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20,500 | pandas-dev/pandas | pandas/core/strings.py | str_contains | def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
"""
Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is
contained within a string of a Series or Index.
Parameters
--------... | python | def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
"""
Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is
contained within a string of a Series or Index.
Parameters
--------... | [
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Return boolean Series or Index based on whether a given pattern or regex is
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20,501 | pandas-dev/pandas | pandas/core/strings.py | str_startswith | def str_startswith(arr, pat, na=np.nan):
"""
Test if the start of each string element matches a pattern.
Equivalent to :meth:`str.startswith`.
Parameters
----------
pat : str
Character sequence. Regular expressions are not accepted.
na : object, default NaN
Object shown if ... | python | def str_startswith(arr, pat, na=np.nan):
"""
Test if the start of each string element matches a pattern.
Equivalent to :meth:`str.startswith`.
Parameters
----------
pat : str
Character sequence. Regular expressions are not accepted.
na : object, default NaN
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20,502 | pandas-dev/pandas | pandas/core/strings.py | str_endswith | def str_endswith(arr, pat, na=np.nan):
"""
Test if the end of each string element matches a pattern.
Equivalent to :meth:`str.endswith`.
Parameters
----------
pat : str
Character sequence. Regular expressions are not accepted.
na : object, default NaN
Object shown if elemen... | python | def str_endswith(arr, pat, na=np.nan):
"""
Test if the end of each string element matches a pattern.
Equivalent to :meth:`str.endswith`.
Parameters
----------
pat : str
Character sequence. Regular expressions are not accepted.
na : object, default NaN
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20,503 | pandas-dev/pandas | pandas/core/strings.py | str_repeat | def str_repeat(arr, repeats):
"""
Duplicate each string in the Series or Index.
Parameters
----------
repeats : int or sequence of int
Same value for all (int) or different value per (sequence).
Returns
-------
Series or Index of object
Series or Index of repeated strin... | python | def str_repeat(arr, repeats):
"""
Duplicate each string in the Series or Index.
Parameters
----------
repeats : int or sequence of int
Same value for all (int) or different value per (sequence).
Returns
-------
Series or Index of object
Series or Index of repeated strin... | [
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20,504 | pandas-dev/pandas | pandas/core/strings.py | str_match | def str_match(arr, pat, case=True, flags=0, na=np.nan):
"""
Determine if each string matches a regular expression.
Parameters
----------
pat : str
Character sequence or regular expression.
case : bool, default True
If True, case sensitive.
flags : int, default 0 (no flags)
... | python | def str_match(arr, pat, case=True, flags=0, na=np.nan):
"""
Determine if each string matches a regular expression.
Parameters
----------
pat : str
Character sequence or regular expression.
case : bool, default True
If True, case sensitive.
flags : int, default 0 (no flags)
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20,505 | pandas-dev/pandas | pandas/core/strings.py | _groups_or_na_fun | def _groups_or_na_fun(regex):
"""Used in both extract_noexpand and extract_frame"""
if regex.groups == 0:
raise ValueError("pattern contains no capture groups")
empty_row = [np.nan] * regex.groups
def f(x):
if not isinstance(x, str):
return empty_row
m = regex.search... | python | def _groups_or_na_fun(regex):
"""Used in both extract_noexpand and extract_frame"""
if regex.groups == 0:
raise ValueError("pattern contains no capture groups")
empty_row = [np.nan] * regex.groups
def f(x):
if not isinstance(x, str):
return empty_row
m = regex.search... | [
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20,506 | pandas-dev/pandas | pandas/core/strings.py | str_extract | def str_extract(arr, pat, flags=0, expand=True):
r"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the
first match of regular expression `pat`.
Parameters
----------
pat : str
Regular expression patt... | python | def str_extract(arr, pat, flags=0, expand=True):
r"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the
first match of regular expression `pat`.
Parameters
----------
pat : str
Regular expression patt... | [
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20,507 | pandas-dev/pandas | pandas/core/strings.py | str_slice | def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in the Series or Index.
Parameters
----------
start : int, optional
Start position for slice operation.
stop : int, optional
Stop position for slice operation.
step : int, optional
... | python | def str_slice(arr, start=None, stop=None, step=None):
"""
Slice substrings from each element in the Series or Index.
Parameters
----------
start : int, optional
Start position for slice operation.
stop : int, optional
Stop position for slice operation.
step : int, optional
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20,508 | pandas-dev/pandas | pandas/core/strings.py | str_slice_replace | def str_slice_replace(arr, start=None, stop=None, repl=None):
"""
Replace a positional slice of a string with another value.
Parameters
----------
start : int, optional
Left index position to use for the slice. If not specified (None),
the slice is unbounded on the left, i.e. slice ... | python | def str_slice_replace(arr, start=None, stop=None, repl=None):
"""
Replace a positional slice of a string with another value.
Parameters
----------
start : int, optional
Left index position to use for the slice. If not specified (None),
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20,509 | pandas-dev/pandas | pandas/core/strings.py | str_get | def str_get(arr, i):
"""
Extract element from each component at specified position.
Extract element from lists, tuples, or strings in each element in the
Series/Index.
Parameters
----------
i : int
Position of element to extract.
Returns
-------
Series or Index
Ex... | python | def str_get(arr, i):
"""
Extract element from each component at specified position.
Extract element from lists, tuples, or strings in each element in the
Series/Index.
Parameters
----------
i : int
Position of element to extract.
Returns
-------
Series or Index
Ex... | [
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>>> s = p... | [
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20,510 | pandas-dev/pandas | pandas/core/groupby/base.py | GroupByMixin._dispatch | def _dispatch(name, *args, **kwargs):
"""
Dispatch to apply.
"""
def outer(self, *args, **kwargs):
def f(x):
x = self._shallow_copy(x, groupby=self._groupby)
return getattr(x, name)(*args, **kwargs)
return self._groupby.apply(f)
... | python | def _dispatch(name, *args, **kwargs):
"""
Dispatch to apply.
