com.intel.analytics.bigdl.example.loadmodel.AlexNet.scala Maven / Gradle / Ivy

/*
 * Copyright 2016 The BigDL Authors.
 *
 * 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.
 */

package com.intel.analytics.bigdl.example.loadmodel

import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.numeric.NumericFloat

object AlexNet_OWT {
  def apply(classNum: Int, hasDropout : Boolean = true, firstLayerPropagateBack :
  Boolean = false): Module[Float] = {
    val model = Sequential()
    model.add(SpatialConvolution(3, 64, 11, 11, 4, 4, 2, 2, 1, firstLayerPropagateBack)
      .setName("conv1"))
    model.add(ReLU(true).setName("relu1"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("pool1"))
    model.add(SpatialConvolution(64, 192, 5, 5, 1, 1, 2, 2).setName("conv2"))
    model.add(ReLU(true).setName("relu2"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("pool2"))
    model.add(SpatialConvolution(192, 384, 3, 3, 1, 1, 1, 1).setName("conv3"))
    model.add(ReLU(true).setName("relu3"))
    model.add(SpatialConvolution(384, 256, 3, 3, 1, 1, 1, 1).setName("conv4"))
    model.add(ReLU(true).setName("relu4"))
    model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).setName("conv5"))
    model.add(ReLU(true).setName("relu5"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("poo5"))
    model.add(View(256 * 6 * 6).setName("view"))
    model.add(Linear(256 * 6 * 6, 4096).setName("fc6"))
    model.add(ReLU(true).setName("relu6"))
    if (hasDropout) model.add(Dropout(0.5).setName("drop6"))
    model.add(Linear(4096, 4096).setName("fc7"))
    model.add(ReLU(true).setName("relu7"))
    if (hasDropout) model.add(Dropout(0.5).setName("drop7"))
    model.add(Linear(4096, classNum).setName("fc8"))
    model.add(LogSoftMax().setName("logsoftmax"))
    model
  }

  def graph(classNum: Int, hasDropout : Boolean = true, firstLayerPropagateBack :
  Boolean = false): Module[Float] = {
    val conv1 = SpatialConvolution(3, 64, 11, 11, 4, 4, 2, 2, 1, firstLayerPropagateBack)
      .setName("conv1").inputs()
    val relu1 = ReLU(true).setName("relu1").inputs(conv1)
    val pool1 = SpatialMaxPooling(3, 3, 2, 2).setName("pool1").inputs(relu1)
    val conv2 = SpatialConvolution(64, 192, 5, 5, 1, 1, 2, 2).setName("conv2").inputs(pool1)
    val relu2 = ReLU(true).setName("relu2").inputs(conv2)
    val pool2 = SpatialMaxPooling(3, 3, 2, 2).setName("pool2").inputs(relu2)
    val conv3 = SpatialConvolution(192, 384, 3, 3, 1, 1, 1, 1).setName("conv3").inputs(pool2)
    val relu3 = ReLU(true).setName("relu3").inputs(conv3)
    val conv4 = SpatialConvolution(384, 256, 3, 3, 1, 1, 1, 1).setName("conv4").inputs(relu3)
    val relu4 = ReLU(true).setName("relu4").inputs(conv4)
    val conv5 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).setName("conv5").inputs(relu4)
    val relu5 = ReLU(true).setName("relu5").inputs(conv5)
    val pool5 = SpatialMaxPooling(3, 3, 2, 2).setName("poo5").inputs(relu5)
    val view1 = View(256 * 6 * 6).inputs(pool5)
    val fc6 = Linear(256 * 6 * 6, 4096).setName("fc6").inputs(view1)
    val relu6 = ReLU(true).setName("relu6").inputs(fc6)
    val drop6 = if (hasDropout) Dropout(0.5).setName("drop6").inputs(relu6) else relu6
    val fc7 = Linear(4096, 4096).setName("fc7").inputs(drop6)
    val relu7 = ReLU(true).setName("relu7").inputs(fc7)
    val drop7 = if (hasDropout) Dropout(0.5).setName("drop7").inputs(relu7) else relu7
    val fc8 = Linear(4096, classNum).setName("fc8").inputs(drop7)
    val output = LogSoftMax().inputs(fc8)
    Graph(conv1, output)
  }
}

