id
int32 0
252k
| repo
stringlengths 7
55
| path
stringlengths 4
127
| func_name
stringlengths 1
88
| original_string
stringlengths 75
19.8k
| language
stringclasses 1
value | code
stringlengths 75
19.8k
| code_tokens
list | docstring
stringlengths 3
17.3k
| docstring_tokens
list | sha
stringlengths 40
40
| url
stringlengths 87
242
|
|---|---|---|---|---|---|---|---|---|---|---|---|
20,700
|
fastai/fastai
|
fastai/core.py
|
show_some
|
def show_some(items:Collection, n_max:int=5, sep:str=','):
"Return the representation of the first `n_max` elements in `items`."
if items is None or len(items) == 0: return ''
res = sep.join([f'{o}' for o in items[:n_max]])
if len(items) > n_max: res += '...'
return res
|
python
|
def show_some(items:Collection, n_max:int=5, sep:str=','):
"Return the representation of the first `n_max` elements in `items`."
if items is None or len(items) == 0: return ''
res = sep.join([f'{o}' for o in items[:n_max]])
if len(items) > n_max: res += '...'
return res
|
[
"def",
"show_some",
"(",
"items",
":",
"Collection",
",",
"n_max",
":",
"int",
"=",
"5",
",",
"sep",
":",
"str",
"=",
"','",
")",
":",
"if",
"items",
"is",
"None",
"or",
"len",
"(",
"items",
")",
"==",
"0",
":",
"return",
"''",
"res",
"=",
"sep",
".",
"join",
"(",
"[",
"f'{o}'",
"for",
"o",
"in",
"items",
"[",
":",
"n_max",
"]",
"]",
")",
"if",
"len",
"(",
"items",
")",
">",
"n_max",
":",
"res",
"+=",
"'...'",
"return",
"res"
] |
Return the representation of the first `n_max` elements in `items`.
|
[
"Return",
"the",
"representation",
"of",
"the",
"first",
"n_max",
"elements",
"in",
"items",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/core.py#L340-L345
|
20,701
|
fastai/fastai
|
fastai/core.py
|
get_tmp_file
|
def get_tmp_file(dir=None):
"Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it."
with tempfile.NamedTemporaryFile(delete=False, dir=dir) as f: return f.name
|
python
|
def get_tmp_file(dir=None):
"Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it."
with tempfile.NamedTemporaryFile(delete=False, dir=dir) as f: return f.name
|
[
"def",
"get_tmp_file",
"(",
"dir",
"=",
"None",
")",
":",
"with",
"tempfile",
".",
"NamedTemporaryFile",
"(",
"delete",
"=",
"False",
",",
"dir",
"=",
"dir",
")",
"as",
"f",
":",
"return",
"f",
".",
"name"
] |
Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it.
|
[
"Create",
"and",
"return",
"a",
"tmp",
"filename",
"optionally",
"at",
"a",
"specific",
"path",
".",
"os",
".",
"remove",
"when",
"done",
"with",
"it",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/core.py#L347-L349
|
20,702
|
fastai/fastai
|
fastai/core.py
|
ItemBase.show
|
def show(self, ax:plt.Axes, **kwargs):
"Subclass this method if you want to customize the way this `ItemBase` is shown on `ax`."
ax.set_title(str(self))
|
python
|
def show(self, ax:plt.Axes, **kwargs):
"Subclass this method if you want to customize the way this `ItemBase` is shown on `ax`."
ax.set_title(str(self))
|
[
"def",
"show",
"(",
"self",
",",
"ax",
":",
"plt",
".",
"Axes",
",",
"*",
"*",
"kwargs",
")",
":",
"ax",
".",
"set_title",
"(",
"str",
"(",
"self",
")",
")"
] |
Subclass this method if you want to customize the way this `ItemBase` is shown on `ax`.
|
[
"Subclass",
"this",
"method",
"if",
"you",
"want",
"to",
"customize",
"the",
"way",
"this",
"ItemBase",
"is",
"shown",
"on",
"ax",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/core.py#L157-L159
|
20,703
|
fastai/fastai
|
fastai/vision/models/darknet.py
|
conv_bn_lrelu
|
def conv_bn_lrelu(ni:int, nf:int, ks:int=3, stride:int=1)->nn.Sequential:
"Create a seuence Conv2d->BatchNorm2d->LeakyReLu layer."
return nn.Sequential(
nn.Conv2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding=ks//2),
nn.BatchNorm2d(nf),
nn.LeakyReLU(negative_slope=0.1, inplace=True))
|
python
|
def conv_bn_lrelu(ni:int, nf:int, ks:int=3, stride:int=1)->nn.Sequential:
"Create a seuence Conv2d->BatchNorm2d->LeakyReLu layer."
return nn.Sequential(
nn.Conv2d(ni, nf, kernel_size=ks, bias=False, stride=stride, padding=ks//2),
nn.BatchNorm2d(nf),
nn.LeakyReLU(negative_slope=0.1, inplace=True))
|
[
"def",
"conv_bn_lrelu",
"(",
"ni",
":",
"int",
",",
"nf",
":",
"int",
",",
"ks",
":",
"int",
"=",
"3",
",",
"stride",
":",
"int",
"=",
"1",
")",
"->",
"nn",
".",
"Sequential",
":",
"return",
"nn",
".",
"Sequential",
"(",
"nn",
".",
"Conv2d",
"(",
"ni",
",",
"nf",
",",
"kernel_size",
"=",
"ks",
",",
"bias",
"=",
"False",
",",
"stride",
"=",
"stride",
",",
"padding",
"=",
"ks",
"//",
"2",
")",
",",
"nn",
".",
"BatchNorm2d",
"(",
"nf",
")",
",",
"nn",
".",
"LeakyReLU",
"(",
"negative_slope",
"=",
"0.1",
",",
"inplace",
"=",
"True",
")",
")"
] |
Create a seuence Conv2d->BatchNorm2d->LeakyReLu layer.
|
[
"Create",
"a",
"seuence",
"Conv2d",
"-",
">",
"BatchNorm2d",
"-",
">",
"LeakyReLu",
"layer",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/models/darknet.py#L6-L11
|
20,704
|
fastai/fastai
|
fastai/vision/models/darknet.py
|
Darknet.make_group_layer
|
def make_group_layer(self, ch_in:int, num_blocks:int, stride:int=1):
"starts with conv layer - `ch_in` channels in - then has `num_blocks` `ResLayer`"
return [conv_bn_lrelu(ch_in, ch_in*2,stride=stride)
] + [(ResLayer(ch_in*2)) for i in range(num_blocks)]
|
python
|
def make_group_layer(self, ch_in:int, num_blocks:int, stride:int=1):
"starts with conv layer - `ch_in` channels in - then has `num_blocks` `ResLayer`"
return [conv_bn_lrelu(ch_in, ch_in*2,stride=stride)
] + [(ResLayer(ch_in*2)) for i in range(num_blocks)]
|
[
"def",
"make_group_layer",
"(",
"self",
",",
"ch_in",
":",
"int",
",",
"num_blocks",
":",
"int",
",",
"stride",
":",
"int",
"=",
"1",
")",
":",
"return",
"[",
"conv_bn_lrelu",
"(",
"ch_in",
",",
"ch_in",
"*",
"2",
",",
"stride",
"=",
"stride",
")",
"]",
"+",
"[",
"(",
"ResLayer",
"(",
"ch_in",
"*",
"2",
")",
")",
"for",
"i",
"in",
"range",
"(",
"num_blocks",
")",
"]"
] |
starts with conv layer - `ch_in` channels in - then has `num_blocks` `ResLayer`
|
[
"starts",
"with",
"conv",
"layer",
"-",
"ch_in",
"channels",
"in",
"-",
"then",
"has",
"num_blocks",
"ResLayer"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/models/darknet.py#L24-L27
|
20,705
|
fastai/fastai
|
fastai/collab.py
|
collab_learner
|
def collab_learner(data, n_factors:int=None, use_nn:bool=False, emb_szs:Dict[str,int]=None, layers:Collection[int]=None,
ps:Collection[float]=None, emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True,
bn_final:bool=False, **learn_kwargs)->Learner:
"Create a Learner for collaborative filtering on `data`."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
u,m = data.train_ds.x.classes.values()
if use_nn: model = EmbeddingNN(emb_szs=emb_szs, layers=layers, ps=ps, emb_drop=emb_drop, y_range=y_range,
use_bn=use_bn, bn_final=bn_final, **learn_kwargs)
else: model = EmbeddingDotBias(n_factors, len(u), len(m), y_range=y_range)
return CollabLearner(data, model, **learn_kwargs)
|
python
|
def collab_learner(data, n_factors:int=None, use_nn:bool=False, emb_szs:Dict[str,int]=None, layers:Collection[int]=None,
ps:Collection[float]=None, emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True,
bn_final:bool=False, **learn_kwargs)->Learner:
"Create a Learner for collaborative filtering on `data`."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
u,m = data.train_ds.x.classes.values()
if use_nn: model = EmbeddingNN(emb_szs=emb_szs, layers=layers, ps=ps, emb_drop=emb_drop, y_range=y_range,
use_bn=use_bn, bn_final=bn_final, **learn_kwargs)
else: model = EmbeddingDotBias(n_factors, len(u), len(m), y_range=y_range)
return CollabLearner(data, model, **learn_kwargs)
|
[
"def",
"collab_learner",
"(",
"data",
",",
"n_factors",
":",
"int",
"=",
"None",
",",
"use_nn",
":",
"bool",
"=",
"False",
",",
"emb_szs",
":",
"Dict",
"[",
"str",
",",
"int",
"]",
"=",
"None",
",",
"layers",
":",
"Collection",
"[",
"int",
"]",
"=",
"None",
",",
"ps",
":",
"Collection",
"[",
"float",
"]",
"=",
"None",
",",
"emb_drop",
":",
"float",
"=",
"0.",
",",
"y_range",
":",
"OptRange",
"=",
"None",
",",
"use_bn",
":",
"bool",
"=",
"True",
",",
"bn_final",
":",
"bool",
"=",
"False",
",",
"*",
"*",
"learn_kwargs",
")",
"->",
"Learner",
":",
"emb_szs",
"=",
"data",
".",
"get_emb_szs",
"(",
"ifnone",
"(",
"emb_szs",
",",
"{",
"}",
")",
")",
"u",
",",
"m",
"=",
"data",
".",
"train_ds",
".",
"x",
".",
"classes",
".",
"values",
"(",
")",
"if",
"use_nn",
":",
"model",
"=",
"EmbeddingNN",
"(",
"emb_szs",
"=",
"emb_szs",
",",
"layers",
"=",
"layers",
",",
"ps",
"=",
"ps",
",",
"emb_drop",
"=",
"emb_drop",
",",
"y_range",
"=",
"y_range",
",",
"use_bn",
"=",
"use_bn",
",",
"bn_final",
"=",
"bn_final",
",",
"*",
"*",
"learn_kwargs",
")",
"else",
":",
"model",
"=",
"EmbeddingDotBias",
"(",
"n_factors",
",",
"len",
"(",
"u",
")",
",",
"len",
"(",
"m",
")",
",",
"y_range",
"=",
"y_range",
")",
"return",
"CollabLearner",
"(",
"data",
",",
"model",
",",
"*",
"*",
"learn_kwargs",
")"
] |
Create a Learner for collaborative filtering on `data`.
|
[
"Create",
"a",
"Learner",
"for",
"collaborative",
"filtering",
"on",
"data",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/collab.py#L98-L107
|
20,706
|
fastai/fastai
|
fastai/collab.py
|
CollabDataBunch.from_df
|
def from_df(cls, ratings:DataFrame, valid_pct:float=0.2, user_name:Optional[str]=None, item_name:Optional[str]=None,
rating_name:Optional[str]=None, test:DataFrame=None, seed:int=None, path:PathOrStr='.', bs:int=64,
val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None,
device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False) -> 'CollabDataBunch':
"Create a `DataBunch` suitable for collaborative filtering from `ratings`."
user_name = ifnone(user_name, ratings.columns[0])
item_name = ifnone(item_name, ratings.columns[1])
rating_name = ifnone(rating_name,ratings.columns[2])
cat_names = [user_name,item_name]
src = (CollabList.from_df(ratings, cat_names=cat_names, procs=Categorify)
.split_by_rand_pct(valid_pct=valid_pct, seed=seed).label_from_df(cols=rating_name))
if test is not None: src.add_test(CollabList.from_df(test, cat_names=cat_names))
return src.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, device=device,
collate_fn=collate_fn, no_check=no_check)
|
python
|
def from_df(cls, ratings:DataFrame, valid_pct:float=0.2, user_name:Optional[str]=None, item_name:Optional[str]=None,
rating_name:Optional[str]=None, test:DataFrame=None, seed:int=None, path:PathOrStr='.', bs:int=64,
val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None,
device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False) -> 'CollabDataBunch':
"Create a `DataBunch` suitable for collaborative filtering from `ratings`."
user_name = ifnone(user_name, ratings.columns[0])
item_name = ifnone(item_name, ratings.columns[1])
rating_name = ifnone(rating_name,ratings.columns[2])
cat_names = [user_name,item_name]
src = (CollabList.from_df(ratings, cat_names=cat_names, procs=Categorify)
.split_by_rand_pct(valid_pct=valid_pct, seed=seed).label_from_df(cols=rating_name))
if test is not None: src.add_test(CollabList.from_df(test, cat_names=cat_names))
return src.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, device=device,
collate_fn=collate_fn, no_check=no_check)
|
[
"def",
"from_df",
"(",
"cls",
",",
"ratings",
":",
"DataFrame",
",",
"valid_pct",
":",
"float",
"=",
"0.2",
",",
"user_name",
":",
"Optional",
"[",
"str",
"]",
"=",
"None",
",",
"item_name",
":",
"Optional",
"[",
"str",
"]",
"=",
"None",
",",
"rating_name",
":",
"Optional",
"[",
"str",
"]",
"=",
"None",
",",
"test",
":",
"DataFrame",
"=",
"None",
",",
"seed",
":",
"int",
"=",
"None",
",",
"path",
":",
"PathOrStr",
"=",
"'.'",
",",
"bs",
":",
"int",
"=",
"64",
",",
"val_bs",
":",
"int",
"=",
"None",
",",
"num_workers",
":",
"int",
"=",
"defaults",
".",
"cpus",
",",
"dl_tfms",
":",
"Optional",
"[",
"Collection",
"[",
"Callable",
"]",
"]",
"=",
"None",
",",
"device",
":",
"torch",
".",
"device",
"=",
"None",
",",
"collate_fn",
":",
"Callable",
"=",
"data_collate",
",",
"no_check",
":",
"bool",
"=",
"False",
")",
"->",
"'CollabDataBunch'",
":",
"user_name",
"=",
"ifnone",
"(",
"user_name",
",",
"ratings",
".",
"columns",
"[",
"0",
"]",
")",
"item_name",
"=",
"ifnone",
"(",
"item_name",
",",
"ratings",
".",
"columns",
"[",
"1",
"]",
")",
"rating_name",
"=",
"ifnone",
"(",
"rating_name",
",",
"ratings",
".",
"columns",
"[",
"2",
"]",
")",
"cat_names",
"=",
"[",
"user_name",
",",
"item_name",
"]",
"src",
"=",
"(",
"CollabList",
".",
"from_df",
"(",
"ratings",
",",
"cat_names",
"=",
"cat_names",
",",
"procs",
"=",
"Categorify",
")",
".",
"split_by_rand_pct",
"(",
"valid_pct",
"=",
"valid_pct",
",",
"seed",
"=",
"seed",
")",
".",
"label_from_df",
"(",
"cols",
"=",
"rating_name",
")",
")",
"if",
"test",
"is",
"not",
"None",
":",
"src",
".",
"add_test",
"(",
"CollabList",
".",
"from_df",
"(",
"test",
",",
"cat_names",
"=",
"cat_names",
")",
")",
"return",
"src",
".",
"databunch",
"(",
"path",
"=",
"path",
",",
"bs",
"=",
"bs",
",",
"val_bs",
"=",
"val_bs",
",",
"num_workers",
"=",
"num_workers",
",",
"device",
"=",
"device",
",",
"collate_fn",
"=",
"collate_fn",
",",
"no_check",
"=",
"no_check",
")"
] |
Create a `DataBunch` suitable for collaborative filtering from `ratings`.
|
[
"Create",
"a",
"DataBunch",
"suitable",
"for",
"collaborative",
"filtering",
"from",
"ratings",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/collab.py#L55-L68
|
20,707
|
fastai/fastai
|
old/fastai/structured.py
|
set_rf_samples
|
def set_rf_samples(n):
""" Changes Scikit learn's random forests to give each tree a random sample of
n random rows.
"""
forest._generate_sample_indices = (lambda rs, n_samples:
forest.check_random_state(rs).randint(0, n_samples, n))
|
python
|
def set_rf_samples(n):
""" Changes Scikit learn's random forests to give each tree a random sample of
n random rows.
"""
forest._generate_sample_indices = (lambda rs, n_samples:
forest.check_random_state(rs).randint(0, n_samples, n))
|
[
"def",
"set_rf_samples",
"(",
"n",
")",
":",
"forest",
".",
"_generate_sample_indices",
"=",
"(",
"lambda",
"rs",
",",
"n_samples",
":",
"forest",
".",
"check_random_state",
"(",
"rs",
")",
".",
"randint",
"(",
"0",
",",
"n_samples",
",",
"n",
")",
")"
] |
Changes Scikit learn's random forests to give each tree a random sample of
n random rows.
|
[
"Changes",
"Scikit",
"learn",
"s",
"random",
"forests",
"to",
"give",
"each",
"tree",
"a",
"random",
"sample",
"of",
"n",
"random",
"rows",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/structured.py#L382-L387
|
20,708
|
fastai/fastai
|
old/fastai/structured.py
|
reset_rf_samples
|
def reset_rf_samples():
""" Undoes the changes produced by set_rf_samples.
"""
forest._generate_sample_indices = (lambda rs, n_samples:
forest.check_random_state(rs).randint(0, n_samples, n_samples))
|
python
|
def reset_rf_samples():
""" Undoes the changes produced by set_rf_samples.
"""
forest._generate_sample_indices = (lambda rs, n_samples:
forest.check_random_state(rs).randint(0, n_samples, n_samples))
|
[
"def",
"reset_rf_samples",
"(",
")",
":",
"forest",
".",
"_generate_sample_indices",
"=",
"(",
"lambda",
"rs",
",",
"n_samples",
":",
"forest",
".",
"check_random_state",
"(",
"rs",
")",
".",
"randint",
"(",
"0",
",",
"n_samples",
",",
"n_samples",
")",
")"
] |
Undoes the changes produced by set_rf_samples.
|
[
"Undoes",
"the",
"changes",
"produced",
"by",
"set_rf_samples",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/structured.py#L389-L393
|
20,709
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
get_global_vars
|
def get_global_vars(mod):
"Return globally assigned variables."
# https://stackoverflow.com/questions/8820276/docstring-for-variable/31764368#31764368
import ast,re
with open(mod.__file__, 'r') as f: fstr = f.read()
flines = fstr.splitlines()
d = {}
for node in ast.walk(ast.parse(fstr)):
if isinstance(node,ast.Assign) and hasattr(node.targets[0], 'id'):
key,lineno = node.targets[0].id,node.targets[0].lineno
codestr = flines[lineno]
match = re.match(f"^({key})\s*=\s*.*", codestr)
if match and match.group(1) != '__all__': # only top level assignment
d[key] = f'`{codestr}` {get_source_link(mod, lineno)}'
return d
|
python
|
def get_global_vars(mod):
"Return globally assigned variables."
# https://stackoverflow.com/questions/8820276/docstring-for-variable/31764368#31764368
import ast,re
with open(mod.__file__, 'r') as f: fstr = f.read()
flines = fstr.splitlines()
d = {}
for node in ast.walk(ast.parse(fstr)):
if isinstance(node,ast.Assign) and hasattr(node.targets[0], 'id'):
key,lineno = node.targets[0].id,node.targets[0].lineno
codestr = flines[lineno]
match = re.match(f"^({key})\s*=\s*.*", codestr)
if match and match.group(1) != '__all__': # only top level assignment
d[key] = f'`{codestr}` {get_source_link(mod, lineno)}'
return d
|
[
"def",
"get_global_vars",
"(",
"mod",
")",
":",
"# https://stackoverflow.com/questions/8820276/docstring-for-variable/31764368#31764368",
"import",
"ast",
",",
"re",
"with",
"open",
"(",
"mod",
".",
"__file__",
",",
"'r'",
")",
"as",
"f",
":",
"fstr",
"=",
"f",
".",
"read",
"(",
")",
"flines",
"=",
"fstr",
".",
"splitlines",
"(",
")",
"d",
"=",
"{",
"}",
"for",
"node",
"in",
"ast",
".",
"walk",
"(",
"ast",
".",
"parse",
"(",
"fstr",
")",
")",
":",
"if",
"isinstance",
"(",
"node",
",",
"ast",
".",
"Assign",
")",
"and",
"hasattr",
"(",
"node",
".",
"targets",
"[",
"0",
"]",
",",
"'id'",
")",
":",
"key",
",",
"lineno",
"=",
"node",
".",
"targets",
"[",
"0",
"]",
".",
"id",
",",
"node",
".",
"targets",
"[",
"0",
"]",
".",
"lineno",
"codestr",
"=",
"flines",
"[",
"lineno",
"]",
"match",
"=",
"re",
".",
"match",
"(",
"f\"^({key})\\s*=\\s*.*\"",
",",
"codestr",
")",
"if",
"match",
"and",
"match",
".",
"group",
"(",
"1",
")",
"!=",
"'__all__'",
":",
"# only top level assignment",
"d",
"[",
"key",
"]",
"=",
"f'`{codestr}` {get_source_link(mod, lineno)}'",
"return",
"d"
] |
Return globally assigned variables.
|
[
"Return",
"globally",
"assigned",
"variables",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L52-L66
|
20,710
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
execute_nb
|
def execute_nb(fname, metadata=None, save=True, show_doc_only=False):
"Execute notebook `fname` with `metadata` for preprocessing."
