From 0bdd5a3f3a264a7a5341ce2afb52fa1d231a3f86 Mon Sep 17 00:00:00 2001 From: nihuini Date: Tue, 5 Aug 2025 16:49:56 +0800 Subject: [PATCH] revert doc --- docs/developer-guide/operators.md | 2259 ++++++++++++++--------------- 1 file changed, 1049 insertions(+), 1210 deletions(-) diff --git a/docs/developer-guide/operators.md b/docs/developer-guide/operators.md index 94b3f2f95..cab7bdca1 100644 --- a/docs/developer-guide/operators.md +++ b/docs/developer-guide/operators.md @@ -1,177 +1,168 @@ -- [AbsVal](#absval) -- [ArgMax](#argmax) -- [BatchNorm](#batchnorm) -- [Bias](#bias) -- [BinaryOp](#binaryop) -- [BNLL](#bnll) -- [Cast](#cast) -- [CELU](#celu) -- [Clip](#clip) -- [Concat](#concat) -- [Convolution](#convolution) -- [Convolution1D](#convolution1d) -- [Convolution3D](#convolution3d) -- [ConvolutionDepthWise](#convolutiondepthwise) -- [ConvolutionDepthWise1D](#convolutiondepthwise1d) -- [ConvolutionDepthWise3D](#convolutiondepthwise3d) -- [CopyTo](#copyto) -- [Crop](#crop) -- [CumulativeSum](#cumulativesum) -- [Deconvolution](#deconvolution) -- [Deconvolution1D](#deconvolution1d) -- [Deconvolution3D](#deconvolution3d) -- [DeconvolutionDepthWise](#deconvolutiondepthwise) -- [DeconvolutionDepthWise1D](#deconvolutiondepthwise1d) -- [DeconvolutionDepthWise3D](#deconvolutiondepthwise3d) -- [DeformableConv2D](#deformableconv2d) -- [Dequantize](#dequantize) -- [Diag](#diag) -- [Dropout](#dropout) -- [Eltwise](#eltwise) -- [ELU](#elu) -- [Embed](#embed) -- [Exp](#exp) -- [Flatten](#flatten) -- [Flip](#flip) -- [Fold](#fold) -- [GELU](#gelu) -- [GLU](#glu) -- [Gemm](#gemm) -- [GridSample](#gridsample) -- [GroupNorm](#groupnorm) -- [GRU](#gru) -- [HardSigmoid](#hardsigmoid) -- [HardSwish](#hardswish) -- [InnerProduct](#innerproduct) -- [Input](#input) -- [InstanceNorm](#instancenorm) -- [Interp](#interp) -- [InverseSpectrogram](#inversespectrogram) -- [LayerNorm](#layernorm) -- [Log](#log) -- [LRN](#lrn) -- [LSTM](#lstm) -- [MemoryData](#memorydata) -- [Mish](#mish) -- [MultiHeadAttention](#multiheadattention) -- [MVN](#mvn) -- [Noop](#noop) -- [Normalize](#normalize) -- [Packing](#packing) -- [Padding](#padding) -- [Permute](#permute) -- [PixelShuffle](#pixelshuffle) -- [Pooling](#pooling) -- [Pooling1D](#pooling1d) -- [Pooling3D](#pooling3d) -- [Power](#power) -- [PReLU](#prelu) -- [Quantize](#quantize) -- [Reduction](#reduction) -- [ReLU](#relu) -- [Reorg](#reorg) -- [Requantize](#requantize) -- [Reshape](#reshape) -- [RMSNorm](#rmsnorm) -- [RNN](#rnn) -- [Scale](#scale) -- [SELU](#selu) -- [Shrink](#shrink) -- [ShuffleChannel](#shufflechannel) -- [Sigmoid](#sigmoid) -- [Slice](#slice) -- [Softmax](#softmax) -- [Softplus](#softplus) -- [Spectrogram](#spectrogram) -- [Split](#split) -- [Swish](#swish) -- [TanH](#tanh) -- [Threshold](#threshold) -- [Tile](#tile) -- [UnaryOp](#unaryop) -- [Unfold](#unfold) -# AbsVal +* [AbsVal](#absval) +* [ArgMax](#argmax) +* [BatchNorm](#batchnorm) +* [Bias](#bias) +* [BinaryOp](#binaryop) +* [BNLL](#bnll) +* [Cast](#cast) +* [CELU](#celu) +* [Clip](#clip) +* [Concat](#concat) +* [Convolution](#convolution) +* [Convolution1D](#convolution1d) +* [Convolution3D](#convolution3d) +* [ConvolutionDepthWise](#convolutiondepthwise) +* [ConvolutionDepthWise1D](#convolutiondepthwise1d) +* [ConvolutionDepthWise3D](#convolutiondepthwise3d) +* [CopyTo](#copyto) +* [Crop](#crop) +* [CumulativeSum](#cumulativesum) +* [Deconvolution](#deconvolution) +* [Deconvolution1D](#deconvolution1d) +* [Deconvolution3D](#deconvolution3d) +* [DeconvolutionDepthWise](#deconvolutiondepthwise) +* [DeconvolutionDepthWise1D](#deconvolutiondepthwise1d) +* [DeconvolutionDepthWise3D](#deconvolutiondepthwise3d) +* [DeformableConv2D](#deformableconv2d) +* [Dequantize](#dequantize) +* [Diag](#diag) +* [Dropout](#dropout) +* [Eltwise](#eltwise) +* [ELU](#elu) +* [Embed](#embed) +* [Exp](#exp) +* [Flatten](#flatten) +* [Fold](#fold) +* [GELU](#gelu) +* [GLU](#glu) +* [Gemm](#gemm) +* [GridSample](#gridsample) +* [GroupNorm](#groupnorm) +* [GRU](#gru) +* [HardSigmoid](#hardsigmoid) +* [HardSwish](#hardswish) +* [InnerProduct](#innerproduct) +* [Input](#input) +* [InstanceNorm](#instancenorm) +* [Interp](#interp) +* [InverseSpectrogram](#inversespectrogram) +* [LayerNorm](#layernorm) +* [Log](#log) +* [LRN](#lrn) +* [LSTM](#lstm) +* [MemoryData](#memorydata) +* [Mish](#mish) +* [MultiHeadAttention](#multiheadattention) +* [MVN](#mvn) +* [Noop](#noop) +* [Normalize](#normalize) +* [Packing](#packing) +* [Padding](#padding) +* [Permute](#permute) +* [PixelShuffle](#pixelshuffle) +* [Pooling](#pooling) +* [Pooling1D](#pooling1d) +* [Pooling3D](#pooling3d) +* [Power](#power) +* [PReLU](#prelu) +* [Quantize](#quantize) +* [Reduction](#reduction) +* [ReLU](#relu) +* [Reorg](#reorg) +* [Requantize](#requantize) +* [Reshape](#reshape) +* [RMSNorm](#rmsnorm) +* [RNN](#rnn) +* [Scale](#scale) +* [SELU](#selu) +* [Shrink](#shrink) +* [ShuffleChannel](#shufflechannel) +* [Sigmoid](#sigmoid) +* [Slice](#slice) +* [Softmax](#softmax) +* [Softplus](#softplus) +* [Spectrogram](#spectrogram) +* [Split](#split) +* [Swish](#swish) +* [TanH](#tanh) +* [Threshold](#threshold) +* [Tile](#tile) +* [UnaryOp](#unaryop) +* [Unfold](#unfold) +# AbsVal ``` y = abs(x) ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace # ArgMax - ``` y = argmax(x, out_max_val, topk) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------- | ---- | ------- | ----------- | -| 0 | out_max_val | int | 0 | | -| 1 | topk | int | 1 | | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | -------- | ----- | ------- | ----------- | -| 0 | channels | int | 0 | | -| 1 | eps | float | 0.f | | +| 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] | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | -------------- | ---- | ------- | ----------- | -| 0 | bias_data_size | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | bias_data_size| int | 0 | | -| weight | type | shape | -| --------- | ----- | ---------- | -| bias_data | float | [channels] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| bias_data | float | [channels] | # BinaryOp - -This operation is used for binary computation, and the calculation rule depends on the [broadcasting rule](https://github.com/Tencent/ncnn/wiki/binaryop-broadcasting). - + This operation is used for binary computation, and the calculation rule depends on the [broadcasting rule](https://github.com/Tencent/ncnn/wiki/binaryop-broadcasting). ``` 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 | +| 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 @@ -186,31 +177,28 @@ Operation type: - 11 = RATAN2 # BNLL - ``` y = log(1 + e^(-x)) , x > 0 y = log(1 + e^x), x < 0 ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace # Cast - ``` y = cast(x) ``` -- one_blob_only -- support_packing +* one_blob_only +* support_packing -| param id | name | type | default | description | -| -------- | --------- | ---- | ------- | ----------- | -| 0 | type_from | int | 0 | | -| 1 | type_to | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | type_from | int | 0 | | +| 1 | type_to | int | 0 | | Element type: - - 0 = auto - 1 = float32 - 2 = float16 @@ -218,288 +206,276 @@ Element type: - 4 = bfloat16 # CELU - ``` if x < 0 y = (exp(x / alpha) - 1.f) * alpha else y = x ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | alpha | float | 1.f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | alpha | float | 1.f | | # Clip - ``` y = clamp(x, min, max) ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ---- | ----- | -------- | ----------- | -| 0 | min | float | -FLT_MAX | | -| 1 | max | float | FLT_MAX | | +| 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 | | +| 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------- | ----------- | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 18 | pad_value | float | 0.f | | -| 19 | dynamic_weight | int | 0 | | - -| 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] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 18 | pad_value | float | 0.f | | +| 19 | dynamic_weight| int | 0 | | + +| 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] | # Convolution1D - ``` x2 = pad(x, pads, pad_value) x3 = conv1d(x2, weight, kernel, stride, dilation) + bias y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | -------- | ----------- | -| 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 | | -| 9 | activation_type | int | 0 | | -| 10 | activation_params | array | [ ] | | -| 15 | pad_right | int | pad_left | | -| 18 | pad_value | float | 0.f | | -| 19 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | --------------- | --------------------------------- | -| weight_data | float/fp16/int8 | [kernel_w, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 9 | activation_type| int | 0 | | +| 10 | activation_params| array | [ ] | | +| 15 | pad_right | int | pad_left | | +| 18 | pad_value | float | 0.f | | +| 19 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16/int8 | [kernel_w, num_input, num_output] | +| bias_data | float | [num_output] | # Convolution3D - ``` x2 = pad(x, pads, pad_value) x3 = conv3d(x2, weight, kernel, stride, dilation) + bias y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 17 | pad_behind | int | pad_front | | -| 18 | pad_value | float | 0.f | | -| 21 | kernel_d | int | kernel_w | | -| 22 | dilation_d | int | dilation_w | | -| 23 | stride_d | int | stride_w | | -| 24 | pad_front | int | pad_left | | - -| weight | type | shape | -| ----------- | --------------- | ----------------------------------------------------- | -| weight_data | float/fp16/int8 | [kernel_w, kernel_h, kernel_d, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 17 | pad_behind | int | pad_front | | +| 18 | pad_value | float | 0.f | | +| 21 | kernel_d | int | kernel_w | | +| 22 | dilation_d | int | dilation_w | | +| 23 | stride_d | int | stride_w | | +| 24 | pad_front | int | pad_left | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16/int8 | [kernel_w, kernel_h, kernel_d, num_input, num_output] | +| bias_data | float | [num_output] | # 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------- | ----------- | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 18 | pad_value | float | 0.f | | -| 19 | dynamic_weight | int | 0 | | - -| 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] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 18 | pad_value | float | 0.f | | +| 19 | dynamic_weight| int | 0 | | + +| 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] | # ConvolutionDepthWise1D - ``` x2 = pad(x, pads, pad_value) x3 = conv1d(x2, weight, kernel, stride, dilation, group) + bias y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | -------- | ----------- | -| 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 | | -| 9 | activation_type | int | 0 | | -| 10 | activation_params | array | [ ] | | -| 15 | pad_right | int | pad_left | | -| 18 | pad_value | float | 0.f | | -| 19 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | --------------- | -------------------------------------------------------- | -| weight_data | float/fp16/int8 | [kernel_w, num_input / group, num_output / group, group] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 9 | activation_type| int | 0 | | +| 10 | activation_params| array | [ ] | | +| 15 | pad_right | int | pad_left | | +| 18 | pad_value | float | 0.f | | +| 19 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16/int8 | [kernel_w, num_input / group, num_output / group, group] | +| bias_data | float | [num_output] | # ConvolutionDepthWise3D - ``` x2 = pad(x, pads, pad_value) x3 = conv3d(x2, weight, kernel, stride, dilation, group) + bias y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 17 | pad_behind | int | pad_front | | -| 18 | pad_value | float | 0.f | | -| 21 | kernel_d | int | kernel_w | | -| 22 | dilation_d | int | dilation_w | | -| 23 | stride_d | int | stride_w | | -| 24 | pad_front | int | pad_left | | - -| weight | type | shape | -| ----------- | --------------- | ---------------------------------------------------------------------------- | -| weight_data | float/fp16/int8 | [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 17 | pad_behind | int | pad_front | | +| 18 | pad_value | float | 0.f | | +| 21 | kernel_d | int | kernel_w | | +| 22 | dilation_d | int | dilation_w | | +| 23 | stride_d | int | stride_w | | +| 24 | pad_front | int | pad_left | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16/int8 | [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group] | +| bias_data | float | [num_output] | # CopyTo - ``` self[offset] = src ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ------- | ----- | ------- | ----------- | -| 0 | woffset | int | 0 | | -| 1 | hoffset | int | 0 | | -| 13 | doffset | int | 0 | | -| 2 | coffset | int | 0 | | -| 9 | starts | array | [ ] | | -| 11 | axes | array | [ ] | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | woffset | int | 0 | | +| 1 | hoffset | int | 0 | | +| 13 | doffset | int | 0 | | +| 2 | coffset | int | 0 | | +| 9 | starts | array | [ ] | | +| 11 | axes | array | [ ] | | # Crop - ``` y = crop(x) ``` -- one_blob_only +* one_blob_only -<<<<<<< HEAD | param id | name | type | default | description | | --------- | ------------- | ----- | --------- | ----------------- | | 0 | woffset | int | 0 | | @@ -520,25 +496,6 @@ y = crop(x) | 19 | starts_expr | str | "" | | | 20 | ends_expr | str | "" | | | 21 | axes_expr | str | "" | | -======= -| param id | name | type | default | description | -| -------- | -------- | ----- | ------- | ----------- | -| 0 | woffset | int | 0 | | -| 1 | hoffset | int | 0 | | -| 13 | doffset | int | 0 | | -| 2 | coffset | int | 0 | | -| 3 | outw | int | 0 | | -| 4 | outh | int | 0 | | -| 14 | outd | int | 0 | | -| 5 | outc | int | 0 | | -| 6 | woffset2 | int | 0 | | -| 7 | hoffset2 | int | 0 | | -| 15 | doffset2 | int | 0 | | -| 8 | coffset2 | int | 0 | | -| 9 | starts | array | [ ] | | -| 10 | ends | array | [ ] | | -| 11 | axes | array | [ ] | | ->>>>>>> flip0/torch_flip # CumulativeSum @@ -546,433 +503,408 @@ If axis < 0, we use axis = x.dims + axis It implements https://pytorch.org/docs/stable/generated/torch.cumsum.html -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace + +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | axis | int | 0 | | -| param id | name | type | default | description | -| -------- | ---- | ---- | ------- | ----------- | -| 0 | axis | int | 0 | | # 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 18 | output_pad_right | int | 0 | | -| 19 | output_pad_bottom | int | output_pad_right | | -| 20 | output_w | int | 0 | | -| 21 | output_h | int | output_w | | -| 28 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | ---------- | ------------------------------------------- | -| weight_data | float/fp16 | [kernel_w, kernel_h, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 18 | output_pad_right| int | 0 | | +| 19 | output_pad_bottom| int | output_pad_right | | +| 20 | output_w | int | 0 | | +| 21 | output_h | int | output_w | | +| 28 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, kernel_h, num_input, num_output] | +| bias_data | float | [num_output] | # Deconvolution1D - ``` x2 = deconv1d(x, weight, kernel, stride, dilation) + bias x3 = depad(x2, pads, pad_value) y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | -------- | ----------- | -| 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 | | -| 9 | activation_type | int | 0 | | -| 10 | activation_params | array | [ ] | | -| 15 | pad_right | int | pad_left | | -| 18 | output_pad_right | int | 0 | | -| 20 | output_w | int | 0 | | -| 28 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | ---------- | --------------------------------- | -| weight_data | float/fp16 | [kernel_w, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 9 | activation_type| int | 0 | | +| 10 | activation_params| array | [ ] | | +| 15 | pad_right | int | pad_left | | +| 18 | output_pad_right| int | 0 | | +| 20 | output_w | int | 0 | | +| 28 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, num_input, num_output] | +| bias_data | float | [num_output] | # Deconvolution3D - ``` x2 = deconv3d(x, weight, kernel, stride, dilation) + bias x3 = depad(x2, pads, pad_value) y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 17 | pad_behind | int | pad_front | | -| 18 | output_pad_right | int | 0 | | -| 19 | output_pad_bottom | int | output_pad_right | | -| 20 | output_pad_behind | int | output_pad_right | | -| 21 | kernel_d | int | kernel_w | | -| 22 | dilation_d | int | dilation_w | | -| 23 | stride_d | int | stride_w | | -| 24 | pad_front | int | pad_left | | -| 25 | output_w | int | 0 | | -| 26 | output_h | int | output_w | | -| 27 | output_d | int | output_w | | - -| weight | type | shape | -| ----------- | ---------- | ----------------------------------------------------- | -| weight_data | float/fp16 | [kernel_w, kernel_h, kernel_d, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 17 | pad_behind | int | pad_front | | +| 18 | output_pad_right| int | 0 | | +| 19 | output_pad_bottom| int | output_pad_right | | +| 20 | output_pad_behind| int | output_pad_right | | +| 21 | kernel_d | int | kernel_w | | +| 22 | dilation_d | int | dilation_w | | +| 23 | stride_d | int | stride_w | | +| 24 | pad_front | int | pad_left | | +| 25 | output_w | int | 0 | | +| 26 | output_h | int | output_w | | +| 27 | output_d | int | output_w | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, kernel_h, kernel_d, 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 18 | output_pad_right | int | 0 | | -| 19 | output_pad_bottom | int | output_pad_right | | -| 20 | output_w | int | 0 | | -| 21 | output_h | int | output_w | | -| 28 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | ---------- | ------------------------------------------------------------------ | -| weight_data | float/fp16 | [kernel_w, kernel_h, num_input / group, num_output / group, group] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 18 | output_pad_right| int | 0 | | +| 19 | output_pad_bottom| int | output_pad_right | | +| 20 | output_w | int | 0 | | +| 21 | output_h | int | output_w | | +| 28 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, kernel_h, num_input / group, num_output / group, group] | +| bias_data | float | [num_output] | # DeconvolutionDepthWise1D - ``` x2 = deconv1d(x, weight, kernel, stride, dilation, group) + bias x3 = depad(x2, pads, pad_value) y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | -------- | ----------- | -| 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 | | -| 9 | activation_type | int | 0 | | -| 10 | activation_params | array | [ ] | | -| 15 | pad_right | int | pad_left | | -| 18 | output_pad_right | int | 0 | | -| 20 | output_w | int | 0 | | -| 28 | dynamic_weight | int | 0 | | - -| weight | type | shape | -| ----------- | ---------- | -------------------------------------------------------- | -| weight_data | float/fp16 | [kernel_w, num_input / group, num_output / group, group] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 9 | activation_type| int | 0 | | +| 10 | activation_params| array | [ ] | | +| 15 | pad_right | int | pad_left | | +| 18 | output_pad_right| int | 0 | | +| 20 | output_w | int | 0 | | +| 28 | dynamic_weight| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, num_input / group, num_output / group, group] | +| bias_data | float | [num_output] | # DeconvolutionDepthWise3D - ``` x2 = deconv3d(x, weight, kernel, stride, dilation, group) + bias x3 = depad(x2, pads, pad_value) y = activation(x3, act_type, act_params) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 17 | pad_behind | int | pad_front | | -| 18 | output_pad_right | int | 0 | | -| 19 | output_pad_bottom | int | output_pad_right | | -| 20 | output_pad_behind | int | output_pad_right | | -| 21 | kernel_d | int | kernel_w | | -| 22 | dilation_d | int | dilation_w | | -| 23 | stride_d | int | stride_w | | -| 24 | pad_front | int | pad_left | | -| 25 | output_w | int | 0 | | -| 26 | output_h | int | output_w | | -| 27 | output_d | int | output_w | | - -| weight | type | shape | -| ----------- | ---------- | ---------------------------------------------------------------------------- | -| weight_data | float/fp16 | [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 17 | pad_behind | int | pad_front | | +| 18 | output_pad_right| int | 0 | | +| 19 | output_pad_bottom| int | output_pad_right | | +| 20 | output_pad_behind| int | output_pad_right | | +| 21 | kernel_d | int | kernel_w | | +| 22 | dilation_d | int | dilation_w | | +| 23 | stride_d | int | stride_w | | +| 24 | pad_front | int | pad_left | | +| 25 | output_w | int | 0 | | +| 26 | output_h | int | output_w | | +| 27 | output_d | int | output_w | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16 | [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group] | +| bias_data | float | [num_output] | # DeformableConv2D - ``` x2 = deformableconv2d(x, offset, mask, weight, kernel, stride, dilation) + bias y = activation(x2, act_type, act_params) ``` -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ---------- | ----------- | -| 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 | | -| 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 | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | - -| weight | type | shape | -| ----------- | --------------- | ------------------------------------------- | -| weight_data | float/fp16/int8 | [kernel_w, kernel_h, num_input, num_output] | -| bias_data | float | [num_output] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 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 | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float/fp16/int8 | [kernel_w, kernel_h, num_input, num_output] | +| bias_data | float | [num_output] | # Dequantize - ``` y = x * scale + bias ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | --------------- | ---- | ------- | ----------- | -| 0 | scale_data_size | int | 1 | | -| 1 | bias_data_size | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | scale_data_size| int | 1 | | +| 1 | bias_data_size| int | 0 | | -| weight | type | shape | -| ---------- | ----- | ----------------- | -| scale_data | float | [scale_data_size] | -| bias_data | float | [bias_data_size] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| scale_data | float | [scale_data_size] | +| bias_data | float | [bias_data_size] | # Diag - ``` y = diag(x, diagonal) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | -------- | ---- | ------- | ----------- | -| 0 | diagonal | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | diagonal | int | 0 | | # Dropout - ``` y = x * scale ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | scale | float | 1.f | | +| 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 | [ ] | | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | alpha | float | 0.1f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | alpha | float | 0.1f | | # Embed - ``` y = embedding(x) ``` -| param id | name | type | default | description | -| -------- | ---------------- | ---- | ------- | ----------- | -| 0 | num_output | int | 0 | | -| 1 | input_dim | int | 0 | | -| 2 | bias_term | int | 0 | | -| 3 | weight_data_size | int | 0 | | -| 18 | int8_scale_term | int | 0 | | - -| weight | type | shape | -| ----------------------- | ----- | ------------------ | -| weight_data | float | [weight_data_size] | -| bias_term | float | [num_output] | -| weight_data_int8_scales | float | [1] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | num_output | int | 0 | | +| 1 | input_dim | int | 0 | | +| 2 | bias_term | int | 0 | | +| 3 | weight_data_size | int | 0 | | +| 18 | int8_scale_term| int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_data | float | [weight_data_size] | +| bias_term | float | [num_output] | +| weight_data_int8_scales| float | [1] | # Exp - ``` if base == -1 y = exp(shift + x * scale) else y = pow(base, (shift + x * scale)) ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | base | float | -1.f | | -| 1 | scale | float | 1.f | | -| 2 | shift | float | 0.f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | base | float | -1.f | | +| 1 | scale | float | 1.f | | +| 2 | shift | float | 0.f | | # Flatten - Reshape blob to 1 dimension -- one_blob_only - -# Flip - -- one_blob_only - -| param id | name | type | default | description | -| -------- | ---- | ----- | ------- | ----------- | -| 0 | axis | array | [] | | +* one_blob_only # Fold - ``` y = fold(x) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ---------- | ---- | ---------- | ----------- | -| 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 | | -| 11 | kernel_h | int | kernel_w | | -| 12 | dilation_h | int | dilation_w | | -| 13 | stride_h | int | stride_w | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | -| 20 | output_w | int | 0 | | -| 21 | output_h | int | output_w | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 11 | kernel_h | int | kernel_w | | +| 12 | dilation_h | int | dilation_w | | +| 13 | stride_h | int | stride_w | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | | +| 20 | output_w | int | 0 | | +| 21 | output_h | int | output_w | | # 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | --------- | ---- | ------- | ----------------- | -| 0 | fast_gelu | int | 0 | use approximation | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | fast_gelu | int | 0 | use approximation | # GLU @@ -984,14 +916,13 @@ where a is the first half of the input matrix and b is the second half. axis specifies the dimension to split the input -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ---- | ---- | ------- | ----------- | -| 0 | axis | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | axis | int | 0 | | # Gemm - ``` a = transA ? transpose(x0) : x0 b = transb ? transpose(x1) : x1 @@ -999,91 +930,88 @@ c = x2 y = (gemm(a, b) + c * beta) * alpha ``` -| param id | name | type | default | description | -| -------- | ------------------------- | ----- | ------- | ----------- | -| 0 | alpha | float | 1.f | | -| 1 | beta | float | 1.f | | -| 2 | transA | int | 0 | | -| 3 | transb | int | 0 | | -| 4 | constantA | int | 0 | | -| 5 | constantB | int | 0 | | -| 6 | constantC | int | 0 | | -| 7 | constantM | int | 0 | | -| 8 | constantN | int | 0 | | -| 9 | constantK | int | 0 | | -| 10 | constant_broadcast_type_C | int | 0 | | -| 11 | output_N1M | int | 0 | | -| 12 | output_elempack | int | 0 | | -| 13 | output_elemtype | int | 0 | | -| 14 | output_transpose | int | 0 | | -| 18 | int8_scale_term | int | 0 | | -| 20 | constant_TILE_M | int | 0 | | -| 21 | constant_TILE_N | int | 0 | | -| 22 | constant_TILE_K | int | 0 | | - -| weight | type | shape | -| ------------------ | --------------- | -------------------------------------------- | -| A_data | float/fp16/int8 | [M, K] or [K, M] | -| B_data | float/fp16/int8 | [N, K] or [K, N] | -| C_data | float | [1], [M] or [N] or [1, M] or [N,1] or [N, M] | -| A_data_int8_scales | float | [M] | -| B_data_int8_scales | float | [1] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | alpha | float | 1.f | | +| 1 | beta | float | 1.f | | +| 2 | transA | int | 0 | | +| 3 | transb | int | 0 | | +| 4 | constantA | int | 0 | | +| 5 | constantB | int | 0 | | +| 6 | constantC | int | 0 | | +| 7 | constantM | int | 0 | | +| 8 | constantN | int | 0 | | +| 9 | constantK | int | 0 | | +| 10 | constant_broadcast_type_C | int | 0 | | +| 11 | output_N1M | int | 0 | | +| 12 | output_elempack | int | 0 | | +| 13 | output_elemtype | int | 0 | | +| 14 | output_transpose | int| 0 | | +| 18 | int8_scale_term | int | 0 | | +| 20 | constant_TILE_M | int | 0 | | +| 21 | constant_TILE_N | int | 0 | | +| 22 | constant_TILE_K | int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| A_data | float/fp16/int8 | [M, K] or [K, M] | +| B_data | float/fp16/int8 | [N, K] or [K, N] | +| C_data | float | [1], [M] or [N] or [1, M] or [N,1] or [N, M] | +| A_data_int8_scales| float | [M] | +| B_data_int8_scales| float | [1] | # GridSample - ``` Given an input and a flow-field grid, computes the output using input values and pixel locations from grid. -For each output location output[:, h2, w2], the size-2 vector grid[h2, w2, 2] specifies input pixel[:, h1, w1] locations x and y, +For each output location output[:, h2, w2], the size-2 vector grid[h2, w2, 2] specifies input pixel[:, h1, w1] locations x and y, which are used to interpolate the output value output[:, h2, w2] This function is often used in conjunction with affine_grid() to build Spatial Transformer Networks . ``` -| param id | name | type | default | description | -| -------- | -------------- | ---- | ------- | ----------------- | -| 0 | sample_type | int | 1 | | -| 1 | padding_mode | int | 1 | | -| 2 | align_corner | int | 0 | | -| 3 | permute_fusion | int | 0 | fuse with permute | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | sample_type | int | 1 | | +| 1 | padding_mode | int | 1 | | +| 2 | align_corner | int | 0 | | +| 3 | permute_fusion| int | 0 | fuse with permute | + Sample type: - - 1 = Nearest - 2 = Bilinear - 3 = Bicubic Padding mode: - - 1 = zeros - 2 = border - 3 = reflection -# GroupNorm +# 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 +* 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 | | +| 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] | +| 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]`. ``` @@ -1091,126 +1019,118 @@ y = gru(x) y0, hidden y1 = gru(x0, hidden x1) ``` -- one_blob_only if bidirectional +* one_blob_only if bidirectional -| param id | name | type | default | description | -| -------- | ---------------- | ---- | ------- | ------------------------------------- | -| 0 | num_output | int | 0 | hidden size of output | -| 1 | weight_data_size | int | 0 | total size of weight matrix | -| 2 | direction | int | 0 | 0=forward, 1=reverse, 2=bidirectional | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | num_output | int | 0 | hidden size of output | +| 1 | weight_data_size| int | 0 | total size of weight matrix | +| 2 | direction | int | 0 | 0=forward, 1=reverse, 2=bidirectional | -| weight | type | shape | -| -------------- | --------------- | -------------------------------------------- | -| weight_xc_data | float/fp16/int8 | [input_size, num_output * 3, num_directions] | -| bias_c_data | float/fp16/int8 | [num_output, 4, num_directions] | -| weight_hc_data | float/fp16/int8 | [num_output, num_output * 3, num_directions] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_xc_data| float/fp16/int8 | [input_size, num_output * 3, num_directions] | +| bias_c_data | float/fp16/int8 | [num_output, 4, num_directions] | +| weight_hc_data| float/fp16/int8 | [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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | alpha | float | 0.2f | | -| 1 | beta | float | 0.5f | | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | alpha | float | 0.2f | | -| 1 | beta | float | 0.5f | | +| 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ----- | ------- | ----------- | -| 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] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 +* support_inplace -| param id | name | type | default | description | -| -------- | ---- | ---- | ------- | ----------- | -| 0 | w | int | 0 | | -| 1 | h | int | 0 | | -| 11 | d | int | 0 | | -| 2 | c | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | w | int | 0 | | +| 1 | h | int | 0 | | +| 11 | d | int | 0 | | +| 2 | c | int | 0 | | # 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 +* 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 | | +| 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] | +| 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 +* one_blob_only if dynamic_target_size == 0 -<<<<<<< HEAD | param id | name | type | default | description | | --------- | ------------- | ----- | --------- | ----------------- | | 0 | resize_type | int | 0 | | @@ -1221,26 +1141,13 @@ else y = resize(x0, size(x1)) | 5 | dynamic_target_size| int | 0 | | | 6 | align_corner | int | 0 | | | 9 | size_expr | str | "" | | -======= -| param id | name | type | default | description | -| -------- | ------------------- | ----- | ------- | ----------- | -| 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 | | ->>>>>>> flip0/torch_flip Resize type: - - 1 = Nearest - 2 = Bilinear - 3 = Bicubic # InverseSpectrogram - ``` x1 = x as complex x1 = x1 * sqrt(norm) if normalized @@ -1252,82 +1159,77 @@ if returns == 1 return y1 real if returns == 2 return y1 imag ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------- | ---- | --------- | ------------------------------- | -| 0 | n_fft | int | 0 | | -| 1 | returns | int | 1 | | -| 2 | hoplen | int | n_fft / 4 | | -| 3 | winlen | int | n_fft | | -| 4 | window_type | int | 0 | 0=ones 1=hann 2=hamming | -| 5 | center | int | 1 | | -| 7 | normalized | int | 0 | 0=no 1=n_fft 2=window-l2-energy | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | n_fft | int | 0 | | +| 1 | returns | int | 1 | | +| 2 | hoplen | int | n_fft / 4 | | +| 3 | winlen | int | n_fft | | +| 4 | window_type | int | 0 | 0=ones 1=hann 2=hamming | +| 5 | center | int | 1 | | +| 7 | normalized | int | 0 | 0=no 1=n_fft 2=window-l2-energy | # 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 +* 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 | | +| 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] | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | base | float | -1.f | | -| 1 | scale | float | 1.f | | -| 2 | shift | float | 0.f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----------- | ----- | ------- | ----------- | -| 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 