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LSTM layer for TensorFlow importer

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This commit is contained in:
Dmitry Kurtaev
2017-08-25 14:45:03 +03:00
parent d9107601c3
commit 84cec17913
7 changed files with 323 additions and 71 deletions
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modules/dnn/include/opencv2/dnn/all_layers.hpp
+41 -7
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@@ -84,7 +84,9 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** Creates instance of LSTM layer */
static Ptr<LSTMLayer> create(const LayerParams& params);
/** Set trained weights for LSTM layer.
/** @deprecated Use LayerParams::blobs instead.
@brief Set trained weights for LSTM layer.
LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.
Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state.
@@ -114,7 +116,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
@param Wx is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_x @f$)
@param b is bias vector (i.e. according to abovemtioned notation is @f$ b @f$)
*/
virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
/** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
* @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
@@ -122,7 +124,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
/** @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample.
/** @deprecated Use flag `produce_cell_output` in LayerParams.
* @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample.
*
* If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams.
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
@@ -130,12 +133,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`].
* In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
*/
virtual void setUseTimstampsDim(bool use = true) = 0;
CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
/** @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
/** @deprecated Use flag `use_timestamp_dim` in LayerParams.
* @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
* @details Shape of the second output is the same as first output.
*/
virtual void setProduceCellOutput(bool produce = false) = 0;
CV_DEPRECATED virtual void setProduceCellOutput(bool produce = false) = 0;
/* In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$).
* @param input should contain packed values @f$x_t@f$
@@ -322,11 +326,41 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
static Ptr<SplitLayer> create(const LayerParams &params);
};
/**
* Slice layer has several modes:
* 1. Caffe mode
* @param[in] axis Axis of split operation
* @param[in] slice_point Array of split points
*
* Number of output blobs equals to number of split points plus one. The
* first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis,
* the second output blob is a slice of input from @p slice_point[0] to
* @p slice_point[1] - 1 by @p axis and the last output blob is a slice of
* input from @p slice_point[-1] up to the end of @p axis size.
*
* 2. TensorFlow mode
* @param begin Vector of start indices
* @param size Vector of sizes
*
* More convinient numpy-like slice. One and only output blob
* is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]`
*
* 3. Torch mode
* @param axis Axis of split operation
*
* Split input blob on the equal parts by @p axis.
*/
class CV_EXPORTS SliceLayer : public Layer
{
public:
/**
* @brief Vector of slice ranges.
*
* The first dimension equals number of output blobs.
* Inner vector has slice ranges for the first number of input dimensions.
*/
std::vector<std::vector<Range> > sliceRanges;
int axis;
std::vector<int> sliceIndices;
static Ptr<SliceLayer> create(const LayerParams &params);
};