# Any module used in the notebook that isn't inside must be in the same directory as this script
with open(fname) as f: nb = nbformat.read(f, as_version=4)
ep_class = ExecuteShowDocPreprocessor if show_doc_only else ExecutePreprocessor
ep = ep_class(timeout=600, kernel_name='python3')
metadata = metadata or {}
ep.preprocess(nb, metadata)
if save:
with open(fname, 'wt') as f: nbformat.write(nb, f)
NotebookNotary().sign(nb)
|
python
|
def execute_nb(fname, metadata=None, save=True, show_doc_only=False):
"Execute notebook `fname` with `metadata` for preprocessing."
# Any module used in the notebook that isn't inside must be in the same directory as this script
with open(fname) as f: nb = nbformat.read(f, as_version=4)
ep_class = ExecuteShowDocPreprocessor if show_doc_only else ExecutePreprocessor
ep = ep_class(timeout=600, kernel_name='python3')
metadata = metadata or {}
ep.preprocess(nb, metadata)
if save:
with open(fname, 'wt') as f: nbformat.write(nb, f)
NotebookNotary().sign(nb)
|
[
"def",
"execute_nb",
"(",
"fname",
",",
"metadata",
"=",
"None",
",",
"save",
"=",
"True",
",",
"show_doc_only",
"=",
"False",
")",
":",
"# Any module used in the notebook that isn't inside must be in the same directory as this script",
"with",
"open",
"(",
"fname",
")",
"as",
"f",
":",
"nb",
"=",
"nbformat",
".",
"read",
"(",
"f",
",",
"as_version",
"=",
"4",
")",
"ep_class",
"=",
"ExecuteShowDocPreprocessor",
"if",
"show_doc_only",
"else",
"ExecutePreprocessor",
"ep",
"=",
"ep_class",
"(",
"timeout",
"=",
"600",
",",
"kernel_name",
"=",
"'python3'",
")",
"metadata",
"=",
"metadata",
"or",
"{",
"}",
"ep",
".",
"preprocess",
"(",
"nb",
",",
"metadata",
")",
"if",
"save",
":",
"with",
"open",
"(",
"fname",
",",
"'wt'",
")",
"as",
"f",
":",
"nbformat",
".",
"write",
"(",
"nb",
",",
"f",
")",
"NotebookNotary",
"(",
")",
".",
"sign",
"(",
"nb",
")"
] |
Execute notebook `fname` with `metadata` for preprocessing.
|
[
"Execute",
"notebook",
"fname",
"with",
"metadata",
"for",
"preprocessing",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L79-L89
|
20,711
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
create_module_page
|
def create_module_page(mod, dest_path, force=False):
"Create the documentation notebook for module `mod_name` in path `dest_path`"
nb = get_empty_notebook()
mod_name = mod.__name__
strip_name = strip_fastai(mod_name)
init_cell = [get_md_cell(f'## Title for {strip_name} (use plain english, not module name!)'), get_md_cell('Type an introduction of the package here.')]
cells = [get_code_cell(f'from fastai.gen_doc.nbdoc import *\nfrom {mod_name} import * ', True)]
gvar_map = get_global_vars(mod)
if gvar_map: cells.append(get_md_cell('### Global Variable Definitions:'))
for name in get_exports(mod):
if name in gvar_map: cells.append(get_md_cell(gvar_map[name]))
for ft_name in get_ft_names(mod, include_inner=True):
if not hasattr(mod, ft_name):
warnings.warn(f"Module {strip_name} doesn't have a function named {ft_name}.")
continue
cells += _symbol_skeleton(ft_name)
elt = getattr(mod, ft_name)
nb['cells'] = init_cell + cells + [get_md_cell(UNDOC_HEADER)]
doc_path = get_doc_path(mod, dest_path)
write_nb(nb, doc_path, 'w' if force else 'x')
execute_nb(doc_path)
return doc_path
|
python
|
def create_module_page(mod, dest_path, force=False):
"Create the documentation notebook for module `mod_name` in path `dest_path`"
nb = get_empty_notebook()
mod_name = mod.__name__
strip_name = strip_fastai(mod_name)
init_cell = [get_md_cell(f'## Title for {strip_name} (use plain english, not module name!)'), get_md_cell('Type an introduction of the package here.')]
cells = [get_code_cell(f'from fastai.gen_doc.nbdoc import *\nfrom {mod_name} import * ', True)]
gvar_map = get_global_vars(mod)
if gvar_map: cells.append(get_md_cell('### Global Variable Definitions:'))
for name in get_exports(mod):
if name in gvar_map: cells.append(get_md_cell(gvar_map[name]))
for ft_name in get_ft_names(mod, include_inner=True):
if not hasattr(mod, ft_name):
warnings.warn(f"Module {strip_name} doesn't have a function named {ft_name}.")
continue
cells += _symbol_skeleton(ft_name)
elt = getattr(mod, ft_name)
nb['cells'] = init_cell + cells + [get_md_cell(UNDOC_HEADER)]
doc_path = get_doc_path(mod, dest_path)
write_nb(nb, doc_path, 'w' if force else 'x')
execute_nb(doc_path)
return doc_path
|
[
"def",
"create_module_page",
"(",
"mod",
",",
"dest_path",
",",
"force",
"=",
"False",
")",
":",
"nb",
"=",
"get_empty_notebook",
"(",
")",
"mod_name",
"=",
"mod",
".",
"__name__",
"strip_name",
"=",
"strip_fastai",
"(",
"mod_name",
")",
"init_cell",
"=",
"[",
"get_md_cell",
"(",
"f'## Title for {strip_name} (use plain english, not module name!)'",
")",
",",
"get_md_cell",
"(",
"'Type an introduction of the package here.'",
")",
"]",
"cells",
"=",
"[",
"get_code_cell",
"(",
"f'from fastai.gen_doc.nbdoc import *\\nfrom {mod_name} import * '",
",",
"True",
")",
"]",
"gvar_map",
"=",
"get_global_vars",
"(",
"mod",
")",
"if",
"gvar_map",
":",
"cells",
".",
"append",
"(",
"get_md_cell",
"(",
"'### Global Variable Definitions:'",
")",
")",
"for",
"name",
"in",
"get_exports",
"(",
"mod",
")",
":",
"if",
"name",
"in",
"gvar_map",
":",
"cells",
".",
"append",
"(",
"get_md_cell",
"(",
"gvar_map",
"[",
"name",
"]",
")",
")",
"for",
"ft_name",
"in",
"get_ft_names",
"(",
"mod",
",",
"include_inner",
"=",
"True",
")",
":",
"if",
"not",
"hasattr",
"(",
"mod",
",",
"ft_name",
")",
":",
"warnings",
".",
"warn",
"(",
"f\"Module {strip_name} doesn't have a function named {ft_name}.\"",
")",
"continue",
"cells",
"+=",
"_symbol_skeleton",
"(",
"ft_name",
")",
"elt",
"=",
"getattr",
"(",
"mod",
",",
"ft_name",
")",
"nb",
"[",
"'cells'",
"]",
"=",
"init_cell",
"+",
"cells",
"+",
"[",
"get_md_cell",
"(",
"UNDOC_HEADER",
")",
"]",
"doc_path",
"=",
"get_doc_path",
"(",
"mod",
",",
"dest_path",
")",
"write_nb",
"(",
"nb",
",",
"doc_path",
",",
"'w'",
"if",
"force",
"else",
"'x'",
")",
"execute_nb",
"(",
"doc_path",
")",
"return",
"doc_path"
] |
Create the documentation notebook for module `mod_name` in path `dest_path`
|
[
"Create",
"the",
"documentation",
"notebook",
"for",
"module",
"mod_name",
"in",
"path",
"dest_path"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L93-L117
|
20,712
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
get_module_names
|
def get_module_names(path_dir, exclude=None):
if exclude is None: exclude = _default_exclude
"Search a given `path_dir` and return all the modules contained inside except those in `exclude`"
files = sorted(path_dir.glob('*'), key=lambda x: (x.is_dir(), x.name), reverse=True) # directories first
res = [f'{path_dir.name}']
for f in files:
if f.is_dir() and f.name in exclude: continue # exclude directories
if any([f.name.endswith(ex) for ex in exclude]): continue # exclude extensions
if f.suffix == '.py': res.append(f'{path_dir.name}.{f.stem}')
elif f.is_dir(): res += [f'{path_dir.name}.{name}' for name in get_module_names(f)]
return res
|
python
|
def get_module_names(path_dir, exclude=None):
if exclude is None: exclude = _default_exclude
"Search a given `path_dir` and return all the modules contained inside except those in `exclude`"
files = sorted(path_dir.glob('*'), key=lambda x: (x.is_dir(), x.name), reverse=True) # directories first
res = [f'{path_dir.name}']
for f in files:
if f.is_dir() and f.name in exclude: continue # exclude directories
if any([f.name.endswith(ex) for ex in exclude]): continue # exclude extensions
if f.suffix == '.py': res.append(f'{path_dir.name}.{f.stem}')
elif f.is_dir(): res += [f'{path_dir.name}.{name}' for name in get_module_names(f)]
return res
|
[
"def",
"get_module_names",
"(",
"path_dir",
",",
"exclude",
"=",
"None",
")",
":",
"if",
"exclude",
"is",
"None",
":",
"exclude",
"=",
"_default_exclude",
"files",
"=",
"sorted",
"(",
"path_dir",
".",
"glob",
"(",
"'*'",
")",
",",
"key",
"=",
"lambda",
"x",
":",
"(",
"x",
".",
"is_dir",
"(",
")",
",",
"x",
".",
"name",
")",
",",
"reverse",
"=",
"True",
")",
"# directories first",
"res",
"=",
"[",
"f'{path_dir.name}'",
"]",
"for",
"f",
"in",
"files",
":",
"if",
"f",
".",
"is_dir",
"(",
")",
"and",
"f",
".",
"name",
"in",
"exclude",
":",
"continue",
"# exclude directories",
"if",
"any",
"(",
"[",
"f",
".",
"name",
".",
"endswith",
"(",
"ex",
")",
"for",
"ex",
"in",
"exclude",
"]",
")",
":",
"continue",
"# exclude extensions",
"if",
"f",
".",
"suffix",
"==",
"'.py'",
":",
"res",
".",
"append",
"(",
"f'{path_dir.name}.{f.stem}'",
")",
"elif",
"f",
".",
"is_dir",
"(",
")",
":",
"res",
"+=",
"[",
"f'{path_dir.name}.{name}'",
"for",
"name",
"in",
"get_module_names",
"(",
"f",
")",
"]",
"return",
"res"
] |
Search a given `path_dir` and return all the modules contained inside except those in `exclude`
|
[
"Search",
"a",
"given",
"path_dir",
"and",
"return",
"all",
"the",
"modules",
"contained",
"inside",
"except",
"those",
"in",
"exclude"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L121-L132
|
20,713
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
read_nb
|
def read_nb(fname):
"Read a notebook in `fname` and return its corresponding json"
with open(fname,'r') as f: return nbformat.reads(f.read(), as_version=4)
|
python
|
def read_nb(fname):
"Read a notebook in `fname` and return its corresponding json"
with open(fname,'r') as f: return nbformat.reads(f.read(), as_version=4)
|
[
"def",
"read_nb",
"(",
"fname",
")",
":",
"with",
"open",
"(",
"fname",
",",
"'r'",
")",
"as",
"f",
":",
"return",
"nbformat",
".",
"reads",
"(",
"f",
".",
"read",
"(",
")",
",",
"as_version",
"=",
"4",
")"
] |
Read a notebook in `fname` and return its corresponding json
|
[
"Read",
"a",
"notebook",
"in",
"fname",
"and",
"return",
"its",
"corresponding",
"json"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L134-L136
|
20,714
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
read_nb_content
|
def read_nb_content(cells, mod_name):
"Build a dictionary containing the position of the `cells`."
doc_fns = {}
for i, cell in enumerate(cells):
if cell['cell_type'] == 'code':
for match in SHOW_DOC_RE.findall(cell['source']):
doc_fns[match] = i
return doc_fns
|
python
|
def read_nb_content(cells, mod_name):
"Build a dictionary containing the position of the `cells`."
doc_fns = {}
for i, cell in enumerate(cells):
if cell['cell_type'] == 'code':
for match in SHOW_DOC_RE.findall(cell['source']):
doc_fns[match] = i
return doc_fns
|
[
"def",
"read_nb_content",
"(",
"cells",
",",
"mod_name",
")",
":",
"doc_fns",
"=",
"{",
"}",
"for",
"i",
",",
"cell",
"in",
"enumerate",
"(",
"cells",
")",
":",
"if",
"cell",
"[",
"'cell_type'",
"]",
"==",
"'code'",
":",
"for",
"match",
"in",
"SHOW_DOC_RE",
".",
"findall",
"(",
"cell",
"[",
"'source'",
"]",
")",
":",
"doc_fns",
"[",
"match",
"]",
"=",
"i",
"return",
"doc_fns"
] |
Build a dictionary containing the position of the `cells`.
|
[
"Build",
"a",
"dictionary",
"containing",
"the",
"position",
"of",
"the",
"cells",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L139-L146
|
20,715
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
link_markdown_cells
|
def link_markdown_cells(cells, modules):
"Create documentation links for all cells in markdown with backticks."
for i, cell in enumerate(cells):
if cell['cell_type'] == 'markdown':
cell['source'] = link_docstring(modules, cell['source'])
|
python
|
def link_markdown_cells(cells, modules):
"Create documentation links for all cells in markdown with backticks."
for i, cell in enumerate(cells):
if cell['cell_type'] == 'markdown':
cell['source'] = link_docstring(modules, cell['source'])
|
[
"def",
"link_markdown_cells",
"(",
"cells",
",",
"modules",
")",
":",
"for",
"i",
",",
"cell",
"in",
"enumerate",
"(",
"cells",
")",
":",
"if",
"cell",
"[",
"'cell_type'",
"]",
"==",
"'markdown'",
":",
"cell",
"[",
"'source'",
"]",
"=",
"link_docstring",
"(",
"modules",
",",
"cell",
"[",
"'source'",
"]",
")"
] |
Create documentation links for all cells in markdown with backticks.
|
[
"Create",
"documentation",
"links",
"for",
"all",
"cells",
"in",
"markdown",
"with",
"backticks",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L156-L160
|
20,716
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
get_insert_idx
|
def get_insert_idx(pos_dict, name):
"Return the position to insert a given function doc in a notebook."
keys,i = list(pos_dict.keys()),0
while i < len(keys) and str.lower(keys[i]) < str.lower(name): i+=1
if i == len(keys): return -1
else: return pos_dict[keys[i]]
|
python
|
def get_insert_idx(pos_dict, name):
"Return the position to insert a given function doc in a notebook."
keys,i = list(pos_dict.keys()),0
while i < len(keys) and str.lower(keys[i]) < str.lower(name): i+=1
if i == len(keys): return -1
else: return pos_dict[keys[i]]
|
[
"def",
"get_insert_idx",
"(",
"pos_dict",
",",
"name",
")",
":",
"keys",
",",
"i",
"=",
"list",
"(",
"pos_dict",
".",
"keys",
"(",
")",
")",
",",
"0",
"while",
"i",
"<",
"len",
"(",
"keys",
")",
"and",
"str",
".",
"lower",
"(",
"keys",
"[",
"i",
"]",
")",
"<",
"str",
".",
"lower",
"(",
"name",
")",
":",
"i",
"+=",
"1",
"if",
"i",
"==",
"len",
"(",
"keys",
")",
":",
"return",
"-",
"1",
"else",
":",
"return",
"pos_dict",
"[",
"keys",
"[",
"i",
"]",
"]"
] |
Return the position to insert a given function doc in a notebook.
|
[
"Return",
"the",
"position",
"to",
"insert",
"a",
"given",
"function",
"doc",
"in",
"a",
"notebook",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L162-L167
|
20,717
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
update_pos
|
def update_pos(pos_dict, start_key, nbr=2):
"Update the `pos_dict` by moving all positions after `start_key` by `nbr`."
for key,idx in pos_dict.items():
if str.lower(key) >= str.lower(start_key): pos_dict[key] += nbr
return pos_dict
|
python
|
def update_pos(pos_dict, start_key, nbr=2):
"Update the `pos_dict` by moving all positions after `start_key` by `nbr`."
for key,idx in pos_dict.items():
if str.lower(key) >= str.lower(start_key): pos_dict[key] += nbr
return pos_dict
|
[
"def",
"update_pos",
"(",
"pos_dict",
",",
"start_key",
",",
"nbr",
"=",
"2",
")",
":",
"for",
"key",
",",
"idx",
"in",
"pos_dict",
".",
"items",
"(",
")",
":",
"if",
"str",
".",
"lower",
"(",
"key",
")",
">=",
"str",
".",
"lower",
"(",
"start_key",
")",
":",
"pos_dict",
"[",
"key",
"]",
"+=",
"nbr",
"return",
"pos_dict"
] |
Update the `pos_dict` by moving all positions after `start_key` by `nbr`.
|
[
"Update",
"the",
"pos_dict",
"by",
"moving",
"all",
"positions",
"after",
"start_key",
"by",
"nbr",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L169-L173
|
20,718
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
insert_cells
|
def insert_cells(cells, pos_dict, ft_name, append=False):
"Insert the function doc `cells` at their correct position and updates `pos_dict`."
idx = get_insert_idx(pos_dict, ft_name)
if append or idx == -1: cells += [get_doc_cell(ft_name), get_empty_cell()]
else:
cells.insert(idx, get_doc_cell(ft_name))
cells.insert(idx+1, get_empty_cell())
pos_dict = update_pos(pos_dict, ft_name, 2)
return cells, pos_dict
|
python
|
def insert_cells(cells, pos_dict, ft_name, append=False):
"Insert the function doc `cells` at their correct position and updates `pos_dict`."
idx = get_insert_idx(pos_dict, ft_name)
if append or idx == -1: cells += [get_doc_cell(ft_name), get_empty_cell()]
else:
cells.insert(idx, get_doc_cell(ft_name))
cells.insert(idx+1, get_empty_cell())
pos_dict = update_pos(pos_dict, ft_name, 2)
return cells, pos_dict
|
[
"def",
"insert_cells",
"(",
"cells",
",",
"pos_dict",
",",
"ft_name",
",",
"append",
"=",
"False",
")",
":",
"idx",
"=",
"get_insert_idx",
"(",
"pos_dict",
",",
"ft_name",
")",
"if",
"append",
"or",
"idx",
"==",
"-",
"1",
":",
"cells",
"+=",
"[",
"get_doc_cell",
"(",
"ft_name",
")",
",",
"get_empty_cell",
"(",
")",
"]",
"else",
":",
"cells",
".",
"insert",
"(",
"idx",
",",
"get_doc_cell",
"(",
"ft_name",
")",
")",
"cells",
".",
"insert",
"(",
"idx",
"+",
"1",
",",
"get_empty_cell",
"(",
")",
")",
"pos_dict",
"=",
"update_pos",
"(",
"pos_dict",
",",
"ft_name",
",",
"2",
")",
"return",
"cells",
",",
"pos_dict"
] |
Insert the function doc `cells` at their correct position and updates `pos_dict`.
|
[
"Insert",
"the",
"function",
"doc",
"cells",
"at",
"their",
"correct",
"position",
"and",
"updates",
"pos_dict",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L175-L183
|
20,719
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
update_nb_metadata
|
def update_nb_metadata(nb_path=None, title=None, summary=None, keywords='fastai', overwrite=True, **kwargs):
"Creates jekyll metadata for given notebook path."
nb = read_nb(nb_path)
data = {'title': title, 'summary': summary, 'keywords': keywords, **kwargs}
data = {k:v for (k,v) in data.items() if v is not None} # remove none values
if not data: return
nb['metadata']['jekyll'] = data
write_nb(nb, nb_path)
NotebookNotary().sign(nb)
|
python
|
def update_nb_metadata(nb_path=None, title=None, summary=None, keywords='fastai', overwrite=True, **kwargs):
"Creates jekyll metadata for given notebook path."
