pytorch_pfn_extras.nn.modules.lazy.UninitializedParameter#
- class pytorch_pfn_extras.nn.modules.lazy.UninitializedParameter(data=None, requires_grad=True)#
Bases:
Parameter
Methods
__init__
()abs
()See
torch.abs()
abs_
()In-place version of
abs()
absolute
()Alias for
abs()
absolute_
()In-place version of
absolute()
Alias forabs_()
acos
()See
torch.acos()
acos_
()In-place version of
acos()
acosh
()See
torch.acosh()
acosh_
()In-place version of
acosh()
add
(other, *[, alpha])Add a scalar or tensor to
self
tensor.add_
(other, *[, alpha])In-place version of
add()
addbmm
(batch1, batch2, *[, beta, alpha])See
torch.addbmm()
addbmm_
(batch1, batch2, *[, beta, alpha])In-place version of
addbmm()
addcdiv
(tensor1, tensor2, *[, value])See
torch.addcdiv()
addcdiv_
(tensor1, tensor2, *[, value])In-place version of
addcdiv()
addcmul
(tensor1, tensor2, *[, value])See
torch.addcmul()
addcmul_
(tensor1, tensor2, *[, value])In-place version of
addcmul()
addmm
(mat1, mat2, *[, beta, alpha])See
torch.addmm()
addmm_
(mat1, mat2, *[, beta, alpha])In-place version of
addmm()
addmv
(mat, vec, *[, beta, alpha])See
torch.addmv()
addmv_
(mat, vec, *[, beta, alpha])In-place version of
addmv()
addr
(vec1, vec2, *[, beta, alpha])See
torch.addr()
addr_
(vec1, vec2, *[, beta, alpha])In-place version of
addr()
adjoint
()Alias for
adjoint()
align_as
(other)Permutes the dimensions of the
self
tensor to match the dimension order in theother
tensor, adding size-one dims for any new names.align_to
(*names)Permutes the dimensions of the
self
tensor to match the order specified innames
, adding size-one dims for any new names.all
([dim, keepdim])See
torch.all()
allclose
(other[, rtol, atol, equal_nan])See
torch.allclose()
amax
([dim, keepdim])See
torch.amax()
amin
([dim, keepdim])See
torch.amin()
aminmax
(*[, dim, keepdim])See
torch.aminmax()
angle
()See
torch.angle()
any
([dim, keepdim])See
torch.any()
apply_
(callable)Applies the function
callable
to each element in the tensor, replacing each element with the value returned bycallable
.arccos
()See
torch.arccos()
arccos_
()In-place version of
arccos()
arccosh
acosh() -> Tensor
arccosh_
acosh_() -> Tensor
arcsin
()See
torch.arcsin()
arcsin_
()In-place version of
arcsin()
arcsinh
()See
torch.arcsinh()
arcsinh_
()In-place version of
arcsinh()
arctan
()See
torch.arctan()
arctan2
(other)See
torch.arctan2()
arctan2_
atan2_(other) -> Tensor
arctan_
()In-place version of
arctan()
arctanh
()See
torch.arctanh()
arctanh_
(other)In-place version of
arctanh()
argmax
([dim, keepdim])See
torch.argmax()
argmin
([dim, keepdim])See
torch.argmin()
argsort
([dim, descending])See
torch.argsort()
argwhere
()See
torch.argwhere()
as_strided
(size, stride[, storage_offset])See
torch.as_strided()
as_strided_
(size, stride[, storage_offset])In-place version of
as_strided()
as_strided_scatter
(src, size, stride[, ...])See
torch.as_strided_scatter()
as_subclass
(cls)Makes a
cls
instance with the same data pointer asself
.asin
()See
torch.asin()
asin_
()In-place version of
asin()
asinh
()See
torch.asinh()
asinh_
()In-place version of
asinh()
atan
()See
torch.atan()
atan2
(other)See
torch.atan2()
atan2_
(other)In-place version of
atan2()
atan_
()In-place version of
atan()
atanh
()See
torch.atanh()
atanh_
(other)In-place version of
atanh()
backward
([gradient, retain_graph, ...])Computes the gradient of current tensor wrt graph leaves.
baddbmm
(batch1, batch2, *[, beta, alpha])See
torch.baddbmm()
baddbmm_
(batch1, batch2, *[, beta, alpha])In-place version of
baddbmm()
bernoulli
(*[, generator])Returns a result tensor where each \(\texttt{result[i]}\) is independently sampled from \(\text{Bernoulli}(\texttt{self[i]})\).
