ncnn/docs/developer-guide/operators.md

• absval
• argmax
• batchnorm
• bias
• binaryop
• bnll
• cast
• clip
• concat
• convolution
• convolutiondepthwise
• crop
• dequantize
• lstm
• pooling
• sigmoid
• softmax
• tanh

• absval
• argmax
• batchnorm
• bias
• binaryop
• bnll
• cast
• clip
• concat
• convolution
• convolutiondepthwise
• crop
• dequantize
• lstm
• pooling
• sigmoid
• softmax
• tanh

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
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
0	channels	int	0
1	eps	float	0.f

weight	type
slope_data	float
mean_data	float
var_data	float
bias_data	float

bias

y = x + bias

• one_blob_only
• support_inplace

param id	name	type	default
0	bias_data_size	int	0

weight	type
bias_data	float

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
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
0	min	float	-FLT_MAX
1	max	float	FLT_MAX

concat

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

param id	name	type	default
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
weight_data	float/fp16/int8
bias_data	float

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
weight_data	float/fp16/int8
bias_data	float

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	[ ]

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

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].

• one_blob_only

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

weight	type	shape	description
weight_xc_data	float	[w=input_size, h=num_output * 4, c=num_directions]
bias_c_data	float	[w=num_output, h=4, c=num_directions]
weight_hc_data	float	[w=num_output, h=num_output * 4, c=num_directions]

pooling

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

param id	name	type	default	description
0	pooling_type	int	0	0: max 1: avg
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	0: full padding 1: valid padding 2: tensorflow padding=SAME or onnx padding=SAME_UPPER 3: onnx padding=SAME_LOWER
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

sigmoid

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

• one_blob_only
• support_inplace

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

tanh

y = tanh(x)

• one_blob_only
• support_inplace