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train | DataBunch.one_item | Get `item` into a batch. Optionally `detach` and `denorm`. | fastai/basic_data.py | 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) | 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
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train | DataBunch.show_batch | Show a batch of data in `ds_type` on a few `rows`. | fastai/basic_data.py | def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
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train | DataBunch.export | Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer) | fastai/basic_data.py | def export(self, file:PathLikeOrBinaryStream='export.pkl'):
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xtra = dict(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {}
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train | DataBunch.sanity_check | Check the underlying data in the training set can be properly loaded. | fastai/basic_data.py | 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`."
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train | one_cycle_scheduler | Instantiate a `OneCycleScheduler` with `lr_max`. | fastai/train.py | def one_cycle_scheduler(lr_max:float, **kwargs:Any)->OneCycleScheduler:
"Instantiate a `OneCycleScheduler` with `lr_max`."
return partial(OneCycleScheduler, lr_max=lr_max, **kwargs) | def one_cycle_scheduler(lr_max:float, **kwargs:Any)->OneCycleScheduler:
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train | fit_one_cycle | Fit a model following the 1cycle policy. | fastai/train.py | 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)... | def fit_one_cycle(learn:Learner, cyc_len:int, max_lr:Union[Floats,slice]=defaults.lr,
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train | lr_find | Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges. | fastai/train.py | 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):
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train | to_fp16 | Put `learn` in FP16 precision mode. | fastai/train.py | def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,
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... | 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:
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train | to_fp32 | Put `learn` back to FP32 precision mode. | fastai/train.py | def to_fp32(learn:Learner):
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learn.data.remove_tfm(batch_to_half)
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train | mixup | Add mixup https://arxiv.org/abs/1710.09412 to `learn`. | fastai/train.py | def mixup(learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True) -> Learner:
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learn.callback_fns.append(partial(MixUpCallback, alpha=alpha, stack_x=stack_x, stack_y=stack_y))
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learn.callback_fns.append(partial(MixUpCallback, alpha=alpha, stack_x=stack_x, stack_y=stack_y))
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train | clip_grad | Add gradient clipping of `clip` during training. | fastai/train.py | def clip_grad(learn:Learner, clip:float=0.1)->Learner:
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learn.callback_fns.append(partial(GradientClipping, clip=clip))
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train | _learner_interpret | Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`. | fastai/train.py | 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) | def _learner_interpret(learn:Learner, ds_type:DatasetType=DatasetType.Valid):
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train | ShowGraph.on_epoch_end | If we have `last_metrics` plot them in our pbar graph | fastai/train.py | def on_epoch_end(self, n_epochs:int, last_metrics:MetricsList, **kwargs)->bool:
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if last_metrics is not None and np.any(last_metrics):
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train | GradientClipping.on_backward_end | Clip the gradient before the optimizer step. | fastai/train.py | def on_backward_end(self, **kwargs):
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if self.clip: nn.utils.clip_grad_norm_(self.learn.model.parameters(), self.clip) | def on_backward_end(self, **kwargs):
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train | AccumulateScheduler.on_train_begin | check if loss is reduction | fastai/train.py | def on_train_begin(self, **kwargs):
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train | AccumulateScheduler.on_batch_begin | accumulate samples and batches | fastai/train.py | def on_batch_begin(self, last_input, last_target, **kwargs):
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self.acc_samples += last_input.shape[0]
self.acc_batches += 1 | def on_batch_begin(self, last_input, last_target, **kwargs):
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train | AccumulateScheduler.on_backward_end | accumulated step and reset samples, True will result in no stepping | fastai/train.py | def on_backward_end(self, **kwargs):
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train | AccumulateScheduler.on_epoch_end | step the rest of the accumulated grads if not perfectly divisible | fastai/train.py | def on_epoch_end(self, **kwargs):
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train | ClassificationInterpretation.from_learner | Create an instance of `ClassificationInterpretation` | fastai/train.py | 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)
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train | ClassificationInterpretation.confusion_matrix | Confusion matrix as an `np.ndarray`. | fastai/train.py | def confusion_matrix(self, slice_size:int=1):
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train | ClassificationInterpretation.plot_confusion_matrix | Plot the confusion matrix, with `title` and using `cmap`. | fastai/train.py | def plot_confusion_matrix(self, normalize:bool=False, title:str='Confusion matrix', cmap:Any="Blues", slice_size:int=1,
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train | ClassificationInterpretation.most_confused | Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences. | fastai/train.py | def most_confused(self, min_val:int=1, slice_size:int=1)->Collection[Tuple[str,str,int]]:
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cm = self.confusion_matrix(slice_size=slice_size)
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train | ClassificationInterpretation.top_losses | `k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`). | fastai/train.py | 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) | def top_losses(self, k:int=None, largest=True):
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train | fbeta | 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 empha... | old/fastai/metrics.py | 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
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"""Calculates the F-beta score (the weighted harmonic mean of precision and recall).
