INSTRUCTION
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Applies a dropout mask whose size is determined by passed argument 'sz'.
Args:
x (nn.Variable): A torch Variable object
sz (tuple(int, int, int)): The expected size of the new tensor
dropout (float): The dropout fraction to apply
This method uses the bernoulli distribution to decide which activations to keep.
Additionally, the sampled activations is rescaled is using the factor 1/(1 - dropout).
In the example given below, one can see that approximately .8 fraction of the
returned tensors are zero. Rescaling with the factor 1/(1 - 0.8) returns a tensor
with 5's in the unit places.
The official link to the pytorch bernoulli function is here:
http://pytorch.org/docs/master/torch.html#torch.bernoulli
Examples:
>>> a_Var = torch.autograd.Variable(torch.Tensor(2, 3, 4).uniform_(0, 1), requires_grad=False)
>>> a_Var
Variable containing:
(0 ,.,.) =
0.6890 0.5412 0.4303 0.8918
0.3871 0.7944 0.0791 0.5979
0.4575 0.7036 0.6186 0.7217
(1 ,.,.) =
0.8354 0.1690 0.1734 0.8099
0.6002 0.2602 0.7907 0.4446
0.5877 0.7464 0.4257 0.3386
[torch.FloatTensor of size 2x3x4]
>>> a_mask = dropout_mask(a_Var.data, (1,a_Var.size(1),a_Var.size(2)), dropout=0.8)
>>> a_mask
(0 ,.,.) =
0 5 0 0
0 0 0 5
5 0 5 0
[torch.FloatTensor of size 1x3x4] | def dropout_mask(x, sz, dropout):
""" Applies a dropout mask whose size is determined by passed argument 'sz'.
Args:
x (nn.Variable): A torch Variable object
sz (tuple(int, int, int)): The expected size of the new tensor
dropout (float): The dropout fraction to apply
This method uses the bernoulli distribution to decide which activations to keep.
Additionally, the sampled activations is rescaled is using the factor 1/(1 - dropout).
In the example given below, one can see that approximately .8 fraction of the
returned tensors are zero. Rescaling with the factor 1/(1 - 0.8) returns a tensor
with 5's in the unit places.
The official link to the pytorch bernoulli function is here:
http://pytorch.org/docs/master/torch.html#torch.bernoulli
Examples:
>>> a_Var = torch.autograd.Variable(torch.Tensor(2, 3, 4).uniform_(0, 1), requires_grad=False)
>>> a_Var
Variable containing:
(0 ,.,.) =
0.6890 0.5412 0.4303 0.8918
0.3871 0.7944 0.0791 0.5979
0.4575 0.7036 0.6186 0.7217
(1 ,.,.) =
0.8354 0.1690 0.1734 0.8099
0.6002 0.2602 0.7907 0.4446
0.5877 0.7464 0.4257 0.3386
[torch.FloatTensor of size 2x3x4]
>>> a_mask = dropout_mask(a_Var.data, (1,a_Var.size(1),a_Var.size(2)), dropout=0.8)
>>> a_mask
(0 ,.,.) =
0 5 0 0
0 0 0 5
5 0 5 0
[torch.FloatTensor of size 1x3x4]
"""
return x.new(*sz).bernoulli_(1-dropout)/(1-dropout) |
for each string defined in self.weights, the corresponding
attribute in the wrapped module is referenced, then deleted, and subsequently
registered as a new parameter with a slightly modified name.
Args:
None
Returns:
None | def _setup(self):
""" for each string defined in self.weights, the corresponding
attribute in the wrapped module is referenced, then deleted, and subsequently
registered as a new parameter with a slightly modified name.
Args:
None
Returns:
None
"""
if isinstance(self.module, torch.nn.RNNBase): self.module.flatten_parameters = noop
for name_w in self.weights:
w = getattr(self.module, name_w)
del self.module._parameters[name_w]
self.module.register_parameter(name_w + '_raw', nn.Parameter(w.data)) |
Uses pytorch's built-in dropout function to apply dropout to the parameters of
the wrapped module.
Args:
None
Returns:
None | def _setweights(self):
""" Uses pytorch's built-in dropout function to apply dropout to the parameters of
the wrapped module.
Args:
None
Returns:
None
"""
for name_w in self.weights:
raw_w = getattr(self.module, name_w + '_raw')
w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training)
if hasattr(self.module, name_w):
delattr(self.module, name_w)
setattr(self.module, name_w, w) |
Load a saved `DataBunch` from `path/file`. `file` can be file-like (file or buffer) | def load_data(path:PathOrStr, file:PathLikeOrBinaryStream='data_save.pkl', bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus,
dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate,
no_check:bool=False, **kwargs)->DataBunch:
"Load a saved `DataBunch` from `path/file`. `file` can be file-like (file or buffer)"
source = Path(path)/file if is_pathlike(file) else file
ll = torch.load(source, map_location='cpu') if defaults.device == torch.device('cpu') else torch.load(source)
return ll.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, dl_tfms=dl_tfms, device=device,
collate_fn=collate_fn, no_check=no_check, **kwargs) |
Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()` | def create(cls, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, path:PathOrStr='.', bs:int=64,
val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None,
device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, **dl_kwargs)->'DataBunch':
"Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()`"
datasets = cls._init_ds(train_ds, valid_ds, test_ds)
val_bs = ifnone(val_bs, bs)
dls = [DataLoader(d, b, shuffle=s, drop_last=s, num_workers=num_workers, **dl_kwargs) for d,b,s in
zip(datasets, (bs,val_bs,val_bs,val_bs), (True,False,False,False)) if d is not None]
return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check) |
Returns appropriate `Dataset` for validation, training, or test (`ds_type`). | def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
"Returns appropriate `Dataset` for validation, training, or test (`ds_type`)."
#TODO: refactor
return (self.train_dl if ds_type == DatasetType.Train else
self.test_dl if ds_type == DatasetType.Test else
self.valid_dl if ds_type == DatasetType.Valid else
self.single_dl if ds_type == DatasetType.Single else
self.fix_dl) |
Returns a list of all DeviceDataLoaders. If you need a specific DeviceDataLoader, access via the relevant property (`train_dl`, `valid_dl`, etc) as the index of DLs in this list is not guaranteed to remain constant. | def dls(self)->List[DeviceDataLoader]:
"Returns a list of all DeviceDataLoaders. If you need a specific DeviceDataLoader, access via the relevant property (`train_dl`, `valid_dl`, etc) as the index of DLs in this list is not guaranteed to remain constant."
res = [self.train_dl, self.fix_dl, self.single_dl]
# Preserve the original ordering of Train, Valid, Fix, Single, Test Data Loaders
# (Unknown/not verified as of 1.0.47 whether there are other methods explicitly using DLs their list index)
if self.valid_dl: res.insert(1, self.valid_dl)
return res if not self.test_dl else res + [self.test_dl] |
Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer) | def save(self, file:PathLikeOrBinaryStream= 'data_save.pkl')->None:
"Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer)"
if not getattr(self, 'label_list', False):
warn("Serializing the `DataBunch` only works when you created it using the data block API.")
