I wondered about that - didn’t have time to think thru whether it’s possible.
1 Like
I think I saw something similar, Jeremy used a hard-coded size/position of 320, which then failed if the chosen image was less than 320 pixels, I just used a different image other than the 0’th and it worked, but you could check the dimensions and use smaller re-size.
I don’t have an intuition, but this pytorch LSUV implementation aborts after n attempts.
Perhaps it’s more likely to happen if one doesn’t use a good weights init in first place?
I found this which suggest this only happens with a custom collate_fn. I’ll see if I can narrow it down.
The model ends up learning that a middle aged person with a smile must be a fish.
Anyway the idea of a middle aged person certainly has changed quite a bit over the years.
2 Likes
In the notebook 09c_add_progress_bar.ipynb, the line
learn.fit(2)
throws AttributeError: ‘NBMasterBar’ object has no attribute 'update’
Anyone know how to fix this? I'm running Windows 10 64-bit. Thanks!
details below:
Total time: 00:00
epoch train_loss train_accuracy valid_loss valid_accuracy time
AttributeError Traceback (most recent call last)
in
----> 1 learn.fit(2)
~\fastai\fastai_docs\dev_course\dl2_jcat\exp\nb_09b.py in fit(self, epochs)
60 self.do_begin_fit(epochs)
61 for epoch in range(epochs):
—> 62 self.do_begin_epoch(epoch)
63 if not self(‘begin_epoch’): self.all_batches()
64
~\fastai\fastai_docs\dev_course\dl2_jcat\exp\nb_09b.py in do_begin_epoch(self, epoch)
54 def do_begin_epoch(self, epoch):
55 self.epoch,self.dl = epoch,self.data.train_dl
—> 56 return self(‘begin_epoch’)
57
58 def fit(self, epochs):
~\fastai\fastai_docs\dev_course\dl2_jcat\exp\nb_09b.py in call(self, cb_name)
79 res = False
80 assert cb_name in self.ALL_CBS
—> 81 for cb in sorted(self.cbs, key=lambda x: x._order): res = cb(cb_name) and res
82 return res
83
~\fastai\fastai_docs\dev_course\dl2_jcat\exp\nb_05b.py in call(self, cb_name)
19 def call(self, cb_name):
20 f = getattr(self, cb_name, None)
—> 21 if f and f(): return True
22 return False
23
in begin_epoch(self)
9 def after_fit(self): self.mbar.on_iter_end()
10 def after_batch(self): self.pb.update(self.iter)
—> 11 def begin_epoch (self): self.set_pb()
12 def begin_validate(self): self.set_pb()
13
in set_pb(self)
14 def set_pb(self):
15 self.pb = progress_bar(self.dl, parent=self.mbar, auto_update=False)
—> 16 self.mbar.update(self.epoch)
AttributeError: ‘NBMasterBar’ object has no attribute ‘update’
See the top post re fastprogress.
D’oh! Thanks Jeremy!
I shall not smile again. It makes me look like a fish 
2 Likes
Relevant question bécause de do normalisé at inférence time. one could argue that data that hasn’t been normaliséd By méan and std of the training data is a wast of time
Haha don’t blame me, blame the algorithm 
On a serious note, I guess since we are using a classification model, the only thing the model can tell us is what it is or is not, an identification of an object; never someone is doing something, an identification of an action. For that we’d need some picture captioning models…
1 Like
It seems the worker threads are dying unless they are wrapped in a “if name == ‘main’:” The error below suggest I should be using fork, but that isn’t applicable to Windows as far as I am aware. I’m wondering if the ‘IS_WINDOWS’ part of https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/worker.py isn’t getting used - or has a bug. I’ll ask around at work to see if anyone knows a way around this.
I could repro and see the more complete error and also fix in a py program, but not sure how to get this running in Jupyter.
