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未验证 提交 d6952087 编写于 作者: L Liubov Batanina 提交者： GitHub
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Merge pull request #17967 from l-bat:non_const_weights_for_conv 

* Supported convolution with non-const weights

* Fix opencl blobs

* Update tests
上级 65b02cc8
隐藏空白更改
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Showing with 178 addition and 53 deletion
modules/dnn/src/layers/convolution_layer.cpp
浏览文件 @ d6952087
......@@ -106,18 +106,19 @@ public:
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
CV_Assert(inputs.size() > 0);
CV_Assert((inputs.size() > outputs.size() && blobs.empty()) ||
(!inputs.empty() && (blobs.size() == 1 || blobs.size() == 2)));
MatSize weightShape = blobs.empty() ? inputs[1].size : blobs[0].size;
CV_Assert(blobs.size() == 1 || blobs.size() == 2);
CV_Assert(inputs[0].dims == outputs[0].dims);
CV_Assert(blobs[0].dims == kernel_size.size() + 2);
CV_Assert(weightShape.dims() == kernel_size.size() + 2);
for (int i = 0; i < kernel_size.size(); i++) {
CV_Assert(blobs[0].size[i + 2] == kernel_size[i]);
CV_Assert(weightShape[i + 2] == kernel_size[i]);
}
const Mat &input = inputs[0];
CV_Assert((input.dims == 4 || input.dims == 5) && (input.type() == CV_32F || input.type() == CV_16S));
for (size_t i = 0; i < inputs.size(); i++)
for (size_t i = 0; i < outputs.size(); i++)
{
CV_Assert(inputs[i].type() == input.type());
CV_Assert((inputs[i].dims == 4 || inputs[i].dims == 5) && inputs[i].size[1] == input.size[1]);
......@@ -245,6 +246,7 @@ public:
MatShape computeColRowShape(const MatShape &inpShape, const MatShape &outShape) const CV_OVERRIDE
{
CV_Assert(!blobs.empty());
int dims = inpShape.size();
int inpD = dims == 5 ? inpShape[2] : 1;
int inpH = inpShape[dims - 2];
......@@ -262,12 +264,14 @@ public:
{
if (kernel_size.size() == 3)
return preferableTarget == DNN_TARGET_CPU;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableTarget != DNN_TARGET_MYRIAD) && blobs.empty())
return false;
return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
}
else
#endif
return (kernel_size.size() == 3 && preferableTarget == DNN_TARGET_CPU && backendId == DNN_BACKEND_OPENCV) ||
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
(kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || (backendId == DNN_BACKEND_HALIDE && !blobs.empty())));
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
......@@ -275,16 +279,16 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(blobs.size() != 0);
CV_Assert(!hasBias() || blobs[1].total() == (size_t)blobs[0].size[0]);
CV_Assert(inputs.size() == (size_t)1);
CV_Assert(!blobs.empty() || inputs.size() > 1);
const int* weightShape = blobs.empty() ? &inputs[1][0] : blobs[0].size.p;
CV_Assert(!hasBias() || blobs[1].total() == (size_t)weightShape[0]);
internals.clear();
CV_Assert(inputs.size() != 0);
std::vector<int> inpShape(inputs[0].begin() + 2, inputs[0].end());
int outCn = blobs[0].size[0];
int outCn = weightShape[0];
std::vector<int> outShape;
outShape.push_back(inputs[0][0]);
outShape.push_back(outCn);
......@@ -300,10 +304,10 @@ public:
getConvPoolOutParams(inpShape, kernel_size, strides, padMode, dilations, outShape);
}
int ngroups = inpCn / blobs[0].size[1];
if (ngroups == 0 || ngroups * blobs[0].size[1] != inpCn)
int ngroups = inpCn / weightShape[1];
if (ngroups == 0 || ngroups * weightShape[1] != inpCn)
CV_Error(Error::StsError, format("Number of input channels should "
"be multiple of %d but got %d", blobs[0].size[1], inpCn));
"be multiple of %d but got %d", weightShape[1], inpCn));
CV_Assert(ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0);
outputs.resize(1, outShape);
......@@ -315,15 +319,15 @@ public:
{
BaseConvolutionLayerImpl::finalize(inputs_arr, outputs_arr);
CV_Assert(!blobs.empty());
const int outCn = blobs[0].size[0];
std::vector<Mat> inputs;
inputs_arr.getMatVector(inputs);
// prepare weightsMat where each row is aligned and has enough zero padding on the right to
