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Merge pull request #21542 from rogday:split_expand

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pull/21488/head
Alexander Alekhin 3 years ago
parent b863c25d21 375fe81311
commit 5cc27fd3b5
6 changed files with 58 additions and 36 deletions
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  1. 2
      modules/dnn/src/darknet/darknet_io.cpp
  2. 52
      modules/dnn/src/layers/slice_layer.cpp
  3. 23
      modules/dnn/src/onnx/onnx_importer.cpp
  4. 13
      modules/dnn/src/tensorflow/tf_importer.cpp
  5. 2
      modules/dnn/src/torch/torch_importer.cpp
  6. 2
      modules/dnn/test/test_layers.cpp

2
modules/dnn/src/darknet/darknet_io.cpp
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@ -376,7 +376,7 @@ namespace cv {
int begin[] = {0, split_size * group_id, 0, 0};
cv::dnn::DictValue paramBegin = cv::dnn::DictValue::arrayInt(begin, 4);
int end[] = {-1, begin[1] + split_size, -1, -1};
int end[] = {INT_MAX, begin[1] + split_size, INT_MAX, INT_MAX};
cv::dnn::DictValue paramEnd = cv::dnn::DictValue::arrayInt(end, 4);
darknet::LayerParameter lp;

52
modules/dnn/src/layers/slice_layer.cpp
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@ -58,12 +58,32 @@ namespace cv
namespace dnn
{
void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector<std::vector<cv::Range> >& sliceRanges)
Range normalizeRange(const Range& input_range, int n)
{
Range range = input_range;
range.start = std::min(std::max(range.start, -n), n - 1);
if (range.start < 0)
{
range.start += n;
}
range.end = std::min(std::max(range.end, -n), n);
if (range.end < 0)
{
range.end += n;
}
return range;
}
std::vector<std::vector<cv::Range> > finalizeSliceRange(const MatShape& inpShape, int& axis,
const std::vector<std::vector<cv::Range> >& inputSliceRanges)
{
std::vector<std::vector<cv::Range> > sliceRanges = inputSliceRanges;
CV_Assert(inpShape.size() > 0);
bool axisNeg = (axis < 0);
axis = (axis + static_cast<int>(inpShape.size())) % inpShape.size();
int n = inpShape[axis];
for (size_t i = 0; i < sliceRanges.size(); ++i){
std::vector<Range>& ranges = sliceRanges[i];
@ -71,16 +91,20 @@ void sliceRangesFromShape(const MatShape& inpShape, int& axis, std::vector<std::
{
ranges.insert(ranges.begin(), axis, Range::all());
}
Range& range = ranges.back();
if (range.start >= 0)
for (size_t j = 0; j < ranges.size(); ++j)
{
continue;
}
int n = inpShape[j];
if (n <= 0)
{
continue;
}
CV_Assert(n != 0);
range.start = (n + range.start) % n;
ranges[j] = normalizeRange(ranges[j], n);
}
}
return sliceRanges;
}
class SliceLayerImpl : public SliceLayer
@ -130,7 +154,7 @@ public:
{
int size = sizeOrEnd;
CV_Assert(size == -1 || size > 0); // -1 value means range [start, axis_size).
sliceRanges[0][i].end = size > 0 ? (start + size) : -1; // We'll finalize a negative value later.
sliceRanges[0][i].end = size > 0 ? (start + size) : INT_MAX; // We'll finalize a negative value later.
}
else
{
@ -176,8 +200,7 @@ public:
MatShape inpShape = inputs[0];
int axis_rw = axis;
std::vector<std::vector<cv::Range> > sliceRanges_rw = sliceRanges;
sliceRangesFromShape(inpShape, axis_rw, sliceRanges_rw);
std::vector<std::vector<cv::Range> > sliceRanges_rw = finalizeSliceRange(inpShape, axis_rw, sliceRanges);
if (!sliceRanges_rw.empty())
{
@ -188,7 +211,7 @@ public:
for (int j = 0; j < sliceRanges_rw[i].size(); ++j)
{
if (shapesInitialized || inpShape[j] > 0)
outputs[i][j] = normalize_axis_range(sliceRanges_rw[i][j], inpShape[j]).size();
outputs[i][j] = normalizeRange(sliceRanges_rw[i][j], inpShape[j]).size();
if (!sliceSteps.empty() && (i < sliceSteps.size()) && (j < sliceSteps[i].size()) && (sliceSteps[i][j] > 1))
outputs[i][j] = (outputs[i][j] + sliceSteps[i][j] - 1) / sliceSteps[i][j];
@ -225,8 +248,7 @@ public:
CV_Assert(inputs.size() == 1);
const MatSize& inpShape = inputs[0].size;
sliceRangesFromShape(shape(inputs[0]), axis, sliceRanges);
finalSliceRanges = sliceRanges;
finalSliceRanges = finalizeSliceRange(shape(inputs[0]), axis, sliceRanges);
if (sliceRanges.empty())
{
@ -256,7 +278,7 @@ public:
// Clamp.
for (int j = 0; j < finalSliceRanges[i].size(); ++j)
{
finalSliceRanges[i][j] = normalize_axis_range(finalSliceRanges[i][j], inpShape[j]);
finalSliceRanges[i][j] = normalizeRange(finalSliceRanges[i][j], inpShape[j]);
}
}

