INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Function is used to get a message from the iopub channel.
Timeout is None by default
When timeout is reached | def get_message(self, stream, timeout=None):
"""
Function is used to get a message from the iopub channel.
Timeout is None by default
When timeout is reached
"""
try:
if stream == 'iopub':
msg = self.kc.get_iopub_msg(timeout=timeout)
... |
Executes a string of python code in cell input.
We do not allow the kernel to make requests to the stdin
this is the norm for notebooks
Function returns a unique message id of the reply from
the kernel. | def execute_cell_input(self, cell_input, allow_stdin=None):
"""
Executes a string of python code in cell input.
We do not allow the kernel to make requests to the stdin
this is the norm for notebooks
Function returns a unique message id of the reply from
the kernel.... |
Continuously poll the kernel 'shell' stream for messages until:
- It receives an 'execute_reply' status for the given message id
- The timeout is reached awaiting a message, in which case
a `Queue.Empty` exception will be raised. | def await_reply(self, msg_id, timeout=None):
"""
Continuously poll the kernel 'shell' stream for messages until:
- It receives an 'execute_reply' status for the given message id
- The timeout is reached awaiting a message, in which case
a `Queue.Empty` exception will be rais... |
Poll the iopub stream until an idle message is received for the given parent ID | def await_idle(self, parent_id, timeout):
"""Poll the iopub stream until an idle message is received for the given parent ID"""
while True:
# Get a message from the kernel iopub channel
msg = self.get_message(timeout=timeout, stream='iopub') # raises Empty on timeout!
... |
Instructs the kernel process to stop channels
and the kernel manager to then shutdown the process. | def stop(self):
"""
Instructs the kernel process to stop channels
and the kernel manager to then shutdown the process.
"""
logger.debug('Stopping kernel')
self.kc.stop_channels()
self.km.shutdown_kernel(now=True)
del self.km |
Get a list of index values for Validation set from a dataset
Arguments:
n : int, Total number of elements in the data set.
cv_idx : int, starting index [idx_start = cv_idx*int(val_pct*n)]
val_pct : (int, float), validation set percentage
seed : seed value for RandomState
... | def get_cv_idxs(n, cv_idx=0, val_pct=0.2, seed=42):
""" Get a list of index values for Validation set from a dataset
Arguments:
n : int, Total number of elements in the data set.
cv_idx : int, starting index [idx_start = cv_idx*int(val_pct*n)]
val_pct : (int, float), validation set... |
Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ. | def resize_img(fname, targ, path, new_path, fn=None):
"""
Enlarge or shrink a single image to scale, such that the smaller of the height or width dimension is equal to targ.
"""
if fn is None:
fn = resize_fn(targ)
dest = os.path.join(path_for(path, new_path, targ), fname)
if os.path.exis... |
Enlarge or shrink a set of images in the same directory to scale, such that the smaller of the height or width dimension is equal to targ.
Note:
-- This function is multithreaded for efficiency.
-- When destination file or folder already exist, function exists without raising an error. | def resize_imgs(fnames, targ, path, new_path, resume=True, fn=None):
"""
Enlarge or shrink a set of images in the same directory to scale, such that the smaller of the height or width dimension is equal to targ.
Note:
-- This function is multithreaded for efficiency.
-- When destination file or fo... |
Returns a list of relative file paths to `path` for all files within `folder` | def read_dir(path, folder):
""" Returns a list of relative file paths to `path` for all files within `folder` """
full_path = os.path.join(path, folder)
fnames = glob(f"{full_path}/*.*")
directories = glob(f"{full_path}/*/")
if any(fnames):
return [os.path.relpath(f,path) for f in fnames]
... |
Fetches name of all files in path in long form, and labels associated by extrapolation of directory names. | def read_dirs(path, folder):
'''
Fetches name of all files in path in long form, and labels associated by extrapolation of directory names.