// use vectorized (i.e. with intrinsics) loops without tail processing
Mat wm = blobs[0].reshape(1, outCn);
Mat wm = blobs.empty() ? inputs[1].reshape(1, numOutput) : blobs[0].reshape(1, numOutput);
if( wm.step1() % VEC_ALIGN != 0 )
{
int newcols = (int)alignSize(wm.step1(), VEC_ALIGN);
Mat wm_buffer = Mat(outCn, newcols, wm.type());
Mat wm_buffer = Mat(numOutput, newcols, wm.type());
Mat wm_padding = wm_buffer.colRange(wm.cols, newcols);
wm_padding.setTo(Scalar::all(0.));
Mat wm_aligned = wm_buffer.colRange(0, wm.cols);
......@@ -331,18 +335,18 @@ public:
wm = wm_aligned;
}
weightsMat = wm;
weightsMultipliers.assign(outCn, 1.0);
weightsMultipliers.assign(numOutput, 1.0);
Mat biasMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat();
biasvec.resize(outCn+2);
Mat biasMat = hasBias() ? blobs[1].reshape(1, numOutput) : Mat();
biasvec.resize(numOutput+2);
if( biasMat.empty() )
{
for(int i = 0; i < outCn; i++ )
for(int i = 0; i < numOutput; i++ )
biasvec[i] = 0.f;
}
else
{
for(int i = 0; i < outCn; i++ )
for(int i = 0; i < numOutput; i++ )
biasvec[i] = biasMat.at<float>(i);
}
#ifdef HAVE_OPENCL
......@@ -352,7 +356,7 @@ public:
bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
if (!activ.empty() && !layer.empty())
if ((!activ.empty() && !layer.empty()) || blobs.empty())
return false;
activ = layer;
......@@ -537,37 +541,48 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
CV_Assert_N(inputs.size() == 1, nodes.size() == 1);
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
CV_Assert(dims.size() == 4 || dims.size() == 5);
std::shared_ptr<ngraph::Node> ieWeights = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
const int inpCn = dims[1];
const int outCn = blobs[0].size[0];
const int inpGroupCn = blobs[0].size[1];
const int inpGroupCn = nodes.size() > 1 ? ieWeights->get_shape()[1] : blobs[0].size[1];
const int group = inpCn / inpGroupCn;
std::vector<size_t> kernel_shape = getShape<size_t>(blobs[0]);
std::vector<size_t> kernel_shape;
if (group != 1)
{
kernel_shape[0] /= group;
kernel_shape.insert(kernel_shape.begin(), group);
kernel_shape.push_back(group);
}
kernel_shape.push_back(numOutput / group);
kernel_shape.push_back(inpCn / group);
std::copy(kernel_size.begin(), kernel_size.end(), back_inserter(kernel_shape));
auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
if (nodes.size() == 1)
{
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
}
else
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, blobs[0].data);
if (fusedWeights)
{
Mat newWeights;
Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / outCn);
cvWeights.copyTo(newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
if (weightsMat.isContinuous())
{
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, weightsMat.data);
}
else
{
Mat newWeights;
Mat cvWeights = weightsMat.colRange(0, blobs[0].total() / numOutput);
cvWeights.copyTo(newWeights);
ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, kernel_shape, newWeights.data);
}
}
}
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{kernel_shape.size()}, kernel_shape.data());
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
}
ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT;
if (!padMode.empty())
......@@ -592,11 +607,21 @@ public:
pad_type);
}
if (hasBias() || fusedBias)
if (hasBias() || fusedBias || nodes.size() == 3)
{
std::vector<size_t> shape(conv_node->get_shape().size(), 1);
shape[1] = outCn;
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), biasvec.data());
shape[1] = conv_node->get_shape()[1];
std::shared_ptr<ngraph::Node> bias;
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
ngraph::Shape{shape.size()}, shape.data());
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
}
else
{
bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), biasvec.data());
}
auto conv_bias = std::make_shared<ngraph::op::v1::Add>(conv_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(conv_bias));