"""
def outer(self, *args, **kwargs):
def f(x):
x = self._shallow_copy(x, groupby=self._groupby)
return getattr(x, name)(*args, **kwargs)
return self._groupby.apply(f)
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20,511 | pandas-dev/pandas | pandas/compat/__init__.py | to_str | def to_str(s):
"""
Convert bytes and non-string into Python 3 str
"""
if isinstance(s, bytes):
s = s.decode('utf-8')
elif not isinstance(s, str):
s = str(s)
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"""
Convert bytes and non-string into Python 3 str
"""
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s = s.decode('utf-8')
elif not isinstance(s, str):
s = str(s)
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20,512 | pandas-dev/pandas | pandas/core/apply.py | frame_apply | def frame_apply(obj, func, axis=0, broadcast=None,
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ignore_failures=False,
args=None, kwds=None):
""" construct and return a row or column based frame apply object """
axis = obj._get_axis_number(axis)
if axis == 0:
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raw=False, reduce=None, result_type=None,
ignore_failures=False,
args=None, kwds=None):
""" construct and return a row or column based frame apply object """
axis = obj._get_axis_number(axis)
if axis == 0:
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20,513 | pandas-dev/pandas | pandas/core/apply.py | FrameApply.get_result | def get_result(self):
""" compute the results """
# dispatch to agg
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return self.obj.aggregate(self.f, axis=self.axis,
*self.args, **self.kwds)
# all empty
if len(self.columns) == ... | python | def get_result(self):
""" compute the results """
# dispatch to agg
if is_list_like(self.f) or is_dict_like(self.f):
return self.obj.aggregate(self.f, axis=self.axis,
*self.args, **self.kwds)
# all empty
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20,514 | pandas-dev/pandas | pandas/core/apply.py | FrameApply.apply_empty_result | def apply_empty_result(self):
"""
we have an empty result; at least 1 axis is 0
we will try to apply the function to an empty
series in order to see if this is a reduction function
"""
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20,515 | pandas-dev/pandas | pandas/core/apply.py | FrameApply.apply_raw | def apply_raw(self):
""" apply to the values as a numpy array """
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result = reduction.reduce(self.values, self.f, axis=self.axis)
except Exception:
result = np.apply_along_axis(self.f, self.axis, self.values)
# TODO: mixed type case
if result.ndim ==... | python | def apply_raw(self):
""" apply to the values as a numpy array """
try:
result = reduction.reduce(self.values, self.f, axis=self.axis)
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result = np.apply_along_axis(self.f, self.axis, self.values)
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20,516 | pandas-dev/pandas | pandas/core/apply.py | FrameRowApply.wrap_results_for_axis | def wrap_results_for_axis(self):
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20,517 | pandas-dev/pandas | pandas/core/apply.py | FrameColumnApply.wrap_results_for_axis | def wrap_results_for_axis(self):
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20,518 | pandas-dev/pandas | pandas/core/apply.py | FrameColumnApply.infer_to_same_shape | def infer_to_same_shape(self):
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20,519 | fastai/fastai | fastai/vision/models/xception.py | xception | def xception(c, k=8, n_middle=8):
"Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet."
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conv(3, k*4, 3, 2),
conv(k*4, k*8, 3),
ConvSkip(k*8, k*16, act=False),
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ConvSkip(k*32, k*91),
]
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"Preview version of Xception network. Not tested yet - use at own risk. No pretrained model yet."
layers = [
conv(3, k*4, 3, 2),
conv(k*4, k*8, 3),
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20,520 | fastai/fastai | old/fastai/nlp.py | LanguageModelData.get_model | def get_model(self, opt_fn, emb_sz, n_hid, n_layers, **kwargs):
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Args:
opt_fn (Optimizer): the torch optimizer function to use
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""" Method returns a RNN_Learner object, that wraps an instance of the RNN_Encoder module.
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20,521 | fastai/fastai | old/fastai/nlp.py | LanguageModelData.from_text_files | def from_text_files(cls, path, field, train, validation, test=None, bs=64, bptt=70, **kwargs):
""" Method used to instantiate a LanguageModelData object that can be used for a
supported nlp task.
Args:
path (str): the absolute path in which temporary model data will be saved
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""" Method used to instantiate a LanguageModelData object that can be used for a
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20,522 | fastai/fastai | fastai/data_block.py | get_files | def get_files(path:PathOrStr, extensions:Collection[str]=None, recurse:bool=False,
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res = []
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20,523 | fastai/fastai | fastai/data_block.py | ItemList.process | def process(self, processor:PreProcessors=None):
"Apply `processor` or `self.processor` to `self`."
if processor is not None: self.processor = processor
self.processor = listify(self.processor)
for p in self.processor: p.process(self)
return self | python | def process(self, processor:PreProcessors=None):
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20,524 | fastai/fastai | fastai/data_block.py | ItemList.process_one | def process_one(self, item:ItemBase, processor:PreProcessors=None):
"Apply `processor` or `self.processor` to `item`."
if processor is not None: self.processor = processor
self.processor = listify(self.processor)
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return item | python | def process_one(self, item:ItemBase, processor:PreProcessors=None):
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20,525 | fastai/fastai | fastai/data_block.py | ItemList.reconstruct | def reconstruct(self, t:Tensor, x:Tensor=None):
"Reconstruct one of the underlying item for its data `t`."
return self[0].reconstruct(t,x) if has_arg(self[0].reconstruct, 'x') else self[0].reconstruct(t) | python | def reconstruct(self, t:Tensor, x:Tensor=None):
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20,526 | fastai/fastai | fastai/data_block.py | ItemList.from_folder | def from_folder(cls, path:PathOrStr, extensions:Collection[str]=None, recurse:bool=True,
include:Optional[Collection[str]]=None, processor:PreProcessors=None, **kwargs)->'ItemList':
"""Create an `ItemList` in `path` from the filenames that have a suffix in `extensions`.
