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fastai/fastai | fastai/basic_train.py | Recorder.on_batch_begin | def on_batch_begin(self, train, **kwargs:Any)->None:
"Record learning rate and momentum at beginning of batch."
if train:
self.lrs.append(self.opt.lr)
self.moms.append(self.opt.mom) | python | def on_batch_begin(self, train, **kwargs:Any)->None:
"Record learning rate and momentum at beginning of batch."
if train:
self.lrs.append(self.opt.lr)
self.moms.append(self.opt.mom) | [
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fastai/fastai | fastai/basic_train.py | Recorder.on_backward_begin | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
"Record the loss before any other callback has a chance to modify it."
self.losses.append(smooth_loss)
if self.pbar is not None and hasattr(self.pbar,'child'):
self.pbar.child.comment = f'{smooth_loss:.4f}' | python | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
"Record the loss before any other callback has a chance to modify it."
self.losses.append(smooth_loss)
if self.pbar is not None and hasattr(self.pbar,'child'):
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fastai/fastai | fastai/basic_train.py | Recorder.on_epoch_end | def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
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self.nb_batches.append(num_batch)
if last_metrics is not None: self.val_losses.append(last_metrics[0])
... | python | def on_epoch_end(self, epoch:int, num_batch:int, smooth_loss:Tensor,
last_metrics=MetricsList, **kwargs:Any)->bool:
"Save epoch info: num_batch, smooth_loss, metrics."
self.nb_batches.append(num_batch)
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fastai/fastai | fastai/basic_train.py | Recorder.format_stats | def format_stats(self, stats:TensorOrNumList)->None:
"Format stats before printing."
str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
if self.add_time: str_stats.append(f... | python | def format_stats(self, stats:TensorOrNumList)->None:
"Format stats before printing."
str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
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fastai/fastai | fastai/basic_train.py | Recorder.add_metric_names | def add_metric_names(self, names):
"Add `names` to the inner metric names."
if hasattr(self, '_added_met_names'): self._added_met_names += names
else: self._added_met_names = names | python | def add_metric_names(self, names):
"Add `names` to the inner metric names."
if hasattr(self, '_added_met_names'): self._added_met_names += names
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fastai/fastai | fastai/basic_train.py | Recorder.plot_lr | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
... | python | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
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fastai/fastai | fastai/basic_train.py | Recorder.plot | def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
**kwargs)->Optional[plt.Figure]:
"Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
lrs = self._split_list(self.lrs, skip_start, ... | python | def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
**kwargs)->Optional[plt.Figure]:
"Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
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fastai/fastai | fastai/basic_train.py | Recorder.plot_losses | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
losses = self._split_list(self.losses, skip_start, skip_end)
iterations = self._split_list(range_of(self.losses), skip_s... | python | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
losses = self._split_list(self.losses, skip_start, skip_end)
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fastai/fastai | fastai/basic_train.py | Recorder.plot_metrics | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
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... | python | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
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fastai/fastai | fastai/script.py | anno_parser | def anno_parser(func):
"Look at params (annotated with `Param`) in func and return an `ArgumentParser`"
p = ArgumentParser(description=func.__doc__)
for k,v in inspect.signature(func).parameters.items():
param = func.__annotations__.get(k, Param())
kwargs = param.kwargs
if v.default ... | python | def anno_parser(func):
"Look at params (annotated with `Param`) in func and return an `ArgumentParser`"
p = ArgumentParser(description=func.__doc__)
for k,v in inspect.signature(func).parameters.items():
param = func.__annotations__.get(k, Param())
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fastai/fastai | fastai/script.py | call_parse | def call_parse(func):
"Decorator to create a simple CLI from `func` using `anno_parser`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == "__main__":
args = anno_parser(func).parse_args()
func(**args.__dict__)
else: return func | python | def call_parse(func):
"Decorator to create a simple CLI from `func` using `anno_parser`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == "__main__":
args = anno_parser(func).parse_args()
func(**args.__dict__)
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fastai/fastai | fastai/script.py | call_plac | def call_plac(f):
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"Decorator to create a simple CLI from `func` using `plac`"
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fastai/fastai | old/fastai/text.py | numericalize_tok | def numericalize_tok(tokens, max_vocab=50000, min_freq=0, unk_tok="_unk_", pad_tok="_pad_", bos_tok="_bos_", eos_tok="_eos_"):
"""Takes in text tokens and returns int2tok and tok2int converters
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tokens(list): List of tokens. Can be a list of strings, or a list of lists of strings.
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"""Takes in text tokens and returns int2tok and tok2int converters
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tokens(list): List of tokens. Can be a list of strings, or a list of lists of strings.
