Add quantized mobilenet for android demo

mace/examples/android/README.md

@@ -12,3 +12,7 @@ cd mace/exampls/android
 Pre-built APK
 --------------
 Pre-built Android APK can be downloaded [here](https://cnbj1.fds.api.xiaomi.com/mace/demo/mace_android_demo.apk).
+
+Note
+--------------
+We use two big cores for CPU inference.

mace/examples/android/app/src/main/java/com/xiaomi/mace/demo/CameraActivity.java

@@ -180,11 +180,23 @@ public class CameraActivity extends Activity implements View.OnClickListener, Ap
             public void onCLickItem(String content) {
                 mSelectMode.setText(content);
                 initData.setModel(content);
+                handleOnlyCpuSupportByModel(content);
                 AppModel.instance.maceMobilenetCreateEngine(initData, CameraActivity.this);
             }
         });
     }
 
+    private void handleOnlyCpuSupportByModel(String model) {
+        if (InitData.isOnlySupportCpuByModel(model)) {
+            String device = InitData.getCpuDevice();
+            mSelectPhoneType.setText(device);
+            mSelectPhoneType.setEnabled(false);
+            initData.setDevice(device);
+        } else {
+            mSelectPhoneType.setEnabled(true);
+        }
+    }
+
     @Override
     public void onCreateEngineFail(final boolean quit) {
         AlertDialog.Builder builder = new AlertDialog.Builder(this);

mace/examples/android/app/src/main/java/com/xiaomi/mace/demo/result/InitData.java

@@ -21,7 +21,8 @@ import java.io.File;
 public class InitData {
 
     public static final String[] DEVICES = new String[]{"CPU", "GPU"};
-    public static final String[] MODELS = new String[]{"mobilenet_v1", "mobilenet_v2"};
+    public static final String[] MODELS = new String[]{"mobilenet_v1", "mobilenet_v2", "mobilenet_v1_quant", "mobilenet_v2_quant"};
+    private static final String[] ONLY_CPU_MODELS = new String[]{"mobilenet_v1_quant", "mobilenet_v2_quant"};
 
     private String model;
     private String device = "";
@@ -34,7 +35,7 @@ public class InitData {
     public InitData() {
         model = MODELS[0];
         ompNumThreads = 2;
-        cpuAffinityPolicy = 0;
+        cpuAffinityPolicy = 1;
         gpuPerfHint = 3;
         gpuPriorityHint = 3;
         device = DEVICES[0];
@@ -101,4 +102,17 @@ public class InitData {
     public void setStoragePath(String storagePath) {
         this.storagePath = storagePath;
     }
+
+    public static String getCpuDevice() {
+        return DEVICES[0];
+    }
+
+    public static boolean isOnlySupportCpuByModel(String model) {
+        for (String m : ONLY_CPU_MODELS) {
+            if (m.equals(model)) {
+                return true;
+            }
+        }
+        return false;
+    }
 }

mace/examples/android/macelibrary/src/main/cpp/image_classify.cc

@@ -46,6 +46,10 @@ struct MaceContext {
       {"mobilenet_v1", {"input", "MobilenetV1/Predictions/Reshape_1",
                             {1, 224, 224, 3}, {1, 1001}}},
       {"mobilenet_v2", {"input", "MobilenetV2/Predictions/Reshape_1",
+                            {1, 224, 224, 3}, {1, 1001}}},
+      {"mobilenet_v1_quant", {"input", "MobilenetV1/Predictions/Softmax:0",
+                                  {1, 224, 224, 3}, {1, 1001}}},
+      {"mobilenet_v2_quant", {"input", "output",
                                   {1, 224, 224, 3}, {1, 1001}}}
   };
 };
@@ -107,7 +111,8 @@ Java_com_xiaomi_mace_JniMaceUtils_maceMobilenetCreateEngine(
   mace::MaceEngineConfig config(mace_context.device_type);
   status = config.SetCPUThreadPolicy(
       omp_num_threads,
-      static_cast<mace::CPUAffinityPolicy>(cpu_affinity_policy));
+      static_cast<mace::CPUAffinityPolicy>(cpu_affinity_policy),
+      true);
   if (status != mace::MACE_SUCCESS) {
     __android_log_print(ANDROID_LOG_ERROR,
                         "image_classify attrs",

mace/examples/android/mobilenet.yml

@@ -39,3 +39,45 @@ models:
     nnlib_graph_mode: 0
     obfuscate: 0
     winograd: 0
+  mobilenet_v1_quant:
+    platform: tensorflow
+    model_file_path: http://cnbj1.fds.api.xiaomi.com/mace/miai-models/mobilenet-v1/mobilenet_v1_1.0_224_quant_retrain.pb
+    model_sha256_checksum: c3faa9bd790310eb3dec411d2f2e2dd8802394e31ccb47daaa5e3b6369fadfa3
+    subgraphs:
+      - input_tensors:
+          - input
+        input_shapes:
+          - 1,224,224,3
+        input_ranges:
+          - -1.0,1.0
+        output_tensors:
+          - MobilenetV1/Predictions/Softmax:0
+        output_shapes:
+          - 1,1001
+    runtime: cpu
+    limit_opencl_kernel_time: 0
+    nnlib_graph_mode: 0
+    obfuscate: 0
+    winograd: 0
+    quantize: 1
+  mobilenet_v2_quant:
+    platform: tensorflow
+    model_file_path: https://cnbj1.fds.api.xiaomi.com/mace/miai-models/mobilenet-v2/mobilenet_v2_1.0_224_quant_retrain.pb
+    model_sha256_checksum: 1429c8e2a91b251c9fa0dae43dfa5817c5a798bd30284f8c0690e7569a8817f1
+    subgraphs:
+      - input_tensors:
+          - input
+        input_shapes:
+          - 1,224,224,3
+        input_ranges:
+          - -1.0,1.0
+        output_tensors:
+          - output
+        output_shapes:
+          - 1,1001
+    runtime: cpu
+    limit_opencl_kernel_time: 0
+    nnlib_graph_mode: 0
+    obfuscate: 0
+    winograd: 0
+    quantize: 1