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include:Optional[Collection[str]]=None, processor:PreProcessors=None, **kwargs)->'ItemList':
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20,527 | fastai/fastai | fastai/data_block.py | ItemList.from_df | def from_df(cls, df:DataFrame, path:PathOrStr='.', cols:IntsOrStrs=0, processor:PreProcessors=None, **kwargs)->'ItemList':
"Create an `ItemList` in `path` from the inputs in the `cols` of `df`."
inputs = df.iloc[:,df_names_to_idx(cols, df)]
assert inputs.isna().sum().sum() == 0, f"You have NaN v... | python | def from_df(cls, df:DataFrame, path:PathOrStr='.', cols:IntsOrStrs=0, processor:PreProcessors=None, **kwargs)->'ItemList':
"Create an `ItemList` in `path` from the inputs in the `cols` of `df`."
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20,528 | fastai/fastai | fastai/data_block.py | ItemList.use_partial_data | def use_partial_data(self, sample_pct:float=0.01, seed:int=None)->'ItemList':
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if seed is not None: np.random.seed(seed)
rand_idx = np.random.permutation(range_of(self))
cut = int(sample_pct * len(self))
... | python | def use_partial_data(self, sample_pct:float=0.01, seed:int=None)->'ItemList':
"Use only a sample of `sample_pct`of the full dataset and an optional `seed`."
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20,529 | fastai/fastai | fastai/data_block.py | ItemList.to_text | def to_text(self, fn:str):
"Save `self.items` to `fn` in `self.path`."
with open(self.path/fn, 'w') as f: f.writelines([f'{o}\n' for o in self._relative_item_paths()]) | python | def to_text(self, fn:str):
"Save `self.items` to `fn` in `self.path`."
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20,530 | fastai/fastai | fastai/data_block.py | ItemList.filter_by_func | def filter_by_func(self, func:Callable)->'ItemList':
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self.items = array([o for o in self.items if func(o)])
return self | python | def filter_by_func(self, func:Callable)->'ItemList':
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20,531 | fastai/fastai | fastai/data_block.py | ItemList.filter_by_folder | def filter_by_folder(self, include=None, exclude=None):
"Only keep filenames in `include` folder or reject the ones in `exclude`."
include,exclude = listify(include),listify(exclude)
def _inner(o):
if isinstance(o, Path): n = o.relative_to(self.path).parts[0]
else: n = o.... | python | def filter_by_folder(self, include=None, exclude=None):
"Only keep filenames in `include` folder or reject the ones in `exclude`."
include,exclude = listify(include),listify(exclude)
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20,532 | fastai/fastai | fastai/data_block.py | ItemList.filter_by_rand | def filter_by_rand(self, p:float, seed:int=None):
"Keep random sample of `items` with probability `p` and an optional `seed`."
if seed is not None: np.random.seed(seed)
return self.filter_by_func(lambda o: rand_bool(p)) | python | def filter_by_rand(self, p:float, seed:int=None):
"Keep random sample of `items` with probability `p` and an optional `seed`."
if seed is not None: np.random.seed(seed)
return self.filter_by_func(lambda o: rand_bool(p)) | [
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20,533 | fastai/fastai | fastai/data_block.py | ItemList.split_none | def split_none(self):
"Don't split the data and create an empty validation set."
val = self[[]]
val.ignore_empty = True
return self._split(self.path, self, val) | python | def split_none(self):
"Don't split the data and create an empty validation set."
val = self[[]]
val.ignore_empty = True
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20,534 | fastai/fastai | fastai/data_block.py | ItemList.split_by_list | def split_by_list(self, train, valid):
"Split the data between `train` and `valid`."
return self._split(self.path, train, valid) | python | def split_by_list(self, train, valid):
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20,535 | fastai/fastai | fastai/data_block.py | ItemList.split_by_idxs | def split_by_idxs(self, train_idx, valid_idx):
"Split the data between `train_idx` and `valid_idx`."
return self.split_by_list(self[train_idx], self[valid_idx]) | python | def split_by_idxs(self, train_idx, valid_idx):
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20,536 | fastai/fastai | fastai/data_block.py | ItemList.split_by_idx | def split_by_idx(self, valid_idx:Collection[int])->'ItemLists':
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#train_idx = [i for i in range_of(self.items) if i not in valid_idx]
train_idx = np.setdiff1d(arange_of(self.items), valid_idx)
return self.split_by_idxs(train_idx, v... | python | def split_by_idx(self, valid_idx:Collection[int])->'ItemLists':
"Split the data according to the indexes in `valid_idx`."
#train_idx = [i for i in range_of(self.items) if i not in valid_idx]
train_idx = np.setdiff1d(arange_of(self.items), valid_idx)
return self.split_by_idxs(train_idx, v... | [
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20,537 | fastai/fastai | fastai/data_block.py | ItemList.split_by_rand_pct | def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists':
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if valid_pct==0.: return self.split_none()
if seed is not None: np.random.seed(seed)
rand_idx = np.random.... | python | def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists':
"Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed."
if valid_pct==0.: return self.split_none()
if seed is not None: np.random.seed(seed)
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20,538 | fastai/fastai | fastai/data_block.py | ItemList.split_by_files | def split_by_files(self, valid_names:'ItemList')->'ItemLists':
"Split the data by using the names in `valid_names` for validation."
if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names)
else: return self.split_by_valid_func(lambda o: os.path.basenam... | python | def split_by_files(self, valid_names:'ItemList')->'ItemLists':
"Split the data by using the names in `valid_names` for validation."
if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names)
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20,539 | fastai/fastai | fastai/data_block.py | ItemList.split_by_fname_file | def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists':
"Split the data by using the names in `fname` for the validation set. `path` will override `self.path`."
path = Path(ifnone(path, self.path))
valid_names = loadtxt_str(path/fname)
return self.split_by_files... | python | def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists':
"Split the data by using the names in `fname` for the validation set. `path` will override `self.path`."
path = Path(ifnone(path, self.path))
valid_names = loadtxt_str(path/fname)
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20,540 | fastai/fastai | fastai/data_block.py | ItemList.split_from_df | def split_from_df(self, col:IntsOrStrs=2):
"Split the data from the `col` in the dataframe in `self.inner_df`."
valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0]
return self.split_by_idx(valid_idx) | python | def split_from_df(self, col:IntsOrStrs=2):
"Split the data from the `col` in the dataframe in `self.inner_df`."
valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0]
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20,541 | fastai/fastai | fastai/data_block.py | ItemList.get_label_cls | def get_label_cls(self, labels, label_cls:Callable=None, label_delim:str=None, **kwargs):
"Return `label_cls` or guess one from the first element of `labels`."
if label_cls is not None: return label_cls
if self.label_cls is not None: return self.label_cls
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"Return `label_cls` or guess one from the first element of `labels`."
if label_cls is not None: return label_cls
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20,542 | fastai/fastai | fastai/data_block.py | ItemList._label_from_list | def _label_from_list(self, labels:Iterator, label_cls:Callable=None, from_item_lists:bool=False, **kwargs)->'LabelList':
"Label `self.items` with `labels`."
if not from_item_lists:
raise Exception("Your data isn't split, if you don't want a validation set, please use `split_none`.")
