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Merge pull request #11141 from dkurt:dnn_no_aspect_ratios

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This commit is contained in:
Alexander Alekhin 2018-03-24 20:38:55 +00:00
commit e06d1e8083
4 changed files with 73 additions and 13 deletions
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22
modules/dnn/src/caffe/caffe_importer.cpp
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@ -335,6 +335,28 @@ public:
}
continue;
}
else if (type == "BatchNorm")
{
if (!layerParams.get<bool>("use_global_stats", true))
{
CV_Assert(layer.bottom_size() == 1, layer.top_size() == 1);
LayerParams mvnParams;
mvnParams.set("eps", layerParams.get<float>("eps", 1e-5));
std::string mvnName = name + "/mvn";
int repetitions = layerCounter[mvnName]++;
if (repetitions)
mvnName += String("_") + toString(repetitions);
int mvnId = dstNet.addLayer(mvnName, "MVN", mvnParams);
addInput(layer.bottom(0), mvnId, 0, dstNet);
addOutput(layer, mvnId, 0);
net.mutable_layer(li)->set_bottom(0, layer.top(0));
layerParams.blobs[0].setTo(0); // mean
layerParams.blobs[1].setTo(1); // std
}
}
int id = dstNet.addLayer(name, type, layerParams);

8
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -36,6 +36,7 @@ public:
hasWeights = params.get<bool>("has_weight", false);
hasBias = params.get<bool>("has_bias", false);
useGlobalStats = params.get<bool>("use_global_stats", true);
if(params.get<bool>("scale_bias", false))
hasWeights = hasBias = true;
epsilon = params.get<float>("eps", 1E-5);
@ -46,7 +47,7 @@ public:
blobs[0].type() == CV_32F && blobs[1].type() == CV_32F);
float varMeanScale = 1.f;
if (!hasWeights && !hasBias && blobs.size() > 2) {
if (!hasWeights && !hasBias && blobs.size() > 2 && useGlobalStats) {
CV_Assert(blobs.size() == 3, blobs[2].type() == CV_32F);
varMeanScale = blobs[2].at<float>(0);
if (varMeanScale != 0)
@ -100,6 +101,8 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const
{
if (!useGlobalStats && inputs[0][0] != 1)
CV_Error(Error::StsNotImplemented, "Batch normalization in training mode with batch size > 1");
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
return true;
}
@ -304,6 +307,9 @@ public:
}
return flops;
}
private:
bool useGlobalStats;
};
Ptr<BatchNormLayer> BatchNormLayer::create(const LayerParams& params)

22
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -109,15 +109,11 @@ public:
for (int i = 0; i < aspectRatioParameter.size(); ++i)
{
float aspectRatio = aspectRatioParameter.get<float>(i);
bool alreadyExists = false;
bool alreadyExists = fabs(aspectRatio - 1.f) < 1e-6f;
for (size_t j = 0; j < _aspectRatios.size(); ++j)
for (size_t j = 0; j < _aspectRatios.size() && !alreadyExists; ++j)
{
if (fabs(aspectRatio - _aspectRatios[j]) < 1e-6)
{
alreadyExists = true;
break;
}
alreadyExists = fabs(aspectRatio - _aspectRatios[j]) < 1e-6;
}
if (!alreadyExists)
{
@ -215,7 +211,7 @@ public:
}
else
{
CV_Assert(!_aspectRatios.empty(), _minSize > 0);
CV_Assert(_minSize > 0);
_boxWidths.resize(1 + (_maxSize > 0 ? 1 : 0) + _aspectRatios.size());
_boxHeights.resize(_boxWidths.size());
_boxWidths[0] = _boxHeights[0] = _minSize;
@ -492,10 +488,12 @@ public:
ieLayer->params["min_size"] = format("%f", _minSize);
ieLayer->params["max_size"] = _maxSize > 0 ? format("%f", _maxSize) : "";
CV_Assert(!_aspectRatios.empty());
ieLayer->params["aspect_ratio"] = format("%f", _aspectRatios[0]);
for (int i = 1; i < _aspectRatios.size(); ++i)
ieLayer->params["aspect_ratio"] += format(",%f", _aspectRatios[i]);
if (!_aspectRatios.empty())
{
ieLayer->params["aspect_ratio"] = format("%f", _aspectRatios[0]);
for (int i = 1; i < _aspectRatios.size(); ++i)
ieLayer->params["aspect_ratio"] += format(",%f", _aspectRatios[i]);
}
ieLayer->params["flip"] = _flip ? "1" : "0";
ieLayer->params["clip"] = _clip ? "1" : "0";

34
modules/dnn/test/test_layers.cpp
View File

@ -252,6 +252,11 @@ TEST(Layer_Test_BatchNorm, Accuracy)
testLayerUsingCaffeModels("layer_batch_norm", DNN_TARGET_CPU, true);
}
TEST(Layer_Test_BatchNorm, local_stats)
{
testLayerUsingCaffeModels("layer_batch_norm_local_stats", DNN_TARGET_CPU, true, false);
}
TEST(Layer_Test_ReLU, Accuracy)
{
testLayerUsingCaffeModels("layer_relu");
@ -831,4 +836,33 @@ TEST(Layer_Test_Average_pooling_kernel_area, Accuracy)
normAssert(out, blobFromImage(target));
}
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
TEST(Layer_PriorBox, squares)
{
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
lp.set("min_size", 32);
lp.set("flip", true);
lp.set("clip", true);
float variance[] = {0.1f, 0.1f, 0.2f, 0.2f};
float aspectRatios[] = {1.0f}; // That should be ignored.
lp.set("variance", DictValue::arrayReal<float*>(&variance[0], 4));
lp.set("aspect_ratio", DictValue::arrayReal<float*>(&aspectRatios[0], 1));
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 1); // The second input is an input image. Shapes are used for boxes normalization.
Mat inp(1, 2, CV_32F);
randu(inp, -1, 1);
net.setInput(blobFromImage(inp));
Mat out = net.forward();
Mat target = (Mat_<float>(4, 4) << -7.75f, -15.5f, 8.25f, 16.5f,
-7.25f, -15.5f, 8.75f, 16.5f,
0.1f, 0.1f, 0.2f, 0.2f,
0.1f, 0.1f, 0.2f, 0.2f);
normAssert(out.reshape(1, 4), target);
}
}} // namespace