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Faster-RCNN anf RFCN models on CPU using Intel's Inference Engine backend.

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Enable Torch layers tests with Intel's Inference Engine backend.
This commit is contained in:
Dmitry Kurtaev
2018-07-24 19:12:58 +03:00
parent 236f383969
commit faa6c4e1e1
12 changed files with 309 additions and 159 deletions
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modules/dnn/test/test_torch_importer.cpp
+110 -79
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@@ -69,100 +69,119 @@ TEST(Torch_Importer, simple_read)
ASSERT_FALSE(net.empty());
}
static void runTorchNet(String prefix, int targetId = DNN_TARGET_CPU, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false)
class Test_Torch_layers : public DNNTestLayer
{
String suffix = (isBinary) ? ".dat" : ".txt";
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
net.setInput(inp);
std::vector<Mat> outBlobs;
net.forward(outBlobs, outLayerName);
normAssert(outRef, outBlobs[0]);
if (check2ndBlob)
public:
void runTorchNet(const String& prefix, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false,
double l1 = 0.0, double lInf = 0.0)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2);
}
}
String suffix = (isBinary) ? ".dat" : ".txt";
typedef testing::TestWithParam<Target> Test_Torch_layers;
Mat inp, outRef;
ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
checkBackend(backend, target, &inp, &outRef);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (outLayerName.empty())
outLayerName = net.getLayerNames().back();
net.setInput(inp);
std::vector<Mat> outBlobs;
net.forward(outBlobs, outLayerName);
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
normAssert(outRef, outBlobs[0], "", l1, lInf);
if (check2ndBlob && backend != DNN_BACKEND_INFERENCE_ENGINE)
{
Mat out2 = outBlobs[1];
Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
normAssert(out2, ref2, "", l1, lInf);
}
}
};
TEST_P(Test_Torch_layers, run_convolution)
{
runTorchNet("net_conv", GetParam(), "", false, true);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTorchNet("net_conv", "", false, true);
}
TEST_P(Test_Torch_layers, run_pool_max)
{
runTorchNet("net_pool_max", GetParam(), "", true);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
runTorchNet("net_pool_max", "", true);
}
TEST_P(Test_Torch_layers, run_pool_ave)
{
runTorchNet("net_pool_ave", GetParam());
runTorchNet("net_pool_ave");
}
TEST_P(Test_Torch_layers, run_reshape)
{
int targetId = GetParam();
runTorchNet("net_reshape", targetId);
runTorchNet("net_reshape_batch", targetId);
runTorchNet("net_reshape_single_sample", targetId);
runTorchNet("net_reshape_channels", targetId, "", false, true);
runTorchNet("net_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)
throw SkipTestException("");
runTorchNet("net_reshape_single_sample", "", false, false,
(target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0052 : 0.0);
}
TEST_P(Test_Torch_layers, run_linear)
{
runTorchNet("net_linear_2d", GetParam());
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
runTorchNet("net_linear_2d");
}
TEST_P(Test_Torch_layers, run_concat)
{
int targetId = GetParam();
runTorchNet("net_concat", targetId, "l5_torchMerge");
runTorchNet("net_depth_concat", targetId, "", false, true);
runTorchNet("net_concat", "l5_torchMerge");
runTorchNet("net_depth_concat", "", false, true, 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
}
TEST_P(Test_Torch_layers, run_deconv)
{
runTorchNet("net_deconv", GetParam());
runTorchNet("net_deconv");
}
TEST_P(Test_Torch_layers, run_batch_norm)
{
runTorchNet("net_batch_norm", GetParam(), "", false, true);
runTorchNet("net_batch_norm", "", false, true);
}
TEST_P(Test_Torch_layers, net_prelu)
{