nb = read_nb(nb_path)
data = {'title': title, 'summary': summary, 'keywords': keywords, **kwargs}
data = {k:v for (k,v) in data.items() if v is not None} # remove none values
if not data: return
nb['metadata']['jekyll'] = data
write_nb(nb, nb_path)
NotebookNotary().sign(nb)
|
[
"def",
"update_nb_metadata",
"(",
"nb_path",
"=",
"None",
",",
"title",
"=",
"None",
",",
"summary",
"=",
"None",
",",
"keywords",
"=",
"'fastai'",
",",
"overwrite",
"=",
"True",
",",
"*",
"*",
"kwargs",
")",
":",
"nb",
"=",
"read_nb",
"(",
"nb_path",
")",
"data",
"=",
"{",
"'title'",
":",
"title",
",",
"'summary'",
":",
"summary",
",",
"'keywords'",
":",
"keywords",
",",
"*",
"*",
"kwargs",
"}",
"data",
"=",
"{",
"k",
":",
"v",
"for",
"(",
"k",
",",
"v",
")",
"in",
"data",
".",
"items",
"(",
")",
"if",
"v",
"is",
"not",
"None",
"}",
"# remove none values",
"if",
"not",
"data",
":",
"return",
"nb",
"[",
"'metadata'",
"]",
"[",
"'jekyll'",
"]",
"=",
"data",
"write_nb",
"(",
"nb",
",",
"nb_path",
")",
"NotebookNotary",
"(",
")",
".",
"sign",
"(",
"nb",
")"
] |
Creates jekyll metadata for given notebook path.
|
[
"Creates",
"jekyll",
"metadata",
"for",
"given",
"notebook",
"path",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L204-L212
|
20,720
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
get_imported_modules
|
def get_imported_modules(cells, nb_module_name=''):
"Finds all submodules of notebook - sorted by submodules > top level modules > manual imports. This gives notebook imports priority"
module_names = get_top_level_modules()
nb_imports = [match.group(1) for cell in cells for match in IMPORT_RE.finditer(cell['source']) if cell['cell_type'] == 'code']
parts = nb_module_name.split('.')
parent_modules = ['.'.join(parts[:(x+1)]) for x in range_of(parts)] # Imports parent modules - a.b.c = [a, a.b, a.b.c]
all_modules = module_names + nb_imports + parent_modules
mods = [import_mod(m, ignore_errors=True) for m in all_modules]
return [m for m in mods if m is not None]
|
python
|
def get_imported_modules(cells, nb_module_name=''):
"Finds all submodules of notebook - sorted by submodules > top level modules > manual imports. This gives notebook imports priority"
module_names = get_top_level_modules()
nb_imports = [match.group(1) for cell in cells for match in IMPORT_RE.finditer(cell['source']) if cell['cell_type'] == 'code']
parts = nb_module_name.split('.')
parent_modules = ['.'.join(parts[:(x+1)]) for x in range_of(parts)] # Imports parent modules - a.b.c = [a, a.b, a.b.c]
all_modules = module_names + nb_imports + parent_modules
mods = [import_mod(m, ignore_errors=True) for m in all_modules]
return [m for m in mods if m is not None]
|
[
"def",
"get_imported_modules",
"(",
"cells",
",",
"nb_module_name",
"=",
"''",
")",
":",
"module_names",
"=",
"get_top_level_modules",
"(",
")",
"nb_imports",
"=",
"[",
"match",
".",
"group",
"(",
"1",
")",
"for",
"cell",
"in",
"cells",
"for",
"match",
"in",
"IMPORT_RE",
".",
"finditer",
"(",
"cell",
"[",
"'source'",
"]",
")",
"if",
"cell",
"[",
"'cell_type'",
"]",
"==",
"'code'",
"]",
"parts",
"=",
"nb_module_name",
".",
"split",
"(",
"'.'",
")",
"parent_modules",
"=",
"[",
"'.'",
".",
"join",
"(",
"parts",
"[",
":",
"(",
"x",
"+",
"1",
")",
"]",
")",
"for",
"x",
"in",
"range_of",
"(",
"parts",
")",
"]",
"# Imports parent modules - a.b.c = [a, a.b, a.b.c]",
"all_modules",
"=",
"module_names",
"+",
"nb_imports",
"+",
"parent_modules",
"mods",
"=",
"[",
"import_mod",
"(",
"m",
",",
"ignore_errors",
"=",
"True",
")",
"for",
"m",
"in",
"all_modules",
"]",
"return",
"[",
"m",
"for",
"m",
"in",
"mods",
"if",
"m",
"is",
"not",
"None",
"]"
] |
Finds all submodules of notebook - sorted by submodules > top level modules > manual imports. This gives notebook imports priority
|
[
"Finds",
"all",
"submodules",
"of",
"notebook",
"-",
"sorted",
"by",
"submodules",
">",
"top",
"level",
"modules",
">",
"manual",
"imports",
".",
"This",
"gives",
"notebook",
"imports",
"priority"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L221-L229
|
20,721
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
update_module_page
|
def update_module_page(mod, dest_path='.'):
"Update the documentation notebook of a given module."
doc_path = get_doc_path(mod, dest_path)
strip_name = strip_fastai(mod.__name__)
nb = read_nb(doc_path)
cells = nb['cells']
link_markdown_cells(cells, get_imported_modules(cells, mod.__name__))
type_dict = read_nb_types(cells)
gvar_map = get_global_vars(mod)
for name in get_exports(mod):
if name not in gvar_map: continue
code = gvar_map[name]
if name in type_dict: cells[type_dict[name]] = get_md_cell(code)
else: cells.append(get_md_cell(code))
pos_dict = read_nb_content(cells, strip_name)
ft_names = get_ft_names(mod, include_inner=True)
new_fts = list(set(ft_names) - set(pos_dict.keys()))
if new_fts: print(f'Found new fuctions for {mod}. Please document:\n{new_fts}')
existing, undoc_cells, new_cells = parse_sections(cells)
for ft_name in new_fts: new_cells.extend([get_doc_cell(ft_name), get_empty_cell()])
if len(new_cells) > 1: nb['cells'] = existing + undoc_cells + new_cells
write_nb(nb, doc_path)
return doc_path
|
python
|
def update_module_page(mod, dest_path='.'):
"Update the documentation notebook of a given module."
doc_path = get_doc_path(mod, dest_path)
strip_name = strip_fastai(mod.__name__)
nb = read_nb(doc_path)
cells = nb['cells']
link_markdown_cells(cells, get_imported_modules(cells, mod.__name__))
type_dict = read_nb_types(cells)
gvar_map = get_global_vars(mod)
for name in get_exports(mod):
if name not in gvar_map: continue
code = gvar_map[name]
if name in type_dict: cells[type_dict[name]] = get_md_cell(code)
else: cells.append(get_md_cell(code))
pos_dict = read_nb_content(cells, strip_name)
ft_names = get_ft_names(mod, include_inner=True)
new_fts = list(set(ft_names) - set(pos_dict.keys()))
if new_fts: print(f'Found new fuctions for {mod}. Please document:\n{new_fts}')
existing, undoc_cells, new_cells = parse_sections(cells)
for ft_name in new_fts: new_cells.extend([get_doc_cell(ft_name), get_empty_cell()])
if len(new_cells) > 1: nb['cells'] = existing + undoc_cells + new_cells
write_nb(nb, doc_path)
return doc_path
|
[
"def",
"update_module_page",
"(",
"mod",
",",
"dest_path",
"=",
"'.'",
")",
":",
"doc_path",
"=",
"get_doc_path",
"(",
"mod",
",",
"dest_path",
")",
"strip_name",
"=",
"strip_fastai",
"(",
"mod",
".",
"__name__",
")",
"nb",
"=",
"read_nb",
"(",
"doc_path",
")",
"cells",
"=",
"nb",
"[",
"'cells'",
"]",
"link_markdown_cells",
"(",
"cells",
",",
"get_imported_modules",
"(",
"cells",
",",
"mod",
".",
"__name__",
")",
")",
"type_dict",
"=",
"read_nb_types",
"(",
"cells",
")",
"gvar_map",
"=",
"get_global_vars",
"(",
"mod",
")",
"for",
"name",
"in",
"get_exports",
"(",
"mod",
")",
":",
"if",
"name",
"not",
"in",
"gvar_map",
":",
"continue",
"code",
"=",
"gvar_map",
"[",
"name",
"]",
"if",
"name",
"in",
"type_dict",
":",
"cells",
"[",
"type_dict",
"[",
"name",
"]",
"]",
"=",
"get_md_cell",
"(",
"code",
")",
"else",
":",
"cells",
".",
"append",
"(",
"get_md_cell",
"(",
"code",
")",
")",
"pos_dict",
"=",
"read_nb_content",
"(",
"cells",
",",
"strip_name",
")",
"ft_names",
"=",
"get_ft_names",
"(",
"mod",
",",
"include_inner",
"=",
"True",
")",
"new_fts",
"=",
"list",
"(",
"set",
"(",
"ft_names",
")",
"-",
"set",
"(",
"pos_dict",
".",
"keys",
"(",
")",
")",
")",
"if",
"new_fts",
":",
"print",
"(",
"f'Found new fuctions for {mod}. Please document:\\n{new_fts}'",
")",
"existing",
",",
"undoc_cells",
",",
"new_cells",
"=",
"parse_sections",
"(",
"cells",
")",
"for",
"ft_name",
"in",
"new_fts",
":",
"new_cells",
".",
"extend",
"(",
"[",
"get_doc_cell",
"(",
"ft_name",
")",
",",
"get_empty_cell",
"(",
")",
"]",
")",
"if",
"len",
"(",
"new_cells",
")",
">",
"1",
":",
"nb",
"[",
"'cells'",
"]",
"=",
"existing",
"+",
"undoc_cells",
"+",
"new_cells",
"write_nb",
"(",
"nb",
",",
"doc_path",
")",
"return",
"doc_path"
] |
Update the documentation notebook of a given module.
|
[
"Update",
"the",
"documentation",
"notebook",
"of",
"a",
"given",
"module",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L262-L288
|
20,722
|
fastai/fastai
|
fastai/gen_doc/gen_notebooks.py
|
update_notebooks
|
def update_notebooks(source_path, dest_path=None, update_html=True, document_new_fns=False,
update_nb_links=True, html_path=None, force=False):
"`source_path` can be a directory or a file. Assume all modules reside in the fastai directory."
from .convert2html import convert_nb
source_path = Path(source_path)
if source_path.is_file():
dest_path = source_path.parent if dest_path is None else Path(dest_path)
html_path = dest_path/'..'/'docs' if html_path is None else Path(html_path)
doc_path = source_path
assert source_path.suffix == '.ipynb', 'Must update from notebook or module'
if document_new_fns:
mod = import_mod(get_module_from_notebook(source_path))
if not mod: print('Could not find module for path:', source_path)
elif mod.__file__.endswith('__init__.py'): pass
else: update_module_page(mod, dest_path)
generate_missing_metadata(doc_path)
if update_nb_links:
print(f'Updating notebook {doc_path}. Please wait...')
link_nb(doc_path)
execute_nb(doc_path, {'metadata': {'path': doc_path.parent}}, show_doc_only=True)
if update_html:
check_nbconvert_version()
html_fn = html_path/doc_path.with_suffix('.html').name
if not force and html_fn.is_file():
in_mod = os.path.getmtime(doc_path)
out_mod = os.path.getmtime(html_fn)
if in_mod < out_mod: return
convert_nb(doc_path, html_path)
elif (source_path.name.startswith('fastai.')):
# Do module update
assert dest_path is not None, 'To update a module, you must specify a destination folder for where notebook resides'
mod = import_mod(source_path.name)
if not mod: return print('Could not find module for:', source_path)
doc_path = Path(dest_path)/(strip_fastai(mod.__name__)+'.ipynb')
if not doc_path.exists():
print('Notebook does not exist. Creating:', doc_path)
create_module_page(mod, dest_path)
update_notebooks(doc_path, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
update_nb_links=update_nb_links, html_path=html_path)
elif source_path.is_dir():
for f in sorted(Path(source_path).glob('*.ipynb')):
update_notebooks(f, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
update_nb_links=update_nb_links, html_path=html_path)
else: print('Could not resolve source file:', source_path)
|
python
|
def update_notebooks(source_path, dest_path=None, update_html=True, document_new_fns=False,
update_nb_links=True, html_path=None, force=False):
"`source_path` can be a directory or a file. Assume all modules reside in the fastai directory."
from .convert2html import convert_nb
source_path = Path(source_path)
if source_path.is_file():
dest_path = source_path.parent if dest_path is None else Path(dest_path)
html_path = dest_path/'..'/'docs' if html_path is None else Path(html_path)
doc_path = source_path
assert source_path.suffix == '.ipynb', 'Must update from notebook or module'
if document_new_fns:
mod = import_mod(get_module_from_notebook(source_path))
if not mod: print('Could not find module for path:', source_path)
elif mod.__file__.endswith('__init__.py'): pass
else: update_module_page(mod, dest_path)
generate_missing_metadata(doc_path)
if update_nb_links:
print(f'Updating notebook {doc_path}. Please wait...')
link_nb(doc_path)
execute_nb(doc_path, {'metadata': {'path': doc_path.parent}}, show_doc_only=True)
if update_html:
check_nbconvert_version()
html_fn = html_path/doc_path.with_suffix('.html').name
if not force and html_fn.is_file():
in_mod = os.path.getmtime(doc_path)
out_mod = os.path.getmtime(html_fn)
if in_mod < out_mod: return
convert_nb(doc_path, html_path)
elif (source_path.name.startswith('fastai.')):
# Do module update
assert dest_path is not None, 'To update a module, you must specify a destination folder for where notebook resides'
mod = import_mod(source_path.name)
if not mod: return print('Could not find module for:', source_path)
doc_path = Path(dest_path)/(strip_fastai(mod.__name__)+'.ipynb')
if not doc_path.exists():
print('Notebook does not exist. Creating:', doc_path)
create_module_page(mod, dest_path)
update_notebooks(doc_path, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
update_nb_links=update_nb_links, html_path=html_path)
elif source_path.is_dir():
for f in sorted(Path(source_path).glob('*.ipynb')):
update_notebooks(f, dest_path=dest_path, update_html=update_html, document_new_fns=document_new_fns,
update_nb_links=update_nb_links, html_path=html_path)
else: print('Could not resolve source file:', source_path)
|
[
"def",
"update_notebooks",
"(",
"source_path",
",",
"dest_path",
"=",
"None",
",",
"update_html",
"=",
"True",
",",
"document_new_fns",
"=",
"False",
",",
"update_nb_links",
"=",
"True",
",",
"html_path",
"=",
"None",
",",
"force",
"=",
"False",
")",
":",
"from",
".",
"convert2html",
"import",
"convert_nb",
"source_path",
"=",
"Path",
"(",
"source_path",
")",
"if",
"source_path",
".",
"is_file",
"(",
")",
":",
"dest_path",
"=",
"source_path",
".",
"parent",
"if",
"dest_path",
"is",
"None",
"else",
"Path",
"(",
"dest_path",
")",
"html_path",
"=",
"dest_path",
"/",
"'..'",
"/",
"'docs'",
"if",
"html_path",
"is",
"None",
"else",
"Path",
"(",
"html_path",
")",
"doc_path",
"=",
"source_path",
"assert",
"source_path",
".",
"suffix",
"==",
"'.ipynb'",
",",
"'Must update from notebook or module'",
"if",
"document_new_fns",
":",
"mod",
"=",
"import_mod",
"(",
"get_module_from_notebook",
"(",
"source_path",
")",
")",
"if",
"not",
"mod",
":",
"print",
"(",
"'Could not find module for path:'",
",",
"source_path",
")",
"elif",
"mod",
".",
"__file__",
".",
"endswith",
"(",
"'__init__.py'",
")",
":",
"pass",
"else",
":",
"update_module_page",
"(",
"mod",
",",
"dest_path",
")",
"generate_missing_metadata",
"(",
"doc_path",
")",
"if",
"update_nb_links",
":",
"print",
"(",
"f'Updating notebook {doc_path}. Please wait...'",
")",
"link_nb",
"(",
"doc_path",
")",
"execute_nb",
"(",
"doc_path",
",",
"{",
"'metadata'",
":",
"{",
"'path'",
":",
"doc_path",
".",
"parent",
"}",
"}",
",",
"show_doc_only",
"=",
"True",
")",
"if",
"update_html",
":",
"check_nbconvert_version",
"(",
")",
"html_fn",
"=",
"html_path",
"/",
"doc_path",
".",
"with_suffix",
"(",
"'.html'",
")",
".",
"name",
"if",
"not",
"force",
"and",
"html_fn",
".",
"is_file",
"(",
")",
":",
"in_mod",
"=",
"os",
".",
"path",
".",
"getmtime",
"(",
"doc_path",
")",
"out_mod",
"=",
"os",
".",
"path",
".",
"getmtime",
"(",
"html_fn",
")",
"if",
"in_mod",
"<",
"out_mod",
":",
"return",
"convert_nb",
"(",
"doc_path",
",",
"html_path",
")",
"elif",
"(",
"source_path",
".",
"name",
".",
"startswith",
"(",
"'fastai.'",
")",
")",
":",
"# Do module update",
"assert",
"dest_path",
"is",
"not",
"None",
",",
"'To update a module, you must specify a destination folder for where notebook resides'",
"mod",
"=",
"import_mod",
"(",
"source_path",
".",
"name",
")",
"if",
"not",
"mod",
":",
"return",
"print",
"(",
"'Could not find module for:'",
",",
"source_path",
")",
"doc_path",
"=",
"Path",
"(",
"dest_path",
")",
"/",
"(",
"strip_fastai",
"(",
"mod",
".",
"__name__",
")",
"+",
"'.ipynb'",
")",
"if",
"not",
"doc_path",
".",
"exists",
"(",
")",
":",
"print",
"(",
"'Notebook does not exist. Creating:'",
",",
"doc_path",
")",
"create_module_page",
"(",
"mod",
",",
"dest_path",
")",
"update_notebooks",
"(",
"doc_path",
",",
"dest_path",
"=",
"dest_path",
",",
"update_html",
"=",
"update_html",
",",
"document_new_fns",
"=",
"document_new_fns",
",",
"update_nb_links",
"=",
"update_nb_links",
",",
"html_path",
"=",
"html_path",
")",
"elif",
"source_path",
".",
"is_dir",
"(",
")",
":",
"for",
"f",
"in",
"sorted",
"(",
"Path",
"(",
"source_path",
")",
".",
"glob",
"(",
"'*.ipynb'",
")",
")",
":",
"update_notebooks",
"(",
"f",
",",
"dest_path",
"=",
"dest_path",
",",
"update_html",
"=",
"update_html",
",",
"document_new_fns",
"=",
"document_new_fns",
",",
"update_nb_links",
"=",
"update_nb_links",
",",
"html_path",
"=",
"html_path",
")",
"else",
":",
"print",
"(",
"'Could not resolve source file:'",
",",
"source_path",
")"
] |
`source_path` can be a directory or a file. Assume all modules reside in the fastai directory.
|
[
"source_path",
"can",
"be",
"a",
"directory",
"or",
"a",
"file",
".",
"Assume",
"all",
"modules",
"reside",
"in",
"the",
"fastai",
"directory",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/gen_doc/gen_notebooks.py#L305-L350
|
20,723
|
fastai/fastai
|
fastai/text/models/awd_lstm.py
|
dropout_mask
|
def dropout_mask(x:Tensor, sz:Collection[int], p:float):
"Return a dropout mask of the same type as `x`, size `sz`, with probability `p` to cancel an element."
return x.new(*sz).bernoulli_(1-p).div_(1-p)
|
python
|
def dropout_mask(x:Tensor, sz:Collection[int], p:float):
"Return a dropout mask of the same type as `x`, size `sz`, with probability `p` to cancel an element."
return x.new(*sz).bernoulli_(1-p).div_(1-p)
|
[
"def",
"dropout_mask",
"(",
"x",
":",
"Tensor",
",",
"sz",
":",
"Collection",
"[",
"int",
"]",
",",
"p",
":",
"float",
")",
":",
"return",
"x",
".",
"new",
"(",
"*",
"sz",
")",
".",
"bernoulli_",
"(",
"1",
"-",
"p",
")",
".",
"div_",
"(",
"1",
"-",
"p",
")"
] |
Return a dropout mask of the same type as `x`, size `sz`, with probability `p` to cancel an element.
|
[
"Return",
"a",
"dropout",
"mask",
"of",
"the",
"same",
"type",
"as",
"x",
"size",
"sz",
"with",
"probability",
"p",
"to",
"cancel",
"an",
"element",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/models/awd_lstm.py#L13-L15
|
20,724
|
fastai/fastai
|
fastai/text/models/awd_lstm.py
|
WeightDropout._setweights
|
def _setweights(self):
"Apply dropout to the raw weights."
for layer in self.layer_names:
raw_w = getattr(self, f'{layer}_raw')
self.module._parameters[layer] = F.dropout(raw_w, p=self.weight_p, training=self.training)
|
python
|
def _setweights(self):
"Apply dropout to the raw weights."
for layer in self.layer_names:
raw_w = getattr(self, f'{layer}_raw')
self.module._parameters[layer] = F.dropout(raw_w, p=self.weight_p, training=self.training)
|
[
"def",
"_setweights",
"(",
"self",
")",
":",
"for",
"layer",
"in",
"self",
".",
"layer_names",
":",
"raw_w",
"=",
"getattr",
"(",
"self",
",",
"f'{layer}_raw'",
")",
"self",
".",
"module",
".",
"_parameters",
"[",
"layer",
"]",
"=",
"F",
".",
"dropout",
"(",
"raw_w",
",",
"p",
"=",
"self",
".",
"weight_p",
",",
"training",
"=",
"self",
".",
"training",
")"
] |
Apply dropout to the raw weights.
|
[
"Apply",
"dropout",
"to",
"the",
"raw",
"weights",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/models/awd_lstm.py#L41-L45
|
20,725
|
fastai/fastai
|
fastai/text/models/awd_lstm.py
|
AWD_LSTM._one_hidden
|
def _one_hidden(self, l:int)->Tensor:
"Return one hidden state."
nh = (self.n_hid if l != self.n_layers - 1 else self.emb_sz) // self.n_dir
return one_param(self).new(1, self.bs, nh).zero_()
|
python
|
def _one_hidden(self, l:int)->Tensor:
"Return one hidden state."
nh = (self.n_hid if l != self.n_layers - 1 else self.emb_sz) // self.n_dir
return one_param(self).new(1, self.bs, nh).zero_()
|
[
"def",
"_one_hidden",
"(",
"self",
",",
"l",
":",
"int",
")",
"->",
"Tensor",
":",
"nh",
"=",
"(",
"self",
".",
"n_hid",
"if",
"l",
"!=",
"self",
".",
"n_layers",
"-",
"1",
"else",
"self",
".",
"emb_sz",
")",
"//",
"self",
".",
"n_dir",
"return",
"one_param",
"(",
"self",
")",
".",
"new",
"(",
"1",
",",
"self",
".",
"bs",
",",
"nh",
")",
".",
"zero_",
"(",
")"
] |
Return one hidden state.
|
[
"Return",
"one",
"hidden",
"state",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/models/awd_lstm.py#L125-L128
|
20,726
|
fastai/fastai
|
fastai/text/models/awd_lstm.py
|
AWD_LSTM.reset
|
def reset(self):
"Reset the hidden states."