return
try_save(self.label_list, self.path, file) |
Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`. | def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
"Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
dl = self.dl(ds_type)
w = self.num_workers
self.num_workers = 0
try: x,y = next(iter(dl))
finally: self.num_workers = w
if detach: x,y = to_detach(x,cpu=cpu),to_detach(y,cpu=cpu)
norm = getattr(self,'norm',False)
if denorm and norm:
x = self.denorm(x)
if norm.keywords.get('do_y',False): y = self.denorm(y, do_x=True)
return x,y |
Get `item` into a batch. Optionally `detach` and `denorm`. | def one_item(self, item, detach:bool=False, denorm:bool=False, cpu:bool=False):
"Get `item` into a batch. Optionally `detach` and `denorm`."
ds = self.single_ds
with ds.set_item(item):
return self.one_batch(ds_type=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu) |
Show a batch of data in `ds_type` on a few `rows`. | def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
"Show a batch of data in `ds_type` on a few `rows`."
x,y = self.one_batch(ds_type, True, True)
if reverse: x,y = x.flip(0),y.flip(0)
n_items = rows **2 if self.train_ds.x._square_show else rows
if self.dl(ds_type).batch_size < n_items: n_items = self.dl(ds_type).batch_size
xs = [self.train_ds.x.reconstruct(grab_idx(x, i)) for i in range(n_items)]
#TODO: get rid of has_arg if possible
if has_arg(self.train_ds.y.reconstruct, 'x'):
ys = [self.train_ds.y.reconstruct(grab_idx(y, i), x=x) for i,x in enumerate(xs)]
else : ys = [self.train_ds.y.reconstruct(grab_idx(y, i)) for i in range(n_items)]
self.train_ds.x.show_xys(xs, ys, **kwargs) |
Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer) | def export(self, file:PathLikeOrBinaryStream='export.pkl'):
"Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer)"
xtra = dict(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {}
try_save(self.valid_ds.get_state(**xtra), self.path, file) |
Check the underlying data in the training set can be properly loaded. | def sanity_check(self):
"Check the underlying data in the training set can be properly loaded."
final_message = "You can deactivate this warning by passing `no_check=True`."
if not hasattr(self.train_ds, 'items') or len(self.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): return
if len(self.train_dl) == 0:
warn(f"""Your training dataloader is empty, you have only {len(self.train_dl.dataset)} items in your training set.
Your batch size is {self.train_dl.batch_size}, you should lower it.""")
print(final_message)
return
idx = next(iter(self.train_dl.batch_sampler))
samples,fails = [],[]
for i in idx:
try: samples.append(self.train_dl.dataset[i])
except: fails.append(i)
if len(fails) > 0:
warn_msg = "There seems to be something wrong with your dataset, for example, in the first batch can't access"
if len(fails) == len(idx):
warn_msg += f" any element of self.train_ds.\nTried: {show_some(idx)}"
else:
warn_msg += f" these elements in self.train_ds: {show_some(fails)}"
warn(warn_msg)
print(final_message)
return
try: batch = self.collate_fn(samples)
except:
message = "It's not possible to collate samples of your dataset together in a batch."
try:
shapes = [[o[i].data.shape for o in samples] for i in range(2)]
message += f'\nShapes of the inputs/targets:\n{shapes}'
except: pass
warn(message)
print(final_message) |
Instantiate a `OneCycleScheduler` with `lr_max`. | def one_cycle_scheduler(lr_max:float, **kwargs:Any)->OneCycleScheduler:
"Instantiate a `OneCycleScheduler` with `lr_max`."
return partial(OneCycleScheduler, lr_max=lr_max, **kwargs) |
Fit a model following the 1cycle policy. | def fit_one_cycle(learn:Learner, cyc_len:int, max_lr:Union[Floats,slice]=defaults.lr,
moms:Tuple[float,float]=(0.95,0.85), div_factor:float=25., pct_start:float=0.3, final_div:float=None,
wd:float=None, callbacks:Optional[CallbackList]=None, tot_epochs:int=None, start_epoch:int=None)->None:
"Fit a model following the 1cycle policy."
max_lr = learn.lr_range(max_lr)
callbacks = listify(callbacks)
callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,
final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))
learn.fit(cyc_len, max_lr, wd=wd, callbacks=callbacks) |
Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges. | def lr_find(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None):
"Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges."
start_lr = learn.lr_range(start_lr)
start_lr = np.array(start_lr) if is_listy(start_lr) else start_lr
end_lr = learn.lr_range(end_lr)
end_lr = np.array(end_lr) if is_listy(end_lr) else end_lr
cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
learn.fit(epochs, start_lr, callbacks=[cb], wd=wd) |
Put `learn` in FP16 precision mode. | def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,
flat_master:bool=False, max_scale:float=2**24)->Learner:
"Put `learn` in FP16 precision mode."
learn.to_fp32()
learn.model = model2half(learn.model)
learn.data.add_tfm(batch_to_half)
learn.mp_cb = MixedPrecision(learn, loss_scale=loss_scale, max_noskip=max_noskip, dynamic=dynamic, clip=clip,
flat_master=flat_master, max_scale=max_scale)
learn.callbacks.append(learn.mp_cb)
return learn |
Put `learn` back to FP32 precision mode. | def to_fp32(learn:Learner):
"Put `learn` back to FP32 precision mode."
learn.data.remove_tfm(batch_to_half)
for cb in learn.callbacks:
if isinstance(cb, MixedPrecision): learn.callbacks.remove(cb)
learn.model = learn.model.float()
return learn |
Add mixup https://arxiv.org/abs/1710.09412 to `learn`. | def mixup(learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True) -> Learner:
"Add mixup https://arxiv.org/abs/1710.09412 to `learn`."
learn.callback_fns.append(partial(MixUpCallback, alpha=alpha, stack_x=stack_x, stack_y=stack_y))
return learn |
Add gradient clipping of `clip` during training. | def clip_grad(learn:Learner, clip:float=0.1)->Learner:
"Add gradient clipping of `clip` during training."
learn.callback_fns.append(partial(GradientClipping, clip=clip))
return learn |
Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`. | def _learner_interpret(learn:Learner, ds_type:DatasetType=DatasetType.Valid):
"Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`."
return ClassificationInterpretation.from_learner(learn, ds_type=ds_type) |
If we have `last_metrics` plot them in our pbar graph | def on_epoch_end(self, n_epochs:int, last_metrics:MetricsList, **kwargs)->bool:
"If we have `last_metrics` plot them in our pbar graph"
if last_metrics is not None and np.any(last_metrics):
rec = self.learn.recorder
iters = range_of(rec.losses)
val_iter = np.array(rec.nb_batches).cumsum()
x_bounds = (0, (n_epochs - len(rec.nb_batches)) * rec.nb_batches[-1] + len(rec.losses))
y_bounds = (0, max((max(Tensor(rec.losses)), max(Tensor(rec.val_losses)))))
rec.pbar.update_graph([(iters, rec.losses), (val_iter, rec.val_losses)], x_bounds, y_bounds)
return {} |
Clip the gradient before the optimizer step. | def on_backward_end(self, **kwargs):
"Clip the gradient before the optimizer step."