... | python | def _label_from_list(self, labels:Iterator, label_cls:Callable=None, from_item_lists:bool=False, **kwargs)->'LabelList':
"Label `self.items` with `labels`."
if not from_item_lists:
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20,543 | fastai/fastai | fastai/data_block.py | ItemList.label_from_df | def label_from_df(self, cols:IntsOrStrs=1, label_cls:Callable=None, **kwargs):
"Label `self.items` from the values in `cols` in `self.inner_df`."
labels = self.inner_df.iloc[:,df_names_to_idx(cols, self.inner_df)]
assert labels.isna().sum().sum() == 0, f"You have NaN values in column(s) {cols} o... | python | def label_from_df(self, cols:IntsOrStrs=1, label_cls:Callable=None, **kwargs):
"Label `self.items` from the values in `cols` in `self.inner_df`."
labels = self.inner_df.iloc[:,df_names_to_idx(cols, self.inner_df)]
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20,544 | fastai/fastai | fastai/data_block.py | ItemList.label_const | def label_const(self, const:Any=0, label_cls:Callable=None, **kwargs)->'LabelList':
"Label every item with `const`."
return self.label_from_func(func=lambda o: const, label_cls=label_cls, **kwargs) | python | def label_const(self, const:Any=0, label_cls:Callable=None, **kwargs)->'LabelList':
"Label every item with `const`."
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20,545 | fastai/fastai | fastai/data_block.py | ItemList.label_empty | def label_empty(self, **kwargs):
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kwargs['label_cls'] = EmptyLabelList
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20,546 | fastai/fastai | fastai/data_block.py | ItemList.label_from_func | def label_from_func(self, func:Callable, label_cls:Callable=None, **kwargs)->'LabelList':
"Apply `func` to every input to get its label."
return self._label_from_list([func(o) for o in self.items], label_cls=label_cls, **kwargs) | python | def label_from_func(self, func:Callable, label_cls:Callable=None, **kwargs)->'LabelList':
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20,547 | fastai/fastai | fastai/data_block.py | ItemList.label_from_folder | def label_from_folder(self, label_cls:Callable=None, **kwargs)->'LabelList':
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return self.label_from_func(func=lambda o: (o.parts if isinstance(o, Path) else o.split(os.path.sep))[-2],
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"Give a label to each filename depending on its folder."
return self.label_from_func(func=lambda o: (o.parts if isinstance(o, Path) else o.split(os.path.sep))[-2],
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20,548 | fastai/fastai | fastai/data_block.py | ItemList.label_from_re | def label_from_re(self, pat:str, full_path:bool=False, label_cls:Callable=None, **kwargs)->'LabelList':
"Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name."
pat = re.compile(pat)
def _inner(o):
s = str((os.path.join(self.path,o) ... | python | def label_from_re(self, pat:str, full_path:bool=False, label_cls:Callable=None, **kwargs)->'LabelList':
"Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name."
pat = re.compile(pat)
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s = str((os.path.join(self.path,o) ... | [
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20,549 | fastai/fastai | fastai/data_block.py | MultiCategoryProcessor.generate_classes | def generate_classes(self, items):
"Generate classes from `items` by taking the sorted unique values."
classes = set()
for c in items: classes = classes.union(set(c))
classes = list(classes)
classes.sort()
return classes | python | def generate_classes(self, items):
"Generate classes from `items` by taking the sorted unique values."
classes = set()
for c in items: classes = classes.union(set(c))
classes = list(classes)
classes.sort()
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20,550 | fastai/fastai | fastai/data_block.py | ItemLists.label_from_lists | def label_from_lists(self, train_labels:Iterator, valid_labels:Iterator, label_cls:Callable=None, **kwargs)->'LabelList':
"Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default."
label_cls = self.train.get_label_cls(train_labels, label_cls)
... | python | def label_from_lists(self, train_labels:Iterator, valid_labels:Iterator, label_cls:Callable=None, **kwargs)->'LabelList':
"Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default."
label_cls = self.train.get_label_cls(train_labels, label_cls)
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20,551 | fastai/fastai | fastai/data_block.py | ItemLists.transform | def transform(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the xs of the train and validation set."
if not tfms: tfms=(None,None)
assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two lists of transforms (train and valid)."
... | python | def transform(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the xs of the train and validation set."
if not tfms: tfms=(None,None)
assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two lists of transforms (train and valid)."
