Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
fake_print | (*args, **kwargs) | Dummy replacement for print() that does nothing | Dummy replacement for print() that does nothing | def fake_print(*args, **kwargs):
"""Dummy replacement for print() that does nothing"""
pass | [
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17,
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pytest_collect_file | (parent, path) | Collect all file suitable for use in tests | Collect all file suitable for use in tests | def pytest_collect_file(parent, path):
"""Collect all file suitable for use in tests"""
if path.basename == "README.md":
return ReadmeFile.from_parent(parent, fspath=path) | [
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_with_patched_sleep | (func, *args, **kwargs) | Patch the sleep function so that it does nothing | Patch the sleep function so that it does nothing | def _with_patched_sleep(func, *args, **kwargs):
"""Patch the sleep function so that it does nothing"""
_sleep = time.sleep
time.sleep = lambda *args: None
try:
return func(*args, **kwargs)
finally:
time.sleep = _sleep | [
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ReadmeFile.collect | (self) | Collect all code snippets | Collect all code snippets | def collect(self):
"""Collect all code snippets"""
raw_text = self.fspath.open().read()
for idx, code in enumerate(code_rxp.findall(raw_text), 1):
yield ReadmeItem.from_parent(
self, name=str(idx), spec=code.strip()
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ReadmeItem.runtest | (self) | Run the test | Run the test | def runtest(self):
"""Run the test"""
builtins = dict(self.builtins)
byte_code = compile(self.spec, '<inline>', 'exec')
_with_patched_sleep(exec, byte_code, builtins)
msg, _, error = api.run(['--no-color-output', '-c', self.spec])
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# Ignore missing er... | [
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ReadmeItem.repr_failure | (self, excinfo, **kwargs) | called when self.runtest() raises an exception. | called when self.runtest() raises an exception. | def repr_failure(self, excinfo, **kwargs):
""" called when self.runtest() raises an exception. """
return (
f"Code snippet {self.name} raised an error: {excinfo.value}. "
f"The executed code was: {self.spec}"
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ReadmeItem.reportinfo | (self) | Report some basic information on the test outcome | Report some basic information on the test outcome | def reportinfo(self):
"""Report some basic information on the test outcome"""
return self.fspath, 0, "usecase: {}".format(self.name) | [
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wrap_qat_forward_context | (
quant_cb,
model: pl.core.LightningModule,
func: Callable,
trigger_condition: Optional[Union[Callable, int]] = None
) |
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
Moreover this version has the (de)quantization conditional as it may not be needed for the training all the time
|
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
Moreover this version has the (de)quantization conditional as it may not be needed for the training all the time
| def wrap_qat_forward_context(
quant_cb,
model: pl.core.LightningModule,
func: Callable,
trigger_condition: Optional[Union[Callable, int]] = None
) -> Callable:
"""
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
Moreover this ... | [
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wrap_quantize_forward_context | (model: pl.core.LightningModule, func: Callable) |
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
|
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
| def wrap_quantize_forward_context(model: pl.core.LightningModule, func: Callable) -> Callable:
"""
Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
"""
# todo: consider using registering hook before/after forward
@functools.wraps(func)... | [
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_recursive_hasattr | (obj: Any, attribs: str, state: bool = True) | recursive check if model has some layers denoted with '. | recursive check if model has some layers denoted with '. | def _recursive_hasattr(obj: Any, attribs: str, state: bool = True) -> bool:
"""recursive check if model has some layers denoted with '.'"""
if '.' in attribs:
attrib, attribs = attribs.split('.', 1)
if hasattr(obj, attrib):
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74,
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] | [
81,
42
] | python | en | ['en', 'en', 'en'] | True |
QuantizationAwareTraining.__init__ | (
self,
qconfig: Union[str, QConfig] = 'fbgemm',
observer_type: str = "average",
collect_quantization: Optional[Union[int, Callable]] = None,
modules_to_fuse: Optional[Sequence] = None,
input_compatible: bool = True,
) |
Quantization allows speeding up inference and decreasing memory requirements
by performing computations and storing tensors at lower bitwidths
(such as INT8 or FLOAT16) than floating point precision.
We use native PyTorch API so for more information
see `Quantization <https://py... |
Quantization allows speeding up inference and decreasing memory requirements
by performing computations and storing tensors at lower bitwidths
(such as INT8 or FLOAT16) than floating point precision.
