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fastai/fastai | fastai/torch_core.py | add_metrics | def add_metrics(last_metrics:Collection[Rank0Tensor], mets:Union[Rank0Tensor, Collection[Rank0Tensor]]):
"Return a dictionary for updating `last_metrics` with `mets`."
last_metrics,mets = listify(last_metrics),listify(mets)
return {'last_metrics': last_metrics + mets} | python | def add_metrics(last_metrics:Collection[Rank0Tensor], mets:Union[Rank0Tensor, Collection[Rank0Tensor]]):
"Return a dictionary for updating `last_metrics` with `mets`."
last_metrics,mets = listify(last_metrics),listify(mets)
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fastai/fastai | old/fastai/executors.py | LazyThreadPoolExecutor.map | def map(self, fn, *iterables, timeout=None, chunksize=1, prefetch=None):
"""
Collects iterables lazily, rather than immediately.
Docstring same as parent: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
Implmentation taken from this PR: https://githu... | python | def map(self, fn, *iterables, timeout=None, chunksize=1, prefetch=None):
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Collects iterables lazily, rather than immediately.
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fastai/fastai | old/docs/gen_ascii_docs.py | gen_ascii_docs | def gen_ascii_docs(src='fastai'):
"""Generate documentation for fastai library in HTML (asciidoctor required)
:param str src: The absolute/relative path of source file/dir
"""
os.chdir(Path(__file__).absolute().parent)
with working_directory('..'):
path = Path(src)
if path.is_dir():
... | python | def gen_ascii_docs(src='fastai'):
"""Generate documentation for fastai library in HTML (asciidoctor required)
:param str src: The absolute/relative path of source file/dir
"""
os.chdir(Path(__file__).absolute().parent)
with working_directory('..'):
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._get_new_batch | def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]:
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._update_batches_if_needed | def _update_batches_if_needed(self)->None:
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update_batches = self.data is not self.learn.data
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"one_batch function is extremely slow with large datasets. This is caching the result as an optimization."
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update_batches = self.data is not self.learn.data
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._write_model_stats | def _write_model_stats(self, iteration:int)->None:
"Writes gradient statistics to Tensorboard."
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._write_training_loss | def _write_training_loss(self, iteration:int, last_loss:Tensor)->None:
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scalar_value = to_np(last_loss)
tag = self.metrics_root + 'train_loss'
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration) | python | def _write_training_loss(self, iteration:int, last_loss:Tensor)->None:
"Writes training loss to Tensorboard."
scalar_value = to_np(last_loss)
tag = self.metrics_root + 'train_loss'
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._write_weight_histograms | def _write_weight_histograms(self, iteration:int)->None:
"Writes model weight histograms to Tensorboard."
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._write_scalar | def _write_scalar(self, name:str, scalar_value, iteration:int)->None:
"Writes single scalar value to Tensorboard."
tag = self.metrics_root + name
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration) | python | def _write_scalar(self, name:str, scalar_value, iteration:int)->None:
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tag = self.metrics_root + name
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter._write_metrics | def _write_metrics(self, iteration:int, last_metrics:MetricsList, start_idx:int=2)->None:
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recorder = self.learn.recorder
for i, name in enumerate(recorder.names[start_idx:]):
if last_metrics is None or len(last_metrics) < i+1: return
... | python | def _write_metrics(self, iteration:int, last_metrics:MetricsList, start_idx:int=2)->None:
"Writes training metrics to Tensorboard."
recorder = self.learn.recorder
for i, name in enumerate(recorder.names[start_idx:]):
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter.on_batch_end | def on_batch_end(self, last_loss:Tensor, iteration:int, **kwargs)->None:
"Callback function that writes batch end appropriate data to Tensorboard."
if iteration == 0: return
self._update_batches_if_needed()
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if iteration == 0: return
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter.on_backward_end | def on_backward_end(self, iteration:int, **kwargs)->None:
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if iteration == 0: return
self._update_batches_if_needed()
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"Callback function that writes backward end appropriate data to Tensorboard."