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train | fbeta_np | see fbeta | old/fastai/metrics.py | def fbeta_np(preds, targs, beta, thresh=0.5, epsilon=1e-8):
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train | main | Distributed training of Imagenet. Fastest speed is if you run with: python -m fastai.launch | examples/train_imagenet.py | 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.cud... | 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/')
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train | cnn_config | Get the metadata associated with `arch`. | fastai/vision/learner.py | def cnn_config(arch):
"Get the metadata associated with `arch`."
torch.backends.cudnn.benchmark = True
return model_meta.get(arch, _default_meta) | def cnn_config(arch):
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train | create_body | Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function). | fastai/vision/learner.py | def create_body(arch:Callable, pretrained:bool=True, cut:Optional[Union[int, Callable]]=None):
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model = arch(pretrained)
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train | create_head | Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes. | fastai/vision/learner.py | def create_head(nf:int, nc:int, lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
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lin_ftrs = [nf, 512, nc] if lin_ftrs is None else [nf] + lin_ftrs + [nc]
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train | create_cnn_model | Create custom convnet architecture | fastai/vision/learner.py | 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 c... | 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):
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train | cnn_learner | Build convnet style learner. | fastai/vision/learner.py | 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_norm... | def cnn_learner(data:DataBunch, base_arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
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train | unet_learner | Build Unet learner from `data` and `arch`. | fastai/vision/learner.py | 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,
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norm_type:Optional[NormType]=NormType, split_on:Optional[SplitFuncOrIdxList]=None, blur:bool=False,
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train | _cl_int_from_learner | Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmentation. | fastai/vision/learner.py | 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_l... | 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."
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train | _cl_int_plot_top_losses | Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class. | fastai/vision/learner.py | 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.to... | def _cl_int_plot_top_losses(self, k, largest=True, figsize=(12,12), heatmap:bool=True, heatmap_thresh:int=16,
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"Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class."
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train | _cl_int_plot_multi_top_losses | Show images in `top_losses` along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset. | fastai/vision/learner.py | 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")
retu... | 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")
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train | DatasetFormatter.from_toplosses | Gets indices with top losses. | fastai/widgets/image_cleaner.py | 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 | 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 | [
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train | DatasetFormatter.get_toplosses_idxs | Sorts `ds_type` dataset by top losses and returns dataset and sorted indices. | fastai/widgets/image_cleaner.py | 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 = to... | 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 = to... | [
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train | DatasetFormatter.padded_ds | For a LabelList `ll_input`, resize each image to `size` using `resize_method` and `padding_mode`. | fastai/widgets/image_cleaner.py | 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=p... | 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=p... | [
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train | DatasetFormatter.from_similars | Gets the indices for the most similar images. | fastai/widgets/image_cleaner.py | 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 | 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 | [
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train | DatasetFormatter.get_similars_idxs | Gets the indices for the most similar images in `ds_type` dataset | fastai/widgets/image_cleaner.py | 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)
si... | 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)
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train | DatasetFormatter.get_actns | Gets activations at the layer specified by `hook`, applies `pool` of dim `pool_dim` and concatenates | fastai/widgets/image_cleaner.py | 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... | 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()
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train | DatasetFormatter.comb_similarity | Computes the similarity function between each embedding of `t1` and `t2` matrices. | fastai/widgets/image_cleaner.py | 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 ... | 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)
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train | DatasetFormatter.largest_indices | Returns the `n` largest indices from a numpy array `arr`. | fastai/widgets/image_cleaner.py | 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.argso... | 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.argso... | [
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train | DatasetFormatter.sort_idxs | Sorts `similarities` and return the indexes in pairs ordered by highest similarity. | fastai/widgets/image_cleaner.py | 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] | 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] | [
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train | ImageCleaner.make_button_widget | Return a Button widget with specified `handler`. | fastai/widgets/image_cleaner.py | 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.but... | def make_button_widget(cls, label, file_path=None, handler=None, style=None, layout=Layout(width='auto')):
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btn = widgets.Button(description=label, layout=layout)
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train | ImageCleaner.make_dropdown_widget | Return a Dropdown widget with specified `handler`. | fastai/widgets/image_cleaner.py | 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... | def make_dropdown_widget(cls, description='Description', options=['Label 1', 'Label 2'], value='Label 1',
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"Return a Dropdown widget with specified `handler`."
