435 lines
18 KiB
C
435 lines
18 KiB
C
/*
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* Copyright (c) 2018-2020
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* Jianjia Ma
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* majianjia@live.com
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Change Logs:
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* Date Author Notes
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* 2019-07-23 Jianjia Ma The first version
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*/
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#include <stdint.h>
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#include <string.h>
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#include <stdbool.h>
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#include "nnom.h"
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#include "nnom_local.h"
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#include "nnom_layers.h"
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#include "layers/nnom_conv2d.h"
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#ifdef NNOM_USING_CMSIS_NN
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#include "arm_math.h"
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#include "arm_nnfunctions.h"
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#endif
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// a machine friendly api, with suffix _s for structured configuration.
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nnom_layer_t *conv2d_s(const nnom_conv2d_config_t *config)
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{
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nnom_conv2d_layer_t *layer;
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nnom_buf_t *comp;
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nnom_layer_io_t *in, *out;
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size_t mem_size;
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// allocate a block memory for all the sub handles and shifts.
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mem_size = sizeof(nnom_conv2d_layer_t) + sizeof(nnom_layer_io_t) * 2 + sizeof(nnom_buf_t);
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layer = nnom_mem(mem_size);
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if (layer == NULL)
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return NULL;
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// distribut the memory to sub handles.
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in = (void *)((uint8_t*)layer + sizeof(nnom_conv2d_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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comp = (void *)((uint8_t*)out + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_CONV_2D;
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// set buf state
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in->type = NNOM_TENSOR_BUF_TEMP;
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out->type = NNOM_TENSOR_BUF_TEMP;
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comp->type = NNOM_TENSOR_BUF_TEMP;
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// put in & out on the layer.
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layer->super.in = io_init(layer, in);
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layer->super.out = io_init(layer, out);
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#ifdef NNOM_USING_CMSIS_NN
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layer->super.comp = comp;
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#endif
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// set run method & output shape
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layer->super.run = conv2d_run;
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layer->super.build = conv2d_build;
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layer->super.free = conv2d_free;
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// save the config
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layer->super.config = (void*) config;
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// get the private parameters
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// test: for 1d input, expend h = 1
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if(config->weight->num_dim == 3)
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{
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layer->kernel = kernel(1, config->kernel_size[0]);
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layer->stride = stride(1, config->stride_size[0]);
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layer->dilation = dilation(1, config->dilation_size[0]);
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}
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else
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{
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layer->kernel = kernel(config->kernel_size[0], config->kernel_size[1]);
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layer->stride = stride(config->stride_size[0], config->stride_size[1]);
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layer->dilation = dilation(config->dilation_size[0], config->dilation_size[1]);
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}
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layer->filter_mult = config->filter_size; // for convs, this means filter number
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layer->padding_type = config->padding_type;
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// get bias and weight tensor, this should be created by script.
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layer->weight = config->weight;
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layer->bias = config->bias;
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// get shifts
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layer->output_rshift = (nnom_qformat_param_t *)config->output_shift;
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layer->bias_lshift = (nnom_qformat_param_t *)config->bias_shift;
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// padding
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if (layer->padding_type == PADDING_SAME)
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{
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layer->pad.h = layer->dilation.h * (layer->kernel.h - 1) / 2;
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layer->pad.w = layer->dilation.w * (layer->kernel.w - 1) / 2;
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layer->pad.c = (1 - 1) / 2;
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}
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return (nnom_layer_t *)layer;
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}
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// Conv2D
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// multiplier of (output/input channel),
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// shape of kernal, shape of strides, weight struct, bias struct
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nnom_layer_t *Conv2D(uint32_t filters, nnom_3d_shape_t k, nnom_3d_shape_t s, nnom_3d_shape_t d, nnom_padding_t pad_type,
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const nnom_weight_t *w, const nnom_bias_t *b)
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{
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nnom_conv2d_layer_t *layer;
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nnom_buf_t *comp;
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nnom_layer_io_t *in, *out;
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// apply a block memory for all the sub handles.
