Operators with Channels Last support
Ma Mingfei edited this page May 22, 2022
·
2 revisions
abs
abs_
acos
acos_
adaptive_avg_pool2d
adaptive_max_pool2d
add
add_
addcdiv
addcdiv_
addcmul
addcmul_
asin
asin_
atan2
atan2_
avg_pool2d
batch_norm
bfloat16
bool
byte
ceil
ceil_
char
channel_shuffle
clone
contiguous
conv2d
copy_
digamma
digamma_
div
div_
double
empty_like
erfinv
erfinv_
expm1
expm1_
fill_
float
floor
floor_
full_like
group_norm
gt
half
int
isnan
log
log_
log10
log10_
log1p
log1p_
log2
log2_
long
lt
max_pool2d
max_unpool2d
mul
mul_
ne
neg
neg_
ones_like
pixel_shuffle
pixel_unshuffle
pow
pow_
randint_like
rand_like
randn_like
relu
relu_
resize_
resize_as_
round
round_
rsqrt
rsqrt_
short
sigmoid
sigmoid_
sign
sign_
sin
sin_
sinh
sinh_
sqrt
sqrt_
to
trunc
trunc_
type
upsample_bilinear2d
upsample_nearest2d
zero_
zeros_like
abs
abs_
acos
acos_
adaptive_avg_pool2d
add
add_
addcdiv
addcdiv_
addcmul
addcmul_
asin
asin_
atan2
atan2_
batch_norm
bfloat16
bool
byte
cat
ceil
ceil_
char
clone
contiguous
conv2d
conv_transpose2d
copy_
cpu
cuda
cudnn_convolution
cudnn_convolution_transpose
detach
digamma
digamma_
div
div_
double
empty_like
erfinv
erfinv_
expm1
expm1_
fill_
float
floor
floor_
full_like
gt
half
int
isnan
log
log_
log10
log10_
log1p
log1p_
log2
log2_
long
lt
max_pool2d
mul
mul_
ne
neg
neg_
ones_like
pow
pow_
randint_like
rand_like
randn_like
relu
relu_
requires_grad_
resize_
resize_as_
round
round_
rsqrt
rsqrt_
short
sigmoid
sigmoid_
sign
sign_
sin
sin_
sinh
sinh_
sqrt
sqrt_
to
trunc
trunc_
type
zero_
zeros_like
- Install Prerequisites
- Fork, clone, and checkout the PyTorch source
- Install Dependencies
- Build PyTorch from source
- Tips for developing PyTorch
- PyTorch Workflow Git cheatsheet
- Overview of the Pull Request Lifecycle
- Finding Or Reporting Issues
- Pre Commit Checks
- Create a Pull Request
- Typical Pull Request Workflow
- Pull Request FAQs
- Getting Help
- Codebase structure
- Tensors, Operators, and Testing
- Autograd
- Dispatcher, Structured Kernels, and Codegen
- torch.nn
- CUDA basics
- Data (Optional)
- function transforms (Optional)