return self._new(map(g, self))
373
374 def filter(self, f, negate=False, **kwargs):
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
321 @property
322 def _xtra(self): return None
--> 323 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
324 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
325 def copy(self): return self._new(self.items.copy())
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
39 return x
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
43 return res
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
312 if items is None: items = []
313 if (use_list is not None) or not _is_array(items):
--> 314 items = list(items) if use_list else _listify(items)
315 if match is not None:
316 if is_coll(match): match = len(match)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in _listify(o)
248 if isinstance(o, list): return o
249 if isinstance(o, str) or _is_array(o): return [o]
--> 250 if is_iter(o): return list(o)
251 return [o]
252
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
214 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
215 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 216 return self.fn(*fargs, **kwargs)
217
218 # Cell
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in _call_one(self, event_name)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/learner.py in <listcomp>(.0)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/callback/core.py in __call__(self, event_name)
21 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
22 (self.run_valid and not getattr(self, 'training', False)))
---> 23 if self.run and _run: getattr(self, event_name, noop)()
24 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
25
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/callback/core.py in after_validate(self)
93 "Concatenate all recorded tensors"
94 if self.with_input: self.inputs = detuplify(to_concat(self.inputs, dim=self.concat_dim))
---> 95 if not self.save_preds: self.preds = detuplify(to_concat(self.preds, dim=self.concat_dim))
96 if not self.save_targs: self.targets = detuplify(to_concat(self.targets, dim=self.concat_dim))
97 if self.with_loss: self.losses = to_concat(self.losses)
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/fastai2/torch_core.py in to_concat(xs, dim)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs[0].keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
IndexError: list index out of range
What is the correct procedure here? should I raise an issue somewhere (your repo? fastai2?)? Something else? I am sure I am not the only one struggling with this issue. Iâve been stuck on this for a couple of days, so I doubt I will get much further by myself.
As I said earlier, predict will not work. You can read a few workarounds here: Object detection using fastai v2
2 Likes
Hi @muellerzr and @Saioa,
Thanks for the feedback
Still trying this but I cannot make it work. Using Datablock approach I end up with a dimension issue:
pneumothorax_source = Path('/home/jg/DeepLearning/Datasets/siim/')
items = get_dicom_files(pneumothorax_source/f"dicom-images-train_real/")
df = pd.read_csv(pneumothorax_source/f"train_rle_real.csv")
df['ImageId'] = 'dicom-images-train_real/' + df['ImageId'].astype(str) + ".dcm"
def replace_lambda_getx(x): return pneumothorax_source/x[0]
def replace_lambda_gety(x): return x[1]
pneumothorax = DataBlock(blocks=(ImageBlock(cls=PILDicom), CategoryBlock),
get_x=replace_lambda_getx,
get_y=replace_lambda_gety, splitter=RandomSplitter(),
batch_tfms=aug_transforms(size=224))
dls = pneumothorax.dataloaders(df.values, bs=32)
train_labels = L()
for i in range(len(dls.train_ds)):
train_labels.append(dls.train_ds[i][1])
for i in range(len(dls.valid_ds)):
train_labels.append(dls.valid_ds[i][1])
val_pct = []
tst_preds = []
skf = StratifiedKFold(n_splits=10, shuffle=True)
for _, val_idx in skf.split(np.array(items), train_labels):
splits = IndexSplitter(val_idx)
split = splits(items)
split_list = [split[0], split[1]]
def replace_lambda_getx(x): return pneumothorax_source/x[0]
def replace_lambda_gety(x): return x[1]
pneumothorax = DataBlock(blocks=(ImageBlock(cls=PILDicom), CategoryBlock),
get_x=replace_lambda_getx,
get_y=replace_lambda_gety, splitter=IndexSplitter(val_idx),
batch_tfms=aug_transforms(size=224))
dls = pneumothorax.dataloaders(df.values, bs=32)
learn = cnn_learner(dls, resnet50, pretrained=False, metrics=accuracy)
learn.fit_one_cycle(1)
val_pct.append(learn.validate()[1])
a,b = learn.get_preds(ds_idx=1)
tst_preds.append(a)
epoch train_loss valid_loss accuracy time
0 0.000000 00:01
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-49-3f8dcca8de26> in <module>
14 dls = pneumothorax.dataloaders(df.values, bs=32)
15 learn = cnn_learner(dls, resnet50, pretrained=False, metrics=accuracy)
---> 16 learn.fit_one_cycle(1)
17 val_pct.append(learn.validate()[1])
18 a,b = learn.get_preds(ds_idx=2)
~/anaconda3/envs/fastai2/lib/python3.7/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)
110 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
111 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 112 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
113
114 # Cell