'''
lbls, fnames, all_lbls = [], [], []
full_path = os.path.join(path, folder)
for lbl in sorted(os.listdir(full_path)):
if lbl not in ('.ipynb_che... |
one hot encoding by index. Returns array of length c, where all entries are 0, except for the indecies in ids | def n_hot(ids, c):
'''
one hot encoding by index. Returns array of length c, where all entries are 0, except for the indecies in ids
'''
res = np.zeros((c,), dtype=np.float32)
res[ids] = 1
return res |
Returns the filenames and labels for a folder within a path
Returns:
-------
fnames: a list of the filenames within `folder`
all_lbls: a list of all of the labels in `folder`, where the # of labels is determined by the # of directories within `folder`
lbl_arr: a numpy array of the label indices... | def folder_source(path, folder):
"""
Returns the filenames and labels for a folder within a path
Returns:
-------
fnames: a list of the filenames within `folder`
all_lbls: a list of all of the labels in `folder`, where the # of labels is determined by the # of directories within `folder`
... |
Parse filenames and label sets from a CSV file.
This method expects that the csv file at path :fn: has two columns. If it
has a header, :skip_header: should be set to True. The labels in the
label set are expected to be space separated.
Arguments:
fn: Path to a CSV file.
skip_header: A... | def parse_csv_labels(fn, skip_header=True, cat_separator = ' '):
"""Parse filenames and label sets from a CSV file.
This method expects that the csv file at path :fn: has two columns. If it
has a header, :skip_header: should be set to True. The labels in the
label set are expected to be space separated... |
True if the fn points to a DICOM image | def isdicom(fn):
'''True if the fn points to a DICOM image'''
fn = str(fn)
if fn.endswith('.dcm'):
return True
# Dicom signature from the dicom spec.
with open(fn,'rb') as fh:
fh.seek(0x80)
return fh.read(4)==b'DICM' |
Opens an image using OpenCV given the file path.
Arguments:
fn: the file path of the image
Returns:
The image in RGB format as numpy array of floats normalized to range between 0.0 - 1.0 | def open_image(fn):
""" Opens an image using OpenCV given the file path.
Arguments:
fn: the file path of the image
Returns:
The image in RGB format as numpy array of floats normalized to range between 0.0 - 1.0
"""
flags = cv2.IMREAD_UNCHANGED+cv2.IMREAD_ANYDEPTH+cv2.IMREAD_ANYCOLO... |
Split each array passed as *a, to a pair of arrays like this (elements selected by idxs, the remaining elements)
This can be used to split multiple arrays containing training data to validation and training set.
:param idxs [int]: list of indexes selected
:param a list: list of np.array, each array should... | def split_by_idx(idxs, *a):
"""
Split each array passed as *a, to a pair of arrays like this (elements selected by idxs, the remaining elements)
This can be used to split multiple arrays containing training data to validation and training set.
:param idxs [int]: list of indexes selected
:param a l... |
resize all images in the dataset and save them to `new_path`
Arguments:
targ (int): the target size
new_path (string): the new folder to save the images
resume (bool): if true (default), allow resuming a partial resize operation by checking for the existence
of individua... | def resize_imgs(self, targ, new_path, resume=True, fn=None):
"""
resize all images in the dataset and save them to `new_path`
Arguments:
targ (int): the target size
new_path (string): the new folder to save the images
resume (bool): if true (default), allow resum... |
Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz) | def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
if len(arr.shape)==3: arr = arr[None]
return self.transform.denorm(np.rollaxis(arr,1,4... |
Return a copy of this dataset resized | def resized(self, dl, targ, new_path, resume = True, fn=None):
"""
Return a copy of this dataset resized
"""
return dl.dataset.resize_imgs(targ, new_path, resume=resume, fn=fn) if dl else None |
Resizes all the images in the train, valid, test folders to a given size.
Arguments:
targ_sz (int): the target size
new_path (str): the path to save the resized images (default tmp)
resume (bool): if True, check for images in the DataSet that haven't been resized yet (useful if a previo... | def resize(self, targ_sz, new_path='tmp', resume=True, fn=None):
"""
Resizes all the images in the train, valid, test folders to a given size.
Arguments:
targ_sz (int): the target size
new_path (str): the path to save the resized images (default tmp)
resume (bool): if Tr... |
Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
trn: a tuple of training data matrix and target label/classification array (e.g. `trn=(x,y)` where `x` has the
shape ... | def from_arrays(cls, path, trn, val, bs=64, tfms=(None,None), classes=None, num_workers=4, test=None, continuous=False):
""" Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
... |
Read in images and their labels given as sub-folder names
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
trn_name:... | def from_paths(cls, path, bs=64, tfms=(None,None), trn_name='train', val_name='valid', test_name=None, test_with_labels=False, num_workers=8):
""" Read in images and their labels given as sub-folder names
Arguments:
path: a root path of the data (used for storing trained models, precomputed... |
Read in images and their labels given as a CSV file.