We use native PyTorch API so for more information
see `Quantization <https://py... | def __init__(
self,
qconfig: Union[str, QConfig] = 'fbgemm',
observer_type: str = "average",
collect_quantization: Optional[Union[int, Callable]] = None,
modules_to_fuse: Optional[Sequence] = None,
input_compatible: bool = True,
) -> None:
"""
Quantiza... | [
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... | [
87,
4
] | [
162,
31
] | python | en | ['en', 'error', 'th'] | False |
run_and_parse_first_match | (run_lambda, command, regex) | Runs command using run_lambda, returns the first regex match if it exists | Runs command using run_lambda, returns the first regex match if it exists | def run_and_parse_first_match(run_lambda, command, regex):
"""Runs command using run_lambda, returns the first regex match if it exists"""
rc, out, _ = run_lambda(command)
if rc != 0:
return None
match = re.search(regex, out)
if match is None:
return None
return match.group(1) | [
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35,
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] | [
43,
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] | python | en | ['en', 'lb', 'en'] | True |
test_model_saves_with_input_sample | (tmpdir) | Test that ONNX model saves with input sample and size is greater than 3 MB | Test that ONNX model saves with input sample and size is greater than 3 MB | def test_model_saves_with_input_sample(tmpdir):
"""Test that ONNX model saves with input sample and size is greater than 3 MB"""
model = BoringModel()
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)
file_path = os.path.join(tmpdir, "model.onnx")
input_sample = torch.randn((1, 32))
m... | [
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test_model_saves_on_gpu | (tmpdir) | Test that model saves on gpu | Test that model saves on gpu | def test_model_saves_on_gpu(tmpdir):
"""Test that model saves on gpu"""
model = BoringModel()
trainer = Trainer(gpus=1, fast_dev_run=True)
trainer.fit(model)
file_path = os.path.join(tmpdir, "model.onnx")
input_sample = torch.randn((1, 32))
model.to_onnx(file_path, input_sample)
assert ... | [
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test_model_saves_with_example_output | (tmpdir) | Test that ONNX model saves when provided with example output | Test that ONNX model saves when provided with example output | def test_model_saves_with_example_output(tmpdir):
"""Test that ONNX model saves when provided with example output"""
model = BoringModel()
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)
file_path = os.path.join(tmpdir, "model.onnx")
input_sample = torch.randn((1, 32))
model.eval()
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test_model_saves_with_example_input_array | (tmpdir) | Test that ONNX model saves with_example_input_array and size is greater than 3 MB | Test that ONNX model saves with_example_input_array and size is greater than 3 MB | def test_model_saves_with_example_input_array(tmpdir):
"""Test that ONNX model saves with_example_input_array and size is greater than 3 MB"""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
file_path = os.path.join(tmpdir, "model.onnx")
model.to_onnx(file_path)
assert os.p... | [
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test_model_saves_on_multi_gpu | (tmpdir) | Test that ONNX model saves on a distributed backend | Test that ONNX model saves on a distributed backend | def test_model_saves_on_multi_gpu(tmpdir):
"""Test that ONNX model saves on a distributed backend"""
tutils.set_random_master_port()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=10,
limit_val_batches=10,
gpus=[0, 1],
acce... | [
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test_verbose_param | (tmpdir, capsys) | Test that output is present when verbose parameter is set | Test that output is present when verbose parameter is set | def test_verbose_param(tmpdir, capsys):
"""Test that output is present when verbose parameter is set"""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
file_path = os.path.join(tmpdir, "model.onnx")
model.to_onnx(file_path, verbose=True)
captured = capsys.readouterr()
a... | [
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test_error_if_no_input | (tmpdir) | Test that an error is thrown when there is no input tensor | Test that an error is thrown when there is no input tensor | def test_error_if_no_input(tmpdir):
"""Test that an error is thrown when there is no input tensor"""
model = BoringModel()
model.example_input_array = None
file_path = os.path.join(tmpdir, "model.onnx")
with pytest.raises(
ValueError,
match=r'Could not export to ONNX since neither `i... | [
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124,
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test_if_inference_output_is_valid | (tmpdir) | Test that the output inferred from ONNX model is same as from PyTorch | Test that the output inferred from ONNX model is same as from PyTorch | def test_if_inference_output_is_valid(tmpdir):
"""Test that the output inferred from ONNX model is same as from PyTorch"""
model = BoringModel()
model.example_input_array = torch.randn(5, 32)
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)
model.eval()
with torch.no_grad():
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add_hide_option | (options) | Mark message/enum/field/enum value as hidden.
Hidden messages are ignored when generating output.
Args:
options: MessageOptions/EnumOptions/FieldOptions/EnumValueOptions message.
| Mark message/enum/field/enum value as hidden. | def add_hide_option(options):
"""Mark message/enum/field/enum value as hidden.
Hidden messages are ignored when generating output.
Args:
options: MessageOptions/EnumOptions/FieldOptions/EnumValueOptions message.
"""
hide_option = options.uninterpreted_option.add()
hide_option.name.add(... | [
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has_hide_option | (options) | Is message/enum/field/enum value hidden?
Hidden messages are ignored when generating output.
Args:
options: MessageOptions/EnumOptions/FieldOptions/EnumValueOptions message.
Returns:
Hidden status.
| Is message/enum/field/enum value hidden? | def has_hide_option(options):
"""Is message/enum/field/enum value hidden?
