Merge branch 'develop' into develop

core/general-server/op/general_dist_kv_infer_op.cpp

@@ -186,9 +186,9 @@ int GeneralDistKVInferOp::inference() {
   if (values.size() != keys.size() || values[0].buff.size() == 0) {
     LOG(ERROR) << "cube value return null";
   }
-  // size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
+   size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
   // size_t EMBEDDING_SIZE = (values[0].buff.size() - 10) / sizeof(float);
-  size_t EMBEDDING_SIZE = 9;
+  //size_t EMBEDDING_SIZE = 9;
   TensorVector sparse_out;
   sparse_out.resize(sparse_count);
   TensorVector dense_out;
@@ -241,7 +241,7 @@ int GeneralDistKVInferOp::inference() {
 
       // The data generated by pslib has 10 bytes of information to be filtered
       // out
-      memcpy(data_ptr, cur_val->buff.data() + 10, cur_val->buff.size() - 10);
+      memcpy(data_ptr, cur_val->buff.data(), cur_val->buff.size() );
       // VLOG(3) <<  keys[cube_val_idx] << ":" << data_ptr[0] << ", " <<
       // data_ptr[1] << ", " <<data_ptr[2] << ", " <<data_ptr[3] << ", "
       // <<data_ptr[4] << ", " <<data_ptr[5] << ", " <<data_ptr[6] << ", "

core/predictor/framework/infer.h

@@ -277,7 +277,7 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
     LOG(WARNING) << "Loading cube cache[" << next_idx << "] ...";
     std::string model_path = conf.model_dir();
     if (access(model_path.c_str(), F_OK) == 0) {
-      std::string cube_cache_path = model_path + "/" + "cube_cache";
+      std::string cube_cache_path = model_path + "/cube_cache";
       int reload_cache_ret = md->caches[next_idx]->reload_data(cube_cache_path);
       LOG(WARNING) << "Loading cube cache[" << next_idx << "] done.";
     } else {
@@ -437,7 +437,7 @@ class CloneDBReloadableInferEngine
       // create caches
       std::string model_path = conf.model_dir();
       if (access(model_path.c_str(), F_OK) == 0) {
-        std::string cube_cache_path = model_path + "cube_cache";
+        std::string cube_cache_path = model_path + "/cube_cache";
         int reload_cache_ret =
             md->caches[next_idx]->reload_data(cube_cache_path);
         LOG(WARNING) << "create cube cache[" << next_idx << "] done.";

python/examples/criteo_ctr_with_cube/cube/conf/cube.conf

+[{
+    "dict_name": "test_dict",
+    "shard": 1,
+    "dup": 1,
+    "timeout": 200,
+    "retry": 3,
+    "backup_request": 100,
+    "type": "ipport_list",
+    "load_balancer": "rr",
+    "nodes": [{
+        "ipport_list": "list://127.0.0.1:8027"
+    }]
+}]

python/examples/criteo_ctr_with_cube/cube/conf/gflags.conf

+--port=8027
+--dict_split=1
+--in_mem=true
+--log_dir=./log/

python/examples/criteo_ctr_with_cube/cube/keys

+1
+2
+3
+4
+5
+6
+7
+8
+9
+10

python/examples/criteo_ctr_with_cube/test_client.py

@@ -16,7 +16,7 @@
 from paddle_serving_client import Client
 import sys
 import os
-import criteo as criteo
+import criteo_reader as criteo
 import time
 from paddle_serving_client.metric import auc
 import numpy as np
@@ -35,22 +35,23 @@ reader = dataset.infer_reader(test_filelists, batch, buf_size)
 label_list = []
 prob_list = []
 start = time.time()
-for ei in range(10000):
+for ei in range(100):
     if py_version == 2:
         data = reader().next()
     else:
         data = reader().__next__()
     feed_dict = {}
-    feed_dict['dense_input'] = data[0][0]
+    feed_dict['dense_input'] = np.array(data[0][0]).reshape(1, len(data[0][0]))
+
     for i in range(1, 27):
-        feed_dict["embedding_{}.tmp_0".format(i - 1)] = np.array(data[0][i]).reshape(-1)
+        feed_dict["embedding_{}.tmp_0".format(i - 1)] = np.array(data[0][i]).reshape(len(data[0][i]))
         feed_dict["embedding_{}.tmp_0.lod".format(i - 1)] = [0, len(data[0][i])]
-    fetch_map = client.predict(feed=feed_dict, fetch=["prob"])
+    fetch_map = client.predict(feed=feed_dict, fetch=["prob"],batch=True)
     print(fetch_map)
     prob_list.append(fetch_map['prob'][0][1])
     label_list.append(data[0][-1][0])
 
-print(auc(label_list, prob_list))
+
 end = time.time()
 print(end - start)
 

python/examples/pipeline/PaddleDetection/faster_rcnn/web_service.py

@@ -25,7 +25,7 @@ class FasterRCNNOp(Op):
         self.img_preprocess = Sequential([
             BGR2RGB(), Div(255.0),
             Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
-            Resize((640, 640)), Transpose((2, 0, 1))
+            Resize(640, 640), Transpose((2, 0, 1))
         ])
         self.img_postprocess = RCNNPostprocess("label_list.txt", "output")