bernoulli_
([p, generator])Fills each location of
self
with an independent sample from \(\text{Bernoulli}(\texttt{p})\).bfloat16
([memory_format])self.bfloat16()
is equivalent toself.to(torch.bfloat16)
.bincount
([weights, minlength])See
torch.bincount()
bitwise_and
()See
torch.bitwise_and()
bitwise_and_
()In-place version of
bitwise_and()
bitwise_left_shift
(other)See
torch.bitwise_left_shift()
bitwise_left_shift_
(other)In-place version of
bitwise_left_shift()
bitwise_not
()See
torch.bitwise_not()
bitwise_not_
()In-place version of
bitwise_not()
bitwise_or
()See
torch.bitwise_or()
bitwise_or_
()In-place version of
bitwise_or()
bitwise_right_shift
(other)See
torch.bitwise_right_shift()
bitwise_right_shift_
(other)In-place version of
bitwise_right_shift()
bitwise_xor
()See
torch.bitwise_xor()
bitwise_xor_
()In-place version of
bitwise_xor()
bmm
(batch2)See
torch.bmm()
bool
([memory_format])self.bool()
is equivalent toself.to(torch.bool)
.broadcast_to
(shape)See
torch.broadcast_to()
.byte
([memory_format])self.byte()
is equivalent toself.to(torch.uint8)
.cauchy_
([median, sigma, generator])Fills the tensor with numbers drawn from the Cauchy distribution:
ccol_indices
cdouble
([memory_format])self.cdouble()
is equivalent toself.to(torch.complex128)
.ceil
()See
torch.ceil()
ceil_
()In-place version of
ceil()
cfloat
([memory_format])self.cfloat()
is equivalent toself.to(torch.complex64)
.chalf
([memory_format])self.chalf()
is equivalent toself.to(torch.complex32)
.char
([memory_format])self.char()
is equivalent toself.to(torch.int8)
.cholesky
([upper])See
torch.cholesky()
cholesky_inverse
([upper])See
torch.cholesky_inverse()
cholesky_solve
(input2[, upper])See
torch.cholesky_solve()
chunk
(chunks[, dim])See
torch.chunk()
clamp
([min, max])See
torch.clamp()
clamp_
([min, max])In-place version of
clamp()
clamp_max
clamp_max_
clamp_min
clamp_min_
clip
([min, max])Alias for
clamp()
.clip_
([min, max])Alias for
clamp_()
.clone
(*[, memory_format])See
torch.clone()
coalesce
()Returns a coalesced copy of
self
ifself
is an uncoalesced tensor.col_indices
()Returns the tensor containing the column indices of the
self
tensor whenself
is a sparse CSR tensor of layoutsparse_csr
.conj
()See
torch.conj()
conj_physical
()See
torch.conj_physical()
conj_physical_
()In-place version of
conj_physical()
contiguous
([memory_format])Returns a contiguous in memory tensor containing the same data as
self
tensor.copy_
(src[, non_blocking])Copies the elements from
src
intoself
tensor and returnsself
.copysign
(other)See
torch.copysign()
copysign_
(other)In-place version of
copysign()
corrcoef
()See
torch.corrcoef()
cos
()See
torch.cos()
cos_
()In-place version of
cos()
cosh
()See
torch.cosh()
cosh_
()In-place version of
cosh()
count_nonzero
([dim])See
torch.count_nonzero()
cov
(*[, correction, fweights, aweights])See
torch.cov()
cpu
([memory_format])Returns a copy of this object in CPU memory.
cross
(other[, dim])See
torch.cross()
crow_indices
()Returns the tensor containing the compressed row indices of the
self
tensor whenself
is a sparse CSR tensor of layoutsparse_csr
.cuda
([device, non_blocking, memory_format])Returns a copy of this object in CUDA memory.
cummax
(dim)See
torch.cummax()
cummin
(dim)See
torch.cummin()
cumprod
(dim[, dtype])See
torch.cumprod()
cumprod_
(dim[, dtype])In-place version of
cumprod()
cumsum
(dim[, dtype])See
torch.cumsum()
cumsum_
(dim[, dtype])In-place version of
cumsum()
data_ptr
()Returns the address of the first element of
self
tensor.deg2rad
()See
torch.deg2rad()
deg2rad_
()In-place version of
deg2rad()
dense_dim
()Return the number of dense dimensions in a sparse tensor
self
.dequantize
()Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
det
()See
torch.det()
detach
Returns a new Tensor, detached from the current graph.
detach_
Detaches the Tensor from the graph that created it, making it a leaf.
