105 lines
2.4 KiB
C
105 lines
2.4 KiB
C
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/*
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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_sumpool.h"
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nnom_layer_t *sumpool_s(const nnom_pool_config_t * config)
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{
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nnom_sumpool_layer_t *cl;
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if(config->num_dim == 1)
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{
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cl = (nnom_sumpool_layer_t *)SumPool(kernel(1, config->kernel_size[0]),
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stride(1, config->stride_size[0]),
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config->padding_type);
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}
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else
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{
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cl = (nnom_sumpool_layer_t *)SumPool(kernel(config->kernel_size[0], config->kernel_size[1]),
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stride(config->stride_size[0], config->stride_size[1]),
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config->padding_type);
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}
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if(cl)
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{
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cl->super.config = (void*) config;
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cl->output_shift = config->output_shift; // no idea if we need it
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}
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return (nnom_layer_t *)cl;
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}
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nnom_layer_t *SumPool(nnom_3d_shape_t k, nnom_3d_shape_t s, nnom_padding_t pad_type)
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{
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nnom_layer_t *layer = MaxPool(k, s, pad_type);
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if (layer != NULL)
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{
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layer->type = NNOM_SUMPOOL;
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layer->run = sumpool_run;
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layer->build = sumpool_build;
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}
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return (nnom_layer_t *)layer;
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}
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nnom_status_t sumpool_build(nnom_layer_t *layer)
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{
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// avg pooling share the same output shape, stride, padding setting.
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maxpool_build(layer);
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// however, avg pooling require a computational buffer.
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layer->comp->size = 4 * tensor_size(layer->out->tensor);
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return NN_SUCCESS;
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}
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// sum pooling, dynamic change Q format, must be used in the last layer before softmax in current version
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nnom_status_t sumpool_run(nnom_layer_t *layer)
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{
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nnom_sumpool_layer_t *cl = (nnom_sumpool_layer_t *)(layer);
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uint16_t out_x, out_y;
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// if global pooling
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if(layer->out->tensor->num_dim == 1)
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{
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out_x = 1; out_y = 1;
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}
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else // normal pooling.
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{
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out_x = layer->out->tensor->dim[1]; //W
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out_y = layer->out->tensor->dim[0]; //h
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}
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#ifdef NNOM_USING_CHW
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local_sumpool_q7_CHW(
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#else
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local_sumpool_q7_HWC(
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#endif
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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->kernel.w, cl->kernel.h,
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cl->pad.w, cl->pad.h,
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cl->stride.w, cl->stride.h,
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out_x, out_y,
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layer->comp->mem->blk,
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layer->out->tensor->p_data);
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return NN_SUCCESS;
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}
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