Enable Myriad tests with batch size > 1

modules/dnn/test/test_halide_layers.cpp

@@ -93,8 +93,10 @@ TEST_P(Convolution, Accuracy)
     Backend backendId = get<0>(get<7>(GetParam()));
     Target targetId = get<1>(get<7>(GetParam()));
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
 
     bool skipCheck = false;
     if (cvtest::skipUnstableTests && backendId == DNN_BACKEND_OPENCV &&
@@ -274,7 +276,8 @@ TEST_P(AvePooling, Accuracy)
     Size stride = get<3>(GetParam());
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD &&
+        stride == Size(3, 2) && kernel == Size(3, 3) && outSize != Size(1, 1))
         throw SkipTestException("");
 
     const int inWidth = (outSize.width - 1) * stride.width + kernel.width;

modules/dnn/test/test_layers.cpp

@@ -215,8 +215,10 @@ TEST(Layer_Test_Reshape, Accuracy)
 
 TEST_P(Test_Caffe_layers, BatchNorm)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     testLayerUsingCaffeModels("layer_batch_norm", true);
     testLayerUsingCaffeModels("layer_batch_norm_local_stats", true, false);
 }
@@ -729,8 +731,10 @@ INSTANTIATE_TEST_CASE_P(Layer_Test, Crop, Combine(
 // into the normalization area.
 TEST_P(Test_Caffe_layers, Average_pooling_kernel_area)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     LayerParams lp;
     lp.name = "testAvePool";
     lp.type = "Pooling";

modules/dnn/test/test_precomp.hpp

@@ -111,6 +111,7 @@ public:
            {
                throw SkipTestException("Myriad is not available/disabled in OpenCV");
            }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
            if (inp && ref && inp->size[0] != 1)
            {
                // Myriad plugin supports only batch size 1. Slice a single sample.
@@ -127,6 +128,12 @@ public:
                else
                    throw SkipTestException("Myriad plugin supports only batch size 1");
            }
+#else
+           if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
+               inp->size[0] != 1 && inp->size[0] != ref->size[0])
+               throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
+
+#endif
        }
    }
 

modules/dnn/test/test_tf_importer.cpp

@@ -144,8 +144,10 @@ TEST_P(Test_TensorFlow_layers, eltwise_add_mul)
 
 TEST_P(Test_TensorFlow_layers, pad_and_concat)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     runTensorFlowNet("pad_and_concat");
 }
 
@@ -180,8 +182,10 @@ TEST_P(Test_TensorFlow_layers, pooling)
 // TODO: fix tests and replace to pooling
 TEST_P(Test_TensorFlow_layers, ave_pool_same)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     runTensorFlowNet("ave_pool_same");
 }
 
@@ -218,9 +222,16 @@ TEST_P(Test_TensorFlow_layers, reshape)
 TEST_P(Test_TensorFlow_layers, flatten)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
-        (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD))
         throw SkipTestException("");
     runTensorFlowNet("flatten", true);
+}
+
+TEST_P(Test_TensorFlow_layers, unfused_flatten)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
+        (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
     runTensorFlowNet("unfused_flatten");
     runTensorFlowNet("unfused_flatten_unknown_batch");
 }
@@ -500,8 +511,10 @@ TEST_P(Test_TensorFlow_layers, fp16_pad_and_concat)
 {
     const float l1 = 0.00071;
     const float lInf = 0.012;
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+#endif
     runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
 }
 

modules/dnn/test/test_torch_importer.cpp

@@ -111,10 +111,10 @@ public:
 
 TEST_P(Test_Torch_layers, run_convolution)
 {
-    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
+    // Output reference values are in range [23.4018, 72.0181]
-        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
+    double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.08 : default_l1;
-        throw SkipTestException("");
+    double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.42 : default_lInf;
-    runTorchNet("net_conv", "", false, true);
+    runTorchNet("net_conv", "", false, true, l1, lInf);
 }
 
 TEST_P(Test_Torch_layers, run_pool_max)
@@ -129,19 +129,23 @@ TEST_P(Test_Torch_layers, run_pool_ave)
     runTorchNet("net_pool_ave");
 }
 
-TEST_P(Test_Torch_layers, run_reshape)
+TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
 {
     runTorchNet("net_reshape");
+}
+
+TEST_P(Test_Torch_layers, run_reshape)
+{
     runTorchNet("net_reshape_batch");
     runTorchNet("net_reshape_channels", "", false, true);
 }
 
 TEST_P(Test_Torch_layers, run_reshape_single_sample)
 {
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+    // Reference output values in range [14.4586, 18.4492].
-        throw SkipTestException("");
     runTorchNet("net_reshape_single_sample", "", false, false,
-                (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0052 : 0.0);
+                (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0073 : default_l1,
+                (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.025 : default_lInf);
 }
 
 TEST_P(Test_Torch_layers, run_linear)
@@ -154,6 +158,10 @@ TEST_P(Test_Torch_layers, run_linear)
 TEST_P(Test_Torch_layers, run_concat)
 {
     runTorchNet("net_concat", "l5_torchMerge");
+}
+
+TEST_P(Test_Torch_layers, run_depth_concat)
+{
     runTorchNet("net_depth_concat", "", false, true, 0.0,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
 }
@@ -207,6 +215,10 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
 
 TEST_P(Test_Torch_layers, net_inception_block)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018030000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     runTorchNet("net_inception_block", "", false, true);
 }