Hidden messages are ignored when generating output.
Args:
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Returns:
Hidden status.
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set_versioning_annotation | (options, previous_message_type) | Set the udpa.annotations.versioning option.
Used by Envoy to chain back through the message type history.
Args:
options: MessageOptions message.
previous_message_type: string with earlier API type name for the message.
| Set the udpa.annotations.versioning option. | def set_versioning_annotation(options, previous_message_type):
"""Set the udpa.annotations.versioning option.
Used by Envoy to chain back through the message type history.
Args:
options: MessageOptions message.
previous_message_type: string with earlier API type name for the message.
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get_versioning_annotation | (options) | Get the udpa.annotations.versioning option.
Used by Envoy to chain back through the message type history.
Args:
options: MessageOptions message.
Returns:
versioning.Annotation if set otherwise None.
| Get the udpa.annotations.versioning option. | def get_versioning_annotation(options):
"""Get the udpa.annotations.versioning option.
Used by Envoy to chain back through the message type history.
Args:
options: MessageOptions message.
Returns:
versioning.Annotation if set otherwise None.
"""
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_new_duck_type_and_implements | () | Create a duck array type and implements functions. | Create a duck array type and implements functions. | def _new_duck_type_and_implements():
"""Create a duck array type and implements functions."""
HANDLED_FUNCTIONS = {}
class MyArray(object):
def __array_function__(self, func, types, args, kwargs):
if func not in HANDLED_FUNCTIONS:
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poly | (seq_of_zeros) |
Find the coefficients of a polynomial with the given sequence of roots.
Returns the coefficients of the polynomial whose leading coefficient
is one for the given sequence of zeros (multiple roots must be included
in the sequence as many times as their multiplicity; see Examples).
A square matrix (... |
Find the coefficients of a polynomial with the given sequence of roots. | def poly(seq_of_zeros):
"""
Find the coefficients of a polynomial with the given sequence of roots.
Returns the coefficients of the polynomial whose leading coefficient
is one for the given sequence of zeros (multiple roots must be included
in the sequence as many times as their multiplicity; see E... | [
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roots | (p) |
Return the roots of a polynomial with coefficients given in p.
The values in the rank-1 array `p` are coefficients of a polynomial.
If the length of `p` is n+1 then the polynomial is described by::
p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]
Parameters
----------
p : array_li... |
Return the roots of a polynomial with coefficients given in p. | def roots(p):
"""
Return the roots of a polynomial with coefficients given in p.
The values in the rank-1 array `p` are coefficients of a polynomial.
If the length of `p` is n+1 then the polynomial is described by::
p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]
Parameters
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polyint | (p, m=1, k=None) |
Return an antiderivative (indefinite integral) of a polynomial.
The returned order `m` antiderivative `P` of polynomial `p` satisfies
:math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
integration constants `k`. The constants determine the low-order
polynomial part
.. math:: \\... |
Return an antiderivative (indefinite integral) of a polynomial. | def polyint(p, m=1, k=None):
"""
Return an antiderivative (indefinite integral) of a polynomial.
The returned order `m` antiderivative `P` of polynomial `p` satisfies
:math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`
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polyder | (p, m=1) |
Return the derivative of the specified order of a polynomial.
Parameters
----------
p : poly1d or sequence
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
m : int, optional
Order of differentiation (default: 1)
Returns
... |
Return the derivative of the specified order of a polynomial. | def polyder(p, m=1):
"""
Return the derivative of the specified order of a polynomial.
Parameters
----------
p : poly1d or sequence
Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients, see `poly1d`.
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polyfit | (x, y, deg, rcond=None, full=False, w=None, cov=False) |
Least squares polynomial fit.
Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
to points `(x, y)`. Returns a vector of coefficients `p` that minimises
the squared error in the order `deg`, `deg-1`, ... `0`.
The `Polynomial.fit <numpy.polynomial.polynomial.Polynomial.fit>` ... |
Least squares polynomial fit. | def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
"""
Least squares polynomial fit.
Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
to points `(x, y)`. Returns a vector of coefficients `p` that minimises
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polyval | (p, x) |
Evaluate a polynomial at specific values.
If `p` is of length N, this function returns the value:
``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]``
If `x` is a sequence, then `p(x)` is returned for each element of `x`.
If `x` is another polynomial then the composite polynomial `p(x... |
Evaluate a polynomial at specific values. | def polyval(p, x):
"""
Evaluate a polynomial at specific values.
If `p` is of length N, this function returns the value:
``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]``
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polyadd | (a1, a2) |
Find the sum of two polynomials.
Returns the polynomial resulting from the sum of two input polynomials.
Each input must be either a poly1d object or a 1D sequence of polynomial
coefficients, from highest to lowest degree.
