make predict adaptable to shape

core/general-client/include/general_model.h

@@ -85,8 +85,10 @@ class PredictorClient {
 
   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);
@@ -94,8 +96,10 @@ class PredictorClient {
   int batch_predict(
       const std::vector<std::vector<std::vector<float>>>& float_feed_batch,
       const std::vector<std::string>& float_feed_name,
+      const std::vector<std::vector<int>>& float_shape,
       const std::vector<std::vector<std::vector<int64_t>>>& int_feed_batch,
       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_batch,  // NOLINT
       const int& pid);

core/general-client/src/general_model.cpp

@@ -134,8 +134,10 @@ int PredictorClient::create_predictor() {
 
 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
@@ -164,11 +166,17 @@ int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
   }
 
   int vec_idx = 0;
-  for (auto &name : float_feed_name) {
-    int idx = _feed_name_to_idx[name];
+  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];
-    for (int j = 0; j < _shape[idx].size(); ++j) {
-      tensor->add_shape(_shape[idx][j]);
+    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) {
@@ -180,11 +188,17 @@ int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
   VLOG(2) << "feed float feed var done.";
   vec_idx = 0;
 
-  for (auto &name : int_feed_name) {
-    int idx = _feed_name_to_idx[name];
+  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];
-    for (int j = 0; j < _shape[idx].size(); ++j) {
-      tensor->add_shape(_shape[idx][j]);
+    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) {
@@ -269,8 +283,10 @@ int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
 int PredictorClient::batch_predict(
     const std::vector<std::vector<std::vector<float>>> &float_feed_batch,
     const std::vector<std::string> &float_feed_name,
+    const std::vector<std::vector<int>> &float_shape,
     const std::vector<std::vector<std::vector<int64_t>>> &int_feed_batch,
     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_batch,
     const int &pid) {
@@ -312,11 +328,17 @@ int PredictorClient::batch_predict(
     VLOG(2) << "batch [" << bi << "] int_feed_name and float_feed_name "
             << "prepared";
     int vec_idx = 0;
-    for (auto &name : float_feed_name) {
-      int idx = _feed_name_to_idx[name];
+    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];
-      for (int j = 0; j < _shape[idx].size(); ++j) {
-        tensor->add_shape(_shape[idx][j]);
+      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]);
+        }
       }
       tensor->set_elem_type(1);
       for (int j = 0; j < float_feed[vec_idx].size(); ++j) {
@@ -329,14 +351,20 @@ int PredictorClient::batch_predict(
             << "float feed value prepared";
 
     vec_idx = 0;
-    for (auto &name : int_feed_name) {
-      int idx = _feed_name_to_idx[name];
+    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];
-      for (int j = 0; j < _shape[idx].size(); ++j) {
-        tensor->add_shape(_shape[idx][j]);
+      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]);
+        }
       }
       tensor->set_elem_type(0);
-      VLOG(3) << "feed var name " << name << " index " << vec_idx
+      VLOG(3) << "feed var name " << float_feed_name[i] << " 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]);

core/general-client/src/pybind_general_model.cpp

@@ -71,15 +71,19 @@ PYBIND11_MODULE(serving_client, m) {
            [](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);
@@ -89,16 +93,20 @@ PYBIND11_MODULE(serving_client, m) {
               const std::vector<std::vector<std::vector<float>>>
                   &float_feed_batch,
               const std::vector<std::string> &float_feed_name,
+              const std::vector<std::vector<int>> &float_shape,
               const std::vector<std::vector<std::vector<int64_t>>>
                   &int_feed_batch,
               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_batch,
               const int &pid) {
              return self.batch_predict(float_feed_batch,
                                        float_feed_name,
+                                       float_shape,
                                        int_feed_batch,
                                        int_feed_name,
+                                       int_shape,
                                        fetch_name,
                                        predict_res_batch,
                                        pid);

python/paddle_serving_client/__init__.py

@@ -18,6 +18,8 @@ import os
 from .proto import sdk_configure_pb2 as sdk
 from .proto import general_model_config_pb2 as m_config
 import google.protobuf.text_format
+import numpy as np
+import time
 import sys
 
 int_type = 0
@@ -203,6 +205,8 @@ class Client(object):
         float_slot_batch = []
         int_feed_names = []
         float_feed_names = []
+        int_shape = []
+        float_shape = []
         fetch_names = []
         counter = 0
         batch_size = len(feed_batch)
@@ -219,6 +223,8 @@ class Client(object):
         for i, feed_i in enumerate(feed_batch):
             int_slot = []
             float_slot = []
+            int_shape = []
+            float_shape = []
             for key in feed_i:
                 if key not in self.feed_names_:
                     raise ValueError("Wrong feed name: {}.".format(key))
@@ -226,13 +232,21 @@ class Client(object):
                 if self.feed_types_[key] == int_type:
                     if i == 0:
                         int_feed_names.append(key)
-                    int_slot.append(feed_i[key])
+                        if isinstance(feed_i[key], np.ndarray):
+                            int_shape.append(feed_i[key].shape)
+                    if isinstance(feed_i[key], np.ndarray):
+                        int_slot.append(feed_i[key].tolist())
+                    else:
+                        int_slot.append(feed_i[key])
                 elif self.feed_types_[key] == float_type:
                     if i == 0:
                         float_feed_names.append(key)
-                    float_slot.append(feed_i[key])
-            if len(int_slot) + len(float_slot) == 0:
-                raise ValueError("No feed data for predict.")
+                        if isinstance(feed_i[key], np.ndarray):
+                            float_shape.append(feed_i[key].shape)
+                    if isinstance(feed_i[key], np.ndarray):
+                        float_slot.append(feed_i[key].tolist())
+                    else:
+                        float_slot.append(feed_i[key])
             int_slot_batch.append(int_slot)
             float_slot_batch.append(float_slot)