if self.clip: nn.utils.clip_grad_norm_(self.learn.model.parameters(), self.clip) |
check if loss is reduction | def on_train_begin(self, **kwargs):
"check if loss is reduction"
if hasattr(self.loss_func, "reduction") and (self.loss_func.reduction != "sum"):
warn("For better gradients consider 'reduction=sum'") |
accumulate samples and batches | def on_batch_begin(self, last_input, last_target, **kwargs):
"accumulate samples and batches"
self.acc_samples += last_input.shape[0]
self.acc_batches += 1 |
accumulated step and reset samples, True will result in no stepping | def on_backward_end(self, **kwargs):
"accumulated step and reset samples, True will result in no stepping"
if (self.acc_batches % self.n_step) == 0:
for p in (self.learn.model.parameters()):
if p.requires_grad: p.grad.div_(self.acc_samples)
self.acc_samples = 0
else: return {'skip_step':True, 'skip_zero':True} |
step the rest of the accumulated grads if not perfectly divisible | def on_epoch_end(self, **kwargs):
"step the rest of the accumulated grads if not perfectly divisible"
for p in (self.learn.model.parameters()):
if p.requires_grad: p.grad.div_(self.acc_samples)
if not self.drop_last: self.learn.opt.step()
self.learn.opt.zero_grad() |
Create an instance of `ClassificationInterpretation` | def from_learner(cls, learn: Learner, ds_type:DatasetType=DatasetType.Valid):
"Create an instance of `ClassificationInterpretation`"
preds = learn.get_preds(ds_type=ds_type, with_loss=True)
return cls(learn, *preds) |
Confusion matrix as an `np.ndarray`. | def confusion_matrix(self, slice_size:int=1):
"Confusion matrix as an `np.ndarray`."
x=torch.arange(0,self.data.c)
if slice_size is None: cm = ((self.pred_class==x[:,None]) & (self.y_true==x[:,None,None])).sum(2)
else:
cm = torch.zeros(self.data.c, self.data.c, dtype=x.dtype)
for i in range(0, self.y_true.shape[0], slice_size):
cm_slice = ((self.pred_class[i:i+slice_size]==x[:,None])
& (self.y_true[i:i+slice_size]==x[:,None,None])).sum(2)
torch.add(cm, cm_slice, out=cm)
return to_np(cm) |
Plot the confusion matrix, with `title` and using `cmap`. | def plot_confusion_matrix(self, normalize:bool=False, title:str='Confusion matrix', cmap:Any="Blues", slice_size:int=1,
norm_dec:int=2, plot_txt:bool=True, return_fig:bool=None, **kwargs)->Optional[plt.Figure]:
"Plot the confusion matrix, with `title` and using `cmap`."
# This function is mainly copied from the sklearn docs
cm = self.confusion_matrix(slice_size=slice_size)
if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
fig = plt.figure(**kwargs)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
tick_marks = np.arange(self.data.c)
plt.xticks(tick_marks, self.data.y.classes, rotation=90)
plt.yticks(tick_marks, self.data.y.classes, rotation=0)
if plot_txt:
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
coeff = f'{cm[i, j]:.{norm_dec}f}' if normalize else f'{cm[i, j]}'
plt.text(j, i, coeff, horizontalalignment="center", verticalalignment="center", color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.grid(False)
if ifnone(return_fig, defaults.return_fig): return fig |
Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences. | def most_confused(self, min_val:int=1, slice_size:int=1)->Collection[Tuple[str,str,int]]:
"Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences."
cm = self.confusion_matrix(slice_size=slice_size)
np.fill_diagonal(cm, 0)
res = [(self.data.classes[i],self.data.classes[j],cm[i,j])
for i,j in zip(*np.where(cm>=min_val))]
return sorted(res, key=itemgetter(2), reverse=True) |
`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`). | def top_losses(self, k:int=None, largest=True):
"`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`)."
return self.losses.topk(ifnone(k, len(self.losses)), largest=largest) |
Calculates the F-beta score (the weighted harmonic mean of precision and recall).
This is the micro averaged version where the true positives, false negatives and
false positives are calculated globally (as opposed to on a per label basis).
beta == 1 places equal weight on precision and recall, b < 1 emphasizes precision and
beta > 1 favors recall. | def fbeta(log_preds, targs, beta, thresh=0.5, epsilon=1e-8):
"""Calculates the F-beta score (the weighted harmonic mean of precision and recall).
This is the micro averaged version where the true positives, false negatives and
false positives are calculated globally (as opposed to on a per label basis).
beta == 1 places equal weight on precision and recall, b < 1 emphasizes precision and
beta > 1 favors recall.
"""
assert beta > 0, 'beta needs to be greater than 0'
beta2 = beta ** 2
rec = recall(log_preds, targs, thresh)
prec = precision(log_preds, targs, thresh)
return (1 + beta2) * prec * rec / (beta2 * prec + rec + epsilon) |
see fbeta | def fbeta_np(preds, targs, beta, thresh=0.5, epsilon=1e-8):
""" see fbeta """
assert beta > 0, 'beta needs to be greater than 0'
beta2 = beta ** 2
rec = recall_np(preds, targs, thresh)
prec = precision_np(preds, targs, thresh)
return (1 + beta2) * prec * rec / (beta2 * prec + rec + epsilon) |
Distributed training of Imagenet. Fastest speed is if you run with: python -m fastai.launch | def main( gpu:Param("GPU to run on", str)=None ):
"""Distributed training of Imagenet. Fastest speed is if you run with: python -m fastai.launch"""
path = Path('/mnt/fe2_disk/')
tot_epochs,size,bs,lr = 60,224,256,3e-1
dirname = 'imagenet'
gpu = setup_distrib(gpu)
if gpu is None: bs *= torch.cuda.device_count()
n_gpus = num_distrib() or 1
workers = min(12, num_cpus()//n_gpus)
data = get_data(path/dirname, size, bs, workers)
b_its = len(data.train_dl)//n_gpus
# Using bs 256 on single GPU as baseline, scale the LR linearly
tot_bs = bs*n_gpus
bs_rat = tot_bs/256
lr *= bs_rat
ph1 = (TrainingPhase(tot_epochs*0.10*b_its)
.schedule_hp('lr', (lr/10,lr), anneal=annealing_cos))
ph2 = (TrainingPhase(tot_epochs*0.90*b_its)
.schedule_hp('lr', (lr,lr/1e5), anneal=annealing_cos))
opt_func = partial(optim.Adam, eps=0.1, betas=(0.9,0.99))
learn = Learner(data, models.xresnet50(), metrics=[accuracy,top_k_accuracy], wd=1e-3,
opt_func=opt_func, bn_wd=False, true_wd=True,
loss_func = LabelSmoothingCrossEntropy()).mixup(alpha=0.2)
learn.callback_fns += [
partial(GeneralScheduler, phases=(ph1,ph2)),
partial(SaveModelCallback, every='epoch', name='model')
]
learn.split(lambda m: (children(m)[-2],))
if gpu is None: learn.model = nn.DataParallel(learn.model)
else: learn.to_distributed(gpu)
learn.to_fp16(dynamic=True)
learn.fit(tot_epochs, 1)
if rank_distrib(): time.sleep(1)
learn.save('done') |
Get the metadata associated with `arch`. | def cnn_config(arch):
"Get the metadata associated with `arch`."
