code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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import torch
from .optimizer import Optimizer
class RMSprop(Optimizer):
r"""Implements RMSprop algorithm.
Proposed by G. Hinton in his
`course <http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_.
The centered version first appears in `Generating Sequences
With Recurrent N... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/rmsprop.py | 0.934197 | 0.732137 | rmsprop.py | pypi |
import torch
from .optimizer import Optimizer
class Adagrad(Optimizer):
"""Implements Adagrad algorithm.
It has been proposed in `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/adagrad.py | 0.908942 | 0.536009 | adagrad.py | pypi |
import types
import math
from torch._six import inf
from functools import wraps
import warnings
import weakref
from collections import Counter
from bisect import bisect_right
from .optimizer import Optimizer
EPOCH_DEPRECATION_WARNING = (
"The epoch parameter in `scheduler.step()` was not necessary and is being "... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/lr_scheduler.py | 0.878471 | 0.343218 | lr_scheduler.py | pypi |
import math
import torch
from .optimizer import Optimizer
class Adam(Optimizer):
r"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/adam.py | 0.896217 | 0.583411 | adam.py | pypi |
from collections import defaultdict
from torch._six import container_abcs
import torch
from copy import deepcopy
from itertools import chain
class _RequiredParameter(object):
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self):
return "<required parameter>"
r... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/optimizer.py | 0.954584 | 0.355691 | optimizer.py | pypi |
import torch
from .optimizer import Optimizer
class Adadelta(Optimizer):
"""Implements Adadelta algorithm.
It has been proposed in `ADADELTA: An Adaptive Learning Rate Method`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/adadelta.py | 0.937813 | 0.512449 | adadelta.py | pypi |
import torch
from .optimizer import Optimizer, required
class SGD(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
Args:
params (iterable): ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/sgd.py | 0.949634 | 0.70078 | sgd.py | pypi |
import math
import torch
from .optimizer import Optimizer
class AdamW(Optimizer):
r"""Implements AdamW algorithm.
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
Arguments:
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/adamw.py | 0.923303 | 0.623936 | adamw.py | pypi |
import math
import torch
from .optimizer import Optimizer
class ASGD(Optimizer):
"""Implements Averaged Stochastic Gradient Descent.
It has been proposed in `Acceleration of stochastic approximation by
averaging`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts def... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/asgd.py | 0.870212 | 0.416025 | asgd.py | pypi |
import torch
from .optimizer import Optimizer
class Adamax(Optimizer):
"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
It has been proposed in `Adam: A Method for Stochastic Optimization`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defi... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/adamax.py | 0.920437 | 0.566618 | adamax.py | pypi |
import math
import torch
from .optimizer import Optimizer
class SparseAdam(Optimizer):
r"""Implements lazy version of Adam algorithm suitable for sparse tensors.
In this variant, only moments that show up in the gradient get updated, and
only those portions of the gradient get applied to the parameters.
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/optim/sparse_adam.py | 0.882706 | 0.608158 | sparse_adam.py | pypi |
import math
from torch._six import inf, nan
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Cauchy(Distribution):
r"""
Samples from a Cauchy (Lorentz) distri... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/cauchy.py | 0.948454 | 0.525308 | cauchy.py | pypi |
from numbers import Number
import math
import torch
from torch.distributions import constraints
from torch.distributions.uniform import Uniform
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, ExpTransform
from torch.distributio... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/gumbel.py | 0.937797 | 0.468487 | gumbel.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, lazy_property, logits_to_probs
def _clamp_by_zero(x):
# works like clamp(x, min=0) but has grad at 0 i... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/binomial.py | 0.909793 | 0.354433 | binomial.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.transforms import Transform
from torch.distributions.utils import _sum_rightmost
class TransformedDistribution(Distribution):
r"""
Extension of the Distribution class, wh... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/transformed_distribution.py | 0.939345 | 0.706849 | transformed_distribution.py | pypi |
import torch
import warnings
from torch.distributions import constraints
from torch.distributions.utils import lazy_property
class Distribution(object):
r"""
Distribution is the abstract base class for probability distributions.
