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- added support for NAMM loader (not finishe)
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hi_tools/hi_neural/RTNeural/RTNeural/conv1d/strided_conv1d.h
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| #pragma once | ||
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| #include "conv1d.h" | ||
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| namespace RTNEURAL_NAMESPACE | ||
| { | ||
| /** | ||
| * Dynamic implementation of a 1-dimensional convolutional layer | ||
| * with strides. | ||
| * | ||
| * Internally, this is just a wrapper around the Conv1D layer. | ||
| */ | ||
| template <typename T> | ||
| class StridedConv1D final : public Layer<T> | ||
| { | ||
| public: | ||
| /** | ||
| * Constructs a strided convolution layer for the given dimensions. | ||
| * | ||
| * @param in_size: the input size for the layer | ||
| * @param out_size: the output size for the layer | ||
| * @param kernel_size: the size of the convolution kernel | ||
| * @param dilation: the dilation rate to use for dilated convolution | ||
| * @param stride: the stride of the convolution | ||
| */ | ||
| StridedConv1D(int in_size, int out_size, int kernel_size, int dilation, int stride, int groups = 1) | ||
| : Layer<T>(in_size, out_size) | ||
| , internal(in_size, out_size, kernel_size, dilation, groups) | ||
| , stride(stride) | ||
| { | ||
| skip_output.resize(out_size, T {}); | ||
| } | ||
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| StridedConv1D(std::initializer_list<int> sizes) | ||
| : StridedConv1D<T>(*sizes.begin(), *(sizes.begin() + 1), *(sizes.begin() + 2), | ||
| *(sizes.begin() + 3), *(sizes.begin() + 4), *(sizes.begin() + 5)) | ||
| { | ||
| } | ||
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| StridedConv1D(const StridedConv1D& other) = default; | ||
| StridedConv1D& operator=(const StridedConv1D& other) = default; | ||
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| /** Resets the layer state. */ | ||
| RTNEURAL_REALTIME void reset() override | ||
| { | ||
| strides_counter = 0; | ||
| std::fill(std::begin(skip_output), std::end(skip_output), T {}); | ||
| internal.reset(); | ||
| } | ||
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| /** Returns the name of this layer. */ | ||
| std::string getName() const noexcept override { return "strided_conv1d"; } | ||
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| /** Performs a stride step for this layer. */ | ||
| RTNEURAL_REALTIME inline void skip(const T* input) | ||
| { | ||
| internal.skip(input); | ||
| } | ||
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| /** Performs forward propagation for this layer. */ | ||
| RTNEURAL_REALTIME inline void forward(const T* input, T* h) noexcept override | ||
| { | ||
| if(strides_counter == 0) | ||
| { | ||
| internal.forward(input, h); | ||
| std::copy(h, h + Layer<T>::out_size, std::begin(skip_output)); | ||
| } | ||
| else | ||
| { | ||
| internal.skip(input); | ||
| std::copy(std::begin(skip_output), std::end(skip_output), h); | ||
| } | ||
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| strides_counter = (strides_counter == stride - 1) ? 0 : strides_counter + 1; | ||
| } | ||
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| /** | ||
| * Sets the layer weights. | ||
| * | ||
| * The weights vector must have size weights[out_size][in_size][kernel_size * dilation] | ||
| */ | ||
| RTNEURAL_REALTIME void setWeights(const std::vector<std::vector<std::vector<T>>>& weights) | ||
| { | ||
| internal.setWeights(weights); | ||
| } | ||
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| /** | ||
| * Sets the layer biases. | ||
| * | ||
| * The bias vector must have size bias[out_size] | ||
| */ | ||
| RTNEURAL_REALTIME void setBias(const std::vector<T>& biasVals) | ||
| { | ||
| internal.setBias(biasVals); | ||
| } | ||
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| /** Returns the size of the convolution kernel. */ | ||
| RTNEURAL_REALTIME int getKernelSize() const noexcept { return internal.getKernelSize(); } | ||
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| /** Returns the convolution dilation rate. */ | ||
| RTNEURAL_REALTIME int getDilationRate() const noexcept { return internal.getDilationRate(); } | ||
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| /** Returns the number of "groups" in the convolution. */ | ||
| int getGroups() const noexcept { return internal.getGroups(); } | ||