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fastai/fastai | fastai/text/models/qrnn.py | QRNN.reset | def reset(self):
"If your convolutional window is greater than 1 and you save previous xs, you must reset at the beginning of each new sequence."
for layer in self.layers: layer.reset()
if self.bidirectional:
for layer in self.layers_bwd: layer.reset() | python | def reset(self):
"If your convolutional window is greater than 1 and you save previous xs, you must reset at the beginning of each new sequence."
for layer in self.layers: layer.reset()
if self.bidirectional:
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fastai/fastai | docs_src/nbval/kernel.py | start_new_kernel | def start_new_kernel(startup_timeout=60, kernel_name='python', **kwargs):
"""Start a new kernel, and return its Manager and Client"""
logger.debug('Starting new kernel: "%s"' % kernel_name)
km = KernelManager(kernel_name=kernel_name,
kernel_spec_manager=NbvalKernelspecManager())
k... | python | def start_new_kernel(startup_timeout=60, kernel_name='python', **kwargs):
"""Start a new kernel, and return its Manager and Client"""
logger.debug('Starting new kernel: "%s"' % kernel_name)
km = KernelManager(kernel_name=kernel_name,
kernel_spec_manager=NbvalKernelspecManager())
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fastai/fastai | docs_src/nbval/kernel.py | NbvalKernelspecManager.get_kernel_spec | def get_kernel_spec(self, kernel_name):
"""Returns a :class:`KernelSpec` instance for the given kernel_name.
Raises :exc:`NoSuchKernel` if the given kernel name is not found.
"""
if kernel_name == CURRENT_ENV_KERNEL_NAME:
return self.kernel_spec_class(
resour... | python | def get_kernel_spec(self, kernel_name):
"""Returns a :class:`KernelSpec` instance for the given kernel_name.
Raises :exc:`NoSuchKernel` if the given kernel name is not found.
"""
if kernel_name == CURRENT_ENV_KERNEL_NAME:
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fastai/fastai | docs_src/nbval/kernel.py | RunningKernel.stop | def stop(self):
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"""
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Arguments:
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""" Get a list of index values for Validation set from a dataset
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fastai/fastai | old/fastai/dataset.py | resize_img | def resize_img(fname, targ, path, new_path, fn=None):
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"""
if fn is None:
fn = resize_fn(targ)
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"""
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fastai/fastai | old/fastai/dataset.py | read_dir | def read_dir(path, folder):
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fnames = glob(f"{full_path}/*.*")
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""" Returns a list of relative file paths to `path` for all files within `folder` """
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fnames = glob(f"{full_path}/*.*")
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'''
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res = np.zeros((c,), dtype=np.float32)
res[ids] = 1
return res | python | def n_hot(ids, c):
'''
one hot encoding by index. Returns array of length c, where all entries are 0, except for the indecies in ids
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res[ids] = 1
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fastai/fastai | old/fastai/dataset.py | parse_csv_labels | def parse_csv_labels(fn, skip_header=True, cat_separator = ' '):
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has a header, :skip_header: should be set to True. The labels in the
label set are expected to be space separated... | python | def parse_csv_labels(fn, skip_header=True, cat_separator = ' '):
"""Parse filenames and label sets from a CSV file.
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fn = str(fn)
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# Dicom signature from the dicom spec.
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'''True if the fn points to a DICOM image'''
fn = str(fn)
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fn: the file path of the image
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fn: the file path of the image
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"""
Split each array passed as *a, to a pair of arrays like this (elements selected by idxs, the remaining elements)
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fastai/fastai | old/fastai/dataset.py | FilesDataset.resize_imgs | def resize_imgs(self, targ, new_path, resume=True, fn=None):
"""
resize all images in the dataset and save them to `new_path`
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targ (int): the target size
new_path (string): the new folder to save the images
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"""
resize all images in the dataset and save them to `new_path`
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targ (int): the target size
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fastai/fastai | old/fastai/dataset.py | FilesDataset.denorm | def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
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return self.transform.denorm(np.rollaxis(arr,1,4... | python | def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
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fastai/fastai | old/fastai/dataset.py | ImageData.resized | def resized(self, dl, targ, new_path, resume = True, fn=None):
"""
Return a copy of this dataset resized
"""
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Return a copy of this dataset resized