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20,552 | fastai/fastai | fastai/data_block.py | ItemLists.transform_y | def transform_y(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the ys of the train and validation set."
if not tfms: tfms=(None,None)
self.train.transform_y(tfms[0], **kwargs)
self.valid.transform_y(tfms[1], **kwargs)
if self.test... | python | def transform_y(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the ys of the train and validation set."
if not tfms: tfms=(None,None)
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20,553 | fastai/fastai | fastai/data_block.py | LabelLists.get_processors | def get_processors(self):
"Read the default class processors if none have been set."
procs_x,procs_y = listify(self.train.x._processor),listify(self.train.y._processor)
xp = ifnone(self.train.x.processor, [p(ds=self.train.x) for p in procs_x])
yp = ifnone(self.train.y.processor, [p(ds=se... | python | def get_processors(self):
"Read the default class processors if none have been set."
procs_x,procs_y = listify(self.train.x._processor),listify(self.train.y._processor)
xp = ifnone(self.train.x.processor, [p(ds=self.train.x) for p in procs_x])
yp = ifnone(self.train.y.processor, [p(ds=se... | [
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20,554 | fastai/fastai | fastai/data_block.py | LabelLists.process | def process(self):
"Process the inner datasets."
xp,yp = self.get_processors()
for ds,n in zip(self.lists, ['train','valid','test']): ds.process(xp, yp, name=n)
#progress_bar clear the outputs so in some case warnings issued during processing disappear.
for ds in self.lists:
... | python | def process(self):
"Process the inner datasets."
xp,yp = self.get_processors()
for ds,n in zip(self.lists, ['train','valid','test']): ds.process(xp, yp, name=n)
#progress_bar clear the outputs so in some case warnings issued during processing disappear.
for ds in self.lists:
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20,555 | fastai/fastai | fastai/data_block.py | LabelLists.databunch | def databunch(self, path:PathOrStr=None, bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus,
dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate,
no_check:bool=False, **kwargs)->'DataBunch':
"Create an `DataBunch` fro... | python | def databunch(self, path:PathOrStr=None, 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,
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20,556 | fastai/fastai | fastai/data_block.py | LabelLists.load_state | def load_state(cls, path:PathOrStr, state:dict):
"Create a `LabelLists` with empty sets from the serialized `state`."
path = Path(path)
train_ds = LabelList.load_state(path, state)
valid_ds = LabelList.load_state(path, state)
return LabelLists(path, train=train_ds, valid=valid_ds... | python | def load_state(cls, path:PathOrStr, state:dict):
"Create a `LabelLists` with empty sets from the serialized `state`."
path = Path(path)
train_ds = LabelList.load_state(path, state)
valid_ds = LabelList.load_state(path, state)
return LabelLists(path, train=train_ds, valid=valid_ds... | [
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20,557 | fastai/fastai | fastai/data_block.py | LabelList.set_item | def set_item(self,item):
"For inference, will briefly replace the dataset with one that only contains `item`."
self.item = self.x.process_one(item)
yield None
self.item = None | python | def set_item(self,item):
"For inference, will briefly replace the dataset with one that only contains `item`."
self.item = self.x.process_one(item)
yield None
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20,558 | fastai/fastai | fastai/data_block.py | LabelList.to_df | def to_df(self)->None:
"Create `pd.DataFrame` containing `items` from `self.x` and `self.y`."
return pd.DataFrame(dict(x=self.x._relative_item_paths(), y=[str(o) for o in self.y])) | python | def to_df(self)->None:
"Create `pd.DataFrame` containing `items` from `self.x` and `self.y`."
return pd.DataFrame(dict(x=self.x._relative_item_paths(), y=[str(o) for o in self.y])) | [
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20,559 | fastai/fastai | fastai/data_block.py | LabelList.get_state | def get_state(self, **kwargs):
"Return the minimal state for export."
state = {'x_cls':self.x.__class__, 'x_proc':self.x.processor,
'y_cls':self.y.__class__, 'y_proc':self.y.processor,
'tfms':self.tfms, 'tfm_y':self.tfm_y, 'tfmargs':self.tfmargs}
if hasattr(self... | python | def get_state(self, **kwargs):
"Return the minimal state for export."
state = {'x_cls':self.x.__class__, 'x_proc':self.x.processor,
'y_cls':self.y.__class__, 'y_proc':self.y.processor,
'tfms':self.tfms, 'tfm_y':self.tfm_y, 'tfmargs':self.tfmargs}
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20,560 | fastai/fastai | fastai/data_block.py | LabelList.export | def export(self, fn:PathOrStr, **kwargs):
"Export the minimal state and save it in `fn` to load an empty version for inference."
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20,561 | fastai/fastai | fastai/data_block.py | LabelList.load_empty | def load_empty(cls, path:PathOrStr, fn:PathOrStr):
"Load the state in `fn` to create an empty `LabelList` for inference."
return cls.load_state(path, pickle.load(open(Path(path)/fn, 'rb'))) | python | def load_empty(cls, path:PathOrStr, fn:PathOrStr):
"Load the state in `fn` to create an empty `LabelList` for inference."
return cls.load_state(path, pickle.load(open(Path(path)/fn, 'rb'))) | [
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20,562 | fastai/fastai | fastai/data_block.py | LabelList.load_state | def load_state(cls, path:PathOrStr, state:dict) -> 'LabelList':
"Create a `LabelList` from `state`."
x = state['x_cls']([], path=path, processor=state['x_proc'], ignore_empty=True)
y = state['y_cls']([], path=path, processor=state['y_proc'], ignore_empty=True)
res = cls(x, y, tfms=state[... | python | def load_state(cls, path:PathOrStr, state:dict) -> 'LabelList':
"Create a `LabelList` from `state`."
x = state['x_cls']([], path=path, processor=state['x_proc'], ignore_empty=True)
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20,563 | fastai/fastai | fastai/data_block.py | LabelList.process | def process(self, xp:PreProcessor=None, yp:PreProcessor=None, name:str=None):
"Launch the processing on `self.x` and `self.y` with `xp` and `yp`."
self.y.process(yp)
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filt = array([o is None for o in self.y.items])
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"Launch the processing on `self.x` and `self.y` with `xp` and `yp`."
self.y.process(yp)
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20,564 | fastai/fastai | fastai/data_block.py | LabelList.transform | def transform(self, tfms:TfmList, tfm_y:bool=None, **kwargs):
"Set the `tfms` and `tfm_y` value to be applied to the inputs and targets."
_check_kwargs(self.x, tfms, **kwargs)
if tfm_y is None: tfm_y = self.tfm_y
if tfm_y: _check_kwargs(self.y, tfms, **kwargs)
self.tfms, self.tf... | python | def transform(self, tfms:TfmList, tfm_y:bool=None, **kwargs):
"Set the `tfms` and `tfm_y` value to be applied to the inputs and targets."
