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absval
argmax
batchnorm
bias
binaryop
bnll
cast
clip
concat
convolution
convolutiondepthwise
crop
deconvolution
deconvolutiondepthwise
dequantize
dropout
eltwise
elu
exp
flatten
gelu
gemm
groupnorm
gru
hardsigmoid
hardswish
innerproduct
input
instancenorm
interp
layernorm
log
lrn
lstm
memorydata
mish
multiheadattention
mvn
noop
normalize
packing
padding
permute
pixelshuffle
pooling
power
prelu
quantize
reduction
relu
reorg
requantize
reshape
rnn
scale
selu
shufflechannel
sigmoid
slice
softmax
softplus
split
swish
tanh
threshold
unaryop

absval
argmax
batchnorm
bias
binaryop
bnll
cast
clip
concat
convolution
convolutiondepthwise
crop
deconvolution
deconvolutiondepthwise
dequantize
dropout
eltwise
elu
exp
flatten
gelu
gemm
groupnorm
gru
hardsigmoid
hardswish
innerproduct
input
instancenorm
interp
layernorm
log
lrn
lstm
memorydata
mish
multiheadattention
mvn
noop
normalize
packing
padding
permute
pixelshuffle
pooling
power
prelu
quantize
reduction
relu
reorg
requantize
reshape
rnn
scale
selu
shufflechannel
sigmoid
slice
softmax
softplus
split
swish
tanh
threshold
unaryop

absval

y = abs(x)

one_blob_only
support_inplace

argmax

y = argmax(x, out_max_val, topk)

one_blob_only

param id	name	type	default	description
0	out_max_val	int	0
1	topk	int	1

batchnorm

y = (x - mean) / sqrt(var + eps) * slope + bias

one_blob_only
support_inplace

param id	name	type	default	description
0	channels	int	0
1	eps	float	0.f

weight	type	shape
slope_data	float	[channels]
mean_data	float	[channels]
var_data	float	[channels]
bias_data	float	[channels]

bias

y = x + bias

one_blob_only
support_inplace

param id	name	type	default	description
0	bias_data_size	int	0

weight	type	shape
bias_data	float	[channels]

binaryop

This operation is used for binary computation, and the calculation rule depends on the broadcasting rule.

C = binaryop(A, B)

if with_scalar = 1:

one_blob_only
support_inplace

param id	name	type	default	description
0	op_type	int	0	Operation type as follows
1	with_scalar	int	0	with_scalar=0 B is a matrix, with_scalar=1 B is a scalar
2	b	float	0.f	When B is a scalar, B = b

Operation type:

0 = ADD
1 = SUB
2 = MUL
3 = DIV
4 = MAX
5 = MIN
6 = POW
7 = RSUB
8 = RDIV

bnll

y = log(1 + e^(-x)) , x > 0
y = log(1 + e^x),     x < 0

one_blob_only
support_inplace

cast

y = cast(x)

one_blob_only
support_packing

param id	name	type	default	description
0	type_from	int	0
1	type_to	int	0

Element type:

0 = auto
1 = float32
2 = float16
3 = int8
4 = bfloat16

clip

y = clamp(x, min, max)

one_blob_only
support_inplace

param id	name	type	default	description
0	min	float	-FLT_MAX
1	max	float	FLT_MAX

concat

y = concat(x0, x1, x2, ...) by axis

param id	name	type	default	description
0	axis	int	0

convolution

x2 = pad(x, pads, pad_value)
x3 = conv(x2, weight, kernel, stride, dilation) + bias
y = activation(x3, act_type, act_params)

one_blob_only

param id	name	type	default
0	num_output	int	0
1	kernel_w	int	0
2	dilation_w	int	1
3	stride_w	int	1
4	pad_left	int	0
5	bias_term	int	0
6	weight_data_size	int	0
8	int8_scale_term	int	0
9	activation_type	int	0
10	activation_params	array	[ ]
11	kernel_h	int	kernel_w
12	dilation_h	int	dilation_w
13	stride_h	int	stride_w
15	pad_right	int	pad_left
14	pad_top	int	pad_left
16	pad_bottom	int	pad_top
18	pad_value	float	0.f

