deeplearning4j/deeplearning4j

/*
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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
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 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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 *  * License for the specific language governing permissions and limitations
 *  * under the License.
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 *  * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.samediff.frameworkimport.onnx.definitions.implementations

import org.nd4j.autodiff.samediff.SDVariable
import org.nd4j.autodiff.samediff.SameDiff
import org.nd4j.autodiff.samediff.internal.SameDiffOp
import org.nd4j.linalg.api.buffer.DataType
import org.nd4j.samediff.frameworkimport.ImportGraph
import org.nd4j.samediff.frameworkimport.hooks.PreImportHook
import org.nd4j.samediff.frameworkimport.hooks.annotations.PreHookRule
import org.nd4j.samediff.frameworkimport.registry.OpMappingRegistry
import org.nd4j.shade.protobuf.GeneratedMessageV3
import org.nd4j.shade.protobuf.ProtocolMessageEnum

@PreHookRule(nodeNames = [],opNames = ["GlobalMaxPool"],frameworkName = "onnx")
class GlobalMaxPooling: PreImportHook {

    override fun doImport(
        sd: SameDiff,
        attributes: Map<String, Any>,
        outputNames: List<String>,
        op: SameDiffOp,
        mappingRegistry: OpMappingRegistry<GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, ProtocolMessageEnum, GeneratedMessageV3, GeneratedMessageV3>,
        importGraph: ImportGraph<GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, GeneratedMessageV3, ProtocolMessageEnum>,
        dynamicVariables: Map<String, GeneratedMessageV3>
    ): Map<String, List<SDVariable>> {
        val inputVariable = sd.getVariable(op.inputsToOp[0])
        val rankOf = sd.rank(inputVariable)
        val range = sd.range(sd.constant(0),rankOf,sd.constant(1),DataType.INT64)
        val sizes = sd.concat(0,sd.constant(2).castTo(DataType.INT64),sd.prod(range.shape()).sub(2.0).castTo(DataType.INT64))
        val split = sd.splitV(range,sizes,2,0)
        val output = sd.math.reduceMax(outputNames[0],inputVariable,split[1],true)
        return mapOf(output.name() to listOf(output))
    }

}