diag
([diagonal])See
torch.diag()
diag_embed
([offset, dim1, dim2])See
torch.diag_embed()
diagflat
([offset])See
torch.diagflat()
diagonal
([offset, dim1, dim2])See
torch.diagonal()
diagonal_scatter
(src[, offset, dim1, dim2])See
torch.diagonal_scatter()
diff
([n, dim, prepend, append])See
torch.diff()
digamma
()See
torch.digamma()
digamma_
()In-place version of
digamma()
dim
()Returns the number of dimensions of
self
tensor.dim_order
()Returns a tuple of int describing the dim order or physical layout of
self
.dist
(other[, p])See
torch.dist()
div
(value, *[, rounding_mode])See
torch.div()
div_
(value, *[, rounding_mode])In-place version of
div()
divide
(value, *[, rounding_mode])See
torch.divide()
divide_
(value, *[, rounding_mode])In-place version of
divide()
dot
(other)See
torch.dot()
double
([memory_format])self.double()
is equivalent toself.to(torch.float64)
.dsplit
(split_size_or_sections)See
torch.dsplit()
eig
([eigenvectors])element_size
()Returns the size in bytes of an individual element.
eq
(other)See
torch.eq()
eq_
(other)In-place version of
eq()
equal
(other)See
torch.equal()
erf
()See
torch.erf()
erf_
()In-place version of
erf()
erfc
()See
torch.erfc()
erfc_
()In-place version of
erfc()
erfinv
()See
torch.erfinv()
erfinv_
()In-place version of
erfinv()
exp
()See
torch.exp()
exp2
()See
torch.exp2()
exp2_
()In-place version of
exp2()
exp_
()In-place version of
exp()
expand
(*sizes)Returns a new view of the
self
tensor with singleton dimensions expanded to a larger size.expand_as
(other)Expand this tensor to the same size as
other
.expm1
()See
torch.expm1()
expm1_
()In-place version of
expm1()
exponential_
([lambd, generator])Fills
self
tensor with elements drawn from the PDF (probability density function):fill_
(value)Fills
self
tensor with the specified value.fill_diagonal_
(fill_value[, wrap])Fill the main diagonal of a tensor that has at least 2-dimensions.
fix
()See
torch.fix()
.fix_
()In-place version of
fix()
flatten
([start_dim, end_dim])See
torch.flatten()
flip
(dims)See
torch.flip()
fliplr
()See
torch.fliplr()
flipud
()See
torch.flipud()
float
([memory_format])self.float()
is equivalent toself.to(torch.float32)
.float_power
(exponent)See
torch.float_power()
float_power_
(exponent)In-place version of
float_power()
floor
()See
torch.floor()
floor_
()In-place version of
floor()
floor_divide
(value)See
torch.floor_divide()
floor_divide_
(value)In-place version of
floor_divide()
fmax
(other)See
torch.fmax()
fmin
(other)See
torch.fmin()
fmod
(divisor)See
torch.fmod()
fmod_
(divisor)In-place version of
fmod()
frac
()See
torch.frac()
frac_
()In-place version of
frac()
frexp
(input)See
torch.frexp()
gather
(dim, index)See
torch.gather()
gcd
(other)See
torch.gcd()
gcd_
(other)In-place version of
gcd()
ge
(other)See
torch.ge()
.ge_
(other)In-place version of
ge()
.geometric_
(p, *[, generator])Fills
self
tensor with elements drawn from the geometric distribution:geqrf
()See
torch.geqrf()
ger
(vec2)See
torch.ger()
get_device
()For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.
greater
(other)See
torch.greater()
.greater_
(other)In-place version of
greater()
.greater_equal
(other)See
torch.greater_equal()
.greater_equal_
(other)In-place version of
greater_equal()
.gt
(other)See
torch.gt()
.gt_
(other)In-place version of
gt()
.half
([memory_format])self.half()
is equivalent toself.to(torch.float16)
.hardshrink
([lambd])See
torch.nn.functional.hardshrink()
has_names
Is
True
if any of this tensor's dimensions are named.heaviside
(values)See
torch.heaviside()
heaviside_
(values)In-place version of
heaviside()
histc
([bins, min, max])See
torch.histc()
histogram
(input, bins, *[, range, weight, ...])See
torch.histogram()
hsplit
(split_size_or_sections)See
torch.hsplit()
hypot
(other)See
torch.hypot()
hypot_
(other)In-place version of
hypot()
i0
()See
torch.i0()
i0_
()In-place version of
i0()
igamma
(other)See
torch.igamma()
igamma_
(other)In-place version of
igamma()
igammac
(other)See
torch.igammac()
igammac_
(other)In-place version of
igammac()
index_add
(dim, index, source, *[, alpha])Out-of-place version of
torch.Tensor.index_add_()
.index_add_
(dim, index, source, *[, alpha])Accumulate the elements of
alpha
timessource
into theself
tensor by adding to the indices in the order given inindex
.index_copy
(dim, index, tensor2)Out-of-place version of
torch.Tensor.index_copy_()
.index_copy_
(dim, index, tensor)Copies the elements of
tensor
into theself
tensor by selecting the indices in the order given inindex
.index_fill
(dim, index, value)Out-of-place version of
torch.Tensor.index_fill_()
.index_fill_
(dim, index, value)Fills the elements of the
self
tensor with valuevalue
by selecting the indices in the order given inindex
.index_put
(indices, values[, accumulate])Out-place version of
index_put_()
.index_put_
(indices, values[, accumulate])Puts values from the tensor
values
into the tensorself
using the indices specified inindices
(which is a tuple of Tensors).index_reduce
index_reduce_
(dim, index, source, reduce, *)Accumulate the elements of
source
into theself
tensor by accumulating to the indices in the order given inindex
using the reduction given by thereduce
argument.index_select
(dim, index)See
torch.index_select()
indices
()Return the indices tensor of a sparse COO tensor.