Parameters
----------
a1, a2 : array_like or poly1d object
... |
Find the sum of two polynomials. | def polyadd(a1, a2):
"""
Find the sum of two polynomials.
Returns the polynomial resulting from the sum of two input polynomials.
Each input must be either a poly1d object or a 1D sequence of polynomial
coefficients, from highest to lowest degree.
Parameters
----------
a1, a2 : array_l... | [
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polysub | (a1, a2) |
Difference (subtraction) of two polynomials.
Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
`a1` and `a2` can be either array_like sequences of the polynomials'
coefficients (including coefficients equal to zero), or `poly1d` objects.
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a1, a2 : array_like o... |
Difference (subtraction) of two polynomials. | def polysub(a1, a2):
"""
Difference (subtraction) of two polynomials.
Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
`a1` and `a2` can be either array_like sequences of the polynomials'
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Parameters
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polymul | (a1, a2) |
Find the product of two polynomials.
Finds the polynomial resulting from the multiplication of the two input
polynomials. Each input must be either a poly1d object or a 1D sequence
of polynomial coefficients, from highest to lowest degree.
Parameters
----------
a1, a2 : array_like or poly... |
Find the product of two polynomials. | def polymul(a1, a2):
"""
Find the product of two polynomials.
Finds the polynomial resulting from the multiplication of the two input
polynomials. Each input must be either a poly1d object or a 1D sequence
of polynomial coefficients, from highest to lowest degree.
Parameters
----------
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polydiv | (u, v) |
Returns the quotient and remainder of polynomial division.
The input arrays are the coefficients (including any coefficients
equal to zero) of the "numerator" (dividend) and "denominator"
(divisor) polynomials, respectively.
Parameters
----------
u : array_like or poly1d
Dividend ... |
Returns the quotient and remainder of polynomial division. | def polydiv(u, v):
"""
Returns the quotient and remainder of polynomial division.
The input arrays are the coefficients (including any coefficients
equal to zero) of the "numerator" (dividend) and "denominator"
(divisor) polynomials, respectively.
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----------
u : array_like o... | [
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mean_squared_error | (preds: torch.Tensor, target: torch.Tensor) |
Computes mean squared error
Args:
preds: estimated labels
target: ground truth labels
Return:
Tensor with MSE
Example:
>>> from pytorch_lightning.metrics.functional import mean_squared_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0.,... |
Computes mean squared error | def mean_squared_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Computes mean squared error
Args:
preds: estimated labels
target: ground truth labels
Return:
Tensor with MSE
Example:
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parse_cmd | () | Parses the command-line arguments.
libfile, deffile = parse_cmd() | Parses the command-line arguments. | def parse_cmd():
"""Parses the command-line arguments.
libfile, deffile = parse_cmd()"""
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getnm | (nm_cmd = ['nm', '-Cs', 'python%s.lib' % py_ver]) | Returns the output of nm_cmd via a pipe.
nm_output = getnam(nm_cmd = 'nm -Cs py_lib') | Returns the output of nm_cmd via a pipe. | def getnm(nm_cmd = ['nm', '-Cs', 'python%s.lib' % py_ver]):
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nm_output = getnam(nm_cmd = 'nm -Cs py_lib')"""
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parse_nm | (nm_output) | Returns a tuple of lists: dlist for the list of data
symbols and flist for the list of function symbols.
dlist, flist = parse_nm(nm_output) | Returns a tuple of lists: dlist for the list of data
symbols and flist for the list of function symbols. | def parse_nm(nm_output):
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symbols and flist for the list of function symbols.
dlist, flist = parse_nm(nm_output)"""
data = DATA_RE.findall(nm_output)
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output_def | (dlist, flist, header, file = sys.stdout) | Outputs the final DEF file to a file defaulting to stdout.
output_def(dlist, flist, header, file = sys.stdout) | Outputs the final DEF file to a file defaulting to stdout. | def output_def(dlist, flist, header, file = sys.stdout):
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output_def(dlist, flist, header, file = sys.stdout)"""
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test_result_reduce_ddp | () | Make sure result logging works with DDP | Make sure result logging works with DDP | def test_result_reduce_ddp():
"""Make sure result logging works with DDP"""
tutils.reset_seed()
tutils.set_random_master_port()
worldsize = 2
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is_free_format | (file) | Check if file is in free format Fortran. | Check if file is in free format Fortran. | def is_free_format(file):
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readfortrancode | (ffile, dowithline=show, istop=1) |
Read fortran codes from files and
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3) Recursively call itself when statement \"include '<filename>'\" is met.