"""
has_rsample = False
has_enumerate_support = False
_validate_... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/distribution.py | 0.943484 | 0.509581 | distribution.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits
class ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/bernoulli.py | 0.946745 | 0.522324 | bernoulli.py | pypi |
import torch
from torch.distributions.distribution import Distribution
class ExponentialFamily(Distribution):
r"""
ExponentialFamily is the abstract base class for probability distributions belonging to an
exponential family, whose probability mass/density function has the form is defined below
.. ma... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/exp_family.py | 0.918311 | 0.894005 | exp_family.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Uniform(Distribution):
r"""
Generates uniformly distributed random samples from the half-open interval
``... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/uniform.py | 0.900893 | 0.562237 | uniform.py | pypi |
import math
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _standard_normal, lazy_property
def _batch_mv(bmat, bvec):
r"""
Performs a batched matrix-vector product, with compatible but different batch sh... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/multivariate_normal.py | 0.938513 | 0.76708 | multivariate_normal.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.dirichlet import Dirichlet
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Beta(ExponentialFamily):
r"""
Beta distribution parame... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/beta.py | 0.964888 | 0.562297 | beta.py | pypi |
from numbers import Number
import torch
from torch._six import nan
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.gamma import Gamma
from torch.distributions.utils import broadcast_all
class FisherSnedecor(Distribution):
r"""
Crea... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/fishersnedecor.py | 0.949646 | 0.639328 | fishersnedecor.py | pypi |
from numbers import Number
import math
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property, clamp_probs
from torch.nn.functional import binary_cross_ent... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/continuous_bernoulli.py | 0.939824 | 0.492554 | continuous_bernoulli.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Poisson(ExponentialFamily):
r"""
Creates a Poisson distribution parameterized by :attr:`rate`, the rate pa... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/poisson.py | 0.952153 | 0.537406 | poisson.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.utils import clamp_probs, broadcast_all
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDistribution
fro... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/relaxed_categorical.py | 0.959068 | 0.703637 | relaxed_categorical.py | pypi |
import math
import numbers
import weakref
import torch
import torch.nn.functional as F
from torch.distributions import constraints
from torch.distributions.utils import (_sum_rightmost, broadcast_all,
lazy_property)
from torch.nn.functional import pad
from torch.nn.functional imp... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/transforms.py | 0.926495 | 0.651881 | transforms.py | pypi |
import math
import torch
from torch._six import inf
from torch.distributions import constraints
from torch.distributions.transforms import AbsTransform
from torch.distributions.cauchy import Cauchy
from torch.distributions.transformed_distribution import TransformedDistribution
class HalfCauchy(TransformedDistributi... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/half_cauchy.py | 0.931766 | 0.480174 | half_cauchy.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all
class Laplace(Distribution):
r"""
Creates a Laplace distribution parameterized by :attr:`loc` and :attr:'scale'.
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/laplace.py | 0.898288 | 0.455744 | laplace.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _sum_rightmost
class Independent(Distribution):
r"""
Reinterprets some of the batch dims of a distribution as event dims.
This is mainly useful for chan... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/independent.py | 0.948728 | 0.594492 | independent.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.normal import Normal
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import StickBreakingTransform
class LogisticNormal(TransformedDistribution):
r"""
Creates ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/logistic_normal.py | 0.932768 | 0.703685 | logistic_normal.py | pypi |
import torch
from torch._six import inf
from torch.distributions.distribution import Distribution
from torch.distributions import Categorical
from numbers import Number
from torch.distributions import constraints
from torch.distributions.utils import broadcast_all
class Multinomial(Distribution):
r"""
Creates... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/multinomial.py | 0.951176 | 0.69529 | multinomial.py | pypi |
import math
import torch
from torch._six import inf, nan
from torch.distributions import Chi2, constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _standard_normal, broadcast_all
class StudentT(Distribution):
r"""
Creates a Student's t-distribution para... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/studentT.py | 0.945273 | 0.492554 | studentT.py | pypi |
from functools import update_wrapper
from numbers import Number
import torch
import torch.nn.functional as F
def broadcast_all(*values):
r"""
Given a list of values (possibly containing numbers), returns a list where each
value is broadcasted based on the following rules:
- `torch.*Tensor` instances... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/utils.py | 0.945045 | 0.721792 | utils.py | pypi |
