convolution.hpp

/*
// Copyright (c) 2016 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
*/

#pragma once
#include "../C/convolution.h"
#include "primitive.hpp"

namespace cldnn
{

struct convolution : public primitive_base<convolution, CLDNN_PRIMITIVE_DESC(convolution)>
{
    CLDNN_DECLATE_PRIMITIVE(convolution)

    
    convolution(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        const std::vector<primitive_id>& bias,
        tensor stride = { 1, 1, 1, 1 },
        tensor input_offset = { 0,0,0,0 },
        tensor dilation = { 1, 1, 1, 1 },
        bool with_activation = false,
        float activation_slp = 0.0f,
        const padding& output_padding = padding()
    )
        :primitive_base(id, { input }, output_padding)
        , weights(_weights.cpp_ids)
        , bias(_bias.cpp_ids)
        , input_offset(input_offset)
        , stride(stride)
        , dilation(dilation)
        , with_activation(with_activation)
        , activation_negative_slope(activation_slp)
        , with_output_size(false)
        , _weights(weights)
        , _bias(bias)
    {
        if ((bias.size() != 0) && (weights.size() != bias.size()))
            throw std::runtime_error("convolution's weights/bias count does not match");
    }

    convolution(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        tensor stride = { 1, 1, 1, 1 },
        tensor input_offset = { 0,0,0,0 },
        tensor dilation = { 1, 1, 1, 1 },
        bool with_activation = false,
        float activation_slp = 0.0f,
        const padding& output_padding = padding()
    )
        :primitive_base(id, { input }, output_padding)
        , weights(_weights.cpp_ids)
        , bias(_bias.cpp_ids)
        , input_offset(input_offset)
        , stride(stride)
        , dilation(dilation)
        , with_activation(with_activation)
        , activation_negative_slope(activation_slp)
        , with_output_size(false)
        , _weights(weights)
        , _bias(std::vector<primitive_id>(0))
    {
    }

    convolution(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        const std::vector<primitive_id>& bias,
        tensor stride,
        tensor input_offset,
        tensor dilation,
        bool with_activation,
        float activation_slp,
        tensor output_size,
        const padding& output_padding = padding()
    )
        :primitive_base(id, { input }, output_padding)
        , weights(_weights.cpp_ids)
        , bias(_bias.cpp_ids)
        , input_offset(input_offset)
        , stride(stride)
        , dilation(dilation)
        , with_activation(with_activation)
        , activation_negative_slope(activation_slp)
        , with_output_size(true)
        , output_size(output_size)
        , _weights(weights)
        , _bias(bias)
    {
        if ((bias.size() != 0) && (weights.size() != bias.size()))
            throw std::runtime_error("convolution's weights/bias count does not match");
    }

    convolution(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        tensor stride,
        tensor input_offset,
        tensor dilation,
        bool with_activation,
        float activation_slp,
        tensor output_size,
        const padding& output_padding = padding()
    )
        :primitive_base(id, { input }, output_padding)
        , weights(_weights.cpp_ids)
        , bias(_bias.cpp_ids)
        , input_offset(input_offset)
        , stride(stride)
        , dilation(dilation)
        , with_activation(with_activation)
        , activation_negative_slope(activation_slp)
        , with_output_size(true)
        , output_size(output_size)
        , _weights(weights)
        , _bias(std::vector<primitive_id>(0))
    {
    }

    convolution(const dto* dto)
        :primitive_base(dto)
        , weights(_weights.cpp_ids)
        , bias(_bias.cpp_ids)
        , input_offset(dto->input_offset)
        , stride(dto->stride)
        , dilation(dto->dilation)
        , with_activation(dto->with_activation != 0)
        , activation_negative_slope(dto->activation_negative_slope)
        , with_output_size(dto->with_output_size != 0)
        , output_size(dto->output_size)
        , _weights(dto->weights)
        , _bias(dto->bias)
    {
        if (!dto->split || (weights.size() != bias.size() && bias.size() != 0) || dto->split != weights.size())
            throw std::invalid_argument("Invalid convolution dto: bad split value");
    }

    static convolution create_with_output_size(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        const std::vector<primitive_id>& bias,
        tensor output_size,
        tensor stride = { 1, 1, 1, 1 },
        tensor input_offset = { 0,0,0,0 },
        tensor dilation = { 1, 1, 1, 1 },
        bool with_activation = false,
        float activation_slp = 0.0f,
        const padding& output_padding = padding()
    )
    {
        return convolution(id, input, weights, bias, stride, input_offset, dilation, with_activation,
            activation_slp, output_size, output_padding);
    }

    static convolution create_with_output_size(
        const primitive_id& id,
        const primitive_id& input,
        const std::vector<primitive_id>& weights,
        tensor output_size,
        tensor stride = { 1, 1, 1, 1 },
        tensor input_offset = { 0,0,0,0 },
        tensor dilation = { 1, 1, 1, 1 },
        bool with_activation = false,
        float activation_slp = 0.0f,
        const padding& output_padding = padding()
    )
    {
        return convolution(id, input, weights, stride, input_offset, dilation, with_activation,
            activation_slp, output_size, output_padding);
    }

    fixed_size_vector_ref weights;
    fixed_size_vector_ref bias;
    tensor input_offset;
    tensor stride;
    tensor dilation;
    bool with_activation;
    float activation_negative_slope;
    bool with_output_size;
    tensor output_size;

    int32_t split() const { return static_cast<int32_t>(weights.size()); }

protected:
    primitive_id_arr _weights;
    primitive_id_arr _bias;

    std::vector<std::reference_wrapper<const primitive_id>> get_dependencies() const override
    {
        std::vector<std::reference_wrapper<const primitive_id>> ret;
        ret.reserve(weights.size() + bias.size());
        for (auto& w : weights)
            ret.push_back(w);
        for (auto& b : bias)
            ret.push_back(b);

        return ret;
    }

    void update_dto(dto& dto) const override
    {
        dto.weights = _weights.ref();
        dto.bias = _bias.ref();
        dto.input_offset = input_offset;
        dto.stride = stride;
        dto.split = split();
        dto.with_activation = with_activation;
        dto.activation_negative_slope = activation_negative_slope;
        dto.dilation = dilation;
        dto.with_output_size = with_output_size;
        dto.output_size = output_size;
    }
};
}