This method should be used when training image labels are given in an CSV file as opposed to
sub-directories with label names.
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
... | def from_csv(cls, path, folder, csv_fname, bs=64, tfms=(None,None),
val_idxs=None, suffix='', test_name=None, continuous=False, skip_header=True, num_workers=8, cat_separator=' '):
""" Read in images and their labels given as a CSV file.
This method should be used when training image lab... |
Read in images given a sub-folder and their labels given a numpy array
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
y: numpy array which contains targe... | def from_path_and_array(cls, path, folder, y, classes=None, val_idxs=None, test_name=None,
num_workers=8, tfms=(None,None), bs=64):
""" Read in images given a sub-folder and their labels given a numpy array
Arguments:
path: a root path of the data (used for storing trained model... |
Is the code running in the ipython environment (jupyter including) | def is_in_ipython():
"Is the code running in the ipython environment (jupyter including)"
program_name = os.path.basename(os.getenv('_', ''))
if ('jupyter-notebook' in program_name or # jupyter-notebook
'ipython' in program_name or # ipython
'JPY_PARENT_PID' in os.environ): #... |
Free traceback from references to locals() in each frame to avoid circular reference leading to gc.collect() unable to reclaim memory | def get_ref_free_exc_info():
"Free traceback from references to locals() in each frame to avoid circular reference leading to gc.collect() unable to reclaim memory"
type, val, tb = sys.exc_info()
traceback.clear_frames(tb)
return (type, val, tb) |
Reclaim GPU RAM if CUDA out of memory happened, or execution was interrupted | def gpu_mem_restore(func):
"Reclaim GPU RAM if CUDA out of memory happened, or execution was interrupted"
@functools.wraps(func)
def wrapper(*args, **kwargs):
tb_clear_frames = os.environ.get('FASTAI_TB_CLEAR_FRAMES', None)
if not IS_IN_IPYTHON or tb_clear_frames=="0":
return fun... |
Fits a model
Arguments:
model (model): any pytorch module
net = to_gpu(net)
data (ModelData): see ModelData class and subclasses (can be a list)
opts: an optimizer. Example: optim.Adam.
If n_epochs is a list, it needs to be the layer_optimizer to get the optimizer as it chan... | def fit(model, data, n_epochs, opt, crit, metrics=None, callbacks=None, stepper=Stepper,
swa_model=None, swa_start=None, swa_eval_freq=None, visualize=False, **kwargs):
""" Fits a model
Arguments:
model (model): any pytorch module
net = to_gpu(net)
data (ModelData): see ModelDa... |
Computes the loss on the next minibatch of the validation set. | def validate_next(stepper, metrics, val_iter):
"""Computes the loss on the next minibatch of the validation set."""
stepper.reset(False)
with no_grad_context():
(*x,y) = val_iter.next()
preds,l = stepper.evaluate(VV(x), VV(y))
res = [delistify(to_np(l))]
res += [f(datafy(pred... |
Create link to documentation. | def link_type(arg_type, arg_name=None, include_bt:bool=True):
"Create link to documentation."
arg_name = arg_name or fn_name(arg_type)
if include_bt: arg_name = code_esc(arg_name)
if belongs_to_module(arg_type, 'torch') and ('Tensor' not in arg_name): return f'[{arg_name}]({get_pytorch_link(arg_type)})'... |
Check if `t` belongs to `module_name`. | def belongs_to_module(t, module_name):
"Check if `t` belongs to `module_name`."
if hasattr(t, '__func__'): return belongs_to_module(t.__func__, module_name)
if not inspect.getmodule(t): return False
return inspect.getmodule(t).__name__.startswith(module_name) |
Formats function param to `param1:Type=val`. Font weights: param1=bold, val=bold+italic | def format_param(p):
"Formats function param to `param1:Type=val`. Font weights: param1=bold, val=bold+italic"
arg_prefix = arg_prefixes.get(p.kind, '') # asterisk prefix for *args and **kwargs
res = f"**{arg_prefix}{code_esc(p.name)}**"
if hasattr(p, 'annotation') and p.annotation != p.empty: res += f'... |
Format and link `func` definition to show in documentation | def format_ft_def(func, full_name:str=None)->str:
"Format and link `func` definition to show in documentation"
sig = inspect.signature(func)
name = f'<code>{full_name or func.__name__}</code>'
fmt_params = [format_param(param) for name,param
in sig.parameters.items() if name not in ('s... |
Formatted enum documentation. | def get_enum_doc(elt, full_name:str)->str:
"Formatted enum documentation."