import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
# This helper is exposed for testing.
def _Dirichlet_backward(x, concentration, grad_output):
total = co... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/dirichlet.py | 0.966315 | 0.569374 | dirichlet.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.distribution import Distribution
class OneHotCategorical(Distribution):
r"""
Creates a one-hot categorical distribution parameterized by :attr:`probs` or
:attr:`logits`... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/one_hot_categorical.py | 0.957118 | 0.640819 | one_hot_categorical.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
class Exponential(ExponentialFamily):
r"""
Creates a Exponential distribution parameterized by :attr:`rate`.
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/exponential.py | 0.956012 | 0.438184 | exponential.py | pypi |
import torch
from torch.distributions.distribution import Distribution
from torch.distributions import Categorical
from torch.distributions import constraints
class MixtureSameFamily(Distribution):
r"""
The `MixtureSameFamily` distribution implements a (batch of) mixture
distribution where all component a... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/mixture_same_family.py | 0.943112 | 0.713145 | mixture_same_family.py | pypi |
r"""
The following constraints are implemented:
- ``constraints.boolean``
- ``constraints.cat``
- ``constraints.dependent``
- ``constraints.greater_than(lower_bound)``
- ``constraints.integer_interval(lower_bound, upper_bound)``
- ``constraints.interval(lower_bound, upper_bound)``
- ``constraints.lower_cholesky``
- ``... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/constraints.py | 0.95593 | 0.593904 | constraints.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits
class Geo... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/geometric.py | 0.942015 | 0.618636 | geometric.py | pypi |
import torch
from torch._six import nan
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import probs_to_logits, logits_to_probs, lazy_property
class Categorical(Distribution):
r"""
Creates a categorical distribution parameter... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/categorical.py | 0.949295 | 0.643098 | categorical.py | pypi |
from __future__ import absolute_import, division, print_function
import math
import torch
import torch.jit
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, lazy_property
def _eval_poly(y, coef):
coef = list... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/von_mises.py | 0.892931 | 0.515986 | von_mises.py | pypi |
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import _standard_normal, broadcast_all
class Normal(ExponentialFamily):
r"""
Creates a normal (also called Gaussian) dist... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/normal.py | 0.952717 | 0.467879 | normal.py | pypi |
r"""
The ``distributions`` package contains parameterizable probability distributions
and sampling functions. This allows the construction of stochastic computation
graphs and stochastic gradient estimators for optimization. This package
generally follows the design of the `TensorFlow Distributions`_ package.
.. _`Ten... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/__init__.py | 0.961732 | 0.89974 | __init__.py | pypi |
import torch
from torch.distributions import constraints
from torch.distributions.exponential import Exponential
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, PowerTransform
from torch.distributions.utils import broadcast_all... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/weibull.py | 0.944931 | 0.590425 | weibull.py | pypi |
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all
def _standard_gamma(concentration):
return torch._standard_gamma(concentration)
class Gamma(ExponentialFamily):
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/gamma.py | 0.960482 | 0.626024 | gamma.py | pypi |
import torch
import torch.nn.functional as F
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, lazy_property, logits_to_probs
class NegativeBinomial(Distribution):
r"""
Creates a Negative ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/negative_binomial.py | 0.921118 | 0.582966 | negative_binomial.py | pypi |
import torch
from numbers import Number
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import SigmoidTransform
from torch.distributions.utils import... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributions/relaxed_bernoulli.py | 0.93769 | 0.6346 | relaxed_bernoulli.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from .qconfig import QConfig
from torch.jit._recursive import wrap_cpp_module
class ConvPackedParams(torch.nn.Module):
def __init__(self):
super(ConvPackedParams, self).__init__()
wq = torch._empty_affi... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quantization/_quantize_script.py | 0.878295 | 0.357806 | _quantize_script.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import itertools
