Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #18077 from l-bat:reduce_sum

Browse Source 
* Supported ReduceSum op

* Skip test
pull/18084/head
Liubov Batanina 4 years ago committed by GitHub
parent 2b227f00f2
commit f3cebb3e1b
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 72 additions and 9 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 26
      modules/dnn/src/layers/pooling_layer.cpp
  2. 41
      modules/dnn/src/tensorflow/tf_importer.cpp
  3. 2
      modules/dnn/test/test_darknet_importer.cpp
  4. 12
      modules/dnn/test/test_tf_importer.cpp

26
modules/dnn/src/layers/pooling_layer.cpp
Unescape View File

@ -98,6 +98,8 @@ public:
type = AVE; type = AVE;
else if (pool == "stochastic") else if (pool == "stochastic")
type = STOCHASTIC; type = STOCHASTIC;
else if (pool == "sum")
type = SUM;
else else
CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\""); CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
@ -195,7 +197,7 @@ public:
return type == MAX || type == AVE; return type == MAX || type == AVE;
} }
else else
return type != STOCHASTIC; return type != STOCHASTIC && type != SUM;
} }
#endif #endif
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@ -288,7 +290,7 @@ public:
maxPooling(inputs[0], outputs[0], mask); maxPooling(inputs[0], outputs[0], mask);
break; break;
} }
case AVE: case AVE: case SUM:
CV_Assert_N(inputs.size() == 1, outputs.size() == 1); CV_Assert_N(inputs.size() == 1, outputs.size() == 1);
avePooling(inputs[0], outputs[0]); avePooling(inputs[0], outputs[0]);
break; break;
@ -366,7 +368,7 @@ public:
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{ {
CV_Assert_N((inputs.size() == 1 && (type == MAX || type == AVE)) || inputs.size() == 2, nodes.size() == inputs.size()); CV_Assert_N((inputs.size() == 1 && (type == MAX || type == AVE || type == SUM)) || inputs.size() == 2, nodes.size() == inputs.size());
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node; auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT; ngraph::op::PadType pad_type = ngraph::op::PadType::EXPLICIT;
@ -381,6 +383,19 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
exclude_pad, rounding_type, pad_type); exclude_pad, rounding_type, pad_type);
return Ptr<BackendNode>(new InfEngineNgraphNode(ave_pool)); return Ptr<BackendNode>(new InfEngineNgraphNode(ave_pool));
} }
else if (type == SUM) {
ngraph::Shape inpShape = ieInpNode->get_shape();
CV_Assert(inpShape.size() == 2 + kernel_size.size());
std::vector<int64_t> axes;
for (size_t i = 0; i < kernel_size.size(); i++)
{
if (inpShape[2 + i] == kernel_size[i])
axes.push_back(2 + i);
}
auto reduction_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes.size()}, axes);
auto reduce_sum = std::make_shared<ngraph::op::v1::ReduceSum>(ieInpNode, reduction_axes, true);
return Ptr<BackendNode>(new InfEngineNgraphNode(reduce_sum));
}
else if (type == MAX) { else if (type == MAX) {
auto max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides), auto max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size), ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
@ -739,7 +754,7 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
} }
} }
} }
else if (poolingType == AVE) else if (poolingType == AVE || poolingType == SUM)
{ {
for( ; x0 < x1; ++x0) for( ; x0 < x1; ++x0)
{ {
@ -750,7 +765,7 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
xend = min(xend, inp_width); xend = min(xend, inp_width);
float inv_kernel_area = avePoolPaddedArea ? xdelta * ydelta * ddelta : float inv_kernel_area = avePoolPaddedArea ? xdelta * ydelta * ddelta :
((dend - dstart) * (yend - ystart) * (xend - xstart)); ((dend - dstart) * (yend - ystart) * (xend - xstart));
inv_kernel_area = 1.0 / inv_kernel_area; inv_kernel_area = poolingType == AVE ? 1.0 / inv_kernel_area : 1.0;
#if CV_SIMD128 #if CV_SIMD128
if( isPool2D && xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width ) if( isPool2D && xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_l + kernel_w < inp_width )
{ {
@ -1095,6 +1110,7 @@ private:
MAX, MAX,
AVE, AVE,
STOCHASTIC, STOCHASTIC,
SUM,
ROI, // RoI pooling, https://arxiv.org/pdf/1504.08083.pdf ROI, // RoI pooling, https://arxiv.org/pdf/1504.08083.pdf
PSROI // Position-sensitive RoI pooling, https://arxiv.org/pdf/1605.06409.pdf PSROI // Position-sensitive RoI pooling, https://arxiv.org/pdf/1605.06409.pdf
}; };