[r.reset() for r in self.rnns if hasattr(r, 'reset')]
if self.qrnn: self.hidden = [self._one_hidden(l) for l in range(self.n_layers)]
else: self.hidden = [(self._one_hidden(l), self._one_hidden(l)) for l in range(self.n_layers)]
|
python
|
def reset(self):
"Reset the hidden states."
[r.reset() for r in self.rnns if hasattr(r, 'reset')]
if self.qrnn: self.hidden = [self._one_hidden(l) for l in range(self.n_layers)]
else: self.hidden = [(self._one_hidden(l), self._one_hidden(l)) for l in range(self.n_layers)]
|
[
"def",
"reset",
"(",
"self",
")",
":",
"[",
"r",
".",
"reset",
"(",
")",
"for",
"r",
"in",
"self",
".",
"rnns",
"if",
"hasattr",
"(",
"r",
",",
"'reset'",
")",
"]",
"if",
"self",
".",
"qrnn",
":",
"self",
".",
"hidden",
"=",
"[",
"self",
".",
"_one_hidden",
"(",
"l",
")",
"for",
"l",
"in",
"range",
"(",
"self",
".",
"n_layers",
")",
"]",
"else",
":",
"self",
".",
"hidden",
"=",
"[",
"(",
"self",
".",
"_one_hidden",
"(",
"l",
")",
",",
"self",
".",
"_one_hidden",
"(",
"l",
")",
")",
"for",
"l",
"in",
"range",
"(",
"self",
".",
"n_layers",
")",
"]"
] |
Reset the hidden states.
|
[
"Reset",
"the",
"hidden",
"states",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/models/awd_lstm.py#L135-L139
|
20,727
|
fastai/fastai
|
fastai/text/models/awd_lstm.py
|
TextClassificationInterpretation.show_top_losses
|
def show_top_losses(self, k:int, max_len:int=70)->None:
"""
Create a tabulation showing the first `k` texts in top_losses along with their prediction, actual,loss, and probability of
actual class. `max_len` is the maximum number of tokens displayed.
"""
from IPython.display import display, HTML
items = []
tl_val,tl_idx = self.top_losses()
for i,idx in enumerate(tl_idx):
if k <= 0: break
k -= 1
tx,cl = self.data.dl(self.ds_type).dataset[idx]
cl = cl.data
classes = self.data.classes
txt = ' '.join(tx.text.split(' ')[:max_len]) if max_len is not None else tx.text
tmp = [txt, f'{classes[self.pred_class[idx]]}', f'{classes[cl]}', f'{self.losses[idx]:.2f}',
f'{self.probs[idx][cl]:.2f}']
items.append(tmp)
items = np.array(items)
names = ['Text', 'Prediction', 'Actual', 'Loss', 'Probability']
df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
with pd.option_context('display.max_colwidth', -1):
display(HTML(df.to_html(index=False)))
|
python
|
def show_top_losses(self, k:int, max_len:int=70)->None:
"""
Create a tabulation showing the first `k` texts in top_losses along with their prediction, actual,loss, and probability of
actual class. `max_len` is the maximum number of tokens displayed.
"""
from IPython.display import display, HTML
items = []
tl_val,tl_idx = self.top_losses()
for i,idx in enumerate(tl_idx):
if k <= 0: break
k -= 1
tx,cl = self.data.dl(self.ds_type).dataset[idx]
cl = cl.data
classes = self.data.classes
txt = ' '.join(tx.text.split(' ')[:max_len]) if max_len is not None else tx.text
tmp = [txt, f'{classes[self.pred_class[idx]]}', f'{classes[cl]}', f'{self.losses[idx]:.2f}',
f'{self.probs[idx][cl]:.2f}']
items.append(tmp)
items = np.array(items)
names = ['Text', 'Prediction', 'Actual', 'Loss', 'Probability']
df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
with pd.option_context('display.max_colwidth', -1):
display(HTML(df.to_html(index=False)))
|
[
"def",
"show_top_losses",
"(",
"self",
",",
"k",
":",
"int",
",",
"max_len",
":",
"int",
"=",
"70",
")",
"->",
"None",
":",
"from",
"IPython",
".",
"display",
"import",
"display",
",",
"HTML",
"items",
"=",
"[",
"]",
"tl_val",
",",
"tl_idx",
"=",
"self",
".",
"top_losses",
"(",
")",
"for",
"i",
",",
"idx",
"in",
"enumerate",
"(",
"tl_idx",
")",
":",
"if",
"k",
"<=",
"0",
":",
"break",
"k",
"-=",
"1",
"tx",
",",
"cl",
"=",
"self",
".",
"data",
".",
"dl",
"(",
"self",
".",
"ds_type",
")",
".",
"dataset",
"[",
"idx",
"]",
"cl",
"=",
"cl",
".",
"data",
"classes",
"=",
"self",
".",
"data",
".",
"classes",
"txt",
"=",
"' '",
".",
"join",
"(",
"tx",
".",
"text",
".",
"split",
"(",
"' '",
")",
"[",
":",
"max_len",
"]",
")",
"if",
"max_len",
"is",
"not",
"None",
"else",
"tx",
".",
"text",
"tmp",
"=",
"[",
"txt",
",",
"f'{classes[self.pred_class[idx]]}'",
",",
"f'{classes[cl]}'",
",",
"f'{self.losses[idx]:.2f}'",
",",
"f'{self.probs[idx][cl]:.2f}'",
"]",
"items",
".",
"append",
"(",
"tmp",
")",
"items",
"=",
"np",
".",
"array",
"(",
"items",
")",
"names",
"=",
"[",
"'Text'",
",",
"'Prediction'",
",",
"'Actual'",
",",
"'Loss'",
",",
"'Probability'",
"]",
"df",
"=",
"pd",
".",
"DataFrame",
"(",
"{",
"n",
":",
"items",
"[",
":",
",",
"i",
"]",
"for",
"i",
",",
"n",
"in",
"enumerate",
"(",
"names",
")",
"}",
",",
"columns",
"=",
"names",
")",
"with",
"pd",
".",
"option_context",
"(",
"'display.max_colwidth'",
",",
"-",
"1",
")",
":",
"display",
"(",
"HTML",
"(",
"df",
".",
"to_html",
"(",
"index",
"=",
"False",
")",
")",
")"
] |
Create a tabulation showing the first `k` texts in top_losses along with their prediction, actual,loss, and probability of
actual class. `max_len` is the maximum number of tokens displayed.
|
[
"Create",
"a",
"tabulation",
"showing",
"the",
"first",
"k",
"texts",
"in",
"top_losses",
"along",
"with",
"their",
"prediction",
"actual",
"loss",
"and",
"probability",
"of",
"actual",
"class",
".",
"max_len",
"is",
"the",
"maximum",
"number",
"of",
"tokens",
"displayed",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/models/awd_lstm.py#L246-L268
|
20,728
|
fastai/fastai
|
fastai/callbacks/general_sched.py
|
GeneralScheduler.on_train_begin
|
def on_train_begin(self, epoch:int, **kwargs:Any)->None:
"Initialize the schedulers for training."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.start_epoch, epoch)
self.scheds = [p.scheds for p in self.phases]
self.opt = self.learn.opt
for k,v in self.scheds[0].items():
v.restart()
self.opt.set_stat(k, v.start)
self.idx_s = 0
return res
|
python
|
def on_train_begin(self, epoch:int, **kwargs:Any)->None:
"Initialize the schedulers for training."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.start_epoch, epoch)
self.scheds = [p.scheds for p in self.phases]
self.opt = self.learn.opt
for k,v in self.scheds[0].items():
v.restart()
self.opt.set_stat(k, v.start)
self.idx_s = 0
return res
|
[
"def",
"on_train_begin",
"(",
"self",
",",
"epoch",
":",
"int",
",",
"*",
"*",
"kwargs",
":",
"Any",
")",
"->",
"None",
":",
"res",
"=",
"{",
"'epoch'",
":",
"self",
".",
"start_epoch",
"}",
"if",
"self",
".",
"start_epoch",
"is",
"not",
"None",
"else",
"None",
"self",
".",
"start_epoch",
"=",
"ifnone",
"(",
"self",
".",
"start_epoch",
",",
"epoch",
")",
"self",
".",
"scheds",
"=",
"[",
"p",
".",
"scheds",
"for",
"p",
"in",
"self",
".",
"phases",
"]",
"self",
".",
"opt",
"=",
"self",
".",
"learn",
".",
"opt",
"for",
"k",
",",
"v",
"in",
"self",
".",
"scheds",
"[",
"0",
"]",
".",
"items",
"(",
")",
":",
"v",
".",
"restart",
"(",
")",
"self",
".",
"opt",
".",
"set_stat",
"(",
"k",
",",
"v",
".",
"start",
")",
"self",
".",
"idx_s",
"=",
"0",
"return",
"res"
] |
Initialize the schedulers for training.
|
[
"Initialize",
"the",
"schedulers",
"for",
"training",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/general_sched.py#L24-L34
|
20,729
|
fastai/fastai
|
fastai/callbacks/general_sched.py
|
GeneralScheduler.on_batch_end
|
def on_batch_end(self, train, **kwargs:Any)->None:
"Take a step in lr,mom sched, start next stepper when the current one is complete."
if train:
if self.idx_s >= len(self.scheds): return {'stop_training': True, 'stop_epoch': True}
sched = self.scheds[self.idx_s]
for k,v in sched.items(): self.opt.set_stat(k, v.step())
if list(sched.values())[0].is_done: self.idx_s += 1
|
python
|
def on_batch_end(self, train, **kwargs:Any)->None:
"Take a step in lr,mom sched, start next stepper when the current one is complete."
if train:
if self.idx_s >= len(self.scheds): return {'stop_training': True, 'stop_epoch': True}
sched = self.scheds[self.idx_s]
for k,v in sched.items(): self.opt.set_stat(k, v.step())
if list(sched.values())[0].is_done: self.idx_s += 1
|
[
"def",
"on_batch_end",
"(",
"self",
",",
"train",
",",
"*",
"*",
"kwargs",
":",
"Any",
")",
"->",
"None",
":",
"if",
"train",
":",
"if",
"self",
".",
"idx_s",
">=",
"len",
"(",
"self",
".",
"scheds",
")",
":",
"return",
"{",
"'stop_training'",
":",
"True",
",",
"'stop_epoch'",
":",
"True",
"}",
"sched",
"=",
"self",
".",
"scheds",
"[",
"self",
".",
"idx_s",
"]",
"for",
"k",
",",
"v",
"in",
"sched",
".",
"items",
"(",
")",
":",
"self",
".",
"opt",
".",
"set_stat",
"(",
"k",
",",
"v",
".",
"step",
"(",
")",
")",
"if",
"list",
"(",
"sched",
".",
"values",
"(",
")",
")",
"[",
"0",
"]",
".",
"is_done",
":",
"self",
".",
"idx_s",
"+=",
"1"
] |
Take a step in lr,mom sched, start next stepper when the current one is complete.
|
[
"Take",
"a",
"step",
"in",
"lr",
"mom",
"sched",
"start",
"next",
"stepper",
"when",
"the",
"current",
"one",
"is",
"complete",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/general_sched.py#L40-L46
|
20,730
|
fastai/fastai
|
fastai/torch_core.py
|
tensor
|
def tensor(x:Any, *rest)->Tensor:
"Like `torch.as_tensor`, but handle lists too, and can pass multiple vector elements directly."
if len(rest): x = (x,)+rest
# XXX: Pytorch bug in dataloader using num_workers>0; TODO: create repro and report
if is_listy(x) and len(x)==0: return tensor(0)
res = torch.tensor(x) if is_listy(x) else as_tensor(x)
if res.dtype is torch.int32:
warn('Tensor is int32: upgrading to int64; for better performance use int64 input')
return res.long()
return res
|
python
|
def tensor(x:Any, *rest)->Tensor:
"Like `torch.as_tensor`, but handle lists too, and can pass multiple vector elements directly."
if len(rest): x = (x,)+rest
# XXX: Pytorch bug in dataloader using num_workers>0; TODO: create repro and report
if is_listy(x) and len(x)==0: return tensor(0)
res = torch.tensor(x) if is_listy(x) else as_tensor(x)
if res.dtype is torch.int32:
warn('Tensor is int32: upgrading to int64; for better performance use int64 input')
return res.long()
return res
|
[
"def",
"tensor",
"(",
"x",
":",
"Any",
",",
"*",
"rest",
")",
"->",
"Tensor",
":",
"if",
"len",
"(",
"rest",
")",
":",
"x",
"=",
"(",
"x",
",",
")",
"+",
"rest",
"# XXX: Pytorch bug in dataloader using num_workers>0; TODO: create repro and report",
"if",
"is_listy",
"(",
"x",
")",
"and",
"len",
"(",
"x",
")",
"==",
"0",
":",
"return",
"tensor",
"(",
"0",
")",
"res",
"=",
"torch",
".",
"tensor",
"(",
"x",
")",
"if",
"is_listy",
"(",
"x",
")",
"else",
"as_tensor",
"(",
"x",
")",
"if",
"res",
".",
"dtype",
"is",
"torch",
".",
"int32",
":",
"warn",
"(",
"'Tensor is int32: upgrading to int64; for better performance use int64 input'",
")",
"return",
"res",
".",
"long",
"(",
")",
"return",
"res"
] |
Like `torch.as_tensor`, but handle lists too, and can pass multiple vector elements directly.
|
[
"Like",
"torch",
".",
"as_tensor",
"but",
"handle",
"lists",
"too",
"and",
"can",
"pass",
"multiple",
"vector",
"elements",
"directly",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L76-L85
|
20,731
|
fastai/fastai
|
fastai/torch_core.py
|
to_detach
|
def to_detach(b:Tensors, cpu:bool=True):
"Recursively detach lists of tensors in `b `; put them on the CPU if `cpu=True`."
if is_listy(b): return [to_detach(o, cpu) for o in b]
if not isinstance(b,Tensor): return b
b = b.detach()
return b.cpu() if cpu else b
|
python
|
def to_detach(b:Tensors, cpu:bool=True):
"Recursively detach lists of tensors in `b `; put them on the CPU if `cpu=True`."
if is_listy(b): return [to_detach(o, cpu) for o in b]
if not isinstance(b,Tensor): return b
b = b.detach()
return b.cpu() if cpu else b
|
[
"def",
"to_detach",
"(",
"b",
":",
"Tensors",
",",
"cpu",
":",
"bool",
"=",
"True",
")",
":",
"if",
"is_listy",
"(",
"b",
")",
":",
"return",
"[",
"to_detach",
"(",
"o",
",",
"cpu",
")",
"for",
"o",
"in",
"b",
"]",
"if",
"not",
"isinstance",
"(",
"b",
",",
"Tensor",
")",
":",
"return",
"b",
"b",
"=",
"b",
".",
"detach",
"(",
")",
"return",
"b",
".",
"cpu",
"(",
")",
"if",
"cpu",
"else",
"b"
] |
Recursively detach lists of tensors in `b `; put them on the CPU if `cpu=True`.
|
[
"Recursively",
"detach",
"lists",
"of",
"tensors",
"in",
"b",
";",
"put",
"them",
"on",
"the",
"CPU",
"if",
"cpu",
"=",
"True",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L91-L96
|
20,732
|
fastai/fastai
|
fastai/torch_core.py
|
to_data
|
def to_data(b:ItemsList):
"Recursively map lists of items in `b ` to their wrapped data."
if is_listy(b): return [to_data(o) for o in b]
return b.data if isinstance(b,ItemBase) else b
|
python
|
def to_data(b:ItemsList):
"Recursively map lists of items in `b ` to their wrapped data."
if is_listy(b): return [to_data(o) for o in b]
return b.data if isinstance(b,ItemBase) else b
|
[
"def",
"to_data",
"(",
"b",
":",
"ItemsList",
")",
":",
"if",
"is_listy",
"(",
"b",
")",
":",
"return",
"[",
"to_data",
"(",
"o",
")",
"for",
"o",
"in",
"b",
"]",
"return",
"b",
".",
"data",
"if",
"isinstance",
"(",
"b",
",",
"ItemBase",
")",
"else",
"b"
] |
Recursively map lists of items in `b ` to their wrapped data.
|
[
"Recursively",
"map",
"lists",
"of",
"items",
"in",
"b",
"to",
"their",
"wrapped",
"data",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L98-L101
|
20,733
|
fastai/fastai
|
fastai/torch_core.py
|
to_cpu
|
def to_cpu(b:ItemsList):
"Recursively map lists of tensors in `b ` to the cpu."
if is_listy(b): return [to_cpu(o) for o in b]
return b.cpu() if isinstance(b,Tensor) else b
|
python
|
def to_cpu(b:ItemsList):
"Recursively map lists of tensors in `b ` to the cpu."
if is_listy(b): return [to_cpu(o) for o in b]
return b.cpu() if isinstance(b,Tensor) else b
|
[
"def",
"to_cpu",
"(",
"b",
":",
"ItemsList",
")",
":",
"if",
"is_listy",
"(",
"b",
")",
":",
"return",
"[",
"to_cpu",
"(",
"o",
")",
"for",
"o",
"in",
"b",
"]",
"return",
"b",
".",
"cpu",
"(",
")",
"if",
"isinstance",
"(",
"b",
",",
"Tensor",
")",
"else",
"b"
] |
Recursively map lists of tensors in `b ` to the cpu.
|
[
"Recursively",
"map",
"lists",
"of",
"tensors",
"in",
"b",
"to",
"the",
"cpu",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L103-L106
|
20,734
|
fastai/fastai
|
fastai/torch_core.py
|
to_device
|
def to_device(b:Tensors, device:torch.device):
"Recursively put `b` on `device`."
device = ifnone(device, defaults.device)
if is_listy(b): return [to_device(o, device) for o in b]
if is_dict(b): return {k: to_device(v, device) for k, v in b.items()}
return b.to(device, non_blocking=True)
|
python
|
def to_device(b:Tensors, device:torch.device):
"Recursively put `b` on `device`."
device = ifnone(device, defaults.device)
if is_listy(b): return [to_device(o, device) for o in b]
if is_dict(b): return {k: to_device(v, device) for k, v in b.items()}
return b.to(device, non_blocking=True)
|
[
"def",
"to_device",
"(",
"b",
":",
"Tensors",
",",
"device",
":",
"torch",
".",
"device",
")",
":",
"device",
"=",
"ifnone",
"(",
"device",
",",
"defaults",
".",
"device",
")",
"if",
"is_listy",
"(",
"b",
")",
":",
"return",
"[",
"to_device",
"(",
"o",
",",
"device",
")",
"for",
"o",
"in",
"b",
"]",
"if",
"is_dict",
"(",
"b",
")",
":",
"return",
"{",
"k",
":",
"to_device",
"(",
"v",
",",
"device",
")",
"for",
"k",
",",
"v",
"in",
"b",
".",
"items",
"(",
")",
"}",
"return",
"b",
".",
"to",
"(",
"device",
",",
"non_blocking",
"=",
"True",
")"
] |
Recursively put `b` on `device`.
|
[
"Recursively",
"put",
"b",
"on",
"device",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L118-L123
|
20,735
|
fastai/fastai
|
fastai/torch_core.py
|
data_collate
|
def data_collate(batch:ItemsList)->Tensor:
"Convert `batch` items to tensor data."
return torch.utils.data.dataloader.default_collate(to_data(batch))
|
python
|
def data_collate(batch:ItemsList)->Tensor:
"Convert `batch` items to tensor data."