import warnings
import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.quantized as nnq
from .default_mappings import (DEFAULT_DYNAMIC_MODULE_MAPPING,
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quantization/quantize.py | 0.800341 | 0.213336 | quantize.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
import copy
import torch.nn.intrinsic.modules.fused as torch_fused
def fuse_conv_bn(conv, bn):
r"""Given the conv and bn modules, fuses them and returns the fused module
Args:
conv: Module instance of typ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quantization/fuse_modules.py | 0.94066 | 0.50952 | fuse_modules.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from torch.nn import Module
from .observer import MovingAverageMinMaxObserver, HistogramObserver, MovingAveragePerChannelMinMaxObserver, _with_args
class FakeQuantize(Module):
r""" Simulate the quantize and dequantize o... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quantization/fake_quantize.py | 0.923962 | 0.576721 | fake_quantize.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
from collections import namedtuple
from .observer import *
from .fake_quantize import *
import torch.nn as nn
class QConfig(namedtuple('QConfig', ['activation', 'weight'])):
"""
Describes how to quantize a layer or a part of the... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quantization/qconfig.py | 0.903114 | 0.27389 | qconfig.py | pypi |
import torch.jit
import inspect
import textwrap
# this file is for generating documentation using sphinx autodoc
# > help(torch.jit.supported_ops) will also give a nice listed of the
# supported ops programmatically
def _hidden(name):
return name.startswith('_') and not name.startswith('__')
def _emit_type(type):... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/jit/supported_ops.py | 0.637934 | 0.155559 | supported_ops.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
class MkldnnLinear(torch.jit.ScriptModule):
def __init__(self, dense_module):
super(MkldnnLinear, self).__init__()
self.register_buffer('weight', dense_module.weight.to_mkldnn())
if dense_modul... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/mkldnn.py | 0.74008 | 0.406626 | mkldnn.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch._C
def format_time(time_us=None, time_ms=None, time_s=None):
'''Defines how to format time'''
assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1
US_IN_SECOND = 1e6
US_IN_MS = 1... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/throughput_benchmark.py | 0.903804 | 0.327561 | throughput_benchmark.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import six
import time
import torch
from tensorboard.compat import tf
from tensorboard.compat.proto.event_pb2 import SessionLog
from tensorboard.compat.proto.event_pb2 import Event
from tensorboard.c... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/tensorboard/writer.py | 0.818737 | 0.291176 | writer.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import logging
import numpy as np
import os
# pylint: disable=unused-import
from six.moves import range
from tensorboard.compat.proto.summary_pb2 import Summary
from tensorboard.compat.proto.summa... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/tensorboard/summary.py | 0.814754 | 0.2135 | summary.py | pypi |
import math
import numpy as np
from ._convert_np import make_np
from ._utils import make_grid
from tensorboard.compat import tf
from tensorboard.plugins.projector.projector_config_pb2 import EmbeddingInfo
def make_tsv(metadata, save_path, metadata_header=None):
if not metadata_header:
metadata = [str(x) f... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/tensorboard/_embedding.py | 0.703549 | 0.425128 | _embedding.py | pypi |
import math
import torch
from . import Sampler
import torch.distributed as dist
class DistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
proc... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/data/distributed.py | 0.904924 | 0.63744 | distributed.py | pypi |
r""""Signal handling for multiprocessing data loading.
NOTE [ Signal handling in multiprocessing data loading ]
In cases like DataLoader, if a worker process dies due to bus error/segfault
or just hang, the main process will hang waiting for data. This is difficult
to avoid on PyTorch side as it can be caused by limi... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/data/_utils/signal_handling.py | 0.691289 | 0.559531 | signal_handling.py | pypi |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter to put
fetched tensors into pinned memory.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
from torch._six import queue, container_abcs, string_classes
from . import MP_STATUS_CHECK_IN... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/data/_utils/pin_memory.py | 0.721056 | 0.32336 | pin_memory.py | pypi |
r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers to
collate samples fetched from dataset into Tensor(s).
