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com.intel.analytics.bigdl.models.vgg.VggForCifar10.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.models.vgg
import com.intel.analytics.bigdl._
import com.intel.analytics.bigdl.nn.Graph._
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.numeric.NumericFloat
object VggForCifar10 {
def apply(classNum: Int, hasDropout: Boolean = true): Module[Float] = {
val vggBnDo = Sequential[Float]()
def convBNReLU(nInputPlane: Int, nOutPutPlane: Int)
: Sequential[Float] = {
vggBnDo.add(SpatialConvolution(nInputPlane, nOutPutPlane, 3, 3, 1, 1, 1, 1))
vggBnDo.add(SpatialBatchNormalization(nOutPutPlane, 1e-3))
vggBnDo.add(ReLU(true))
vggBnDo
}
convBNReLU(3, 64)
if (hasDropout) vggBnDo.add(Dropout((0.3)))
convBNReLU(64, 64)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(64, 128)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(128, 128)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(128, 256)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(256, 256)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(256, 256)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(256, 512)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(512, 512)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(512, 512)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
convBNReLU(512, 512)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(512, 512)
if (hasDropout) vggBnDo.add(Dropout(0.4))
convBNReLU(512, 512)
vggBnDo.add(SpatialMaxPooling(2, 2, 2, 2).ceil())
vggBnDo.add(View(512))
val classifier = Sequential[Float]()
if (hasDropout) classifier.add(Dropout(0.5))
classifier.add(Linear(512, 512))
classifier.add(BatchNormalization(512))
classifier.add(ReLU(true))
if (hasDropout) classifier.add(Dropout(0.5))
classifier.add(Linear(512, classNum))
classifier.add(LogSoftMax())
vggBnDo.add(classifier)
vggBnDo
}
def graph(classNum: Int, hasDropout: Boolean = true)
: Module[Float] = {
val input = Input()
def convBNReLU(nInputPlane: Int, nOutPutPlane: Int)(input: ModuleNode[Float])
: ModuleNode[Float] = {
val conv = SpatialConvolution(nInputPlane, nOutPutPlane, 3, 3, 1, 1, 1, 1).inputs(input)
val bn = SpatialBatchNormalization(nOutPutPlane, 1e-3).inputs(conv)
ReLU(true).inputs(bn)
}
val relu1 = convBNReLU(3, 64)(input)
val drop1 = if (hasDropout) Dropout(0.3).inputs(relu1) else relu1
val relu2 = convBNReLU(64, 64)(drop1)
val pool1 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu2)
val relu3 = convBNReLU(64, 128)(pool1)
val drop2 = if (hasDropout) Dropout(0.4).inputs(relu3) else relu3
val relu4 = convBNReLU(128, 128)(drop2)
val pool2 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu4)
val relu5 = convBNReLU(128, 256)(pool2)
val drop3 = if (hasDropout) Dropout(0.4).inputs(relu5) else relu5
val relu6 = convBNReLU(256, 256)(drop3)
val drop4 = if (hasDropout) Dropout(0.4).inputs(relu6) else relu6
val relu7 = convBNReLU(256, 256)(drop4)
val pool3 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu7)
val relu8 = convBNReLU(256, 512)(pool3)
val drop5 = if (hasDropout) Dropout(0.4).inputs(relu8) else relu8
val relu9 = convBNReLU(512, 512)(drop5)
val drop6 = if (hasDropout) Dropout(0.4).inputs(relu9) else relu9
val relu10 = convBNReLU(512, 512)(drop6)
val pool4 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu10)
val relu11 = convBNReLU(512, 512)(pool4)
val drop7 = if (hasDropout) Dropout(0.4).inputs(relu11) else relu11
val relu12 = convBNReLU(512, 512)(drop7)
val drop8 = if (hasDropout) Dropout(0.4).inputs(relu12) else relu12
val relu13 = convBNReLU(512, 512)(drop8)
val pool5 = SpatialMaxPooling(2, 2, 2, 2).ceil().inputs(relu13)
val view = View(512).inputs(pool5)
val drop9 = if (hasDropout) Dropout(0.5).inputs(view) else view
val linear1 = Linear(512, 512).inputs(drop9)
val bn = BatchNormalization(512).inputs(linear1)
val relu = ReLU(true).inputs(bn)
val drop10 = if (hasDropout) Dropout(0.5).inputs(relu) else relu
val linear2 = Linear(512, classNum).inputs(drop10)
val output = LogSoftMax().inputs(linear2)
Graph(input, output)
}
}
object Vgg_16 {
def apply(classNum: Int, hasDropout: Boolean = true): Module[Float] = {
val model = Sequential()
model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(View(512 * 7 * 7))
model.add(Linear(512 * 7 * 7, 4096))
model.add(Threshold(0, 1e-6))
if (hasDropout) model.add(Dropout(0.5))
model.add(Linear(4096, 4096))
model.add(Threshold(0, 1e-6))
if (hasDropout) model.add(Dropout(0.5))
model.add(Linear(4096, classNum))
model.add(LogSoftMax())
model
}
def graph(classNum: Int, hasDropout: Boolean = true)
: Module[Float] = {
val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs()
val relu1 = ReLU(true).inputs(conv1)
val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1)
val relu2 = ReLU(true).inputs(conv2)
val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu2)
val conv3 = SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1).inputs(pool1)
val relu3 = ReLU(true).inputs(conv3)
val conv4 = SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1).inputs(relu3)