weight	type	shape
weight_data	float/fp16/int8	[kernel_w, kernel_h, num_input, num_output]
bias_data	float	[num_output]
weight_data_int8_scales	float	[num_output]
bottom_blob_int8_scales	float	[1]
top_blob_int8_scales	float	[1]

convolutiondepthwise

x2 = pad(x, pads, pad_value)
x3 = conv(x2, weight, kernel, stride, dilation, group) + bias
y = activation(x3, act_type, act_params)

one_blob_only

param id	name	type	default
0	num_output	int	0
1	kernel_w	int	0
2	dilation_w	int	1
3	stride_w	int	1
4	pad_left	int	0
5	bias_term	int	0
6	weight_data_size	int	0
7	group	int	1
8	int8_scale_term	int	0
9	activation_type	int	0
10	activation_params	array	[ ]
11	kernel_h	int	kernel_w
12	dilation_h	int	dilation_w
13	stride_h	int	stride_w
15	pad_right	int	pad_left
14	pad_top	int	pad_left
16	pad_bottom	int	pad_top
18	pad_value	float	0.f

weight	type	shape
weight_data	float/fp16/int8	[kernel_w, kernel_h, num_input / group, num_output / group, group]
bias_data	float	[num_output]
weight_data_int8_scales	float	[group]
bottom_blob_int8_scales	float	[1]
top_blob_int8_scales	float	[1]

crop

y = crop(x)

one_blob_only

param id	name	type	default
0	woffset	int	0
1	hoffset	int	0
2	coffset	int	1
3	outw	int	1
4	outh	int	0
5	outc	int	0
6	woffset2	int	0
7	hoffset2	int	1
8	coffset2	int	0
9	starts	array	[ ]
10	ends	array	[ ]
11	axes	array	[ ]

deconvolution

x2 = deconv(x, weight, kernel, stride, dilation) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)

one_blob_only

param id	name	type	default
0	num_output	int	0
1	kernel_w	int	0
2	dilation_w	int	1
3	stride_w	int	1
4	pad_left	int	0
5	bias_term	int	0
6	weight_data_size	int	0
8	int8_scale_term	int	0
9	activation_type	int	0
10	activation_params	array	[ ]
11	kernel_h	int	kernel_w
12	dilation_h	int	dilation_w
13	stride_h	int	stride_w
15	pad_right	int	pad_left
14	pad_top	int	pad_left
16	pad_bottom	int	pad_top
18	pad_value	float	0.f

weight	type	shape
weight_data	float/fp16/int8	[kernel_w, kernel_h, num_input, num_output]
bias_data	float	[num_output]

deconvolutiondepthwise

x2 = deconv(x, weight, kernel, stride, dilation, group) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)

one_blob_only

param id	name	type	default
0	num_output	int	0
1	kernel_w	int	0
2	dilation_w	int	1
3	stride_w	int	1
4	pad_left	int	0
5	bias_term	int	0
6	weight_data_size	int	0
7	group	int	1
8	int8_scale_term	int	0
9	activation_type	int	0
10	activation_params	array	[ ]
11	kernel_h	int	kernel_w
12	dilation_h	int	dilation_w
13	stride_h	int	stride_w
15	pad_right	int	pad_left
14	pad_top	int	pad_left
16	pad_bottom	int	pad_top
18	pad_value	float	0.f

weight	type	shape
weight_data	float/fp16/int8	[kernel_w, kernel_h, num_input / group, num_output / group, group]
bias_data	float	[num_output]

dequantize

y = x * scale + bias

one_blob_only
support_inplace

param id	name	type	default
0	scale	float	1.f
1	bias_term	int	0
2	bias_data_size	int	0

dropout

y = x * scale

one_blob_only

param id	name	type	default	description
0	scale	float	1.f

eltwise

y = elementwise_op(x0, x1, ...)

param id	name	type	default	description
0	op_type	int	0
1	coeffs	array	[ ]

Operation type:

0 = PROD
1 = SUM
2 = MAX

elu

if x < 0    y = (exp(x) - 1) * alpha
else        y = x

one_blob_only
support_inplace

param id	name	type	default	description
0	alpha	float	0.1f

exp

if base == -1   y = exp(shift + x * scale)
else            y = pow(base, (shift + x * scale))

one_blob_only
support_inplace

param id	name	type	default
0	base	float	-1.f
1	scale	float	1.f
2	shift	float	0.f

flatten

Reshape blob to 1 dimension

one_blob_only

gelu

if fast_gelu == 1   y = 0.5 * x * (1 + tanh(0.79788452 * (x + 0.044715 * x * x * x)));
else                y = 0.5 * x * erfc(-0.70710678 * x)

one_blob_only
support_inplace

param id	name	type	default	description
0	fast_gelu	int	0	use approximation

gemm

a = transA ? transpose(x0) : x0
b = transb ? transpose(x1) : x1
c = x2
y = gemm(a, b) * alpha + c * beta

param id	name	type	default
0	alpha	float	1.f
1	beta	float	1.f
2	transA	int	0
3	transb	int	0

groupnorm

split x along channel axis into group x0, x1 ...
l2 normalize for each group x0, x1 ...
y = x * gamma + beta

one_blob_only
support_inplace

param id	name	type	default	description
0	group	int	1
1	channels	int	0
2	eps	float	0.001f	x = x / sqrt(var + eps)
3	affine	int	1

weight	type	shape
gamma_data	float	[channels]
beta_data	float	[channels]

gru

Apply a single-layer GRU to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = gru(x)
y0, hidden y1 = gru(x0, hidden x1)

one_blob_only if bidirectional

param id	name	type	description
0	num_output	int	hidden size of output
1	weight_data_size	int	total size of weight matrix
2	direction	int	0=forward, 1=reverse, 2=bidirectional

weight	type	shape
weight_xc_data	float	[input_size, num_output * 3, num_directions]
bias_c_data	float	[num_output, 4, num_directions]
weight_hc_data	float	[num_output, num_output * 3, num_directions]

Direction flag:

0 = forward only
1 = reverse only
2 = bidirectional

hardsigmoid

y = clamp(x * alpha + beta, 0, 1)

one_blob_only
support_inplace

param id	name	type	default	description
0	alpha	float	0.2f
1	beta	float	0.5f

hardswish

y = x * clamp(x * alpha + beta, 0, 1)

one_blob_only
support_inplace

param id	name	type	default	description
0	alpha	float	0.2f
1	beta	float	0.5f

innerproduct

x2 = innerproduct(x, weight) + bias
y = activation(x2, act_type, act_params)

one_blob_only

param id	name	type	default
0	num_output	int	0
1	bias_term	int	0
2	weight_data_size	int	0
8	int8_scale_term	int	0
9	activation_type	int	0
10	activation_params	array	[ ]

weight	type	shape
weight_data	float/fp16/int8	[num_input, num_output]
bias_data	float	[num_output]
weight_data_int8_scales	float	[num_output]
bottom_blob_int8_scales	float	[1]

input

y = input

support_inplace

param id	name	type
0	w	int
1	h	int
2	c	int

instancenorm

split x along channel axis into instance x0, x1 ...
l2 normalize for each channel instance x0, x1 ...
y = x * gamma + beta

one_blob_only
support_inplace

param id	name	type	default	description
0	channels	int	0
1	eps	float	0.001f	x = x / sqrt(var + eps)
2	affine	int	1

weight	type	shape
gamma_data	float	[channels]
beta_data	float	[channels]

interp

if dynamic_target_size == 0     y = resize(x) by fixed size or scale
else                            y = resize(x0, size(x1))

one_blob_only if dynamic_target_size == 0

param id	name	type	default
0	resize_type	int	0
1	height_scale	float	1.f
2	width_scale	float	1.f
3	output_height	int	0
4	output_width	int	0
5	dynamic_target_size	int	0
6	align_corner	int	0

Resize type:

1 = Nearest
2 = Bilinear
3 = Bicubic

layernorm

split x along outmost axis into part x0, x1 ...
l2 normalize for each part x0, x1 ...
y = x * gamma + beta by elementwise

one_blob_only
support_inplace

param id	name	type	default	description
0	affine_size	int	0
1	eps	float	0.001f	x = x / sqrt(var + eps)
2	affine	int	1

weight	type	shape
gamma_data	float	[affine_size]
beta_data	float	[affine_size]

log

if base == -1   y = log(shift + x * scale)
else            y = log(shift + x * scale) / log(base)

one_blob_only
support_inplace

param id	name	type	default
0	base	float	-1.f
1	scale	float	1.f
2	shift	float	0.f

lrn

if region_type == ACROSS_CHANNELS   square_sum = sum of channel window of local_size
if region_type == WITHIN_CHANNEL    square_sum = sum of spatial window of local_size
y = x * pow(bias + alpha * square_sum / (local_size * local_size), -beta)

one_blob_only
support_inplace

param id	name	type	default
0	region_type	int	0
1	local_size	int	5
2	alpha	float	1.f
3	beta	float	0.75f
4	bias	float	1.f

Region type:

0 = ACROSS_CHANNELS
1 = WITHIN_CHANNEL

lstm

Apply a single-layer LSTM to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = lstm(x)
y0, hidden y1, cell y2 = lstm(x0, hidden x1, cell x2)

one_blob_only if bidirectional

param id	name	type	description
0	num_output	int	hidden size of output
1	weight_data_size	int	total size of IFOG weight matrix
2	direction	int	0=forward, 1=reverse, 2=bidirectional

weight	type	shape
weight_xc_data	float	[input_size, num_output * 4, num_directions]
bias_c_data	float	[num_output, 4, num_directions]
weight_hc_data	float	[num_output, num_output * 4, num_directions]

Direction flag:

0 = forward only
1 = reverse only
2 = bidirectional

memorydata

y = data

param id	name	type
0	w	int
1	h	int
2	c	int

weight	type	shape
data	float	[w, h, c]

mish

y = x * tanh(log(exp(x) + 1))

one_blob_only
support_inplace

multiheadattention

split q k v into num_head part q0, k0, v0, q1, k1, v1 ...
for each num_head part
    xq = affine(q) / (embed_dim / num_head)
    xk = affine(k)
    xv = affine(v)
    xqk = xq * xk
    softmax_inplace(xqk)
    xqkv = xqk * xv
    merge xqkv to out
y = affine(out)

param id	name	type	default
0	embed_dim	int	0
1	num_head	int	1
2	weight_data_size	int	0

weight	type	shape
q_weight_data	float/fp16/int8	[weight_data_size]
q_bias_data	float	[embed_dim]
k_weight_data	float/fp16/int8	[weight_data_size]
k_bias_data	float	[embed_dim]
v_weight_data	float/fp16/int8	[weight_data_size]
v_bias_data	float	[embed_dim]
out_weight_data	float/fp16/int8	[weight_data_size]
out_bias_data	float	[embed_dim]

mvn

if normalize_variance == 1 && across_channels == 1      y = (x - mean) / (sqrt(var) + eps) of whole blob
if normalize_variance == 1 && across_channels == 0      y = (x - mean) / (sqrt(var) + eps) of each channel
if normalize_variance == 0 && across_channels == 1      y = x - mean of whole blob
if normalize_variance == 0 && across_channels == 0      y = x - mean of each channel

one_blob_only

param id	name	type	default	description
0	normalize_variance	int	0
1	across_channels	int	0
2	eps	float	0.0001f	x = x / (sqrt(var) + eps)

noop

y = x

normalize

if across_spatial == 1 && across_channel == 1      x2 = normalize(x) of whole blob
if across_spatial == 1 && across_channel == 0      x2 = normalize(x) of each channel
if across_spatial == 0 && across_channel == 1      x2 = normalize(x) of each position
y = x2 * scale

one_blob_only
support_inplace

param id	name	type	default	description
0	across_spatial	int	0
1	channel_shared	int	0
2	eps	float	0.0001f	see eps mode
3	scale_data_size	int	0
4	across_channel	int	0
9	eps_mode	int	0

weight	type	shape
scale_data	float	[scale_data_size]