torch.backends.cudnn.benchmark = True
return model_meta.get(arch, _default_meta) |
Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function). | def create_body(arch:Callable, pretrained:bool=True, cut:Optional[Union[int, Callable]]=None):
"Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function)."
model = arch(pretrained)
cut = ifnone(cut, cnn_config(arch)['cut'])
if cut is None:
ll = list(enumerate(model.children()))
cut = next(i for i,o in reversed(ll) if has_pool_type(o))
if isinstance(cut, int): return nn.Sequential(*list(model.children())[:cut])
elif isinstance(cut, Callable): return cut(model)
else: raise NamedError("cut must be either integer or a function") |
Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes. | def create_head(nf:int, nc:int, lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
concat_pool:bool=True, bn_final:bool=False):
"Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes."
lin_ftrs = [nf, 512, nc] if lin_ftrs is None else [nf] + lin_ftrs + [nc]
ps = listify(ps)
if len(ps) == 1: ps = [ps[0]/2] * (len(lin_ftrs)-2) + ps
actns = [nn.ReLU(inplace=True)] * (len(lin_ftrs)-2) + [None]
pool = AdaptiveConcatPool2d() if concat_pool else nn.AdaptiveAvgPool2d(1)
layers = [pool, Flatten()]
for ni,no,p,actn in zip(lin_ftrs[:-1], lin_ftrs[1:], ps, actns):
layers += bn_drop_lin(ni, no, True, p, actn)
if bn_final: layers.append(nn.BatchNorm1d(lin_ftrs[-1], momentum=0.01))
return nn.Sequential(*layers) |
Create custom convnet architecture | def create_cnn_model(base_arch:Callable, nc:int, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, custom_head:Optional[nn.Module]=None,
split_on:Optional[SplitFuncOrIdxList]=None, bn_final:bool=False, concat_pool:bool=True):
"Create custom convnet architecture"
body = create_body(base_arch, pretrained, cut)
if custom_head is None:
nf = num_features_model(nn.Sequential(*body.children())) * (2 if concat_pool else 1)
head = create_head(nf, nc, lin_ftrs, ps=ps, concat_pool=concat_pool, bn_final=bn_final)
else: head = custom_head
return nn.Sequential(body, head) |
Build convnet style learner. | def cnn_learner(data:DataBunch, base_arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, custom_head:Optional[nn.Module]=None,
split_on:Optional[SplitFuncOrIdxList]=None, bn_final:bool=False, init=nn.init.kaiming_normal_,
concat_pool:bool=True, **kwargs:Any)->Learner:
"Build convnet style learner."
meta = cnn_config(base_arch)
model = create_cnn_model(base_arch, data.c, cut, pretrained, lin_ftrs, ps=ps, custom_head=custom_head,
split_on=split_on, bn_final=bn_final, concat_pool=concat_pool)
learn = Learner(data, model, **kwargs)
learn.split(split_on or meta['split'])
if pretrained: learn.freeze()
if init: apply_init(model[1], init)
return learn |
Build Unet learner from `data` and `arch`. | def unet_learner(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
norm_type:Optional[NormType]=NormType, split_on:Optional[SplitFuncOrIdxList]=None, blur:bool=False,
self_attention:bool=False, y_range:Optional[Tuple[float,float]]=None, last_cross:bool=True,
bottle:bool=False, cut:Union[int,Callable]=None, **learn_kwargs:Any)->Learner:
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained, cut)
model = to_device(models.unet.DynamicUnet(body, n_classes=data.c, blur=blur, blur_final=blur_final,
self_attention=self_attention, y_range=y_range, norm_type=norm_type, last_cross=last_cross,
bottle=bottle), data.device)
learn = Learner(data, model, **learn_kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained: learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn |
Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmentation. | def _cl_int_from_learner(cls, learn:Learner, ds_type:DatasetType=DatasetType.Valid, tta=False):
"Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmentation."
preds = learn.TTA(ds_type=ds_type, with_loss=True) if tta else learn.get_preds(ds_type=ds_type, with_loss=True)
return cls(learn, *preds, ds_type=ds_type) |
Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class. | def _cl_int_plot_top_losses(self, k, largest=True, figsize=(12,12), heatmap:bool=True, heatmap_thresh:int=16,
return_fig:bool=None)->Optional[plt.Figure]:
"Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class."
tl_val,tl_idx = self.top_losses(k, largest)
classes = self.data.classes
cols = math.ceil(math.sqrt(k))
rows = math.ceil(k/cols)
fig,axes = plt.subplots(rows, cols, figsize=figsize)
fig.suptitle('prediction/actual/loss/probability', weight='bold', size=14)
for i,idx in enumerate(tl_idx):
im,cl = self.data.dl(self.ds_type).dataset[idx]
cl = int(cl)
im.show(ax=axes.flat[i], title=
f'{classes[self.pred_class[idx]]}/{classes[cl]} / {self.losses[idx]:.2f} / {self.probs[idx][cl]:.2f}')
if heatmap:
xb,_ = self.data.one_item(im, detach=False, denorm=False)
m = self.learn.model.eval()
with hook_output(m[0]) as hook_a:
with hook_output(m[0], grad= True) as hook_g:
preds = m(xb)
preds[0,cl].backward()
acts = hook_a.stored[0].cpu()
if (acts.shape[-1]*acts.shape[-2]) >= heatmap_thresh:
grad = hook_g.stored[0][0].cpu()
grad_chan = grad.mean(1).mean(1)
mult = F.relu(((acts*grad_chan[...,None,None])).sum(0))
sz = list(im.shape[-2:])
axes.flat[i].imshow(mult, alpha=0.6, extent=(0,*sz[::-1],0), interpolation='bilinear', cmap='magma')
if ifnone(return_fig, defaults.return_fig): return fig |
Show images in `top_losses` along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset. | def _cl_int_plot_multi_top_losses(self, samples:int=3, figsize:Tuple[int,int]=(8,8), save_misclassified:bool=False):
"Show images in `top_losses` along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset."
