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Make `p` listy and the same length as `q`.
def listify(p:OptListOrItem=None, q:OptListOrItem=None):
"Make `p` listy and the same length as `q`."
if p is None: p=[]
elif isinstance(p, str): p = [p]
elif not isinstance(p, Iterable): p = [p]
#Rank 0 tensors in PyTorch are Iterable but don't h... |
Change `name` from camel to snake style.
def camel2snake(name:str)->str:
"Change `name` from camel to snake style."
s1 = re.sub(_camel_re1, r'\1_\2', name)
return re.sub(_camel_re2, r'\1_\2', s1).lower() |
Build log-stepped array from `start` to `stop` in `n` steps.
def even_mults(start:float, stop:float, n:int)->np.ndarray:
"Build log-stepped array from `start` to `stop` in `n` steps."
mult = stop/start
step = mult**(1/(n-1))
return np.array([start*(step**i) for i in range(n)]) |
Extract the keys in `names` from the `kwargs`.
def extract_kwargs(names:Collection[str], kwargs:KWArgs):
"Extract the keys in `names` from the `kwargs`."
new_kwargs = {}
for arg_name in names:
if arg_name in kwargs:
arg_val = kwargs.pop(arg_name)
new_kwargs[arg_name] = arg_v... |
Split iterables `a` in equal parts of size `sz`
def partition(a:Collection, sz:int)->List[Collection]:
"Split iterables `a` in equal parts of size `sz`"
return [a[i:i+sz] for i in range(0, len(a), sz)] |
Split data in `a` equally among `n_cpus` cores
def partition_by_cores(a:Collection, n_cpus:int)->List[Collection]:
"Split data in `a` equally among `n_cpus` cores"
return partition(a, len(a)//n_cpus + 1) |
Categorifies the columns `col_names` in `df`.
def series2cat(df:DataFrame, *col_names):
"Categorifies the columns `col_names` in `df`."
for c in listify(col_names): df[c] = df[c].astype('category').cat.as_ordered() |
Download `url` to `dest` unless it exists and not `overwrite`.
def download_url(url:str, dest:str, overwrite:bool=False, pbar:ProgressBar=None,
show_progress=True, chunk_size=1024*1024, timeout=4, retries=5)->None:
"Download `url` to `dest` unless it exists and not `overwrite`."
if os.path.exi... |
Return `Path(path)/Path(fname)`, `path` defaults to current dir.
def join_path(fname:PathOrStr, path:PathOrStr='.')->Path:
"Return `Path(path)/Path(fname)`, `path` defaults to current dir."
return Path(path)/Path(fname) |
Join `path` to every file name in `fnames`.
def join_paths(fnames:FilePathList, path:PathOrStr='.')->Collection[Path]:
"Join `path` to every file name in `fnames`."
path = Path(path)
return [join_path(o,path) for o in fnames] |
Return `ndarray` of `str` of lines of text from `path`.
def loadtxt_str(path:PathOrStr)->np.ndarray:
"Return `ndarray` of `str` of lines of text from `path`."
with open(path, 'r') as f: lines = f.readlines()
return np.array([l.strip() for l in lines]) |
Save in `fname` the content of `texts`.
def save_texts(fname:PathOrStr, texts:Collection[str]):
"Save in `fname` the content of `texts`."
with open(fname, 'w') as f:
for t in texts: f.write(f'{t}\n') |
Return the column indexes of `names` in `df`.
def df_names_to_idx(names:IntsOrStrs, df:DataFrame):
"Return the column indexes of `names` in `df`."
if not is_listy(names): names = [names]
if isinstance(names[0], int): return names
return [df.columns.get_loc(c) for c in names] |
One-hot encode `x` with `c` classes.
def one_hot(x:Collection[int], c:int):
"One-hot encode `x` with `c` classes."
res = np.zeros((c,), np.float32)
res[listify(x)] = 1.
return res |
Return the slice of `a` corresponding to `idxs`.
def index_row(a:Union[Collection,pd.DataFrame,pd.Series], idxs:Collection[int])->Any:
"Return the slice of `a` corresponding to `idxs`."
