627 lines
21 KiB
Python
627 lines
21 KiB
Python
"""This file contains utilities for initializing neural network parameters."""
|
|
import math
|
|
import warnings
|
|
|
|
from torch import Tensor
|
|
import torch
|
|
from typing import Optional as _Optional
|
|
|
|
# These no_grad_* functions are necessary as wrappers around the parts of these
|
|
# functions that use `with torch.no_grad()`. The JIT doesn't support context
|
|
# managers, so these need to be implemented as builtins. Using these wrappers
|
|
# lets us keep those builtins small and re-usable.
|
|
def _no_grad_uniform_(tensor, a, b, generator=None):
|
|
with torch.no_grad():
|
|
return tensor.uniform_(a, b, generator=generator)
|
|
|
|
|
|
def _no_grad_normal_(tensor, mean, std, generator=None):
|
|
with torch.no_grad():
|
|
return tensor.normal_(mean, std, generator=generator)
|
|
|
|
|
|
def _no_grad_trunc_normal_(tensor, mean, std, a, b, generator=None):
|
|
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
|
def norm_cdf(x):
|
|
# Computes standard normal cumulative distribution function
|
|
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
|
|
|
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
|
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
|
"The distribution of values may be incorrect.",
|
|
stacklevel=2)
|
|
|
|
with torch.no_grad():
|
|
# Values are generated by using a truncated uniform distribution and
|
|
# then using the inverse CDF for the normal distribution.
|
|
# Get upper and lower cdf values
|
|
l = norm_cdf((a - mean) / std)
|
|
u = norm_cdf((b - mean) / std)
|
|
|
|
# Uniformly fill tensor with values from [l, u], then translate to
|
|
# [2l-1, 2u-1].
|
|
tensor.uniform_(2 * l - 1, 2 * u - 1, generator=generator)
|
|
|
|
# Use inverse cdf transform for normal distribution to get truncated
|
|
# standard normal
|
|
tensor.erfinv_()
|
|
|
|
# Transform to proper mean, std
|
|
tensor.mul_(std * math.sqrt(2.))
|
|
tensor.add_(mean)
|
|
|
|
# Clamp to ensure it's in the proper range
|
|
tensor.clamp_(min=a, max=b)
|
|
return tensor
|
|
|
|
|
|
def _no_grad_fill_(tensor, val):
|
|
with torch.no_grad():
|
|
return tensor.fill_(val)
|
|
|
|
|
|
def _no_grad_zero_(tensor):
|
|
with torch.no_grad():
|
|
return tensor.zero_()
|
|
|
|
|
|
def calculate_gain(nonlinearity, param=None):
|
|
r"""Return the recommended gain value for the given nonlinearity function.
|
|
|
|
The values are as follows:
|
|
|
|
================= ====================================================
|
|
nonlinearity gain
|
|
================= ====================================================
|
|
Linear / Identity :math:`1`
|
|
Conv{1,2,3}D :math:`1`
|
|
Sigmoid :math:`1`
|
|
Tanh :math:`\frac{5}{3}`
|
|
ReLU :math:`\sqrt{2}`
|
|
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
|
|
SELU :math:`\frac{3}{4}`
|
|
================= ====================================================
|
|
|
|
.. warning::
|
|
In order to implement `Self-Normalizing Neural Networks`_ ,
|
|
you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``.
|
|
This gives the initial weights a variance of ``1 / N``,
|
|
which is necessary to induce a stable fixed point in the forward pass.
|
|
In contrast, the default gain for ``SELU`` sacrifices the normalization
|
|
effect for more stable gradient flow in rectangular layers.
|
|
|
|
Args:
|
|
nonlinearity: the non-linear function (`nn.functional` name)
|
|
param: optional parameter for the non-linear function
|
|
|
|
Examples:
|
|
>>> gain = nn.init.calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
|
|
|
|
.. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html
|
|
"""
|
|
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
|
|
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
|
|
return 1
|
|
elif nonlinearity == 'tanh':
|
|
return 5.0 / 3
|
|
elif nonlinearity == 'relu':
|
|
return math.sqrt(2.0)
|
|
elif nonlinearity == 'leaky_relu':
|
|
if param is None:
|
|
negative_slope = 0.01
|
|
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
|
|
# True/False are instances of int, hence check above
|
|
negative_slope = param
|
|
else:
|
|
raise ValueError(f"negative_slope {param} not a valid number")
|
|
return math.sqrt(2.0 / (1 + negative_slope ** 2))
|
|
elif nonlinearity == 'selu':
|
|
return 3.0 / 4 # Value found empirically (https://github.com/pytorch/pytorch/pull/50664)
|
|
else:
|
|
raise ValueError(f"Unsupported nonlinearity {nonlinearity}")
|
|
|
|
|
|
def uniform_(
|
|
tensor: Tensor,
|
|
a: float = 0.0,
|
|
b: float = 1.0,
|
|
generator: _Optional[torch.Generator] = None,
|
|
) -> Tensor:
|
|
r"""Fill the input Tensor with values drawn from the uniform distribution.