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size_t mem_size = sizeof(nnom_conv2d_layer_t) + sizeof(nnom_layer_io_t) * 2 + sizeof(nnom_buf_t);
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layer = nnom_mem(mem_size);
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if (layer == NULL)
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return NULL;
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// distribut the memory to sub handles.
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in = (void *)((uint8_t*)layer + sizeof(nnom_conv2d_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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comp = (void *)((uint8_t*)out + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_CONV_2D;
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// set buf state
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in->type = NNOM_TENSOR_BUF_TEMP;
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out->type = NNOM_TENSOR_BUF_TEMP;
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comp->type = NNOM_TENSOR_BUF_TEMP;
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// put in & out on the layer.
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layer->super.in = io_init(layer, in);
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layer->super.out = io_init(layer, out);
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#ifdef NNOM_USING_CMSIS_NN
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layer->super.comp = comp;
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#endif
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// set run method & output shape
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layer->super.run = conv2d_run;
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layer->super.build = conv2d_build;
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// get the private parameters
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layer->kernel = k;
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layer->stride = s;
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layer->dilation = d;
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layer->filter_mult = filters; // for convs, this means filter number
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layer->padding_type = pad_type;
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// create weight and bias tensor
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layer->weight = new_tensor(NNOM_QTYPE_PER_TENSOR, 4, filters);
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layer->bias = new_tensor(NNOM_QTYPE_PER_TENSOR, 1, filters);
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// configure weight tensor manually to support new tensor based backends.
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// needs to be very careful
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{
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// config weight
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nnom_shape_data_t dim[4] = {k.h, k.w, k.c, filters};
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*(layer->weight->q_offset) = 0; // we have no support of offset here
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*(layer->weight->q_dec) = 0; // not using it
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layer->weight->p_data = (void*)w->p_value;
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layer->weight->bitwidth = 8;
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layer->weight->qtype = NNOM_QTYPE_PER_TENSOR;
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nnom_memcpy(layer->weight->dim, dim, layer->weight->num_dim * sizeof(nnom_shape_data_t));
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// config bias
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dim[0] = filters;
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*(layer->bias->q_offset) = 0; // we have no support of offset here
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*(layer->bias->q_dec) = 0; // not using it
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layer->bias->p_data = (void*) b->p_value;
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layer->bias->bitwidth = 8;
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layer->weight->qtype = NNOM_QTYPE_PER_TENSOR;
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nnom_memcpy(layer->bias->dim, dim, layer->bias->num_dim * sizeof(nnom_shape_data_t));
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// output shift and bias shift
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layer->output_rshift = (nnom_qformat_param_t *)&w->shift;
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layer->bias_lshift = (nnom_qformat_param_t *)&b->shift;
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}
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return (nnom_layer_t *)layer;
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}
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// keras's implementation.
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// source: https://github.com/keras-team/keras/blob/7a39b6c62d43c25472b2c2476bd2a8983ae4f682/keras/utils/conv_utils.py#L85
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uint32_t conv_output_length(uint32_t input_length, uint32_t filter_size, nnom_padding_t padding, uint32_t stride, uint32_t dilation)
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{
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if (input_length == 0)
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return 0;
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uint32_t dilated_filter_size = (filter_size - 1) * dilation + 1;
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uint32_t output_length;
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if(padding == PADDING_SAME)
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output_length = input_length;
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else
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output_length = input_length - dilated_filter_size + 1;
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return (output_length + stride - 1) / stride;
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}
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nnom_status_t conv2d_build(nnom_layer_t *layer)
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{
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nnom_conv2d_layer_t *cl = (nnom_conv2d_layer_t *)layer;
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// get the tensor from last layer's output
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layer->in->tensor = layer->in->hook.io->tensor;
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// create new tensor for the output
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layer->out->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, layer->in->tensor->num_dim, cl->filter_mult);
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// copy then change later.
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tensor_cpy_attr(layer->out->tensor, layer->in->tensor);
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// calculate the output tensor q format, only support per tensor quantise now
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layer->out->tensor->q_dec[0] = layer->in->tensor->q_dec[0] + cl->weight->q_dec[0] - cl->output_rshift[0]; // need some modification for 16bit.
