89 lines
2.2 KiB
C
89 lines
2.2 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_cropping.h"
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nnom_layer_t * cropping_s(const nnom_cropping_config_t *config)
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{
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nnom_layer_t *layer = Cropping(config->pad);
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if(layer)
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layer->config = (void*) config;
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return layer;
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}
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// Cropping layer
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nnom_layer_t *Cropping(nnom_border_t pad)
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{
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nnom_layer_t *layer;
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// most setting are the same as zero padding
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layer = ZeroPadding(pad);
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// now change to cropping
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layer->type = NNOM_CROPPING;
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layer->run = cropping_run;
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layer->build = cropping_build;
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return layer;
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}
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nnom_status_t cropping_build(nnom_layer_t* layer)
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{
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nnom_cropping_layer_t *cl = (nnom_cropping_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 output
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layer->out->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, layer->in->tensor->num_dim, tensor_get_num_channel(layer->in->tensor));
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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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// output shape
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if(layer->in->tensor->dim[1] <= (cl->pad.left + cl->pad.right) ||
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layer->in->tensor->dim[0] <= (cl->pad.top + cl->pad.bottom))
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return NN_ARGUMENT_ERROR;
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layer->out->tensor->dim[0] = layer->in->tensor->dim[0] - (cl->pad.top + cl->pad.bottom);
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layer->out->tensor->dim[1] = layer->in->tensor->dim[1] - (cl->pad.left + cl->pad.right);
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layer->out->tensor->dim[2] = layer->in->tensor->dim[2];
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return NN_SUCCESS;
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}
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nnom_status_t cropping_run(nnom_layer_t * layer)
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{
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nnom_cropping_layer_t *cl = (nnom_cropping_layer_t*)layer;
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#ifdef NNOM_USING_CHW
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local_cropping_CHW_q7(
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#else
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local_cropping_HWC_q7(
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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->pad.top,
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cl->pad.bottom,
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cl->pad.left,
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cl->pad.right,
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layer->out->tensor->p_data,
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layer->out->tensor->dim[1], layer->out->tensor->dim[0]);
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return NN_SUCCESS;
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}
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