|
|
|
|
:math:`\mathcal{U}(a, b)`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
a: the lower bound of the uniform distribution
|
|
b: the upper bound of the uniform distribution
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.uniform_(w)
|
|
"""
|
|
if torch.overrides.has_torch_function_variadic(tensor):
|
|
return torch.overrides.handle_torch_function(
|
|
uniform_, (tensor,), tensor=tensor, a=a, b=b, generator=generator
|
|
)
|
|
return _no_grad_uniform_(tensor, a, b, generator)
|
|
|
|
|
|
def normal_(
|
|
tensor: Tensor,
|
|
mean: float = 0.0,
|
|
std: float = 1.0,
|
|
generator: _Optional[torch.Generator] = None,
|
|
) -> Tensor:
|
|
r"""Fill the input Tensor with values drawn from the normal distribution.
|
|
|
|
:math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
mean: the mean of the normal distribution
|
|
std: the standard deviation of the normal distribution
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.normal_(w)
|
|
"""
|
|
if torch.overrides.has_torch_function_variadic(tensor):
|
|
return torch.overrides.handle_torch_function(
|
|
normal_, (tensor,), tensor=tensor, mean=mean, std=std, generator=generator
|
|
)
|
|
return _no_grad_normal_(tensor, mean, std, generator)
|
|
|
|
def trunc_normal_(
|
|
tensor: Tensor,
|
|
mean: float = 0.,
|
|
std: float = 1.,
|
|
a: float = -2.,
|
|
b: float = 2.,
|
|
generator: _Optional[torch.Generator] = None
|
|
) -> Tensor:
|
|
r"""Fill the input Tensor with values drawn from a truncated normal distribution.
|
|
|
|
The values are effectively drawn from the
|
|
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
|
|
with values outside :math:`[a, b]` redrawn until they are within
|
|
the bounds. The method used for generating the random values works
|
|
best when :math:`a \leq \text{mean} \leq b`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
mean: the mean of the normal distribution
|
|
std: the standard deviation of the normal distribution
|
|
a: the minimum cutoff value
|
|
b: the maximum cutoff value
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.trunc_normal_(w)
|
|
"""
|
|
return _no_grad_trunc_normal_(tensor, mean, std, a, b, generator=generator)
|
|
|
|
|
|
def constant_(tensor: Tensor, val: float) -> Tensor:
|
|
r"""Fill the input Tensor with the value :math:`\text{val}`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
val: the value to fill the tensor with
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.constant_(w, 0.3)
|
|
"""
|
|
if torch.overrides.has_torch_function_variadic(tensor):
|
|
return torch.overrides.handle_torch_function(constant_, (tensor,), tensor=tensor, val=val)
|
|
return _no_grad_fill_(tensor, val)
|
|
|
|
|
|
def ones_(tensor: Tensor) -> Tensor:
|
|
r"""Fill the input Tensor with the scalar value `1`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.ones_(w)
|
|
"""
|
|
return _no_grad_fill_(tensor, 1.)
|
|
|
|
|
|
def zeros_(tensor: Tensor) -> Tensor:
|
|
r"""Fill the input Tensor with the scalar value `0`.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.zeros_(w)
|
|
"""
|
|
return _no_grad_zero_(tensor)
|
|
|
|
|
|
def eye_(tensor):
|
|
r"""Fill the 2-dimensional input `Tensor` with the identity matrix.
|
|
|
|
Preserves the identity of the inputs in `Linear` layers, where as
|
|
many inputs are preserved as possible.