if samples >20:
print("Max 20 samples")
return
losses, idxs = self.top_losses(self.data.c)
l_dim = len(losses.size())
if l_dim == 1: losses, idxs = self.top_losses()
infolist, ordlosses_idxs, mismatches_idxs, mismatches, losses_mismatches, mismatchescontainer = [],[],[],[],[],[]
truthlabels = np.asarray(self.y_true, dtype=int)
classes_ids = [k for k in enumerate(self.data.classes)]
predclass = np.asarray(self.pred_class)
for i,pred in enumerate(predclass):
where_truth = np.nonzero((truthlabels[i]>0))[0]
mismatch = np.all(pred!=where_truth)
if mismatch:
mismatches_idxs.append(i)
if l_dim > 1 : losses_mismatches.append((losses[i][pred], i))
else: losses_mismatches.append((losses[i], i))
if l_dim > 1: infotup = (i, pred, where_truth, losses[i][pred], np.round(self.probs[i], decimals=3)[pred], mismatch)
else: infotup = (i, pred, where_truth, losses[i], np.round(self.probs[i], decimals=3)[pred], mismatch)
infolist.append(infotup)
ds = self.data.dl(self.ds_type).dataset
mismatches = ds[mismatches_idxs]
ordlosses = sorted(losses_mismatches, key = lambda x: x[0], reverse=True)
for w in ordlosses: ordlosses_idxs.append(w[1])
mismatches_ordered_byloss = ds[ordlosses_idxs]
print(f'{str(len(mismatches))} misclassified samples over {str(len(self.data.valid_ds))} samples in the validation set.')
samples = min(samples, len(mismatches))
for ima in range(len(mismatches_ordered_byloss)):
mismatchescontainer.append(mismatches_ordered_byloss[ima][0])
for sampleN in range(samples):
actualclasses = ''
for clas in infolist[ordlosses_idxs[sampleN]][2]:
actualclasses = f'{actualclasses} -- {str(classes_ids[clas][1])}'
imag = mismatches_ordered_byloss[sampleN][0]
imag = show_image(imag, figsize=figsize)
imag.set_title(f"""Predicted: {classes_ids[infolist[ordlosses_idxs[sampleN]][1]][1]} \nActual: {actualclasses}\nLoss: {infolist[ordlosses_idxs[sampleN]][3]}\nProbability: {infolist[ordlosses_idxs[sampleN]][4]}""",
loc='left')
plt.show()
if save_misclassified: return mismatchescontainer |
Gets indices with top losses. | def from_toplosses(cls, learn, n_imgs=None, **kwargs):
"Gets indices with top losses."
train_ds, train_idxs = cls.get_toplosses_idxs(learn, n_imgs, **kwargs)
return train_ds, train_idxs |
Sorts `ds_type` dataset by top losses and returns dataset and sorted indices. | def get_toplosses_idxs(cls, learn, n_imgs, **kwargs):
"Sorts `ds_type` dataset by top losses and returns dataset and sorted indices."
dl = learn.data.fix_dl
if not n_imgs: n_imgs = len(dl.dataset)
_,_,top_losses = learn.get_preds(ds_type=DatasetType.Fix, with_loss=True)
idxs = torch.topk(top_losses, n_imgs)[1]
return cls.padded_ds(dl.dataset, **kwargs), idxs |
For a LabelList `ll_input`, resize each image to `size` using `resize_method` and `padding_mode`. | def padded_ds(ll_input, size=(250, 300), resize_method=ResizeMethod.CROP, padding_mode='zeros', **kwargs):
"For a LabelList `ll_input`, resize each image to `size` using `resize_method` and `padding_mode`."
return ll_input.transform(tfms=crop_pad(), size=size, resize_method=resize_method, padding_mode=padding_mode) |
Gets the indices for the most similar images. | def from_similars(cls, learn, layer_ls:list=[0, 7, 2], **kwargs):
"Gets the indices for the most similar images."
train_ds, train_idxs = cls.get_similars_idxs(learn, layer_ls, **kwargs)
return train_ds, train_idxs |
Gets the indices for the most similar images in `ds_type` dataset | def get_similars_idxs(cls, learn, layer_ls, **kwargs):
"Gets the indices for the most similar images in `ds_type` dataset"
hook = hook_output(learn.model[layer_ls[0]][layer_ls[1]][layer_ls[2]])
dl = learn.data.fix_dl
ds_actns = cls.get_actns(learn, hook=hook, dl=dl, **kwargs)
similarities = cls.comb_similarity(ds_actns, ds_actns, **kwargs)
idxs = cls.sort_idxs(similarities)
return cls.padded_ds(dl, **kwargs), idxs |
Gets activations at the layer specified by `hook`, applies `pool` of dim `pool_dim` and concatenates | def get_actns(learn, hook:Hook, dl:DataLoader, pool=AdaptiveConcatPool2d, pool_dim:int=4, **kwargs):
"Gets activations at the layer specified by `hook`, applies `pool` of dim `pool_dim` and concatenates"
print('Getting activations...')
actns = []
learn.model.eval()
with torch.no_grad():
for (xb,yb) in progress_bar(dl):
learn.model(xb)
actns.append((hook.stored).cpu())
if pool:
pool = pool(pool_dim)
return pool(torch.cat(actns)).view(len(dl.x),-1)
else: return torch.cat(actns).view(len(dl.x),-1) |
Computes the similarity function between each embedding of `t1` and `t2` matrices. | def comb_similarity(t1: torch.Tensor, t2: torch.Tensor, **kwargs):
# https://github.com/pytorch/pytorch/issues/11202
"Computes the similarity function between each embedding of `t1` and `t2` matrices."
print('Computing similarities...')
w1 = t1.norm(p=2, dim=1, keepdim=True)
w2 = w1 if t2 is t1 else t2.norm(p=2, dim=1, keepdim=True)
t = torch.mm(t1, t2.t()) / (w1 * w2.t()).clamp(min=1e-8)
return torch.tril(t, diagonal=-1) |
Returns the `n` largest indices from a numpy array `arr`. | def largest_indices(arr, n):
"Returns the `n` largest indices from a numpy array `arr`."