Eps Mode:

0 = caffe/mxnet x = x / sqrt(var + eps)
1 = pytorch x = x / max(sqrt(var), eps)
2 = tensorflow x = x / sqrt(max(var, eps))

packing

y = wrap_packing(x)

one_blob_only

param id	name	type	default
0	out_elempack	int	1
1	use_padding	int	0
2	cast_type_from	float	0
3	cast_type_to	int	0
4	storage_type_from	int	0
5	storage_type_to	int	0

padding

if pads != -233/-234    y = pad(x, pads)
else                    y = pad(x0, pads param from x1)

param id	name	type	default
0	top	int	0
1	bottom	int	0
2	left	int	0
3	right	int	0
4	type	int	0
5	value	int	0
6	per_channel_pad_data_size	int	0
7	front	int	stride_w
8	behind	int	pad_left

weight	type	shape
per_channel_pad_data	float	[per_channel_pad_data_size]

permute

y = reorder(x)

param id	name	type	default	description
0	order_type	int	0

Order Type:

0 = WH WHC
1 = HW HWC
2 = WCH
3 = CWH
4 = HCW
5 = CHW

pixelshuffle

if mode == 0    y = depth_to_space(x) where x channel order is sw-sh-outc
if mode == 1    y = depth_to_space(x) where x channel order is outc-sw-sh

one_blob_only

param id	name	type	default	description
0	upscale_factor	int	1
1	mode	int	0

pooling

x2 = pad(x, pads)
x3 = pooling(x2, kernel, stride)

param id	name	type	default
0	pooling_type	int	0
1	kernel_w	int	0
2	stride_w	int	1
3	pad_left	int	0
4	global_pooling	int	0
5	pad_mode	int	0
11	kernel_h	int	kernel_w
12	stride_h	int	stride_w
13	pad_top	int	pad_left
14	pad_right	int	pad_left
15	pad_bottom	int	pad_top

Pooling type:

0 = MAX
1 = AVG

Pad mode:

0 = full padding
1 = valid padding
2 = tensorflow padding=SAME or onnx padding=SAME_UPPER
3 = onnx padding=SAME_LOWER

power

y = pow((shift + x * scale), power)

one_blob_only
support_inplace

param id	name	type	default
0	power	float	1.f
1	scale	float	1.f
2	shift	float	0.f

prelu

if x < 0    y = x * slope
else        y = x

one_blob_only
support_inplace

param id	name	type	default	description
0	num_slope	int	0

weight	type	shape
slope_data	float	[num_slope]

quantize

y = float2int8(x * scale)

one_blob_only

param id	name	type	default	description
0	scale_data_size	int	0

weight	type	shape
scale_data	float	[scale_data_size]

reduction

y = reduce_op(x * coeff)

one_blob_only

param id	name	type	default
0	operation	int	0
1	reduce_all	int	1
2	coeff	float	1.f
3	axes	array	[ ]
4	keepdims	int	0

Operation type:

0 = SUM
1 = ASUM
2 = SUMSQ
3 = MEAN
4 = MAX
5 = MIN
6 = PROD
7 = L1
8 = L2
9 = LogSum
10 = LogSumExp

relu

if x < 0    y = x * slope
else        y = x

one_blob_only
support_inplace

param id	name	type	default	description
0	slope	float	0.f

reorg

if mode == 0    y = space_to_depth(x) where x channel order is sw-sh-outc
if mode == 1    y = space_to_depth(x) where x channel order is outc-sw-sh

one_blob_only

param id	name	type	default	description
0	stride	int	1
1	mode	int	0

requantize

x2 = x * scale_in + bias
x3 = activation(x2)
y = float2int8(x3 * scale_out)

one_blob_only

param id	name	type	default
0	scale_in_data_size	int	1
1	scale_out_data_size	int	1
2	bias_data_size	int	0
3	activation_type	int	0
4	activation_params	int	[ ]

weight	type	shape
scale_in_data	float	[scale_in_data_size]
scale_out_data	float	[scale_out_data_size]
bias_data	float	[bias_data_size]

reshape

if permute == 1     y = hwc2chw(reshape(chw2hwc(x)))
else                y = reshape(x)

one_blob_only

param id	name	type	default
0	w	int	-233
1	h	int	-233
2	c	int	-233
3	permute	int	0

