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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)
}
}