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| private: | ||
| Conv1D<T> internal; | ||
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| const int stride; | ||
| int strides_counter = 0; | ||
| std::vector<T> skip_output {}; | ||
| }; | ||
|
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| //==================================================== | ||
| /** | ||
| * Static implementation of a 1-dimensional convolution layer | ||
| * with strides. | ||
| * | ||
| * Internally, this is just a wrapper around the Conv1DT layer. | ||
| * | ||
| * @param in_sizet: the input size for the layer | ||
| * @param out_sizet: the output size for the layer | ||
| * @param kernel_size: the size of the convolution kernel | ||
| * @param dilation_rate: the dilation rate to use for dilated convolution | ||
| * @param stride: the stride of the convolution | ||
| * @param groups: controls connections between inputs and outputs | ||
| * @param dynamic_state: use dynamically allocated layer state | ||
| */ | ||
| template <typename T, int in_sizet, int out_sizet, int kernel_size, int dilation_rate, int stride, int groups = 1, bool dynamic_state = false> | ||
| class StridedConv1DT | ||
| { | ||
| Conv1DT<T, in_sizet, out_sizet, kernel_size, dilation_rate, groups, dynamic_state> internal; | ||
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| int strides_counter = 0; | ||
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| public: | ||
| static constexpr auto in_size = in_sizet; | ||
| static constexpr auto out_size = out_sizet; | ||
| static constexpr auto filters_per_group = in_size / groups; | ||
| static constexpr auto channels_per_group = out_size / groups; | ||
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| StridedConv1DT() | ||
| : outs(internal.outs) | ||
| { | ||
| } | ||
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| /** Returns the name of this layer. */ | ||
| std::string getName() const noexcept { return "strided_conv1d"; } | ||
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| /** Returns false since convolution is not an activation layer. */ | ||
| constexpr bool isActivation() const noexcept { return false; } | ||
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| /** Resets the layer state. */ | ||
| RTNEURAL_REALTIME void reset() | ||
| { | ||
| internal.reset(); | ||
| } | ||
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| /** Performs a stride step for this layer. */ | ||
| template <typename Inputs> | ||
| RTNEURAL_REALTIME inline void skip(const Inputs& ins) noexcept | ||
| { | ||
| internal.skip(ins); | ||
| } | ||
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| /** Performs forward propagation for this layer. */ | ||
| template <typename Inputs> | ||
| RTNEURAL_REALTIME inline void forward(const Inputs& ins) noexcept | ||
| { | ||
| if(strides_counter == 0) | ||
| internal.forward(ins); | ||
| else | ||
| internal.skip(ins); | ||
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| strides_counter = (strides_counter == stride - 1) ? 0 : strides_counter + 1; | ||
| } | ||
|
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| /** | ||
| * Sets the layer weights. | ||
| * | ||
| * The weights vector must have size weights[out_size][group_count][kernel_size * dilation] | ||
| */ | ||
| RTNEURAL_REALTIME void setWeights(const std::vector<std::vector<std::vector<T>>>& weights) | ||
| { | ||
| internal.setWeights(weights); | ||
| } | ||
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| /** | ||
| * Sets the layer biases. | ||
| * | ||
| * The bias vector must have size bias[out_size] | ||
| */ | ||
| RTNEURAL_REALTIME void setBias(const std::vector<T>& biasVals) | ||
| { | ||
| internal.setBias(biasVals); | ||
| } | ||
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| /** Returns the size of the convolution kernel. */ | ||
| RTNEURAL_REALTIME int getKernelSize() const noexcept { return kernel_size; } | ||
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| /** Returns the convolution dilation rate. */ | ||
| RTNEURAL_REALTIME int getDilationRate() const noexcept { return dilation_rate; } | ||
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| /** Returns the number of "groups" in the convolution. */ | ||
| int getGroups() const noexcept { return groups; } | ||
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| /** Reference to the internal layer weights. */ | ||
| decltype(internal.outs)& outs; | ||
| }; | ||
| } |
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