#https://stackoverflow.com/questions/6910641/how-do-i-get-indices-of-n-maximum-values-in-a-numpy-array
flat = arr.flatten()
indices = np.argpartition(flat, -n)[-n:]
indices = indices[np.argsort(-flat[indices])]
return np.unravel_index(indices, arr.shape) |
Sorts `similarities` and return the indexes in pairs ordered by highest similarity. | def sort_idxs(cls, similarities):
"Sorts `similarities` and return the indexes in pairs ordered by highest similarity."
idxs = cls.largest_indices(similarities, len(similarities))
idxs = [(idxs[0][i], idxs[1][i]) for i in range(len(idxs[0]))]
return [e for l in idxs for e in l] |
Returns an image widget for specified file name `img`. | def make_img_widget(cls, img, layout=Layout(), format='jpg'):
"Returns an image widget for specified file name `img`."
return widgets.Image(value=img, format=format, layout=layout) |
Return a Button widget with specified `handler`. | def make_button_widget(cls, label, file_path=None, handler=None, style=None, layout=Layout(width='auto')):
"Return a Button widget with specified `handler`."
btn = widgets.Button(description=label, layout=layout)
if handler is not None: btn.on_click(handler)
if style is not None: btn.button_style = style
btn.file_path = file_path
btn.flagged_for_delete = False
return btn |
Return a Dropdown widget with specified `handler`. | def make_dropdown_widget(cls, description='Description', options=['Label 1', 'Label 2'], value='Label 1',
file_path=None, layout=Layout(), handler=None):
"Return a Dropdown widget with specified `handler`."
dd = widgets.Dropdown(description=description, options=options, value=value, layout=layout)
if file_path is not None: dd.file_path = file_path
if handler is not None: dd.observe(handler, names=['value'])
return dd |
Make a horizontal box with `children` and `layout`. | def make_horizontal_box(cls, children, layout=Layout()):
"Make a horizontal box with `children` and `layout`."
return widgets.HBox(children, layout=layout) |
Make a vertical box with `children` and `layout`. | def make_vertical_box(cls, children, layout=Layout(), duplicates=False):
"Make a vertical box with `children` and `layout`."
if not duplicates: return widgets.VBox(children, layout=layout)
else: return widgets.VBox([children[0], children[2]], layout=layout) |
Create a list of images, filenames and labels but first removing files that are not supposed to be displayed. | def create_image_list(self, dataset, fns_idxs):
"Create a list of images, filenames and labels but first removing files that are not supposed to be displayed."
items = dataset.x.items
if self._duplicates:
chunked_idxs = chunks(fns_idxs, 2)
chunked_idxs = [chunk for chunk in chunked_idxs if Path(items[chunk[0]]).is_file() and Path(items[chunk[1]]).is_file()]
return [(dataset.x[i]._repr_jpeg_(), items[i], self._labels[dataset.y[i].data]) for chunk in chunked_idxs for i in chunk]
else:
return [(dataset.x[i]._repr_jpeg_(), items[i], self._labels[dataset.y[i].data]) for i in fns_idxs if
Path(items[i]).is_file()] |
Relabel images by moving from parent dir with old label `class_old` to parent dir with new label `class_new`. | def relabel(self, change):
"Relabel images by moving from parent dir with old label `class_old` to parent dir with new label `class_new`."
class_new,class_old,file_path = change.new,change.old,change.owner.file_path
fp = Path(file_path)
parent = fp.parents[1]
self._csv_dict[fp] = class_new |
Handler for 'Next Batch' button click. Delete all flagged images and renders next batch. | def next_batch(self, _):
"Handler for 'Next Batch' button click. Delete all flagged images and renders next batch."
for img_widget, delete_btn, fp, in self._batch:
fp = delete_btn.file_path
if (delete_btn.flagged_for_delete == True):
self.delete_image(fp)
self._deleted_fns.append(fp)
self._all_images = self._all_images[self._batch_size:]
self.empty_batch()
self.render() |
Flag this image as delete or keep. | def on_delete(self, btn):
"Flag this image as delete or keep."
btn.button_style = "" if btn.flagged_for_delete else "danger"
btn.flagged_for_delete = not btn.flagged_for_delete |
Create and format widget set. | def get_widgets(self, duplicates):
"Create and format widget set."
widgets = []
for (img,fp,human_readable_label) in self._all_images[:self._batch_size]:
img_widget = self.make_img_widget(img, layout=Layout(height='250px', width='300px'))
dropdown = self.make_dropdown_widget(description='', options=self._labels, value=human_readable_label,
file_path=fp, handler=self.relabel, layout=Layout(width='auto'))
delete_btn = self.make_button_widget('Delete', file_path=fp, handler=self.on_delete)
widgets.append(self.make_vertical_box([img_widget, dropdown, delete_btn],
layout=Layout(width='auto', height='300px',
overflow_x="hidden"), duplicates=duplicates))
self._batch.append((img_widget, delete_btn, fp))
return widgets |
Check if current batch contains already deleted images. | def batch_contains_deleted(self):
"Check if current batch contains already deleted images."
if not self._duplicates: return False
imgs = [self._all_images[:self._batch_size][0][1], self._all_images[:self._batch_size][1][1]]
return any(img in self._deleted_fns for img in imgs) |
Re-render Jupyter cell for batch of images. | def render(self):
"Re-render Jupyter cell for batch of images."
clear_output()
self.write_csv()
if self.empty() and self._skipped>0:
return display(f'No images to show :). {self._skipped} pairs were '
f'skipped since at least one of the images was deleted by the user.')
elif self.empty():
return display('No images to show :)')
if self.batch_contains_deleted():
self.next_batch(None)
self._skipped += 1
else:
display(self.make_horizontal_box(self.get_widgets(self._duplicates)))
display(self.make_button_widget('Next Batch', handler=self.next_batch, style="primary")) |
Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal. | def _line_shift(x:Tensor, mask:bool=False):
"Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal."
bs,nh,n,p = x.size()
x_pad = torch.cat([x.new_zeros(bs,nh,n,1), x], dim=3)
x_shift = x_pad.view(bs,nh,p + 1,n)[:,:,1:].view_as(x)
if mask: x_shift.mul_(torch.tril(x.new_ones(n,p), p-n)[None,None,])
return x_shift |
Split a RNN `model` in groups for differential learning rates. | def tfmer_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]), list(encoder.layers[n:2*n]), list(encoder.layers[2*n:])]
return groups + [[encoder.encoder, model[1]]] |
Split a RNN `model` in groups for differential learning rates. | def tfmer_clas_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0].module
n = len(encoder.layers)//3
groups = [[encoder.encoder], list(encoder.layers[:n]), list(encoder.layers[n:2*n]), list(encoder.layers[2*n:])]
return groups + [[model[1]]] |
Split a RNN `model` in groups for differential learning rates. | def tfmerXL_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]) + [ParameterModule(encoder.u), ParameterModule(encoder.v)]]
return groups + [list(encoder.layers[n:2*n]), list(encoder.layers[2*n:]), [encoder.encoder, model[1]]] |
Reset the internal memory. | def reset(self):
"Reset the internal memory."
self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)] |
Make report in form of two notebooks.
Use nbdime diff-web to present the difference between reference
cells and test cells. | def make_report(self, outcome):
"""Make report in form of two notebooks.
Use nbdime diff-web to present the difference between reference
cells and test cells.