_check_kwargs(self.x, tfms, **kwargs)
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20,565 | fastai/fastai | fastai/data_block.py | LabelList.transform_y | def transform_y(self, tfms:TfmList=None, **kwargs):
"Set `tfms` to be applied to the targets only."
_check_kwargs(self.y, tfms, **kwargs)
self.tfm_y=True
if tfms is None:
self.tfms_y = list(filter(lambda t: t.use_on_y, listify(self.tfms)))
self.tfmargs_y = {**self... | python | def transform_y(self, tfms:TfmList=None, **kwargs):
"Set `tfms` to be applied to the targets only."
_check_kwargs(self.y, tfms, **kwargs)
self.tfm_y=True
if tfms is None:
self.tfms_y = list(filter(lambda t: t.use_on_y, listify(self.tfms)))
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20,566 | fastai/fastai | fastai/utils/collect_env.py | get_env | def get_env(name):
"Return env var value if it's defined and not an empty string, or return Unknown"
res = os.environ.get(name,'')
return res if len(res) else "Unknown" | python | def get_env(name):
"Return env var value if it's defined and not an empty string, or return Unknown"
res = os.environ.get(name,'')
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20,567 | fastai/fastai | fastai/utils/collect_env.py | pypi_module_version_is_available | def pypi_module_version_is_available(module, version):
"Check whether module==version is available on pypi"
# returns True/False (or None if failed to execute the check)
# using a hack that when passing "module==" w/ no version number to pip
# it "fails" and returns all the available versions in stderr... | python | def pypi_module_version_is_available(module, version):
"Check whether module==version is available on pypi"
# returns True/False (or None if failed to execute the check)
# using a hack that when passing "module==" w/ no version number to pip
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20,568 | fastai/fastai | fastai/callback.py | annealing_linear | def annealing_linear(start:Number, end:Number, pct:float)->Number:
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return start + pct * (end-start) | python | def annealing_linear(start:Number, end:Number, pct:float)->Number:
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
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20,569 | fastai/fastai | fastai/callback.py | annealing_exp | def annealing_exp(start:Number, end:Number, pct:float)->Number:
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20,570 | fastai/fastai | fastai/callback.py | annealing_cos | def annealing_cos(start:Number, end:Number, pct:float)->Number:
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = np.cos(np.pi * pct) + 1
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"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = np.cos(np.pi * pct) + 1
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20,571 | fastai/fastai | fastai/callback.py | do_annealing_poly | def do_annealing_poly(start:Number, end:Number, pct:float, degree:Number)->Number:
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20,572 | fastai/fastai | fastai/callback.py | OptimWrapper.create | def create(cls, opt_func:Union[type,Callable], lr:Union[float,Tuple,List], layer_groups:ModuleList, wd:Floats=0.,
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split_params = split_no_wd_params(la... | python | def create(cls, opt_func:Union[type,Callable], lr:Union[float,Tuple,List], layer_groups:ModuleList, wd:Floats=0.,
true_wd:bool=False, bn_wd:bool=True)->optim.Optimizer:
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20,573 | fastai/fastai | fastai/callback.py | OptimWrapper.step | def step(self)->None:
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for lr,wd,pg1,pg2 in zip(self._lr,self._wd,self.opt.param_groups[::2],self.opt.param_groups[1::2]):
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"Set weight decay and step optimizer."
# weight decay outside of optimizer step (AdamW)
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20,574 | fastai/fastai | fastai/callback.py | OptimWrapper.wd | def wd(self, val:float)->None:
"Set weight decay."
if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
self._wd = listify(val, self._wd) | python | def wd(self, val:float)->None:
"Set weight decay."
if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
self._wd = listify(val, self._wd) | [
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20,575 | fastai/fastai | fastai/callback.py | OptimWrapper.read_defaults | def read_defaults(self)->None:
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"Read the values inside the optimizer for the hyper-parameters."
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20,576 | fastai/fastai | fastai/callback.py | OptimWrapper.set_val | def set_val(self, key:str, val:Any, bn_groups:bool=True)->Any:
"Set `val` inside the optimizer dictionary at `key`."
if is_tuple(val): val = [(v1,v2) for v1,v2 in zip(*val)]
for v,pg1,pg2 in zip(val,self.opt.param_groups[::2],self.opt.param_groups[1::2]):
pg1[key] = v
if ... | python | def set_val(self, key:str, val:Any, bn_groups:bool=True)->Any:
"Set `val` inside the optimizer dictionary at `key`."
if is_tuple(val): val = [(v1,v2) for v1,v2 in zip(*val)]
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20,577 | fastai/fastai | fastai/callback.py | OptimWrapper.read_val | def read_val(self, key:str) -> Union[List[float],Tuple[List[float],List[float]]]:
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val = [pg[key] for pg in self.opt.param_groups[::2]]
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"Read a hyperparameter `key` in the optimizer dictionary."
val = [pg[key] for pg in self.opt.param_groups[::2]]
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20,578 | fastai/fastai | fastai/callback.py | OptimWrapper.get_state | def get_state(self):
"Return the inner state minus the layer groups."
return {'opt_state':self.opt.state_dict(), 'lr':self._lr, 'wd':self._wd, 'beta':self._beta, 'mom':self._mom,
'opt_func':self.opt_func, 'true_wd':self.true_wd, 'bn_wd':self.bn_wd} | python | def get_state(self):
"Return the inner state minus the layer groups."
return {'opt_state':self.opt.state_dict(), 'lr':self._lr, 'wd':self._wd, 'beta':self._beta, 'mom':self._mom,
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"Return the inner state of the `Callback`, `minimal` or not."