val relu4 = ReLU(true).inputs(conv4)
val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu4)
val conv5 = SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1).inputs(pool2)
val relu5 = ReLU(true).inputs(conv5)
val conv6 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu5)
val relu6 = ReLU(true).inputs(conv6)
val conv7 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu6)
val relu7 = ReLU(true).inputs(conv7)
val pool3 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu7)
val conv8 = SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1).inputs(pool3)
val relu8 = ReLU(true).inputs(conv8)
val conv9 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu8)
val relu9 = ReLU(true).inputs(conv9)
val conv10 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu9)
val relu10 = ReLU(true).inputs(conv10)
val pool4 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu10)
val conv11 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(pool4)
val relu11 = ReLU(true).inputs(conv11)
val conv12 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu11)
val relu12 = ReLU(true).inputs(conv12)
val conv13 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu12)
val relu13 = ReLU(true).inputs(conv13)
val pool5 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu13)
val view1 = View(512 * 7 * 7).inputs(pool5)
val linear1 = Linear(512 * 7 * 7, 4096).inputs(view1)
val th1 = Threshold(0, 1e-6).inputs(linear1)
val drop1 = if (hasDropout) Dropout(0.5).inputs(th1) else th1
val linear2 = Linear(4096, 4096).inputs(drop1)
val th2 = Threshold(0, 1e-6).inputs(linear2)
val drop2 = if (hasDropout) Dropout(0.5).inputs(th2) else th2
val linear3 = Linear(4096, classNum).inputs(drop2)
val output = LogSoftMax().inputs(linear3)
Graph(conv1, output)
}
}
object Vgg_19 {
def apply(classNum: Int, hasDropout: Boolean = true): Module[Float] = {
val model = Sequential()
model.add(SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1))
model.add(ReLU(true))
model.add(SpatialMaxPooling(2, 2, 2, 2))
model.add(View(512 * 7 * 7))
model.add(Linear(512 * 7 * 7, 4096))
model.add(Threshold(0, 1e-6))
if (hasDropout) model.add(Dropout(0.5))
model.add(Linear(4096, 4096))
model.add(Threshold(0, 1e-6))
if (hasDropout) model.add(Dropout(0.5))
model.add(Linear(4096, classNum))
model.add(LogSoftMax())
model
}
def graph(classNum: Int, hasDropout: Boolean = true)
: Module[Float] = {
val conv1 = SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1).inputs()
val relu1 = ReLU(true).inputs(conv1)
val conv2 = SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1).inputs(relu1)
val relu2 = ReLU(true).inputs(conv2)
val pool1 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu2)
val conv3 = SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1).inputs(pool1)
val relu3 = ReLU(true).inputs(conv3)
val conv4 = SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1).inputs(relu3)
val relu4 = ReLU(true).inputs(conv4)
val pool2 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu4)
val conv5 = SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1).inputs(pool2)
val relu5 = ReLU(true).inputs(conv5)
val conv6 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu5)
val relu6 = ReLU(true).inputs(conv6)
val conv7 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu6)
val relu7 = ReLU(true).inputs(conv7)
val conv8 = SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1).inputs(relu7)
val relu8 = ReLU(true).inputs(conv8)
val pool3 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu8)
val conv9 = SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1).inputs(pool3)
val relu9 = ReLU(true).inputs(conv9)
val conv10 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu9)
val relu10 = ReLU(true).inputs(conv10)
val conv11 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu10)
val relu11 = ReLU(true).inputs(conv11)
val conv12 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu11)
val relu12 = ReLU(true).inputs(conv12)
val pool4 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu12)
val conv13 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(pool4)
val relu13 = ReLU(true).inputs(conv13)
val conv14 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu13)
val relu14 = ReLU(true).inputs(conv14)
val conv15 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu14)
val relu15 = ReLU(true).inputs(conv15)
val conv16 = SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1).inputs(relu15)
val relu16 = ReLU(true).inputs(conv16)
val pool5 = SpatialMaxPooling(2, 2, 2, 2).inputs(relu16)
val view1 = View(512 * 7 * 7).inputs(pool5)
val linear1 = Linear(512 * 7 * 7, 4096).inputs(view1)
val th1 = Threshold(0, 1e-6).inputs(linear1)
val drop1 = if (hasDropout) Dropout(0.5).inputs(th1) else th1
val linear2 = Linear(4096, 4096).inputs(drop1)
val th2 = Threshold(0, 1e-6).inputs(linear2)
val drop2 = if (hasDropout) Dropout(0.5).inputs(th2) else th2
val linear3 = Linear(4096, classNum).inputs(drop2)
val output = LogSoftMax().inputs(linear3)
Graph(conv1, output)
}
}