return torch.utils.data.dataloader.default_collate(to_data(batch))
|
[
"def",
"data_collate",
"(",
"batch",
":",
"ItemsList",
")",
"->",
"Tensor",
":",
"return",
"torch",
".",
"utils",
".",
"data",
".",
"dataloader",
".",
"default_collate",
"(",
"to_data",
"(",
"batch",
")",
")"
] |
Convert `batch` items to tensor data.
|
[
"Convert",
"batch",
"items",
"to",
"tensor",
"data",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L125-L127
|
20,736
|
fastai/fastai
|
fastai/torch_core.py
|
requires_grad
|
def requires_grad(m:nn.Module, b:Optional[bool]=None)->Optional[bool]:
"If `b` is not set return `requires_grad` of first param, else set `requires_grad` on all params as `b`"
ps = list(m.parameters())
if not ps: return None
if b is None: return ps[0].requires_grad
for p in ps: p.requires_grad=b
|
python
|
def requires_grad(m:nn.Module, b:Optional[bool]=None)->Optional[bool]:
"If `b` is not set return `requires_grad` of first param, else set `requires_grad` on all params as `b`"
ps = list(m.parameters())
if not ps: return None
if b is None: return ps[0].requires_grad
for p in ps: p.requires_grad=b
|
[
"def",
"requires_grad",
"(",
"m",
":",
"nn",
".",
"Module",
",",
"b",
":",
"Optional",
"[",
"bool",
"]",
"=",
"None",
")",
"->",
"Optional",
"[",
"bool",
"]",
":",
"ps",
"=",
"list",
"(",
"m",
".",
"parameters",
"(",
")",
")",
"if",
"not",
"ps",
":",
"return",
"None",
"if",
"b",
"is",
"None",
":",
"return",
"ps",
"[",
"0",
"]",
".",
"requires_grad",
"for",
"p",
"in",
"ps",
":",
"p",
".",
"requires_grad",
"=",
"b"
] |
If `b` is not set return `requires_grad` of first param, else set `requires_grad` on all params as `b`
|
[
"If",
"b",
"is",
"not",
"set",
"return",
"requires_grad",
"of",
"first",
"param",
"else",
"set",
"requires_grad",
"on",
"all",
"params",
"as",
"b"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L129-L134
|
20,737
|
fastai/fastai
|
fastai/torch_core.py
|
trainable_params
|
def trainable_params(m:nn.Module)->ParamList:
"Return list of trainable params in `m`."
res = filter(lambda p: p.requires_grad, m.parameters())
return res
|
python
|
def trainable_params(m:nn.Module)->ParamList:
"Return list of trainable params in `m`."
res = filter(lambda p: p.requires_grad, m.parameters())
return res
|
[
"def",
"trainable_params",
"(",
"m",
":",
"nn",
".",
"Module",
")",
"->",
"ParamList",
":",
"res",
"=",
"filter",
"(",
"lambda",
"p",
":",
"p",
".",
"requires_grad",
",",
"m",
".",
"parameters",
"(",
")",
")",
"return",
"res"
] |
Return list of trainable params in `m`.
|
[
"Return",
"list",
"of",
"trainable",
"params",
"in",
"m",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L136-L139
|
20,738
|
fastai/fastai
|
fastai/torch_core.py
|
children_and_parameters
|
def children_and_parameters(m:nn.Module):
"Return the children of `m` and its direct parameters not registered in modules."
children = list(m.children())
children_p = sum([[id(p) for p in c.parameters()] for c in m.children()],[])
for p in m.parameters():
if id(p) not in children_p: children.append(ParameterModule(p))
return children
|
python
|
def children_and_parameters(m:nn.Module):
"Return the children of `m` and its direct parameters not registered in modules."
children = list(m.children())
children_p = sum([[id(p) for p in c.parameters()] for c in m.children()],[])
for p in m.parameters():
if id(p) not in children_p: children.append(ParameterModule(p))
return children
|
[
"def",
"children_and_parameters",
"(",
"m",
":",
"nn",
".",
"Module",
")",
":",
"children",
"=",
"list",
"(",
"m",
".",
"children",
"(",
")",
")",
"children_p",
"=",
"sum",
"(",
"[",
"[",
"id",
"(",
"p",
")",
"for",
"p",
"in",
"c",
".",
"parameters",
"(",
")",
"]",
"for",
"c",
"in",
"m",
".",
"children",
"(",
")",
"]",
",",
"[",
"]",
")",
"for",
"p",
"in",
"m",
".",
"parameters",
"(",
")",
":",
"if",
"id",
"(",
"p",
")",
"not",
"in",
"children_p",
":",
"children",
".",
"append",
"(",
"ParameterModule",
"(",
"p",
")",
")",
"return",
"children"
] |
Return the children of `m` and its direct parameters not registered in modules.
|
[
"Return",
"the",
"children",
"of",
"m",
"and",
"its",
"direct",
"parameters",
"not",
"registered",
"in",
"modules",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L161-L167
|
20,739
|
fastai/fastai
|
fastai/torch_core.py
|
split_model_idx
|
def split_model_idx(model:nn.Module, idxs:Collection[int])->ModuleList:
"Split `model` according to the indexes in `idxs`."
layers = flatten_model(model)
if idxs[0] != 0: idxs = [0] + idxs
if idxs[-1] != len(layers): idxs.append(len(layers))
return [nn.Sequential(*layers[i:j]) for i,j in zip(idxs[:-1],idxs[1:])]
|
python
|
def split_model_idx(model:nn.Module, idxs:Collection[int])->ModuleList:
"Split `model` according to the indexes in `idxs`."
layers = flatten_model(model)
if idxs[0] != 0: idxs = [0] + idxs
if idxs[-1] != len(layers): idxs.append(len(layers))
return [nn.Sequential(*layers[i:j]) for i,j in zip(idxs[:-1],idxs[1:])]
|
[
"def",
"split_model_idx",
"(",
"model",
":",
"nn",
".",
"Module",
",",
"idxs",
":",
"Collection",
"[",
"int",
"]",
")",
"->",
"ModuleList",
":",
"layers",
"=",
"flatten_model",
"(",
"model",
")",
"if",
"idxs",
"[",
"0",
"]",
"!=",
"0",
":",
"idxs",
"=",
"[",
"0",
"]",
"+",
"idxs",
"if",
"idxs",
"[",
"-",
"1",
"]",
"!=",
"len",
"(",
"layers",
")",
":",
"idxs",
".",
"append",
"(",
"len",
"(",
"layers",
")",
")",
"return",
"[",
"nn",
".",
"Sequential",
"(",
"*",
"layers",
"[",
"i",
":",
"j",
"]",
")",
"for",
"i",
",",
"j",
"in",
"zip",
"(",
"idxs",
"[",
":",
"-",
"1",
"]",
",",
"idxs",
"[",
"1",
":",
"]",
")",
"]"
] |
Split `model` according to the indexes in `idxs`.
|
[
"Split",
"model",
"according",
"to",
"the",
"indexes",
"in",
"idxs",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L179-L184
|
20,740
|
fastai/fastai
|
fastai/torch_core.py
|
split_model
|
def split_model(model:nn.Module=None, splits:Collection[Union[nn.Module,ModuleList]]=None):
"Split `model` according to the layers in `splits`."
splits = listify(splits)
if isinstance(splits[0], nn.Module):
layers = flatten_model(model)
idxs = [layers.index(first_layer(s)) for s in splits]
return split_model_idx(model, idxs)
return [nn.Sequential(*s) for s in splits]
|
python
|
def split_model(model:nn.Module=None, splits:Collection[Union[nn.Module,ModuleList]]=None):
"Split `model` according to the layers in `splits`."
splits = listify(splits)
if isinstance(splits[0], nn.Module):
layers = flatten_model(model)
idxs = [layers.index(first_layer(s)) for s in splits]
return split_model_idx(model, idxs)
return [nn.Sequential(*s) for s in splits]
|
[
"def",
"split_model",
"(",
"model",
":",
"nn",
".",
"Module",
"=",
"None",
",",
"splits",
":",
"Collection",
"[",
"Union",
"[",
"nn",
".",
"Module",
",",
"ModuleList",
"]",
"]",
"=",
"None",
")",
":",
"splits",
"=",
"listify",
"(",
"splits",
")",
"if",
"isinstance",
"(",
"splits",
"[",
"0",
"]",
",",
"nn",
".",
"Module",
")",
":",
"layers",
"=",
"flatten_model",
"(",
"model",
")",
"idxs",
"=",
"[",
"layers",
".",
"index",
"(",
"first_layer",
"(",
"s",
")",
")",
"for",
"s",
"in",
"splits",
"]",
"return",
"split_model_idx",
"(",
"model",
",",
"idxs",
")",
"return",
"[",
"nn",
".",
"Sequential",
"(",
"*",
"s",
")",
"for",
"s",
"in",
"splits",
"]"
] |
Split `model` according to the layers in `splits`.
|
[
"Split",
"model",
"according",
"to",
"the",
"layers",
"in",
"splits",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L186-L193
|
20,741
|
fastai/fastai
|
fastai/torch_core.py
|
set_bn_eval
|
def set_bn_eval(m:nn.Module)->None:
"Set bn layers in eval mode for all recursive children of `m`."
for l in m.children():
if isinstance(l, bn_types) and not next(l.parameters()).requires_grad:
l.eval()
set_bn_eval(l)
|
python
|
def set_bn_eval(m:nn.Module)->None:
"Set bn layers in eval mode for all recursive children of `m`."
for l in m.children():
if isinstance(l, bn_types) and not next(l.parameters()).requires_grad:
l.eval()
set_bn_eval(l)
|
[
"def",
"set_bn_eval",
"(",
"m",
":",
"nn",
".",
"Module",
")",
"->",
"None",
":",
"for",
"l",
"in",
"m",
".",
"children",
"(",
")",
":",
"if",
"isinstance",
"(",
"l",
",",
"bn_types",
")",
"and",
"not",
"next",
"(",
"l",
".",
"parameters",
"(",
")",
")",
".",
"requires_grad",
":",
"l",
".",
"eval",
"(",
")",
"set_bn_eval",
"(",
"l",
")"
] |
Set bn layers in eval mode for all recursive children of `m`.
|
[
"Set",
"bn",
"layers",
"in",
"eval",
"mode",
"for",
"all",
"recursive",
"children",
"of",
"m",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L216-L221
|
20,742
|
fastai/fastai
|
fastai/torch_core.py
|
bn2float
|
def bn2float(module:nn.Module)->nn.Module:
"If `module` is batchnorm don't use half precision."
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): module.float()
for child in module.children(): bn2float(child)
return module
|
python
|
def bn2float(module:nn.Module)->nn.Module:
"If `module` is batchnorm don't use half precision."
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): module.float()
for child in module.children(): bn2float(child)
return module
|
[
"def",
"bn2float",
"(",
"module",
":",
"nn",
".",
"Module",
")",
"->",
"nn",
".",
"Module",
":",
"if",
"isinstance",
"(",
"module",
",",
"torch",
".",
"nn",
".",
"modules",
".",
"batchnorm",
".",
"_BatchNorm",
")",
":",
"module",
".",
"float",
"(",
")",
"for",
"child",
"in",
"module",
".",
"children",
"(",
")",
":",
"bn2float",
"(",
"child",
")",
"return",
"module"
] |
If `module` is batchnorm don't use half precision.
|
[
"If",
"module",
"is",
"batchnorm",
"don",
"t",
"use",
"half",
"precision",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L227-L231
|
20,743
|
fastai/fastai
|
fastai/torch_core.py
|
init_default
|
def init_default(m:nn.Module, func:LayerFunc=nn.init.kaiming_normal_)->None:
"Initialize `m` weights with `func` and set `bias` to 0."
if func:
if hasattr(m, 'weight'): func(m.weight)
if hasattr(m, 'bias') and hasattr(m.bias, 'data'): m.bias.data.fill_(0.)
return m
|
python
|
def init_default(m:nn.Module, func:LayerFunc=nn.init.kaiming_normal_)->None:
"Initialize `m` weights with `func` and set `bias` to 0."
if func:
if hasattr(m, 'weight'): func(m.weight)
if hasattr(m, 'bias') and hasattr(m.bias, 'data'): m.bias.data.fill_(0.)
return m
|
[
"def",
"init_default",
"(",
"m",
":",
"nn",
".",
"Module",
",",
"func",
":",
"LayerFunc",
"=",
"nn",
".",
"init",
".",
"kaiming_normal_",
")",
"->",
"None",
":",
"if",
"func",
":",
"if",
"hasattr",
"(",
"m",
",",
"'weight'",
")",
":",
"func",
"(",
"m",
".",
"weight",
")",
"if",
"hasattr",
"(",
"m",
",",
"'bias'",
")",
"and",
"hasattr",
"(",
"m",
".",
"bias",
",",
"'data'",
")",
":",
"m",
".",
"bias",
".",
"data",
".",
"fill_",
"(",
"0.",
")",
"return",
"m"
] |
Initialize `m` weights with `func` and set `bias` to 0.
|
[
"Initialize",
"m",
"weights",
"with",
"func",
"and",
"set",
"bias",
"to",
"0",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L237-L242
|
20,744
|
fastai/fastai
|
fastai/torch_core.py
|
cond_init
|
def cond_init(m:nn.Module, init_func:LayerFunc):
"Initialize the non-batchnorm layers of `m` with `init_func`."
if (not isinstance(m, bn_types)) and requires_grad(m): init_default(m, init_func)
|
python
|
def cond_init(m:nn.Module, init_func:LayerFunc):
"Initialize the non-batchnorm layers of `m` with `init_func`."
if (not isinstance(m, bn_types)) and requires_grad(m): init_default(m, init_func)
|
[
"def",
"cond_init",
"(",
"m",
":",
"nn",
".",
"Module",
",",
"init_func",
":",
"LayerFunc",
")",
":",
"if",
"(",
"not",
"isinstance",
"(",
"m",
",",
"bn_types",
")",
")",
"and",
"requires_grad",
"(",
"m",
")",
":",
"init_default",
"(",
"m",
",",
"init_func",
")"
] |
Initialize the non-batchnorm layers of `m` with `init_func`.
|
[
"Initialize",
"the",
"non",
"-",
"batchnorm",
"layers",
"of",
"m",
"with",
"init_func",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L244-L246
|
20,745
|
fastai/fastai
|
fastai/torch_core.py
|
apply_init
|
def apply_init(m, init_func:LayerFunc):
"Initialize all non-batchnorm layers of `m` with `init_func`."
apply_leaf(m, partial(cond_init, init_func=init_func))
|
python
|
def apply_init(m, init_func:LayerFunc):
"Initialize all non-batchnorm layers of `m` with `init_func`."
apply_leaf(m, partial(cond_init, init_func=init_func))
|
[
"def",
"apply_init",
"(",
"m",
",",
"init_func",
":",
"LayerFunc",
")",
":",
"apply_leaf",
"(",
"m",
",",
"partial",
"(",
"cond_init",
",",
"init_func",
"=",
"init_func",
")",
")"
] |
Initialize all non-batchnorm layers of `m` with `init_func`.
|
[
"Initialize",
"all",
"non",
"-",
"batchnorm",
"layers",
"of",
"m",
"with",
"init_func",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L254-L256
|
20,746
|
fastai/fastai
|
fastai/torch_core.py
|
in_channels
|
def in_channels(m:nn.Module) -> List[int]:
"Return the shape of the first weight layer in `m`."
for l in flatten_model(m):
if hasattr(l, 'weight'): return l.weight.shape[1]
raise Exception('No weight layer')
|
python
|
def in_channels(m:nn.Module) -> List[int]:
"Return the shape of the first weight layer in `m`."
for l in flatten_model(m):
if hasattr(l, 'weight'): return l.weight.shape[1]
raise Exception('No weight layer')
|
[
"def",
"in_channels",
"(",
"m",
":",
"nn",
".",
"Module",
")",
"->",
"List",
"[",
"int",
"]",
":",
"for",
"l",
"in",
"flatten_model",
"(",
"m",
")",
":",
"if",
"hasattr",
"(",
"l",
",",
"'weight'",
")",
":",
"return",
"l",
".",
"weight",
".",
"shape",
"[",
"1",
"]",
"raise",
"Exception",
"(",
"'No weight layer'",
")"
] |
Return the shape of the first weight layer in `m`.
|
[
"Return",
"the",
"shape",
"of",
"the",
"first",
"weight",
"layer",
"in",
"m",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L258-L262
|
20,747
|
fastai/fastai
|
fastai/torch_core.py
|
model_type
|
def model_type(dtype):
"Return the torch type corresponding to `dtype`."
return (torch.float32 if np.issubdtype(dtype, np.floating) else
torch.int64 if np.issubdtype(dtype, np.integer)
else None)
|
python
|
def model_type(dtype):
"Return the torch type corresponding to `dtype`."
return (torch.float32 if np.issubdtype(dtype, np.floating) else
torch.int64 if np.issubdtype(dtype, np.integer)
else None)
|
[
"def",
"model_type",
"(",
"dtype",
")",
":",
"return",
"(",
"torch",
".",
"float32",
"if",
"np",
".",
"issubdtype",
"(",
"dtype",
",",
"np",
".",
"floating",
")",
"else",
"torch",
".",
"int64",
"if",
"np",
".",
"issubdtype",
"(",
"dtype",
",",
"np",
".",
"integer",
")",
"else",
"None",
")"
] |
Return the torch type corresponding to `dtype`.
|
[
"Return",
"the",
"torch",
"type",
"corresponding",
"to",
"dtype",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L292-L296
|
20,748
|
fastai/fastai
|
fastai/torch_core.py
|
np2model_tensor
|
def np2model_tensor(a):
"Tranform numpy array `a` to a tensor of the same type."
dtype = model_type(a.dtype)
res = as_tensor(a)
if not dtype: return res
return res.type(dtype)
|
python
|
def np2model_tensor(a):
"Tranform numpy array `a` to a tensor of the same type."
dtype = model_type(a.dtype)
res = as_tensor(a)
if not dtype: return res
return res.type(dtype)
|
[
"def",
"np2model_tensor",
"(",
"a",
")",
":",
"dtype",
"=",
"model_type",
"(",
"a",
".",
"dtype",
")",
"res",
"=",
"as_tensor",
"(",
"a",
")",
"if",
"not",
"dtype",
":",
"return",
"res",
"return",
"res",
".",
"type",
"(",
"dtype",
")"
] |
Tranform numpy array `a` to a tensor of the same type.
|
[
"Tranform",
"numpy",
"array",
"a",
"to",
"a",
"tensor",
"of",
"the",
"same",
"type",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L298-L303
|
20,749
|
fastai/fastai
|
fastai/torch_core.py
|
_pca
|
def _pca(x, k=2):
"Compute PCA of `x` with `k` dimensions."
x = x-torch.mean(x,0)
U,S,V = torch.svd(x.t())
return torch.mm(x,U[:,:k])
|
python
|
def _pca(x, k=2):
"Compute PCA of `x` with `k` dimensions."
x = x-torch.mean(x,0)
U,S,V = torch.svd(x.t())
return torch.mm(x,U[:,:k])
|
[
"def",
"_pca",
"(",
"x",
",",
"k",
"=",
"2",
")",
":",
"x",
"=",
"x",
"-",
"torch",
".",
"mean",
"(",
"x",
",",
"0",
")",
"U",
",",
"S",
",",
"V",
"=",
"torch",
".",
"svd",
"(",
"x",
".",
"t",
"(",
")",
")",
"return",
"torch",
".",
"mm",
"(",
"x",
",",
"U",
"[",
":",
",",
":",
"k",
"]",
")"
] |
Compute PCA of `x` with `k` dimensions.
|
[
"Compute",
"PCA",
"of",
"x",
"with",
"k",
"dimensions",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L305-L309
|
20,750
|
fastai/fastai
|
fastai/torch_core.py
|
grab_idx
|
def grab_idx(x,i,batch_first:bool=True):
"Grab the `i`-th batch in `x`, `batch_first` stating the batch dimension."
if batch_first: return ([o[i].cpu() for o in x] if is_listy(x) else x[i].cpu())
else: return ([o[:,i].cpu() for o in x] if is_listy(x) else x[:,i].cpu())
|
python
|
def grab_idx(x,i,batch_first:bool=True):
"Grab the `i`-th batch in `x`, `batch_first` stating the batch dimension."
if batch_first: return ([o[i].cpu() for o in x] if is_listy(x) else x[i].cpu())
else: return ([o[:,i].cpu() for o in x] if is_listy(x) else x[:,i].cpu())
|
[
"def",
"grab_idx",
"(",
"x",
",",
"i",
",",
"batch_first",
":",
"bool",
"=",
"True",
")",
":",
"if",
"batch_first",
":",
"return",
"(",
"[",
"o",
"[",
"i",
"]",
".",
"cpu",
"(",
")",
"for",
"o",
"in",
"x",
"]",
"if",
"is_listy",
"(",
"x",
")",
"else",
"x",
"[",
"i",
"]",
".",
"cpu",
"(",
")",
")",
"else",
":",
"return",
"(",
"[",
"o",
"[",
":",
",",
"i",
"]",
".",
"cpu",
"(",
")",
"for",
"o",
"in",
"x",
"]",
"if",
"is_listy",
"(",
"x",
")",
"else",
"x",
"[",
":",
",",
"i",
"]",
".",
"cpu",
"(",
")",
")"
] |
Grab the `i`-th batch in `x`, `batch_first` stating the batch dimension.
|
[
"Grab",
"the",
"i",
"-",
"th",
"batch",
"in",
"x",
"batch_first",
"stating",
"the",
"batch",
"dimension",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L328-L331
|
20,751
|
fastai/fastai
|
fastai/torch_core.py
|
logit_
|
def logit_(x:Tensor)->Tensor:
"Inplace logit of `x`, clamped to avoid inf"
x.clamp_(1e-7, 1-1e-7)
return (x.reciprocal_().sub_(1)).log_().neg_()
|
python
|
def logit_(x:Tensor)->Tensor:
"Inplace logit of `x`, clamped to avoid inf"
x.clamp_(1e-7, 1-1e-7)
return (x.reciprocal_().sub_(1)).log_().neg_()
|
[
"def",
"logit_",
"(",
"x",
":",
"Tensor",
")",
"->",
"Tensor",
":",
"x",
".",
"clamp_",
"(",
"1e-7",
",",
"1",
"-",
"1e-7",
")",
"return",
"(",
"x",
".",
"reciprocal_",
"(",
")",
".",
"sub_",
"(",
"1",
")",
")",
".",
"log_",
"(",
")",
".",
"neg_",
"(",
")"
] |
Inplace logit of `x`, clamped to avoid inf
|
[
"Inplace",
"logit",
"of",
"x",
"clamped",
"to",
"avoid",
"inf"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L338-L341
|
20,752
|
fastai/fastai
|
fastai/torch_core.py
|
try_int
|
def try_int(o:Any)->Any:
"Try to convert `o` to int, default to `o` if not possible."