if iteration == 0: return
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fastai/fastai | fastai/callbacks/tensorboard.py | LearnerTensorboardWriter.on_epoch_end | def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
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fastai/fastai | fastai/callbacks/tensorboard.py | GANTensorboardWriter._write_weight_histograms | def _write_weight_histograms(self, iteration:int)->None:
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... | python | def _write_weight_histograms(self, iteration:int)->None:
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generator, critic = self.learn.gan_trainer.generator, self.learn.gan_trainer.critic
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fastai/fastai | fastai/callbacks/tensorboard.py | GANTensorboardWriter._write_gen_model_stats | def _write_gen_model_stats(self, iteration:int)->None:
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generator = self.learn.gan_trainer.generator
self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats')
self.gen_stats_upda... | python | def _write_gen_model_stats(self, iteration:int)->None:
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generator = self.learn.gan_trainer.generator
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fastai/fastai | fastai/callbacks/tensorboard.py | ModelImageSet.get_list_from_model | def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
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fastai/fastai | fastai/callbacks/tensorboard.py | HistogramTBRequest._write_histogram | def _write_histogram(self, param_name:str, values)->None:
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fastai/fastai | fastai/callbacks/tensorboard.py | ImageTBRequest._write_images | def _write_images(self, name:str, images:[Tensor])->None:
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fastai/fastai | old/fastai/lm_rnn.py | repackage_var | def repackage_var(h):
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if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h)
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fastai/fastai | old/fastai/lm_rnn.py | get_language_model | def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token,
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fastai/fastai | fastai/text/transform.py | replace_rep | def replace_rep(t:str) -> str:
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def _replace_rep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_REP} {len(cc)+1} {c} '
re_rep = re.compile(r'(\S)(\1{3,})')
return re_rep.sub(_replace_rep, t) | python | def replace_rep(t:str) -> str:
"Replace repetitions at the character level in `t`."
def _replace_rep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_REP} {len(cc)+1} {c} '
re_rep = re.compile(r'(\S)(\1{3,})')
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fastai/fastai | fastai/text/transform.py | replace_all_caps | def replace_all_caps(x:Collection[str]) -> Collection[str]:
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res = []
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fastai/fastai | fastai/text/transform.py | deal_caps | def deal_caps(x:Collection[str]) -> Collection[str]:
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fastai/fastai | fastai/text/transform.py | Tokenizer.process_text | def process_text(self, t:str, tok:BaseTokenizer) -> List[str]:
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for rule in self.pre_rules: t = rule(t)
toks = tok.tokenizer(t)
for rule in self.post_rules: toks = rule(toks)
return toks | python | def process_text(self, t:str, tok:BaseTokenizer) -> List[str]:
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for rule in self.pre_rules: t = rule(t)
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fastai/fastai | fastai/text/transform.py | Tokenizer._process_all_1 | def _process_all_1(self, texts:Collection[str]) -> List[List[str]]:
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tok = self.tok_func(self.lang)
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fastai/fastai | fastai/text/transform.py | Tokenizer.process_all | def process_all(self, texts:Collection[str]) -> List[List[str]]:
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fastai/fastai | fastai/text/transform.py | Vocab.numericalize | def numericalize(self, t:Collection[str]) -> List[int]:
"Convert a list of tokens `t` to their ids."
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fastai/fastai | fastai/text/transform.py | Vocab.textify | def textify(self, nums:Collection[int], sep=' ') -> List[str]:
"Convert a list of `nums` to their tokens."
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fastai/fastai | fastai/text/transform.py | Vocab.create | def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':
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freq = Counter(p for o in tokens for p in o)
itos = [o for o,c in freq.most_common(max_vocab) if c >= min_freq]
for o in reversed(defaults.text_spec_tok):
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"Create a vocabulary from a set of `tokens`."
freq = Counter(p for o in tokens for p in o)
itos = [o for o,c in freq.most_common(max_vocab) if c >= min_freq]
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fastai/fastai | fastai/text/transform.py | Vocab.load | def load(cls, path):
"Load the `Vocab` contained in `path`"
itos = pickle.load(open(path, 'rb'))
return cls(itos) | python | def load(cls, path):
"Load the `Vocab` contained in `path`"
itos = pickle.load(open(path, 'rb'))
return cls(itos) | [
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fastai/fastai | old/fastai/sgdr.py | LossRecorder.plot_loss | def plot_loss(self, n_skip=10, n_skip_end=5):
'''
plots loss function as function of iterations.
