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Merge pull request #11875 from dkurt:dnn_fix_reshape

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pull/11874/head^2
Vadim Pisarevsky 7 years ago
parent 9a66331984 7ed5d85f25
commit a0baae8a55
5 changed files with 44 additions and 17 deletions
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  1. 11
      modules/dnn/src/layers/reshape_layer.cpp
  2. 38
      modules/dnn/src/tensorflow/tf_importer.cpp
  3. 4
      modules/dnn/src/torch/torch_importer.cpp
  4. 7
      modules/dnn/test/test_layers.cpp
  5. 1
      modules/dnn/test/test_tf_importer.cpp

11
modules/dnn/src/layers/reshape_layer.cpp
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@ -82,17 +82,26 @@ static void computeShapeByReshapeMask(const MatShape &srcShape,
{
if (matched)
{
if (i == 0 || total(srcShape, i, srcRange.end) != maskTotal)
if (total(srcShape, i, srcRange.end) != maskTotal)
{
srcRange.start = i + 1;
break;
}
else if (i == 0)
{
srcRange.start = 0;
break;
}
}
else
{
matched = total(srcShape, i, srcRange.end) == maskTotal;
}
}
while (total(srcShape, srcRange.start, srcRange.end) != maskTotal && srcRange.start > 0)
{
srcRange.start -= 1;
}
CV_Assert(total(srcShape, srcRange.start, srcRange.end) == maskTotal);
}

38
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -262,6 +262,18 @@ static int getDataLayout(const tensorflow::NodeDef& layer)
return DATA_LAYOUT_UNKNOWN;
}
static inline std::string getNodeName(const std::string& tensorName)
{
return tensorName.substr(0, tensorName.rfind(':'));
}
static inline int getDataLayout(const std::string& layerName,
const std::map<String, int>& data_layouts)
{
std::map<String, int>::const_iterator it = data_layouts.find(getNodeName(layerName));
return it != data_layouts.end() ? it->second : DATA_LAYOUT_UNKNOWN;
}
void setStrides(LayerParams &layerParams, const tensorflow::NodeDef &layer)
{
if (hasLayerAttr(layer, "strides"))
@ -604,11 +616,6 @@ static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& cons
}
}
static inline std::string getNodeName(const std::string& tensorName)
{
return tensorName.substr(0, tensorName.rfind(':'));
}
// If all inputs of specific layer have the same data layout we can say that
// this layer's output has this data layout too. Returns DATA_LAYOUT_UNKNOWN otherwise.
static int predictOutputDataLayout(const tensorflow::GraphDef& net,
@ -830,7 +837,8 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect(layer_id, dstNet, parsePin(input), id, 0);
if (data_layouts[name] == DATA_LAYOUT_UNKNOWN)
if (getDataLayout(name, data_layouts) == DATA_LAYOUT_UNKNOWN)
data_layouts[name] = DATA_LAYOUT_NHWC;
}
else if (type == "BiasAdd" || type == "Add")
@ -956,7 +964,8 @@ void TFImporter::populateNet(Net dstNet)
Pin inpId = parsePin(layer.input(0));
Mat newShape = getTensorContent(getConstBlob(layer, value_id, 1));
if (newShape.total() != 4 && data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
int inpLayout = getDataLayout(layer.input(0), data_layouts);
if (newShape.total() != 4 && inpLayout == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
@ -969,7 +978,7 @@ void TFImporter::populateNet(Net dstNet)
connect(layer_id, dstNet, inpId, permId, 0);
inpId = Pin(permName);
}
else if (newShape.total() == 4 && data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
else if (newShape.total() == 4 && inpLayout == DATA_LAYOUT_NHWC)
{
// NHWC->NCHW
std::swap(*newShape.ptr<int32_t>(0, 2), *newShape.ptr<int32_t>(0, 3));
@ -987,7 +996,7 @@ void TFImporter::populateNet(Net dstNet)
else if (type == "Flatten" || type == "Squeeze")
{
Pin inpId = parsePin(layer.input(0));
int inpLayout = data_layouts[layer.input(0)];
int inpLayout = getDataLayout(layer.input(0), data_layouts);
if (type == "Squeeze")
{
CV_Assert(hasLayerAttr(layer, "squeeze_dims"));
@ -1032,7 +1041,8 @@ void TFImporter::populateNet(Net dstNet)
{
// Only NHWC <-> NCHW permutations are allowed. OpenCV is always
// keep NCHW layout this way.
if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
int inpLayout = getDataLayout(layer.input(0), data_layouts);
if (inpLayout == DATA_LAYOUT_NHWC)
{
if (permData[0] == 0 && permData[1] == 3 && permData[2] == 1 && permData[3] == 2)
{
@ -1049,7 +1059,7 @@ void TFImporter::populateNet(Net dstNet)
else
CV_Error(Error::StsParseError, "Only NHWC <-> NCHW permutations are allowed.");
}
else if (data_layouts[layer.input(0)] == DATA_LAYOUT_NCHW)
else if (inpLayout == DATA_LAYOUT_NCHW)
{
if (permData[0] == 0 && permData[1] == 2 && permData[2] == 3 && permData[3] == 1)
{
@ -1112,7 +1122,7 @@ void TFImporter::populateNet(Net dstNet)
int axisId = (type == "Concat" ? 0 : layer.input_size() - 1);
int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0);
if (data_layouts[name] == DATA_LAYOUT_NHWC)
if (getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
axis = toNCHW(axis);
layerParams.set("axis", axis);
@ -1197,7 +1207,7 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(!begins.empty(), !sizes.empty(), begins.type() == CV_32SC1,
sizes.type() == CV_32SC1);
if (begins.total() == 4 && data_layouts[name] == DATA_LAYOUT_NHWC)
if (begins.total() == 4 && getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
{
// Swap NHWC parameters' order to NCHW.
std::swap(*begins.ptr<int32_t>(0, 2), *begins.ptr<int32_t>(0, 3));
@ -1597,7 +1607,7 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(reductionIndices.type() == CV_32SC1);
const int numAxes = reductionIndices.total();
if (data_layouts[name] == DATA_LAYOUT_NHWC)
if (getDataLayout(name, data_layouts) == DATA_LAYOUT_NHWC)
for (int i = 0; i < numAxes; ++i)
reductionIndices.at<int>(i) = toNCHW(reductionIndices.at<int>(i));