# NB: single-item rank-1 array/tensor can be converted to int, but we don't want to do this
if isinstance(o, (np.ndarray,Tensor)): return o if o.ndim else int(o)
if isinstance(o, collections.Sized) or getattr(o,'__array_interface__',False): return o
try: return int(o)
except: return o
|
python
|
def try_int(o:Any)->Any:
"Try to convert `o` to int, default to `o` if not possible."
# NB: single-item rank-1 array/tensor can be converted to int, but we don't want to do this
if isinstance(o, (np.ndarray,Tensor)): return o if o.ndim else int(o)
if isinstance(o, collections.Sized) or getattr(o,'__array_interface__',False): return o
try: return int(o)
except: return o
|
[
"def",
"try_int",
"(",
"o",
":",
"Any",
")",
"->",
"Any",
":",
"# NB: single-item rank-1 array/tensor can be converted to int, but we don't want to do this",
"if",
"isinstance",
"(",
"o",
",",
"(",
"np",
".",
"ndarray",
",",
"Tensor",
")",
")",
":",
"return",
"o",
"if",
"o",
".",
"ndim",
"else",
"int",
"(",
"o",
")",
"if",
"isinstance",
"(",
"o",
",",
"collections",
".",
"Sized",
")",
"or",
"getattr",
"(",
"o",
",",
"'__array_interface__'",
",",
"False",
")",
":",
"return",
"o",
"try",
":",
"return",
"int",
"(",
"o",
")",
"except",
":",
"return",
"o"
] |
Try to convert `o` to int, default to `o` if not possible.
|
[
"Try",
"to",
"convert",
"o",
"to",
"int",
"default",
"to",
"o",
"if",
"not",
"possible",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L365-L371
|
20,753
|
fastai/fastai
|
fastai/torch_core.py
|
get_model
|
def get_model(model:nn.Module):
"Return the model maybe wrapped inside `model`."
return model.module if isinstance(model, (DistributedDataParallel, nn.DataParallel)) else model
|
python
|
def get_model(model:nn.Module):
"Return the model maybe wrapped inside `model`."
return model.module if isinstance(model, (DistributedDataParallel, nn.DataParallel)) else model
|
[
"def",
"get_model",
"(",
"model",
":",
"nn",
".",
"Module",
")",
":",
"return",
"model",
".",
"module",
"if",
"isinstance",
"(",
"model",
",",
"(",
"DistributedDataParallel",
",",
"nn",
".",
"DataParallel",
")",
")",
"else",
"model"
] |
Return the model maybe wrapped inside `model`.
|
[
"Return",
"the",
"model",
"maybe",
"wrapped",
"inside",
"model",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L373-L375
|
20,754
|
fastai/fastai
|
fastai/torch_core.py
|
flatten_check
|
def flatten_check(out:Tensor, targ:Tensor) -> Tensor:
"Check that `out` and `targ` have the same number of elements and flatten them."
out,targ = out.contiguous().view(-1),targ.contiguous().view(-1)
assert len(out) == len(targ), f"Expected output and target to have the same number of elements but got {len(out)} and {len(targ)}."
return out,targ
|
python
|
def flatten_check(out:Tensor, targ:Tensor) -> Tensor:
"Check that `out` and `targ` have the same number of elements and flatten them."
out,targ = out.contiguous().view(-1),targ.contiguous().view(-1)
assert len(out) == len(targ), f"Expected output and target to have the same number of elements but got {len(out)} and {len(targ)}."
return out,targ
|
[
"def",
"flatten_check",
"(",
"out",
":",
"Tensor",
",",
"targ",
":",
"Tensor",
")",
"->",
"Tensor",
":",
"out",
",",
"targ",
"=",
"out",
".",
"contiguous",
"(",
")",
".",
"view",
"(",
"-",
"1",
")",
",",
"targ",
".",
"contiguous",
"(",
")",
".",
"view",
"(",
"-",
"1",
")",
"assert",
"len",
"(",
"out",
")",
"==",
"len",
"(",
"targ",
")",
",",
"f\"Expected output and target to have the same number of elements but got {len(out)} and {len(targ)}.\"",
"return",
"out",
",",
"targ"
] |
Check that `out` and `targ` have the same number of elements and flatten them.
|
[
"Check",
"that",
"out",
"and",
"targ",
"have",
"the",
"same",
"number",
"of",
"elements",
"and",
"flatten",
"them",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L377-L381
|
20,755
|
fastai/fastai
|
fastai/torch_core.py
|
remove_module_load
|
def remove_module_load(state_dict):
"""create new OrderedDict that does not contain `module.`"""
new_state_dict = OrderedDict()
for k, v in state_dict.items(): new_state_dict[k[7:]] = v
return new_state_dict
|
python
|
def remove_module_load(state_dict):
"""create new OrderedDict that does not contain `module.`"""
new_state_dict = OrderedDict()
for k, v in state_dict.items(): new_state_dict[k[7:]] = v
return new_state_dict
|
[
"def",
"remove_module_load",
"(",
"state_dict",
")",
":",
"new_state_dict",
"=",
"OrderedDict",
"(",
")",
"for",
"k",
",",
"v",
"in",
"state_dict",
".",
"items",
"(",
")",
":",
"new_state_dict",
"[",
"k",
"[",
"7",
":",
"]",
"]",
"=",
"v",
"return",
"new_state_dict"
] |
create new OrderedDict that does not contain `module.`
|
[
"create",
"new",
"OrderedDict",
"that",
"does",
"not",
"contain",
"module",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L388-L392
|
20,756
|
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)
return {'last_metrics': last_metrics + mets}
|
[
"def",
"add_metrics",
"(",
"last_metrics",
":",
"Collection",
"[",
"Rank0Tensor",
"]",
",",
"mets",
":",
"Union",
"[",
"Rank0Tensor",
",",
"Collection",
"[",
"Rank0Tensor",
"]",
"]",
")",
":",
"last_metrics",
",",
"mets",
"=",
"listify",
"(",
"last_metrics",
")",
",",
"listify",
"(",
"mets",
")",
"return",
"{",
"'last_metrics'",
":",
"last_metrics",
"+",
"mets",
"}"
] |
Return a dictionary for updating `last_metrics` with `mets`.
|
[
"Return",
"a",
"dictionary",
"for",
"updating",
"last_metrics",
"with",
"mets",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/torch_core.py#L402-L405
|
20,757
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
LearnerTensorboardWriter._get_new_batch
|
def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]:
"Retrieves new batch of DatasetType, and detaches it."
return self.learn.data.one_batch(ds_type=ds_type, detach=True, denorm=False, cpu=False)
|
python
|
def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]:
"Retrieves new batch of DatasetType, and detaches it."
return self.learn.data.one_batch(ds_type=ds_type, detach=True, denorm=False, cpu=False)
|
[
"def",
"_get_new_batch",
"(",
"self",
",",
"ds_type",
":",
"DatasetType",
")",
"->",
"Collection",
"[",
"Tensor",
"]",
":",
"return",
"self",
".",
"learn",
".",
"data",
".",
"one_batch",
"(",
"ds_type",
"=",
"ds_type",
",",
"detach",
"=",
"True",
",",
"denorm",
"=",
"False",
",",
"cpu",
"=",
"False",
")"
] |
Retrieves new batch of DatasetType, and detaches it.
|
[
"Retrieves",
"new",
"batch",
"of",
"DatasetType",
"and",
"detaches",
"it",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L40-L42
|
20,758
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
LearnerTensorboardWriter._update_batches_if_needed
|
def _update_batches_if_needed(self)->None:
"one_batch function is extremely slow with large datasets. This is caching the result as an optimization."
if self.learn.data.valid_dl is None: return # Running learning rate finder, so return
update_batches = self.data is not self.learn.data
if not update_batches: return
self.data = self.learn.data
self.trn_batch = self._get_new_batch(ds_type=DatasetType.Train)
self.val_batch = self._get_new_batch(ds_type=DatasetType.Valid)
|
python
|
def _update_batches_if_needed(self)->None:
"one_batch function is extremely slow with large datasets. This is caching the result as an optimization."
if self.learn.data.valid_dl is None: return # Running learning rate finder, so return
update_batches = self.data is not self.learn.data
if not update_batches: return
self.data = self.learn.data
self.trn_batch = self._get_new_batch(ds_type=DatasetType.Train)
self.val_batch = self._get_new_batch(ds_type=DatasetType.Valid)
|
[
"def",
"_update_batches_if_needed",
"(",
"self",
")",
"->",
"None",
":",
"if",
"self",
".",
"learn",
".",
"data",
".",
"valid_dl",
"is",
"None",
":",
"return",
"# Running learning rate finder, so return",
"update_batches",
"=",
"self",
".",
"data",
"is",
"not",
"self",
".",
"learn",
".",
"data",
"if",
"not",
"update_batches",
":",
"return",
"self",
".",
"data",
"=",
"self",
".",
"learn",
".",
"data",
"self",
".",
"trn_batch",
"=",
"self",
".",
"_get_new_batch",
"(",
"ds_type",
"=",
"DatasetType",
".",
"Train",
")",
"self",
".",
"val_batch",
"=",
"self",
".",
"_get_new_batch",
"(",
"ds_type",
"=",
"DatasetType",
".",
"Valid",
")"
] |
one_batch function is extremely slow with large datasets. This is caching the result as an optimization.
|
[
"one_batch",
"function",
"is",
"extremely",
"slow",
"with",
"large",
"datasets",
".",
"This",
"is",
"caching",
"the",
"result",
"as",
"an",
"optimization",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L44-L51
|
20,759
|
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:
"Writes single scalar value to Tensorboard."
tag = self.metrics_root + name
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
|
[
"def",
"_write_scalar",
"(",
"self",
",",
"name",
":",
"str",
",",
"scalar_value",
",",
"iteration",
":",
"int",
")",
"->",
"None",
":",
"tag",
"=",
"self",
".",
"metrics_root",
"+",
"name",
"self",
".",
"tbwriter",
".",
"add_scalar",
"(",
"tag",
"=",
"tag",
",",
"scalar_value",
"=",
"scalar_value",
",",
"global_step",
"=",
"iteration",
")"
] |
Writes single scalar value to Tensorboard.
|
[
"Writes",
"single",
"scalar",
"value",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L67-L70
|
20,760
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
LearnerTensorboardWriter._write_metrics
|
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:]):
if last_metrics is None or len(last_metrics) < i+1: return
scalar_value = last_metrics[i]
self._write_scalar(name=name, scalar_value=scalar_value, iteration=iteration)
|
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:]):
if last_metrics is None or len(last_metrics) < i+1: return
scalar_value = last_metrics[i]
self._write_scalar(name=name, scalar_value=scalar_value, iteration=iteration)
|
[
"def",
"_write_metrics",
"(",
"self",
",",
"iteration",
":",
"int",
",",
"last_metrics",
":",
"MetricsList",
",",
"start_idx",
":",
"int",
"=",
"2",
")",
"->",
"None",
":",
"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",
"scalar_value",
"=",
"last_metrics",
"[",
"i",
"]",
"self",
".",
"_write_scalar",
"(",
"name",
"=",
"name",
",",
"scalar_value",
"=",
"scalar_value",
",",
"iteration",
"=",
"iteration",
")"
] |
Writes training metrics to Tensorboard.
|
[
"Writes",
"training",
"metrics",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L73-L79
|
20,761
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
LearnerTensorboardWriter.on_epoch_end
|
def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
"Callback function that writes epoch end appropriate data to Tensorboard."
self._write_metrics(iteration=iteration, last_metrics=last_metrics)
|
python
|
def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
"Callback function that writes epoch end appropriate data to Tensorboard."
self._write_metrics(iteration=iteration, last_metrics=last_metrics)
|
[
"def",
"on_epoch_end",
"(",
"self",
",",
"last_metrics",
":",
"MetricsList",
",",
"iteration",
":",
"int",
",",
"*",
"*",
"kwargs",
")",
"->",
"None",
":",
"self",
".",
"_write_metrics",
"(",
"iteration",
"=",
"iteration",
",",
"last_metrics",
"=",
"last_metrics",
")"
] |
Callback function that writes epoch end appropriate data to Tensorboard.
|
[
"Callback",
"function",
"that",
"writes",
"epoch",
"end",
"appropriate",
"data",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L99-L101
|
20,762
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
GANTensorboardWriter._write_gen_model_stats
|
def _write_gen_model_stats(self, iteration:int)->None:
"Writes gradient statistics for generator to Tensorboard."
generator = self.learn.gan_trainer.generator
self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats')
self.gen_stats_updated = True
|
python
|
def _write_gen_model_stats(self, iteration:int)->None:
"Writes gradient statistics for generator to Tensorboard."
generator = self.learn.gan_trainer.generator
self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats')
self.gen_stats_updated = True
|
[
"def",
"_write_gen_model_stats",
"(",
"self",
",",
"iteration",
":",
"int",
")",
"->",
"None",
":",
"generator",
"=",
"self",
".",
"learn",
".",
"gan_trainer",
".",
"generator",
"self",
".",
"stats_writer",
".",
"write",
"(",
"model",
"=",
"generator",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"self",
".",
"tbwriter",
",",
"name",
"=",
"'gen_model_stats'",
")",
"self",
".",
"gen_stats_updated",
"=",
"True"
] |
Writes gradient statistics for generator to Tensorboard.
|
[
"Writes",
"gradient",
"statistics",
"for",
"generator",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L120-L124
|
20,763
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
GANTensorboardWriter._write_critic_model_stats
|
def _write_critic_model_stats(self, iteration:int)->None:
"Writes gradient statistics for critic to Tensorboard."
critic = self.learn.gan_trainer.critic
self.stats_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='crit_model_stats')
self.crit_stats_updated = True
|
python
|
def _write_critic_model_stats(self, iteration:int)->None:
"Writes gradient statistics for critic to Tensorboard."
critic = self.learn.gan_trainer.critic
self.stats_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='crit_model_stats')
self.crit_stats_updated = True
|
[
"def",
"_write_critic_model_stats",
"(",
"self",
",",
"iteration",
":",
"int",
")",
"->",
"None",
":",
"critic",
"=",
"self",
".",
"learn",
".",
"gan_trainer",
".",
"critic",
"self",
".",
"stats_writer",
".",
"write",
"(",
"model",
"=",
"critic",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"self",
".",
"tbwriter",
",",
"name",
"=",
"'crit_model_stats'",
")",
"self",
".",
"crit_stats_updated",
"=",
"True"
] |
Writes gradient statistics for critic to Tensorboard.
|
[
"Writes",
"gradient",
"statistics",
"for",
"critic",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L126-L130
|
20,764
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
GANTensorboardWriter._write_images
|
def _write_images(self, iteration:int)->None:
"Writes model generated, original and real images to Tensorboard."
trainer = self.learn.gan_trainer
#TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way?
gen_mode = trainer.gen_mode
try:
trainer.switch(gen_mode=True)
self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch,
iteration=iteration, tbwriter=self.tbwriter)
finally: trainer.switch(gen_mode=gen_mode)
|
python
|
def _write_images(self, iteration:int)->None:
"Writes model generated, original and real images to Tensorboard."
trainer = self.learn.gan_trainer
#TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way?
gen_mode = trainer.gen_mode
try:
trainer.switch(gen_mode=True)
self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch,
iteration=iteration, tbwriter=self.tbwriter)
finally: trainer.switch(gen_mode=gen_mode)
|
[
"def",
"_write_images",
"(",
"self",
",",
"iteration",
":",
"int",
")",
"->",
"None",
":",
"trainer",
"=",
"self",
".",
"learn",
".",
"gan_trainer",
"#TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way?",
"gen_mode",
"=",
"trainer",
".",
"gen_mode",
"try",
":",
"trainer",
".",
"switch",
"(",
"gen_mode",
"=",
"True",
")",
"self",
".",
"img_gen_vis",
".",
"write",
"(",
"learn",
"=",
"self",
".",
"learn",
",",
"trn_batch",
"=",
"self",
".",
"trn_batch",
",",
"val_batch",
"=",
"self",
".",
"val_batch",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"self",
".",
"tbwriter",
")",
"finally",
":",
"trainer",
".",
"switch",
"(",
"gen_mode",
"=",
"gen_mode",
")"
] |
Writes model generated, original and real images to Tensorboard.
|
[
"Writes",
"model",
"generated",
"original",
"and",
"real",
"images",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L147-L156
|
20,765
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ImageGenTensorboardWriter._write_images
|
def _write_images(self, iteration:int)->None:
"Writes model generated, original and real images to Tensorboard"
self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch, iteration=iteration,
tbwriter=self.tbwriter)
|
python
|
def _write_images(self, iteration:int)->None:
"Writes model generated, original and real images to Tensorboard"
self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch, iteration=iteration,
tbwriter=self.tbwriter)
|
[
"def",
"_write_images",
"(",
"self",
",",
"iteration",
":",
"int",
")",
"->",
"None",
":",
"self",
".",
"img_gen_vis",
".",
"write",
"(",
"learn",
"=",
"self",
".",
"learn",
",",
"trn_batch",
"=",
"self",
".",
"trn_batch",
",",
"val_batch",
"=",
"self",
".",
"val_batch",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"self",
".",
"tbwriter",
")"
] |
Writes model generated, original and real images to Tensorboard
|
[
"Writes",
"model",
"generated",
"original",
"and",
"real",
"images",
"to",
"Tensorboard"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L182-L185
|
20,766
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
AsyncTBWriter.request_write
|
def request_write(self, request: TBWriteRequest)->None:
"Queues up an asynchronous write request to Tensorboard."
if self.stop_request.isSet(): return
self.queue.put(request)
|
python
|
def request_write(self, request: TBWriteRequest)->None:
"Queues up an asynchronous write request to Tensorboard."
if self.stop_request.isSet(): return
self.queue.put(request)
|
[
"def",
"request_write",
"(",
"self",
",",
"request",
":",
"TBWriteRequest",
")",
"->",
"None",
":",
"if",
"self",
".",
"stop_request",
".",
"isSet",
"(",
")",
":",
"return",
"self",
".",
"queue",
".",
"put",
"(",
"request",
")"
] |
Queues up an asynchronous write request to Tensorboard.
|
[
"Queues",
"up",
"an",
"asynchronous",
"write",
"request",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L216-L219
|
20,767
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
AsyncTBWriter._queue_processor
|
def _queue_processor(self)->None:
"Processes queued up write requests asynchronously to Tensorboard."
while not self.stop_request.isSet():
while not self.queue.empty():
if self.stop_request.isSet(): return
request = self.queue.get()
request.write()
sleep(0.2)
|
python
|
def _queue_processor(self)->None:
"Processes queued up write requests asynchronously to Tensorboard."
while not self.stop_request.isSet():
while not self.queue.empty():
if self.stop_request.isSet(): return
request = self.queue.get()
request.write()
sleep(0.2)
|
[
"def",
"_queue_processor",
"(",
"self",
")",
"->",
"None",
":",
"while",
"not",
"self",
".",
"stop_request",
".",
"isSet",
"(",
")",
":",
"while",
"not",
"self",
".",
"queue",
".",
"empty",
"(",
")",
":",
"if",
"self",
".",
"stop_request",
".",
"isSet",
"(",
")",
":",
"return",
"request",
"=",
"self",
".",
"queue",
".",
"get",
"(",
")",
"request",
".",
"write",
"(",
")",
"sleep",
"(",
"0.2",
")"
] |
Processes queued up write requests asynchronously to Tensorboard.