Reshape flag:

0 = copy from bottom
-1 = remaining
-233 = drop this dim(default)

rnn

Apply a single-layer RNN to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = rnn(x)
y0, hidden y1 = rnn(x0, hidden x1)

one_blob_only if bidirectional

param id	name	type	description
0	num_output	int	hidden size of output
1	weight_data_size	int	total size of weight matrix
2	direction	int	0=forward, 1=reverse, 2=bidirectional

weight	type	shape
weight_xc_data	float	[input_size, num_output, num_directions]
bias_c_data	float	[num_output, 1, num_directions]
weight_hc_data	float	[num_output, num_output, num_directions]

Direction flag:

0 = forward only
1 = reverse only
2 = bidirectional

scale

if scale_data_size == -233  y = x0 * x1
else                        y = x * scale + bias

one_blob_only if scale_data_size != -233
support_inplace

param id	name	type	default	description
0	scale_data_size	int	0
1	bias_term	int	0

weight	type	shape
scale_data	float	[scale_data_size]
bias_data	float	[scale_data_size]

selu

if x < 0    y = (exp(x) - 1.f) * alpha * lambda
else        y = x * lambda

one_blob_only
support_inplace

param id	name	type	default	description
0	alpha	float	1.67326324f
1	lambda	float	1.050700987f

shufflechannel

if reverse == 0     y = shufflechannel(x) by group
if reverse == 1     y = shufflechannel(x) by channel / group

one_blob_only

param id	name	type	default	description
0	group	int	1
1	reverse	int	0

sigmoid

y = 1 / (1 + exp(-x))

one_blob_only
support_inplace

slice

split x along axis into slices, each part slice size is based on slices array

param id	name	type	default	description
0	slices	array	[ ]
1	axis	int	0

softmax

softmax(x, axis)

one_blob_only
support_inplace

param id	name	type	default	description
0	axis	int	0
1	fixbug0	int	0	hack for bug fix, should be 1

softplus

y = log(exp(x) + 1)

one_blob_only
support_inplace

split

y0, y1 ... = x

swish

y = x / (1 + exp(-x))

one_blob_only
support_inplace

tanh

y = tanh(x)

one_blob_only
support_inplace

threshold

if x > threshold    y = 1
else                y = 0

one_blob_only
support_inplace

param id	name	type	default	description
0	threshold	float	0.f

unaryop

y = unaryop(x)

one_blob_only
support_inplace

param id	name	type	default	description
0	op_type	int	0	Operation type as follows

Operation type:

0 = ABS
1 = NEG
2 = FLOOR
3 = CEIL
4 = SQUARE
5 = SQRT
6 = RSQ
7 = EXP
8 = LOG
9 = SIN
10 = COS
11 = TAN
12 = ASIN
13 = ACOS
14 = ATAN
15 = RECIPROCAL
16 = TANH

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absval

argmax

batchnorm

bias

binaryop

bnll

cast

clip

concat

convolution

convolutiondepthwise

crop

deconvolution

deconvolutiondepthwise

dequantize

dropout

eltwise

elu

exp

flatten

gelu

gemm

groupnorm

gru

hardsigmoid

hardswish

innerproduct

input

instancenorm

interp

layernorm

log

lrn

lstm

memorydata

mish

multiheadattention

mvn

noop

normalize

packing

padding

permute

pixelshuffle

pooling

power

prelu

quantize

reduction

relu

reorg

requantize

reshape

rnn

scale

selu

shufflechannel

sigmoid

slice

softmax

softplus

split

swish

tanh

threshold

unaryop

41 kB

Raw Blame History