if a is None: return a
if isinstance(a,(pd.DataFrame,pd.Series)):
res = a.iloc[idxs]
if isinstance(res,(p... |
Return the arguments of `func`.
def func_args(func)->bool:
"Return the arguments of `func`."
code = func.__code__
return code.co_varnames[:code.co_argcount] |
Split `kwargs` between those expected by `func` and the others.
def split_kwargs_by_func(kwargs, func):
"Split `kwargs` between those expected by `func` and the others."
args = func_args(func)
func_kwargs = {a:kwargs.pop(a) for a in args if a in kwargs}
return func_kwargs, kwargs |
Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`.
def array(a, dtype:type=None, **kwargs)->np.ndarray:
"Same as `np.array` but also handles generators. `kwargs` are passed to `np.array` with `dtype`."
if not isinstance(a, collections.Sized) and not getattr(a,'__arr... |
Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %.
def text2html_table(items:Collection[Collection[str]])->str:
"Put the texts in `items` in an HTML table, `widths` are the widths of the columns in %."
html_code = f"""<table border="1" class="dataframe">"""
html_code +=... |
Call `func` on every element of `arr` in parallel using `max_workers`.
def parallel(func, arr:Collection, max_workers:int=None):
"Call `func` on every element of `arr` in parallel using `max_workers`."
max_workers = ifnone(max_workers, defaults.cpus)
if max_workers<2: results = [func(o,i) for i,o in progre... |
Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`
def subplots(rows:int, cols:int, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, title=None, **kwargs):
"Like `plt.subplots` but with consistent axs shape, `kwargs` passed to `fig.suptitle` with `title`"
fig... |
Return the representation of the first `n_max` elements in `items`.
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(it... |
Create and return a tmp filename, optionally at a specific path. `os.remove` when done with it.
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 |
Compose `funcs`
def compose(funcs:List[Callable])->Callable:
"Compose `funcs`"
def compose_(funcs, x, *args, **kwargs):
for f in listify(funcs): x = f(x, *args, **kwargs)
return x
return partial(compose_, funcs) |
Subclass this method if you want to customize the way this `ItemBase` is shown on `ax`.
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)) |
Init layer parameters.
def init_params(net):
'''Init layer parameters.'''
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
... |
Create a seuence Conv2d->BatchNorm2d->LeakyReLu layer.
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.BatchNo... |
starts with conv layer - `ch_in` channels in - then has `num_blocks` `ResLayer`
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)
] + [... |
Create a Learner for collaborative filtering on `data`.
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=F... |
Create a `DataBunch` suitable for collaborative filtering from `ratings`.
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,
... |
Fetch item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)
def get_idx(self, arr:Collection, is_item:bool=True):
"Fetch item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)"
m = self.model.eval().cpu()
requires_grad(m,False)
... |
Bias for item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)
def bias(self, arr:Collection, is_item:bool=True):
"Bias for item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)"
idx = self.get_idx(arr, is_item)
m = self.model
... |
Bias for item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)
def weight(self, arr:Collection, is_item:bool=True):
"Bias for item or user (based on `is_item`) for all in `arr`. (Set model to `cpu` and no grad.)"
idx = self.get_idx(arr, is_item)
m = self.model
... |
Draws a representation of a random forest in IPython.
Parameters:
-----------
t: The tree you wish to draw
df: The data used to train the tree. This is used to get the names of the features.
def draw_tree(t, df, size=10, ratio=0.6, precision=0):
""" Draws a representation of a random forest in IPyt... |
Gets a random sample of n rows from df, without replacement.
Parameters:
-----------
df: A pandas data frame, that you wish to sample from.
n: The number of rows you wish to sample.
Returns:
--------
return value: A random sample of n rows of df.
Examples:
---------
>>> df = pd.D... |
add_datepart converts a column of df from a datetime64 to many columns containing
the information from the date. This applies changes inplace.
Parameters:
-----------
df: A pandas data frame. df gain several new columns.
fldname: A string that is the name of the date column you wish to expand.