23
modules/dnn/src/onnx/onnx_importer.cpp
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@ -1040,13 +1040,12 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
end.resize(axis, INT_MAX);
}
for (int i = 0; i < starts.size(); ++i)
{
begin.push_back(starts.get<int>(i));
int finish = ends.get<int>(i);
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
end.push_back(ends.get<int>(i));
}
} else { // inp_size > 1
CV_Assert(inp_size >= 3);
@ -1070,14 +1069,10 @@ void ONNXImporter::parseSlice(LayerParams& layerParams, const opencv_onnx::NodeP
const int* ends = end_blob.ptr<int>();
if (axis > 0) {
begin.resize(axis, 0);
end.resize(axis, -1);
end.resize(axis, INT_MAX);
}
std::copy(starts, starts + start_blob.total(), std::back_inserter(begin));
for (int i = 0; i < end_blob.total(); ++i)
{
int finish = ends[i];
end.push_back((finish < 0) ? --finish : finish); // numpy doesn't include last dim
}
std::copy(ends, ends + end_blob.total(), std::back_inserter(end));
if (inp_size == 5) {
CV_Assert(constBlobs.find(node_proto.input(4)) != constBlobs.end());
@ -2160,9 +2155,15 @@ void ONNXImporter::parseExpand(LayerParams& layerParams, const opencv_onnx::Node
if (!haveVariables)
{
if (broadcast_axes.size() != 1)
if (broadcast_axes.size() > 1)
CV_Error(Error::StsNotImplemented, "Expand op doesn't support multiple axes for constant input");
if (broadcast_axes.empty())
{
addConstant(output_name, getBlob(node_proto, 0));
return;
}
Mat input = getBlob(node_proto, 0);
input = input.reshape(0, total(inpShape, 0, broadcast_axes[0]));
Mat output = cv::repeat(input, 1, targetShape[broadcast_axes[0]]);
@ -2381,7 +2382,7 @@ void ONNXImporter::parseGather(LayerParams& layerParams, const opencv_onnx::Node
sliceLp.type = "Slice";
sliceLp.name = inpShape.size() > 1 ? layerParams.name + "/slice" : layerParams.name;
std::vector<int> begin(inpShape.size(), 0);
std::vector<int> end(inpShape.size(), -1);
std::vector<int> end(inpShape.size(), INT_MAX);
begin[axis] = index;
end[axis] = index + 1;

13
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1493,10 +1493,8 @@ void TFImporter::parseStridedSlice(tensorflow::GraphDef& net, const tensorflow::
int end_mask = getLayerAttr(layer, "end_mask").i();
for (int i = 0; i < num; ++i)
{
if (ends.at<int>(i) < 0)
ends.at<int>(i) -= 1;
if (end_mask & (1 << i))
ends.at<int>(i) = -1;
ends.at<int>(i) = INT_MAX;
if (strides.at<int>(i) != 1)
CV_Error(Error::StsNotImplemented,
format("StridedSlice with stride %d", strides.at<int>(i)));
@ -1794,15 +1792,16 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
int64_t pads[8];
bool explicit_pads = getExplicitPadding(layerParams, layer, pads);
int64_t begs[4] = {};
int64_t ends[4] = {-1, -1, -1, -1};
int64_t ends[4] = {};
if (explicit_pads)
{
name += "/deconv";
layerParams.set("pad_mode", "VALID");
ends[0] = ends[1] = INT_MAX;
for (int i = 2; i < 4; ++i) // begins=[0, 0, a, b], ends=[-1, -1, c, d]
{
begs[i] = pads[2*i];
ends[i] = -1 - pads[2*i + 1];
ends[i] = -pads[2*i + 1];
}
}
@ -1822,8 +1821,8 @@ void TFImporter::parseConv2DBackpropInput(tensorflow::GraphDef& net, const tenso
const int strideX = layerParams.get<int>("stride_w");
Mat outShape = getTensorContent(getConstBlob(layer, value_id, 0));
int shift = (getDataLayout(layer) == DATA_LAYOUT_NCHW);
const int outH = outShape.at<int>(1 + shift) + begs[2] - 1 - ends[2];
const int outW = outShape.at<int>(2 + shift) + begs[3] - 1 - ends[3];
const int outH = outShape.at<int>(1 + shift) + begs[2] - ends[2];
const int outW = outShape.at<int>(2 + shift) + begs[3] - ends[3];
if (layerParams.get<String>("pad_mode") == "SAME")
{
layerParams.set("adj_w", (outW - 1) % strideX);

2
modules/dnn/src/torch/torch_importer.cpp
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@ -954,7 +954,7 @@ struct TorchImporter
int size = scalarParams.get<int>("size");
int begins[] = {0, 0, size, size};
int ends[] = {-1, -1, -size - 1, -size - 1};
int ends[] = {INT_MAX, INT_MAX, -size, -size};
newModule->apiType = "Slice";
layerParams.set("begin", DictValue::arrayInt<int*>(&begins[0], 4));

2
modules/dnn/test/test_layers.cpp
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@ -2013,7 +2013,7 @@ TEST_P(Layer_Test_Slice, variable_input_shape)
int targetId = get<1>(GetParam());
int begin[] = {0, 0, 0, 0};
int end[] = {-1, -1, -1, -1};
int end[] = {INT_MAX, INT_MAX, INT_MAX, INT_MAX};
Net net;
LayerParams lp;

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