vals = ', '.join(elt.__members__.keys())
return f'{code_esc(full_name)}',f'<code>Enum</code> = [{vals}]' |
Class definition. | def get_cls_doc(elt, full_name:str)->str:
"Class definition."
parent_class = inspect.getclasstree([elt])[-1][0][1][0]
name,args = format_ft_def(elt, full_name)
if parent_class != object: args += f' :: {link_type(parent_class, include_bt=True)}'
return name,args |
Show documentation for element `elt`. Supported types: class, Callable, and enum. | def show_doc(elt, doc_string:bool=True, full_name:str=None, arg_comments:dict=None, title_level=None, alt_doc_string:str='',
ignore_warn:bool=False, markdown=True, show_tests=True):
"Show documentation for element `elt`. Supported types: class, Callable, and enum."
arg_comments = ifnone(arg_comment... |
Show `show_doc` info in preview window along with link to full docs. | def doc(elt):
"Show `show_doc` info in preview window along with link to full docs."
global use_relative_links
use_relative_links = False
elt = getattr(elt, '__func__', elt)
md = show_doc(elt, markdown=False)
if is_fastai_class(elt):
md += f'\n\n<a href="{get_fn_link(elt)}" target="_blan... |
Merge and format the docstring definition with `arg_comments` and `alt_doc_string`. | def format_docstring(elt, arg_comments:dict={}, alt_doc_string:str='', ignore_warn:bool=False)->str:
"Merge and format the docstring definition with `arg_comments` and `alt_doc_string`."
parsed = ""
doc = parse_docstring(inspect.getdoc(elt))
description = alt_doc_string or f"{doc['short_description']} {... |
Search `docstring` for backticks and attempt to link those functions to respective documentation. | def link_docstring(modules, docstring:str, overwrite:bool=False)->str:
"Search `docstring` for backticks and attempt to link those functions to respective documentation."
mods = listify(modules)
for mod in mods: _modvars.update(mod.__dict__) # concat all module definitions
return re.sub(BT_REGEX, replac... |
Attempt to resolve keywords such as Learner.lr_find. `match_last` starts matching from last component. | def find_elt(modvars, keyword, match_last=False):
"Attempt to resolve keywords such as Learner.lr_find. `match_last` starts matching from last component."
keyword = strip_fastai(keyword)
if keyword in modvars: return modvars[keyword]
comps = keyword.split('.')
comp_elt = modvars.get(comps[0])
if... |
Return module from `mod_name`. | def import_mod(mod_name:str, ignore_errors=False):
"Return module from `mod_name`."
splits = str.split(mod_name, '.')
try:
if len(splits) > 1 : mod = importlib.import_module('.' + '.'.join(splits[1:]), splits[0])
else: mod = importlib.import_module(mod_name)
return mod
except:
... |
Show documentation for `ft_name`, see `show_doc`. | def show_doc_from_name(mod_name, ft_name:str, doc_string:bool=True, arg_comments:dict={}, alt_doc_string:str=''):
"Show documentation for `ft_name`, see `show_doc`."
mod = import_mod(mod_name)
splits = str.split(ft_name, '.')
assert hasattr(mod, splits[0]), print(f"Module {mod_name} doesn't have a funct... |
Return all the functions of module `mod`. | def get_ft_names(mod, include_inner=False)->List[str]:
"Return all the functions of module `mod`."