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train | ImageCleaner.make_horizontal_box | Make a horizontal box with `children` and `layout`. | fastai/widgets/image_cleaner.py | def make_horizontal_box(cls, children, layout=Layout()):
"Make a horizontal box with `children` and `layout`."
return widgets.HBox(children, layout=layout) | def make_horizontal_box(cls, children, layout=Layout()):
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train | ImageCleaner.make_vertical_box | Make a vertical box with `children` and `layout`. | fastai/widgets/image_cleaner.py | 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) | def make_vertical_box(cls, children, layout=Layout(), duplicates=False):
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train | ImageCleaner.create_image_list | Create a list of images, filenames and labels but first removing files that are not supposed to be displayed. | fastai/widgets/image_cleaner.py | def create_image_list(self, dataset, fns_idxs):
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items = dataset.x.items
if self._duplicates:
chunked_idxs = chunks(fns_idxs, 2)
chunked_idxs = [chunk for chunk ... | 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 ... | [
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train | ImageCleaner.relabel | Relabel images by moving from parent dir with old label `class_old` to parent dir with new label `class_new`. | fastai/widgets/image_cleaner.py | 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
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parent = fp.parents[1]
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"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)
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train | ImageCleaner.next_batch | Handler for 'Next Batch' button click. Delete all flagged images and renders next batch. | fastai/widgets/image_cleaner.py | 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)
... | 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)
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train | ImageCleaner.on_delete | Flag this image as delete or keep. | fastai/widgets/image_cleaner.py | def on_delete(self, btn):
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btn.button_style = "" if btn.flagged_for_delete else "danger"
btn.flagged_for_delete = not btn.flagged_for_delete | def on_delete(self, btn):
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btn.button_style = "" if btn.flagged_for_delete else "danger"
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train | ImageCleaner.get_widgets | Create and format widget set. | fastai/widgets/image_cleaner.py | 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_wid... | def get_widgets(self, duplicates):
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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'))
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train | ImageCleaner.batch_contains_deleted | Check if current batch contains already deleted images. | fastai/widgets/image_cleaner.py | 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) | 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]]
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train | ImageCleaner.render | Re-render Jupyter cell for batch of images. | fastai/widgets/image_cleaner.py | def render(self):
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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 ... | def render(self):
"Re-render Jupyter cell for batch of images."
clear_output()
self.write_csv()
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return display(f'No images to show :). {self._skipped} pairs were '
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train | _line_shift | Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal. | fastai/text/models/transformer.py | 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.n... | 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.n... | [
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train | tfmer_lm_split | Split a RNN `model` in groups for differential learning rates. | fastai/text/models/transformer.py | def tfmer_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
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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... | 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:])]
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train | tfmer_clas_split | Split a RNN `model` in groups for differential learning rates. | fastai/text/models/transformer.py | def tfmer_clas_split(model:nn.Module) -> List[nn.Module]:
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train | tfmerXL_lm_split | Split a RNN `model` in groups for differential learning rates. | fastai/text/models/transformer.py | def tfmerXL_lm_split(model:nn.Module) -> List[nn.Module]:
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train | TransformerXL.reset | Reset the internal memory. | fastai/text/models/transformer.py | def reset(self):
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train | NbdimeReporter.make_report | Make report in form of two notebooks.
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train | copy_model_to_fp32 | Creates a fp32 copy of model parameters and sets optimizer parameters | old/fastai/fp16.py | def copy_model_to_fp32(m, optim):
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train | setup_coverage | Start coverage reporting in kernel.