|
|
|
|
Args:
|
|
tensor: a 2-dimensional `torch.Tensor`
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.eye_(w)
|
|
"""
|
|
if tensor.ndimension() != 2:
|
|
raise ValueError("Only tensors with 2 dimensions are supported")
|
|
|
|
with torch.no_grad():
|
|
torch.eye(*tensor.shape, out=tensor, requires_grad=tensor.requires_grad)
|
|
return tensor
|
|
|
|
|
|
def dirac_(tensor, groups=1):
|
|
r"""Fill the {3, 4, 5}-dimensional input `Tensor` with the Dirac delta function.
|
|
|
|
Preserves the identity of the inputs in `Convolutional`
|
|
layers, where as many input channels are preserved as possible. In case
|
|
of groups>1, each group of channels preserves identity
|
|
|
|
Args:
|
|
tensor: a {3, 4, 5}-dimensional `torch.Tensor`
|
|
groups (int, optional): number of groups in the conv layer (default: 1)
|
|
Examples:
|
|
>>> w = torch.empty(3, 16, 5, 5)
|
|
>>> nn.init.dirac_(w)
|
|
>>> w = torch.empty(3, 24, 5, 5)
|
|
>>> nn.init.dirac_(w, 3)
|
|
"""
|
|
dimensions = tensor.ndimension()
|
|
if dimensions not in [3, 4, 5]:
|
|
raise ValueError("Only tensors with 3, 4, or 5 dimensions are supported")
|
|
|
|
sizes = tensor.size()
|
|
|
|
if sizes[0] % groups != 0:
|
|
raise ValueError('dim 0 must be divisible by groups')
|
|
|
|
out_chans_per_grp = sizes[0] // groups
|
|
min_dim = min(out_chans_per_grp, sizes[1])
|
|
|
|
with torch.no_grad():
|
|
tensor.zero_()
|
|
|
|
for g in range(groups):
|
|
for d in range(min_dim):
|
|
if dimensions == 3: # Temporal convolution
|
|
tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2] = 1
|
|
elif dimensions == 4: # Spatial convolution
|
|
tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2,
|
|
tensor.size(3) // 2] = 1
|
|
else: # Volumetric convolution
|
|
tensor[g * out_chans_per_grp + d, d, tensor.size(2) // 2,
|
|
tensor.size(3) // 2, tensor.size(4) // 2] = 1
|
|
return tensor
|
|
|
|
|
|
def _calculate_fan_in_and_fan_out(tensor):
|
|
dimensions = tensor.dim()
|
|
if dimensions < 2:
|
|
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
|
|
|
|
num_input_fmaps = tensor.size(1)
|
|
num_output_fmaps = tensor.size(0)
|
|
receptive_field_size = 1
|
|
if tensor.dim() > 2:
|
|
# math.prod is not always available, accumulate the product manually
|
|
# we could use functools.reduce but that is not supported by TorchScript
|
|
for s in tensor.shape[2:]:
|
|
receptive_field_size *= s
|
|
fan_in = num_input_fmaps * receptive_field_size
|
|
fan_out = num_output_fmaps * receptive_field_size
|
|
|
|
return fan_in, fan_out
|
|
|
|
|
|
def xavier_uniform_(
|
|
tensor: Tensor, gain: float = 1.0, generator: _Optional[torch.Generator] = None
|
|
) -> Tensor:
|
|
r"""Fill the input `Tensor` with values using a Xavier uniform distribution.
|
|
|
|
The method is described in `Understanding the difficulty of training
|
|
deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010).
|
|
The resulting tensor will have values sampled from
|
|
:math:`\mathcal{U}(-a, a)` where
|
|
|
|
.. math::
|
|
a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}
|
|
|
|
Also known as Glorot initialization.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
gain: an optional scaling factor
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
|
|
"""
|
|
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
|
std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
|
|
a = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation
|
|
|
|
return _no_grad_uniform_(tensor, -a, a, generator)
|
|
|
|
|
|
def xavier_normal_(
|
|
tensor: Tensor,
|
|
gain: float = 1.0,
|
|
generator: _Optional[torch.Generator] = None,
|
|
) -> Tensor:
|
|
r"""Fill the input `Tensor` with values using a Xavier normal distribution.