# If the module has an attribute __all__, it picks those.
# Otherwise, it returns all the functions defined inside a module.
fn_names = []
for elt_name in get_exports(mod):
elt = getattr(mod,el... |
List the inner functions of a class. | def get_inner_fts(elt)->List[str]:
"List the inner functions of a class."
fts = []
for ft_name in elt.__dict__.keys():
if ft_name.startswith('_'): continue
ft = getattr(elt, ft_name)
if inspect.isfunction(ft): fts.append(f'{elt.__name__}.{ft_name}')
if inspect.ismethod(ft): f... |
Display table of contents for given `mod_name`. | def get_module_toc(mod_name):
"Display table of contents for given `mod_name`."
mod = import_mod(mod_name)
ft_names = mod.__all__ if hasattr(mod,'__all__') else get_ft_names(mod)
ft_names.sort(key = str.lower)
tabmat = ''
for ft_name in ft_names:
tabmat += f'- [{ft_name}](#{ft_name})\n'
... |
Return function link to notebook documentation of `ft`. Private functions link to source code | def get_fn_link(ft)->str:
"Return function link to notebook documentation of `ft`. Private functions link to source code"
ft = getattr(ft, '__func__', ft)
anchor = strip_fastai(get_anchor(ft))
module_name = strip_fastai(get_module_name(ft))
base = '' if use_relative_links else FASTAI_DOCS
return... |
Returns link to pytorch docs of `ft`. | def get_pytorch_link(ft)->str:
"Returns link to pytorch docs of `ft`."
name = ft.__name__
ext = '.html'
if name == 'device': return f'{PYTORCH_DOCS}tensor_attributes{ext}#torch-device'
if name == 'Tensor': return f'{PYTORCH_DOCS}tensors{ext}#torch-tensor'
if name.startswith('torchvision'):
... |
Returns github link for given file | def get_source_link(file, line, display_text="[source]", **kwargs)->str:
"Returns github link for given file"
link = f"{SOURCE_URL}{file}#L{line}"
if display_text is None: return link
return f'<a href="{link}" class="source_link" style="float:right">{display_text}</a>' |
Returns link to `ft` in source code. | def get_function_source(ft, **kwargs)->str:
"Returns link to `ft` in source code."
try: line = inspect.getsourcelines(ft)[1]
except Exception: return ''
mod_path = get_module_name(ft).replace('.', '/') + '.py'
return get_source_link(mod_path, line, **kwargs) |
Look through the cell source for comments which affect nbval's behaviour
Yield an iterable of ``(MARKER_TYPE, True)``. | def find_comment_markers(cellsource):
"""Look through the cell source for comments which affect nbval's behaviour
Yield an iterable of ``(MARKER_TYPE, True)``.
"""
found = {}
for line in cellsource.splitlines():
line = line.strip()
if line.startswith('#'):
# print("Found... |
Merge all stream outputs with shared names into single streams
to ensure deterministic outputs.
Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed | def coalesce_streams(outputs):
"""
Merge all stream outputs with shared names into single streams
to ensure deterministic outputs.
Parameters
----------
outputs : iterable of NotebookNodes
Outputs being processed
"""
if not outputs:
return outputs
new_outputs = []
... |
Trim and hash base64 strings | def _trim_base64(s):
"""Trim and hash base64 strings"""
if len(s) > 64 and _base64.match(s.replace('\n', '')):
h = hash_string(s)
s = '%s...<snip base64, md5=%s...>' % (s[:8], h[:16])
return s |
Intent each line with indent | def _indent(s, indent=' '):
"""Intent each line with indent"""
if isinstance(s, six.string_types):
return '\n'.join(('%s%s' % (indent, line) for line in s.splitlines()))
return s |
Called by pytest to setup the collector cells in .
Here we start a kernel and setup the sanitize patterns. | def setup(self):
"""
Called by pytest to setup the collector cells in .
Here we start a kernel and setup the sanitize patterns.
"""
if self.parent.config.option.current_env:
kernel_name = CURRENT_ENV_KERNEL_NAME
else:
kernel_name = self.nb.metadat... |
For each of the sanitize files that were specified as command line options
load the contents of the file into the sanitise patterns dictionary. | def setup_sanitize_files(self):
"""
For each of the sanitize files that were specified as command line options
load the contents of the file into the sanitise patterns dictionary.
"""
for fname in self.get_sanitize_files():
with open(fname, 'r') as f:
... |
Return list of all sanitize files provided by the user on the command line.
N.B.: We only support one sanitize file at the moment, but
this is likely to change in the future | def get_sanitize_files(self):
"""
Return list of all sanitize files provided by the user on the command line.
N.B.: We only support one sanitize file at the moment, but
this is likely to change in the future
"""
if self.parent.config.option.sanitize_with is not No... |
Gets a message from the iopub channel of the notebook kernel. | def get_kernel_message(self, timeout=None, stream='iopub'):
"""
Gets a message from the iopub channel of the notebook kernel.