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// see if the activation will change the q format
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if(layer->actail)
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layer->out->tensor->q_dec[0] = act_get_dec_bit(layer->actail->type, layer->out->tensor->q_dec[0]);
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// now we set up the tensor shape, always HWC format
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layer->out->tensor->dim[0] = conv_output_length(layer->in->tensor->dim[0], cl->kernel.h, cl->padding_type, cl->stride.h, cl->dilation.h);
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layer->out->tensor->dim[1] = conv_output_length(layer->in->tensor->dim[1], cl->kernel.w, cl->padding_type, cl->stride.w, cl->dilation.w);
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layer->out->tensor->dim[2] = cl->filter_mult; // channel stays the same
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// fill padding
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if (cl->padding_type == PADDING_SAME)
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{
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cl->pad.w = cl->dilation.w * (cl->kernel.w - 1) / 2;
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cl->pad.h = cl->dilation.h * (cl->kernel.h - 1) / 2;
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cl->pad.c = 0;
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}
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#ifdef NNOM_USING_CMSIS_NN
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// bufferA size: (1D shape)
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// 2*ch_im_in*dim_kernel*dim_kernel
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layer->comp->size = 2 * 2 * layer->in->tensor->dim[2] * cl->kernel.w * cl->kernel.h;
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#endif
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// computational cost: K x K x Cin x Hour x Wout x Cout
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layer->stat.macc = cl->kernel.w * cl->kernel.h * layer->in->tensor->dim[2] * tensor_size(layer->out->tensor);
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return NN_SUCCESS;
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}
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nnom_status_t conv2d_free(nnom_layer_t *layer)
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{
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// free weight and bias tensor when we are not initialised from structured configuration.
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if(!layer->config)
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{
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nnom_conv2d_layer_t* cl = (nnom_conv2d_layer_t*)layer;
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delete_tensor(cl->weight);
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delete_tensor(cl->bias);
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}
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return NN_SUCCESS;
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}
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nnom_status_t conv2d_run(nnom_layer_t *layer)
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{
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nnom_conv2d_layer_t *cl = (nnom_conv2d_layer_t *)layer;
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#ifdef NNOM_USING_CHW
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// CHW format
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if(layer->in->tensor->bitwidth == 16)
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local_convolve_CHW_q15_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
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cl->weight->p_data, layer->out->tensor->dim[2],
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cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h, cl->dilation.w, cl->dilation.h,
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cl->bias->p_data, cl->bias_lshift, cl->output_rshift, cl->weight->qtype,
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layer->out->tensor->p_data,
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layer->out->tensor->dim[1], layer->out->tensor->dim[0], NULL, NULL);
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else
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local_convolve_CHW_q7_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
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cl->weight->p_data, layer->out->tensor->dim[2],
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cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h, cl->dilation.w, cl->dilation.h,
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cl->bias->p_data, cl->bias_lshift, cl->output_rshift, cl->weight->qtype,
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layer->out->tensor->p_data,
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layer->out->tensor->dim[1], layer->out->tensor->dim[0], NULL, NULL);
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return NN_SUCCESS;
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#else
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// HWC format
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#ifdef NNOM_USING_CMSIS_NN
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// current cmsis nn does not support dilation
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if(cl->dilation.w == 1 && cl->dilation.h == 1 && cl->weight->qtype == NNOM_QTYPE_PER_TENSOR)
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{
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// 8 bit cmsis nn