|
[
"Processes",
"queued",
"up",
"write",
"requests",
"asynchronously",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L221-L228
|
20,768
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelImageSet.get_list_from_model
|
def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
"Factory method to convert a batch of model images to a list of ModelImageSet."
image_sets = []
x,y = batch[0],batch[1]
preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True)
for orig_px, real_px, gen in zip(x,y,preds):
orig, real = Image(px=orig_px), Image(px=real_px)
image_set = ModelImageSet(orig=orig, real=real, gen=gen)
image_sets.append(image_set)
return image_sets
|
python
|
def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
"Factory method to convert a batch of model images to a list of ModelImageSet."
image_sets = []
x,y = batch[0],batch[1]
preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True)
for orig_px, real_px, gen in zip(x,y,preds):
orig, real = Image(px=orig_px), Image(px=real_px)
image_set = ModelImageSet(orig=orig, real=real, gen=gen)
image_sets.append(image_set)
return image_sets
|
[
"def",
"get_list_from_model",
"(",
"learn",
":",
"Learner",
",",
"ds_type",
":",
"DatasetType",
",",
"batch",
":",
"Tuple",
")",
"->",
"[",
"]",
":",
"image_sets",
"=",
"[",
"]",
"x",
",",
"y",
"=",
"batch",
"[",
"0",
"]",
",",
"batch",
"[",
"1",
"]",
"preds",
"=",
"learn",
".",
"pred_batch",
"(",
"ds_type",
"=",
"ds_type",
",",
"batch",
"=",
"(",
"x",
",",
"y",
")",
",",
"reconstruct",
"=",
"True",
")",
"for",
"orig_px",
",",
"real_px",
",",
"gen",
"in",
"zip",
"(",
"x",
",",
"y",
",",
"preds",
")",
":",
"orig",
",",
"real",
"=",
"Image",
"(",
"px",
"=",
"orig_px",
")",
",",
"Image",
"(",
"px",
"=",
"real_px",
")",
"image_set",
"=",
"ModelImageSet",
"(",
"orig",
"=",
"orig",
",",
"real",
"=",
"real",
",",
"gen",
"=",
"gen",
")",
"image_sets",
".",
"append",
"(",
"image_set",
")",
"return",
"image_sets"
] |
Factory method to convert a batch of model images to a list of ModelImageSet.
|
[
"Factory",
"method",
"to",
"convert",
"a",
"batch",
"of",
"model",
"images",
"to",
"a",
"list",
"of",
"ModelImageSet",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L248-L257
|
20,769
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
HistogramTBRequest._write_histogram
|
def _write_histogram(self, param_name:str, values)->None:
"Writes single model histogram to Tensorboard."
tag = self.name + '/weights/' + param_name
self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration)
|
python
|
def _write_histogram(self, param_name:str, values)->None:
"Writes single model histogram to Tensorboard."
tag = self.name + '/weights/' + param_name
self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration)
|
[
"def",
"_write_histogram",
"(",
"self",
",",
"param_name",
":",
"str",
",",
"values",
")",
"->",
"None",
":",
"tag",
"=",
"self",
".",
"name",
"+",
"'/weights/'",
"+",
"param_name",
"self",
".",
"tbwriter",
".",
"add_histogram",
"(",
"tag",
"=",
"tag",
",",
"values",
"=",
"values",
",",
"global_step",
"=",
"self",
".",
"iteration",
")"
] |
Writes single model histogram to Tensorboard.
|
[
"Writes",
"single",
"model",
"histogram",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L268-L271
|
20,770
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._add_gradient_scalar
|
def _add_gradient_scalar(self, name:str, scalar_value)->None:
"Writes a single scalar value for a gradient statistic to Tensorboard."
tag = self.name + '/gradients/' + name
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration)
|
python
|
def _add_gradient_scalar(self, name:str, scalar_value)->None:
"Writes a single scalar value for a gradient statistic to Tensorboard."
tag = self.name + '/gradients/' + name
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration)
|
[
"def",
"_add_gradient_scalar",
"(",
"self",
",",
"name",
":",
"str",
",",
"scalar_value",
")",
"->",
"None",
":",
"tag",
"=",
"self",
".",
"name",
"+",
"'/gradients/'",
"+",
"name",
"self",
".",
"tbwriter",
".",
"add_scalar",
"(",
"tag",
"=",
"tag",
",",
"scalar_value",
"=",
"scalar_value",
",",
"global_step",
"=",
"self",
".",
"iteration",
")"
] |
Writes a single scalar value for a gradient statistic to Tensorboard.
|
[
"Writes",
"a",
"single",
"scalar",
"value",
"for",
"a",
"gradient",
"statistic",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L294-L297
|
20,771
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_avg_norm
|
def _write_avg_norm(self, norms:[])->None:
"Writes the average norm of the gradients to Tensorboard."
avg_norm = sum(norms)/len(self.gradients)
self._add_gradient_scalar('avg_norm', scalar_value=avg_norm)
|
python
|
def _write_avg_norm(self, norms:[])->None:
"Writes the average norm of the gradients to Tensorboard."
avg_norm = sum(norms)/len(self.gradients)
self._add_gradient_scalar('avg_norm', scalar_value=avg_norm)
|
[
"def",
"_write_avg_norm",
"(",
"self",
",",
"norms",
":",
"[",
"]",
")",
"->",
"None",
":",
"avg_norm",
"=",
"sum",
"(",
"norms",
")",
"/",
"len",
"(",
"self",
".",
"gradients",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'avg_norm'",
",",
"scalar_value",
"=",
"avg_norm",
")"
] |
Writes the average norm of the gradients to Tensorboard.
|
[
"Writes",
"the",
"average",
"norm",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L299-L302
|
20,772
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_median_norm
|
def _write_median_norm(self, norms:[])->None:
"Writes the median norm of the gradients to Tensorboard."
median_norm = statistics.median(norms)
self._add_gradient_scalar('median_norm', scalar_value=median_norm)
|
python
|
def _write_median_norm(self, norms:[])->None:
"Writes the median norm of the gradients to Tensorboard."
median_norm = statistics.median(norms)
self._add_gradient_scalar('median_norm', scalar_value=median_norm)
|
[
"def",
"_write_median_norm",
"(",
"self",
",",
"norms",
":",
"[",
"]",
")",
"->",
"None",
":",
"median_norm",
"=",
"statistics",
".",
"median",
"(",
"norms",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'median_norm'",
",",
"scalar_value",
"=",
"median_norm",
")"
] |
Writes the median norm of the gradients to Tensorboard.
|
[
"Writes",
"the",
"median",
"norm",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L304-L307
|
20,773
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_max_norm
|
def _write_max_norm(self, norms:[])->None:
"Writes the maximum norm of the gradients to Tensorboard."
max_norm = max(norms)
self._add_gradient_scalar('max_norm', scalar_value=max_norm)
|
python
|
def _write_max_norm(self, norms:[])->None:
"Writes the maximum norm of the gradients to Tensorboard."
max_norm = max(norms)
self._add_gradient_scalar('max_norm', scalar_value=max_norm)
|
[
"def",
"_write_max_norm",
"(",
"self",
",",
"norms",
":",
"[",
"]",
")",
"->",
"None",
":",
"max_norm",
"=",
"max",
"(",
"norms",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'max_norm'",
",",
"scalar_value",
"=",
"max_norm",
")"
] |
Writes the maximum norm of the gradients to Tensorboard.
|
[
"Writes",
"the",
"maximum",
"norm",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L309-L312
|
20,774
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_min_norm
|
def _write_min_norm(self, norms:[])->None:
"Writes the minimum norm of the gradients to Tensorboard."
min_norm = min(norms)
self._add_gradient_scalar('min_norm', scalar_value=min_norm)
|
python
|
def _write_min_norm(self, norms:[])->None:
"Writes the minimum norm of the gradients to Tensorboard."
min_norm = min(norms)
self._add_gradient_scalar('min_norm', scalar_value=min_norm)
|
[
"def",
"_write_min_norm",
"(",
"self",
",",
"norms",
":",
"[",
"]",
")",
"->",
"None",
":",
"min_norm",
"=",
"min",
"(",
"norms",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'min_norm'",
",",
"scalar_value",
"=",
"min_norm",
")"
] |
Writes the minimum norm of the gradients to Tensorboard.
|
[
"Writes",
"the",
"minimum",
"norm",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L314-L317
|
20,775
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_num_zeros
|
def _write_num_zeros(self)->None:
"Writes the number of zeroes in the gradients to Tensorboard."
gradient_nps = [to_np(x.data) for x in self.gradients]
num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps)
self._add_gradient_scalar('num_zeros', scalar_value=num_zeros)
|
python
|
def _write_num_zeros(self)->None:
"Writes the number of zeroes in the gradients to Tensorboard."
gradient_nps = [to_np(x.data) for x in self.gradients]
num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps)
self._add_gradient_scalar('num_zeros', scalar_value=num_zeros)
|
[
"def",
"_write_num_zeros",
"(",
"self",
")",
"->",
"None",
":",
"gradient_nps",
"=",
"[",
"to_np",
"(",
"x",
".",
"data",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
"]",
"num_zeros",
"=",
"sum",
"(",
"(",
"np",
".",
"asarray",
"(",
"x",
")",
"==",
"0.0",
")",
".",
"sum",
"(",
")",
"for",
"x",
"in",
"gradient_nps",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'num_zeros'",
",",
"scalar_value",
"=",
"num_zeros",
")"
] |
Writes the number of zeroes in the gradients to Tensorboard.
|
[
"Writes",
"the",
"number",
"of",
"zeroes",
"in",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L319-L323
|
20,776
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_avg_gradient
|
def _write_avg_gradient(self)->None:
"Writes the average of the gradients to Tensorboard."
avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients)
self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient)
|
python
|
def _write_avg_gradient(self)->None:
"Writes the average of the gradients to Tensorboard."
avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients)
self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient)
|
[
"def",
"_write_avg_gradient",
"(",
"self",
")",
"->",
"None",
":",
"avg_gradient",
"=",
"sum",
"(",
"x",
".",
"data",
".",
"mean",
"(",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
")",
"/",
"len",
"(",
"self",
".",
"gradients",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'avg_gradient'",
",",
"scalar_value",
"=",
"avg_gradient",
")"
] |
Writes the average of the gradients to Tensorboard.
|
[
"Writes",
"the",
"average",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L325-L328
|
20,777
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_median_gradient
|
def _write_median_gradient(self)->None:
"Writes the median of the gradients to Tensorboard."
median_gradient = statistics.median(x.data.median() for x in self.gradients)
self._add_gradient_scalar('median_gradient', scalar_value=median_gradient)
|
python
|
def _write_median_gradient(self)->None:
"Writes the median of the gradients to Tensorboard."
median_gradient = statistics.median(x.data.median() for x in self.gradients)
self._add_gradient_scalar('median_gradient', scalar_value=median_gradient)
|
[
"def",
"_write_median_gradient",
"(",
"self",
")",
"->",
"None",
":",
"median_gradient",
"=",
"statistics",
".",
"median",
"(",
"x",
".",
"data",
".",
"median",
"(",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'median_gradient'",
",",
"scalar_value",
"=",
"median_gradient",
")"
] |
Writes the median of the gradients to Tensorboard.
|
[
"Writes",
"the",
"median",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L330-L333
|
20,778
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_max_gradient
|
def _write_max_gradient(self)->None:
"Writes the maximum of the gradients to Tensorboard."
max_gradient = max(x.data.max() for x in self.gradients)
self._add_gradient_scalar('max_gradient', scalar_value=max_gradient)
|
python
|
def _write_max_gradient(self)->None:
"Writes the maximum of the gradients to Tensorboard."
max_gradient = max(x.data.max() for x in self.gradients)
self._add_gradient_scalar('max_gradient', scalar_value=max_gradient)
|
[
"def",
"_write_max_gradient",
"(",
"self",
")",
"->",
"None",
":",
"max_gradient",
"=",
"max",
"(",
"x",
".",
"data",
".",
"max",
"(",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'max_gradient'",
",",
"scalar_value",
"=",
"max_gradient",
")"
] |
Writes the maximum of the gradients to Tensorboard.
|
[
"Writes",
"the",
"maximum",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L335-L338
|
20,779
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest._write_min_gradient
|
def _write_min_gradient(self)->None:
"Writes the minimum of the gradients to Tensorboard."
min_gradient = min(x.data.min() for x in self.gradients)
self._add_gradient_scalar('min_gradient', scalar_value=min_gradient)
|
python
|
def _write_min_gradient(self)->None:
"Writes the minimum of the gradients to Tensorboard."
min_gradient = min(x.data.min() for x in self.gradients)
self._add_gradient_scalar('min_gradient', scalar_value=min_gradient)
|
[
"def",
"_write_min_gradient",
"(",
"self",
")",
"->",
"None",
":",
"min_gradient",
"=",
"min",
"(",
"x",
".",
"data",
".",
"min",
"(",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
")",
"self",
".",
"_add_gradient_scalar",
"(",
"'min_gradient'",
",",
"scalar_value",
"=",
"min_gradient",
")"
] |
Writes the minimum of the gradients to Tensorboard.
|
[
"Writes",
"the",
"minimum",
"of",
"the",
"gradients",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L340-L343
|
20,780
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ModelStatsTBRequest.write
|
def write(self)->None:
"Writes model gradient statistics to Tensorboard."
if len(self.gradients) == 0: return
norms = [x.data.norm() for x in self.gradients]
self._write_avg_norm(norms=norms)
self._write_median_norm(norms=norms)
self._write_max_norm(norms=norms)
self._write_min_norm(norms=norms)
self._write_num_zeros()
self._write_avg_gradient()
self._write_median_gradient()
self._write_max_gradient()
self._write_min_gradient()
|
python
|
def write(self)->None:
"Writes model gradient statistics to Tensorboard."
if len(self.gradients) == 0: return
norms = [x.data.norm() for x in self.gradients]
self._write_avg_norm(norms=norms)
self._write_median_norm(norms=norms)
self._write_max_norm(norms=norms)
self._write_min_norm(norms=norms)
self._write_num_zeros()
self._write_avg_gradient()
self._write_median_gradient()
self._write_max_gradient()
self._write_min_gradient()
|
[
"def",
"write",
"(",
"self",
")",
"->",
"None",
":",
"if",
"len",
"(",
"self",
".",
"gradients",
")",
"==",
"0",
":",
"return",
"norms",
"=",
"[",
"x",
".",
"data",
".",
"norm",
"(",
")",
"for",
"x",
"in",
"self",
".",
"gradients",
"]",
"self",
".",
"_write_avg_norm",
"(",
"norms",
"=",
"norms",
")",
"self",
".",
"_write_median_norm",
"(",
"norms",
"=",
"norms",
")",
"self",
".",
"_write_max_norm",
"(",
"norms",
"=",
"norms",
")",
"self",
".",
"_write_min_norm",
"(",
"norms",
"=",
"norms",
")",
"self",
".",
"_write_num_zeros",
"(",
")",
"self",
".",
"_write_avg_gradient",
"(",
")",
"self",
".",
"_write_median_gradient",
"(",
")",
"self",
".",
"_write_max_gradient",
"(",
")",
"self",
".",
"_write_min_gradient",
"(",
")"
] |
Writes model gradient statistics to Tensorboard.
|
[
"Writes",
"model",
"gradient",
"statistics",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L345-L357
|
20,781
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ImageTBRequest._write_images
|
def _write_images(self, name:str, images:[Tensor])->None:
"Writes list of images as tensors to Tensorboard."
tag = self.ds_type.name + ' ' + name
self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration)
|
python
|
def _write_images(self, name:str, images:[Tensor])->None:
"Writes list of images as tensors to Tensorboard."
tag = self.ds_type.name + ' ' + name
self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration)
|
[
"def",
"_write_images",
"(",
"self",
",",
"name",
":",
"str",
",",
"images",
":",
"[",
"Tensor",
"]",
")",
"->",
"None",
":",
"tag",
"=",
"self",
".",
"ds_type",
".",
"name",
"+",
"' '",
"+",
"name",
"self",
".",
"tbwriter",
".",
"add_image",
"(",
"tag",
"=",
"tag",
",",
"img_tensor",
"=",
"vutils",
".",
"make_grid",
"(",
"images",
",",
"normalize",
"=",
"True",
")",
",",
"global_step",
"=",
"self",
".",
"iteration",
")"
] |
Writes list of images as tensors to Tensorboard.
|
[
"Writes",
"list",
"of",
"images",
"as",
"tensors",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L373-L376
|
20,782
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ImageTBWriter.write
|
def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None:
"Writes training and validation batch images to Tensorboard."
self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid)
self._write_for_dstype(learn=learn, batch=trn_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Train)
|
python
|
def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None:
"Writes training and validation batch images to Tensorboard."
self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid)
self._write_for_dstype(learn=learn, batch=trn_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Train)
|
[
"def",
"write",
"(",
"self",
",",
"learn",
":",
"Learner",
",",
"trn_batch",
":",
"Tuple",
",",
"val_batch",
":",
"Tuple",
",",
"iteration",
":",
"int",
",",
"tbwriter",
":",
"SummaryWriter",
")",
"->",
"None",
":",
"self",
".",
"_write_for_dstype",
"(",
"learn",
"=",
"learn",
",",
"batch",
"=",
"val_batch",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"tbwriter",
",",
"ds_type",
"=",
"DatasetType",
".",
"Valid",
")",
"self",
".",
"_write_for_dstype",
"(",
"learn",
"=",
"learn",
",",
"batch",
"=",
"trn_batch",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"tbwriter",
",",
"ds_type",
"=",
"DatasetType",
".",
"Train",
")"
] |
Writes training and validation batch images to Tensorboard.
|
[
"Writes",
"training",
"and",
"validation",
"batch",
"images",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L399-L402
|
20,783
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
ImageTBWriter._write_for_dstype
|
def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None:
"Writes batch images of specified DatasetType to Tensorboard."
request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type)
asyncTBWriter.request_write(request)
|
python
|
def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None:
"Writes batch images of specified DatasetType to Tensorboard."
request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type)
asyncTBWriter.request_write(request)
|
[
"def",
"_write_for_dstype",
"(",
"self",
",",
"learn",
":",
"Learner",
",",
"batch",
":",
"Tuple",
",",
"iteration",
":",
"int",
",",
"tbwriter",
":",
"SummaryWriter",
",",
"ds_type",
":",
"DatasetType",
")",
"->",
"None",
":",
"request",
"=",
"ImageTBRequest",
"(",
"learn",
"=",
"learn",
",",
"batch",
"=",
"batch",
",",
"iteration",
"=",
"iteration",
",",
"tbwriter",
"=",
"tbwriter",
",",
"ds_type",
"=",
"ds_type",
")",
"asyncTBWriter",
".",
"request_write",
"(",
"request",
")"
] |
Writes batch images of specified DatasetType to Tensorboard.
|
[
"Writes",
"batch",
"images",
"of",
"specified",
"DatasetType",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L404-L407
|
20,784
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
GraphTBRequest.write
|
def write(self)->None:
"Writes single model graph to Tensorboard."
self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model)
|
python
|
def write(self)->None:
"Writes single model graph to Tensorboard."
self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model)
|
[
"def",
"write",
"(",
"self",
")",
"->",
"None",
":",
"self",
".",
"tbwriter",
".",
"add_graph",
"(",
"model",
"=",
"self",
".",
"model",
",",
"input_to_model",
"=",
"self",
".",
"input_to_model",
")"
] |
Writes single model graph to Tensorboard.
|
[
"Writes",
"single",
"model",
"graph",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L415-L417
|
20,785
|
fastai/fastai
|
fastai/callbacks/tensorboard.py
|
GraphTBWriter.write
|
def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None:
"Writes model graph to Tensorboard."
request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model)
asyncTBWriter.request_write(request)
|
python
|
def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None:
"Writes model graph to Tensorboard."
request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model)
asyncTBWriter.request_write(request)
|
[
"def",
"write",
"(",
"self",
",",
"model",
":",
"nn",
".",
"Module",
",",
"tbwriter",
":",
"SummaryWriter",
",",
"input_to_model",
":",
"torch",
".",
"Tensor",
")",
"->",
"None",
":",
"request",
"=",
"GraphTBRequest",
"(",
"model",
"=",
"model",
",",
"tbwriter",
"=",
"tbwriter",
",",
"input_to_model",
"=",
"input_to_model",
")",
"asyncTBWriter",
".",
"request_write",
"(",
"request",
")"
] |
Writes model graph to Tensorboard.
|
[
"Writes",
"model",
"graph",
"to",
"Tensorboard",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/tensorboard.py#L421-L424
|
20,786
|
fastai/fastai
|
old/fastai/lm_rnn.py
|
repackage_var
|
def repackage_var(h):
"""Wraps h in new Variables, to detach them from their history."""
if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h)
else: return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
|
python
|
def repackage_var(h):
"""Wraps h in new Variables, to detach them from their history."""
if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h)
else: return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
|
[
"def",
"repackage_var",
"(",
"h",
")",
":",
"if",
"IS_TORCH_04",
":",
"return",
"h",
".",
"detach",
"(",
")",
"if",
"type",
"(",
"h",
")",
"==",
"torch",
".",
"Tensor",
"else",
"tuple",
"(",
"repackage_var",
"(",
"v",
")",
"for",
"v",
"in",
"h",
")",
"else",
":",
"return",
"Variable",
"(",
"h",
".",
"data",
")",
"if",
"type",
"(",
"h",
")",
"==",
"Variable",
"else",
"tuple",
"(",
"repackage_var",
"(",
"v",
")",
"for",
"v",
"in",
"h",
")"
] |
Wraps h in new Variables, to detach them from their history.
|
[
"Wraps",
"h",
"in",
"new",
"Variables",
"to",
"detach",
"them",
"from",
"their",
"history",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/lm_rnn.py#L20-L23
|
20,787
|
fastai/fastai
|
old/fastai/lm_rnn.py
|
get_language_model
|
def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token,
dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False):
"""Returns a SequentialRNN model.
