Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
com.intel.analytics.bigdl.nn.mkldnn.models.Vgg16.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.nn.mkldnn.models
import com.intel.analytics.bigdl.mkl.Memory
import com.intel.analytics.bigdl.nn.{ConstInitMethod, Xavier, Zeros}
import com.intel.analytics.bigdl.nn.mkldnn._
object Vgg_16 {
def apply(batchSize: Int, classNum: Int, hasDropout: Boolean = true): Sequential = {
val model = Sequential()
model.add(Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw))
model.add(Conv(3, 64, 3, 3, 1, 1, 1, 1).setName("conv1_1"))
model.add(ReLU().setName("relu1_1"))
model.add(Conv(64, 64, 3, 3, 1, 1, 1, 1).setName("conv1_2"))
model.add(ReLU().setName("relu1_2"))
model.add(MaxPooling(2, 2, 2, 2).setName("pool1"))
model.add(Conv(64, 128, 3, 3, 1, 1, 1, 1).setName("conv2_1"))
model.add(ReLU().setName("relu2_1"))
model.add(Conv(128, 128, 3, 3, 1, 1, 1, 1).setName("conv2_2"))
model.add(ReLU().setName("relu2_2"))
model.add(MaxPooling(2, 2, 2, 2).setName("pool2"))
model.add(Conv(128, 256, 3, 3, 1, 1, 1, 1).setName("conv3_1"))
model.add(ReLU().setName("relu3_1"))
model.add(Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_2"))
model.add(ReLU().setName("relu3_2"))
model.add(Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_3"))
model.add(ReLU().setName("relu3_3"))
model.add(MaxPooling(2, 2, 2, 2).setName("pool3"))
model.add(Conv(256, 512, 3, 3, 1, 1, 1, 1).setName("conv4_1"))
model.add(ReLU().setName("relu4_1"))
model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_2"))
model.add(ReLU().setName("relu4_2"))
model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_3"))
model.add(ReLU().setName("relu4_3"))
model.add(MaxPooling(2, 2, 2, 2).setName("pool4"))
model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_1"))
model.add(ReLU().setName("relu5_1"))
model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_2"))
model.add(ReLU().setName("relu5_2"))
model.add(Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_3"))
model.add(ReLU().setName("relu5_3"))
model.add(MaxPooling(2, 2, 2, 2).setName("pool5"))
model.add(Linear(512 * 7 * 7, 4096).setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc6"))
model.add(ReLU().setName("relu6"))
if (hasDropout) model.add(Dropout(0.5).setName("drop6"))
model.add(Linear(4096, 4096).setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc7"))
model.add(ReLU().setName("relu7"))
if (hasDropout) model.add(Dropout(0.5).setName("drop7"))
model.add(Linear(4096, classNum).setInitMethod(Xavier, ConstInitMethod(0.1)).setName(("fc8")))
model.add(ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc)))
model
}
private object Conv {
def apply(
nInputPlane: Int,
nOutputPlane: Int,
kernelW: Int,
kernelH: Int,
strideW: Int = 1,
strideH: Int = 1,
padW: Int = 0,
padH: Int = 0,
nGroup: Int = 1,
propagateBack: Boolean = true): SpatialConvolution = {
val conv = SpatialConvolution(nInputPlane, nOutputPlane, kernelW, kernelH,
strideW, strideH, padW, padH, nGroup, propagateBack)
conv.setInitMethod(Xavier.setVarianceNormAverage(false), Zeros)
conv
}
}
def graph(batchSize: Int, classNum: Int, hasDropout: Boolean = true): DnnGraph = {
val input = Input(Array(batchSize, 3, 224, 224), Memory.Format.nchw).inputs()
val conv1_1 = Conv(3, 64, 3, 3, 1, 1, 1, 1).setName("conv1_1").inputs(input)
val relu1_1 = ReLU().setName("relu1_1").inputs(conv1_1)
val conv1_2 = Conv(64, 64, 3, 3, 1, 1, 1, 1).setName("conv1_2").inputs(relu1_1)
val relu1_2 = ReLU().setName("relu1_2").inputs(conv1_2)
val pool1 = MaxPooling(2, 2, 2, 2).setName("pool1").inputs(relu1_2)
val conv2_1 = Conv(64, 128, 3, 3, 1, 1, 1, 1).setName("conv2_1").inputs(pool1)
val relu2_1 = ReLU().setName("relu2_1").inputs(conv2_1)
val conv2_2 = Conv(128, 128, 3, 3, 1, 1, 1, 1).setName("conv2_2").inputs(relu2_1)
val relu2_2 = ReLU().setName("relu2_2").inputs(conv2_2)
val pool2 = MaxPooling(2, 2, 2, 2).setName("pool2").inputs(relu2_2)
val conv3_1 = Conv(128, 256, 3, 3, 1, 1, 1, 1).setName("conv3_1").inputs(pool2)
val relu3_1 = ReLU().setName("relu3_1").inputs(conv3_1)
val conv3_2 = Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_2").inputs(relu3_1)
val relu3_2 = ReLU().setName("relu3_2").inputs(conv3_2)
val conv3_3 = Conv(256, 256, 3, 3, 1, 1, 1, 1).setName("conv3_3").inputs(relu3_2)
val relu3_3 = ReLU().setName("relu3_3").inputs(conv3_3)
val pool3 = MaxPooling(2, 2, 2, 2).setName("pool3").inputs(relu3_3)
val conv4_1 = Conv(256, 512, 3, 3, 1, 1, 1, 1).setName("conv4_1").inputs(pool3)
val relu4_1 = ReLU().setName("relu4_1").inputs(conv4_1)
val conv4_2 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_2").inputs(relu4_1)
val relu4_2 = ReLU().setName("relu4_2").inputs(conv4_2)
val conv4_3 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv4_3").inputs(relu4_2)
val relu4_3 = ReLU().setName("relu4_3").inputs(conv4_3)
val pool4 = MaxPooling(2, 2, 2, 2).setName("pool4").inputs(relu4_3)
val conv5_1 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_1").inputs(pool4)
val relu5_1 = ReLU().setName("relu5_1").inputs(conv5_1)
val conv5_2 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_2").inputs(relu5_1)
val relu5_2 = ReLU().setName("relu5_2").inputs(conv5_2)
val conv5_3 = Conv(512, 512, 3, 3, 1, 1, 1, 1).setName("conv5_3").inputs(relu5_2)
val relu5_3 = ReLU().setName("relu5_3").inputs(conv5_3)
val pool5 = MaxPooling(2, 2, 2, 2).setName("pool5").inputs(relu5_3)
val fc6 = Linear(512 * 7 * 7, 4096).
setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc6").inputs(pool5)
val relu6 = ReLU().setName("relu6").inputs(fc6)
val drop6 = if (hasDropout) {
Dropout(0.5).setName("drop6").inputs(relu6)
} else {
relu6
}
val fc7 = Linear(4096, 4096).
setInitMethod(Xavier, ConstInitMethod(0.1)).setName("fc7").inputs(drop6)
val relu7 = ReLU().setName("relu7").inputs(fc7)
val drop7 = if (hasDropout) {
Dropout(0.5).setName("drop7").inputs(relu7)
} else {
relu7
}
val fc8 = Linear(4096, classNum).
setInitMethod(Xavier, ConstInitMethod(0.1)).setName(("fc8")).inputs(drop7)
val output = ReorderMemory(HeapData(Array(batchSize, classNum), Memory.Format.nc)).inputs(fc8)
DnnGraph(Array(input), Array(output))
}
}