runTorchNet("net_prelu", GetParam());
runTorchNet("net_prelu");
}
TEST_P(Test_Torch_layers, net_cadd_table)
{
runTorchNet("net_cadd_table", GetParam());
runTorchNet("net_cadd_table");
}
TEST_P(Test_Torch_layers, net_softmax)
{
int targetId = GetParam();
runTorchNet("net_softmax", targetId);
runTorchNet("net_softmax_spatial", targetId);
runTorchNet("net_softmax");
runTorchNet("net_softmax_spatial");
}
TEST_P(Test_Torch_layers, net_logsoftmax)
@@ -173,40 +192,55 @@ TEST_P(Test_Torch_layers, net_logsoftmax)
TEST_P(Test_Torch_layers, net_lp_pooling)
{
int targetId = GetParam();
runTorchNet("net_lp_pooling_square", targetId, "", false, true);
runTorchNet("net_lp_pooling_power", targetId, "", false, true);
runTorchNet("net_lp_pooling_square", "", false, true);
runTorchNet("net_lp_pooling_power", "", false, true);
}
TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
runTorchNet("net_conv_gemm_lrn", GetParam(), "", false, true);
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTorchNet("net_conv_gemm_lrn", "", false, true,
target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
}
TEST_P(Test_Torch_layers, net_inception_block)
{
runTorchNet("net_inception_block", GetParam(), "", false, true);
runTorchNet("net_inception_block", "", false, true);
}
TEST_P(Test_Torch_layers, net_normalize)
{
runTorchNet("net_normalize", GetParam(), "", false, true);
runTorchNet("net_normalize", "", false, true);
}
TEST_P(Test_Torch_layers, net_padding)
{
int targetId = GetParam();
runTorchNet("net_padding", targetId, "", false, true);
runTorchNet("net_spatial_zero_padding", targetId, "", false, true);
runTorchNet("net_spatial_reflection_padding", targetId, "", false, true);
runTorchNet("net_padding", "", false, true);
runTorchNet("net_spatial_zero_padding", "", false, true);
runTorchNet("net_spatial_reflection_padding", "", false, true);
}
TEST_P(Test_Torch_layers, net_non_spatial)
{
runTorchNet("net_non_spatial", GetParam(), "", false, true);
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
runTorchNet("net_non_spatial", "", false, true);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, availableDnnTargets());
TEST_P(Test_Torch_layers, run_paralel)
{
if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException("");
runTorchNet("net_parallel", "l5_torchMerge");
}
TEST_P(Test_Torch_layers, net_residual)
{
runTorchNet("net_residual", "", false, true);
}
typedef testing::TestWithParam<Target> Test_Torch_nets;
@@ -313,21 +347,6 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, availableDnnTargets());
// TODO: fix OpenCL and add to the rest of tests
TEST(Torch_Importer, run_paralel)
{
runTorchNet("net_parallel", DNN_TARGET_CPU, "l5_torchMerge");
}
TEST(Torch_Importer, DISABLED_run_paralel)
{
runTorchNet("net_parallel", DNN_TARGET_OPENCL, "l5_torchMerge");
}
TEST(Torch_Importer, net_residual)
{
runTorchNet("net_residual", DNN_TARGET_CPU, "", false, true);
}
// Test a custom layer
// https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest
@@ -374,17 +393,29 @@ public:
}
}
virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
private:
int scale;
};
TEST(Torch_Importer, upsampling_nearest)
TEST_P(Test_Torch_layers, upsampling_nearest)
{
// Test a custom layer.
CV_DNN_REGISTER_LAYER_CLASS(SpatialUpSamplingNearest, SpatialUpSamplingNearestLayer);
runTorchNet("net_spatial_upsampling_nearest", DNN_TARGET_CPU, "", false, true);
try
{
runTorchNet("net_spatial_upsampling_nearest", "", false, true);
}
catch (...)
{
LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
throw;
}
LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
// Test an implemented layer.
runTorchNet("net_spatial_upsampling_nearest", "", false, true);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, dnnBackendsAndTargets());
}