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.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 = ["Unsqueeze"],frameworkName = "onnx")
class Unsqueeze  : 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>> {
        // Parameter docs below are from the onnx operator docs:
        // https://github.com/onnx/onnx/blob/master/docs/Operators.md#unsqueeze
        val axes = if(op.inputsToOp.size < 2) attributes["axes"] as List<Int> else {
            sd.getVariable(op.inputsToOp[1]).arr.toIntVector().toList()
        }
        var ret: SDVariable? = null

        val input = sd.getVariable(op.inputsToOp[0])

        if(axes.size != 1) {
            for(i in axes.indices) {
                if(i < axes.size - 1)
                    ret = sd.expandDims(input,axes[i])
                else {
                    ret = sd.expandDims(outputNames[0],input,axes[i])
                }
            }
        } else {
            val input = sd.getVariable(op.inputsToOp[0])
            ret = sd.expandDims(outputNames[0],input,axes[0])

        }

        return mapOf(ret!!.name() to listOf(ret!!))
    }
}