... |
Change any columns of strings in a panda's dataframe to a column of
categorical values. This applies the changes inplace.
Parameters:
-----------
df: A pandas dataframe. Any columns of strings will be changed to
categorical values.
Examples:
---------
>>> df = pd.DataFrame({'col1' : ... |
Changes any columns of strings in df into categorical variables using trn as
a template for the category codes.
Parameters:
-----------
df: A pandas dataframe. Any columns of strings will be changed to
categorical values. The category codes are determined by trn.
trn: A pandas dataframe. Whe... |
Fill missing data in a column of df with the median, and add a {name}_na column
which specifies if the data was missing.
Parameters:
-----------
df: The data frame that will be changed.
col: The column of data to fix by filling in missing data.
name: The name of the new filled column in df.
... |
Changes the column col from a categorical type to it's integer codes.
Parameters:
-----------
df: A pandas dataframe. df[name] will be filled with the integer codes from
col.
col: The column you wish to change into the categories.
name: The column name you wish to insert into df. This column... |
proc_df takes a data frame df and splits off the response variable, and
changes the df into an entirely numeric dataframe. For each column of df
which is not in skip_flds nor in ignore_flds, na values are replaced by the
median value of the column.
Parameters:
-----------
df: The data frame you... |
Changes Scikit learn's random forests to give each tree a random sample of
n random rows.
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_sta... |
Undoes the changes produced by set_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)) |
Return globally assigned variables.
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 n... |
Execute notebook `fname` with `metadata` for preprocessing.
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) a... |
Create the documentation notebook for module `mod_name` in path `dest_path`
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_c... |
Search a given `path_dir` and return all the modules contained inside except those in `exclude`
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_... |
Read a notebook in `fname` and return its corresponding json
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) |
Build a dictionary containing the position of the `cells`.
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']):
... |
Create documentation links for all cells in markdown with backticks.
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, ... |
Return the position to insert a given function doc in a notebook.
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
... |
Update the `pos_dict` by moving all positions after `start_key` by `nbr`.
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 p... |
Insert the function doc `cells` at their correct position and updates `pos_dict`.
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_c... |
Creates jekyll metadata for given notebook path.
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}
... |
Finds all submodules of notebook - sorted by submodules > top level modules > manual imports. This gives notebook imports priority
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"... |
Update the documentation notebook of a given module.
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... |
`source_path` can be a directory or a file. Assume all modules reside in the fastai directory.
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... |
Return a dropout mask of the same type as `x`, size `sz`, with probability `p` to cancel an element.
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) |
Split a RNN `model` in groups for differential learning rates.
def awd_lstm_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
groups = [[rnn, dp] for rnn, dp in zip(model[0].rnns, model[0].hidden_dps)]
return groups + [[model[0].encoder, model[0]... |
Convert a value `x` from 0 to 1 (inclusive) to an RGBA tuple according to `cmap` times transparency `alpha_mult`.
def value2rgba(x:float, cmap:Callable=cm.RdYlGn, alpha_mult:float=1.0)->Tuple:
"Convert a value `x` from 0 to 1 (inclusive) to an RGBA tuple according to `cmap` times transparency `alpha_mult`."
c ... |
Apply dropout to the raw weights.
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) |
Return one hidden state.
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_() |
Reset the hidden states.
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.... |
Calculate the intrinsic attention of the input w.r.t to an output `class_id`, or the classification given by the model if `None`.
For reference, see the Sequential Jacobian session at https://www.cs.toronto.edu/~graves/preprint.pdf
def intrinsic_attention(self, text:str, class_id:int=None):
"""Calculat... |
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.
def show_top_losses(self, k:int, max_len:int=70)->None:
"""
Create a tabulation showing the first `k` tex... |
Initialize the schedulers for training.
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.sched... |
Take a step in lr,mom sched, start next stepper when the current one is complete.
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... |
Like `torch.as_tensor`, but handle lists too, and can pass multiple vector elements directly.
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; T... |
Recursively detach lists of tensors in `b `; put them on the CPU if `cpu=True`.