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if minimal: to_remove += getattr(self, 'not_min', []).copy()
return {k:v for k,v in self.__dict__.items() if k no... | python | def get_state(self, minimal:bool=True):
"Return the inner state of the `Callback`, `minimal` or not."
to_remove = ['exclude', 'not_min'] + getattr(self, 'exclude', []).copy()
if minimal: to_remove += getattr(self, 'not_min', []).copy()
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20,580 | fastai/fastai | fastai/callback.py | SmoothenValue.add_value | def add_value(self, val:float)->None:
"Add `val` to calculate updated smoothed value."
self.n += 1
self.mov_avg = self.beta * self.mov_avg + (1 - self.beta) * val
self.smooth = self.mov_avg / (1 - self.beta ** self.n) | python | def add_value(self, val:float)->None:
"Add `val` to calculate updated smoothed value."
self.n += 1
self.mov_avg = self.beta * self.mov_avg + (1 - self.beta) * val
self.smooth = self.mov_avg / (1 - self.beta ** self.n) | [
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20,581 | fastai/fastai | fastai/callback.py | AverageMetric.on_batch_end | def on_batch_end(self, last_output, last_target, **kwargs):
"Update metric computation with `last_output` and `last_target`."
if not is_listy(last_target): last_target=[last_target]
self.count += last_target[0].size(0)
val = self.func(last_output, *last_target)
if self.world:
... | python | def on_batch_end(self, last_output, last_target, **kwargs):
"Update metric computation with `last_output` and `last_target`."
if not is_listy(last_target): last_target=[last_target]
self.count += last_target[0].size(0)
val = self.func(last_output, *last_target)
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20,582 | fastai/fastai | fastai/callback.py | AverageMetric.on_epoch_end | def on_epoch_end(self, last_metrics, **kwargs):
"Set the final result in `last_metrics`."
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"Set the final result in `last_metrics`."
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20,583 | fastai/fastai | fastai/callback.py | Scheduler.step | def step(self)->Number:
"Return next value along annealed schedule."
self.n += 1
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"Return next value along annealed schedule."
self.n += 1
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20,584 | fastai/fastai | fastai/callbacks/one_cycle.py | OneCycleScheduler.steps | def steps(self, *steps_cfg:StartOptEnd):
"Build anneal schedule for all of the parameters."
return [Scheduler(step, n_iter, func=func)
for (step,(n_iter,func)) in zip(steps_cfg, self.phases)] | python | def steps(self, *steps_cfg:StartOptEnd):
"Build anneal schedule for all of the parameters."
return [Scheduler(step, n_iter, func=func)
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20,585 | fastai/fastai | fastai/callbacks/one_cycle.py | OneCycleScheduler.on_train_begin | def on_train_begin(self, n_epochs:int, epoch:int, **kwargs:Any)->None:
"Initialize our optimization params based on our annealing schedule."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.start_epoch, epoch)
self.tot_epochs = ifn... | python | def on_train_begin(self, n_epochs:int, epoch:int, **kwargs:Any)->None:
"Initialize our optimization params based on our annealing schedule."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.start_epoch, epoch)
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20,586 | fastai/fastai | fastai/callbacks/one_cycle.py | OneCycleScheduler.on_batch_end | def on_batch_end(self, train, **kwargs:Any)->None:
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if train:
if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
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... | python | def on_batch_end(self, train, **kwargs:Any)->None:
"Take one step forward on the annealing schedule for the optim params."
if train:
if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
self.opt.lr = self.lr_scheds[self.idx_s].step()
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20,587 | fastai/fastai | fastai/vision/gan.py | basic_critic | def basic_critic(in_size:int, n_channels:int, n_features:int=64, n_extra_layers:int=0, **conv_kwargs):
"A basic critic for images `n_channels` x `in_size` x `in_size`."
layers = [conv_layer(n_channels, n_features, 4, 2, 1, leaky=0.2, norm_type=None, **conv_kwargs)]#norm_type=None?
cur_size, cur_ftrs = in_si... | python | def basic_critic(in_size:int, n_channels:int, n_features:int=64, n_extra_layers:int=0, **conv_kwargs):
"A basic critic for images `n_channels` x `in_size` x `in_size`."
layers = [conv_layer(n_channels, n_features, 4, 2, 1, leaky=0.2, norm_type=None, **conv_kwargs)]#norm_type=None?
cur_size, cur_ftrs = in_si... | [
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20,588 | fastai/fastai | fastai/vision/gan.py | basic_generator | def basic_generator(in_size:int, n_channels:int, noise_sz:int=100, n_features:int=64, n_extra_layers=0, **conv_kwargs):
"A basic generator from `noise_sz` to images `n_channels` x `in_size` x `in_size`."
cur_size, cur_ftrs = 4, n_features//2
while cur_size < in_size: cur_size *= 2; cur_ftrs *= 2
layers... | python | def basic_generator(in_size:int, n_channels:int, noise_sz:int=100, n_features:int=64, n_extra_layers=0, **conv_kwargs):
"A basic generator from `noise_sz` to images `n_channels` x `in_size` x `in_size`."
cur_size, cur_ftrs = 4, n_features//2
while cur_size < in_size: cur_size *= 2; cur_ftrs *= 2
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] | 9fb84a5cdefe5a766cdb792b8f5d8971737b7e67 | https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/gan.py#L28-L39 |
20,589 | fastai/fastai | fastai/vision/gan.py | gan_loss_from_func | def gan_loss_from_func(loss_gen, loss_crit, weights_gen:Tuple[float,float]=None):
"Define loss functions for a GAN from `loss_gen` and `loss_crit`."
def _loss_G(fake_pred, output, target, weights_gen=weights_gen):
ones = fake_pred.new_ones(fake_pred.shape[0])
weights_gen = ifnone(weights_gen, (1... | python | def gan_loss_from_func(loss_gen, loss_crit, weights_gen:Tuple[float,float]=None):
"Define loss functions for a GAN from `loss_gen` and `loss_crit`."
def _loss_G(fake_pred, output, target, weights_gen=weights_gen):
ones = fake_pred.new_ones(fake_pred.shape[0])
weights_gen = ifnone(weights_gen, (1... | [
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20,590 | fastai/fastai | fastai/vision/gan.py | gan_critic | def gan_critic(n_channels:int=3, nf:int=128, n_blocks:int=3, p:int=0.15):
"Critic to train a `GAN`."
layers = [
_conv(n_channels, nf, ks=4, stride=2),
nn.Dropout2d(p/2),
res_block(nf, dense=True,**_conv_args)]
nf *= 2 # after dense block
for i in range(n_blocks):
layers +... | python | def gan_critic(n_channels:int=3, nf:int=128, n_blocks:int=3, p:int=0.15):
"Critic to train a `GAN`."