When used in Jupyternotebook, plot will be displayed in notebook. Else, plot will be displayed in console and both plot and loss are saved in save_path.
'''
if not in_ipynb(): plt.s... | python | def plot_loss(self, n_skip=10, n_skip_end=5):
'''
plots loss function as function of iterations.
When used in Jupyternotebook, plot will be displayed in notebook. Else, plot will be displayed in console and both plot and loss are saved in save_path.
'''
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fastai/fastai | old/fastai/sgdr.py | LossRecorder.plot_lr | def plot_lr(self):
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plt.switch_backend('agg')
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for i in range(0,2): axs[i].se... | python | def plot_lr(self):
'''Plots learning rate in jupyter notebook or console, depending on the enviroment of the learner.'''
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plt.switch_backend('agg')
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fastai/fastai | old/fastai/sgdr.py | LR_Finder.plot | def plot(self, n_skip=10, n_skip_end=5):
'''
Plots the loss function with respect to learning rate, in log scale.
'''
plt.ylabel("validation loss")
plt.xlabel("learning rate (log scale)")
plt.plot(self.lrs[n_skip:-(n_skip_end+1)], self.losses[n_skip:-(n_skip_end+1)])
... | python | def plot(self, n_skip=10, n_skip_end=5):
'''
Plots the loss function with respect to learning rate, in log scale.
'''
plt.ylabel("validation loss")
plt.xlabel("learning rate (log scale)")
plt.plot(self.lrs[n_skip:-(n_skip_end+1)], self.losses[n_skip:-(n_skip_end+1)])
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fastai/fastai | old/fastai/sgdr.py | OptimScheduler.plot_lr | def plot_lr(self, show_text=True, show_moms=True):
"""Plots the lr rate/momentum schedule"""
phase_limits = [0]
for nb_batch, phase in zip(self.nb_batches, self.phases):
phase_limits.append(phase_limits[-1] + nb_batch * phase.epochs)
if not in_ipynb():
plt.switch_... | python | def plot_lr(self, show_text=True, show_moms=True):
"""Plots the lr rate/momentum schedule"""
phase_limits = [0]
for nb_batch, phase in zip(self.nb_batches, self.phases):
phase_limits.append(phase_limits[-1] + nb_batch * phase.epochs)
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fastai/fastai | examples/train_imagenette.py | main | def main(
gpu:Param("GPU to run on", str)=None,
woof: Param("Use imagewoof (otherwise imagenette)", int)=0,
lr: Param("Learning rate", float)=1e-3,
size: Param("Size (px: 128,192,224)", int)=128,
alpha: Param("Alpha", float)=0.99,
mom: Param("Momentum", float)=0.9,
... | python | def main(
gpu:Param("GPU to run on", str)=None,
woof: Param("Use imagewoof (otherwise imagenette)", int)=0,
lr: Param("Learning rate", float)=1e-3,
size: Param("Size (px: 128,192,224)", int)=128,
alpha: Param("Alpha", float)=0.99,
mom: Param("Momentum", float)=0.9,
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fastai/fastai | fastai/callbacks/tracker.py | TerminateOnNaNCallback.on_batch_end | def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None:
"Test if `last_loss` is NaN and interrupts training."
if self.stop: return True #to skip validation after stopping during training
if torch.isnan(last_loss):
print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid l... | python | def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None:
"Test if `last_loss` is NaN and interrupts training."
if self.stop: return True #to skip validation after stopping during training
if torch.isnan(last_loss):
print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid l... | [
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fastai/fastai | fastai/callbacks/tracker.py | TrackerCallback.on_train_begin | def on_train_begin(self, **kwargs:Any)->None:
"Initializes the best value."
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"Initializes the best value."