|
|
|
|
The method is described in `Understanding the difficulty of training deep feedforward
|
|
neural networks` - Glorot, X. & Bengio, Y. (2010). The resulting tensor
|
|
will have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
|
|
|
|
.. math::
|
|
\text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}
|
|
|
|
Also known as Glorot initialization.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
gain: an optional scaling factor
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.xavier_normal_(w)
|
|
"""
|
|
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
|
std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
|
|
|
|
return _no_grad_normal_(tensor, 0., std, generator)
|
|
|
|
|
|
def _calculate_correct_fan(tensor, mode):
|
|
mode = mode.lower()
|
|
valid_modes = ['fan_in', 'fan_out']
|
|
if mode not in valid_modes:
|
|
raise ValueError(f"Mode {mode} not supported, please use one of {valid_modes}")
|
|
|
|
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
|
return fan_in if mode == 'fan_in' else fan_out
|
|
|
|
|
|
def kaiming_uniform_(
|
|
tensor: Tensor,
|
|
a: float = 0,
|
|
mode: str = "fan_in",
|
|
nonlinearity: str = "leaky_relu",
|
|
generator: _Optional[torch.Generator] = None,
|
|
):
|
|
r"""Fill the input `Tensor` with values using a Kaiming uniform distribution.
|
|
|
|
The method is described in `Delving deep into rectifiers: Surpassing
|
|
human-level performance on ImageNet classification` - He, K. et al. (2015).
|
|
The resulting tensor will have values sampled from
|
|
:math:`\mathcal{U}(-\text{bound}, \text{bound})` where
|
|
|
|
.. math::
|
|
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
|
|
|
|
Also known as He initialization.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
a: the negative slope of the rectifier used after this layer (only
|
|
used with ``'leaky_relu'``)
|
|
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
|
preserves the magnitude of the variance of the weights in the
|
|
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
|
backwards pass.
|
|
nonlinearity: the non-linear function (`nn.functional` name),
|
|
recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu')
|
|
"""
|
|
if torch.overrides.has_torch_function_variadic(tensor):
|
|
return torch.overrides.handle_torch_function(
|
|
kaiming_uniform_,
|
|
(tensor,),
|
|
tensor=tensor,
|
|
a=a,
|
|
mode=mode,
|
|
nonlinearity=nonlinearity,
|
|
generator=generator)
|
|
|
|
if 0 in tensor.shape:
|
|
warnings.warn("Initializing zero-element tensors is a no-op")
|
|
return tensor
|
|
fan = _calculate_correct_fan(tensor, mode)
|
|
gain = calculate_gain(nonlinearity, a)
|
|
std = gain / math.sqrt(fan)
|
|
bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation
|
|
with torch.no_grad():
|
|
return tensor.uniform_(-bound, bound, generator=generator)
|
|
|
|
|
|
def kaiming_normal_(
|
|
tensor: Tensor,
|
|
a: float = 0,
|
|
mode: str = "fan_in",
|
|
nonlinearity: str = "leaky_relu",
|
|
generator: _Optional[torch.Generator] = None,
|
|
):
|
|
r"""Fill the input `Tensor` with values using a Kaiming normal distribution.
|
|
|
|
The method is described in `Delving deep into rectifiers: Surpassing
|
|
human-level performance on ImageNet classification` - He, K. et al. (2015).
|
|
The resulting tensor will have values sampled from
|
|
:math:`\mathcal{N}(0, \text{std}^2)` where
|
|
|
|
.. math::
|
|
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
|
|
|
|
Also known as He initialization.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
a: the negative slope of the rectifier used after this layer (only
|
|
used with ``'leaky_relu'``)
|
|
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
|
preserves the magnitude of the variance of the weights in the
|
|
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
|
backwards pass.
|
|
nonlinearity: the non-linear function (`nn.functional` name),
|
|
recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')
|
|
"""
|
|
if 0 in tensor.shape:
|
|
warnings.warn("Initializing zero-element tensors is a no-op")
|
|
return tensor
|
|
fan = _calculate_correct_fan(tensor, mode)
|
|
gain = calculate_gain(nonlinearity, a)
|
|
std = gain / math.sqrt(fan)
|
|
with torch.no_grad():
|
|
return tensor.normal_(0, std, generator=generator)
|
|
|
|
|
|
def orthogonal_(
|
|
tensor,
|
|
gain=1,
|
|
generator: _Optional[torch.Generator] = None,
|
|
):
|
|
r"""Fill the input `Tensor` with a (semi) orthogonal matrix.
|
|
|
|
Described in `Exact solutions to the nonlinear dynamics of learning in deep
|
|
linear neural networks` - Saxe, A. et al. (2013). The input tensor must have
|
|
at least 2 dimensions, and for tensors with more than 2 dimensions the
|
|
trailing dimensions are flattened.