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| def readfortrancode(ffile, dowithline=show, istop=1):
"""
Read fortran codes from files and
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split_by_unquoted | (line, characters) |
Splits the line into (line[:i], line[i:]),
where i is the index of first occurrence of one of the characters
not within quotes, or len(line) if no such index exists
|
Splits the line into (line[:i], line[i:]),
where i is the index of first occurrence of one of the characters
not within quotes, or len(line) if no such index exists
| def split_by_unquoted(line, characters):
"""
Splits the line into (line[:i], line[i:]),
where i is the index of first occurrence of one of the characters
not within quotes, or len(line) if no such index exists
"""
assert not (set('"\'') & set(characters)), "cannot split by unquoted quotes"
r... | [
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crackline | (line, reset=0) |
reset=-1 --- initialize
reset=0 --- crack the line
reset=1 --- final check if mismatch of blocks occurred
Cracked data is saved in grouplist[0].
|
reset=-1 --- initialize
reset=0 --- crack the line
reset=1 --- final check if mismatch of blocks occurred | def crackline(line, reset=0):
"""
reset=-1 --- initialize
reset=0 --- crack the line
reset=1 --- final check if mismatch of blocks occurred
Cracked data is saved in grouplist[0].
"""
global beginpattern, groupcounter, groupname, groupcache, grouplist
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postcrack | (block, args=None, tab='') |
TODO:
function return values
determine expression types if in argument list
|
TODO:
function return values
determine expression types if in argument list
| def postcrack(block, args=None, tab=''):
"""
TODO:
function return values
determine expression types if in argument list
"""
global usermodules, onlyfunctions
if isinstance(block, list):
gret = []
uret = []
for g in block:
setmesstext(g)
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test_eval_train_calls | (test_train_mock, test_eval_mock, val_train_mock, val_eval_mock, tmpdir) |
Tests that only training_step can be used
|
Tests that only training_step can be used
| def test_eval_train_calls(test_train_mock, test_eval_mock, val_train_mock, val_eval_mock, tmpdir):
"""
Tests that only training_step can be used
"""
model = BoringModel()
model.validation_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
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test_invalid_input_img_type | () | Test Whether the module successfully handles invalid input data type | Test Whether the module successfully handles invalid input data type | def test_invalid_input_img_type():
"""Test Whether the module successfully handles invalid input data type"""
invalid_dummy_input = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
with pytest.raises(TypeError):
image_gradients(invalid_dummy_input) | [
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test_invalid_input_ndims | () |
Test whether the module successfully handles invalid number of dimensions
of input tensor
|
Test whether the module successfully handles invalid number of dimensions
of input tensor
| def test_invalid_input_ndims():
"""
Test whether the module successfully handles invalid number of dimensions
of input tensor
"""
BATCH_SIZE = 1
HEIGHT = 5
WIDTH = 5
CHANNELS = 1
image = torch.arange(0, BATCH_SIZE * HEIGHT * WIDTH * CHANNELS, dtype=torch.float32)
image = torch.... | [
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test_multi_batch_image_gradients | () | Test whether the module correctly calculates gradients for known input
with non-unity batch size.Example input-output pair taken from TF's implementation of i
mage-gradients
| Test whether the module correctly calculates gradients for known input
with non-unity batch size.Example input-output pair taken from TF's implementation of i
mage-gradients
| def test_multi_batch_image_gradients():
"""Test whether the module correctly calculates gradients for known input
with non-unity batch size.Example input-output pair taken from TF's implementation of i
mage-gradients
"""
BATCH_SIZE = 5
HEIGHT = 5
WIDTH = 5
CHANNELS = 1
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test_image_gradients | () | Test whether the module correctly calculates gradients for known input.
Example input-output pair taken from TF's implementation of image-gradients
| Test whether the module correctly calculates gradients for known input.
Example input-output pair taken from TF's implementation of image-gradients
| def test_image_gradients():
"""Test whether the module correctly calculates gradients for known input.
Example input-output pair taken from TF's implementation of image-gradients
"""
BATCH_SIZE = 1
HEIGHT = 5
WIDTH = 5
CHANNELS = 1
image = torch.arange(0, BATCH_SIZE * HEIGHT * WIDTH * ... | [
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process_tex | (lines) |
Remove unnecessary section titles from the LaTeX file.
|
Remove unnecessary section titles from the LaTeX file. | def process_tex(lines):
"""
Remove unnecessary section titles from the LaTeX file.
"""
new_lines = []
for line in lines:
if (line.startswith(r'\section{numpy.')
or line.startswith(r'\subsection{numpy.')
or line.startswith(r'\subsubsection{numpy.')
or line... | [
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overwrite_by_env_vars | (fn: Callable) |
Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
input arguments should be moved automatically to the correct device.
|
Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
input arguments should be moved automatically to the correct device. | def overwrite_by_env_vars(fn: Callable) -> Callable:
"""
Decorator for :class:`~pytorch_lightning.trainer.trainer.Trainer` methods for which
input arguments should be moved automatically to the correct device.
"""
@wraps(fn)
def overwrite_by_env_vars(self, *args, **kwargs):
# get the c... | [
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filepath_from_subprocess_output | (output) |
Convert `bytes` in the encoding used by a subprocess into a filesystem-appropriate `str`.
