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Merge pull request #9649 from dkurt:dnn_reshape_transpose

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This commit is contained in:
Vadim Pisarevsky 2017-09-21 19:25:58 +00:00
commit a0d3d11470
5 changed files with 11 additions and 61 deletions
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1
modules/dnn/src/layers/permute_layer.cpp
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@ -132,6 +132,7 @@ public:
for (size_t i = 0; i < inputs.size(); i++)
{
CV_Assert(inputs[i].size() == 4);
CV_Assert(inputs[i][2] == shapeBefore[2] && inputs[i][3] == shapeBefore[3]);
CV_Assert(total(inputs[i]) == total(shapeAfter));
outputs.push_back(shapeAfter);

60
modules/dnn/src/layers/reshape_layer.cpp
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@ -146,13 +146,11 @@ static void computeShapeByReshapeMask(const MatShape &srcShape,
class ReshapeLayerImpl : public ReshapeLayer
{
public:
ReshapeLayerImpl(const LayerParams& params):
performReordering(false)
ReshapeLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
int axis = params.get<int>("axis", 0);
int numAxes = params.get<int>("num_axes", -1);
enableReordering = params.get<bool>("reorder_dims", false);
CV_Assert(numAxes >= -1);
newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);
@ -184,25 +182,6 @@ public:
return true;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs)
{
CV_Assert(inputs.size());
CV_Assert(outputs.size());
Mat srcBlob = *inputs[0];
int dims = srcBlob.dims;
MatShape inputShape = shape(srcBlob), outShape = shape(outputs[0]);
// input.total() == output.total(). So if reordering is require,
// one of the sizes will be are not equal.
// Example where reordering is require: from 1x128x4x4 to 1x2048
// Example where reordering is NOT require: from 1x1024x1x1 to 1x1024.
bool reorderingRequire = false;
const int minDims = min(dims, (int)outShape.size());
for (int i = 0; !reorderingRequire && i < minDims; ++i)
reorderingRequire = inputShape[i] != outShape[i];
performReordering = enableReordering && reorderingRequire;
}
void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
{
CV_TRACE_FUNCTION();
@ -211,43 +190,10 @@ public:
for (size_t i = 0; i < inputs.size(); i++)
{
Mat srcBlob = *inputs[i];
MatShape inputShape = shape(srcBlob), outShape = shape(outputs[i]);
if (performReordering)
{
float *dstData = internals[i].ptr<float>();
const float *srcData = srcBlob.ptr<float>();
int num = inputShape[0], channels = inputShape[1], height = inputShape[2], width = inputShape[3];
int total = num*channels*height*width;
for(int i_n = 0; i_n < num; i_n++) {
for(int i_c = 0; i_c < channels; i_c++) {
for(int i_h = 0; i_h < height; i_h++) {
for(int i_w = 0; i_w < width; i_w++) {
int src_i = channels*height*width*i_n + height*width*i_c + width*i_h + i_w;
int dst_i = channels*height*width*i_n + i_c + channels*width*i_h + channels*i_w;
CV_Assert(dst_i < total);
CV_Assert(src_i < total);
dstData[dst_i] = srcData[src_i];
}
}
}
}
internals[i].copyTo(outputs[i]);
}
else
{
if (outputs[i].data != srcBlob.data)
srcBlob.reshape(1, outShape).copyTo(outputs[i]);
}
if (outputs[i].data != srcBlob.data)
srcBlob.reshape(1, shape(outputs[i])).copyTo(outputs[i]);
}
}
private:
std::vector<std::vector<int> > outShapes;
bool enableReordering, performReordering;
};
Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params)

1
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -771,7 +771,6 @@ void TFImporter::populateNet(Net dstNet)
else if (type == "Reshape")
{
layerParams.set("dim", parseDims(getConstBlob(layer, value_id, 1)));
layerParams.set("reorder_dims", true);
int id = dstNet.addLayer(name, "Reshape", layerParams);
layer_id[name] = id;

5
modules/dnn/test/test_layers.cpp
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@ -166,13 +166,12 @@ TEST(Layer_Test_MVN, Accuracy)
}
void testReshape(const MatShape& inputShape, const MatShape& targetShape,
int axis = 0, int num_axes = -1, bool reorder_dims = false,
int axis = 0, int num_axes = -1,
MatShape mask = MatShape())
{
LayerParams params;
params.set("axis", axis);
params.set("num_axes", num_axes);
params.set("reorder_dims", reorder_dims);
if (!mask.empty())
{
params.set("dim", DictValue::arrayInt<int*>(&mask[0], mask.size()));
@ -201,7 +200,7 @@ TEST(Layer_Test_Reshape, Accuracy)
int inp[] = {1, 128, 4, 4};
int out[] = {1, 2048};
int mask[] = {-1, 2048};
testReshape(MatShape(inp, inp + 4), MatShape(out, out + 2), 0, -1, true,
testReshape(MatShape(inp, inp + 4), MatShape(out, out + 2), 0, -1,
MatShape(mask, mask + 2));
}
}

5
modules/dnn/test/test_tf_importer.cpp
View File

@ -143,6 +143,11 @@ TEST(Test_TensorFlow, defun)
runTensorFlowNet("defun_dropout");
}
TEST(Test_TensorFlow, reshape)
{
runTensorFlowNet("shift_reshape_no_reorder");
}
TEST(Test_TensorFlow, fp16)
{
const float l1 = 1e-3;