"""
return self.kernel.get_message(stream, timeout=timeout) |
The collect function is required by pytest and is used to yield pytest
Item objects. We specify an Item for each code cell in the notebook. | def collect(self):
"""
The collect function is required by pytest and is used to yield pytest
Item objects. We specify an Item for each code cell in the notebook.
"""
self.nb = nbformat.read(str(self.fspath), as_version=4)
# Start the cell count
cell_num = 0
... |
Format an output for printing | def format_output_compare(self, key, left, right):
"""Format an output for printing"""
if isinstance(left, six.string_types):
left = _trim_base64(left)
if isinstance(right, six.string_types):
right = _trim_base64(right)
cc = self.colors
self.comparison_t... |
called when self.runtest() raises an exception. | def repr_failure(self, excinfo):
""" called when self.runtest() raises an exception. """
exc = excinfo.value
cc = self.colors
if isinstance(exc, NbCellError):
msg_items = [
cc.FAIL + "Notebook cell execution failed" + cc.ENDC]
formatstring = (
... |
sanitize a string for comparison. | def sanitize(self, s):
"""sanitize a string for comparison.
"""
if not isinstance(s, six.string_types):
return s
"""
re.sub matches a regex and replaces it with another.
The regex replacements are taken from a file if the option
is passed when py.test... |
Computes the outputs for several augmented inputs for TTA | def _tta_only(learn:Learner, ds_type:DatasetType=DatasetType.Valid, scale:float=1.35) -> Iterator[List[Tensor]]:
"Computes the outputs for several augmented inputs for TTA"
dl = learn.dl(ds_type)
ds = dl.dataset
old = ds.tfms
augm_tfm = [o for o in learn.data.train_ds.tfms if o.tfm not in
... |
Applies TTA to predict on `ds_type` dataset. | def _TTA(learn:Learner, beta:float=0.4, scale:float=1.35, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False) -> Tensors:
"Applies TTA to predict on `ds_type` dataset."
preds,y = learn.get_preds(ds_type)
all_preds = list(learn.tta_only(scale=scale, ds_type=ds_type))
avg_preds = torch.stack(all_... |
Computes the f_beta between `preds` and `targets` | def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
"Computes the f_beta between `preds` and `targets`"
beta2 = beta ** 2
if sigmoid: y_pred = y_pred.sigmoid()
y_pred = (y_pred>thresh).float()
y_true = y_true.float()
TP = (y_pr... |
Compute accuracy with `targs` when `input` is bs * n_classes. | def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
"Compute accuracy with `targs` when `input` is bs * n_classes."
n = targs.shape[0]
input = input.argmax(dim=-1).view(n,-1)
targs = targs.view(n,-1)
return (input==targs).float().mean() |
Compute accuracy when `y_pred` and `y_true` are the same size. | def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
"Compute accuracy when `y_pred` and `y_true` are the same size."
if sigmoid: y_pred = y_pred.sigmoid()
return ((y_pred>thresh)==y_true.byte()).float().mean() |
Computes the Top-k accuracy (target is in the top k predictions). | def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
"Computes the Top-k accuracy (target is in the top k predictions)."
input = input.topk(k=k, dim=-1)[1]
targs = targs.unsqueeze(dim=-1).expand_as(input)
return (input == targs).max(dim=-1)[0].float().mean() |
Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems. | def dice(input:Tensor, targs:Tensor, iou:bool=False)->Rank0Tensor:
"Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
n = targs.shape[0]
input = input.argmax(dim=1).view(n,-1)
targs = targs.view(n,-1)
intersect = (input * targs).sum().flo... |
Exp RMSE between `pred` and `targ`. | def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Exp RMSE between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
pred, targ = torch.exp(pred), torch.exp(targ)
pct_var = (targ - pred)/targ
return torch.sqrt((pct_var**2).mean()) |
Mean absolute error between `pred` and `targ`. | def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean absolute error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return torch.abs(targ - pred).mean() |
Mean squared error between `pred` and `targ`. | def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean squared error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return F.mse_loss(pred, targ) |
Root mean squared error between `pred` and `targ`. | def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Root mean squared error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return torch.sqrt(F.mse_loss(pred, targ)) |
Mean squared logarithmic error between `pred` and `targ`. | def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Mean squared logarithmic error between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ)) |
Explained variance between `pred` and `targ`. | def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
"Explained variance between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
var_pct = torch.var(targ - pred) / torch.var(targ)
return 1 - var_pct |
R2 score (coefficient of determination) between `pred` and `targ`. | def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
"R2 score (coefficient of determination) between `pred` and `targ`."