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
190 try:
191 self.epoch=epoch; self('begin_epoch')
--> 192 self._do_epoch_train()
193 self._do_epoch_validate()
194 except CancelEpochException: self('after_cancel_epoch')
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/fastai2/learner.py in _do_epoch_train(self)
163 try:
164 self.dl = self.dls.train; self('begin_train')
--> 165 self.all_batches()
166 except CancelTrainException: self('after_cancel_train')
167 finally: self('after_train')
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/fastai2/learner.py in all_batches(self)
141 def all_batches(self):
142 self.n_iter = len(self.dl)
--> 143 for o in enumerate(self.dl): self.one_batch(*o)
144
145 def one_batch(self, i, b):
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/fastai2/learner.py in one_batch(self, i, b)
147 try:
148 self._split(b); self('begin_batch')
--> 149 self.pred = self.model(*self.xb); self('after_pred')
150 if len(self.yb) == 0: return
151 self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
98 def forward(self, input):
99 for module in self:
--> 100 input = module(input)
101 return input
102
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
98 def forward(self, input):
99 for module in self:
--> 100 input = module(input)
101 return input
102
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai2/lib/python3.7/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):
~/anaconda3/envs/fastai2/lib/python3.7/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[32, 1, 224, 224] to have 3 channels, but got 1 channels instead
However, if I recreate the dataset to use Datasets it works:
pneumothorax_source = Path('/home/jg/DeepLearning/Datasets/SIIM_reshaped/')
train_imgs = get_dicom_files(pneumothorax_source)
random.shuffle(train_imgs)
start_val = len(train_imgs) - int(len(train_imgs)*.2)
idxs = list(range(start_val, len(train_imgs)))
splits = IndexSplitter(idxs)
split = splits(train_imgs)
split_list = [split[0], split[1]]
dsrc = Datasets(train_imgs, tfms=[[PILDicom.create], [parent_label, Categorize]],
splits = split_list)
batch_tfms = [IntToFloatTensor(), *aug_transforms(size=224), Normalize.from_stats(*imagenet_stats)]
item_tfms = [ToTensor(), RandomResizedCrop(460, min_scale=0.75, ratio=(1.,1.))]
bs=64
dls = dsrc.dataloaders(bs=bs, after_item=item_tfms, after_batch=batch_tfms)
train_labels = L()
for i in range(len(dsrc.train)):
train_labels.append(dsrc.train[i][1])
for i in range(len(dsrc.valid)):
train_labels.append(dsrc.valid[i][1])
val_pct = []
skf = StratifiedKFold(n_splits=10, shuffle=True)
for _, val_idx in skf.split(np.array(train_imgs), train_labels):
splits = IndexSplitter(val_idx)
split = splits(train_imgs)
split_list = [split[0], split[1]]
dsrc = Datasets(train_imgs, tfms=[[PILDicom.create], [parent_label, Categorize]], splits=split_list)
dls = dsrc.dataloaders(bs=bs, after_item=item_tfms, after_batch=batch_tfms)
learn = cnn_learner(dls, resnet34, pretrained=False, metrics=accuracy)
learn.fit_one_cycle(1)
val_pct.append(learn.validate()[1])
Any idea where is the problem? Thanks!
Hi Joan!
I donât know much about Cross Validation, but I will try to understand a little more what you are doing.
I think your images only have one channel, while your network is asking for RGB channels. Is that so?
That brings me to the next question: why when you use Datasets do you add transformations (batch_tfms / item_tfms) that when you use DataBlocks you donât add? Could it be that your images are not normalized in the first case?
2 Likes
@Joan also the two codes arenât the exact same, you have some item transforms that arenât being done on the first set that you do in Datasets
1 Like
Uhm⌠Thanks for the feedback. You nailed it.
I got confused by different things. First DICOM images are RGB.
patient = 4
xray_sample = dcmread(items[patient])
xray_sample.show()
But they become grayscale after DataBlock:
And yes, I recall somewhere that Normalize was automatic from vision since last update but I have to admit I didnât double check it. So, after adding normalize to batch_tfms everything works nicely.
Thanks again.
3 Likes
Awesome! Glad we got it working 
Yes if you donât include normalize say by accident then itâll automatically be done with cnn_learner or unet_learner (but Iâd personally just declare it to double check myself each time)
1 Like
Hi @muellerzr
Has the get_transforms() function been totally removed? That was handy to add a set of default transformations.
Is there any other way to add default transforms?
It seems that in the audio notebook, the out_features of the model isnât changed to the number of classes of the data.
Yes this is now aug_transforms, this should be in a notebook somewhere.
1 Like
Oh okay, and can we use the defaults of aug_tranforms for item_tfms also?
If not where to get the list of item transformation functions?
Aug_transforms is a batch transform to be run efficiently. Any transform that affects only augmentation should be run as a batch transform. Item transforms should only be resizing your image to fit into a batch so your transforms can be run more efficiently. Look at vision.augmentation notebook or docs for more information on augmentation.
1 Like