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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]`. ``` @@ -1335,57 +1237,53 @@ y = lstm(x) y0, hidden y1, cell y2 = lstm(x0, hidden x1, cell x2) ``` -- one_blob_only if bidirectional +* one_blob_only if bidirectional -| param id | name | type | default | description | -| -------- | ---------------- | ---- | ---------- | ------------------------------------- | -| 0 | num_output | int | 0 | output 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 | -| 3 | hidden_size | int | num_output | hidden size | - -| weight | type | shape | -| -------------- | --------------- | --------------------------------------------- | -| weight_xc_data | float/fp16/int8 | [input_size, hidden_size * 4, num_directions] | -| bias_c_data | float/fp16/int8 | [hidden_size, 4, num_directions] | -| weight_hc_data | float/fp16/int8 | [num_output, hidden_size * 4, num_directions] | -| weight_hr_data | float/fp16/int8 | [hidden_size, num_output, num_directions] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | num_output | int | 0 | output 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 | +| 3 | hidden_size | int | num_output| hidden size | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_xc_data| float/fp16/int8 | [input_size, hidden_size * 4, num_directions] | +| bias_c_data | float/fp16/int8 | [hidden_size, 4, num_directions] | +| weight_hc_data| float/fp16/int8 | [num_output, hidden_size * 4, num_directions] | +| weight_hr_data| float/fp16/int8 | [hidden_size, num_output, num_directions] | Direction flag: - - 0 = forward only - 1 = reverse only - 2 = bidirectional # MemoryData - ``` y = data ``` -| param id | name | type | default | description | -| -------- | --------- | ---- | ------- | ----------- | -| 0 | w | int | 0 | | -| 1 | h | int | 0 | | -| 11 | d | int | 0 | | -| 2 | c | int | 0 | | -| 21 | load_type | int | 1 | 1=fp32 | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | w | int | 0 | | +| 1 | h | int | 0 | | +| 11 | d | int | 0 | | +| 2 | c | int | 0 | | +| 21 | load_type | int | 1 | 1=fp32 | -| weight | type | shape | -| ------ | ----- | ------------ | -| data | float | [w, h, d, c] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| data | float | [w, h, d, c] | # Mish - ``` y = x * tanh(log(exp(x) + 1)) ``` -- one_blob_only -- support_inplace +* 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 @@ -1400,34 +1298,33 @@ for each num_head part y = affine(out) ``` -| param id | name | type | default | description | -| -------- | ---------------- | ----- | --------------------------------- | ----------------------------------- | -| 0 | embed_dim | int | 0 | | -| 1 | num_heads | int | 1 | | -| 2 | weight_data_size | int | 0 | qdim = weight_data_size / embed_dim | -| 3 | kdim | int | embed_dim | | -| 4 | vdim | int | embed_dim | | -| 5 | attn_mask | int | 0 | | -| 6 | scale | float | 1.f / sqrt(embed_dim / num_heads) | | -| 18 | int8_scale_term | int | 0 | | - -| weight | type | shape | -| --------------------------- | --------------- | ------------------ | -| q_weight_data | float/fp16/int8 | [embed_dim * qdim] | -| q_bias_data | float | [embed_dim] | -| k_weight_data | float/fp16/int8 | [embed_dim * kdim] | -| k_bias_data | float | [embed_dim] | -| v_weight_data | float/fp16/int8 | [embed_dim * vdim] | -| v_bias_data | float | [embed_dim] | -| out_weight_data | float/fp16/int8 | [qdim * embed_dim] | -| out_bias_data | float | [qdim] | -| q_weight_data_int8_scales | float | [embed_dim] | -| k_weight_data_int8_scales | float | [embed_dim] | -| v_weight_data_int8_scales | float | [embed_dim] | -| out_weight_data_int8_scales | float | [1] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | embed_dim | int | 0 | | +| 1 | num_heads | int | 1 | | +| 2 | weight_data_size| int | 0 | qdim = weight_data_size / embed_dim | +| 3 | kdim | int | embed_dim | | +| 4 | vdim | int | embed_dim | | +| 5 | attn_mask | int | 0 | | +| 6 | scale | float | 1.f / sqrt(embed_dim / num_heads) | | +| 18 | int8_scale_term | int | 0 | | + +| weight | type | shape | +| ------------- | ----- | --------------------- | +| q_weight_data | float/fp16/int8 | [embed_dim * qdim] | +| q_bias_data | float | [embed_dim] | +| k_weight_data | float/fp16/int8 | [embed_dim * kdim] | +| k_bias_data | float | [embed_dim] | +| v_weight_data | float/fp16/int8 | [embed_dim * vdim] | +| v_bias_data | float | [embed_dim] | +| out_weight_data| float/fp16/int8 | [qdim * embed_dim] | +| out_bias_data | float | [qdim] | +| q_weight_data_int8_scales| float | [embed_dim] | +| k_weight_data_int8_scales| float | [embed_dim] | +| v_weight_data_int8_scales| float | [embed_dim] | +| out_weight_data_int8_scales| float | [1] | # 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 @@ -1435,22 +1332,20 @@ if normalize_variance == 0 && across_channels == 1 y = x - mean of whole bl if normalize_variance == 0 && across_channels == 0 y = x - mean of each channel ``` -- one_blob_only +* 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) | +| 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 @@ -1458,85 +1353,79 @@ if across_spatial == 0 && across_channel == 1 x2 = normalize(x) of each pos y = x2 * scale ``` -- one_blob_only -- support_inplace +* 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 | | +| 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] | +| 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)) +- 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------------- | ---- | ------- | ----------- | -| 0 | out_elempack | int | 1 | | -| 1 | use_padding | int | 0 | | -| 2 | cast_type_from | int | 0 | | -| 3 | cast_type_to | int | 0 | | -| 4 | storage_type_from | int | 0 | | -| 5 | storage_type_to | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | out_elempack | int | 1 | | +| 1 | use_padding | int | 0 | | +| 2 | cast_type_from| int | 0 | | +| 3 | cast_type_to | int | 0 | | +| 4 | storage_type_from| int | 0 | | +| 5 | storage_type_to| int | 0 | | # Padding - ``` y = pad(x, pads) ``` -| param id | name | type | default | description | -| -------- | ------------------------- | ----- | -------- | ----------- | -| 0 | top | int | 0 | | -| 1 | bottom | int | 0 | | -| 2 | left | int | 0 | | -| 3 | right | int | 0 | | -| 4 | type | int | 0 | | -| 5 | value | float | 0 | | -| 6 | per_channel_pad_data_size | int | 0 | | -| 7 | front | int | stride_w | | -| 8 | behind | int | pad_left | | +| param id | name | type | default | description | +| --------- | ------------- | ---- | --------- | ----------------- | +| 0 | top | int | 0 | | +| 1 | bottom | int | 0 | | +| 2 | left | int | 0 | | +| 3 | right | int | 0 | | +| 4 | type | int | 0 | | +| 5 | value | float | 