146 lines
4.9 KiB
C
146 lines
4.9 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_input.h"
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nnom_layer_t *input_s(const nnom_io_config_t* config)
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{
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nnom_io_layer_t *layer;
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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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layer = nnom_mem(sizeof(nnom_io_layer_t) + sizeof(nnom_layer_io_t) * 2);
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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_io_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_INPUT;
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layer->super.run = input_run;
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layer->super.build = input_build;
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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_NULL;
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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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/*
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// some other layers (Conv, pooling) are not supporting 12 d input, we still expand the 1,2 dimension to 3
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// test -> native support 1,2,3 D input.
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layer->super.in->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, config->tensor->num_dim, tensor_get_num_channel(config->tensor));
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tensor_cpy_attr(layer->super.in->tensor, config->tensor);
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layer->buf = config->tensor->p_data;
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layer->dec_bit = config->tensor->q_dec[0];
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*/
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// set parameters
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if(config->tensor->num_dim == 1) // test for 1d input, expend h = 1
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layer->shape = shape(1, 1, config->tensor->dim[0]);
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else if (config->tensor->num_dim == 2) // test for 1d input, expend h = 1
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layer->shape = shape(1, config->tensor->dim[0], config->tensor->dim[1]);
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else
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layer->shape = shape(config->tensor->dim[0], config->tensor->dim[1], config->tensor->dim[2]);
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layer->buf = config->tensor->p_data;
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layer->dec_bit = config->tensor->q_dec[0];
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// experimental: fixed input dim to 3
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// input normally dont have a tensor, so we create one to store the initial data.
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nnom_shape_data_t dim[3] = {layer->shape.h, layer->shape.w, layer->shape.c};
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layer->super.in->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, 3, tensor_get_num_channel(config->tensor));
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tensor_set_attr_v(layer->super.in->tensor, layer->dec_bit, 0, dim, sizeof(dim)/sizeof(nnom_shape_data_t), 8);
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return (nnom_layer_t *)layer;
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}
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nnom_layer_t *Input(nnom_3d_shape_t input_shape, void *p_buf)
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{
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nnom_io_layer_t *layer;
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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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layer = nnom_mem(sizeof(nnom_io_layer_t) + sizeof(nnom_layer_io_t) * 2);
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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_io_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_INPUT;
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layer->super.run = input_run;
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layer->super.build = input_build;
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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_NULL;
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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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// set parameters
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layer->shape = input_shape;
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layer->buf = p_buf;
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layer->dec_bit = 7;
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// experimental: fixed input dim to 3
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// input normally dont have a tensor, so we create one to store the initial data.
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nnom_shape_data_t dim[3] = { input_shape.h, input_shape.w, input_shape.c };
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layer->super.in->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, 3, input_shape.c);
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tensor_set_attr_v(layer->super.in->tensor, layer->dec_bit, 0, dim, sizeof(dim)/sizeof(nnom_shape_data_t), 8);
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return (nnom_layer_t *)layer;
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}
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nnom_status_t input_build(nnom_layer_t* layer)
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{
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// the input tensor of inputlayer has assigned previously
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// output tensor
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// 1. allocate a new tensor for output
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// 2. set the same dim, qfmt to the new tensor.
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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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tensor_cpy_attr(layer->out->tensor, layer->in->tensor);
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// now this build has passed the input tensors (shapes, formats) to the new tensors.
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return NN_SUCCESS;
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}
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nnom_status_t input_run(nnom_layer_t *layer)
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{
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nnom_io_layer_t *cl = (nnom_io_layer_t *)layer;
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#ifdef NNOM_USING_CHW
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if(layer->in->tensor->num_dim == 3)
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{
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nnom_3d_shape_t shape = {layer->in->tensor->dim[0], layer->in->tensor->dim[1], layer->in->tensor->dim[2]};
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hwc2chw_q7(shape, cl->buf, layer->in->tensor->p_data);
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}
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else if (layer->in->tensor->num_dim == 2)
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{
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nnom_3d_shape_t shape = {1, layer->in->tensor->dim[0], layer->in->tensor->dim[1]};
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hwc2chw_q7(shape, cl->buf, layer->in->tensor->p_data);
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}
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else
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#endif
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nnom_memcpy(layer->in->tensor->p_data, cl->buf, tensor_size(layer->in->tensor));
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return NN_SUCCESS;
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}
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