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
import re
from torch._six import container_abcs, string_classes, int_cla... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/utils/data/_utils/collate.py | 0.79162 | 0.551211 | collate.py | pypi |
import sys
import torch
def is_built():
r"""Returns whether PyTorch is built with CUDA support. Note that this
doesn't necessarily mean CUDA is available; just that if this PyTorch
binary were run a machine with working CUDA drivers and devices, we
would be able to use it."""
return torch._C.has_... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/backends/cuda/__init__.py | 0.701406 | 0.382545 | __init__.py | pypi |
try:
from urllib.parse import urlparse, urlunparse
except ImportError:
from urlparse import urlparse, urlunparse
import torch._six as six
import numbers
import os
from . import FileStore, TCPStore
from .constants import default_pg_timeout
_rendezvous_handlers = {}
def register_rendezvous_handler(scheme, ha... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributed/rendezvous.py | 0.752377 | 0.282779 | rendezvous.py | pypi |
import torch.distributed.rpc as rpc
import torch.distributed.autograd as dist_autograd
from collections import defaultdict
from threading import Lock
class _LocalOptimizer:
# Ideally we would only need to share a lock for instances of
# _LocalOptimizer that deal with the same parameters. We are
# making ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/distributed/optim/optimizer.py | 0.770983 | 0.378861 | optimizer.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import multiprocessing
import multiprocessing.connection
import signal
import sys
import warnings
from . import _prctl_pr_set_pdeathsig
def _wrap(fn, i, args, error_queue):
# prctl(2) is a Linux specific system call.
# On oth... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/multiprocessing/spawn.py | 0.562657 | 0.169028 | spawn.py | pypi |
import torch
import functools
import inspect
class _DecoratorContextManager:
"""Allow a context manager to be used as a decorator"""
def __call__(self, func):
if inspect.isgeneratorfunction(func):
return self._wrap_generator(func)
@functools.wraps(func)
def decorate_contex... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/autograd/grad_mode.py | 0.857709 | 0.410993 | grad_mode.py | pypi |
import torch
import warnings
class detect_anomaly(object):
r"""Context-manager that enable anomaly detection for the autograd engine.
This does two things:
- Running the forward pass with detection enabled will allow the backward
pass to print the traceback of the forward operation that created the fa... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/autograd/anomaly_mode.py | 0.842604 | 0.465873 | anomaly_mode.py | pypi |
from functools import reduce
def maybe_view(tensor, size, check_same_size=True):
if check_same_size and tensor.size() == size:
return tensor
return tensor.contiguous().view(size)
def maybe_unexpand(tensor, old_size, check_same_size=True):
if check_same_size and tensor.size() == old_size:
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/autograd/_functions/utils.py | 0.827967 | 0.66266 | utils.py | pypi |
import warnings
from torchvision import models
from torchvision import datasets
from torchvision import ops
from torchvision import transforms
from torchvision import utils
from torchvision import io
from .extension import _HAS_OPS
import torch
try:
from .version import __version__ # noqa: F401
except ImportErr... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/__init__.py | 0.894427 | 0.361841 | __init__.py | pypi |
import numbers
import random
from torchvision.transforms import (
RandomCrop,
RandomResizedCrop,
)
from . import _functional_video as F
__all__ = [
"RandomCropVideo",
"RandomResizedCropVideo",
"CenterCropVideo",
"NormalizeVideo",
"ToTensorVideo",
"RandomHorizontalFlipVideo",
]
cla... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/transforms/_transforms_video.py | 0.912988 | 0.280719 | _transforms_video.py | pypi |
import torch
import torchvision.transforms.functional as F
from torch import Tensor
from torch.jit.annotations import Optional, List, BroadcastingList2, Tuple
def _is_tensor_a_torch_image(input):
return len(input.shape) == 3
def vflip(img):
# type: (Tensor) -> Tensor
"""Vertically flip the given the Ima... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/transforms/functional_tensor.py | 0.962108 | 0.916633 | functional_tensor.py | pypi |
import torch
def _is_tensor_video_clip(clip):
if not torch.is_tensor(clip):
raise TypeError("clip should be Tesnor. Got %s" % type(clip))
if not clip.ndimension() == 4:
raise ValueError("clip should be 4D. Got %dD" % clip.dim())
return True
def crop(clip, i, j, h, w):
"""
Args:... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/transforms/_functional_video.py | 0.91191 | 0.733667 | _functional_video.py | pypi |
import torch