inner
(other)See
torch.inner()
.int
([memory_format])self.int()
is equivalent toself.to(torch.int32)
.int_repr
()Given a quantized Tensor,
self.int_repr()
returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.inverse
()See
torch.inverse()
ipu
([device, non_blocking, memory_format])Returns a copy of this object in IPU memory.
is_coalesced
()Returns
True
ifself
is a sparse COO tensor that is coalesced,False
otherwise.is_complex
()Returns True if the data type of
self
is a complex data type.is_conj
()Returns True if the conjugate bit of
self
is set to true.is_contiguous
([memory_format])Returns True if
self
tensor is contiguous in memory in the order specified by memory format.is_distributed
is_floating_point
()Returns True if the data type of
self
is a floating point data type.is_inference
()See
torch.is_inference()
is_neg
()Returns True if the negative bit of
self
is set to true.is_nonzero
is_pinned
Returns true if this tensor resides in pinned memory.
is_same_size
is_set_to
(tensor)Returns True if both tensors are pointing to the exact same memory (same storage, offset, size and stride).
is_shared
()Checks if tensor is in shared memory.
is_signed
()Returns True if the data type of
self
is a signed data type.isclose
(other[, rtol, atol, equal_nan])See
torch.isclose()
isfinite
()See
torch.isfinite()
isinf
()See
torch.isinf()
isnan
()See
torch.isnan()
isneginf
()See
torch.isneginf()
isposinf
()See
torch.isposinf()
isreal
()See
torch.isreal()
istft
(n_fft[, hop_length, win_length, ...])See
torch.istft()
item
()Returns the value of this tensor as a standard Python number.
kron
(other)See
torch.kron()
kthvalue
(k[, dim, keepdim])See
torch.kthvalue()
lcm
(other)See
torch.lcm()
lcm_
(other)In-place version of
lcm()
ldexp
(other)See
torch.ldexp()
ldexp_
(other)In-place version of
ldexp()
le
(other)See
torch.le()
.le_
(other)In-place version of
le()
.lerp
(end, weight)See
torch.lerp()
lerp_
(end, weight)In-place version of
lerp()
less
lt(other) -> Tensor
less_
(other)In-place version of
less()
.less_equal
(other)See
torch.less_equal()
.less_equal_
(other)In-place version of
less_equal()
.lgamma
()See
torch.lgamma()
lgamma_
()In-place version of
lgamma()
log
()See
torch.log()
log10
()See
torch.log10()
log10_
()In-place version of
log10()
log1p
()See
torch.log1p()
log1p_
()In-place version of
log1p()
log2
()See
torch.log2()
log2_
()In-place version of
log2()
log_
()In-place version of
log()
log_normal_
([mean, std, generator])Fills
self
tensor with numbers samples from the log-normal distribution parameterized by the given mean \(\mu\) and standard deviation \(\sigma\).log_softmax
logaddexp
(other)See
torch.logaddexp()
logaddexp2
(other)See
torch.logaddexp2()
logcumsumexp
(dim)See
torch.logcumsumexp()
logdet
()See
torch.logdet()
logical_and
()See
torch.logical_and()
logical_and_
()In-place version of
logical_and()
logical_not
()See
torch.logical_not()
logical_not_
()In-place version of
logical_not()
logical_or
()See
torch.logical_or()
logical_or_
()In-place version of
logical_or()
logical_xor
()See
torch.logical_xor()
logical_xor_
()In-place version of
logical_xor()
logit
()See
torch.logit()
logit_
()In-place version of
logit()
logsumexp
(dim[, keepdim])See
torch.logsumexp()
long
([memory_format])self.long()
is equivalent toself.to(torch.int64)
.lstsq
(other)lt
(other)See
torch.lt()
.lt_
(other)In-place version of
lt()
.lu
([pivot, get_infos])See
torch.lu()
lu_solve
(LU_data, LU_pivots)See
torch.lu_solve()
map2_
map_
(tensor, callable)Applies
callable
for each element inself
tensor and the giventensor
and stores the results inself
tensor.masked_fill
(mask, value)Out-of-place version of
torch.Tensor.masked_fill_()
masked_fill_
(mask, value)Fills elements of
self
tensor withvalue
wheremask
is True.masked_scatter
(mask, tensor)Out-of-place version of
torch.Tensor.masked_scatter_()
masked_scatter_
(mask, source)Copies elements from
source
intoself
tensor at positions where themask
is True.masked_select
(mask)See
torch.masked_select()
materialize
(shape[, device, dtype])Create a Parameter with the same properties of the uninitialized one.