41
modules/dnn/src/tensorflow/tf_importer.cpp
Unescape View File

@ -2067,7 +2067,7 @@ void TFImporter::populateNet(Net dstNet)
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0); connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 1); connect(layer_id, dstNet, parsePin(layer.input(1)), id, 1);
} }
else if (type == "Mean") else if (type == "Mean" || type == "Sum")
{ {
// Computes the mean of elements across dimensions of a tensor. // Computes the mean of elements across dimensions of a tensor.
// If keepdims is false (default) reduces input_tensor along the dimensions given in axis, // If keepdims is false (default) reduces input_tensor along the dimensions given in axis,
@ -2116,7 +2116,7 @@ void TFImporter::populateNet(Net dstNet)
LayerParams avgLp; LayerParams avgLp;
std::string avgName = name + "/avg"; std::string avgName = name + "/avg";
CV_Assert(layer_id.find(avgName) == layer_id.end()); CV_Assert(layer_id.find(avgName) == layer_id.end());
avgLp.set("pool", "ave"); avgLp.set("pool", type == "Mean" ? "ave" : "sum");
// pooling kernel H x 1 // pooling kernel H x 1
avgLp.set("global_pooling_h", true); avgLp.set("global_pooling_h", true);
avgLp.set("kernel_w", 1); avgLp.set("kernel_w", 1);
@ -2153,11 +2153,44 @@ void TFImporter::populateNet(Net dstNet)
layer_id[name] = id; layer_id[name] = id;
connect(layer_id, dstNet, Pin(avgName), id, 0); connect(layer_id, dstNet, Pin(avgName), id, 0);
connect(layer_id, dstNet, Pin(layerShapeName), id, 1); connect(layer_id, dstNet, Pin(layerShapeName), id, 1);
} else if (indices.total() == 1) {
int axis = toNCHW(indices.at<int>(0));
if (axis == 2 || axis == 3)
{
layerParams.set("pool", type == "Mean" ? "ave" : "sum");
layerParams.set(axis == 2 ? "kernel_w" : "kernel_h", 1);
layerParams.set(axis == 2 ? "global_pooling_h" : "global_pooling_w", true);
int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
if (!keepDims)
{
// To keep correct order after squeeze dims we first need to change layout from NCHW to NHWC
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
permLP.set("order", DictValue::arrayInt<int*>(order, 4));
std::string permName = name + "/nchw";
CV_Assert(layer_id.find(permName) == layer_id.end());
int permId = dstNet.addLayer(permName, "Permute", permLP);
layer_id[permName] = permId;
connect(layer_id, dstNet, Pin(name), permId, 0);
LayerParams squeezeLp;
std::string squeezeName = name + "/squeeze";
CV_Assert(layer_id.find(squeezeName) == layer_id.end());
squeezeLp.set("axis", indices.at<int>(0));
squeezeLp.set("end_axis", indices.at<int>(0) + 1);
int squeezeId = dstNet.addLayer(squeezeName, "Flatten", squeezeLp);
layer_id[squeezeName] = squeezeId;
connect(layer_id, dstNet, Pin(permName), squeezeId, 0);
}
}
} else { } else {
if (indices.total() != 2 || indices.at<int>(0) != 1 || indices.at<int>(1) != 2) if (indices.total() != 2 || indices.at<int>(0) != 1 || indices.at<int>(1) != 2)
CV_Error(Error::StsNotImplemented, "Unsupported mode of reduce_mean operation."); CV_Error(Error::StsNotImplemented, "Unsupported mode of reduce_mean or reduce_sum operation.");
layerParams.set("pool", "ave"); layerParams.set("pool", type == "Mean" ? "ave" : "sum");
layerParams.set("global_pooling", true); layerParams.set("global_pooling", true);
int id = dstNet.addLayer(name, "Pooling", layerParams); int id = dstNet.addLayer(name, "Pooling", layerParams);
layer_id[name] = id; layer_id[name] = id;

2
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -755,6 +755,8 @@ TEST_P(Test_Darknet_layers, connected)
TEST_P(Test_Darknet_layers, relu) TEST_P(Test_Darknet_layers, relu)
{ {
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
testDarknetLayer("relu"); testDarknetLayer("relu");
} }

12
modules/dnn/test/test_tf_importer.cpp
Unescape View File

@ -128,6 +128,13 @@ TEST_P(Test_TensorFlow_layers, reduce_mean)
runTensorFlowNet("global_pool_by_axis"); runTensorFlowNet("global_pool_by_axis");
} }
TEST_P(Test_TensorFlow_layers, reduce_sum)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
runTensorFlowNet("sum_pool_by_axis");
}
TEST_P(Test_TensorFlow_layers, conv_single_conv) TEST_P(Test_TensorFlow_layers, conv_single_conv)
{ {
runTensorFlowNet("single_conv"); runTensorFlowNet("single_conv");
@ -340,6 +347,11 @@ TEST_P(Test_TensorFlow_layers, pooling_reduce_mean)
runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions. runTensorFlowNet("reduce_mean"); // an average pooling over all spatial dimensions.
} }
TEST_P(Test_TensorFlow_layers, pooling_reduce_sum)
{
runTensorFlowNet("reduce_sum"); // a SUM pooling over all spatial dimensions.
}
TEST_P(Test_TensorFlow_layers, max_pool_grad) TEST_P(Test_TensorFlow_layers, max_pool_grad)
{ {
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)

Write Preview
Loading…
Cancel
Save