Inherited from `exec_command`, and possibly incorrect.
|
Convert `bytes` in the encoding used by a subprocess into a filesystem-appropriate `str`. | def filepath_from_subprocess_output(output):
"""
Convert `bytes` in the encoding used by a subprocess into a filesystem-appropriate `str`.
Inherited from `exec_command`, and possibly incorrect.
"""
mylocale = locale.getpreferredencoding(False)
if mylocale is None:
mylocale = 'ascii'
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forward_bytes_to_stdout | (val) |
Forward bytes from a subprocess call to the console, without attempting to
decode them.
The assumption is that the subprocess call already returned bytes in
a suitable encoding.
|
Forward bytes from a subprocess call to the console, without attempting to
decode them. | def forward_bytes_to_stdout(val):
"""
Forward bytes from a subprocess call to the console, without attempting to
decode them.
The assumption is that the subprocess call already returned bytes in
a suitable encoding.
"""
if sys.version_info.major < 3:
# python 2 has binary output any... | [
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find_executable | (exe, path=None, _cache={}) | Return full path of a executable or None.
Symbolic links are not followed.
| Return full path of a executable or None. | def find_executable(exe, path=None, _cache={}):
"""Return full path of a executable or None.
Symbolic links are not followed.
"""
key = exe, path
try:
return _cache[key]
except KeyError:
pass
log.debug('find_executable(%r)' % exe)
orig_exe = exe
if path is None:
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exec_command | (command, execute_in='', use_shell=None, use_tee=None,
_with_python = 1, **env ) |
Return (status,output) of executed command.
.. deprecated:: 1.17
Use subprocess.Popen instead
Parameters
----------
command : str
A concatenated string of executable and arguments.
execute_in : str
Before running command ``cd execute_in`` and after ``cd -``.
use_sh... |
Return (status,output) of executed command. | def exec_command(command, execute_in='', use_shell=None, use_tee=None,
_with_python = 1, **env ):
"""
Return (status,output) of executed command.
.. deprecated:: 1.17
Use subprocess.Popen instead
Parameters
----------
command : str
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_exec_command | (command, use_shell=None, use_tee = None, **env) |
Internal workhorse for exec_command().
|
Internal workhorse for exec_command().
| def _exec_command(command, use_shell=None, use_tee = None, **env):
"""
Internal workhorse for exec_command().
"""
if use_shell is None:
use_shell = os.name=='posix'
if use_tee is None:
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_quote_arg | (arg) |
Quote the argument for safe use in a shell command line.
|
Quote the argument for safe use in a shell command line.
| def _quote_arg(arg):
"""
Quote the argument for safe use in a shell command line.
"""
# If there is a quote in the string, assume relevants parts of the
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hide_not_implemented | (comment) | Should a given type_context.Comment be hidden because it is tagged as [#not-implemented-hide:]? | Should a given type_context.Comment be hidden because it is tagged as [#not-implemented-hide:]? | def hide_not_implemented(comment):
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github_url | (text, type_context) | Obtain data plane API Github URL by path from a TypeContext.
Args:
type_context: type_context.TypeContext for node.
Returns:
A string with a corresponding data plane API GitHub Url.
| Obtain data plane API Github URL by path from a TypeContext. | def github_url(text, type_context):
"""Obtain data plane API Github URL by path from a TypeContext.
Args:
type_context: type_context.TypeContext for node.
Returns:
A string with a corresponding data plane API GitHub Url.
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format_comment_with_annotations | (comment, type_name='') | Format a comment string with additional RST for annotations.
Args:
comment: comment string.
type_name: optional, 'message' or 'enum' may be specified for additional
message/enum specific annotations.
Returns:
A string with additional RST from annotations.
| Format a comment string with additional RST for annotations. | def format_comment_with_annotations(comment, type_name=''):
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Args:
comment: comment string.
type_name: optional, 'message' or 'enum' may be specified for additional
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map_lines | (f, s) | Apply a function across each line in a flat string.
Args:
f: A string transform function for a line.
s: A string consisting of potentially multiple lines.
Returns:
A flat string with f applied to each line.
| Apply a function across each line in a flat string. | def map_lines(f, s):
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Args:
f: A string transform function for a line.
s: A string consisting of potentially multiple lines.
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indent | (spaces, line) | Indent a string. | Indent a string. | def indent(spaces, line):
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indent_lines | (spaces, lines) | Indent a list of strings. | Indent a list of strings. | def indent_lines(spaces, lines):
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format_header | (style, text) | Format RST header.
Args:
style: underline style, e.g. '=', '-'.
text: header text
Returns:
RST formatted header.
| Format RST header. | def format_header(style, text):
"""Format RST header.
Args:
style: underline style, e.g. '=', '-'.
text: header text
Returns:
RST formatted header.
