未验证 提交 1d3754aa 编写于 作者: S Santa An 提交者: GitHub

[LITE][BM] support multiclass_nms2 and fix some issues, test=develop (#4379)

上级 26c0ecc8
......@@ -73,10 +73,16 @@ int BoxCoderConverter(void* ctx, OpLite* op, KernelBase* kernel) {
if (op_info->HasAttr("variance")) {
variance = op_info->GetAttr<std::vector<float>>("variance");
}
int variance_len = variance.size();
user_cpu_param_t bm_param;
bm_param.op_type = USER_PADDLE_BOX_CODER;
bm_param.u.box_coder_param.axis = axis;
bm_param.u.box_coder_param.variance = &variance[0];
CHECK_LE(variance_len, 2000);
memset(bm_param.u.box_coder_param.variance, 0, 2000 * sizeof(float));
memcpy(bm_param.u.box_coder_param.variance,
&variance[0],
variance_len * sizeof(float));
bm_param.u.box_coder_param.variance_len = variance_len;
bm_param.u.box_coder_param.code_type =
(code_type == "encode_center_size") ? 0 : 1;
bm_param.u.box_coder_param.normalized = box_normalized;
......
......@@ -32,7 +32,8 @@ bool CvtDtype(int dtype, int* ptype) {
*ptype = DTYPE_INT16;
break;
case 2:
*ptype = DTYPE_FP32;
case 3:
*ptype = DTYPE_INT32;
break;
case 5:
*ptype = DTYPE_FP32;
......
......@@ -127,7 +127,7 @@ int ElementwiseConverter(void* ctx, OpLite* op, KernelBase* kernel) {
const float* y_data = const_cast<const float*>(y->mutable_data<float>());
const float* x_data = const_cast<const float*>(x->mutable_data<float>());
auto unique_op_name = lite::subgraph::bm::UniqueName("expand_ndims");
std::vector<int32_t> i_expand_shape_data(3);
std::vector<int32_t> i_expand_shape_data;
if (x_is_const && y_is_const) {
float* cpu_data = compute_elementwise_both_const(op);
bm_add_const_tensor(graph->GetCompilerHandle(),
......@@ -157,12 +157,31 @@ int ElementwiseConverter(void* ctx, OpLite* op, KernelBase* kernel) {
static_cast<const char*>(unique_op_name.c_str()));
name[1] = static_cast<const char*>(unique_op_name.c_str());
dim[1] = 3;
i_expand_shape_data[0] = i_y_shape_data[0];
i_expand_shape_data[1] = 1;
i_expand_shape_data[2] = 1;
i_expand_shape_data.push_back(i_y_shape_data[0]);
i_expand_shape_data.push_back(1);
i_expand_shape_data.push_back(1);
shape[1] = &i_expand_shape_data[0];
y_data = nullptr;
}
} else {
if (dim[1] < dim[0]) {
for (size_t i = 0; i < dim[1]; i++) {
i_expand_shape_data.push_back(i_y_shape_data[i]);
}
for (size_t i = dim[1]; i < dim[0]; i++) {
i_expand_shape_data.push_back(1);
}
add_reshape_layer_v2(graph->GetCompilerHandle(),
name[1],
shape[1],
dim[1],
static_cast<const char*>(unique_op_name.c_str()),
const_cast<const int*>(&i_expand_shape_data[0]),
i_expand_shape_data.size());
dim[1] = dim[0];
shape[1] = &i_expand_shape_data[0];
name[1] = static_cast<const char*>(unique_op_name.c_str());
}
}
add_binary_layer_v2(graph->GetCompilerHandle(),
name[0],
......
......@@ -51,7 +51,7 @@ int MultiClassNMSConverter(void* ctx, OpLite* op, KernelBase* kernel) {
auto score_threshold = op_info->GetAttr<float>("score_threshold");
auto nms_threshold = op_info->GetAttr<float>("nms_threshold");
auto nms_eta = op_info->GetAttr<float>("nms_eta");
bool normalized;
bool normalized = false;
if (op_info->HasAttr("normalized")) {
normalized = op_info->GetAttr<bool>("normalized");
}
......@@ -97,12 +97,39 @@ int MultiClassNMSConverter(void* ctx, OpLite* op, KernelBase* kernel) {
in_dim[1] = score_dims.size();
in_name[0] = static_cast<const char*>(boxes_var_name.c_str());
in_name[1] = static_cast<const char*>(score_var_name.c_str());
int32_t* out_shape[1];
int32_t out_dim[1];
const char* out_name[1];
int32_t* out_shape[2];
int32_t out_dim[2];
const char* out_name[2];
out_shape[0] = &i_out_shape_data[0];
out_dim[0] = out_dims.size();
out_name[0] = static_cast<const char*>(out_var_name.c_str());
std::vector<int64_t> vec_index_dim(score_dims.size());
std::vector<int32_t> i_out_index_shape_data(score_dims.size());
std::string out_index_name = "";
if (op_type == "multiclass_nms2") {
output_num = 2;
out_index_name = op_info->Output("Index").front();
auto out_index = scope->FindVar(out_index_name)->GetMutable<lite::Tensor>();
if (3 == score_dims.size()) {
vec_index_dim[0] = score_dims[0];
vec_index_dim[1] = keep_top_k;
vec_index_dim[2] = 1;
} else {
vec_index_dim[0] = keep_top_k;
vec_index_dim[1] = 1;
}
DDimLite index_dims(vec_index_dim);
out_index->Resize(index_dims);
out_index->mutable_data<float>();
for (size_t i = 0; i < index_dims.size(); i++) {
i_out_index_shape_data[i] = static_cast<int32_t>(index_dims[i]);
}
out_shape[1] = &i_out_index_shape_data[0];
out_dim[1] = index_dims.size();
out_name[1] = static_cast<const char*>(out_index_name.c_str());
}
add_user_cpu_layer(graph->GetCompilerHandle(),
input_num,
in_shape,
......@@ -126,3 +153,6 @@ int MultiClassNMSConverter(void* ctx, OpLite* op, KernelBase* kernel) {
REGISTER_SUBGRAPH_BRIDGE(multiclass_nms,
kBM,
paddle::lite::subgraph::bm::MultiClassNMSConverter);
REGISTER_SUBGRAPH_BRIDGE(multiclass_nms2,
kBM,
paddle::lite::subgraph::bm::MultiClassNMSConverter);
......@@ -39,6 +39,7 @@ USE_SUBGRAPH_BRIDGE(norm, kBM);
USE_SUBGRAPH_BRIDGE(prior_box, kBM);
USE_SUBGRAPH_BRIDGE(box_coder, kBM);
USE_SUBGRAPH_BRIDGE(multiclass_nms, kBM);
USE_SUBGRAPH_BRIDGE(multiclass_nms2, kBM);
USE_SUBGRAPH_BRIDGE(nearest_interp, kBM);
USE_SUBGRAPH_BRIDGE(bilinear_interp, kBM);
USE_SUBGRAPH_BRIDGE(yolo_box, kBM);
......
......@@ -67,17 +67,17 @@ int YoloBoxConverter(void* ctx, OpLite* op, KernelBase* kernel) {
auto downsample_ratio = op_info->GetAttr<int>("downsample_ratio");
auto conf_thresh = op_info->GetAttr<float>("conf_thresh");
auto anchors = op_info->GetAttr<std::vector<int>>("anchors");
int* anchors_buffer = static_cast<int*>(malloc(sizeof(int) * anchors.size()));
CHECK(anchors_buffer != nullptr);
memcpy(anchors_buffer, &anchors[0], sizeof(int) * anchors.size());
CHECK_LE(anchors.size(), 2000);
user_cpu_param_t bm_param;
bm_param.op_type = USER_PADDLE_YOLO_BOX;
bm_param.u.yolo_box_param.class_num = class_num;
bm_param.u.yolo_box_param.downsample_ratio = downsample_ratio;
bm_param.u.yolo_box_param.conf_thresh = conf_thresh;
bm_param.u.yolo_box_param.anchors = anchors_buffer;
memset(bm_param.u.yolo_box_param.anchors, 0, 2000 * sizeof(int));
memcpy(bm_param.u.yolo_box_param.anchors,
&anchors[0],
anchors.size() * sizeof(int));
bm_param.u.yolo_box_param.anchors_size = anchors.size();
memcpy(anchors_buffer, &anchors[0], sizeof(int) * anchors.size());
int32_t input_num = 2;
int32_t output_num = 2;
int32_t* in_shape[2];
......
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