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.de... |
Recursively map lists of items in `b ` to their wrapped 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 |
Recursively map lists of tensors in `b ` to the 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 |
Recursively map lists of tensors in `b ` to FP16.
def to_half(b:Collection[Tensor])->Collection[Tensor]:
"Recursively map lists of tensors in `b ` to FP16."
if is_listy(b): return [to_half(o) for o in b]
return b.half() if b.dtype not in [torch.int64, torch.int32, torch.int16] else b |
Recursively map lists of tensors in `b ` to FP16.
def to_float(b:Collection[Tensor])->Collection[Tensor]:
"Recursively map lists of tensors in `b ` to FP16."
if is_listy(b): return [to_float(o) for o in b]
return b.float() if b.dtype not in [torch.int64, torch.int32, torch.int16] else b |
Recursively put `b` on `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(devi... |
Convert `batch` items to tensor data.
def data_collate(batch:ItemsList)->Tensor:
"Convert `batch` items to tensor data."
return torch.utils.data.dataloader.default_collate(to_data(batch)) |
If `b` is not set return `requires_grad` of first param, else set `requires_grad` on all params as `b`
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... |
Return list of trainable params in `m`.
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 |
Return the children of `m` and its direct parameters not registered in modules.
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()],... |
Split `model` according to the indexes in `idxs`.
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 ... |
Split `model` according to the layers in `splits`.
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)
idx... |
Separate the parameters in `layer_groups` between `no_wd_types` and bias (`bias_types`) from the rest.
def split_no_wd_params(layer_groups:Collection[nn.Module])->List[List[nn.Parameter]]:
"Separate the parameters in `layer_groups` between `no_wd_types` and bias (`bias_types`) from the rest."
split_params = ... |
Set bn layers in eval mode for all recursive children of `m`.
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) |
If `module` is batchnorm don't use half precision.
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 |
Initialize `m` weights with `func` and set `bias` to 0.
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.bi... |
Initialize the non-batchnorm layers of `m` with `init_func`.
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) |
Initialize all non-batchnorm layers of `m` with `init_func`.
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)) |
Return the shape of the first weight layer in `m`.
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') |
Return the torch type corresponding to `dtype`.
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) |
Tranform numpy array `a` to a tensor of the same type.
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) |
Compute PCA of `x` with `k` dimensions.
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]) |
Grab the `i`-th batch in `x`, `batch_first` stating the batch dimension.
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(... |
Inplace logit of `x`, clamped to avoid inf
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_() |
Draw 1 or shape=`size` random floats from uniform dist: min=`low`, max=`high`.
def uniform(low:Number, high:Number=None, size:Optional[List[int]]=None)->FloatOrTensor:
"Draw 1 or shape=`size` random floats from uniform dist: min=`low`, max=`high`."
if high is None: high=low
return random.uniform(low,high) ... |
Draw 1 or shape=`size` random floats from uniform dist: min=log(`low`), max=log(`high`).
def log_uniform(low, high, size:Optional[List[int]]=None)->FloatOrTensor:
"Draw 1 or shape=`size` random floats from uniform dist: min=log(`low`), max=log(`high`)."
res = uniform(log(low), log(high), size)
return exp(r... |
Draw 1 or shape=`size` random booleans (`True` occuring with probability `p`).
def rand_bool(p:float, size:Optional[List[int]]=None)->BoolOrTensor:
"Draw 1 or shape=`size` random booleans (`True` occuring with probability `p`)."
return uniform(0,1,size)<p |
Generate int or tensor `size` of ints between `low` and `high` (included).
def uniform_int(low:int, high:int, size:Optional[List[int]]=None)->IntOrTensor:
"Generate int or tensor `size` of ints between `low` and `high` (included)."
return random.randint(low,high) if size is None else torch.randint(low,high+1,s... |
Try to convert `o` to int, default to `o` if not possible.
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)
... |
Return the model maybe wrapped inside `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 |
Check that `out` and `targ` have the same number of elements and flatten them.
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),... |
create new OrderedDict that does not contain `module.`
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 |
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