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] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| per_channel_pad_data| float | [per_channel_pad_data_size] | Padding type: - - 0 = CONSTANT - 1 = REPLICATE - 2 = REFLECT # Permute - ``` y = reorder(x) ``` -| param id | name | type | default | description | -| -------- | ---------- | ---- | ------- | ----------- | -| 0 | order_type | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ---- | --------- | ----------------- | +| 0 | order_type | int | 0 | | Order Type: - - 0 = WH WHC WHDC - 1 = HW HWC HWDC - 2 = WCH WDHC @@ -1563,198 +1452,183 @@ Order Type: - 23 = CDHW # 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | -------------- | ---- | ------- | ----------- | -| 0 | upscale_factor | int | 1 | | -| 1 | mode | int | 0 | | +| 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 | description | -| -------- | ------------------------- | ---- | -------- | ----------- | -| 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 | | -| 6 | avgpool_count_include_pad | int | 0 | | -| 7 | adaptive_pooling | int | 0 | | -| 8 | out_w | 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 | | -| 18 | out_h | int | out_w | | +| param id | name | type | default | description | +| --------- | --------------| ---- | --------- | ----------------- | +| 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 | | +| 6 | avgpool_count_include_pad| int | 0 | | +| 7 | adaptive_pooling| int | 0 | | +| 8 | out_w | 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 | | +| 18 | out_h | int | out_w | | 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 # Pooling1D - ``` x2 = pad(x, pads) x3 = pooling1d(x2, kernel, stride) ``` -| param id | name | type | default | description | -| -------- | ------------------------- | ---- | -------- | ----------- | -| 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 | | -| 6 | avgpool_count_include_pad | int | 0 | | -| 7 | adaptive_pooling | int | 0 | | -| 8 | out_w | int | 0 | | -| 14 | pad_right | int | pad_left | | +| param id | name | type | default | description | +| --------- | --------------| ---- | --------- | ----------------- | +| 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 | | +| 6 | avgpool_count_include_pad| int | 0 | | +| 7 | adaptive_pooling| int | 0 | | +| 8 | out_w | int | 0 | | +| 14 | pad_right | int | pad_left | | 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 # Pooling3D - ``` x2 = pad(x, pads) x3 = pooling3d(x2, kernel, stride) ``` -| param id | name | type | default | description | -| -------- | ------------------------- | ---- | --------- | ----------- | -| 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 | | -| 6 | avgpool_count_include_pad | int | 0 | | -| 7 | adaptive_pooling | int | 0 | | -| 8 | out_w | 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 | | -| 16 | pad_behind | int | pad_front | | -| 18 | out_h | int | out_w | | -| 21 | kernel_d | int | kernel_w | | -| 22 | stride_d | int | stride_w | | -| 23 | pad_front | int | pad_left | | -| 28 | out_d | int | out_w | | +| param id | name | type | default | description | +| --------- | --------------| ---- | --------- | ----------------- | +| 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 | | +| 6 | avgpool_count_include_pad| int | 0 | | +| 7 | adaptive_pooling| int | 0 | | +| 8 | out_w | 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 | | +| 16 | pad_behind | int | pad_front | | +| 18 | out_h | int | out_w | | +| 21 | kernel_d | int | kernel_w | | +| 22 | stride_d | int | stride_w | | +| 23 | pad_front | int | pad_left | | +| 28 | out_d | int | out_w | | 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | power | float | 1.f | | -| 1 | scale | float | 1.f | | -| 2 | shift | float | 0.f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | --------- | ---- | ------- | ----------- | -| 0 | num_slope | int | 0 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | num_slope | int | 0 | | -| weight | type | shape | -| ---------- | ----- | ----------- | -| slope_data | float | [num_slope] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| slope_data | float | [num_slope] | # Quantize - ``` y = float2int8(x * scale) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | --------------- | ---- | ------- | ----------- | -| 0 | scale_data_size | int | 1 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | scale_data_size| int | 1 | | -| weight | type | shape | -| ---------- | ----- | ----------------- | -| scale_data | float | [scale_data_size] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| scale_data | float | [scale_data_size] | # Reduction - ``` y = reduce_op(x * coeff) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ---------- | ----- | ------- | ----------------------------- | -| 0 | operation | int | 0 | | -| 1 | reduce_all | int | 1 | | -| 2 | coeff | float | 1.f | | -| 3 | axes | array | [ ] | | -| 4 | keepdims | int | 0 | | -| 5 | fixbug0 | int | 0 | hack for bug fix, should be 1 | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | operation | int | 0 | | +| 1 | reduce_all | int | 1 | | +| 2 | coeff | float | 1.f | | +| 3 | axes | array | [ ] | | +| 4 | keepdims | int | 0 | | +| 5 | fixbug0 | int | 0 | hack for bug fix, should be 1 | Operation type: - - 0 = SUM - 1 = ASUM - 2 = SUMSQ @@ -1768,66 +1642,61 @@ Operation type: - 10 = LogSumExp # ReLU - ``` if x < 0 y = x * slope else y = x ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | slope | float | 0.f | | +| 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 +* one_blob_only -| param id | name | type | default | description | -| -------- | ------ | ---- | ------- | ----------- | -| 0 | stride | int | 1 | | -| 1 | mode | int | 0 | | +| 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 | description | -| -------- | ------------------- | ---- | ------- | ----------- | -| 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 | [ ] | | +* one_blob_only -| 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] | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 - ``` y = reshape(x) ``` -- one_blob_only +* one_blob_only -<<<<<<< HEAD | param id | name | type | default | description | | --------- | ------------- | ----- | --------- | ----------------- | | 0 | w | int | -233 | | @@ -1835,45 +1704,33 @@ y = reshape(x) | 11 | d | int | -233 | | | 2 | c | int | -233 | | | 6 | shape_expr | str | "" | | -======= -| param id | name | type | default | description | -| -------- | ------- | ---- | ------- | ----------- | -| 0 | w | int | -233 | | -| 1 | h | int | -233 | | -| 11 | d | int | -233 | | -| 2 | c | int | -233 | | -| 3 | permute | int | 0 | | ->>>>>>> flip0/torch_flip Reshape flag: - - 0 = copy from bottom - -1 = remaining - -233 = drop this dim(default) # RMSNorm - ``` split x