object AlexNet {
  def apply(classNum: Int, hasDropout : Boolean = true): Module[Float] = {
    val model = Sequential()
    model.add(SpatialConvolution(3, 96, 11, 11, 4, 4, 0, 0, 1, false).setName("conv1"))
    model.add(ReLU(true).setName("relu1"))
    model.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("norm1"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("pool1"))
    model.add(SpatialConvolution(96, 256, 5, 5, 1, 1, 2, 2, 2).setName("conv2"))
    model.add(ReLU(true).setName("relu2"))
    model.add(SpatialCrossMapLRN(5, 0.0001, 0.75).setName("norm2"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("pool2"))
    model.add(SpatialConvolution(256, 384, 3, 3, 1, 1, 1, 1).setName("conv3"))
    model.add(ReLU(true).setName("relu3"))
    model.add(SpatialConvolution(384, 384, 3, 3, 1, 1, 1, 1, 2).setName("conv4"))
    model.add(ReLU(true).setName("relu4"))
    model.add(SpatialConvolution(384, 256, 3, 3, 1, 1, 1, 1, 2).setName("conv5"))
    model.add(ReLU(true).setName("relu5"))
    model.add(SpatialMaxPooling(3, 3, 2, 2).setName("pool5"))
    model.add(View(256 * 6 * 6))
    model.add(Linear(256 * 6 * 6, 4096).setName("fc6"))
    model.add(ReLU(true).setName("relu6"))
    if (hasDropout) model.add(Dropout(0.5).setName("drop6"))
    model.add(Linear(4096, 4096).setName("fc7"))
    model.add(ReLU(true).setName("relu7"))
    if (hasDropout) model.add(Dropout(0.5).setName("drop7"))
    model.add(Linear(4096, classNum).setName("fc8"))
    model.add(LogSoftMax().setName("loss"))
    model
  }

  def graph(classNum: Int, hasDropout : Boolean = true): Module[Float] = {
    val conv1 = SpatialConvolution(3, 96, 11, 11, 4, 4, 0, 0, 1, false)
      .setName("conv1").inputs()
    val relu1 = ReLU(true).setName("relu1").inputs(conv1)
    val norm1 = SpatialCrossMapLRN(5, 0.0001, 0.75).setName("norm1").inputs(relu1)
    val pool1 = SpatialMaxPooling(3, 3, 2, 2).setName("pool1").inputs(norm1)
    val conv2 = SpatialConvolution(96, 256, 5, 5, 1, 1, 2, 2, 2).setName("conv2").inputs(pool1)
    val relu2 = ReLU(true).setName("relu2").inputs(conv2)
    val norm2 = SpatialCrossMapLRN(5, 0.0001, 0.75).setName("norm2").inputs(relu2)
    val pool2 = SpatialMaxPooling(3, 3, 2, 2).setName("pool2").inputs(norm2)
    val conv3 = SpatialConvolution(256, 384, 3, 3, 1, 1, 1, 1).setName("conv3").inputs(pool2)
    val relu3 = ReLU(true).setName("relu3").inputs(conv3)
    val conv4 = SpatialConvolution(384, 384, 3, 3, 1, 1, 1, 1, 2).setName("conv4").inputs(relu3)
    val relu4 = ReLU(true).setName("relu4").inputs(conv4)
    val conv5 = SpatialConvolution(384, 256, 3, 3, 1, 1, 1, 1, 2).setName("conv5").inputs(relu4)
    val relu5 = ReLU(true).setName("relu5").inputs(conv5)
    val pool5 = SpatialMaxPooling(3, 3, 2, 2).setName("pool5").inputs(relu5)
    val view1 = View(256 * 6 * 6).inputs(pool5)
    val fc6 = Linear(256 * 6 * 6, 4096).setName("fc6").inputs(view1)
    val relu6 = ReLU(true).setName("relu6").inputs(fc6)
    val drop6 = if (hasDropout) Dropout(0.5).setName("drop6").inputs(relu6) else relu6
    val fc7 = Linear(4096, 4096).setName("fc7").inputs(drop6)
    val relu7 = ReLU(true).setName("relu7").inputs(fc7)
    val drop7 = if (hasDropout) Dropout(0.5).setName("drop7").inputs(relu7) else relu7
    val fc8 = Linear(4096, classNum).setName("fc8").inputs(drop7)
    val loss = LogSoftMax().setName("loss").inputs(fc8)
    Graph(conv1, loss)
  }
}