matmul
(tensor2)See
torch.matmul()
matrix_exp
()See
torch.matrix_exp()
matrix_power
(n)Note
matrix_power()
is deprecated, usetorch.linalg.matrix_power()
instead.max
([dim, keepdim])See
torch.max()
maximum
(other)See
torch.maximum()
mean
([dim, keepdim, dtype])See
torch.mean()
median
([dim, keepdim])See
torch.median()
min
([dim, keepdim])See
torch.min()
minimum
(other)See
torch.minimum()
mm
(mat2)See
torch.mm()
mode
([dim, keepdim])See
torch.mode()
moveaxis
(source, destination)See
torch.moveaxis()
movedim
(source, destination)See
torch.movedim()
msort
()See
torch.msort()
mul
(value)See
torch.mul()
.mul_
(value)In-place version of
mul()
.multinomial
(num_samples[, replacement, ...])See
torch.multinomial()
multiply
(value)See
torch.multiply()
.multiply_
(value)In-place version of
multiply()
.mv
(vec)See
torch.mv()
mvlgamma
(p)See
torch.mvlgamma()
mvlgamma_
(p)In-place version of
mvlgamma()
nan_to_num
([nan, posinf, neginf])See
torch.nan_to_num()
.nan_to_num_
([nan, posinf, neginf])In-place version of
nan_to_num()
.nanmean
([dim, keepdim, dtype])See
torch.nanmean()
nanmedian
([dim, keepdim])See
torch.nanmedian()
nanquantile
(q[, dim, keepdim, interpolation])See
torch.nanquantile()
nansum
([dim, keepdim, dtype])See
torch.nansum()
narrow
(dimension, start, length)See
torch.narrow()
.narrow_copy
(dimension, start, length)See
torch.narrow_copy()
.ndimension
()Alias for
dim()
ne
(other)See
torch.ne()
.ne_
(other)In-place version of
ne()
.neg
()See
torch.neg()
neg_
()In-place version of
neg()
negative
()See
torch.negative()
negative_
()In-place version of
negative()
nelement
()Alias for
numel()
new
new_empty
(size, *[, dtype, device, ...])Returns a Tensor of size
size
filled with uninitialized data.new_empty_strided
(size, stride[, dtype, ...])Returns a Tensor of size
size
and stridesstride
filled with uninitialized data.new_full
(size, fill_value, *[, dtype, ...])Returns a Tensor of size
size
filled withfill_value
.new_ones
(size, *[, dtype, device, ...])Returns a Tensor of size
size
filled with1
.new_tensor
(data, *[, dtype, device, ...])Returns a new Tensor with
data
as the tensor data.new_zeros
(size, *[, dtype, device, ...])Returns a Tensor of size
size
filled with0
.nextafter
(other)See
torch.nextafter()
nextafter_
(other)In-place version of
nextafter()
nonzero
()See
torch.nonzero()
nonzero_static
(input, *, size[, fill_value])Returns a 2-D tensor where each row is the index for a non-zero value.
norm
([p, dim, keepdim, dtype])See
torch.norm()
normal_
([mean, std, generator])Fills
self
tensor with elements samples from the normal distribution parameterized bymean
andstd
.not_equal
(other)See
torch.not_equal()
.not_equal_
(other)In-place version of
not_equal()
.numel
()See
torch.numel()
numpy
(*[, force])Returns the tensor as a NumPy
ndarray
.orgqr
(input2)See
torch.orgqr()
ormqr
(input2, input3[, left, transpose])See
torch.ormqr()
outer
(vec2)See
torch.outer()
.permute
(*dims)See
torch.permute()
pin_memory
()Copies the tensor to pinned memory, if it's not already pinned.
pinverse
()See
torch.pinverse()
polygamma
(n)See
torch.polygamma()
polygamma_
(n)In-place version of
polygamma()
positive
()See
torch.positive()
pow
(exponent)See
torch.pow()
pow_
(exponent)In-place version of
pow()
prelu
prod
([dim, keepdim, dtype])See
torch.prod()
put
(input, index, source[, accumulate])Out-of-place version of
torch.Tensor.put_()
.put_
(index, source[, accumulate])Copies the elements from
source
into the positions specified byindex
.q_per_channel_axis
()Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.
q_per_channel_scales
()Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer.
q_per_channel_zero_points
()Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer.
