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Merge pull request #21036 from fengyuentau:timvx_backend_support

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dnn: TIM-VX NPU backend support

* Add TimVX NPU backend for DNN module.

* use official branch from tim-vx repo; fix detecting viv sdk

Co-authored-by: fytao <yuantao.feng@outlook.com>
This commit is contained in:
Zihao Mu
2022-04-01 05:42:11 +08:00
committed by GitHub
parent 9390c56831
commit 7b582b71ba
37 changed files with 2982 additions and 30 deletions
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modules/dnn/test/test_int8_layers.cpp
+68 -12
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@@ -12,6 +12,9 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
{
std::vector< tuple<Backend, Target> > targets;
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
#ifdef HAVE_TIMVX
targets.push_back(make_tuple(DNN_BACKEND_TIMVX, DNN_TARGET_NPU));
#endif
return testing::ValuesIn(targets);
}
@@ -104,14 +107,29 @@ TEST_P(Test_Int8_layers, Convolution1D)
TEST_P(Test_Int8_layers, Convolution2D)
{
testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true);
testLayer("single_conv", "TensorFlow", 0.00413, 0.02201);
testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169);
if(backend == DNN_BACKEND_TIMVX)
testLayer("single_conv", "TensorFlow", 0.00424, 0.02201);
else
testLayer("single_conv", "TensorFlow", 0.00413, 0.02201);
testLayer("atrous_conv2d_valid", "TensorFlow", 0.0193, 0.0633);
testLayer("atrous_conv2d_same", "TensorFlow", 0.0185, 0.1322);
testLayer("keras_atrous_conv2d_same", "TensorFlow", 0.0056, 0.0244);
testLayer("convolution", "ONNX", 0.0052, 0.01516);
testLayer("two_convolution", "ONNX", 0.00295, 0.00840);
if(backend == DNN_BACKEND_TIMVX)
testLayer("convolution", "ONNX", 0.00534, 0.01516);
else
testLayer("convolution", "ONNX", 0.0052, 0.01516);
if(backend == DNN_BACKEND_TIMVX)
testLayer("two_convolution", "ONNX", 0.0033, 0.01);
else
testLayer("two_convolution", "ONNX", 0.00295, 0.00840);
if(backend == DNN_BACKEND_TIMVX)
applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true);
testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169);
}
TEST_P(Test_Int8_layers, Convolution3D)
@@ -130,9 +148,21 @@ TEST_P(Test_Int8_layers, Flatten)
TEST_P(Test_Int8_layers, Padding)
{
testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064);
testLayer("padding_same", "TensorFlow", 0.0081, 0.032);
testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028);
if (backend == DNN_BACKEND_TIMVX)
testLayer("padding_valid", "TensorFlow", 0.0292, 0.0105);
else
testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064);
if (backend == DNN_BACKEND_TIMVX)
testLayer("padding_same", "TensorFlow", 0.0085, 0.032);
else
testLayer("padding_same", "TensorFlow", 0.0081, 0.032);
if (backend == DNN_BACKEND_TIMVX)
testLayer("spatial_padding", "TensorFlow", 0.0079, 0.028);
else
testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028);
testLayer("mirror_pad", "TensorFlow", 0.0064, 0.013);
testLayer("pad_and_concat", "TensorFlow", 0.0021, 0.0098);
testLayer("padding", "ONNX", 0.0005, 0.0069);
@@ -283,20 +313,35 @@ TEST_P(Test_Int8_layers, InnerProduct)
{
testLayer("layer_inner_product", "Caffe", 0.005, 0.02, 1, 1, true);
testLayer("matmul", "TensorFlow", 0.0061, 0.019);
testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091);
if (backend == DNN_BACKEND_TIMVX)
testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0018, 0.0175);
else
testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091);
testLayer("nhwc_reshape_matmul", "TensorFlow", 0.03, 0.071);
testLayer("matmul_layout", "TensorFlow", 0.035, 0.06);
testLayer("tf2_dense", "TensorFlow", 0, 0);
testLayer("matmul_add", "ONNX", 0.041, 0.082);
testLayer("linear", "ONNX", 0.0018, 0.0029);
testLayer("constant", "ONNX", 0.00021, 0.0006);
if (backend == DNN_BACKEND_TIMVX)
testLayer("constant", "ONNX", 0.00048, 0.0013);
else
testLayer("constant", "ONNX", 0.00021, 0.0006);
testLayer("lin_with_constant", "ONNX", 0.0011, 0.0016);
}
TEST_P(Test_Int8_layers, Reshape)
{
testLayer("reshape_layer", "TensorFlow", 0.0032, 0.0082);
testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029);
if (backend == DNN_BACKEND_TIMVX)
testLayer("reshape_nchw", "TensorFlow", 0.0092, 0.0495);
else
testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029);
testLayer("reshape_conv", "TensorFlow", 0.035, 0.054);
testLayer("reshape_reduce", "TensorFlow", 0.0042, 0.0078);
testLayer("reshape_as_shape", "TensorFlow", 0.0014, 0.0028);
@@ -307,7 +352,12 @@ TEST_P(Test_Int8_layers, Reshape)
testLayer("flatten_by_prod", "ONNX", 0.0048, 0.0081);
testLayer("squeeze", "ONNX", 0.0048, 0.0081);
testLayer("unsqueeze", "ONNX", 0.0033, 0.0053);
testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154);
if (backend == DNN_BACKEND_TIMVX)
testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.006, 0.0212);
else
testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154);
testLayer("unsqueeze_and_conv_dynamic_axes", "ONNX", 0.0037, 0.0151);
}
@@ -378,6 +428,10 @@ TEST_P(Test_Int8_layers, Dropout)
TEST_P(Test_Int8_layers, Eltwise)
{
testLayer("layer_eltwise", "Caffe", 0.062, 0.15);
if (backend == DNN_BACKEND_TIMVX)
applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
testLayer("conv_2_inps", "Caffe", 0.0086, 0.0232, 2, 1, true, false);
testLayer("eltwise_sub", "TensorFlow", 0.015, 0.047);
testLayer("eltwise_add_vec", "TensorFlow", 0.037, 0.21); // tflite 0.0095, 0.0365
@@ -862,6 +916,8 @@ TEST_P(Test_Int8_nets, EfficientDet)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
if (backend == DNN_BACKEND_TIMVX)
applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
if (target != DNN_TARGET_CPU)
{