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format_extension | (extension) | Format extension metadata as RST.
Args:
extension: the name of the extension, e.g. com.acme.foo.
Returns:
RST formatted extension description.
| Format extension metadata as RST. | def format_extension(extension):
"""Format extension metadata as RST.
Args:
extension: the name of the extension, e.g. com.acme.foo.
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RST formatted extension description.
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try:
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format_extension_category | (extension_category) | Format extension metadata as RST.
Args:
extension_category: the name of the extension_category, e.g. com.acme.
Returns:
RST formatted extension category description.
| Format extension metadata as RST. | def format_extension_category(extension_category):
"""Format extension metadata as RST.
Args:
extension_category: the name of the extension_category, e.g. com.acme.
Returns:
RST formatted extension category description.
"""
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format_header_from_file | (style, source_code_info, proto_name, v2_link) | Format RST header based on special file level title
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style: underline style, e.g. '=', '-'.
source_code_info: SourceCodeInfo object.
proto_name: If the file_level_comment does not contain a user specified
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format_field_type_as_json | (type_context, field) | Format FieldDescriptorProto.Type as a pseudo-JSON string.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Return: RST formatted pseudo-JSON string representation of field type.
| Format FieldDescriptorProto.Type as a pseudo-JSON string. | def format_field_type_as_json(type_context, field):
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field: FieldDescriptor proto.
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format_message_as_json | (type_context, msg) | Format a message definition DescriptorProto as a pseudo-JSON block.
Args:
type_context: contextual information for message/enum/field.
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| Format a message definition DescriptorProto as a pseudo-JSON block. | def format_message_as_json(type_context, msg):
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normalize_field_type_name | (field_fqn) | Normalize a fully qualified field type name, e.g.
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Strips leading ENVOY_API_NAMESPACE_PREFIX and ENVOY_PREFIX.
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| Normalize a fully qualified field type name, e.g. | def normalize_field_type_name(field_fqn):
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Strips leading ENVOY_API_NAMESPACE_PREFIX and ENVOY_PREFIX.
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field_fqn: a fully qualified type name from FieldDescriptorProto.type_name.
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normalize_type_context_name | (type_name) | Normalize a type name, e.g.
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Strips leading ENVOY_API_NAMESPACE_PREFIX and ENVOY_PREFIX.
Args:
type_name: a name from a TypeContext.
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| Normalize a type name, e.g. | def normalize_type_context_name(type_name):
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Strips leading ENVOY_API_NAMESPACE_PREFIX and ENVOY_PREFIX.
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format_field_type | (type_context, field) | Format a FieldDescriptorProto type description.
Adds cross-refs for message types.
TODO(htuch): Add cross-refs for enums as well.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Return: RST formatted field type.
| Format a FieldDescriptorProto type description. | def format_field_type(type_context, field):
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TODO(htuch): Add cross-refs for enums as well.
Args:
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strip_leading_space | (s) | Remove leading space in flat comment strings. | Remove leading space in flat comment strings. | def strip_leading_space(s):
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file_cross_ref_label | (msg_name) | File cross reference label. | File cross reference label. | def file_cross_ref_label(msg_name):
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message_cross_ref_label | (msg_name) | Message cross reference label. | Message cross reference label. | def message_cross_ref_label(msg_name):
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enum_cross_ref_label | (enum_name) | Enum cross reference label. | Enum cross reference label. | def enum_cross_ref_label(enum_name):
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field_cross_ref_label | (field_name) | Field cross reference label. | Field cross reference label. | def field_cross_ref_label(field_name):
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enum_value_cross_ref_label | (enum_value_name) | Enum value cross reference label. | Enum value cross reference label. | def enum_value_cross_ref_label(enum_value_name):
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format_anchor | (label) | Format a label as an Envoy API RST anchor. | Format a label as an Envoy API RST anchor. | def format_anchor(label):
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format_field_as_definition_list_item | (
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Args:
outer_type_context: contextual information for enclosing message.
type_context: contextual information for message/enum/field.
field: FieldDescriptorProto.
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format_message_as_definition_list | (type_context, msg, protodoc_manifest) | Format a DescriptorProto as RST definition list.
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type_context: contextual information for message/enum/field.
msg: DescriptorProto.
protodoc_manifest: tools.protodoc.Manifest for proto.
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| Format a DescriptorProto as RST definition list. | def format_message_as_definition_list(type_context, msg, protodoc_manifest):
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type_context: contextual information for message/enum/field.
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format_enum_value_as_definition_list_item | (type_context, enum_value) | Format a EnumValueDescriptorProto as RST definition list item.
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type_context: contextual information for message/enum/field.
enum_value: EnumValueDescriptorProto.
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| Format a EnumValueDescriptorProto as RST definition list item. | def format_enum_value_as_definition_list_item(type_context, enum_value):
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format_enum_as_definition_list | (type_context, enum) | Format a EnumDescriptorProto as RST definition list.
