convolution.cpp

/**
 * \file imperative/src/impl/ops/dnn/convolution.cpp
 * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
 *
 * Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 */

#include "megbrain/opr/dnn/convolution.h"
#include "megbrain/imperative/ops/autogen.h"

#include "../op_trait.h"

namespace mgb {
namespace imperative {

namespace {
namespace convolution {
std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
    auto* node = &node_->cast_final_safe<opr::Convolution>();
    return Convolution::make(node->param(), node->execution_policy());
}

auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
    auto&& conv = static_cast<const Convolution&>(def);
    OperatorNodeConfig config{conv.make_name()};
    return opr::Convolution::make(
            inputs[0], inputs[1], conv.param(), conv.policy(), config);
}

OP_TRAIT_REG(Convolution, Convolution, opr::Convolution)
        .make_from_op_node(make_from_op_node)
        .apply_on_var_node(apply_on_var_node)
        .fallback();
}  // namespace convolution
}  // namespace

namespace {
namespace convolution_backward_data {
auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
    auto&& conv = static_cast<const ConvolutionBackwardData&>(def);
    OperatorNodeConfig config{conv.make_name()};
    DType output_dtype = conv.dtype;
    if (output_dtype.valid()) {
        config.output_dtype(output_dtype);
    }

    if (inputs.size() == 2) {
        return opr::ConvolutionBackwardData::make(
                inputs[0], inputs[1], conv.param(), conv.policy(), config);
    } else {
        mgb_assert(inputs.size() == 3);
        return opr::ConvolutionBackwardData::make(
                inputs[0], inputs[1], inputs[2], conv.param(), conv.policy(), config);
    }
}

OP_TRAIT_REG(ConvolutionBackwardData, ConvolutionBackwardData)
        .apply_on_var_node(apply_on_var_node)
        .fallback();
}  // namespace convolution_backward_data
}  // namespace

namespace {
namespace convolution3d {
std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
    auto* node = &node_->cast_final_safe<opr::Convolution3D>();
    return Convolution3D::make(node->param(), node->execution_policy());
}

auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
    auto&& conv = static_cast<const Convolution3D&>(def);
    return opr::Convolution3D::make(inputs[0], inputs[1], conv.param(), conv.policy());
}

OP_TRAIT_REG(Convolution3D, Convolution3D, opr::Convolution3D)
        .make_from_op_node(make_from_op_node)
        .apply_on_var_node(apply_on_var_node)
        .fallback();
}  // namespace convolution3d
}  // namespace

namespace {
namespace convolution3d_backward_data {
auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
    auto&& conv = static_cast<const Convolution3DBackwardData&>(def);
    OperatorNodeConfig config{conv.make_name()};
    mgb_assert(inputs.size() == 2);
    return opr::Convolution3DBackwardData::make(
            inputs[0], inputs[1], conv.param(), conv.policy(), config);
}

OP_TRAIT_REG(Convolution3DBackwardData, Convolution3DBackwardData)
        .apply_on_var_node(apply_on_var_node)
        .fallback();
}  // namespace convolution3d_backward_data
}  // namespace

}  // namespace imperative
}  // namespace mgb