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if(layer->in->tensor->bitwidth == 8)
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{
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//RGB
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// ch_im_in = 3, w = h
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if (layer->in->tensor->dim[2] == 3 && layer->in->tensor->dim[0] == layer->in->tensor->dim[1])
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// squared
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if((cl->kernel.w == cl->kernel.h) && (cl->pad.w == cl->pad.h) && (cl->stride.w == cl->stride.h))
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return (nnom_status_t)arm_convolve_HWC_q7_RGB(
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layer->in->tensor->p_data, layer->in->tensor->dim[1], layer->in->tensor->dim[2],
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cl->weight->p_data,
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layer->out->tensor->dim[2],
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cl->kernel.w, cl->pad.w, cl->stride.w,
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cl->bias->p_data, cl->bias_lshift[0],
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cl->output_rshift[0], layer->out->tensor->p_data, layer->out->tensor->dim[1],
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(q15_t *)(layer->comp->mem->blk), NULL);
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// check if can use optimized function
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// ch_im_in is multiple of 4
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// ch_im_out is multiple of 2
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if ((layer->in->tensor->dim[2] % 4 == 0) && (layer->out->tensor->dim[2] % 2 == 0))
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{
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// squared
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if((layer->in->tensor->dim[0] == layer->in->tensor->dim[1])
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&& (layer->out->tensor->dim[0] == layer->out->tensor->dim[1])
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&& (cl->kernel.w == cl->kernel.h) && (cl->pad.w == cl->pad.h) && (cl->stride.w == cl->stride.h))
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{
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// 1x1 fast
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if (cl->kernel.w == 1 && cl->kernel.h == 1 && cl->stride.w == 1 && cl->stride.h == 1 && cl->pad.w == 0 && cl->pad.h == 0)
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return (nnom_status_t)arm_convolve_1x1_HWC_q7_fast_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
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cl->weight->p_data,
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layer->out->tensor->dim[2],
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cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h,
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cl->bias->p_data, cl->bias_lshift[0],
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cl->output_rshift[0], layer->out->tensor->p_data, layer->out->tensor->dim[1], layer->out->tensor->dim[0],
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(q15_t *)(layer->comp->mem->blk), NULL);
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// opt square shape
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else
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return (nnom_status_t)arm_convolve_HWC_q7_fast(
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layer->in->tensor->p_data, layer->in->tensor->dim[1], layer->in->tensor->dim[2],
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cl->weight->p_data,
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layer->out->tensor->dim[2], cl->kernel.w, cl->pad.w, cl->stride.w,
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cl->bias->p_data, cl->bias_lshift[0],
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cl->output_rshift[0], layer->out->tensor->p_data,
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layer->out->tensor->dim[1], (q15_t *)(layer->comp->mem->blk), NULL);
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}
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// opt none square shape
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else
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return (nnom_status_t)arm_convolve_HWC_q7_fast_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
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cl->weight->p_data, layer->out->tensor->dim[2],
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cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h,
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cl->bias->p_data, cl->bias_lshift[0], cl->output_rshift[0],
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layer->out->tensor->p_data,
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layer->out->tensor->dim[1], layer->out->tensor->dim[0], (q15_t *)(layer->comp->mem->blk), NULL);
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}
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// none optimized
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else
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{
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// none opt square shape
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if ((layer->in->tensor->dim[0] == layer->in->tensor->dim[1] &&
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layer->out->tensor->dim[0] == layer->out->tensor->dim[1]) &&
