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Paddle-Lite
未验证 提交 c3d15d76 编写于 作者: Z zhupengyang 提交者： GitHub
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add nearest_interp op converter (#1879) 

test=developt branch
上级 e7dc96c1
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Showing with 217 addition and 104 deletion
lite/npu/bridge/CMakeLists.txt
浏览文件 @ c3d15d76
......@@ -15,7 +15,7 @@ lite_cc_library(npu_bridge_batch_norm_op SRCS batch_norm_op.cc DEPS ${npu_bridge
lite_cc_library(npu_bridge_elementwise_op SRCS elementwise_ops.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_reshape_op SRCS reshape_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_conv_transpose_op SRCS conv_transpose_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_bilinear_interp_op SRCS bilinear_interp_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_interpolate_op SRCS interpolate_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_transpose_op SRCS transpose_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_split_op SRCS split_op.cc DEPS ${npu_bridge_deps})
lite_cc_library(npu_bridge_concat_op SRCS concat_op.cc DEPS ${npu_bridge_deps})
......@@ -35,7 +35,7 @@ set(npu_bridges
npu_bridge_elementwise_op
npu_bridge_reshape_op
npu_bridge_conv_transpose_op
npu_bridge_bilinear_interp_op
npu_bridge_interpolate_op
npu_bridge_transpose_op
npu_bridge_split_op
npu_bridge_concat_op
......@@ -55,7 +55,7 @@ lite_cc_test(test_npu_bridge_batch_norm_op SRCS batch_norm_op_test.cc DEPS npu_t
lite_cc_test(test_npu_bridge_elementwise_op SRCS elementwise_ops_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_reshape_op SRCS reshape_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_conv_transpose_op SRCS conv_transpose_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_bilinear_interp_op SRCS bilinear_interp_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_interpolate_op SRCS interpolate_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_transpose_op SRCS transpose_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_split_op SRCS split_op_test.cc DEPS npu_test_helper)
lite_cc_test(test_npu_bridge_concat_op SRCS concat_op_test.cc DEPS npu_test_helper)
......
lite/npu/bridge/bilinear_interp_op.cc → lite/npu/bridge/interpolate_op.cc
浏览文件 @ c3d15d76
......@@ -26,17 +26,20 @@ namespace lite {
namespace npu {
namespace bridge {
node_map_type BilinearInterpConverter(
const std::shared_ptr<lite::OpLite> interp_op,
node_map_type InterpolateConverter(
const std::shared_ptr<lite::OpLite> interpolate_op,
const node_map_type& inputs_map) {
auto scope = interp_op->scope();
auto op_info = interp_op->op_info();
auto scope = interpolate_op->scope();
auto op_info = interpolate_op->op_info();
auto op_type = op_info->Type();
auto unique_op_type = UniqueName(op_type);
LOG(INFO) << "Converting " + op_type + "...";
// get input, output and attributes from lite op
auto x_var_name = op_info->Input("X").front();
CHECK(inputs_map.count(x_var_name));
OpList::Global().add(inputs_map.at(x_var_name));
auto x = scope->FindVar(x_var_name)->GetMutable<lite::Tensor>();
auto x_dims = x->dims();
auto x_h = x_dims[2];
......@@ -46,7 +49,6 @@ node_map_type BilinearInterpConverter(
auto out_w = op_info->GetAttr<int>("out_w");
auto out_h = op_info->GetAttr<int>("out_h");
auto align_corners = op_info->GetAttr<bool>("align_corners");
auto interp_method = op_info->GetAttr<std::string>("interp_method");
int align_mode = op_info->GetAttr<int>("align_mode");
CHECK(!(align_mode == 0 && !align_corners))
<< "align_mode = 0 && align_corners = false isn't supported in NPU DDK";
......@@ -59,56 +61,74 @@ node_map_type BilinearInterpConverter(
out_w = out_w > 0 ? out_w : -1;
}
// create interp node and set input node from inputs_map
auto interp_node = std::make_shared<ge::op::ResizeBilinear>(unique_op_type);
CHECK(inputs_map.count(x_var_name));
interp_node->set_input_x(*inputs_map.at(x_var_name));
OpList::Global().add(inputs_map.at(x_var_name));
OpList::Global().add(interp_node);
// update out_h and out_w if has OutSize
bool is_dyn_out_size = false;
bool inputs_map_has_w = false;
if (HasInputArg(op_info, scope, "OutSize")) {
auto out_size_var_name = op_info->Input("OutSize").front();
if (!inputs_map.count(out_size_var_name)) {
if (inputs_map.count(out_size_var_name)) {
inputs_map_has_w = true;
} else {
auto out_size =
scope->FindVar(out_size_var_name)->GetMutable<lite::Tensor>();
auto out_size_dims = out_size->dims();
CHECK_EQ(out_size_dims.size(), 1);
CHECK_EQ(out_size_dims.production(), 2);
CHECK_EQ(out_size->numel(), 2);
auto out_size_data = out_size->mutable_data<int>();
// update out_h and out_w if has OutSize
out_h = out_size_data[0];
out_w = out_size_data[1];
} else {
interp_node->set_input_w(*inputs_map.at(out_size_var_name));
OpList::Global().add(inputs_map.at(out_size_var_name));
is_dyn_out_size = true; // using dynamic output size
}
}
if (!is_dyn_out_size) {
CHECK_GT(out_h, 0);
CHECK_GT(out_w, 0);
node_map_type outputs_map;
auto interp_method = op_info->GetAttr<std::string>("interp_method");
if (interp_method == "bilinear") {
auto interp_node = std::make_shared<ge::op::ResizeBilinear>(unique_op_type);
OpList::Global().add(interp_node);
interp_node->set_input_x(*inputs_map.at(x_var_name));
if (inputs_map_has_w) {
auto out_size_var_name = op_info->Input("OutSize").front();
interp_node->set_input_w(*inputs_map.at(out_size_var_name));
OpList::Global().add(inputs_map.at(out_size_var_name));
} else {
const float largest_multiple = 7.0f;
float multiple = static_cast<float>(x_h * x_w) / (out_h * out_w);
CHECK_LT(multiple, largest_multiple)
<< "multiple=(ih*iw)/(oh*ow)=" << multiple
<< " is too large, should not exceed " << largest_multiple
<< " in NPU DDK";
auto w_const_node = std::make_shared<ge::op::Const>(unique_op_type + "/w");
auto w_const_node =
std::make_shared<ge::op::Const>(unique_op_type + "/w");
w_const_node->set_attr_value(
CreateTensorAndFillData(std::vector<int>({out_h, out_w})));
interp_node->set_input_w(*w_const_node);
OpList::Global().add(w_const_node);
}
// set attributes
interp_node->set_attr_output_dim_mode(
2); // 0: zoom_factor, 1: shrink_factor, 2: height/width
interp_node->set_attr_align_corners(align_corners);
node_map_type outputs_map;
outputs_map[op_info->Output("Out").front()] = interp_node;
} else if (interp_method == "nearest") {
auto interp_node =
std::make_shared<ge::op::ResizeNearestNeighbor>(unique_op_type);
OpList::Global().add(interp_node);
interp_node->set_input_image(*inputs_map.at(x_var_name));
if (inputs_map_has_w) {
auto out_size_var_name = op_info->Input("OutSize").front();
interp_node->set_input_size(*inputs_map.at(out_size_var_name));
OpList::Global().add(inputs_map.at(out_size_var_name));
} else {
auto w_const_node =
std::make_shared<ge::op::Const>(unique_op_type + "/w");
w_const_node->set_attr_value(
CreateTensorAndFillData(std::vector<int>({out_h, out_w})));
interp_node->set_input_size(*w_const_node);
OpList::Global().add(w_const_node);
}
interp_node->set_attr_align_corners(align_corners);
outputs_map[op_info->Output("Out").front()] = interp_node;
} else {
LOG(FATAL) << "unsupported interpolate method: " << interp_method;
}
return outputs_map;
}
......@@ -118,4 +138,6 @@ node_map_type BilinearInterpConverter(
} // namespace paddle
REGISTER_NPU_BRIDGE(bilinear_interp,
paddle::lite::npu::bridge::BilinearInterpConverter);
paddle::lite::npu::bridge::InterpolateConverter);
REGISTER_NPU_BRIDGE(nearest_interp,
paddle::lite::npu::bridge::InterpolateConverter);
lite/npu/bridge/bilinear_interp_op_test.cc → lite/npu/bridge/interpolate_op_test.cc
浏览文件 @ c3d15d76
......@@ -12,12 +12,12 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "lite/operators/interpolate_op.h"
#include <gtest/gtest.h>
#include <random>
#include "lite/core/op_registry.h"
#include "lite/npu/bridge/registry.h"
#include "lite/npu/bridge/test_helper.h"
#include "lite/operators/interpolate_op.h"
namespace paddle {
namespace lite {
......@@ -161,7 +161,84 @@ void bilinear_interp_ref(const std::shared_ptr<operators::InterpolateOp> op) {
}
}
void test_bilinear_interp(int bs,
template <typename DType>
void nearest_interp_ref(const std::shared_ptr<operators::InterpolateOp> op) {
auto scope = op->scope();
auto op_info = op->op_info();
auto x = scope->FindVar(op_info->Input("X").front())->GetMutable<Tensor>();
auto out =
scope->FindVar(op_info->Output("Out").front())->GetMutable<Tensor>();
auto x_dims = x->dims();
CHECK_EQ(x_dims.size(), 4);
auto scale = op_info->GetAttr<float>("scale");
auto out_w = op_info->GetAttr<int>("out_w");
auto out_h = op_info->GetAttr<int>("out_h");
auto align_corners = op_info->GetAttr<bool>("align_corners");
// int align_mode = op_info->GetAttr<int>("align_mode");
auto interp_method = op_info->GetAttr<std::string>("interp_method");
CHECK_EQ(interp_method, "nearest");
int x_h = x_dims[2];
int x_w = x_dims[3];
if (scale > 0) {
out_h = static_cast<int>(x_h * scale);
out_w = static_cast<int>(x_w * scale);
}
if (op_info->HasInput("OutSize")) {
auto out_size_var_names = op_info->Input("OutSize");
if (out_size_var_names.size() > 0) {
auto out_size_var_name = out_size_var_names.front();
auto out_size =
scope->FindVar(out_size_var_name)->GetMutable<lite::Tensor>();
CHECK_EQ(out_size->numel(), 2);
auto out_size_data = out_size->mutable_data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
}
CHECK_GT(out_h, 0);
CHECK_GT(out_w, 0);
out->Resize({x_dims[0], x_dims[1], out_h, out_w});
float ratio_h = 0.f;
float ratio_w = 0.f;
if (out_h > 1) {
ratio_h = align_corners ? static_cast<float>(x_h - 1.0) / (out_h - 1.0)
: static_cast<float>(x_h) / out_h;
}
if (out_w > 1) {
ratio_w = align_corners ? static_cast<float>(x_w - 1.0) / (out_w - 1.0)
: static_cast<float>(x_w) / out_w;
}
auto x_data = x->data<DType>();
auto out_data = out->mutable_data<DType>();
auto out_dims = out->dims();
std::vector<int64_t> x_strides(x_dims.size(), 1);
for (int idx = x_strides.size() - 2; idx >= 0; idx--) {
x_strides[idx] = x_strides[idx + 1] * x_dims[idx + 1];
}
for (int n = 0; n < out_dims[0]; n++) {
for (int c = 0; c < out_dims[1]; c++) {
for (int h = 0; h < out_dims[2]; h++) {
for (int w = 0; w < out_dims[3]; w++) {
int in_i = ratio_h * h;
int in_j = ratio_w * w;
if (align_corners) {
in_i = ratio_h * h + 0.5;
in_j = ratio_w * w + 0.5;
}
*out_data = x_data[n * x_strides[0] + c * x_strides[1] +
in_i * x_strides[2] + in_j * x_strides[3]];
out_data++;
}
}
}
}
}
void test_interpolate(int bs,
int ic,
int ih,
int iw,
......@@ -171,7 +248,8 @@ void test_bilinear_interp(int bs,
int out_size_h,
int out_size_w,
bool align_corners,
int align_mode) {
int align_mode,
std::string interp_method) {
// prepare input&output variables
Scope scope;
std::string x_var_name("x");
......@@ -190,7 +268,7 @@ void test_bilinear_interp(int bs,
// initialize op desc
cpp::OpDesc opdesc;
opdesc.SetType("bilinear_interp");
opdesc.SetType(interp_method + "_interp");
opdesc.SetInput("X", {x_var_name});
opdesc.SetOutput("Out", {out_var_name});
opdesc.SetAttr("out_h", oh);
......@@ -198,7 +276,7 @@ void test_bilinear_interp(int bs,
opdesc.SetAttr("scale", scale);
opdesc.SetAttr("align_corners", static_cast<bool>(align_corners));
opdesc.SetAttr("align_mode", static_cast<int>(align_mode));
opdesc.SetAttr("interp_method", std::string("bilinear"));
opdesc.SetAttr("interp_method", interp_method);
if (out_size_h > 0 && out_size_w > 0) {
auto out_size_dims = out_size->dims();
CHECK_EQ(out_size_dims.size(), 1);
......@@ -211,7 +289,11 @@ void test_bilinear_interp(int bs,
// create op and execute reference implementation
auto op = CreateOp<operators::InterpolateOp>(opdesc, &scope);
if (interp_method == "bilinear") {
bilinear_interp_ref<float>(op);
} else {
nearest_interp_ref<float>(op);
}
out_ref->CopyDataFrom(*out);
// convert op to NPU model, then run it on NPU
......@@ -245,6 +327,7 @@ TEST(NPUBridges, bilinear_interp) {
for (auto out_size_w : {0, 2, 12}) {
for (auto align_corners : {true, false}) {
for (auto align_mode : {0, 1}) {
for (auto interp_method : {"bilinear", "nearest"}) {
int act_oh = 0, act_ow = 0;
if (out_size_h > 0 && out_size_w > 0) {
act_oh = out_size_h;
......@@ -259,8 +342,10 @@ TEST(NPUBridges, bilinear_interp) {
if (act_oh <= 0 || act_ow <= 0) {
continue;
}
// TODO(hong19860320) multiple=(ih*iw)/(oh*ow) should
// not exceed 7.0 in NPU DDK, delete the following lines
// TODO(hong19860320) multiple=(ih*iw)/(oh*ow)
// should
// not exceed 7.0 in NPU DDK, delete the following
// lines
// if the limination is removed.
const float largest_multiple = 7.0f;
float multiple =
......@@ -271,14 +356,15 @@ TEST(NPUBridges, bilinear_interp) {
if (align_mode == 0 && !align_corners) {
continue;
}
VLOG(3)
<< "bs: " << bs << " ic: " << ic << " ih: " << ih
<< " iw: " << iw << " oh: " << oh << " ow: " << ow
VLOG(3) << "bs: " << bs << " ic: " << ic
<< " ih: " << ih << " iw: " << iw
<< " oh: " << oh << " ow: " << ow
<< " scale: " << scale
<< " out_size: " << out_size_h << "," << out_size_w
<< " out_size: " << out_size_h << ","
<< out_size_w
<< " align_corners: " << align_corners
<< " align_mode: " << align_mode;
test_bilinear_interp(bs,
test_interpolate(bs,
ic,
ih,
iw,
......@@ -288,7 +374,9 @@ TEST(NPUBridges, bilinear_interp) {
out_size_h,
out_size_w,
align_corners,
align_mode);
align_mode,
interp_method);
}
}
}
}
......@@ -301,7 +389,7 @@ TEST(NPUBridges, bilinear_interp) {
}
}
#else
test_bilinear_interp(3, 4, 5, 3, 8, 4, 0.6f, 3, 0, true, 0);
test_interpolate(1, 1, 4, 3, 0, 0, 1.f, 3, 6, false, 1, "nearest");
#endif
}
......@@ -312,3 +400,6 @@ TEST(NPUBridges, bilinear_interp) {
USE_LITE_OP(bilinear_interp);
USE_NPU_BRIDGE(bilinear_interp);
USE_LITE_OP(nearest_interp);
USE_NPU_BRIDGE(nearest_interp);
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