My program that runs ok was based on notebook 3 - as I could repro in the PyTorcj Dataloader section if I used num_workers > 0.:
from nb_03_bs import *
import torch.nn.functional as F
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
import numpy as np
class Dataset():
def init(self, x, y): self.x,self.y = x,y
def len(self): return len(self.x)
def getitem(self, i): return self.x[i],self.y[i]
def fit():
for epoch in range(epochs):
for xb,yb in train_dl:
pred = model(xb)
loss = loss_func(pred, yb)
loss.backward()
opt.step()
opt.zero_grad()
def get_model():
model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))
return model, optim.SGD(model.parameters(), lr=lr)
def collate(b):
xs,ys = zip(*b)
return torch.stack(xs),torch.stack(ys)
bs=64
mpl.rcParams[‘image.cmap’] = ‘gray’
x_train,y_train,x_valid,y_valid = get_data()
n,m = x_train.shape
c = y_train.max()+1
nh = 50
lr = 0.5
epochs = 1
loss_func = F.cross_entropy
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
assert len(train_ds)==len(x_train)
assert len(valid_ds)==len(x_valid)
if name == ‘main’:
train_dl = DataLoader(train_ds, bs, sampler=RandomSampler(train_ds), collate_fn=collate, num_workers=4)
valid_dl = DataLoader(valid_ds, bs, sampler=SequentialSampler(valid_ds), collate_fn=collate, num_workers=4)
xb,yb = next(iter(train_dl))
model,opt = get_model()
fit()
loss,acc = loss_func(model(xb), yb), accuracy(model(xb), yb)
assert acc>0.7
print(loss)
print(acc)
If I exclude the if name == ‘main’: the error I get is:
(fastai-partdeux) D:\OneDrive\AI\fastai_docs\dev_course\dl2\exp>cd d:\OneDrive\AI\fastai_docs\dev_course\dl2\exp && cmd /C "set “PYTHONIOENCODING=UTF-8” && set “PYTHONUNBUFFERED=1” && C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\python.exe c:\Users\bsmi0.vscode\extensions\ms-python.python-2019.3.6558\pythonFiles\ptvsd_launcher.py --default --client --host localhost --port 50869 d:\OneDrive\AI\fastai_docs\dev_course\dl2\exp\standalone.py "
Traceback (most recent call last):
File “”, line 1, in
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 105,
in spawn_main
exitcode = _main(fd)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 114,
in _main
prepare(preparation_data)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 225,
in prepare
_fixup_main_from_path(data[‘init_main_from_path’])
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 277,
in _fixup_main_from_path
run_name=“mp_main”)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 85, in _run_code
exec(code, run_globals)
File “d:\OneDrive\AI\fastai_docs\dev_course\dl2\exp\standalone.py”, line 46, in
xb,yb = next(iter(train_dl))
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 162, in iter
return _DataLoaderIter(self)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 438, in init
w.start()
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\process.py”, line 112, in start
self._popen = self._Popen(self)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\context.py”, line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\context.py”, line 322, in _Popen
return Popen(process_obj)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\popen_spawn_win32.py”, line 46, in init
prep_data = spawn.get_preparation_data(process_obj._name)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 143,
in get_preparation_data
_check_not_importing_main()
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\spawn.py”, line 136,
in _check_not_importing_main
is not going to be frozen to produce an executable.’’’)
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 480, in _try_get_batch
data = self.data_queue.get(timeout=timeout)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\multiprocessing\queues.py”, line 105, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File “c:\Users\bsmi0.vscode\extensions\ms-python.python-2019.3.6558\pythonFiles\ptvsd_launcher.py”, line 45, in
main(ptvsdArgs)
File “c:\Users\bsmi0.vscode\extensions\ms-python.python-2019.3.6558\pythonFiles\lib\python\ptvsd_main_.py”, line 391, in main
run()
File “c:\Users\bsmi0.vscode\extensions\ms-python.python-2019.3.6558\pythonFiles\lib\python\ptvsd_main_.py”, line 272, in run_file
runpy.run_path(target, run_name=‘main’)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\runpy.py”, line 85, in _run_code
exec(code, run_globals)
File “d:\OneDrive\AI\fastai_docs\dev_course\dl2\exp\standalone.py”, line 46, in
xb,yb = next(iter(train_dl))
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 545, in next
idx, batch = self._get_batch()
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 522, in _get_batch
success, data = self._try_get_batch()
File “C:\Users\bsmi0\Anaconda3\envs\fastai-partdeux\lib\site-packages\torch\utils\data\dataloader.py”, line 488, in _try_get_batch
raise RuntimeError(‘DataLoader worker (pid(s) {}) exited unexpectedly’.format(pids_str))
RuntimeError: DataLoader worker (pid(s) 30832) exited unexpectedly
1 Like
How can we handle a case where i have a dataset where a single training item consists of multiple heat maps of the same object but from different perspectives ? Do i use each perspective as a separate training item or is there some other technique for dealing with this ?
Could the perspectives be aligned and layered AS channels ?
Impossible to tell without a lot more detail of the dataset.
If you take the carvana image segmentation challenge (lesson 14 last year) there was enough information to not use the information that there were many images of the same car from different angles.
On the other hand, you can look at the solutions to kaggle’s landmark retrieval task, or even the whale task, and see that because of low sample size it’s best to take a metric/distance/fewshot learning approach.
In the example you suggest say if we have front view and back view of each car provided, do we create a classifier for front view of the car and another for the backview and finally merge them through some weighted mechanism?
DataBlock question.
Why do we labelling after splitting train / val?
In case of rare clases it is possible what whole rare class (or classes) will be in validation, so we miss them when create vocal from train.
If we first label dataset, we then create full label list. And then in case of rare classes we can split every rare class personally.
Because the labeling function was though as a tool to retrieve the label and convert it into the appropriate type used by your model.
The ratio of doing the right split is in the function you use to divide your data which is done before: there you can implement the criterions that best fit your classes so as to avoid unbalanced training set and validation set.