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(cl->kernel.w == cl->kernel.h) && (cl->pad.w == cl->pad.h) && (cl->stride.w == cl->stride.h))
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return (nnom_status_t)arm_convolve_HWC_q7_basic(
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layer->in->tensor->p_data, layer->in->tensor->dim[1], layer->in->tensor->dim[2],
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cl->weight->p_data,
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layer->out->tensor->dim[2], cl->kernel.w, cl->pad.w, cl->stride.w,
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cl->bias->p_data, cl->bias_lshift[0],
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cl->output_rshift[0], layer->out->tensor->p_data,
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layer->out->tensor->dim[1], (q15_t *)(layer->comp->mem->blk), NULL);
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// none opt none square shape
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else
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return (nnom_status_t)arm_convolve_HWC_q7_basic_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
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cl->weight->p_data, layer->out->tensor->dim[2],
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cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h,
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cl->bias->p_data, cl->bias_lshift[0], cl->output_rshift[0],
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layer->out->tensor->p_data,
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layer->out->tensor->dim[1], layer->out->tensor->dim[0], (q15_t *)(layer->comp->mem->blk), NULL);
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} //end of cmsis-nn none-opt
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} //end of 8 bit cmsis-nn
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else if (layer->in->tensor->bitwidth == 16)
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{
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// fast opt
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if ((layer->in->tensor->dim[2] % 2 == 0) && (layer->out->tensor->dim[2] % 2 == 0))
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{
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if((layer->in->tensor->dim[0] == layer->in->tensor->dim[1])
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&& (layer->out->tensor->dim[0] == layer->out->tensor->dim[1])
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&& (cl->kernel.w == cl->kernel.h) && (cl->pad.w == cl->pad.h) && (cl->stride.w == cl->stride.h))
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return (nnom_status_t)arm_convolve_HWC_q15_fast(
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layer->in->tensor->p_data, layer->in->tensor->dim[1], layer->in->tensor->dim[2],
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cl->weight->p_data,
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layer->out->tensor->dim[2], cl->kernel.w, cl->pad.w, cl->stride.w,
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cl->bias->p_data, cl->bias_lshift[0],
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cl->output_rshift[0], layer->out->tensor->p_data,
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layer->out->tensor->dim[1], (q15_t *)(layer->comp->mem->blk), NULL);
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else
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return (nnom_status_t)arm_convolve_HWC_q15_fast_nonsquare(
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layer->in->tensor->p_data,
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layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
|
|
cl->weight->p_data, layer->out->tensor->dim[2],
|
|
cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h,
|
|
cl->bias->p_data, cl->bias_lshift[0], cl->output_rshift[0],
|
|
layer->out->tensor->p_data,
|
|
layer->out->tensor->dim[1], layer->out->tensor->dim[0], (q15_t *)(layer->comp->mem->blk), NULL);
|
|
}
|
|
// none opt basic
|
|
else
|
|
{
|
|
local_convolve_HWC_q7_nonsquare(
|
|
layer->in->tensor->p_data,
|
|
layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
|
|
cl->weight->p_data, layer->out->tensor->dim[2],
|
|
cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h, cl->dilation.w, cl->dilation.h,
|
|
cl->bias->p_data, cl->bias_lshift, cl->output_rshift, cl->weight->qtype,
|
|
layer->out->tensor->p_data,
|
|
layer->out->tensor->dim[1], layer->out->tensor->dim[0], NULL, NULL);
|
|
return NN_SUCCESS;
|
|
}
|
|
|
|
} // end of 16 bit cmsis-nn
|
|
} // end of dilation == 1
|
|
else
|
|
#endif // NNOM_USING_CMSIS_NN
|
|
{
|
|
|
|
if(layer->in->tensor->bitwidth == 16)
|
|
local_convolve_HWC_q15_nonsquare(
|
|
layer->in->tensor->p_data,
|
|
layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
|
|
cl->weight->p_data, layer->out->tensor->dim[2],
|
|
cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h, cl->dilation.w, cl->dilation.h,
|
|
cl->bias->p_data, cl->bias_lshift, cl->output_rshift, cl->weight->qtype,
|
|
layer->out->tensor->p_data,
|
|
layer->out->tensor->dim[1], layer->out->tensor->dim[0], NULL, NULL);
|
|
else
|
|
local_convolve_HWC_q7_nonsquare(
|
|
layer->in->tensor->p_data,
|
|
layer->in->tensor->dim[1], layer->in->tensor->dim[0], layer->in->tensor->dim[2],
|
|
cl->weight->p_data, layer->out->tensor->dim[2],
|
|
cl->kernel.w, cl->kernel.h, cl->pad.w, cl->pad.h, cl->stride.w, cl->stride.h, cl->dilation.w, cl->dilation.h,
|
|
cl->bias->p_data, cl->bias_lshift, cl->output_rshift, cl->weight->qtype,
|
|
layer->out->tensor->p_data,
|
|
layer->out->tensor->dim[1], layer->out->tensor->dim[0], NULL, NULL);
|
|
return NN_SUCCESS;
|
|
}
|
|
#endif // end of CHW/HWC
|
|
// return NN_SUCCESS;
|
|
}
|
|
|