pred,targ = flatten_check(pred,targ)
u = torch.sum((targ - pred) ** 2)
d = torch.sum((targ - targ.mean()) ** 2)
return 1 - u / d |
Using trapezoid method to calculate the area under roc curve | def auc_roc_score(input:Tensor, targ:Tensor):
"Using trapezoid method to calculate the area under roc curve"
fpr, tpr = roc_curve(input, targ)
d = fpr[1:] - fpr[:-1]
sl1, sl2 = [slice(None)], [slice(None)]
sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
return (d * (tpr[tuple(sl1)] + tpr[tupl... |
Returns the false positive and true positive rates | def roc_curve(input:Tensor, targ:Tensor):
"Returns the false positive and true positive rates"
targ = (targ == 1)
desc_score_indices = torch.flip(input.argsort(-1), [-1])
input = input[desc_score_indices]
targ = targ[desc_score_indices]
d = input[1:] - input[:-1]
distinct_value_indices = tor... |
convert iterable object into numpy array | def A(*a):
"""convert iterable object into numpy array"""
return np.array(a[0]) if len(a)==1 else [np.array(o) for o in a] |
Convert numpy array into a pytorch tensor.
if Cuda is available and USE_GPU=True, store resulting tensor in GPU. | def T(a, half=False, cuda=True):
"""
Convert numpy array into a pytorch tensor.
if Cuda is available and USE_GPU=True, store resulting tensor in GPU.
"""
if not torch.is_tensor(a):
a = np.array(np.ascontiguousarray(a))
if a.dtype in (np.int8, np.int16, np.int32, np.int64):
... |
equivalent to create_variable, which creates a pytorch tensor | def V_(x, requires_grad=False, volatile=False):
'''equivalent to create_variable, which creates a pytorch tensor'''
return create_variable(x, volatile=volatile, requires_grad=requires_grad) |
creates a single or a list of pytorch tensors, depending on input x. | def V(x, requires_grad=False, volatile=False):
'''creates a single or a list of pytorch tensors, depending on input x. '''
return map_over(x, lambda o: V_(o, requires_grad, volatile)) |
returns an np.array object given an input of np.array, list, tuple, torch variable or tensor. | def to_np(v):
'''returns an np.array object given an input of np.array, list, tuple, torch variable or tensor.'''
if isinstance(v, float): return np.array(v)
if isinstance(v, (np.ndarray, np.generic)): return v
if isinstance(v, (list,tuple)): return [to_np(o) for o in v]
if isinstance(v, Variable): ... |
puts pytorch variable to gpu, if cuda is available and USE_GPU is set to true. | def to_gpu(x, *args, **kwargs):
'''puts pytorch variable to gpu, if cuda is available and USE_GPU is set to true. '''
return x.cuda(*args, **kwargs) if USE_GPU else x |
A generator that returns sequence pieces, seperated by indexes specified in idxs. | def split_by_idxs(seq, idxs):
'''A generator that returns sequence pieces, seperated by indexes specified in idxs. '''
last = 0
for idx in idxs:
if not (-len(seq) <= idx < len(seq)):
raise KeyError(f'Idx {idx} is out-of-bounds')
yield seq[last:idx]
last = idx
yield seq[... |
splits iterables a in equal parts of size sz | def partition(a, sz):
"""splits iterables a in equal parts of size sz"""
return [a[i:i+sz] for i in range(0, len(a), sz)] |
A generator that yields chunks of iterable, chunk_size at a time. | def chunk_iter(iterable, chunk_size):
'''A generator that yields chunks of iterable, chunk_size at a time. '''
while True:
chunk = []
try:
for _ in range(chunk_size): chunk.append(next(iterable))
yield chunk
except StopIteration:
if chunk: yield chunk
... |
Apply `change` in brightness of image `x`. | def _brightness(x, change:uniform):
"Apply `change` in brightness of image `x`."