layers = [
_conv(n_channels, nf, ks=4, stride=2),
nn.Dropout2d(p/2),
res_block(nf, dense=True,**_conv_args)]
nf *= 2 # after dense block
for i in range(n_blocks):
layers +... | [
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20,591 | fastai/fastai | fastai/vision/gan.py | accuracy_thresh_expand | def accuracy_thresh_expand(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
"Compute accuracy after expanding `y_true` to the size of `y_pred`."
if sigmoid: y_pred = y_pred.sigmoid()
return ((y_pred>thresh)==y_true[:,None].expand_as(y_pred).byte()).float().mean() | python | def accuracy_thresh_expand(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
"Compute accuracy after expanding `y_true` to the size of `y_pred`."
if sigmoid: y_pred = y_pred.sigmoid()
return ((y_pred>thresh)==y_true[:,None].expand_as(y_pred).byte()).float().mean() | [
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20,592 | fastai/fastai | fastai/vision/gan.py | GANModule.switch | def switch(self, gen_mode:bool=None):
"Put the model in generator mode if `gen_mode`, in critic mode otherwise."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode | python | def switch(self, gen_mode:bool=None):
"Put the model in generator mode if `gen_mode`, in critic mode otherwise."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode | [
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20,593 | fastai/fastai | fastai/vision/gan.py | GANLoss.generator | def generator(self, output, target):
"Evaluate the `output` with the critic then uses `self.loss_funcG` to combine it with `target`."
fake_pred = self.gan_model.critic(output)
return self.loss_funcG(fake_pred, target, output) | python | def generator(self, output, target):
"Evaluate the `output` with the critic then uses `self.loss_funcG` to combine it with `target`."
fake_pred = self.gan_model.critic(output)
return self.loss_funcG(fake_pred, target, output) | [
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20,594 | fastai/fastai | fastai/vision/gan.py | GANLoss.critic | def critic(self, real_pred, input):
"Create some `fake_pred` with the generator from `input` and compare them to `real_pred` in `self.loss_funcD`."
fake = self.gan_model.generator(input.requires_grad_(False)).requires_grad_(True)
fake_pred = self.gan_model.critic(fake)
return self.loss_f... | python | def critic(self, real_pred, input):
"Create some `fake_pred` with the generator from `input` and compare them to `real_pred` in `self.loss_funcD`."
fake = self.gan_model.generator(input.requires_grad_(False)).requires_grad_(True)
fake_pred = self.gan_model.critic(fake)
return self.loss_f... | [
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20,595 | fastai/fastai | fastai/vision/gan.py | GANTrainer.on_train_begin | def on_train_begin(self, **kwargs):
"Create the optimizers for the generator and critic if necessary, initialize smootheners."
if not getattr(self,'opt_gen',None):
self.opt_gen = self.opt.new([nn.Sequential(*flatten_model(self.generator))])
else: self.opt_gen.lr,self.opt_gen.wd = sel... | python | def on_train_begin(self, **kwargs):
"Create the optimizers for the generator and critic if necessary, initialize smootheners."
if not getattr(self,'opt_gen',None):
self.opt_gen = self.opt.new([nn.Sequential(*flatten_model(self.generator))])
else: self.opt_gen.lr,self.opt_gen.wd = sel... | [
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20,596 | fastai/fastai | fastai/vision/gan.py | GANTrainer.on_batch_begin | def on_batch_begin(self, last_input, last_target, **kwargs):
"Clamp the weights with `self.clip` if it's not None, return the correct input."
if self.clip is not None:
for p in self.critic.parameters(): p.data.clamp_(-self.clip, self.clip)
return {'last_input':last_input,'last_target... | python | def on_batch_begin(self, last_input, last_target, **kwargs):
"Clamp the weights with `self.clip` if it's not None, return the correct input."
if self.clip is not None:
for p in self.critic.parameters(): p.data.clamp_(-self.clip, self.clip)
return {'last_input':last_input,'last_target... | [
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20,597 | fastai/fastai | fastai/vision/gan.py | GANTrainer.on_backward_begin | def on_backward_begin(self, last_loss, last_output, **kwargs):
"Record `last_loss` in the proper list."
last_loss = last_loss.detach().cpu()
if self.gen_mode:
self.smoothenerG.add_value(last_loss)
self.glosses.append(self.smoothenerG.smooth)
self.last_gen = la... | python | def on_backward_begin(self, last_loss, last_output, **kwargs):
"Record `last_loss` in the proper list."
last_loss = last_loss.detach().cpu()
if self.gen_mode:
self.smoothenerG.add_value(last_loss)
self.glosses.append(self.smoothenerG.smooth)
self.last_gen = la... | [
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20,598 | fastai/fastai | fastai/vision/gan.py | GANTrainer.on_epoch_end | def on_epoch_end(self, pbar, epoch, last_metrics, **kwargs):
"Put the various losses in the recorder and show a sample image."
if not hasattr(self, 'last_gen') or not self.show_img: return
data = self.learn.data
img = self.last_gen[0]
norm = getattr(data,'norm',False)
if ... | python | def on_epoch_end(self, pbar, epoch, last_metrics, **kwargs):
"Put the various losses in the recorder and show a sample image."
if not hasattr(self, 'last_gen') or not self.show_img: return
data = self.learn.data
img = self.last_gen[0]
norm = getattr(data,'norm',False)
if ... | [
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20,599 | fastai/fastai | fastai/vision/gan.py | GANTrainer.switch | def switch(self, gen_mode:bool=None):
"Switch the model, if `gen_mode` is provided, in the desired mode."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode
self.opt.opt = self.opt_gen.opt if self.gen_mode else self.opt_critic.opt
self._set_trainable()
self.mod... | python | def switch(self, gen_mode:bool=None):
"Switch the model, if `gen_mode` is provided, in the desired mode."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode
self.opt.opt = self.opt_gen.opt if self.gen_mode else self.opt_critic.opt
self._set_trainable()
self.mod... | [
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