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2`
|
|
gain: optional scaling factor
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK)
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.orthogonal_(w)
|
|
"""
|
|
if tensor.ndimension() < 2:
|
|
raise ValueError("Only tensors with 2 or more dimensions are supported")
|
|
|
|
if tensor.numel() == 0:
|
|
# no-op
|
|
return tensor
|
|
rows = tensor.size(0)
|
|
cols = tensor.numel() // rows
|
|
flattened = tensor.new(rows, cols).normal_(0, 1, generator=generator)
|
|
|
|
if rows < cols:
|
|
flattened.t_()
|
|
|
|
# Compute the qr factorization
|
|
q, r = torch.linalg.qr(flattened)
|
|
# Make Q uniform according to https://arxiv.org/pdf/math-ph/0609050.pdf
|
|
d = torch.diag(r, 0)
|
|
ph = d.sign()
|
|
q *= ph
|
|
|
|
if rows < cols:
|
|
q.t_()
|
|
|
|
with torch.no_grad():
|
|
tensor.view_as(q).copy_(q)
|
|
tensor.mul_(gain)
|
|
return tensor
|
|
|
|
|
|
def sparse_(
|
|
tensor,
|
|
sparsity,
|
|
std=0.01,
|
|
generator: _Optional[torch.Generator] = None,
|
|
):
|
|
r"""Fill the 2D input `Tensor` as a sparse matrix.
|
|
|
|
The non-zero elements will be drawn from the normal distribution
|
|
:math:`\mathcal{N}(0, 0.01)`, as described in `Deep learning via
|
|
Hessian-free optimization` - Martens, J. (2010).
|
|
|
|
Args:
|
|
tensor: an n-dimensional `torch.Tensor`
|
|
sparsity: The fraction of elements in each column to be set to zero
|
|
std: the standard deviation of the normal distribution used to generate
|
|
the non-zero values
|
|
generator: the torch Generator to sample from (default: None)
|
|
|
|
Examples:
|
|
>>> w = torch.empty(3, 5)
|
|
>>> nn.init.sparse_(w, sparsity=0.1)
|
|
"""
|
|
if tensor.ndimension() != 2:
|
|
raise ValueError("Only tensors with 2 dimensions are supported")
|
|
|
|
rows, cols = tensor.shape
|
|
num_zeros = int(math.ceil(sparsity * rows))
|
|
|
|
with torch.no_grad():
|
|
tensor.normal_(0, std, generator=generator)
|
|
for col_idx in range(cols):
|
|
row_indices = torch.randperm(rows)
|
|
zero_indices = row_indices[:num_zeros]
|
|
tensor[zero_indices, col_idx] = 0
|
|
return tensor
|
|
|
|
|
|
# for backward compatibility
|
|
def _make_deprecate(meth):
|
|
new_name = meth.__name__
|
|
old_name = new_name[:-1]
|
|
|
|
def deprecated_init(*args, **kwargs):
|
|
warnings.warn(f"nn.init.{old_name} is now deprecated in favor of nn.init.{new_name}.", stacklevel=2)
|
|
return meth(*args, **kwargs)
|
|
|
|
deprecated_init.__doc__ = fr"""
|
|
{old_name}(...)
|
|
|
|
.. warning::
|
|
This method is now deprecated in favor of :func:`torch.nn.init.{new_name}`.
|
|
|
|
See :func:`~torch.nn.init.{new_name}` for details."""
|
|
deprecated_init.__name__ = old_name
|
|
return deprecated_init
|
|
|
|
|
|
uniform = _make_deprecate(uniform_)
|
|
normal = _make_deprecate(normal_)
|
|
constant = _make_deprecate(constant_)
|
|
eye = _make_deprecate(eye_)
|
|
dirac = _make_deprecate(dirac_)
|
|
xavier_uniform = _make_deprecate(xavier_uniform_)
|
|
xavier_normal = _make_deprecate(xavier_normal_)
|
|
kaiming_uniform = _make_deprecate(kaiming_uniform_)
|
|
kaiming_normal = _make_deprecate(kaiming_normal_)
|
|
orthogonal = _make_deprecate(orthogonal_)
|
|
sparse = _make_deprecate(sparse_)
|