"""
failures = self.getreports('failed')
if not failures:
return
for rep in failures:
# Check if this is a notebook node
msg = self._getfailureheadline(rep)
lines = rep.longrepr.splitlines()
if len(lines) > 1:
self.section(msg, lines[1])
self._outrep_summary(rep)
tmpdir = tempfile.mkdtemp()
try:
ref_file = os.path.join(tmpdir, 'reference.ipynb')
test_file = os.path.join(tmpdir, 'test_result.ipynb')
with io.open(ref_file, "w", encoding="utf8") as f:
nbformat.write(self.nb_ref, f)
with io.open(test_file, "w", encoding="utf8") as f:
nbformat.write(self.nb_test, f)
run_server(
port=0, # Run on random port
cwd=tmpdir,
closable=True,
on_port=lambda port: browse(
port, ref_file, test_file, None))
finally:
shutil.rmtree(tmpdir) |
BatchNorm layers to have parameters in single precision.
Find all layers and convert them back to float. This can't
be done with built in .apply as that function will apply
fn to all modules, parameters, and buffers. Thus we wouldn't
be able to guard the float conversion based on the module type. | def batchnorm_to_fp32(module):
'''
BatchNorm layers to have parameters in single precision.
Find all layers and convert them back to float. This can't
be done with built in .apply as that function will apply
fn to all modules, parameters, and buffers. Thus we wouldn't
be able to guard the float conversion based on the module type.
'''
if isinstance(module, nn.modules.batchnorm._BatchNorm):
module.float()
for child in module.children():
batchnorm_to_fp32(child)
return module |
Creates a fp32 copy of model parameters and sets optimizer parameters | def copy_model_to_fp32(m, optim):
""" Creates a fp32 copy of model parameters and sets optimizer parameters
"""
fp32_params = [m_param.clone().type(torch.cuda.FloatTensor).detach() for m_param in trainable_params_(m)]
optim_groups = [group['params'] for group in optim.param_groups]
iter_fp32_params = iter(fp32_params)
for group_params in optim_groups:
for i in range(len(group_params)):
if not group_params[i].requires_grad: continue # only update trainable_params_
fp32_param = next(iter_fp32_params)
assert(fp32_param.shape == group_params[i].shape)
fp32_param.requires_grad = group_params[i].requires_grad
group_params[i] = fp32_param
return fp32_params |
Start coverage reporting in kernel.
Currently supported kernel languages are:
- Python | def setup_coverage(config, kernel, floc, output_loc=None):
"""Start coverage reporting in kernel.
Currently supported kernel languages are:
- Python
"""
language = kernel.language
if language.startswith('python'):
# Get the pytest-cov coverage object
cov = get_cov(config)
if cov:
# If present, copy the data file location used by pytest-cov
data_file = os.path.abspath(cov.config.data_file)
else:
# Fall back on output_loc and current dir if not
data_file = os.path.abspath(os.path.join(output_loc or os.getcwd(), '.coverage'))
# Get options from pytest-cov's command line arguments:
source = config.option.cov_source
config_file = config.option.cov_config
if isinstance(config_file, str) and os.path.isfile(config_file):
config_file = os.path.abspath(config_file)
# Copy the suffix of plugin if available
suffix = _make_suffix(cov)
if suffix is True:
# Cannot merge data with autogen suffix, so turn off warning
# for missing data in pytest-cov collector
cov._warn_no_data = False
# Build setup command and execute in kernel:
cmd = _python_setup % (data_file, source, config_file, suffix)
msg_id = kernel.kc.execute(cmd, stop_on_error=False)
kernel.await_idle(msg_id, 60) # A minute should be plenty to enable coverage
else:
config.warn(
'C1',
'Coverage currently not supported for language "%s".' % language,
floc)
return |
Finish coverage reporting in kernel.
The coverage should previously have been started with
setup_coverage. | def teardown_coverage(config, kernel, output_loc=None):
"""Finish coverage reporting in kernel.
The coverage should previously have been started with
setup_coverage.
"""
language = kernel.language
if language.startswith('python'):
# Teardown code does not require any input, simply execute:
msg_id = kernel.kc.execute(_python_teardown)
kernel.await_idle(msg_id, 60) # A minute should be plenty to write out coverage
# Ensure we merge our data into parent data of pytest-cov, if possible
cov = get_cov(config)
_merge_nbval_coverage_data(cov)
else:
# Warnings should be given on setup, or there might be no teardown
# for a specific language, so do nothing here
pass |
Returns the coverage object of pytest-cov. | def get_cov(config):
"""Returns the coverage object of pytest-cov."""
# Check with hasplugin to avoid getplugin exception in older pytest.
if config.pluginmanager.hasplugin('_cov'):
plugin = config.pluginmanager.getplugin('_cov')
if plugin.cov_controller:
return plugin.cov_controller.cov
return None |
Create a suffix for nbval data file depending on pytest-cov config. | def _make_suffix(cov):
"""Create a suffix for nbval data file depending on pytest-cov config."""
# Check if coverage object has data_suffix:
if cov and cov.data_suffix is not None:
# If True, the suffix will be autogenerated by coverage.py.
# The suffixed data files will be automatically combined later.
if cov.data_suffix is True:
return True
# Has a suffix, but we add our own extension
return cov.data_suffix + '.nbval'
return 'nbval' |
Merge nbval coverage data into pytest-cov data. | def _merge_nbval_coverage_data(cov):
"""Merge nbval coverage data into pytest-cov data."""
if not cov:
return
suffix = _make_suffix(cov)
if suffix is True:
# Note: If suffix is true, we are running in parallel, so several
# files will be generated. This will cause some warnings about "no coverage"
# but is otherwise OK. Do nothing.
return
# Get the filename of the nbval coverage:
filename = cov.data_files.filename + '.' + suffix
# Read coverage generated by nbval in this run:
nbval_data = coverage.CoverageData(debug=cov.debug)
try:
nbval_data.read_file(os.path.abspath(filename))
except coverage.CoverageException:
return
# Set up aliases (following internal coverage.py code here)
aliases = None
if cov.config.paths:
aliases = coverage.files.PathAliases()
for paths in cov.config.paths.values():
result = paths[0]
for pattern in paths[1:]:
aliases.add(pattern, result)
# Merge nbval data into pytest-cov data:
cov.data.update(nbval_data, aliases=aliases)
# Delete our nbval coverage data
coverage.misc.file_be_gone(filename) |
Yield successive `n`-sized chunks from `l`. | def chunks(l:Collection, n:int)->Iterable:
"Yield successive `n`-sized chunks from `l`."
for i in range(0, len(l), n): yield l[i:i+n] |
Convert `b` to an int or list of ints (if `is_listy`); raises exception if not convertible | def to_int(b:Any)->Union[int,List[int]]:
"Convert `b` to an int or list of ints (if `is_listy`); raises exception if not convertible"
if is_listy(b): return [to_int(x) for x in b]
else: return int(b) |
Return `True` if `a` is one-dimensional | def is1d(a:Collection)->bool:
"Return `True` if `a` is one-dimensional"
return len(a.shape) == 1 if hasattr(a, 'shape') else True |
Return sorted unique values of `x`. | def uniqueify(x:Series, sort:bool=False)->List:
"Return sorted unique values of `x`."