A RNN_Encoder layer is instantiated using the parameters provided.
This is followed by the creation of a LinearDecoder layer.
Also by default (i.e. tie_weights = True), the embedding matrix used in the RNN_Encoder
is used to instantiate the weights for the LinearDecoder layer.
The SequentialRNN layer is the native torch's Sequential wrapper that puts the RNN_Encoder and
LinearDecoder layers sequentially in the model.
Args:
n_tok (int): number of unique vocabulary words (or tokens) in the source dataset
emb_sz (int): the embedding size to use to encode each token
n_hid (int): number of hidden activation per LSTM layer
n_layers (int): number of LSTM layers to use in the architecture
pad_token (int): the int value used for padding text.
dropouth (float): dropout to apply to the activations going from one LSTM layer to another
dropouti (float): dropout to apply to the input layer.
dropoute (float): dropout to apply to the embedding layer.
wdrop (float): dropout used for a LSTM's internal (or hidden) recurrent weights.
tie_weights (bool): decide if the weights of the embedding matrix in the RNN encoder should be tied to the
weights of the LinearDecoder layer.
qrnn (bool): decide if the model is composed of LSTMS (False) or QRNNs (True).
bias (bool): decide if the decoder should have a bias layer or not.
Returns:
A SequentialRNN model
"""
rnn_enc = RNN_Encoder(n_tok, emb_sz, n_hid=n_hid, n_layers=n_layers, pad_token=pad_token,
dropouth=dropouth, dropouti=dropouti, dropoute=dropoute, wdrop=wdrop, qrnn=qrnn)
enc = rnn_enc.encoder if tie_weights else None
return SequentialRNN(rnn_enc, LinearDecoder(n_tok, emb_sz, dropout, tie_encoder=enc, bias=bias))
|
python
|
def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token,
dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False):
"""Returns a SequentialRNN model.
A RNN_Encoder layer is instantiated using the parameters provided.
This is followed by the creation of a LinearDecoder layer.
Also by default (i.e. tie_weights = True), the embedding matrix used in the RNN_Encoder
is used to instantiate the weights for the LinearDecoder layer.
The SequentialRNN layer is the native torch's Sequential wrapper that puts the RNN_Encoder and
LinearDecoder layers sequentially in the model.
Args:
n_tok (int): number of unique vocabulary words (or tokens) in the source dataset
emb_sz (int): the embedding size to use to encode each token
n_hid (int): number of hidden activation per LSTM layer
n_layers (int): number of LSTM layers to use in the architecture
pad_token (int): the int value used for padding text.
dropouth (float): dropout to apply to the activations going from one LSTM layer to another
dropouti (float): dropout to apply to the input layer.
dropoute (float): dropout to apply to the embedding layer.
wdrop (float): dropout used for a LSTM's internal (or hidden) recurrent weights.
tie_weights (bool): decide if the weights of the embedding matrix in the RNN encoder should be tied to the
weights of the LinearDecoder layer.
qrnn (bool): decide if the model is composed of LSTMS (False) or QRNNs (True).
bias (bool): decide if the decoder should have a bias layer or not.
Returns:
A SequentialRNN model
"""
rnn_enc = RNN_Encoder(n_tok, emb_sz, n_hid=n_hid, n_layers=n_layers, pad_token=pad_token,
dropouth=dropouth, dropouti=dropouti, dropoute=dropoute, wdrop=wdrop, qrnn=qrnn)
enc = rnn_enc.encoder if tie_weights else None
return SequentialRNN(rnn_enc, LinearDecoder(n_tok, emb_sz, dropout, tie_encoder=enc, bias=bias))
|
[
"def",
"get_language_model",
"(",
"n_tok",
",",
"emb_sz",
",",
"n_hid",
",",
"n_layers",
",",
"pad_token",
",",
"dropout",
"=",
"0.4",
",",
"dropouth",
"=",
"0.3",
",",
"dropouti",
"=",
"0.5",
",",
"dropoute",
"=",
"0.1",
",",
"wdrop",
"=",
"0.5",
",",
"tie_weights",
"=",
"True",
",",
"qrnn",
"=",
"False",
",",
"bias",
"=",
"False",
")",
":",
"rnn_enc",
"=",
"RNN_Encoder",
"(",
"n_tok",
",",
"emb_sz",
",",
"n_hid",
"=",
"n_hid",
",",
"n_layers",
"=",
"n_layers",
",",
"pad_token",
"=",
"pad_token",
",",
"dropouth",
"=",
"dropouth",
",",
"dropouti",
"=",
"dropouti",
",",
"dropoute",
"=",
"dropoute",
",",
"wdrop",
"=",
"wdrop",
",",
"qrnn",
"=",
"qrnn",
")",
"enc",
"=",
"rnn_enc",
".",
"encoder",
"if",
"tie_weights",
"else",
"None",
"return",
"SequentialRNN",
"(",
"rnn_enc",
",",
"LinearDecoder",
"(",
"n_tok",
",",
"emb_sz",
",",
"dropout",
",",
"tie_encoder",
"=",
"enc",
",",
"bias",
"=",
"bias",
")",
")"
] |
Returns a SequentialRNN model.
A RNN_Encoder layer is instantiated using the parameters provided.
This is followed by the creation of a LinearDecoder layer.
Also by default (i.e. tie_weights = True), the embedding matrix used in the RNN_Encoder
is used to instantiate the weights for the LinearDecoder layer.
The SequentialRNN layer is the native torch's Sequential wrapper that puts the RNN_Encoder and
LinearDecoder layers sequentially in the model.
Args:
n_tok (int): number of unique vocabulary words (or tokens) in the source dataset
emb_sz (int): the embedding size to use to encode each token
n_hid (int): number of hidden activation per LSTM layer
n_layers (int): number of LSTM layers to use in the architecture
pad_token (int): the int value used for padding text.
dropouth (float): dropout to apply to the activations going from one LSTM layer to another
dropouti (float): dropout to apply to the input layer.
dropoute (float): dropout to apply to the embedding layer.
wdrop (float): dropout used for a LSTM's internal (or hidden) recurrent weights.
tie_weights (bool): decide if the weights of the embedding matrix in the RNN encoder should be tied to the
weights of the LinearDecoder layer.
qrnn (bool): decide if the model is composed of LSTMS (False) or QRNNs (True).
bias (bool): decide if the decoder should have a bias layer or not.
Returns:
A SequentialRNN model
|
[
"Returns",
"a",
"SequentialRNN",
"model",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/old/fastai/lm_rnn.py#L204-L238
|
20,788
|
fastai/fastai
|
fastai/text/transform.py
|
replace_rep
|
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,})')
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,})')
return re_rep.sub(_replace_rep, t)
|
[
"def",
"replace_rep",
"(",
"t",
":",
"str",
")",
"->",
"str",
":",
"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",
")"
] |
Replace repetitions at the character level in `t`.
|
[
"Replace",
"repetitions",
"at",
"the",
"character",
"level",
"in",
"t",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L42-L48
|
20,789
|
fastai/fastai
|
fastai/text/transform.py
|
replace_wrep
|
def replace_wrep(t:str) -> str:
"Replace word repetitions in `t`."
def _replace_wrep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_WREP} {len(cc.split())+1} {c} '
re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})')
return re_wrep.sub(_replace_wrep, t)
|
python
|
def replace_wrep(t:str) -> str:
"Replace word repetitions in `t`."
def _replace_wrep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_WREP} {len(cc.split())+1} {c} '
re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})')
return re_wrep.sub(_replace_wrep, t)
|
[
"def",
"replace_wrep",
"(",
"t",
":",
"str",
")",
"->",
"str",
":",
"def",
"_replace_wrep",
"(",
"m",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"str",
":",
"c",
",",
"cc",
"=",
"m",
".",
"groups",
"(",
")",
"return",
"f' {TK_WREP} {len(cc.split())+1} {c} '",
"re_wrep",
"=",
"re",
".",
"compile",
"(",
"r'(\\b\\w+\\W+)(\\1{3,})'",
")",
"return",
"re_wrep",
".",
"sub",
"(",
"_replace_wrep",
",",
"t",
")"
] |
Replace word repetitions in `t`.
|
[
"Replace",
"word",
"repetitions",
"in",
"t",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L50-L56
|
20,790
|
fastai/fastai
|
fastai/text/transform.py
|
fix_html
|
def fix_html(x:str) -> str:
"List of replacements from html strings in `x`."
re1 = re.compile(r' +')
x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace(
'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace(
'<br />', "\n").replace('\\"', '"').replace('<unk>',UNK).replace(' @.@ ','.').replace(
' @-@ ','-').replace(' @,@ ',',').replace('\\', ' \\ ')
return re1.sub(' ', html.unescape(x))
|
python
|
def fix_html(x:str) -> str:
"List of replacements from html strings in `x`."
re1 = re.compile(r' +')
x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace(
'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace(
'<br />', "\n").replace('\\"', '"').replace('<unk>',UNK).replace(' @.@ ','.').replace(
' @-@ ','-').replace(' @,@ ',',').replace('\\', ' \\ ')
return re1.sub(' ', html.unescape(x))
|
[
"def",
"fix_html",
"(",
"x",
":",
"str",
")",
"->",
"str",
":",
"re1",
"=",
"re",
".",
"compile",
"(",
"r' +'",
")",
"x",
"=",
"x",
".",
"replace",
"(",
"'#39;'",
",",
"\"'\"",
")",
".",
"replace",
"(",
"'amp;'",
",",
"'&'",
")",
".",
"replace",
"(",
"'#146;'",
",",
"\"'\"",
")",
".",
"replace",
"(",
"'nbsp;'",
",",
"' '",
")",
".",
"replace",
"(",
"'#36;'",
",",
"'$'",
")",
".",
"replace",
"(",
"'\\\\n'",
",",
"\"\\n\"",
")",
".",
"replace",
"(",
"'quot;'",
",",
"\"'\"",
")",
".",
"replace",
"(",
"'<br />'",
",",
"\"\\n\"",
")",
".",
"replace",
"(",
"'\\\\\"'",
",",
"'\"'",
")",
".",
"replace",
"(",
"'<unk>'",
",",
"UNK",
")",
".",
"replace",
"(",
"' @.@ '",
",",
"'.'",
")",
".",
"replace",
"(",
"' @-@ '",
",",
"'-'",
")",
".",
"replace",
"(",
"' @,@ '",
",",
"','",
")",
".",
"replace",
"(",
"'\\\\'",
",",
"' \\\\ '",
")",
"return",
"re1",
".",
"sub",
"(",
"' '",
",",
"html",
".",
"unescape",
"(",
"x",
")",
")"
] |
List of replacements from html strings in `x`.
|
[
"List",
"of",
"replacements",
"from",
"html",
"strings",
"in",
"x",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L58-L65
|
20,791
|
fastai/fastai
|
fastai/text/transform.py
|
replace_all_caps
|
def replace_all_caps(x:Collection[str]) -> Collection[str]:
"Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before."
res = []
for t in x:
if t.isupper() and len(t) > 1: res.append(TK_UP); res.append(t.lower())
else: res.append(t)
return res
|
python
|
def replace_all_caps(x:Collection[str]) -> Collection[str]:
"Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before."
res = []
for t in x:
if t.isupper() and len(t) > 1: res.append(TK_UP); res.append(t.lower())
else: res.append(t)
return res
|
[
"def",
"replace_all_caps",
"(",
"x",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"Collection",
"[",
"str",
"]",
":",
"res",
"=",
"[",
"]",
"for",
"t",
"in",
"x",
":",
"if",
"t",
".",
"isupper",
"(",
")",
"and",
"len",
"(",
"t",
")",
">",
"1",
":",
"res",
".",
"append",
"(",
"TK_UP",
")",
"res",
".",
"append",
"(",
"t",
".",
"lower",
"(",
")",
")",
"else",
":",
"res",
".",
"append",
"(",
"t",
")",
"return",
"res"
] |
Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before.
|
[
"Replace",
"tokens",
"in",
"ALL",
"CAPS",
"in",
"x",
"by",
"their",
"lower",
"version",
"and",
"add",
"TK_UP",
"before",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L67-L73
|
20,792
|
fastai/fastai
|
fastai/text/transform.py
|
deal_caps
|
def deal_caps(x:Collection[str]) -> Collection[str]:
"Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before."
res = []
for t in x:
if t == '': continue
if t[0].isupper() and len(t) > 1 and t[1:].islower(): res.append(TK_MAJ)
res.append(t.lower())
return res
|
python
|
def deal_caps(x:Collection[str]) -> Collection[str]:
"Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before."
res = []
for t in x:
if t == '': continue
if t[0].isupper() and len(t) > 1 and t[1:].islower(): res.append(TK_MAJ)
res.append(t.lower())
return res
|
[
"def",
"deal_caps",
"(",
"x",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"Collection",
"[",
"str",
"]",
":",
"res",
"=",
"[",
"]",
"for",
"t",
"in",
"x",
":",
"if",
"t",
"==",
"''",
":",
"continue",
"if",
"t",
"[",
"0",
"]",
".",
"isupper",
"(",
")",
"and",
"len",
"(",
"t",
")",
">",
"1",
"and",
"t",
"[",
"1",
":",
"]",
".",
"islower",
"(",
")",
":",
"res",
".",
"append",
"(",
"TK_MAJ",
")",
"res",
".",
"append",
"(",
"t",
".",
"lower",
"(",
")",
")",
"return",
"res"
] |
Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before.
|
[
"Replace",
"all",
"Capitalized",
"tokens",
"in",
"x",
"by",
"their",
"lower",
"version",
"and",
"add",
"TK_MAJ",
"before",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L75-L82
|
20,793
|
fastai/fastai
|
fastai/text/transform.py
|
Tokenizer.process_text
|
def process_text(self, t:str, tok:BaseTokenizer) -> List[str]:
"Process one text `t` with tokenizer `tok`."
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]:
"Process one text `t` with tokenizer `tok`."
for rule in self.pre_rules: t = rule(t)
toks = tok.tokenizer(t)
for rule in self.post_rules: toks = rule(toks)
return toks
|
[
"def",
"process_text",
"(",
"self",
",",
"t",
":",
"str",
",",
"tok",
":",
"BaseTokenizer",
")",
"->",
"List",
"[",
"str",
"]",
":",
"for",
"rule",
"in",
"self",
".",
"pre_rules",
":",
"t",
"=",
"rule",
"(",
"t",
")",
"toks",
"=",
"tok",
".",
"tokenizer",
"(",
"t",
")",
"for",
"rule",
"in",
"self",
".",
"post_rules",
":",
"toks",
"=",
"rule",
"(",
"toks",
")",
"return",
"toks"
] |
Process one text `t` with tokenizer `tok`.
|
[
"Process",
"one",
"text",
"t",
"with",
"tokenizer",
"tok",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L103-L108
|
20,794
|
fastai/fastai
|
fastai/text/transform.py
|
Tokenizer._process_all_1
|
def _process_all_1(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts` in one process."
tok = self.tok_func(self.lang)
if self.special_cases: tok.add_special_cases(self.special_cases)
return [self.process_text(str(t), tok) for t in texts]
|
python
|
def _process_all_1(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts` in one process."
tok = self.tok_func(self.lang)
if self.special_cases: tok.add_special_cases(self.special_cases)
return [self.process_text(str(t), tok) for t in texts]
|
[
"def",
"_process_all_1",
"(",
"self",
",",
"texts",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"List",
"[",
"List",
"[",
"str",
"]",
"]",
":",
"tok",
"=",
"self",
".",
"tok_func",
"(",
"self",
".",
"lang",
")",
"if",
"self",
".",
"special_cases",
":",
"tok",
".",
"add_special_cases",
"(",
"self",
".",
"special_cases",
")",
"return",
"[",
"self",
".",
"process_text",
"(",
"str",
"(",
"t",
")",
",",
"tok",
")",
"for",
"t",
"in",
"texts",
"]"
] |
Process a list of `texts` in one process.
|
[
"Process",
"a",
"list",
"of",
"texts",
"in",
"one",
"process",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L110-L114
|
20,795
|
fastai/fastai
|
fastai/text/transform.py
|
Tokenizer.process_all
|
def process_all(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts`."
if self.n_cpus <= 1: return self._process_all_1(texts)
with ProcessPoolExecutor(self.n_cpus) as e:
return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), [])
|
python
|
def process_all(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts`."
if self.n_cpus <= 1: return self._process_all_1(texts)
with ProcessPoolExecutor(self.n_cpus) as e:
return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), [])
|
[
"def",
"process_all",
"(",
"self",
",",
"texts",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"List",
"[",
"List",
"[",
"str",
"]",
"]",
":",
"if",
"self",
".",
"n_cpus",
"<=",
"1",
":",
"return",
"self",
".",
"_process_all_1",
"(",
"texts",
")",
"with",
"ProcessPoolExecutor",
"(",
"self",
".",
"n_cpus",
")",
"as",
"e",
":",
"return",
"sum",
"(",
"e",
".",
"map",
"(",
"self",
".",
"_process_all_1",
",",
"partition_by_cores",
"(",
"texts",
",",
"self",
".",
"n_cpus",
")",
")",
",",
"[",
"]",
")"
] |
Process a list of `texts`.
|
[
"Process",
"a",
"list",
"of",
"texts",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L116-L120
|
20,796
|
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."
return [self.stoi[w] for w in t]
|
python
|
def numericalize(self, t:Collection[str]) -> List[int]:
"Convert a list of tokens `t` to their ids."
return [self.stoi[w] for w in t]
|
[
"def",
"numericalize",
"(",
"self",
",",
"t",
":",
"Collection",
"[",
"str",
"]",
")",
"->",
"List",
"[",
"int",
"]",
":",
"return",
"[",
"self",
".",
"stoi",
"[",
"w",
"]",
"for",
"w",
"in",
"t",
"]"
] |
Convert a list of tokens `t` to their ids.
|
[
"Convert",
"a",
"list",
"of",
"tokens",
"t",
"to",
"their",
"ids",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L128-L130
|
20,797
|
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."
return sep.join([self.itos[i] for i in nums]) if sep is not None else [self.itos[i] for i in nums]
|
python
|
def textify(self, nums:Collection[int], sep=' ') -> List[str]:
"Convert a list of `nums` to their tokens."
return sep.join([self.itos[i] for i in nums]) if sep is not None else [self.itos[i] for i in nums]
|
[
"def",
"textify",
"(",
"self",
",",
"nums",
":",
"Collection",
"[",
"int",
"]",
",",
"sep",
"=",
"' '",
")",
"->",
"List",
"[",
"str",
"]",
":",
"return",
"sep",
".",
"join",
"(",
"[",
"self",
".",
"itos",
"[",
"i",
"]",
"for",
"i",
"in",
"nums",
"]",
")",
"if",
"sep",
"is",
"not",
"None",
"else",
"[",
"self",
".",
"itos",
"[",
"i",
"]",
"for",
"i",
"in",
"nums",
"]"
] |
Convert a list of `nums` to their tokens.
|
[
"Convert",
"a",
"list",
"of",
"nums",
"to",
"their",
"tokens",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L132-L134
|
20,798
|
fastai/fastai
|
fastai/text/transform.py
|
Vocab.create
|
def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':
"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]
for o in reversed(defaults.text_spec_tok):
if o in itos: itos.remove(o)
itos.insert(0, o)
return cls(itos)
|
python
|
def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':
"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]
for o in reversed(defaults.text_spec_tok):
if o in itos: itos.remove(o)
itos.insert(0, o)
return cls(itos)
|
[
"def",
"create",
"(",
"cls",
",",
"tokens",
":",
"Tokens",
",",
"max_vocab",
":",
"int",
",",
"min_freq",
":",
"int",
")",
"->",
"'Vocab'",
":",
"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",
")",
":",
"if",
"o",
"in",
"itos",
":",
"itos",
".",
"remove",
"(",
"o",
")",
"itos",
".",
"insert",
"(",
"0",
",",
"o",
")",
"return",
"cls",
"(",
"itos",
")"
] |
Create a vocabulary from a set of `tokens`.
|
[
"Create",
"a",
"vocabulary",
"from",
"a",
"set",
"of",
"tokens",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L148-L155
|
20,799
|
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)
|
[
"def",
"load",
"(",
"cls",
",",
"path",
")",
":",
"itos",
"=",
"pickle",
".",
"load",
"(",
"open",
"(",
"path",
",",
"'rb'",
")",
")",
"return",
"cls",
"(",
"itos",
")"
] |
Load the `Vocab` contained in `path`
|
[
"Load",
"the",
"Vocab",
"contained",
"in",
"path"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/transform.py#L158-L161
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.