along outmost axis into part x0, x1 ... root mean square normalize for each part x0, x1 ... y = x * gamma by elementwise ``` -- one_blob_only -- support_inplace +* 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 | | +| 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] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| gamma_data | float | [affine_size] | # 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]`. ``` @@ -1881,137 +1738,127 @@ y = rnn(x) y0, hidden y1 = rnn(x0, hidden x1) ``` -- one_blob_only if bidirectional +* one_blob_only if bidirectional -| param id | name | type | default | description | -| -------- | ---------------- | ---- | ------- | ------------------------------------- | -| 0 | num_output | int | 0 | hidden size of output | -| 1 | weight_data_size | int | 0 | total size of weight matrix | -| 2 | direction | int | 0 | 0=forward, 1=reverse, 2=bidirectional | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | num_output | int | 0 | hidden size of output | +| 1 | weight_data_size| int | 0 | total size of weight matrix | +| 2 | direction | int | 0 | 0=forward, 1=reverse, 2=bidirectional | -| weight | type | shape | -| -------------- | --------------- | ---------------------------------------- | -| weight_xc_data | float/fp16/int8 | [input_size, num_output, num_directions] | -| bias_c_data | float/fp16/int8 | [num_output, 1, num_directions] | -| weight_hc_data | float/fp16/int8 | [num_output, num_output, num_directions] | +| weight | type | shape | +| ------------- | ----- | --------------------- | +| weight_xc_data| float/fp16/int8 | [input_size, num_output, num_directions] | +| bias_c_data | float/fp16/int8 | [num_output, 1, num_directions] | +| weight_hc_data| float/fp16/int8 | [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 +* 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 | | +| 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] | +| 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 +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ------ | ----- | ------------ | ----------- | -| 0 | alpha | float | 1.67326324f | | -| 1 | lambda | float | 1.050700987f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | alpha | float | 1.67326324f| | +| 1 | lambda | float | 1.050700987f| | # Shrink - ``` if x < -lambd y = x + bias if x > lambd y = x - bias else y = x ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | ----- | ----- | ------- | ----------- | -| 0 | bias | float | 0.0f | | -| 1 | lambd | float | 0.5f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | bias | float | 0.0f | | +| 1 | lambd | float | 0.5f | | # ShuffleChannel - ``` if reverse == 0 y = shufflechannel(x) by group if reverse == 1 y = shufflechannel(x) by channel / group ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ------- | ---- | ------- | ----------- | -| 0 | group | int | 1 | | -| 1 | reverse | int | 0 | | +| 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 +* 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 | | -| 2 | indices | array | [ ] | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | slices | array | [ ] | | +| 1 | axis | int | 0 | | +| 2 | indices | array | [ ] | | # Softmax - ``` softmax(x, axis) ``` -- one_blob_only -- support_inplace +* 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 | +| 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 +* one_blob_only +* support_inplace # Spectrogram - ``` x1 = pad(x) if center y = stft(x1) @@ -2022,74 +1869,68 @@ if power == 1 return magnitude if power == 2 return square of magnitude ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ----------- | ---- | --------- | -------------------------------- | -| 0 | n_fft | int | 0 | | -| 1 | power | int | 0 | | -| 2 | hoplen | int | n_fft / 4 | | -| 3 | winlen | int | n_fft | | -| 4 | window_type | int | 0 | 0=ones 1=hann 2=hamming | -| 5 | center | int | 1 | | -| 6 | pad_type | int | 2 | 0=CONSTANT 1=REPLICATE 2=REFLECT | -| 7 | normalized | int | 0 | 0=no 1=n_fft 2=window-l2-energy | -| 8 | onesided | int | 1 | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | n_fft | int | 0 | | +| 1 | power | int | 0 | | +| 2 | hoplen | int | n_fft / 4 | | +| 3 | winlen | int | n_fft | | +| 4 | window_type | int | 0 | 0=ones 1=hann 2=hamming | +| 5 | center | int | 1 | | +| 6 | pad_type | int | 2 | 0=CONSTANT 1=REPLICATE 2=REFLECT | +| 7 | normalized | int | 0 | 0=no 1=n_fft 2=window-l2-energy | +| 8 | onesided | int | 1 | | # Split - ``` y0, y1 ... = x ``` # Swish - ``` y = x / (1 + exp(-x)) ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace # TanH - ``` y = tanh(x) ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace # Threshold - ``` if x > threshold y = 1 else y = 0 ``` -- one_blob_only -- support_inplace +* one_blob_only +* support_inplace -| param id | name | type | default | description | -| -------- | --------- | ----- | ------- | ----------- | -| 0 | threshold | float | 0.f | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | threshold | float | 0.f | | # Tile - ``` y = repeat tiles along axis for x ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ------- | ----- | ------- | ----------- | -| 0 | axis | int | 0 | | -| 1 | tiles | int | 1 | | -| 2 | repeats | array | [ ] | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | axis | int | 0 | | +| 1 | tiles | int | 1 | | +| 2 | repeats | array | [ ] | | # UnaryOp - ``` y = unaryop(x) ``` @@ -2097,12 +1938,11 @@ y = unaryop(x) - one_blob_only - support_inplace -| param id | name | type | default | description | -| -------- | ------- | ---- | ------- | ------------------------- | -| 0 | op_type | int | 0 | Operation type as follows | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 0 | op_type | int | 0 | Operation type as follows | Operation type: - - 0 = ABS - 1 = NEG - 2 = FLOOR @@ -2125,23 +1965,22 @@ Operation type: - 19 = TRUNC # Unfold - ``` y = unfold(x) ``` -- one_blob_only +* one_blob_only -| param id | name | type | default | description | -| -------- | ---------- | ---- | ---------- | ----------- | -| 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 | | -| 11 | kernel_h | int | kernel_w | | -| 12 | dilation_h | int | dilation_w | | -| 13 | stride_h | int | stride_w | | -| 14 | pad_top | int | pad_left | | -| 15 | pad_right | int | pad_left | | -| 16 | pad_bottom | int | pad_top | | +| param id | name | type | default | description | +| --------- | ------------- | ----- | --------- | ----------------- | +| 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 | | +| 11 | kernel_h | int | kernel_w | | +| 12 | dilation_h | int | dilation_w | | +| 13 | stride_h | int | stride_w | | +| 14 | pad_top | int | pad_left | | +| 15 | pad_right | int | pad_left | | +| 16 | pad_bottom | int | pad_top | |