q_scale
()Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().
q_zero_point
()Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().
qr
([some])See
torch.qr()
qscheme
()Returns the quantization scheme of a given QTensor.
quantile
(q[, dim, keepdim, interpolation])See
torch.quantile()
rad2deg
()See
torch.rad2deg()
rad2deg_
()In-place version of
rad2deg()
random_
([from, to, generator])Fills
self
tensor with numbers sampled from the discrete uniform distribution over[from, to - 1]
.ravel
()see
torch.ravel()
reciprocal
()See
torch.reciprocal()
reciprocal_
()In-place version of
reciprocal()
record_stream
(stream)Marks the tensor as having been used by this stream.
refine_names
(*names)Refines the dimension names of
self
according tonames
.register_hook
(hook)Registers a backward hook.
register_post_accumulate_grad_hook
(hook)Registers a backward hook that runs after grad accumulation.
reinforce
(reward)relu
relu_
remainder
(divisor)See
torch.remainder()
remainder_
(divisor)In-place version of
remainder()
rename
(*names, **rename_map)Renames dimension names of
self
.rename_
(*names, **rename_map)In-place version of
rename()
.renorm
(p, dim, maxnorm)See
torch.renorm()
renorm_
(p, dim, maxnorm)In-place version of
renorm()
repeat
(*sizes)Repeats this tensor along the specified dimensions.
repeat_interleave
(repeats[, dim, output_size])See
torch.repeat_interleave()
.requires_grad_
([requires_grad])Change if autograd should record operations on this tensor: sets this tensor's
requires_grad
attribute in-place.reshape
(*shape)Returns a tensor with the same data and number of elements as
self
but with the specified shape.reshape_as
(other)Returns this tensor as the same shape as
other
.resize
(*sizes)resize_
(*sizes[, memory_format])Resizes
self
tensor to the specified size.resize_as
(tensor)resize_as_
(tensor[, memory_format])Resizes the
self
tensor to be the same size as the specifiedtensor
.resize_as_sparse_
resolve_conj
()See
torch.resolve_conj()
resolve_neg
()See
torch.resolve_neg()
retain_grad
()Enables this Tensor to have their
grad
populated duringbackward()
.roll
(shifts, dims)See
torch.roll()
rot90
(k, dims)See
torch.rot90()
round
([decimals])See
torch.round()
round_
([decimals])In-place version of
round()
row_indices
rsqrt
()See
torch.rsqrt()
rsqrt_
()In-place version of
rsqrt()
scatter
(dim, index, src)Out-of-place version of
torch.Tensor.scatter_()
scatter_
(dim, index, src[, reduce])Writes all values from the tensor
src
intoself
at the indices specified in theindex
tensor.scatter_add
(dim, index, src)Out-of-place version of
torch.Tensor.scatter_add_()
scatter_add_
(dim, index, src)Adds all values from the tensor
src
intoself
at the indices specified in theindex
tensor in a similar fashion asscatter_()
.scatter_reduce
(dim, index, src, reduce, *[, ...])Out-of-place version of
torch.Tensor.scatter_reduce_()
scatter_reduce_
(dim, index, src, reduce, *)Reduces all values from the
src
tensor to the indices specified in theindex
tensor in theself
tensor using the applied reduction defined via thereduce
argument ("sum"
,"prod"
,"mean"
,"amax"
,"amin"
).select
(dim, index)See
torch.select()
select_scatter
(src, dim, index)See
torch.select_scatter()
set_
([source, storage_offset, size, stride])Sets the underlying storage, size, and strides.
sgn
()See
torch.sgn()
sgn_
()In-place version of
sgn()
Moves the underlying storage to shared memory.
short
([memory_format])self.short()
is equivalent toself.to(torch.int16)
.sigmoid
()See
torch.sigmoid()
sigmoid_
()In-place version of
sigmoid()
sign
()See
torch.sign()
sign_
()In-place version of
sign()
signbit
()See
torch.signbit()
sin
()See
torch.sin()
sin_
()In-place version of
sin()
sinc
()See
torch.sinc()
sinc_
()In-place version of
sinc()
sinh
()See
torch.sinh()
sinh_
()In-place version of
sinh()
size
([dim])Returns the size of the
self
tensor.slice_scatter
(src[, dim, start, end, step])See
torch.slice_scatter()
slogdet
()See
torch.slogdet()
smm
(mat)See
torch.smm()
softmax
(dim)Alias for
torch.nn.functional.softmax()
.solve
(other)sort
([dim, descending])See
torch.sort()
sparse_dim
()Return the number of sparse dimensions in a sparse tensor
self
.sparse_mask
(mask)Returns a new sparse tensor with values from a strided tensor
self
filtered by the indices of the sparse tensormask
.sparse_resize_
(size, sparse_dim, dense_dim)Resizes
self
sparse tensor to the desired size and the number of sparse and dense dimensions.sparse_resize_and_clear_
(size, sparse_dim, ...)Removes all specified elements from a sparse tensor
self
and resizesself
to the desired size and the number of sparse and dense dimensions.split
(split_size[, dim])See
torch.split()
split_with_sizes
sqrt
()See
torch.sqrt()
sqrt_
()In-place version of
sqrt()
square
()See
torch.square()
square_
()In-place version of
square()
squeeze
([dim])See
torch.squeeze()
squeeze_
([dim])In-place version of
squeeze()
sspaddmm
(mat1, mat2, *[, beta, alpha])See
torch.sspaddmm()
std
([dim, correction, keepdim])See
torch.std()
stft
(n_fft[, hop_length, win_length, ...])See
torch.stft()
storage
()Returns the underlying
TypedStorage
.storage_offset
()Returns
self
tensor's offset in the underlying storage in terms of number of storage elements (not bytes).storage_type
()Returns the type of the underlying storage.