import torch.nn as nn
import torch.nn.init as init
from .utils import load_state_dict_from_url
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
model_urls = {
'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
'squeezenet1_1': 'https://download.pytorch.or... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/squeezenet.py | 0.899195 | 0.450903 | squeezenet.py | pypi |
from collections import namedtuple
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional
from torch import Tensor
from .utils import load_state_dict_from_url
__all__ = ['Inception3', 'inception_v3', 'InceptionOutputs', '_InceptionOutputs']
mode... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/inception.py | 0.948466 | 0.600716 | inception.py | pypi |
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from .utils import load_state_dict_from_url
from torch import Tensor
from torch.jit.annotations import List
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densene... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/densenet.py | 0.856317 | 0.395718 | densenet.py | pypi |
from torch import nn
from .utils import load_state_dict_from_url
__all__ = ['MobileNetV2', 'mobilenet_v2']
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/mobilenet.py | 0.947539 | 0.446917 | mobilenet.py | pypi |
import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional, Tuple
from torch import Tensor
from .utils import load_state_dict_from_url
__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOutputs", "_GoogLeNetOutputs"]
mod... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/googlenet.py | 0.906511 | 0.339828 | googlenet.py | pypi |
import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = [
'ShuffleNetV2', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0',
'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0'
]
model_urls = {
'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/shufflenetv2.py | 0.897858 | 0.466846 | shufflenetv2.py | pypi |
import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = ['AlexNet', 'alexnet']
model_urls = {
'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/alexnet.py | 0.93759 | 0.364466 | alexnet.py | pypi |
import warnings
import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = ['MNASNet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3']
_MODEL_URLS = {
"mnasnet0_5":
"https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
"mnasnet0_75": None,
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/mnasnet.py | 0.927986 | 0.558026 | mnasnet.py | pypi |
import torch
import torch.nn as nn
from .utils import load_state_dict_from_url
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/vgg.py | 0.930229 | 0.500488 | vgg.py | pypi |
from collections import OrderedDict
import torch
from torch import nn
from torch.jit.annotations import Dict
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules have been registered
into the model ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/_utils.py | 0.952508 | 0.748444 | _utils.py | pypi |
import torch
from torch import nn
from torch.nn import functional as F
from ._utils import _SimpleSegmentationModel
__all__ = ["DeepLabV3"]
class DeepLabV3(_SimpleSegmentationModel):
"""
Implements DeepLabV3 model from
`"Rethinking Atrous Convolution for Semantic Image Segmentation"
<https://arxiv.... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/segmentation/deeplabv3.py | 0.947769 | 0.550728 | deeplabv3.py | pypi |
from .._utils import IntermediateLayerGetter
from ..utils import load_state_dict_from_url
from .. import resnet
from .deeplabv3 import DeepLabHead, DeepLabV3
from .fcn import FCN, FCNHead
__all__ = ['fcn_resnet50', 'fcn_resnet101', 'deeplabv3_resnet50', 'deeplabv3_resnet101']
model_urls = {
'fcn_resnet50_coco':... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/segmentation/segmentation.py | 0.779322 | 0.341994 | segmentation.py | pypi |
import torch
import torch.nn as nn
from ..utils import load_state_dict_from_url
__all__ = ['r3d_18', 'mc3_18', 'r2plus1d_18']
model_urls = {
'r3d_18': 'https://download.pytorch.org/models/r3d_18-b3b3357e.pth',
'mc3_18': 'https://download.pytorch.org/models/mc3_18-a90a0ba3.pth',
'r2plus1d_18': 'https://d... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/video/resnet.py | 0.947284 | 0.546315 | resnet.py | pypi |
import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import inception as inception_module
from torchvision.models.inception import InceptionOutputs
from torch.jit.annotations import Optional
from torchvision.models.utils import lo... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/quantization/inception.py | 0.834407 | 0.566139 | inception.py | pypi |
from torch import nn
from torchvision.models.utils import load_state_dict_from_url
from torchvision.models.mobilenet import InvertedResidual, ConvBNReLU, MobileNetV2, model_urls
from torch.quantization import QuantStub, DeQuantStub, fuse_modules
