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fastai/fastai | fastai/widgets/image_downloader.py | _url_params | def _url_params(size:str='>400*300', format:str='jpg') -> str:
"Build Google Images Search Url params and return them as a string."
_fmts = {'jpg':'ift:jpg','gif':'ift:gif','png':'ift:png','bmp':'ift:bmp', 'svg':'ift:svg','webp':'webp','ico':'ift:ico'}
if size not in _img_sizes:
raise RuntimeError(... | python | def _url_params(size:str='>400*300', format:str='jpg') -> str:
"Build Google Images Search Url params and return them as a string."
_fmts = {'jpg':'ift:jpg','gif':'ift:gif','png':'ift:png','bmp':'ift:bmp', 'svg':'ift:svg','webp':'webp','ico':'ift:ico'}
if size not in _img_sizes:
raise RuntimeError(... | [
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fastai/fastai | fastai/widgets/image_downloader.py | _search_url | def _search_url(search_term:str, size:str='>400*300', format:str='jpg') -> str:
"Return a Google Images Search URL for a given search term."
return ('https://www.google.com/search?q=' + quote(search_term) +
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_url_params(size, fo... | python | def _search_url(search_term:str, size:str='>400*300', format:str='jpg') -> str:
"Return a Google Images Search URL for a given search term."
return ('https://www.google.com/search?q=' + quote(search_term) +
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fastai/fastai | fastai/widgets/image_downloader.py | _fetch_img_tuples | def _fetch_img_tuples(url:str, format:str='jpg', n_images:int=10) -> list:
"Parse the Google Images Search for urls and return the image metadata as tuples (fname, url)."
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'}
html = r... | python | def _fetch_img_tuples(url:str, format:str='jpg', n_images:int=10) -> list:
"Parse the Google Images Search for urls and return the image metadata as tuples (fname, url)."
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'}
html = r... | [
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fastai/fastai | fastai/widgets/image_downloader.py | _html_to_img_tuples | def _html_to_img_tuples(html:str, format:str='jpg', n_images:int=10) -> list:
"Parse the google images html to img tuples containining `(fname, url)`"
bs = BeautifulSoup(html, 'html.parser')
img_tags = bs.find_all('div', {'class': 'rg_meta'})
metadata_dicts = (json.loads(e.text) for e in img_tags)
... | python | def _html_to_img_tuples(html:str, format:str='jpg', n_images:int=10) -> list:
"Parse the google images html to img tuples containining `(fname, url)`"
bs = BeautifulSoup(html, 'html.parser')
img_tags = bs.find_all('div', {'class': 'rg_meta'})
metadata_dicts = (json.loads(e.text) for e in img_tags)
... | [
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fastai/fastai | fastai/widgets/image_downloader.py | _fetch_img_tuples_webdriver | def _fetch_img_tuples_webdriver(url:str, format:str='jpg', n_images:int=150) -> list:
"""
Parse the Google Images Search for urls and return the image metadata as tuples (fname, url).
Use this for downloads of >100 images. Requires `selenium`.
"""
try:
from selenium import webdriver
... | python | def _fetch_img_tuples_webdriver(url:str, format:str='jpg', n_images:int=150) -> list:
"""
Parse the Google Images Search for urls and return the image metadata as tuples (fname, url).
Use this for downloads of >100 images. Requires `selenium`.
"""
try:
from selenium import webdriver
... | [
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fastai/fastai | fastai/widgets/image_downloader.py | _download_images | def _download_images(label_path:PathOrStr, img_tuples:list, max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList:
"""
Downloads images in `img_tuples` to `label_path`.
If the directory doesn't exist, it'll be created automatically.
Uses `parallel` to speed things up in `max_workers` when the s... | python | def _download_images(label_path:PathOrStr, img_tuples:list, max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList:
"""
Downloads images in `img_tuples` to `label_path`.
If the directory doesn't exist, it'll be created automatically.
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fastai/fastai | fastai/widgets/image_downloader.py | _download_single_image | def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None:
"""
Downloads a single image from Google Search results to `label_path`
given an `img_tuple` that contains `(fname, url)` of an image to download.
`i` is just an iteration number `int`.
"""
suffix = re.f... | python | def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None:
"""
Downloads a single image from Google Search results to `label_path`
given an `img_tuple` that contains `(fname, url)` of an image to download.
`i` is just an iteration number `int`.