"""
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targ_sz (int): the target size
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fastai/fastai | old/fastai/dataset.py | ImageClassifierData.from_arrays | def from_arrays(cls, path, trn, val, bs=64, tfms=(None,None), classes=None, num_workers=4, test=None, continuous=False):
""" Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
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""" Read in images and their labels given as numpy arrays
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path: a root path of the data (used for storing trained models, precomputed values, etc)
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fastai/fastai | old/fastai/dataset.py | ImageClassifierData.from_paths | def from_paths(cls, path, bs=64, tfms=(None,None), trn_name='train', val_name='valid', test_name=None, test_with_labels=False, num_workers=8):
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fastai/fastai | old/fastai/dataset.py | ImageClassifierData.from_csv | def from_csv(cls, path, folder, csv_fname, bs=64, tfms=(None,None),
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fastai/fastai | old/fastai/dataset.py | ImageClassifierData.from_path_and_array | def from_path_and_array(cls, path, folder, y, classes=None, val_idxs=None, test_name=None,
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fastai/fastai | fastai/utils/ipython.py | is_in_ipython | def is_in_ipython():
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program_name = os.path.basename(os.getenv('_', ''))
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"Is the code running in the ipython environment (jupyter including)"
program_name = os.path.basename(os.getenv('_', ''))
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fastai/fastai | fastai/utils/ipython.py | get_ref_free_exc_info | def get_ref_free_exc_info():
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type, val, tb = sys.exc_info()
traceback.clear_frames(tb)
return (type, val, tb) | python | def get_ref_free_exc_info():
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if include_bt: arg_name = code_esc(arg_name)
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fastai/fastai | fastai/gen_doc/nbdoc.py | show_doc | def show_doc(elt, doc_string:bool=True, full_name:str=None, arg_comments:dict=None, title_level=None, alt_doc_string:str='',
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fastai/fastai | fastai/gen_doc/nbdoc.py | link_docstring | def link_docstring(modules, docstring:str, overwrite:bool=False)->str:
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mods = listify(modules)
for mod in mods: _modvars.update(mod.__dict__) # concat all module definitions
return re.sub(BT_REGEX, replac... | python | def link_docstring(modules, docstring:str, overwrite:bool=False)->str:
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fastai/fastai | fastai/gen_doc/nbdoc.py | find_elt | def find_elt(modvars, keyword, match_last=False):
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keyword = strip_fastai(keyword)
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fastai/fastai | fastai/gen_doc/nbdoc.py | import_mod | def import_mod(mod_name:str, ignore_errors=False):
"Return module from `mod_name`."
splits = str.split(mod_name, '.')
try:
if len(splits) > 1 : mod = importlib.import_module('.' + '.'.join(splits[1:]), splits[0])
else: mod = importlib.import_module(mod_name)
return mod
except:
... | python | def import_mod(mod_name:str, ignore_errors=False):
"Return module from `mod_name`."
splits = str.split(mod_name, '.')
try:
if len(splits) > 1 : mod = importlib.import_module('.' + '.'.join(splits[1:]), splits[0])
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return mod
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fastai/fastai | fastai/gen_doc/nbdoc.py | show_doc_from_name | def show_doc_from_name(mod_name, ft_name:str, doc_string:bool=True, arg_comments:dict={}, alt_doc_string:str=''):
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_ft_names | def get_ft_names(mod, include_inner=False)->List[str]:
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# If the module has an attribute __all__, it picks those.
# Otherwise, it returns all the functions defined inside a module.
fn_names = []
for elt_name in get_exports(mod):
elt = getattr(mod,el... | python | def get_ft_names(mod, include_inner=False)->List[str]:
"Return all the functions of module `mod`."
# If the module has an attribute __all__, it picks those.
# Otherwise, it returns all the functions defined inside a module.
fn_names = []
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_inner_fts | def get_inner_fts(elt)->List[str]:
"List the inner functions of a class."
fts = []
for ft_name in elt.__dict__.keys():
if ft_name.startswith('_'): continue
ft = getattr(elt, ft_name)
if inspect.isfunction(ft): fts.append(f'{elt.__name__}.{ft_name}')
if inspect.ismethod(ft): f... | python | def get_inner_fts(elt)->List[str]:
"List the inner functions of a class."
fts = []
for ft_name in elt.__dict__.keys():
if ft_name.startswith('_'): continue
ft = getattr(elt, ft_name)
if inspect.isfunction(ft): fts.append(f'{elt.__name__}.{ft_name}')
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_module_toc | def get_module_toc(mod_name):
"Display table of contents for given `mod_name`."
mod = import_mod(mod_name)
ft_names = mod.__all__ if hasattr(mod,'__all__') else get_ft_names(mod)
ft_names.sort(key = str.lower)
tabmat = ''
for ft_name in ft_names:
tabmat += f'- [{ft_name}](#{ft_name})\n'
... | python | def get_module_toc(mod_name):
"Display table of contents for given `mod_name`."
