Binary data for batch normalization test from Torch

diff --git a/modules/dnn/test/test_torch_importer.cpp b/modules/dnn/test/test_torch_importer.cpp
index d83c203..8c894ae 100644 (file)
--- a/modules/dnn/test/test_torch_importer.cpp
+++ b/modules/dnn/test/test_torch_importer.cpp
@@ -169,7 +169,7 @@ TEST(Torch_Importer, run_deconv)
 
 TEST(Torch_Importer, run_batch_norm)
 {
-    runTorchNet("net_batch_norm");
+    runTorchNet("net_batch_norm", DNN_TARGET_CPU, "", false, true);
 }
 
 TEST(Torch_Importer, net_prelu)

diff --git a/samples/dnn/resnet_ssd_face_python.py b/samples/dnn/resnet_ssd_face_python.py
index 922dd9b..f7d88ec 100644 (file)
--- a/samples/dnn/resnet_ssd_face_python.py
+++ b/samples/dnn/resnet_ssd_face_python.py
@@ -24,7 +24,7 @@ if __name__ == '__main__':
         cols = frame.shape[1]
         rows = frame.shape[0]
 
-        net.setInput(dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (104.0, 177.0, 123.0), False))
+        net.setInput(dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (104.0, 177.0, 123.0), False, False))
         detections = net.forward()
 
         perf_stats = net.getPerfProfile()