4
modules/dnn/src/torch/torch_importer.cpp
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@ -592,8 +592,8 @@ struct TorchImporter
DictValue dimParam = scalarParams.get("size");
layerParams.set("dim", dimParam);
if (scalarParams.has("batchMode") && scalarParams.get<bool>("batchMode"))
layerParams.set("axis", 1);
int axis = (int)scalarParams.get<bool>("batchMode", true);
layerParams.set("axis", axis);
curModule->modules.push_back(newModule);
}

7
modules/dnn/test/test_layers.cpp
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@ -201,6 +201,13 @@ TEST(Layer_Test_Reshape, Accuracy)
testReshape(MatShape(inp, inp + 4), MatShape(out, out + 2), 0, -1,
MatShape(mask, mask + 2));
}
{
int inp[] = {1, 2, 3};
int out[] = {3, 1, 2};
int mask[] = {3, 1, 2};
testReshape(MatShape(inp, inp + 3), MatShape(out, out + 3), 0, -1,
MatShape(mask, mask + 3));
}
}
TEST(Layer_Test_BatchNorm, Accuracy)

1
modules/dnn/test/test_tf_importer.cpp
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@ -198,6 +198,7 @@ TEST_P(Test_TensorFlow_layers, reshape)
{
int targetId = GetParam();
runTensorFlowNet("shift_reshape_no_reorder", targetId);
runTensorFlowNet("reshape_no_reorder", targetId);
runTensorFlowNet("reshape_reduce", targetId);
runTensorFlowNet("flatten", targetId, true);
runTensorFlowNet("unfused_flatten", targetId);

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