"""
suffix = re.f... | [
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fastai/fastai | fastai/widgets/image_downloader.py | ImageDownloader._init_ui | def _init_ui(self) -> VBox:
"Initialize the widget UI and return the UI."
self._search_input = Text(placeholder="What images to search for?")
self._count_input = BoundedIntText(placeholder="How many pics?", value=10, min=1, max=5000, step=1,
layout=Layo... | python | def _init_ui(self) -> VBox:
"Initialize the widget UI and return the UI."
self._search_input = Text(placeholder="What images to search for?")
self._count_input = BoundedIntText(placeholder="How many pics?", value=10, min=1, max=5000, step=1,
layout=Layo... | [
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fastai/fastai | fastai/widgets/image_downloader.py | ImageDownloader.clear_imgs | def clear_imgs(self) -> None:
"Clear the widget's images preview pane."
self._preview_header.value = self._heading
self._img_pane.children = tuple() | python | def clear_imgs(self) -> None:
"Clear the widget's images preview pane."
self._preview_header.value = self._heading
self._img_pane.children = tuple() | [
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fastai/fastai | fastai/widgets/image_downloader.py | ImageDownloader.validate_search_input | def validate_search_input(self) -> bool:
"Check if input value is empty."
input = self._search_input
if input.value == str(): input.layout = Layout(border="solid 2px red", height='auto')
else: self._search_input.layout = Layout()
return input.value != str() | python | def validate_search_input(self) -> bool:
"Check if input value is empty."
input = self._search_input
if input.value == str(): input.layout = Layout(border="solid 2px red", height='auto')
else: self._search_input.layout = Layout()
return input.value != str() | [
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fastai/fastai | fastai/widgets/image_downloader.py | ImageDownloader.on_download_button_click | def on_download_button_click(self, btn) -> None:
"Download button click handler: validate search term and download images."
term = self._search_input.value
limit = int(self._count_input.value)
size = self._size_input.value
if not self.validate_search_input(): return
self.... | python | def on_download_button_click(self, btn) -> None:
"Download button click handler: validate search term and download images."
term = self._search_input.value
limit = int(self._count_input.value)
size = self._size_input.value
if not self.validate_search_input(): return
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fastai/fastai | fastai/widgets/image_downloader.py | ImageDownloader.display_images_widgets | def display_images_widgets(self, fnames:list) -> None:
"Display a few preview images in the notebook"
imgs = [widgets.Image(value=open(f, 'rb').read(), width='200px') for f in fnames]
self._img_pane.children = tuple(imgs) | python | def display_images_widgets(self, fnames:list) -> None:
"Display a few preview images in the notebook"
imgs = [widgets.Image(value=open(f, 'rb').read(), width='200px') for f in fnames]
self._img_pane.children = tuple(imgs) | [
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fastai/fastai | fastai/callbacks/lr_finder.py | LRFinder.on_train_begin | def on_train_begin(self, pbar, **kwargs:Any)->None:
"Initialize optimizer and learner hyperparameters."
setattr(pbar, 'clean_on_interrupt', True)
self.learn.save('tmp')
self.opt = self.learn.opt
self.opt.lr = self.sched.start
self.stop,self.best_loss = False,0.
re... | python | def on_train_begin(self, pbar, **kwargs:Any)->None:
"Initialize optimizer and learner hyperparameters."
setattr(pbar, 'clean_on_interrupt', True)
self.learn.save('tmp')
self.opt = self.learn.opt
self.opt.lr = self.sched.start
self.stop,self.best_loss = False,0.
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fastai/fastai | fastai/callbacks/lr_finder.py | LRFinder.on_batch_end | def on_batch_end(self, iteration:int, smooth_loss:TensorOrNumber, **kwargs:Any)->None:
"Determine if loss has runaway and we should stop."
if iteration==0 or smooth_loss < self.best_loss: self.best_loss = smooth_loss
self.opt.lr = self.sched.step()
if self.sched.is_done or (self.stop_div... | python | def on_batch_end(self, iteration:int, smooth_loss:TensorOrNumber, **kwargs:Any)->None:
"Determine if loss has runaway and we should stop."
if iteration==0 or smooth_loss < self.best_loss: self.best_loss = smooth_loss
self.opt.lr = self.sched.step()
if self.sched.is_done or (self.stop_div... | [
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fastai/fastai | fastai/callbacks/lr_finder.py | LRFinder.on_train_end | def on_train_end(self, **kwargs:Any)->None:
"Cleanup learn model weights disturbed during LRFinder exploration."
self.learn.load('tmp', purge=False)
if hasattr(self.learn.model, 'reset'): self.learn.model.reset()
for cb in self.callbacks:
if hasattr(cb, 'reset'): cb.reset()
... | python | def on_train_end(self, **kwargs:Any)->None:
"Cleanup learn model weights disturbed during LRFinder exploration."
self.learn.load('tmp', purge=False)
if hasattr(self.learn.model, 'reset'): self.learn.model.reset()
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fastai/fastai | fastai/basic_data.py | load_data | 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:
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dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate,
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fastai/fastai | fastai/basic_data.py | DataBunch.create | 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,
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fastai/fastai | fastai/basic_data.py | DataBunch.dl | 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
... | python | def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
"Returns appropriate `Dataset` for validation, training, or test (`ds_type`)."