mod = import_mod(mod_name)
ft_names = mod.__all__ if hasattr(mod,'__all__') else get_ft_names(mod)
ft_names.sort(key = str.lower)
tabmat = ''
for ft_name in ft_names:
tabmat += f'- [{ft_name}](#{ft_name})\n'
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_fn_link | def get_fn_link(ft)->str:
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ft = getattr(ft, '__func__', ft)
anchor = strip_fastai(get_anchor(ft))
module_name = strip_fastai(get_module_name(ft))
base = '' if use_relative_links else FASTAI_DOCS
return... | python | def get_fn_link(ft)->str:
"Return function link to notebook documentation of `ft`. Private functions link to source code"
ft = getattr(ft, '__func__', ft)
anchor = strip_fastai(get_anchor(ft))
module_name = strip_fastai(get_module_name(ft))
base = '' if use_relative_links else FASTAI_DOCS
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_pytorch_link | def get_pytorch_link(ft)->str:
"Returns link to pytorch docs of `ft`."
name = ft.__name__
ext = '.html'
if name == 'device': return f'{PYTORCH_DOCS}tensor_attributes{ext}#torch-device'
if name == 'Tensor': return f'{PYTORCH_DOCS}tensors{ext}#torch-tensor'
if name.startswith('torchvision'):
... | python | def get_pytorch_link(ft)->str:
"Returns link to pytorch docs of `ft`."
name = ft.__name__
ext = '.html'
if name == 'device': return f'{PYTORCH_DOCS}tensor_attributes{ext}#torch-device'
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_source_link | def get_source_link(file, line, display_text="[source]", **kwargs)->str:
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link = f"{SOURCE_URL}{file}#L{line}"
if display_text is None: return link
return f'<a href="{link}" class="source_link" style="float:right">{display_text}</a>' | python | def get_source_link(file, line, display_text="[source]", **kwargs)->str:
"Returns github link for given file"
link = f"{SOURCE_URL}{file}#L{line}"
if display_text is None: return link
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fastai/fastai | fastai/gen_doc/nbdoc.py | get_function_source | def get_function_source(ft, **kwargs)->str:
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try: line = inspect.getsourcelines(ft)[1]
except Exception: return ''
mod_path = get_module_name(ft).replace('.', '/') + '.py'
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fastai/fastai | docs_src/nbval/plugin.py | find_comment_markers | def find_comment_markers(cellsource):
"""Look through the cell source for comments which affect nbval's behaviour
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"""
found = {}
for line in cellsource.splitlines():
line = line.strip()
if line.startswith('#'):
# print("Found... | python | def find_comment_markers(cellsource):
"""Look through the cell source for comments which affect nbval's behaviour
Yield an iterable of ``(MARKER_TYPE, True)``.
"""
found = {}
for line in cellsource.splitlines():
line = line.strip()
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"""
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Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed
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if not outputs:
return outputs
new_outputs = []
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"""
Merge all stream outputs with shared names into single streams
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Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed
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if not outputs:
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fastai/fastai | docs_src/nbval/plugin.py | transform_streams_for_comparison | def transform_streams_for_comparison(outputs):
"""Makes failure output for streams better by having key be the stream name"""
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for output in outputs:
if (output.output_type == 'stream'):
# Transform output
new_outputs.append({
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"""Makes failure output for streams better by having key be the stream name"""
new_outputs = []
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fastai/fastai | docs_src/nbval/plugin.py | _trim_base64 | def _trim_base64(s):
"""Trim and hash base64 strings"""
if len(s) > 64 and _base64.match(s.replace('\n', '')):
h = hash_string(s)
s = '%s...<snip base64, md5=%s...>' % (s[:8], h[:16])
return s | python | def _trim_base64(s):
"""Trim and hash base64 strings"""
if len(s) > 64 and _base64.match(s.replace('\n', '')):
h = hash_string(s)
s = '%s...<snip base64, md5=%s...>' % (s[:8], h[:16])
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fastai/fastai | docs_src/nbval/plugin.py | _indent | def _indent(s, indent=' '):
"""Intent each line with indent"""
if isinstance(s, six.string_types):
return '\n'.join(('%s%s' % (indent, line) for line in s.splitlines()))
return s | python | def _indent(s, indent=' '):
"""Intent each line with indent"""
if isinstance(s, six.string_types):
return '\n'.join(('%s%s' % (indent, line) for line in s.splitlines()))
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fastai/fastai | docs_src/nbval/plugin.py | IPyNbCell.format_output_compare | def format_output_compare(self, key, left, right):
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fastai/fastai | docs_src/nbval/plugin.py | IPyNbCell.sanitize | def sanitize(self, s):
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fastai/fastai | fastai/vision/tta.py | _tta_only | def _tta_only(learn:Learner, ds_type:DatasetType=DatasetType.Valid, scale:float=1.35) -> Iterator[List[Tensor]]:
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fastai/fastai | fastai/metrics.py | fbeta | def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
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input = input.topk(k=k, dim=-1)[1]
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] | puts pytorch variable to gpu, if cuda is available and USE_GPU is set to true. | [
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"."
] | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/core.py#L88-L90 | train |
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