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fastai/fastai | fastai/callbacks/tracker.py | TrackerCallback.get_monitor_value | def get_monitor_value(self):
"Pick the monitored value."
if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None
elif len(self.learn.recorder.val_losses) == 0: return None
values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(),
'va... | python | def get_monitor_value(self):
"Pick the monitored value."
if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None
elif len(self.learn.recorder.val_losses) == 0: return None
values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(),
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fastai/fastai | fastai/callbacks/tracker.py | SaveModelCallback.on_epoch_end | def on_epoch_end(self, epoch:int, **kwargs:Any)->None:
"Compare the value monitored to its best score and maybe save the model."
if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}')
else: #every="improvement"
current = self.get_monitor_value()
if current is not... | python | def on_epoch_end(self, epoch:int, **kwargs:Any)->None:
"Compare the value monitored to its best score and maybe save the model."
if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}')
else: #every="improvement"
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fastai/fastai | fastai/callbacks/tracker.py | SaveModelCallback.on_train_end | def on_train_end(self, **kwargs):
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if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file():
self.learn.load(f'{self.name}', purge=False) | python | def on_train_end(self, **kwargs):
"Load the best model."
if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file():
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fastai/fastai | fastai/callbacks/tracker.py | ReduceLROnPlateauCallback.on_train_begin | def on_train_begin(self, **kwargs:Any)->None:
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self.wait, self.opt = 0, self.learn.opt
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"Initialize inner arguments."
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fastai/fastai | fastai/callbacks/tracker.py | ReduceLROnPlateauCallback.on_epoch_end | def on_epoch_end(self, epoch, **kwargs:Any)->None:
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"Compare the value monitored to its best and maybe reduce lr."
current = self.get_monitor_value()
if current is None: return
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fastai/fastai | fastai/gen_doc/convert2html.py | convert_nb | def convert_nb(fname, dest_path='.'):
"Convert a notebook `fname` to html file in `dest_path`."
from .gen_notebooks import remove_undoc_cells, remove_code_cell_jupyter_widget_state_elem
nb = read_nb(fname)
nb['cells'] = remove_undoc_cells(nb['cells'])
nb['cells'] = remove_code_cell_jupyter_widget_st... | python | def convert_nb(fname, dest_path='.'):
"Convert a notebook `fname` to html file in `dest_path`."
from .gen_notebooks import remove_undoc_cells, remove_code_cell_jupyter_widget_state_elem
nb = read_nb(fname)
nb['cells'] = remove_undoc_cells(nb['cells'])
nb['cells'] = remove_code_cell_jupyter_widget_st... | [
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fastai/fastai | fastai/gen_doc/convert2html.py | convert_all | def convert_all(folder, dest_path='.', force_all=False):
"Convert modified notebooks in `folder` to html pages in `dest_path`."
path = Path(folder)
changed_cnt = 0
for fname in path.glob("*.ipynb"):
# only rebuild modified files
fname_out = Path(dest_path)/fname.with_suffix('.html').nam... | python | def convert_all(folder, dest_path='.', force_all=False):
"Convert modified notebooks in `folder` to html pages in `dest_path`."
path = Path(folder)
changed_cnt = 0
for fname in path.glob("*.ipynb"):
# only rebuild modified files
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max_len = max([len(s[0]) for s in samples])
res = torch.zeros(len(sam... | python | def pad_collate(samples:BatchSamples, pad_idx:int=1, pad_first:bool=True, backwards:bool=False) -> Tuple[LongTensor, LongTensor]:
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test_ids:Collection[Collection[int]]=None, train_lbls:Collection[Union[int,float]]=None,
valid_lbls:Collection[Union[int,float]]=None, classes:Collection[Any]=No... | python | def from_ids(cls, path:PathOrStr, vocab:Vocab, train_ids:Collection[Collection[int]], valid_ids:Collection[Collection[int]],
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fastai/fastai | fastai/text/data.py | TextDataBunch.load | def load(cls, path:PathOrStr, cache_name:PathOrStr='tmp', processor:PreProcessor=None, **kwargs):
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val_tok:Collection[Collection[str]], val_lbls:Collection[Union[int,float]], vocab:Vocab=None,
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... | python | def from_csv(cls, path:PathOrStr, csv_name, valid_pct:float=0.2, test:Optional[str]=None,
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num_workers:int=0, device:torch.device=None, collate_fn:Callable=data_collate,
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fastai/fastai | fastai/text/data.py | TextList.label_for_lm | def label_for_lm(self, **kwargs):
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fastai/fastai | fastai/text/data.py | TextList.show_xys | def show_xys(self, xs, ys, max_len:int=70)->None:
"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed."
