update code

java/paddle-serving-sdk-java/src/main/java/io/paddle/serving/client/Client.java

@@ -12,6 +12,7 @@ import com.google.common.util.concurrent.Futures;
 import com.google.common.util.concurrent.ListenableFuture;
 
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.iter.NdIndexIterator;
 import org.nd4j.linalg.factory.Nd4j;
 
 import io.paddle.serving.grpc.*;
@@ -65,7 +66,8 @@ public class Client {
     }
 
     public Boolean connect(List<String> endpoints) {
-        String target = "ipv4:" + String.join(",", endpoints);
+        // String target = "ipv4:" + String.join(",", endpoints);
+        String target = endpoints.get(0);
         // TODO: max_receive_message_length and max_send_message_length
         try {
             channel_ = ManagedChannelBuilder.forTarget(target)
@@ -80,13 +82,17 @@ public class Client {
         }
         GetClientConfigRequest get_client_config_req = GetClientConfigRequest.newBuilder().build();
         GetClientConfigResponse resp;
+        System.out.println("try to call getClientConfig");
         try {
             resp = blockingStub_.getClientConfig(get_client_config_req);
         } catch (StatusRuntimeException e) {
             System.out.format("Get Client config failed: %s", e.toString());
             return false;
         }
+        System.out.println("succ call get client");
+        System.out.println(resp);
         String model_config_str = resp.getClientConfigStr();
+        System.out.println("model_config_str: " + model_config_str);
         _parseModelConfig(model_config_str);
         return true;
     }
@@ -144,7 +150,7 @@ public class Client {
     }
 
     private InferenceRequest _packInferenceRequest(
-            List<Map<String, INDArray>> feed_batch,
+            List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch) throws IllegalArgumentException {
         List<String> feed_var_names = new ArrayList<String>();
         feed_var_names.addAll(feed_batch.get(0).keySet());
@@ -153,25 +159,40 @@ public class Client {
             .addAllFeedVarNames(feed_var_names)
             .addAllFetchVarNames(fetch)
             .setIsPython(false);
-        for (Map<String, INDArray> feed_data: feed_batch) {
+        for (HashMap<String, INDArray> feed_data: feed_batch) {
             FeedInst.Builder inst_builder = FeedInst.newBuilder();
             for (String name: feed_var_names) {
                 Tensor.Builder tensor_builder = Tensor.newBuilder();
                 INDArray variable = feed_data.get(name);
-                INDArray flattened_list = variable.reshape({-1});
+                long[] flattened_shape = {-1};
+                INDArray flattened_list = variable.reshape(flattened_shape);
+                for (Map.Entry<String, Integer> entry : feedTypes_.entrySet()) {
+                    System.out.println("Key = " + entry.getKey() + ", Value = " + entry.getValue());
+                }
                 int v_type = feedTypes_.get(name);
+                NdIndexIterator iter = new NdIndexIterator(flattened_list.shape());
                 if (v_type == 0) { // int64
-                    for (long x: flattened_list) {
+                    while (iter.hasNext()) {
+                        long[] next_index = iter.next();
+                        long x = flattened_list.getLong(next_index);
                         tensor_builder.addInt64Data(x);
                     }
                 } else if (v_type == 1) { // float32
-                    for (float x: flattened_list) {
+                    while (iter.hasNext()) {
+                        long[] next_index = iter.next();
+                        float x = flattened_list.getFloat(next_index);
                         tensor_builder.addFloatData(x);
                     }
                 } else if (v_type == 2) { // int32
-                    for (int x: flattened_list) {
+                    throw new IllegalArgumentException("error tensor value type.");
+                    //TODO
+                    /*
+                    while (iter.hasNext()) {
+                        long[] next_index = iter.next();
+                        int x = flattened_list.getInt(next_index);
+                        // TODO: long to int? 
                         tensor_builder.addIntData(x);
-                    }
+                    }*/
                 } else {
                     throw new IllegalArgumentException("error tensor value type.");
                 }
@@ -183,7 +204,7 @@ public class Client {
         return req_builder.build();
     }
 
-    private Map<String, ? extends Map<String, INDArray>>
+    private HashMap<String, HashMap<String, INDArray>>
         _unpackInferenceResponse(
             InferenceResponse resp,
             Iterable<String> fetch,
@@ -192,7 +213,7 @@ public class Client {
                 resp, fetch, fetchTypes_, lodTensorSet_, need_variant_tag);
     }
 