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train | get_cov | Returns the coverage object of pytest-cov. | docs_src/nbval/cover.py | def get_cov(config):
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train | _make_suffix | Create a suffix for nbval data file depending on pytest-cov config. | docs_src/nbval/cover.py | def _make_suffix(cov):
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train | _merge_nbval_coverage_data | Merge nbval coverage data into pytest-cov data. | docs_src/nbval/cover.py | def _merge_nbval_coverage_data(cov):
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train | to_int | Convert `b` to an int or list of ints (if `is_listy`); raises exception if not convertible | fastai/core.py | def to_int(b:Any)->Union[int,List[int]]:
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train | is1d | Return `True` if `a` is one-dimensional | fastai/core.py | def is1d(a:Collection)->bool:
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train | find_classes | List of label subdirectories in imagenet-style `folder`. | fastai/core.py | def find_classes(folder:Path)->FilePathList:
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train | arrays_split | Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]. | fastai/core.py | 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[m... | def arrays_split(mask:NPArrayMask, *arrs:NPArrayableList)->SplitArrayList:
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assert all([len(arr)==len(arrs[0]) for arr in arrs]), 'All arrays should have same length'
mask = array(mask)
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train | random_split | Randomly split `arrs` with `valid_pct` ratio. good for creating validation set. | fastai/core.py | def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:
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train | listify | Make `p` listy and the same length as `q`. | fastai/core.py | def listify(p:OptListOrItem=None, q:OptListOrItem=None):
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elif isinstance(p, str): p = [p]
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train | camel2snake | Change `name` from camel to snake style. | fastai/core.py | 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() | def camel2snake(name:str)->str:
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s1 = re.sub(_camel_re1, r'\1_\2', name)
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train | even_mults | Build log-stepped array from `start` to `stop` in `n` steps. | fastai/core.py | 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)]) | 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)]) | [
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train | extract_kwargs | Extract the keys in `names` from the `kwargs`. | fastai/core.py | def extract_kwargs(names:Collection[str], kwargs:KWArgs):
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new_kwargs = {}
for arg_name in names:
if arg_name in kwargs:
arg_val = kwargs.pop(arg_name)
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new_kwargs = {}
for arg_name in names:
if arg_name in kwargs:
arg_val = kwargs.pop(arg_name)
new_kwargs[arg_name] = arg_val
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train | partition | Split iterables `a` in equal parts of size `sz` | fastai/core.py | 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)] | def partition(a:Collection, sz:int)->List[Collection]:
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train | partition_by_cores | Split data in `a` equally among `n_cpus` cores | fastai/core.py | def partition_by_cores(a:Collection, n_cpus:int)->List[Collection]:
"Split data in `a` equally among `n_cpus` cores"
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train | series2cat | Categorifies the columns `col_names` in `df`. | fastai/core.py | 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() | def series2cat(df:DataFrame, *col_names):
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train | download_url | Download `url` to `dest` unless it exists and not `overwrite`. | fastai/core.py | 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()
... | def download_url(url:str, dest:str, overwrite:bool=False, pbar:ProgressBar=None,
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"Download `url` to `dest` unless it exists and not `overwrite`."
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train | join_path | Return `Path(path)/Path(fname)`, `path` defaults to current dir. | fastai/core.py | def join_path(fname:PathOrStr, path:PathOrStr='.')->Path:
"Return `Path(path)/Path(fname)`, `path` defaults to current dir."
return Path(path)/Path(fname) | def join_path(fname:PathOrStr, path:PathOrStr='.')->Path:
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train | join_paths | Join `path` to every file name in `fnames`. | fastai/core.py | 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] | def join_paths(fnames:FilePathList, path:PathOrStr='.')->Collection[Path]:
"Join `path` to every file name in `fnames`."
path = Path(path)
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train | loadtxt_str | Return `ndarray` of `str` of lines of text from `path`. | fastai/core.py | 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]) | def loadtxt_str(path:PathOrStr)->np.ndarray:
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with open(path, 'r') as f: lines = f.readlines()
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train | save_texts | Save in `fname` the content of `texts`. | fastai/core.py | 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') | def save_texts(fname:PathOrStr, texts:Collection[str]):
"Save in `fname` the content of `texts`."
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train | df_names_to_idx | Return the column indexes of `names` in `df`. | fastai/core.py | 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] | def df_names_to_idx(names:IntsOrStrs, df:DataFrame):
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if isinstance(names[0], int): return names
return [df.columns.get_loc(c) for c in names] | [
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train | one_hot | One-hot encode `x` with `c` classes. | fastai/core.py | 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 | def one_hot(x:Collection[int], c:int):
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res = np.zeros((c,), np.float32)
res[listify(x)] = 1.
