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tengine: supports conv with asymmetric padding

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pull/22362/head
fengyuentau 3 years ago
parent 0cdff46725
commit 2959286eb5
3 changed files with 33 additions and 28 deletions
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  1. 24
      modules/dnn/src/layers/convolution_layer.cpp
  2. 8
      modules/dnn/src/tengine4dnn/include/tengine_graph_convolution.hpp
  3. 29
      modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp

24
modules/dnn/src/layers/convolution_layer.cpp
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@ -2027,7 +2027,7 @@ public:
}
#ifdef HAVE_TENGINE
bool tengine_ret = false; ;
bool tengine_ret = false;
std::vector<Mat> teng_in, teng_out;
inputs_arr.getMatVector(teng_in);
@ -2052,20 +2052,24 @@ public:
/* tengine_init will run when first time. */
if(NULL == tengine_graph)
{
// pads_begin: 0 - pad_top, 1 - pad_left
// pads_end: 0 - pad_bottom, 1 - pad_right
// pad_h0: pad_top, pad_h1: pad_bottom
// pad_w0: pad_left, pad_w1: pad_right
tengine_graph = tengine_init(name.c_str(), input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
pads_begin[0], pads_end[0], pads_begin[1], pads_end[1], dilation.height, dilation.width,
weightsMat.step1(), padMode, tengine_graph, nstripes);
/*printf("Init(%s): input=%p(%d %d %d %d ),output=%p(%d %d %d %d ),kernel=%p(%ld %d %d ), bias=%p ,"
"stride(%d %d), pad(%d %d), dilation(%d %d) ,weightsMat=%ld, padMode=%s ,tengine_graph = %p \n",
name.c_str(),input_, inch, ngroups, in_h, in_w,
output_, out_b, outch, out_h, out_w,
kernel_, kernel_size.size(), kernel.height, kernel.width,
teg_bias, stride.height, stride.width,
pad.height, pad.width, dilation.height, dilation.width,
weightsMat.step1(), padMode.c_str() ,tengine_graph);*/
// printf("Init(%s): input=%p(%d %d %d %d ),output=%p(%d %d %d %d ),kernel=%p(%ld %d %d ), bias=%p ,"
// "stride(%d %d), pad(%d %d %d %d), dilation(%d %d) ,weightsMat=%ld, padMode=%s ,tengine_graph = %p \n",
// name.c_str(),input_, inch, ngroups, in_h, in_w,
// output_, out_b, outch, out_h, out_w,
// kernel_, kernel_size.size(), kernel.height, kernel.width,
// teg_bias, stride.height, stride.width,
// pads_begin[0], pads_end[0], pads_begin[1], pads_end[1], dilation.height, dilation.width,
// weightsMat.step1(), padMode.c_str() ,tengine_graph);
}
if(NULL != tengine_graph)
{

8
modules/dnn/src/tengine4dnn/include/tengine_graph_convolution.hpp
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@ -34,11 +34,15 @@ namespace cv
{
namespace dnn
{
// pad_h0: pad_top
// pad_h1: pad_bottom
// pad_w0: pad_left
// pad_w1: pad_right
teng_graph_t tengine_init(const char* name , float* input_, int inch, int group, int in_h, int in_w,
float *output_, int out_b, int outch, int out_h, int out_w,
float *kernel_,int kernel_s , int kernel_h, int kernel_w,
float *teg_bias, int stride_h,int stride_w,
int pad_h, int pad_w, int dilation_h, int dilation_w,
float *teg_bias, int stride_h, int stride_w,
int pad_h0, int pad_h1, int pad_w0, int pad_w1, int dilation_h, int dilation_w,
size_t wstep, const std::string padMode , teng_graph_t& graph, int nstripes) ;
bool tengine_forward(teng_graph_t& graph) ;

29
modules/dnn/src/tengine4dnn/src/tengine_graph_convolution.cpp
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@ -56,7 +56,7 @@ static int create_input_node(teng_graph_t graph, const char* node_name, int inch
}
static int create_conv_node(teng_graph_t graph, const char* node_name, const char* input_name, int in_h, int in_w, int out_h, int out_w,
int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, int pad_w, int inch, int outch, int group,
int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h0, int pad_h1, int pad_w0, int pad_w1, int inch, int outch, int group,
int dilation_h, int dilation_w, int activation, std::string padMode)
{
node_t conv_node = teng_create_graph_node(graph, node_name, "Convolution");
@ -107,15 +107,12 @@ static int create_conv_node(teng_graph_t graph, const char* node_name, const cha
teng_release_graph_node(b_node);
teng_release_graph_tensor(b_tensor);
int pad_h1 = pad_h;
int pad_w1 = pad_w;
if (!padMode.empty())
{
if (padMode == "SAME")
{
int out_h_temp = (in_h-kernel_h + 2*pad_h)/stride_h + 1;
int out_w_temp = (in_w-kernel_w + 2*pad_w)/stride_w + 1;
int out_h_temp = (in_h-kernel_h + 2*pad_h0)/stride_h + 1;
int out_w_temp = (in_w-kernel_w + 2*pad_w0)/stride_w + 1;
if (out_h_temp < out_h)
pad_h1 += 1;
@ -129,8 +126,8 @@ static int create_conv_node(teng_graph_t graph, const char* node_name, const cha
teng_set_node_attr_int(conv_node, "kernel_w", &kernel_w);
teng_set_node_attr_int(conv_node, "stride_h", &stride_h);
teng_set_node_attr_int(conv_node, "stride_w", &stride_w);
teng_set_node_attr_int(conv_node, "pad_h0", &pad_h);
teng_set_node_attr_int(conv_node, "pad_w0", &pad_w);
teng_set_node_attr_int(conv_node, "pad_h0", &pad_h0);
teng_set_node_attr_int(conv_node, "pad_w0", &pad_w0);
teng_set_node_attr_int(conv_node, "pad_h1", &pad_h1);
teng_set_node_attr_int(conv_node, "pad_w1", &pad_w1);
teng_set_node_attr_int(conv_node, "output_channel", &outch);
@ -149,7 +146,7 @@ static teng_graph_t create_conv_graph(const char* layer_name, float* input_data,
float* output_data, int outch, int out_h, int out_w,
int kernel_h, int kernel_w,
int stride_h,int stride_w,
int pad_h, int pad_w, int dilation_h, int dilation_w, int activation,
int pad_h0, int pad_h1, int pad_w0, int pad_w1, int dilation_h, int dilation_w, int activation,
float* teg_weight, float* teg_bias, std::string padMode, int nstripes)
{
node_t conv_node = NULL;
@ -188,7 +185,7 @@ static teng_graph_t create_conv_graph(const char* layer_name, float* input_data,
}
if (ok && create_conv_node(graph, conv_name, input_name, in_h, in_w, out_h, out_w, kernel_h, kernel_w,
stride_h, stride_w, pad_h, pad_w, inch, outch, group, dilation_h, dilation_w, activation, padMode) < 0)
stride_h, stride_w, pad_h0, pad_h1, pad_w0, pad_w1, inch, outch, group, dilation_h, dilation_w, activation, padMode) < 0)
{
CV_LOG_WARNING(NULL,"Tengine: create conv node failed." );
ok = false;
@ -289,8 +286,8 @@ static bool tengine_init_flag = false;
teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int group, int in_h, int in_w,
float *output_, int out_b, int outch, int out_h, int out_w,
float *kernel_, int kernel_s ,int kernel_h, int kernel_w,
float *teg_bias, int stride_h,int stride_w,
int pad_h, int pad_w, int dilation_h, int dilation_w,
float *teg_bias, int stride_h, int stride_w,
int pad_h0, int pad_h1, int pad_w0, int pad_w1, int dilation_h, int dilation_w,
size_t wstep, const std::string padMode, teng_graph_t &graph, int nstripes)
{
std::vector<float> teg_weight_vec;
@ -299,9 +296,9 @@ teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int g
// Do not using the activation fuse mode, just convolution only.
int activation = -1;
if (!(kernel_s == 2 && kernel_h == kernel_w && pad_h == pad_w
if (!(kernel_s == 2 && kernel_h == kernel_w
&& dilation_h == dilation_w && stride_h == stride_w
&& out_b == 1 && pad_h < 10)) // just for Conv2D
&& out_b == 1 && pad_h0 < 10 && pad_h1 < 10 && pad_w0 < 10 && pad_w1 < 10)) // just for Conv2D
{
// printf("return : just for Conv2D\n");
return NULL;
@ -314,7 +311,7 @@ teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int g
kernel_w, kernel_h,
stride_w, stride_h,
dilation_w, dilation_h,
pad_w, pad_h);
pad_h0, pad_h1, pad_w0, pad_w1);
*/
// weight
if (kernel_inwh != wstep)
@ -342,7 +339,7 @@ teng_graph_t tengine_init(const char* layer_name, float* input_, int inch, int g
graph = create_conv_graph(layer_name, input_, inch, group, in_h, in_w,
output_, outch, out_h, out_w,
kernel_h, kernel_w, stride_h,stride_w,
pad_h, pad_w, dilation_h, dilation_w, activation,
pad_h0, pad_h1, pad_w0, pad_w1, dilation_h, dilation_w, activation,
teg_weight, teg_bias, padMode, nstripes);
if(NULL == graph )
{

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