-    private static Map<String, ? extends Map<String, INDArray>>
+    private static HashMap<String, HashMap<String, INDArray>>
         _staticUnpackInferenceResponse(
             InferenceResponse resp,
             Iterable<String> fetch,
@@ -203,29 +224,49 @@ public class Client {
             return null;
         }
         String tag = resp.getTag();
-        Map<String, ? extends Map<String, INDArray>> multi_result_map
+        HashMap<String, HashMap<String, INDArray>> multi_result_map
             = new HashMap<String, HashMap<String, INDArray>>();
         for (ModelOutput model_result: resp.getOutputsList()) {
             FetchInst inst = model_result.getInsts(0);
-            Map<String, INDArray> result_map
+            HashMap<String, INDArray> result_map
                 = new HashMap<String, INDArray>();
             int index = 0;
             for (String name: fetch) {
                 Tensor variable = inst.getTensorArray(index);
                 int v_type = fetchTypes.get(name);
                 if (v_type == 0) { // int64
-                    result_map.put(name, variable.getInt64DataList());
+                    List<Long> list = variable.getInt64DataList();
+                    long[] array = new long[list.size()];
+                    for (int i = 0; i < list.size(); i++) {
+                        array[i] = list.get(i);
+                    }
+                    result_map.put(name, Nd4j.create(array));
                 } else if (v_type == 1) { // float32
-                    result_map.put(name, variable.getFloatDataList());
+                    List<Float> list = variable.getFloatDataList();
+                    float[] array = new float[list.size()];
+                    for (int i = 0; i < list.size(); i++) {
+                        array[i] = list.get(i);
+                    }
+                    result_map.put(name, Nd4j.create(array));
                 } else if (v_type == 2) { // int32
-                    result_map.put(name, variable.getIntDataList());
+                    List<Integer> list = variable.getIntDataList();
+                    int[] array = new int[list.size()];
+                    for (int i = 0; i < list.size(); i++) {
+                        array[i] = list.get(i);
+                    }
+                    result_map.put(name, Nd4j.create(array));
                 } else {
                     throw new IllegalArgumentException("error tensor value type.");
                 }
                 // TODO: shape
 
                 if (lodTensorSet.contains(name)) {
-                    result_map.put(name + ".lod", variable.getLodList());
+                    List<Integer> list = variable.getLodList();
+                    int[] array = new int[list.size()];
+                    for (int i = 0; i < list.size(); i++) {
+                        array[i] = list.get(i);
+                    }
+                    result_map.put(name + ".lod", Nd4j.create(array));
                 }
                 index += 1;
             }
@@ -235,8 +276,8 @@ public class Client {
         return multi_result_map;
     }
 
-    public Map<String, ? extends Map<String, INDArray>> predict(
-            Map<String, INDArray> feed,
+    public Map<String, HashMap<String, INDArray>> predict(
+            HashMap<String, INDArray> feed,
             Iterable<String> fetch) {
         return predict(feed, fetch, false);
     }
@@ -247,12 +288,12 @@ public class Client {
         return async_predict(feed, fetch, false);
     }
 */
-    public Map<String, ? extends Map<String, INDArray>> predict(
-            Map<String, INDArray> feed,
+    public Map<String, HashMap<String, INDArray>> predict(
+            HashMap<String, INDArray> feed,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
-        List<? extends Map<String, INDArray>> feed_batch
-            = new ArrayList<? extends Map<String, INDArray>>();
+        List<HashMap<String, INDArray>> feed_batch
+            = new ArrayList<HashMap<String, INDArray>>();
         feed_batch.add(feed);
         return predict(feed_batch, fetch, need_variant_tag);
     }
@@ -267,8 +308,8 @@ public class Client {
         return async_predict(feed_batch, fetch, need_variant_tag);
     }
 */
-    public Map<String, ? extends Map<String, INDArray>> predict(
-            List<? extends Map<String, INDArray>> feed_batch,
+    public Map<String, HashMap<String, INDArray>> predict(
+            List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch) {
         return predict(feed_batch, fetch, false);
     }
@@ -279,8 +320,8 @@ public class Client {
         return async_predict(feed_batch, fetch, false);
     }
 */
-    public Map<String, ? extends Map<String, INDArray>> predict(
-            List<? extends Map<String, INDArray>> feed_batch,
+    public Map<String, HashMap<String, INDArray>> predict(
+            List<HashMap<String, INDArray>> feed_batch,
             Iterable<String> fetch,
             Boolean need_variant_tag) {
         InferenceRequest req = _packInferenceRequest(feed_batch, fetch);
@@ -317,16 +358,22 @@ public class Client {
         System.out.println(npdata);
 
         Client client = new Client();
-        List<String> endpoints = new ArrayList<String>()
-            .add("182.61.111.54:9393");
-        Client.connect(endpoints);
+        List<String> endpoints = Arrays.asList("localhost:9393");
+        boolean succ = client.connect(endpoints);
+        if (succ != true) {
+            System.out.println("connect failed.");
+            return;
+        }
+        HashMap<String, INDArray> feed_data
+            = new HashMap<String, INDArray>() {{
+                put("x", npdata);
+        }};
         List<HashMap<String, INDArray>> feed_batch
-            = new ArrayList<HashMap<String, INDArray>>()
-            .add(new HashMap<String, INDArray>()
-                    .put("x", npdata));
-        List<String> fetch = new ArrayList<String>()
-            .add("price");
-        Map<String, ? extends Map<String, INDArray>> fetch_map
+            = new ArrayList<HashMap<String, INDArray>>() {{
+                add(feed_data);
+        }};
+        List<String> fetch = Arrays.asList("price");
+        Map<String, HashMap<String, INDArray>> fetch_map
             = client.predict(feed_batch, fetch);
         System.out.println( "Hello World!" );
     }