return x.add_(scipy.special.logit(change)) |
Rotate image by `degrees`. | def _rotate(degrees:uniform):
"Rotate image by `degrees`."
angle = degrees * math.pi / 180
return [[cos(angle), -sin(angle), 0.],
[sin(angle), cos(angle), 0.],
[0. , 0. , 1.]] |
`sw`,`sh` scale width,height - `c`,`r` focus col,row. | def _get_zoom_mat(sw:float, sh:float, c:float, r:float)->AffineMatrix:
"`sw`,`sh` scale width,height - `c`,`r` focus col,row."
return [[sw, 0, c],
[0, sh, r],
[0, 0, 1.]] |
Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom. | def _zoom(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Zoom image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
s = 1-1/scale
col_c = s * (2*col_pct - 1)
row_c = s * (2*row_pct - 1)
return _get_zoom_mat(1/scale, 1/scale, col_c, row_c) |
Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom. | def _squish(scale:uniform=1.0, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Squish image by `scale`. `row_pct`,`col_pct` select focal point of zoom."
if scale <= 1:
col_c = (1-scale) * (2*col_pct - 1)
return _get_zoom_mat(scale, 1, col_c, 0.)
else:
row_c = (1-1/scale) * (2*row_pct - 1... |
Replace pixels by random neighbors at `magnitude`. | def _jitter(c, magnitude:uniform):
"Replace pixels by random neighbors at `magnitude`."
c.flow.add_((torch.rand_like(c.flow)-0.5)*magnitude*2)
return c |
Flip `x` horizontally. | def _flip_lr(x):
"Flip `x` horizontally."
#return x.flip(2)
if isinstance(x, ImagePoints):
x.flow.flow[...,0] *= -1
return x
return tensor(np.ascontiguousarray(np.array(x)[...,::-1])) |
Randomly flip `x` image based on `k`. | def _dihedral(x, k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
flips=[]
if k&1: flips.append(1)
if k&2: flips.append(2)
if flips: x = torch.flip(x,flips)
if k&4: x = x.transpose(1,2)
return x.contiguous() |
Randomly flip `x` image based on `k`. | def _dihedral_affine(k:partial(uniform_int,0,7)):
"Randomly flip `x` image based on `k`."
x = -1 if k&1 else 1
y = -1 if k&2 else 1
if k&4: return [[0, x, 0.],
[y, 0, 0],
[0, 0, 1.]]
return [[x, 0, 0.],
[0, y, 0],
[0, 0, 1.]] |
Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border'). | def _pad_default(x, padding:int, mode='reflection'):
"Pad `x` with `padding` pixels. `mode` fills in space ('zeros','reflection','border')."
mode = _pad_mode_convert[mode]
return F.pad(x[None], (padding,)*4, mode=mode)[0] |
Cut out `n_holes` number of square holes of size `length` in image at random locations. | def _cutout(x, n_holes:uniform_int=1, length:uniform_int=40):
"Cut out `n_holes` number of square holes of size `length` in image at random locations."
h,w = x.shape[1:]
for n in range(n_holes):
h_y = np.random.randint(0, h)
h_x = np.random.randint(0, w)
y1 = int(np.clip(h_y - length... |
Randomize one of the channels of the input image | def _rgb_randomize(x, channel:int=None, thresh:float=0.3):
"Randomize one of the channels of the input image"
if channel is None: channel = np.random.randint(0, x.shape[0] - 1)
x[channel] = torch.rand(x.shape[1:]) * np.random.uniform(0, thresh)
return x |
Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop. | def _crop_default(x, size, row_pct:uniform=0.5, col_pct:uniform=0.5):
"Crop `x` to `size` pixels. `row_pct`,`col_pct` select focal point of crop."
rows,cols = tis2hw(size)
row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
row = int((x.size(1)-rows+1) * row_pct)
col = int((x.size(2)-cols+1) * col_pct... |
Crop and pad tfm - `row_pct`,`col_pct` sets focal point. | def _crop_pad_default(x, size, padding_mode='reflection', row_pct:uniform = 0.5, col_pct:uniform = 0.5):
"Crop and pad tfm - `row_pct`,`col_pct` sets focal point."
padding_mode = _pad_mode_convert[padding_mode]
size = tis2hw(size)
if x.shape[1:] == torch.Size(size): return x
rows,cols = size
row... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.