from IPython.display import display, HTML
names = ['idx','text'] if self._is_lm else ['text','target']
items = []
for i, (x,y) in en... | python | def show_xys(self, xs, ys, max_len:int=70)->None:
"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed."
from IPython.display import display, HTML
names = ['idx','text'] if self._is_lm else ['text','target']
items = []
for i, (x,y) in en... | [
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fastai/fastai | old/fastai/models/inceptionv4.py | inceptionv4 | def inceptionv4(pretrained=True):
r"""InceptionV4 model architecture from the
`"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = InceptionV4()
if pretrained:
model... | python | def inceptionv4(pretrained=True):
r"""InceptionV4 model architecture from the
`"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = InceptionV4()
if pretrained:
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fastai/fastai | old/fastai/conv_learner.py | ConvLearner.predict_array | def predict_array(self, arr):
"""
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Args:
arr: a numpy array to be used as input to the model for prediction purposes
Returns:
... | python | def predict_array(self, arr):
"""
This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called
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Args:
arr: a numpy array to be used as input to the model for prediction purposes
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fastai/fastai | examples/train_cifar.py | main | def main( gpu:Param("GPU to run on", str)=None ):
"""Distrubuted training of CIFAR-10.
Fastest speed is if you run as follows:
python -m fastai.launch train_cifar.py"""
gpu = setup_distrib(gpu)
n_gpus = num_distrib()
path = url2path(URLs.CIFAR)
ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5... | python | def main( gpu:Param("GPU to run on", str)=None ):
"""Distrubuted training of CIFAR-10.
Fastest speed is if you run as follows:
python -m fastai.launch train_cifar.py"""
gpu = setup_distrib(gpu)
n_gpus = num_distrib()
path = url2path(URLs.CIFAR)
ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5... | [
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fastai/fastai | setup.py | DepsCommand.initialize_options | def initialize_options(self):
"""Set default values for options."""
self.dep_groups = ''
self.dep_quote = False
self.dep_conda = False | python | def initialize_options(self):
"""Set default values for options."""
self.dep_groups = ''
self.dep_quote = False
self.dep_conda = False | [
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fastai/fastai | setup.py | DepsCommand.run | def run(self):
"""Run command."""
wanted_groups = self.parse()
deps = []
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if grp in dep_groups: deps.extend(dep_groups[grp])
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if invalid_groups or not wa... | python | def run(self):
"""Run command."""
wanted_groups = self.parse()
deps = []
invalid_groups = []
for grp in wanted_groups:
if grp in dep_groups: deps.extend(dep_groups[grp])
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fastai/fastai | old/fastai/models/unet.py | get_sfs_idxs | def get_sfs_idxs(sfs, last=True):
"""
Return the saved feature indexes that will be concatenated
Inputs:
sfs (list): saved features by hook function, in other words intermediate activations
last (bool): whether to concatenate only last different activation, or all from the encoder model
... | python | def get_sfs_idxs(sfs, last=True):
"""
Return the saved feature indexes that will be concatenated
Inputs:
sfs (list): saved features by hook function, in other words intermediate activations
last (bool): whether to concatenate only last different activation, or all from the encoder model
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fastai/fastai | fastai/callbacks/hooks.py | hook_output | def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
"Return a `Hook` that stores activations of `module` in `self.stored`"
return Hook(module, _hook_inner, detach=detach, is_forward=not grad) | python | def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
"Return a `Hook` that stores activations of `module` in `self.stored`"
return Hook(module, _hook_inner, detach=detach, is_forward=not grad) | [
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fastai/fastai | fastai/callbacks/hooks.py | hook_outputs | def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
"Return `Hooks` that store activations of all `modules` in `self.stored`"
return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad) | python | def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
"Return `Hooks` that store activations of all `modules` in `self.stored`"
return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad) | [
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fastai/fastai | fastai/callbacks/hooks.py | dummy_batch | def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
"Create a dummy batch to go through `m` with `size`."
ch_in = in_channels(m)
return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.) | python | def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
"Create a dummy batch to go through `m` with `size`."
ch_in = in_channels(m)
return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.) | [
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