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enum: DescriptorProto.
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| Format a EnumDescriptorProto as RST definition list. | def format_enum_as_definition_list(type_context, enum):
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format_proto_as_block_comment | (proto) | Format a proto as a RST block comment.
Useful in debugging, not usually referenced.
| Format a proto as a RST block comment. | def format_proto_as_block_comment(proto):
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convert_inf | (x: Optional[Union[int, float]]) | The tqdm doesn't support inf values. We have to convert it to None. | The tqdm doesn't support inf values. We have to convert it to None. | def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
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reset | (bar: tqdm, total: Optional[int] = None) | Resets the tqdm bar to 0 progress with a new total, unless it is disabled. | Resets the tqdm bar to 0 progress with a new total, unless it is disabled. | def reset(bar: tqdm, total: Optional[int] = None) -> None:
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tqdm.format_num | (n) | Add additional padding to the formatted numbers | Add additional padding to the formatted numbers | def format_num(n) -> str:
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ProgressBarBase.train_batch_idx | (self) |
The current batch index being processed during training.
Use this to update your progress bar.
|
The current batch index being processed during training.
Use this to update your progress bar.
| def train_batch_idx(self) -> int:
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The current batch index being processed during training.
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ProgressBarBase.val_batch_idx | (self) |
The current batch index being processed during validation.
Use this to update your progress bar.
|
The current batch index being processed during validation.
Use this to update your progress bar.
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ProgressBarBase.test_batch_idx | (self) |
The current batch index being processed during testing.
Use this to update your progress bar.
|
The current batch index being processed during testing.
Use this to update your progress bar.
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ProgressBarBase.predict_batch_idx | (self) |
The current batch index being processed during predicting.
Use this to update your progress bar.
|
The current batch index being processed during predicting.
Use this to update your progress bar.
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ProgressBarBase.total_train_batches | (self) |
The total number of training batches during training, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
training dataloader is of infinite size.
|
The total number of training batches during training, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
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] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.total_val_batches | (self) |
The total number of validation batches during validation, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
validation dataloader is of infinite size.
|
The total number of validation batches during validation, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
validation dataloader is of infinite size.
| def total_val_batches(self) -> int:
"""
The total number of validation batches during validation, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
validation dataloader is of infinite size.
"""
... | [
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144,
4
] | [
154,
32
] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.total_test_batches | (self) |
The total number of testing batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
test dataloader is of infinite size.
|
The total number of testing batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
test dataloader is of infinite size.
| def total_test_batches(self) -> int:
"""
The total number of testing batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
test dataloader is of infinite size.
"""
return... | [
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] | [
157,
4
] | [
163,
49
] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.total_predict_batches | (self) |
The total number of predicting batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
predict dataloader is of infinite size.
|
The total number of predicting batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
predict dataloader is of infinite size.
| def total_predict_batches(self) -> int:
"""
The total number of predicting batches during testing, which may change from epoch to epoch.
Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the
predict dataloader is of infinite size.
"""
... | [
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"num_predict_batches",
")"
] | [
166,
4
] | [
172,
52
] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.disable | (self) |
You should provide a way to disable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the
output on processes that have a rank different from 0, e.g., in multi-node training.
|
You should provide a way to disable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the
output on processes that have a rank different from 0, e.g., in multi-node training.
| def disable(self):
"""
You should provide a way to disable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the
output on processes that have a rank different from 0, e.g., in multi-node training.
"""
raise NotImplemented... | [
"def",
"disable",
"(",
"self",
")",
":",
"raise",
"NotImplementedError"
] | [
174,
4
] | [
180,
33
] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.enable | (self) |
You should provide a way to enable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training
routines like the :ref:`learning rate finder <advanced/lr_finder:Learning Rate Finder>`
to temporarily enable and disable the main progress ba... |
You should provide a way to enable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training
routines like the :ref:`learning rate finder <advanced/lr_finder:Learning Rate Finder>`
to temporarily enable and disable the main progress ba... | def enable(self):
"""
You should provide a way to enable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training
routines like the :ref:`learning rate finder <advanced/lr_finder:Learning Rate Finder>`
to temporarily enable and... | [
"def",
"enable",
"(",
"self",
")",
":",
"raise",
"NotImplementedError"
] | [
182,
4
] | [
189,
33
] | python | en | ['en', 'error', 'th'] | False |
ProgressBarBase.print | (self, *args, **kwargs) |
You should provide a way to print without breaking the progress bar.
|
You should provide a way to print without breaking the progress bar.
| def print(self, *args, **kwargs):
"""
You should provide a way to print without breaking the progress bar.
"""
print(*args, **kwargs) | [
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] | [
191,
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] | [
195,
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] | python | en | ['en', 'error', 'th'] | False |
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