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train | index_row | Return the slice of `a` corresponding to `idxs`. | fastai/core.py | 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()
... | 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()
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train | func_args | Return the arguments of `func`. | fastai/core.py | def func_args(func)->bool:
"Return the arguments of `func`."
code = func.__code__
return code.co_varnames[:code.co_argcount] | def func_args(func)->bool:
"Return the arguments of `func`."
code = func.__code__
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train | split_kwargs_by_func | Split `kwargs` between those expected by `func` and the others. | fastai/core.py | def split_kwargs_by_func(kwargs, func):
"Split `kwargs` between those expected by `func` and the others."
args = func_args(func)
func_kwargs = {a:kwargs.pop(a) for a in args if a in kwargs}
return func_kwargs, kwargs | def split_kwargs_by_func(kwargs, func):
"Split `kwargs` between those expected by `func` and the others."
args = func_args(func)
func_kwargs = {a:kwargs.pop(a) for a in args if a in kwargs}
return func_kwargs, kwargs | [
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train | array | Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`. | fastai/core.py | def array(a, dtype:type=None, **kwargs)->np.ndarray:
"Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`."
if not isinstance(a, collections.Sized) and not getattr(a,'__array_interface__',False):
a = list(a)
if np.int_==np.int32 and dtype is None and is_lis... | def array(a, dtype:type=None, **kwargs)->np.ndarray:
"Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`."
if not isinstance(a, collections.Sized) and not getattr(a,'__array_interface__',False):
a = list(a)
if np.int_==np.int32 and dtype is None and is_lis... | [
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train | text2html_table | Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %. | fastai/core.py | def text2html_table(items:Collection[Collection[str]])->str:
"Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %."
html_code = f"""<table border="1" class="dataframe">"""
html_code += f""" <thead>\n <tr style="text-align: right;">\n"""
for i in items[0]: html_code... | def text2html_table(items:Collection[Collection[str]])->str:
"Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %."
html_code = f"""<table border="1" class="dataframe">"""
html_code += f""" <thead>\n <tr style="text-align: right;">\n"""
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train | parallel | Call `func` on every element of `arr` in parallel using `max_workers`. | fastai/core.py | def parallel(func, arr:Collection, max_workers:int=None):
"Call `func` on every element of `arr` in parallel using `max_workers`."
max_workers = ifnone(max_workers, defaults.cpus)
if max_workers<2: results = [func(o,i) for i,o in progress_bar(enumerate(arr), total=len(arr))]
else:
with ProcessPo... | def parallel(func, arr:Collection, max_workers:int=None):
"Call `func` on every element of `arr` in parallel using `max_workers`."
max_workers = ifnone(max_workers, defaults.cpus)
if max_workers<2: results = [func(o,i) for i,o in progress_bar(enumerate(arr), total=len(arr))]
else:
with ProcessPo... | [
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] | fastai/fastai | python | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/core.py#L320-L329 | [
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train | subplots | Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title` | fastai/core.py | def subplots(rows:int, cols:int, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, title=None, **kwargs):
"Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`"
figsize = ifnone(figsize, (imgsize*cols, imgsize*rows))
fig, axs = plt.subplots(rows,cols,figsize=... | def subplots(rows:int, cols:int, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, title=None, **kwargs):
"Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`"
figsize = ifnone(figsize, (imgsize*cols, imgsize*rows))
fig, axs = plt.subplots(rows,cols,figsize=... | [
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train | show_some | Return the representation of the first `n_max` elements in `items`. | fastai/core.py | def show_some(items:Collection, n_max:int=5, sep:str=','):
"Return the representation of the first `n_max` elements in `items`."
if items is None or len(items) == 0: return ''
res = sep.join([f'{o}' for o in items[:n_max]])
if len(items) > n_max: res += '...'
return res | def show_some(items:Collection, n_max:int=5, sep:str=','):
"Return the representation of the first `n_max` elements in `items`."
if items is None or len(items) == 0: return ''
res = sep.join([f'{o}' for o in items[:n_max]])
if len(items) > n_max: res += '...'
return res | [
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train | get_tmp_file | Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it. | fastai/core.py | def get_tmp_file(dir=None):
"Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it."
with tempfile.NamedTemporaryFile(delete=False, dir=dir) as f: return f.name | def get_tmp_file(dir=None):
"Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it."
with tempfile.NamedTemporaryFile(delete=False, dir=dir) as f: return f.name | [
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train | compose | Compose `funcs` | fastai/core.py | def compose(funcs:List[Callable])->Callable:
"Compose `funcs`"
def compose_(funcs, x, *args, **kwargs):
for f in listify(funcs): x = f(x, *args, **kwargs)
return x
return partial(compose_, funcs) | def compose(funcs:List[Callable])->Callable:
"Compose `funcs`"
def compose_(funcs, x, *args, **kwargs):
for f in listify(funcs): x = f(x, *args, **kwargs)
return x
return partial(compose_, funcs) | [
"Compose",
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] | fastai/fastai | python | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/core.py#L351-L356 | [
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... | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 |
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