#TODO: refactor
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fastai/fastai | fastai/basic_data.py | DataBunch.dls | def dls(self)->List[DeviceDataLoader]:
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fastai/fastai | fastai/basic_data.py | DataBunch.save | def save(self, file:PathLikeOrBinaryStream= 'data_save.pkl')->None:
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if not getattr(self, 'label_list', False):
warn("Serializing the `DataBunch` only works when you created it using the data block API.")
... | python | 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):
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fastai/fastai | fastai/basic_data.py | DataBunch.one_batch | 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
... | python | def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
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fastai/fastai | fastai/basic_data.py | DataBunch.one_item | def one_item(self, item, detach:bool=False, denorm:bool=False, cpu:bool=False):
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ds = self.single_ds
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"Get `item` into a batch. Optionally `detach` and `denorm`."
ds = self.single_ds
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fastai/fastai | fastai/basic_data.py | DataBunch.show_batch | def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
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x,y = self.one_batch(ds_type, True, True)
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fastai/fastai | fastai/basic_data.py | DataBunch.export | def export(self, file:PathLikeOrBinaryStream='export.pkl'):
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xtra = dict(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {}
try_save(self.valid_ds.get_state(**xtra),... | python | 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 {}
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fastai/fastai | fastai/train.py | ClassificationInterpretation.plot_confusion_matrix | def plot_confusion_matrix(self, normalize:bool=False, title:str='Confusion matrix', cmap:Any="Blues", slice_size:int=1,
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fastai/fastai | fastai/train.py | ClassificationInterpretation.most_confused | def most_confused(self, min_val:int=1, slice_size:int=1)->Collection[Tuple[str,str,int]]:
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fastai/fastai | fastai/train.py | ClassificationInterpretation.top_losses | def top_losses(self, k:int=None, largest=True):
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return self.losses.topk(ifnone(k, len(self.losses)), largest=largest) | python | def top_losses(self, k:int=None, largest=True):
"`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`)."
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This is the micro averaged version where the true positives, false negatives and
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fastai/fastai | old/fastai/metrics.py | fbeta_np | def fbeta_np(preds, targs, beta, thresh=0.5, epsilon=1e-8):
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assert beta > 0, 'beta needs to be greater than 0'
beta2 = beta ** 2
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""" see fbeta """
assert beta > 0, 'beta needs to be greater than 0'
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fastai/fastai | examples/train_imagenet.py | main | def main( gpu:Param("GPU to run on", str)=None ):
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path = Path('/mnt/fe2_disk/')
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dirname = 'imagenet'
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if gpu is None: bs *= torch.cud... | python | 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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fastai/fastai | fastai/vision/learner.py | cnn_config | def cnn_config(arch):
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fastai/fastai | fastai/vision/learner.py | create_body | def create_body(arch:Callable, pretrained:bool=True, cut:Optional[Union[int, Callable]]=None):
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model = arch(pretrained)
cut = ifnone(cut, cnn_config(arch)['cut'])
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"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)
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fastai/fastai | fastai/vision/learner.py | create_head | def create_head(nf:int, nc:int, lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
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"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]
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"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]
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fastai/fastai | fastai/vision/learner.py | cnn_learner | def cnn_learner(data:DataBunch, base_arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
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fastai/fastai | fastai/vision/learner.py | unet_learner | def unet_learner(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
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self_attention:bool=False, y_range:Optional[Tuple[float,float]]=None, last_cross:bool=True,
... | python | def unet_learner(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
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fastai/fastai | fastai/vision/learner.py | _cl_int_from_learner | 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... | python | 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... | [
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fastai/fastai | fastai/vision/learner.py | _cl_int_plot_top_losses | 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... | python | 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]:
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fastai/fastai | fastai/vision/learner.py | _cl_int_plot_multi_top_losses | 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... | python | 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."
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print("Max 20 samples")
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.from_toplosses | 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 | python | 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)
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.get_toplosses_idxs | def get_toplosses_idxs(cls, learn, n_imgs, **kwargs):
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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... | python | 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)
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.padded_ds | 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... | python | 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`."
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.from_similars | 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 | python | 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)
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.get_similars_idxs | def get_similars_idxs(cls, learn, layer_ls, **kwargs):
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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... | python | 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]])
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.get_actns | 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... | python | 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...')
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.comb_similarity | 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 ... | python | 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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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.largest_indices | 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... | python | 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:]
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fastai/fastai | fastai/widgets/image_cleaner.py | DatasetFormatter.sort_idxs | 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] | python | 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]))]
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.make_img_widget | 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) | python | def make_img_widget(cls, img, layout=Layout(), format='jpg'):
"Returns an image widget for specified file name `img`."