stride
(dim)Returns the stride of
self
tensor.sub
(other, *[, alpha])See
torch.sub()
.sub_
(other, *[, alpha])In-place version of
sub()
subtract
(other, *[, alpha])See
torch.subtract()
.subtract_
(other, *[, alpha])In-place version of
subtract()
.sum
([dim, keepdim, dtype])See
torch.sum()
sum_to_size
(*size)Sum
this
tensor tosize
.svd
([some, compute_uv])See
torch.svd()
swapaxes
(axis0, axis1)See
torch.swapaxes()
swapaxes_
(axis0, axis1)In-place version of
swapaxes()
swapdims
(dim0, dim1)See
torch.swapdims()
swapdims_
(dim0, dim1)In-place version of
swapdims()
symeig
([eigenvectors])t
()See
torch.t()
t_
()In-place version of
t()
take
(indices)See
torch.take()
take_along_dim
(indices, dim)See
torch.take_along_dim()
tan
()See
torch.tan()
tan_
()In-place version of
tan()
tanh
()See
torch.tanh()
tanh_
()In-place version of
tanh()
tensor_split
(indices_or_sections[, dim])See
torch.tensor_split()
tile
(dims)See
torch.tile()
to
(*args, **kwargs)Performs Tensor dtype and/or device conversion.
to_dense
([dtype, masked_grad])Creates a strided copy of
self
ifself
is not a strided tensor, otherwise returnsself
.to_mkldnn
()Returns a copy of the tensor in
torch.mkldnn
layout.to_padded_tensor
(padding[, output_size])See
to_padded_tensor()
to_sparse
(sparseDims)Returns a sparse copy of the tensor.
to_sparse_bsc
(blocksize, dense_dim)Convert a tensor to a block sparse column (BSC) storage format of given blocksize.
to_sparse_bsr
(blocksize, dense_dim)Convert a tensor to a block sparse row (BSR) storage format of given blocksize.
to_sparse_coo
()Convert a tensor to coordinate format.
to_sparse_csc
()Convert a tensor to compressed column storage (CSC) format.
to_sparse_csr
([dense_dim])Convert a tensor to compressed row storage format (CSR).
tolist
()Returns the tensor as a (nested) list.
topk
(k[, dim, largest, sorted])See
torch.topk()
trace
()See
torch.trace()
transpose
(dim0, dim1)See
torch.transpose()
transpose_
(dim0, dim1)In-place version of
transpose()
triangular_solve
(A[, upper, transpose, ...])See
torch.triangular_solve()
tril
([diagonal])See
torch.tril()
tril_
([diagonal])In-place version of
tril()
triu
([diagonal])See
torch.triu()
triu_
([diagonal])In-place version of
triu()
true_divide
(value)See
torch.true_divide()
true_divide_
(value)In-place version of
true_divide_()
trunc
()See
torch.trunc()
trunc_
()In-place version of
trunc()
type
([dtype, non_blocking])Returns the type if dtype is not provided, else casts this object to the specified type.
type_as
(tensor)Returns this tensor cast to the type of the given tensor.
unbind
([dim])See
torch.unbind()
unflatten
(dim, sizes)See
torch.unflatten()
.unfold
(dimension, size, step)Returns a view of the original tensor which contains all slices of size
size
fromself
tensor in the dimensiondimension
.uniform_
([from, to, generator])Fills
self
tensor with numbers sampled from the continuous uniform distribution:unique
([sorted, return_inverse, ...])Returns the unique elements of the input tensor.
unique_consecutive
([return_inverse, ...])Eliminates all but the first element from every consecutive group of equivalent elements.