from .utils import _replace_relu, quantize_model
__all__ = ['Quantizable... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/quantization/mobilenet.py | 0.857067 | 0.331201 | mobilenet.py | pypi |
import warnings
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.jit.annotations import Optional
from torchvision.models.utils import load_state_dict_from_url
from torchvision.models.googlenet import (
GoogLeNetOutputs, BasicConv2d, Inception, InceptionAux, GoogLeNet, model_urls)
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/quantization/googlenet.py | 0.784319 | 0.500183 | googlenet.py | pypi |
import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
import torchvision.models.shufflenetv2
import sys
from .utils import _replace_relu, quantize_model
shufflenetv2 = sys.modules['torchvision.models.shufflenetv2']
__all__ = [
'QuantizableShuffleNetV2', 'shufflenet_v2_x0... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/quantization/shufflenetv2.py | 0.642881 | 0.333707 | shufflenetv2.py | pypi |
from collections import OrderedDict
from torch import nn
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
from torchvision.ops import misc as misc_nn_ops
from .._utils import IntermediateLayerGetter
from .. import resnet
class BackboneWithFPN(nn.Module):
"""
Adds a ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/detection/backbone_utils.py | 0.949844 | 0.441312 | backbone_utils.py | pypi |
import random
import math
import torch
from torch import nn, Tensor
import torchvision
from torch.jit.annotations import List, Tuple, Dict, Optional
from torchvision.ops import misc as misc_nn_ops
from .image_list import ImageList
from .roi_heads import paste_masks_in_image
@torch.jit.unused
def _resize_image_and_ma... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/detection/transform.py | 0.887926 | 0.544801 | transform.py | pypi |
import math
import torch
from torch.jit.annotations import List, Tuple
from torch import Tensor
import torchvision
# TODO: https://github.com/pytorch/pytorch/issues/26727
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/detection/_utils.py | 0.736306 | 0.674443 | _utils.py | pypi |
from collections import OrderedDict
import torch
from torch import nn
import warnings
from torch.jit.annotations import Tuple, List, Dict, Optional
from torch import Tensor
class GeneralizedRCNN(nn.Module):
"""
Main class for Generalized R-CNN.
Arguments:
backbone (nn.Module):
rpn (nn.Mod... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/models/detection/generalized_rcnn.py | 0.962612 | 0.558086 | generalized_rcnn.py | pypi |
import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import List
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
def ps_roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1):
# type: (Tensor, Tensor, int, float,... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/ps_roi_align.py | 0.902629 | 0.773772 | ps_roi_align.py | pypi |
import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import List
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
def ps_roi_pool(input, boxes, output_size, spatial_scale=1.0):
# type: (Tensor, Tensor, int, float) -> Tensor
"""
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/ps_roi_pool.py | 0.902583 | 0.756627 | ps_roi_pool.py | pypi |
import sys
import torch
_onnx_opset_version = 11
def _register_custom_op():
from torch.onnx.symbolic_helper import parse_args, scalar_type_to_onnx, scalar_type_to_pytorch_type, \
cast_pytorch_to_onnx
from torch.onnx.symbolic_opset9 import select, unsqueeze, squeeze, _cast_Long, reshape
@parse_ar... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/_register_onnx_ops.py | 0.560974 | 0.336713 | _register_onnx_ops.py | pypi |
import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import List, BroadcastingList2
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
def roi_pool(input, boxes, output_size, spatial_scale=1.0):
# type: (Tensor, Tensor, BroadcastingLis... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/roi_pool.py | 0.891923 | 0.762336 | roi_pool.py | pypi |
import math
import torch
from torch import nn, Tensor
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.nn.modules.utils import _pair
from torch.jit.annotations import Optional, Tuple
def deform_conv2d(input, offset, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/deform_conv.py | 0.93847 | 0.707177 | deform_conv.py | pypi |
import torch
from torch import nn, Tensor
from torch.nn.modules.utils import _pair
from torch.jit.annotations import List, BroadcastingList2
from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
def roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1, aligned=False):
# typ... | /rpi_torchvision-0.7.0-cp37-cp37m-linux_armv7l.whl/torchvision/ops/roi_align.py | 0.90928 | 0.773302 | roi_align.py | pypi |
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