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.make_button_widget | 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)
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"Return a Button widget with specified `handler`."
btn = widgets.Button(description=label, layout=layout)
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.make_dropdown_widget | 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... | python | def make_dropdown_widget(cls, description='Description', options=['Label 1', 'Label 2'], value='Label 1',
file_path=None, layout=Layout(), handler=None):
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.make_horizontal_box | def make_horizontal_box(cls, children, layout=Layout()):
"Make a horizontal box with `children` and `layout`."
return widgets.HBox(children, layout=layout) | python | def make_horizontal_box(cls, children, layout=Layout()):
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.make_vertical_box | 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) | python | def make_vertical_box(cls, children, layout=Layout(), duplicates=False):
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.create_image_list | 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 ... | python | 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
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chunked_idxs = chunks(fns_idxs, 2)
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.relabel | 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] =... | python | 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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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.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)
... | python | 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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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.on_delete | 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 | python | def on_delete(self, btn):
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.get_widgets | 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... | python | 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'))
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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.batch_contains_deleted | 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) | python | 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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fastai/fastai | fastai/widgets/image_cleaner.py | ImageCleaner.render | def render(self):
"Re-render Jupyter cell for batch of images."
clear_output()
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return display(f'No images to show :). {self._skipped} pairs were '
f'skipped since at least one of the images was deleted ... | python | def render(self):
"Re-render Jupyter cell for batch of images."
clear_output()
self.write_csv()
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fastai/fastai | fastai/text/models/transformer.py | _line_shift | 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... | python | 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)
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fastai/fastai | fastai/text/models/transformer.py | tfmer_lm_split | 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... | python | 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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fastai/fastai | fastai/text/models/transformer.py | tfmer_clas_split | 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 group... | python | 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
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fastai/fastai | fastai/text/models/transformer.py | tfmerXL_lm_split | def tfmerXL_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]) + [ParameterModule(encoder.u), ParameterModule(encoder.v)]]
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"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)]]
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fastai/fastai | fastai/text/models/transformer.py | TransformerXL.reset | def reset(self):
"Reset the internal memory."
self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)] | python | def reset(self):
"Reset the internal memory."
self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)] | [
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fastai/fastai | docs_src/nbval/nbdime_reporter.py | NbdimeReporter.make_report | 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:
... | python | 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
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fastai/fastai | old/fastai/fp16.py | batchnorm_to_fp32 | 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
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fastai/fastai | old/fastai/fp16.py | copy_model_to_fp32 | def copy_model_to_fp32(m, optim):
""" Creates a fp32 copy of model parameters and sets optimizer parameters
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fp32_params = [m_param.clone().type(torch.cuda.FloatTensor).detach() for m_param in trainable_params_(m)]
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fastai/fastai | docs_src/nbval/cover.py | setup_coverage | 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)
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"""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)
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fastai/fastai | docs_src/nbval/cover.py | teardown_coverage | def teardown_coverage(config, kernel, output_loc=None):
"""Finish coverage reporting in kernel.
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setup_coverage.
"""
language = kernel.language
if language.startswith('python'):
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fastai/fastai | docs_src/nbval/cover.py | get_cov | def get_cov(config):
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# Check with hasplugin to avoid getplugin exception in older pytest.
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plugin = config.pluginmanager.getplugin('_cov')
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"""Returns the coverage object of pytest-cov."""
# Check with hasplugin to avoid getplugin exception in older pytest.
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plugin = config.pluginmanager.getplugin('_cov')
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fastai/fastai | docs_src/nbval/cover.py | _make_suffix | 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.
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fastai/fastai | docs_src/nbval/cover.py | _merge_nbval_coverage_data | def _merge_nbval_coverage_data(cov):
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if not cov:
return
suffix = _make_suffix(cov)
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fastai/fastai | fastai/core.py | chunks | 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] | python | def chunks(l:Collection, n:int)->Iterable:
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fastai/fastai | fastai/core.py | to_int | 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) | python | def to_int(b:Any)->Union[int,List[int]]:
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if is_listy(b): return [to_int(x) for x in b]
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fastai/fastai | fastai/core.py | is1d | def is1d(a:Collection)->bool:
"Return `True` if `a` is one-dimensional"
return len(a.shape) == 1 if hasattr(a, 'shape') else True | python | def is1d(a:Collection)->bool:
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return len(a.shape) == 1 if hasattr(a, 'shape') else True | [
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fastai/fastai | fastai/core.py | uniqueify | 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 | python | 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 | [
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fastai/fastai | fastai/core.py | find_classes | 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) | python | def find_classes(folder:Path)->FilePathList:
"List of label subdirectories in imagenet-style `folder`."
classes = [d for d in folder.iterdir()
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assert(len(classes)>0)
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fastai/fastai | fastai/core.py | arrays_split | 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... | python | 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'
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fastai/fastai | fastai/core.py | random_split | 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... | python | 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'
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