}
......@@ -1103,6 +1128,26 @@ public:
for (int i = 0; i < inputs.size(); ++i)
CV_Assert(inputs[i].u != outputs[0].u);
if (blobs.empty())
{
size_t n = inputs.size() - 1;
umat_blobs.resize(n);
for (size_t i = 0; i < n; i++)
{
if (use_half)
{
Mat matFP32;
convertFp16(inputs[i + 1], matFP32);
matFP32.copyTo(umat_blobs[i]);
}
else
{
inputs[i + 1].copyTo(umat_blobs[i]);
}
}
inputs.resize(1);
}
if (umat_blobs.empty())
{
size_t n = blobs.size();
......@@ -1113,7 +1158,7 @@ public:
}
}
if (convolutionOp.empty())
if (convolutionOp.empty() || blobs.empty())
{
OCL4DNNConvConfig config;
config.in_shape = shape(inputs[0]);
......@@ -1123,7 +1168,7 @@ public:
config.stride = stride;
config.dilation = dilation;
config.group = inputs[0].size[1] / umat_blobs[0].size[1];
config.bias_term = (hasBias()) ? true : false;
config.bias_term = umat_blobs.size() == 2;
config.use_half = use_half;
convolutionOp = Ptr<OCL4DNNConvSpatial<float> >(new OCL4DNNConvSpatial<float>(config));
......@@ -1250,16 +1295,37 @@ public:
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
int outCn = blobs.empty() ? inputs[1].size[0] : blobs[0].size[0];
// Need to align non-const blobs
if (blobs.empty())
{
Mat wm = inputs[1].reshape(1, outCn);
if( wm.step1() % VEC_ALIGN != 0 )
{
wm.copyTo(weightsMat);
if (inputs.size() > 2)
{
Mat biasMat = inputs[2].reshape(1, outCn);
biasMat.col(0).copyTo(biasvec);
biasvec.resize(outCn + 2);
}
else
{
biasvec.resize(outCn + 2, 0);
}
}
}
/*printf("conv %s: input (%d x %d x %d x %d), kernel (%d x %d), pad (%d x %d), stride (%d x %d), dilation (%d x %d)\n",
name.c_str(), inputs[0].size[0], inputs[0].size[1], inputs[0].size[2], inputs[0].size[3],
kernel.width, kernel.height, pad.width, pad.height,
stride.width, stride.height, dilation.width, dilation.height);*/
CV_Assert_N(inputs.size() == (size_t)1, inputs[0].size[1] % blobs[0].size[1] == 0,
int inpGroupCn = blobs.empty() ? inputs[1].size[1] : blobs[0].size[1];
CV_Assert_N(inputs.size() >= (size_t)1, inputs[0].size[1] % inpGroupCn == 0,
outputs.size() == 1, inputs[0].data != outputs[0].data);
int ngroups = inputs[0].size[1]/blobs[0].size[1];
int ngroups = inputs[0].size[1] / inpGroupCn;
CV_Assert(outputs[0].size[1] % ngroups == 0);
int outCn = blobs[0].size[0];
reluslope.clear();
if( activ )
......@@ -1328,11 +1394,11 @@ public:
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_Assert(inputs.size() == outputs.size());
CV_Assert(inputs.size() == outputs.size() || inputs.size() == outputs.size() + blobs.size());
int64 flops = 0;
int karea = std::accumulate(kernel_size.begin(), kernel_size.end(), 1, std::multiplies<size_t>());
for (int i = 0; i < inputs.size(); i++)
for (int i = 0; i < outputs.size(); i++)
{
flops += total(outputs[i])*(CV_BIG_INT(2)*karea*inputs[i][1] + 1);
}
......
modules/dnn/src/onnx/onnx_importer.cpp
浏览文件 @ d6952087
......@@ -1003,10 +1003,13 @@ void ONNXImporter::populateNet(Net dstNet)
CV_Assert(node_proto.input_size() >= 2);
layerParams.type = "Convolution";
for (int j = 1; j < node_proto.input_size(); j++) {
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
if (constBlobs.find(node_proto.input(j)) != constBlobs.end())
{
layerParams.blobs.push_back(getBlob(node_proto, constBlobs, j));
}
}
layerParams.set("num_output", layerParams.blobs[0].size[0]);
layerParams.set("bias_term", node_proto.input_size() == 3);
int outCn = layerParams.blobs.empty() ? outShapes[node_proto.input(1)][0] : layerParams.blobs[0].size[0];
layerParams.set("num_output", outCn);
}
else if (layer_type == "ConvTranspose")
{
......
modules/dnn/test/test_onnx_importer.cpp
浏览文件 @ d6952087
......@@ -111,6 +111,62 @@ TEST_P(Test_ONNX_layers, Convolution)
testONNXModels("convolution");
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String basename = "conv_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Gather)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
......
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