res = list(OrderedDict.fromkeys(x).keys())
if sort: res.sort()
return res |
List of label subdirectories in imagenet-style `folder`. | def find_classes(folder:Path)->FilePathList:
"List of label subdirectories in imagenet-style `folder`."
classes = [d for d in folder.iterdir()
if d.is_dir() and not d.name.startswith('.')]
assert(len(classes)>0)
return sorted(classes, key=lambda d: d.name) |
Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]. | def arrays_split(mask:NPArrayMask, *arrs:NPArrayableList)->SplitArrayList:
"Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]."
assert all([len(arr)==len(arrs[0]) for arr in arrs]), 'All arrays should have same length'
mask = array(mask)
return list(zip(*[(a[mask],a[~mask]) for a in map(np.array, arrs)])) |
Randomly split `arrs` with `valid_pct` ratio. good for creating validation set. | def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:
"Randomly split `arrs` with `valid_pct` ratio. good for creating validation set."
assert (valid_pct>=0 and valid_pct<=1), 'Validation set percentage should be between 0 and 1'
is_train = np.random.uniform(size=(len(arrs[0]),)) > valid_pct
return arrays_split(is_train, *arrs) |
Make `p` listy and the same length as `q`. | def listify(p:OptListOrItem=None, q:OptListOrItem=None):
"Make `p` listy and the same length as `q`."
if p is None: p=[]
elif isinstance(p, str): p = [p]
elif not isinstance(p, Iterable): p = [p]
#Rank 0 tensors in PyTorch are Iterable but don't have a length.
else:
try: a = len(p)
except: p = [p]
n = q if type(q)==int else len(p) if q is None else len(q)
if len(p)==1: p = p * n
assert len(p)==n, f'List len mismatch ({len(p)} vs {n})'
return list(p) |
Change `name` from camel to snake style. | def camel2snake(name:str)->str:
"Change `name` from camel to snake style."
s1 = re.sub(_camel_re1, r'\1_\2', name)
return re.sub(_camel_re2, r'\1_\2', s1).lower() |
Build log-stepped array from `start` to `stop` in `n` steps. | def even_mults(start:float, stop:float, n:int)->np.ndarray:
"Build log-stepped array from `start` to `stop` in `n` steps."
mult = stop/start
step = mult**(1/(n-1))
return np.array([start*(step**i) for i in range(n)]) |
Extract the keys in `names` from the `kwargs`. | def extract_kwargs(names:Collection[str], kwargs:KWArgs):
"Extract the keys in `names` from the `kwargs`."
new_kwargs = {}
for arg_name in names:
if arg_name in kwargs:
arg_val = kwargs.pop(arg_name)
new_kwargs[arg_name] = arg_val
return new_kwargs, kwargs |
Split iterables `a` in equal parts of size `sz` | def partition(a:Collection, sz:int)->List[Collection]:
"Split iterables `a` in equal parts of size `sz`"
return [a[i:i+sz] for i in range(0, len(a), sz)] |
Split data in `a` equally among `n_cpus` cores | def partition_by_cores(a:Collection, n_cpus:int)->List[Collection]:
"Split data in `a` equally among `n_cpus` cores"
return partition(a, len(a)//n_cpus + 1) |
Categorifies the columns `col_names` in `df`. | def series2cat(df:DataFrame, *col_names):
"Categorifies the columns `col_names` in `df`."
for c in listify(col_names): df[c] = df[c].astype('category').cat.as_ordered() |
Download `url` to `dest` unless it exists and not `overwrite`. | def download_url(url:str, dest:str, overwrite:bool=False, pbar:ProgressBar=None,
show_progress=True, chunk_size=1024*1024, timeout=4, retries=5)->None:
"Download `url` to `dest` unless it exists and not `overwrite`."
if os.path.exists(dest) and not overwrite: return
s = requests.Session()
s.mount('http://',requests.adapters.HTTPAdapter(max_retries=retries))
u = s.get(url, stream=True, timeout=timeout)
try: file_size = int(u.headers["Content-Length"])
except: show_progress = False
with open(dest, 'wb') as f:
nbytes = 0
if show_progress: pbar = progress_bar(range(file_size), auto_update=False, leave=False, parent=pbar)
try:
for chunk in u.iter_content(chunk_size=chunk_size):
nbytes += len(chunk)
if show_progress: pbar.update(nbytes)
f.write(chunk)
except requests.exceptions.ConnectionError as e:
fname = url.split('/')[-1]
from fastai.datasets import Config
data_dir = Config().data_path()
timeout_txt =(f'\n Download of {url} has failed after {retries} retries\n'
f' Fix the download manually:\n'
f'$ mkdir -p {data_dir}\n'
f'$ cd {data_dir}\n'
f'$ wget -c {url}\n'
f'$ tar -zxvf {fname}\n\n'
f'And re-run your code once the download is successful\n')
print(timeout_txt)
import sys;sys.exit(1) |
Return `Path(path)/Path(fname)`, `path` defaults to current dir. | def join_path(fname:PathOrStr, path:PathOrStr='.')->Path:
"Return `Path(path)/Path(fname)`, `path` defaults to current dir."
return Path(path)/Path(fname) |
Join `path` to every file name in `fnames`. | def join_paths(fnames:FilePathList, path:PathOrStr='.')->Collection[Path]:
"Join `path` to every file name in `fnames`."
path = Path(path)
return [join_path(o,path) for o in fnames] |
Return `ndarray` of `str` of lines of text from `path`. | def loadtxt_str(path:PathOrStr)->np.ndarray:
"Return `ndarray` of `str` of lines of text from `path`."
with open(path, 'r') as f: lines = f.readlines()
return np.array([l.strip() for l in lines]) |
Save in `fname` the content of `texts`. | def save_texts(fname:PathOrStr, texts:Collection[str]):
"Save in `fname` the content of `texts`."
with open(fname, 'w') as f:
for t in texts: f.write(f'{t}\n') |
Return the column indexes of `names` in `df`. | def df_names_to_idx(names:IntsOrStrs, df:DataFrame):
"Return the column indexes of `names` in `df`."
if not is_listy(names): names = [names]
if isinstance(names[0], int): return names
return [df.columns.get_loc(c) for c in names] |
One-hot encode `x` with `c` classes. | def one_hot(x:Collection[int], c:int):
"One-hot encode `x` with `c` classes."
res = np.zeros((c,), np.float32)
res[listify(x)] = 1.
return res |
Return the slice of `a` corresponding to `idxs`. | def index_row(a:Union[Collection,pd.DataFrame,pd.Series], idxs:Collection[int])->Any:
"Return the slice of `a` corresponding to `idxs`."
if a is None: return a
if isinstance(a,(pd.DataFrame,pd.Series)):
res = a.iloc[idxs]
if isinstance(res,(pd.DataFrame,pd.Series)): return res.copy()
return res
return a[idxs] |
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