unsafe_chunk
(chunks[, dim])See
torch.unsafe_chunk()
unsafe_split
(split_size[, dim])See
torch.unsafe_split()
unsafe_split_with_sizes
unsqueeze
(dim)See
torch.unsqueeze()
unsqueeze_
(dim)In-place version of
unsqueeze()
untyped_storage
()Returns the underlying
UntypedStorage
.values
()Return the values tensor of a sparse COO tensor.
var
([dim, correction, keepdim])See
torch.var()
vdot
(other)See
torch.vdot()
view
(*shape)Returns a new tensor with the same data as the
self
tensor but of a differentshape
.view_as
(other)View this tensor as the same size as
other
.vsplit
(split_size_or_sections)See
torch.vsplit()
where
(condition, y)self.where(condition, y)
is equivalent totorch.where(condition, self, y)
.xlogy
(other)See
torch.xlogy()
xlogy_
(other)In-place version of
xlogy()
xpu
([device, non_blocking, memory_format])Returns a copy of this object in XPU memory.
zero_
()Fills
self
tensor with zeros.Attributes
H
Returns a view of a matrix (2-D tensor) conjugated and transposed.
T
Returns a view of this tensor with its dimensions reversed.
data
device
Is the
torch.device
where this Tensor is.dtype
grad
This attribute is
None
by default and becomes a Tensor the first time a call tobackward()
computes gradients forself
.grad_fn
imag
Returns a new tensor containing imaginary values of the
self
tensor.is_cpu
Is
True
if the Tensor is stored on the CPU,False
otherwise.is_cuda
Is
True
if the Tensor is stored on the GPU,False
otherwise.is_ipu
Is
True
if the Tensor is stored on the IPU,False
otherwise.All Tensors that have
requires_grad
which isFalse
will be leaf Tensors by convention.is_meta
Is
True
if the Tensor is a meta tensor,False
otherwise.is_mkldnn
is_mps
Is
True
if the Tensor is stored on the MPS device,False
otherwise.is_mtia
is_nested
is_ort
is_quantized
Is
True
if the Tensor is quantized,False
otherwise.is_sparse
Is
True
if the Tensor uses sparse COO storage layout,False
otherwise.is_sparse_csr
Is
True
if the Tensor uses sparse CSR storage layout,False
otherwise.is_vulkan
is_xla
Is
True
if the Tensor is stored on an XLA device,False
otherwise.is_xpu
Is
True
if the Tensor is stored on the XPU,False
otherwise.itemsize
Alias for
element_size()
layout
mH
Accessing this property is equivalent to calling
adjoint()
.mT
Returns a view of this tensor with the last two dimensions transposed.
name
names
Stores names for each of this tensor's dimensions.
nbytes
Returns the number of bytes consumed by the "view" of elements of the Tensor if the Tensor does not use sparse storage layout.
ndim
Alias for
dim()
output_nr
real
Returns a new tensor containing real values of the
self
tensor for a complex-valued input tensor.requires_grad
Is
True
if gradients need to be computed for this Tensor,False
otherwise.retains_grad
Is
True
if this Tensor is non-leaf and itsgrad
is enabled to be populated duringbackward()
,False
otherwise.shape
Returns the size of the
self
tensor.volatile
- property is_leaf: bool#
All Tensors that have
requires_grad
which isFalse
will be leaf Tensors by convention.For Tensors that have
requires_grad
which isTrue
, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and sograd_fn
is None.Only leaf Tensors will have their
grad
populated during a call tobackward()
. To getgrad
populated for non-leaf Tensors, you can useretain_grad()
.Example:
>>> a = torch.rand(10, requires_grad=True) >>> a.is_leaf True >>> b = torch.rand(10, requires_grad=True).cuda() >>> b.is_leaf False # b was created by the operation that cast a cpu Tensor into a cuda Tensor >>> c = torch.rand(10, requires_grad=True) + 2 >>> c.is_leaf False # c was created by the addition operation >>> d = torch.rand(10).cuda() >>> d.is_leaf True # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine) >>> e = torch.rand(10).cuda().requires_grad_() >>> e.is_leaf True # e requires gradients and has no operations creating it >>> f = torch.rand(10, requires_grad=True, device="cuda") >>> f.is_leaf True # f requires grad, has no operation creating it
- materialize(shape, device=None, dtype=None)#
Create a Parameter with the same properties of the uninitialized one. Given a shape, it materializes a parameter in the same device and with the same dtype as the current one or the specified ones in the arguments.
- Parameters:
shape (Tuple[int, ...]) – (tuple): the shape for the materialized tensor.
device (
torch.device
) – the desired device of the parameters and buffers in this module. Optional.dtype (
torch.dtype
) – the desired floating point type of the floating point parameters and buffers in this module. Optional.
- Return type:
None
Moves the underlying storage to shared memory.
This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.
See
torch.UntypedStorage.share_memory_()
for more details.- Return type: