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提交 ff9aed92 编写于 作者: D dongdaxiang
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make fetch variable's shape consistent with output of inference. support numpy... 

make fetch variable's shape consistent with output of inference. support numpy array input for predict interface
上级 e5bcdbe5
隐藏空白更改
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Showing with 140 addition and 322 deletion
core/general-client/include/general_model.h
浏览文件 @ ff9aed92
......@@ -45,13 +45,17 @@ class PredictorRes {
~PredictorRes() {}
public:
const std::vector<std::vector<int64_t>>& get_int64_by_name(
const std::string& name) {
return _int64_map[name];
const std::vector<int64_t>& get_int64_by_name(const std::string& name) {
return _int64_value_map[name];
}
const std::vector<std::vector<float>>& get_float_by_name(
const std::string& name) {
return _float_map[name];
const std::vector<float>& get_float_by_name(const std::string& name) {
return _float_value_map[name];
}
const std::vector<int>& get_shape(const std::string& name) {
return _shape_map[name];
}
const std::vector<int>& get_lod(const std::string& name) {
return _lod_map[name];
}
void set_variant_tag(const std::string& variant_tag) {
_variant_tag = variant_tag;
......@@ -59,8 +63,10 @@ class PredictorRes {
const std::string& variant_tag() { return _variant_tag; }
public:
std::map<std::string, std::vector<std::vector<int64_t>>> _int64_map;
std::map<std::string, std::vector<std::vector<float>>> _float_map;
std::map<std::string, std::vector<int64_t>> _int64_value_map;
std::map<std::string, std::vector<float>> _float_value_map;
std::map<std::string, std::vector<int>> _shape_map;
std::map<std::string, std::vector<int>> _lod_map;
private:
std::string _variant_tag;
......@@ -81,17 +87,8 @@ class PredictorClient {
int create_predictor_by_desc(const std::string& sdk_desc);
int create_predictor();
int destroy_predictor();
int predict(const std::vector<std::vector<float>>& float_feed,
const std::vector<std::string>& float_feed_name,
const std::vector<std::vector<int>>& float_shape,
const std::vector<std::vector<int64_t>>& int_feed,
const std::vector<std::string>& int_feed_name,
const std::vector<std::vector<int>>& int_shape,
const std::vector<std::string>& fetch_name,
PredictorRes& predict_res, // NOLINT
const int& pid);
int destroy_predictor();
int batch_predict(
const std::vector<std::vector<std::vector<float>>>& float_feed_batch,
......
core/general-client/src/general_model.cpp
浏览文件 @ ff9aed92
......@@ -132,154 +132,6 @@ int PredictorClient::create_predictor() {
_api.thrd_initialize();
}
int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
const std::vector<std::string> &float_feed_name,
const std::vector<std::vector<int>> &float_shape,
const std::vector<std::vector<int64_t>> &int_feed,
const std::vector<std::string> &int_feed_name,
const std::vector<std::vector<int>> &int_shape,
const std::vector<std::string> &fetch_name,
PredictorRes &predict_res,
const int &pid) { // NOLINT
predict_res._int64_map.clear();
predict_res._float_map.clear();
Timer timeline;
int64_t preprocess_start = timeline.TimeStampUS();
_api.thrd_clear();
std::string variant_tag;
_predictor = _api.fetch_predictor("general_model", &variant_tag);
predict_res.set_variant_tag(variant_tag);
Request req;
for (auto &name : fetch_name) {
req.add_fetch_var_names(name);
}
std::vector<Tensor *> tensor_vec;
FeedInst *inst = req.add_insts();
for (auto &name : float_feed_name) {
tensor_vec.push_back(inst->add_tensor_array());
}
for (auto &name : int_feed_name) {
tensor_vec.push_back(inst->add_tensor_array());
}
int vec_idx = 0;
for (int i = 0; i < float_feed_name.size(); ++i) {
int idx = _feed_name_to_idx[float_feed_name[i]];
Tensor *tensor = tensor_vec[idx];
if (float_shape.size() == 0) {
for (int j = 0; j < _shape[idx].size(); ++j) {
tensor->add_shape(_shape[idx][j]);
}
} else {
for (int j = 0; j < float_shape[i].size(); ++j) {
tensor->add_shape(float_shape[i][j]);
}
}
tensor->set_elem_type(1);
for (int j = 0; j < float_feed[vec_idx].size(); ++j) {
tensor->add_float_data(float_feed[vec_idx][j]);
}
vec_idx++;
}
VLOG(2) << "feed float feed var done.";
vec_idx = 0;
for (int i = 0; i < int_feed_name.size(); ++i) {
int idx = _feed_name_to_idx[int_feed_name[i]];
Tensor *tensor = tensor_vec[idx];
if (int_shape.size() == 0) {
for (int j = 0; j < int_shape[i].size(); ++j) {
tensor->add_shape(int_shape[i][j]);
}
} else {
for (int j = 0; j < _shape[idx].size(); ++j) {
tensor->add_shape(_shape[idx][j]);
}
}
tensor->set_elem_type(0);
for (int j = 0; j < int_feed[vec_idx].size(); ++j) {
tensor->add_int64_data(int_feed[vec_idx][j]);
}
vec_idx++;
}
int64_t preprocess_end = timeline.TimeStampUS();
int64_t client_infer_start = timeline.TimeStampUS();
Response res;
int64_t client_infer_end = 0;
int64_t postprocess_start = 0;
int64_t postprocess_end = 0;
if (FLAGS_profile_client) {
if (FLAGS_profile_server) {
req.set_profile_server(true);
}
}
res.Clear();
if (_predictor->inference(&req, &res) != 0) {
LOG(ERROR) << "failed call predictor with req: " << req.ShortDebugString();
return -1;
} else {
VLOG(2) << "predict done.";
client_infer_end = timeline.TimeStampUS();
postprocess_start = client_infer_end;
for (auto &name : fetch_name) {
int idx = _fetch_name_to_idx[name];
VLOG(2) << "fetch name: " << name;
if (_fetch_name_to_type[name] == 0) {
int len = res.insts(0).tensor_array(idx).int64_data_size();
VLOG(2) << "fetch tensor : " << name << " type: int64 len : " << len;
predict_res._int64_map[name].resize(1);
predict_res._int64_map[name][0].resize(len);
for (int i = 0; i < len; ++i) {
predict_res._int64_map[name][0][i] =
res.insts(0).tensor_array(idx).int64_data(i);
}
} else if (_fetch_name_to_type[name] == 1) {
int len = res.insts(0).tensor_array(idx).float_data_size();
VLOG(2) << "fetch tensor : " << name << " type: float32 len : " << len;
predict_res._float_map[name].resize(1);
predict_res._float_map[name][0].resize(len);
for (int i = 0; i < len; ++i) {
predict_res._float_map[name][0][i] =
res.insts(0).tensor_array(idx).float_data(i);
}
}
postprocess_end = timeline.TimeStampUS();
}
}
if (FLAGS_profile_client) {
std::ostringstream oss;
oss << "PROFILE\t"
<< "pid:" << pid << "\t"
<< "prepro_0:" << preprocess_start << " "
<< "prepro_1:" << preprocess_end << " "
<< "client_infer_0:" << client_infer_start << " "
<< "client_infer_1:" << client_infer_end << " ";
if (FLAGS_profile_server) {
int op_num = res.profile_time_size() / 2;
for (int i = 0; i < op_num; ++i) {
oss << "op" << i << "_0:" << res.profile_time(i * 2) << " ";
oss << "op" << i << "_1:" << res.profile_time(i * 2 + 1) << " ";
}
}
oss << "postpro_0:" << postprocess_start << " ";
oss << "postpro_1:" << postprocess_end;
fprintf(stderr, "%s\n", oss.str().c_str());
}
return 0;
}
int PredictorClient::batch_predict(
const std::vector<std::vector<std::vector<float>>> &float_feed_batch,
const std::vector<std::string> &float_feed_name,
......@@ -292,8 +144,10 @@ int PredictorClient::batch_predict(
const int &pid) {
int batch_size = std::max(float_feed_batch.size(), int_feed_batch.size());
predict_res_batch._int64_map.clear();
predict_res_batch._float_map.clear();
predict_res_batch._int64_value_map.clear();
predict_res_batch._float_value_map.clear();
predict_res_batch._shape_map.clear();
predict_res_batch._lod_map.clear();
Timer timeline;
int64_t preprocess_start = timeline.TimeStampUS();
......@@ -310,7 +164,7 @@ int PredictorClient::batch_predict(
for (auto &name : fetch_name) {
req.add_fetch_var_names(name);
}
//
for (int bi = 0; bi < batch_size; bi++) {
VLOG(2) << "prepare batch " << bi;
std::vector<Tensor *> tensor_vec;
......@@ -325,20 +179,14 @@ int PredictorClient::batch_predict(
tensor_vec.push_back(inst->add_tensor_array());
}
VLOG(2) << "batch [" << bi << "] int_feed_name and float_feed_name "
VLOG(2) << "batch [" << bi << "] int_feed_name and float_feed_name"
<< "prepared";
int vec_idx = 0;
for (int i = 0; i < float_feed_name.size(); ++i) {
int idx = _feed_name_to_idx[float_feed_name[i]];
for (auto &name : float_feed_name) {
int idx = _feed_name_to_idx[name];
Tensor *tensor = tensor_vec[idx];
if (float_shape.size() == float_feed_name.size()) {
for (int j = 0; j < float_shape[i].size(); ++j) {
tensor->add_shape(float_shape[i][j]);
}
} else {
for (int j = 0; j < _shape[idx].size(); ++j) {
tensor->add_shape(_shape[idx][j]);
}
for (int j = 0; j < float_shape[vec_idx].size(); ++j) {
tensor->add_shape(float_shape[vec_idx][j]);
}
tensor->set_elem_type(1);
for (int j = 0; j < float_feed[vec_idx].size(); ++j) {
......@@ -351,20 +199,14 @@ int PredictorClient::batch_predict(
<< "float feed value prepared";
vec_idx = 0;
for (int i = 0; i < int_feed_name.size(); ++i) {
int idx = _feed_name_to_idx[int_feed_name[i]];
for (auto &name : int_feed_name) {
int idx = _feed_name_to_idx[name];
Tensor *tensor = tensor_vec[idx];
if (int_shape.size() == int_feed_name.size()) {
for (int j = 0; j < int_shape[i].size(); ++j) {
tensor->add_shape(int_shape[i][j]);
}
} else {
for (int j = 0; j < _shape[idx].size(); ++j) {
tensor->add_shape(_shape[idx][j]);
}
for (int j = 0; j < int_shape[vec_idx].size(); ++j) {
tensor->add_shape(int_shape[vec_idx][j]);
}
tensor->set_elem_type(0);
VLOG(3) << "feed var name " << float_feed_name[i] << " index " << vec_idx
VLOG(3) << "feed var name " << name << " index " << vec_idx
<< "first data " << int_feed[vec_idx][0];
for (int j = 0; j < int_feed[vec_idx].size(); ++j) {
tensor->add_int64_data(int_feed[vec_idx][j]);
......@@ -395,43 +237,47 @@ int PredictorClient::batch_predict(
res.Clear();
if (_predictor->inference(&req, &res) != 0) {
LOG(ERROR) << "failed call predictor with req: " << req.ShortDebugString();
return -1;
exit(-1);
} else {
client_infer_end = timeline.TimeStampUS();
postprocess_start = client_infer_end;
for (auto &name : fetch_name) {
predict_res_batch._int64_map[name].resize(batch_size);
predict_res_batch._float_map[name].resize(batch_size);
int idx = _fetch_name_to_idx[name];
int shape_size = res.insts(0).tensor_array(idx).shape_size();
predict_res_batch._shape_map[name].resize(shape_size);
for (int i = 0; i < shape_size; ++i) {
predict_res_batch._shape_map[name][i] =
res.insts(0).tensor_array(idx).shape(i);
}
int lod_size = res.insts(0).tensor_array(idx).lod_size();
if (lod_size > 0) {
predict_res_batch._lod_map[name].resize(lod_size);
for (int i = 0; i < lod_size; ++i) {
predict_res_batch._lod_map[name][i] =
res.insts(0).tensor_array(idx).lod(i);
}
}
}
VLOG(2) << "response batch size " << res.insts_size();
VLOG(2) << "response var nmae " << res.insts(0).tensor_array_size();
for (int bi = 0; bi < batch_size; bi++) {
int idx = 0;
for (auto &name : fetch_name) {
int len = res.insts(bi).tensor_array(idx).data_size();
if (_fetch_name_to_type[name] == 0) {
int len = res.insts(bi).tensor_array(idx).int64_data_size();
VLOG(2) << "fetch tensor : " << name << " type: int64 len : " << len;
predict_res_batch._int64_map[name][bi].resize(len);
VLOG(2) << "fetch name " << name << " index " << idx << " first data "
<< res.insts(bi).tensor_array(idx).int64_data(0);
for (int i = 0; i < len; ++i) {
predict_res_batch._int64_map[name][bi][i] =
res.insts(bi).tensor_array(idx).int64_data(i);
}
} else if (_fetch_name_to_type[name] == 1) {
int len = res.insts(bi).tensor_array(idx).float_data_size();
VLOG(2) << "fetch tensor : " << name
<< " type: float32 len : " << len;
predict_res_batch._float_map[name][bi].resize(len);
VLOG(2) << "fetch name " << name << " index " << idx << " first data "
<< res.insts(bi).tensor_array(idx).float_data(0);
for (int i = 0; i < len; ++i) {
predict_res_batch._float_map[name][bi][i] =
res.insts(bi).tensor_array(idx).float_data(i);
}
for (auto &name : fetch_name) {
int idx = _fetch_name_to_idx[name];
if (_fetch_name_to_type[name] == 0) {
predict_res_batch._int64_value_map[name].resize(
res.insts(0).tensor_array(idx).int64_data_size());
int size = res.insts(0).tensor_array(idx).int64_data_size();
for (int i = 0; i < size; ++i) {
predict_res_batch._int64_value_map[name][i] =
res.insts(0).tensor_array(idx).int64_data(i);
}
} else {
predict_res_batch._float_value_map[name].resize(
res.insts(0).tensor_array(idx).float_data_size());
int size = res.insts(0).tensor_array(idx).float_data_size();
for (int i = 0; i < size; ++i) {
predict_res_batch._float_value_map[name][i] =
res.insts(0).tensor_array(idx).float_data(i);
}
idx += 1;
}
}
postprocess_end = timeline.TimeStampUS();
......
core/general-client/src/pybind_general_model.cpp
浏览文件 @ ff9aed92
......@@ -40,6 +40,16 @@ PYBIND11_MODULE(serving_client, m) {
return self.get_float_by_name(name);
},
py::return_value_policy::reference)
.def("get_shape",
[](PredictorRes &self, std::string &name) {
return self.get_shape(name);
},
py::return_value_policy::reference)
.def("get_lod",
[](PredictorRes &self, std::string &name) {
return self.get_lod(name);
},
py::return_value_policy::reference)
.def("variant_tag",
[](PredictorRes &self) { return self.variant_tag(); });
......@@ -67,27 +77,6 @@ PYBIND11_MODULE(serving_client, m) {
[](PredictorClient &self) { self.create_predictor(); })
.def("destroy_predictor",
[](PredictorClient &self) { self.destroy_predictor(); })
.def("predict",
[](PredictorClient &self,
const std::vector<std::vector<float>> &float_feed,
const std::vector<std::string> &float_feed_name,
const std::vector<std::vector<int>> &float_shape,
const std::vector<std::vector<int64_t>> &int_feed,
const std::vector<std::string> &int_feed_name,
const std::vector<std::vector<int>> &int_shape,
const std::vector<std::string> &fetch_name,
PredictorRes &predict_res,
const int &pid) {
return self.predict(float_feed,
float_feed_name,
float_shape,
int_feed,
int_feed_name,
int_shape,
fetch_name,
predict_res,
pid);
})
.def("batch_predict",
[](PredictorClient &self,
const std::vector<std::vector<std::vector<float>>>
......
core/general-server/op/general_response_op.cpp
浏览文件 @ ff9aed92
......@@ -73,22 +73,21 @@ int GeneralResponseOp::inference() {
// response inst with only fetch_var_names
Response *res = mutable_data<Response>();
for (int i = 0; i < batch_size; ++i) {
FetchInst *fetch_inst = res->add_insts();
for (auto &idx : fetch_index) {
Tensor *tensor = fetch_inst->add_tensor_array();
// currently only response float tensor or lod_tensor
tensor->set_elem_type(1);
if (model_config->_is_lod_fetch[idx]) {
VLOG(2) << "out[" << idx << " is lod_tensor";
tensor->add_shape(-1);
} else {
VLOG(2) << "out[" << idx << "] is tensor";
for (int k = 1; k < in->at(idx).shape.size(); ++k) {
VLOG(2) << "shape[" << k - 1 << "]: " << in->at(idx).shape[k];
tensor->add_shape(in->at(idx).shape[k]);
}
FetchInst *fetch_inst = res->add_insts();
for (auto &idx : fetch_index) {
Tensor *tensor = fetch_inst->add_tensor_array();
tensor->set_elem_type(1);
if (model_config->_is_lod_fetch[idx]) {
VLOG(2) << "out[" << idx << "] is lod_tensor";
for (int k = 0; k < in->at(idx).shape.size(); ++k) {
VLOG(2) << "shape[" << k << "]: " << in->at(idx).shape[k];
tensor->add_shape(in->at(idx).shape[k]);
}
} else {
VLOG(2) << "out[" << idx << "] is tensor";
for (int k = 0; k < in->at(idx).shape.size(); ++k) {
VLOG(2) << "shape[" << k << "]: " << in->at(idx).shape[k];
tensor->add_shape(in->at(idx).shape[k]);
}
}
}
......@@ -96,62 +95,42 @@ int GeneralResponseOp::inference() {
int var_idx = 0;
for (auto &idx : fetch_index) {
int cap = 1;
for (int j = 1; j < in->at(idx).shape.size(); ++j) {
for (int j = 0; j < in->at(idx).shape.size(); ++j) {
cap *= in->at(idx).shape[j];
}
if (in->at(idx).dtype == paddle::PaddleDType::INT64) {
int64_t *data_ptr = static_cast<int64_t *>(in->at(idx).data.data());
if (model_config->_is_lod_fetch[idx]) {
for (int j = 0; j < batch_size; ++j) {
for (int k = in->at(idx).lod[0][j]; k < in->at(idx).lod[0][j + 1];
k++) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_int64_data(data_ptr[k]);
}
FetchInst *fetch_p = res->mutable_insts(0);
for (int j = 0; j < in->at(idx).lod[0].size(); ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_lod(
in->at(idx).lod[0][j]);
}
for (int j = 0; j < cap; ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_int64_data(data_ptr[j]);
}
} else {
int var_size = in->at(idx).shape[0];
if (var_size == batch_size) {
for (int j = 0; j < batch_size; ++j) {
for (int k = j * cap; k < (j + 1) * cap; ++k) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_int64_data(
data_ptr[k]);
}
}
} else {
for (int j = 0; j < batch_size; ++j) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_int64_data(data_ptr[0]);
}
FetchInst *fetch_p = res->mutable_insts(0);
for (int j = 0; j < cap; ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_float_data(data_ptr[j]);
}
}
var_idx++;
} else if (in->at(idx).dtype == paddle::PaddleDType::FLOAT32) {
float *data_ptr = static_cast<float *>(in->at(idx).data.data());
if (model_config->_is_lod_fetch[idx]) {
for (int j = 0; j < batch_size; ++j) {
for (int k = in->at(idx).lod[0][j]; k < in->at(idx).lod[0][j + 1];
k++) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_float_data(data_ptr[k]);
}
FetchInst *fetch_p = res->mutable_insts(0);
for (int j = 0; j < in->at(idx).lod[0].size(); ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_lod(
in->at(idx).lod[0][j]);
}
for (int j = 0; j < cap; ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_float_data(data_ptr[j]);
}
} else {
int var_size = in->at(idx).shape[0];
if (var_size == batch_size) {
for (int j = 0; j < batch_size; ++j) {
for (int k = j * cap; k < (j + 1) * cap; ++k) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_float_data(
data_ptr[k]);
}
}
} else {
for (int j = 0; j < batch_size; ++j) {
FetchInst *fetch_p = res->mutable_insts(j);
fetch_p->mutable_tensor_array(var_idx)->add_float_data(data_ptr[0]);
}
FetchInst *fetch_p = res->mutable_insts(0);
for (int j = 0; j < cap; ++j) {
fetch_p->mutable_tensor_array(var_idx)->add_float_data(data_ptr[j]);
}
}
var_idx++;
......
core/general-server/proto/general_model_service.proto
浏览文件 @ ff9aed92
......@@ -26,6 +26,7 @@ message Tensor {
repeated float float_data = 4;
optional int32 elem_type = 5;
repeated int32 shape = 6;
repeated int32 lod = 7; // only for fetch tensor currently
};
message FeedInst { repeated Tensor tensor_array = 1; };
......
core/sdk-cpp/proto/general_model_service.proto
浏览文件 @ ff9aed92
......@@ -26,6 +26,7 @@ message Tensor {
repeated float float_data = 4;
optional int32 elem_type = 5;
repeated int32 shape = 6;
repeated int32 lod = 7; // only for fetch tensor currently
};
message FeedInst { repeated Tensor tensor_array = 1; };
......
python/paddle_serving_client/__init__.py
浏览文件 @ ff9aed92
......@@ -121,6 +121,7 @@ class Client(object):
self.fetch_names_to_idx_ = {}
self.lod_tensor_set = set()
self.feed_tensor_len = {}
for i, var in enumerate(model_conf.feed_var):
self.feed_names_to_idx_[var.alias_name] = i
self.feed_types_[var.alias_name] = var.feed_type
......@@ -133,11 +134,11 @@ class Client(object):
for dim in self.feed_shapes_[var.alias_name]:
counter *= dim
self.feed_tensor_len[var.alias_name] = counter
for i, var in enumerate(model_conf.fetch_var):
self.fetch_names_to_idx_[var.alias_name] = i
self.fetch_names_to_type_[var.alias_name] = var.fetch_type
if var.is_lod_tensor:
self.lod_tensor_set.add(var.alias_name)
return
def add_variant(self, tag, cluster, variant_weight):
......@@ -165,7 +166,6 @@ class Client(object):
"parameter endpoints({}) will not take effect, because you use the add_variant function.".
format(endpoints))
sdk_desc = self.predictor_sdk_.gen_desc()
print(sdk_desc)
self.client_handle_.create_predictor_by_desc(sdk_desc.SerializeToString(
))
......@@ -229,23 +229,25 @@ class Client(object):
for key in feed_i:
if key not in self.feed_names_:
raise ValueError("Wrong feed name: {}.".format(key))
self.shape_check(feed_i, key)
if not isinstance(feed_i[key], np.ndarray):
self.shape_check(feed_i, key)
if self.feed_types_[key] == int_type:
if i == 0:
int_feed_names.append(key)
if isinstance(feed_i[key], np.ndarray):
int_shape.append(feed_i[key].shape)
int_shape.append(list(feed_i[key].shape))
if isinstance(feed_i[key], np.ndarray):
int_slot.append(feed_i[key].tolist())
int_slot.append(np.reshape(feed_i[key], (-1)).tolist())
else:
int_slot.append(feed_i[key])
elif self.feed_types_[key] == float_type:
if i == 0:
float_feed_names.append(key)
if isinstance(feed_i[key], np.ndarray):
float_shape.append(feed_i[key].shape)
float_shape.append(list(feed_i[key].shape))
if isinstance(feed_i[key], np.ndarray):
float_slot.append(feed_i[key].tolist())
float_slot.append(
np.reshape(feed_i[key], (-1)).tolist())
else:
float_slot.append(feed_i[key])
int_slot_batch.append(int_slot)
......@@ -253,31 +255,34 @@ class Client(object):
result_batch = self.result_handle_
res = self.client_handle_.batch_predict(
float_slot_batch, float_feed_names, int_slot_batch, int_feed_names,
fetch_names, result_batch, self.pid)
float_slot_batch, float_feed_names, float_shape, int_slot_batch,
int_feed_names, int_shape, fetch_names, result_batch, self.pid)
if res == -1:
return None
result_map_batch = []
result_map = {}
# result map needs to be a numpy array
for i, name in enumerate(fetch_names):
if self.fetch_names_to_type_[name] == int_type:
result_map[name] = result_batch.get_int64_by_name(name)
shape = result_batch.get_shape(name)
result_map[name] = np.array(result_map[name])
result_map[name].shape = shape
if name in self.lod_tensor_set:
result_map["{}.lod".format(name)] = result_batch.get_lod(
name)
elif self.fetch_names_to_type_[name] == float_type:
result_map[name] = result_batch.get_float_by_name(name)
for i in range(batch_size):
single_result = {}
for key in result_map:
single_result[key] = result_map[key][i]
result_map_batch.append(single_result)
if batch_size == 1:
return [result_map_batch[0], self.result_handle_.variant_tag()
] if need_variant_tag else result_map_batch[0]
else:
return [result_map_batch, self.result_handle_.variant_tag()
] if need_variant_tag else result_map_batch
shape = result_batch.get_shape(name)
result_map[name] = np.array(result_map[name])
result_map[name].shape = shape
if name in self.lod_tensor_set:
result_map["{}.lod".format(name)] = result_batch.get_lod(
name)
return result_map
def release(self):
self.client_handle_.destroy_predictor()
......
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