vm.torch¶
Classes¶
class device : Represent the device spec.
class dtype : The basic data type.
class enable_grad : Context-manager to enable gradient calculation.
class no_grad : Context-manager to disable gradient calculation.
class set_grad_enabled : Context-manager to set gradient calculation on or off.
class Size : Represent the a sequence of dimensions.
class Tensor : A multi-dimensional array containing elements of a single data type.
Functions¶
abs(…) : Compute the absolute value of input.
add(…) : Compute the element-wise addition.
addmm(…) : Add input to the result of matrix-matrix multiplication.
arange(…) : Return a tensor of evenly spaced values within a interval.
argmax(…) : Return the index of maximum elements along the given dimension.
argmin(…) : Return the index of minimum elements along the given dimension.
argsort(…) : Return the index of sorted elements along the given dimension.
atan2(…) : Compute the element-wise arc-tangent of two arguments.
baddbmm(…) : Add input to the result of batched matrix-matrix multiplication.
bitwise_and(…) : Compute the element-wise AND bitwise operation.
bitwise_not(…) : Compute the element-wise NOT bitwise operation.
bitwise_or(…) : Compute the element-wise OR bitwise operation.
bitwise_xor(…) : Compute the element-wise XOR bitwise operation.
bmm(…) : Compute the batched matrix-matrix multiplication.
cat(…) : Concatenate the inputs along the given dimension.
ceil(…) : Compute the smallest integer not less than input.
chunk(…) : Split input into a specific number of chunks.
clamp(…) : Compute the clipped input according to the given bounds.
cos(…) : Compute the cos of input.
cumsum(…) : Compute the cumulative sum of elements along the given dimension.
div(…) : Compute the element-wise division.
empty(…) : Return a tensor filled with uninitialized data.
eq(…) : Compute the element-wise equal comparison.
exp(…) : Compute the exponential of input.
eye(…) : Return a tensor constructed as the identity matrix.
flatten(…) : Return a tensor with dimensions flattened.
flip(…) : Reverse elements along the given dimension.
fliplr(…) : Reverse elements along the second dimension.
flipud(…) : Reverse elements along the first dimension.
floor(…) : Compute the largest integer not greater than input.
from_numpy(…) : Create a tensor converting from the given numpy array.
full(…) : Return a tensor filled with a scalar.
full_like(…) : Return a tensor filled with a scalar with size as input.
gather(…) : Gather elements along the given dimension of index.
ge(…) : Compute the element-wise greater-equal comparison.
gt(…) : Compute the element-wise greater comparison.
index_select(…) : Select elements along the given dimension using index.
isfinite(…) : Check if the elements of input are finite.
isinf(…) : Check if the elements of input are infinite.
isnan(…) : Check if the elements of input are NaN.
le(…) : Compute the element-wise less-equal comparison.
linspace(…) : Generate evenly spaced values within intervals along the given dimension.
log(…) : Compute the natural logarithm of input.
logical_and(…) : Compute the element-wise AND logical operation.
logical_not(…) : Compute the element-wise NOT logical operation.
logical_or(…) : Compute the element-wise OR logical operation.
logical_xor(…) : Compute the element-wise XOR logical operation.
logsumexp(…) : Apply the composite of log, sum, and exp to input.
lt(…) : Compute the element-wise less comparison.
masked_select(…) : Select the input elements where mask is true.
matmul(…) : Compute the matrix multiplication.
max(…) : Compute the max value of elements along the given dimension.
maximum(…) : Compute the maximum value of inputs.
mean(…) : Compute the mean value of elements along the given dimension.
min(…) : Compute the min value of elements along the given dimension.
minimum(…) : Compute the minimum value of inputs.
mm(…) : Compute the matrix-matrix multiplication.
mul(…) : Compute the element-wise multiplication.
multinomial(…) : Return a tensor with index sampled from multinomial distribution.
narrow(…) : Return a new tensor that is a narrowed version of input tensor.
ne(…) : Compute the element-wise not-equal comparison.
neg(…) : Compute the element-wise negative.
nonzero(…) : Return the index of non-zero elements.
norm(…) : Compute the min value of elements along the given dimension.
ones(…) : Return a tensor filled with ones.
ones_like(…) : Return a tensor of ones with shape as the other.
permute(…) : Return a new tensor with the specific order of dimensions.
pow(…) : Compute the power of input.
rand(…) : Return a tensor from the uniform distribution of U(0, 1).
randn(…) : Return a tensor from the normal distribution of N(0, 1).
randperm(…) : Return a tensor with value in the permuted range.
reciprocal(…) : Compute the reciprocal of input.
reshape(…) : Change the shape of input.
roll(…) : Roll elements along the given dimension.
round(…) : Compute the nearest integer of input.
rsqrt(…) : Compute the reciprocal square root of input.
scatter(…) : Update elements along the given dimension of index.
scatter_add(…) : Add elements along the given dimension of index.
sign(…) : Compute the sign indication of input.
sin(…) : Compute the sin of input.
sort(…) : Return the sorted elements along the given dimension.
split(…) : Split input into chunks along the given dimension.
sqrt(…) : Compute the square root of input.
square(…) : Compute the square of input.
squeeze(…) : Remove the dimensions of input with size 1.
stack(…) : Stack the inputs along the given dimension.
sub(…) : Compute the element-wise subtraction.
sum(…) : Compute the sum value of elements along the given dimension.
tensor(…) : Create a tensor initializing from the given data.
tile(…) : Repeat elements along each dimension of input.
topk(…) : Return the top k-largest or k-smallest elements along the given dimension.
transpose(…) : Return a new tensor with two dimensions swapped.
tril(…) : Return the lower triangular part of input.
triu(…) : Return the upper triangular part of input.
unbind(…) : Unpack input into chunks along the given dimension.
unique(…) : Return the unique elements of input.
unsqueeze(…) : Expand the dimensions of input with size 1.
where(…) : Select the elements from two branches under the condition.
var(…) : Compute the variance value of elements along the given dimension.
var_mean(…) : Compute the variance and mean of elements along the given dimension.
zeros(…) : Return a tensor filled with zeros.
zeros_like(…) : Return a tensor of zeros with shape as the other.