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com.intel.analytics.bigdl.models.inception.Inception_v2.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.
*/
/* Still in experimental stage! */
package com.intel.analytics.bigdl.models.inception
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
import com.intel.analytics.bigdl.utils.{T, Table}
object Inception_Layer_v2 {
def apply(inputSize: Int, config: Table, namePrefix : String): Module[Float] = {
val concat = Concat(2)
if (config[Table](1)[Int](1) != 0) {
val conv1 = Sequential()
conv1.add(SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1)
.setName(namePrefix + "1x1"))
conv1.add(SpatialBatchNormalization(config[Table](1)(1), 1e-3)
.setName(namePrefix + "1x1/bn"))
conv1.add(ReLU(true).setName(namePrefix + "1x1/bn/sc/relu"))
concat.add(conv1)
}
val conv3 = Sequential()
conv3.add(SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1)
.setName(namePrefix + "3x3_reduce"))
conv3.add(SpatialBatchNormalization(config[Table](2)(1), 1e-3)
.setName(namePrefix + "3x3_reduce/bn"))
conv3.add(ReLU(true). setName(namePrefix + "3x3_reduce/bn/sc/relu"))
if(config[Table](4)[String](1) == "max" && config[Table](4)[Int](2) == 0) {
conv3.add(SpatialConvolution(config[Table](2)(1),
config[Table](2)(2), 3, 3, 2, 2, 1, 1).setName(namePrefix + "3x3"))
} else {
conv3.add(SpatialConvolution(config[Table](2)(1),
config[Table](2)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "3x3"))
}
conv3.add(SpatialBatchNormalization(config[Table](2)(2), 1e-3)
.setName(namePrefix + "3x3/bn"))
conv3.add(ReLU(true).setName(namePrefix + "3x3/bn/sc/relu"))
concat.add(conv3)
val conv3xx = Sequential()
conv3xx.add(SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1)
.setName(namePrefix + "double3x3_reduce"))
conv3xx.add(SpatialBatchNormalization(config[Table](3)(1), 1e-3)
.setName(namePrefix + "double3x3_reduce/bn"))
conv3xx.add(ReLU(true).setName(namePrefix + "double3x3_reduce/bn/sc/relu"))
conv3xx.add(SpatialConvolution(config[Table](3)(1),
config[Table](3)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "double3x3a"))
conv3xx.add(SpatialBatchNormalization(config[Table](3)(2), 1e-3)
.setName(namePrefix + "double3x3a/bn"))
conv3xx.add(ReLU(true).setName(namePrefix + "double3x3a/bn/sc/relu"))
if(config[Table](4)[String](1) == "max" && config[Table](4)[Int](2) == 0) {
conv3xx.add(SpatialConvolution(config[Table](3)(2),
config[Table](3)(2), 3, 3, 2, 2, 1, 1).setName(namePrefix + "double3x3b"))
} else {
conv3xx.add(SpatialConvolution(config[Table](3)(2),
config[Table](3)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "double3x3b"))
}
conv3xx.add(SpatialBatchNormalization(config[Table](3)(2), 1e-3)
.setName(namePrefix + "double3x3b/bn"))
conv3xx.add(ReLU(true).setName(namePrefix + "double3x3b/bn/sc/relu"))
concat.add(conv3xx)
val pool = Sequential()
config[Table](4)[String](1) match {
case "max" =>
if (config[Table](4)[Int](2) != 0) {
pool.add(SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil().setName(namePrefix + "pool"))
} else {
pool.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName(namePrefix + "pool"))
}
case "avg" => pool.add(SpatialAveragePooling(3, 3, 1, 1, 1, 1).ceil()
.setName(namePrefix + "pool"))
case _ => throw new IllegalArgumentException
}
if (config[Table](4)[Int](2) != 0) {
pool.add(SpatialConvolution(inputSize, config[Table](4)[Int](2), 1, 1, 1, 1)
.setName(namePrefix + "pool_proj"))
pool.add(SpatialBatchNormalization(config[Table](4)(2), 1e-3)
.setName(namePrefix + "pool_proj/bn"))
pool.add(ReLU(true).setName(namePrefix + "pool_proj/bn/sc/relu"))
}
concat.add(pool)
concat.setName(namePrefix + "output")
}
def apply(input: ModuleNode[Float], inputSize: Int, config: Table, namePrefix : String)
: ModuleNode[Float] = {
val relu1 = if (config[Table](1)[Int](1) != 0) {
val conv1 = SpatialConvolution(inputSize, config[Table](1)(1), 1, 1, 1, 1)
.setName(namePrefix + "1x1").inputs(input)
val bn1 = SpatialBatchNormalization(config[Table](1)(1), 1e-3)
.setName(namePrefix + "1x1/bn").inputs(conv1)
ReLU(true).setName(namePrefix + "1x1/bn/sc/relu").inputs(bn1)
} else {
null
}
val conv2 = SpatialConvolution(inputSize, config[Table](2)(1), 1, 1, 1, 1)
.setName(namePrefix + "3x3_reduce").inputs(input)
val bn2 = SpatialBatchNormalization(config[Table](2)(1), 1e-3)
.setName(namePrefix + "3x3_reduce/bn").inputs(conv2)
val relu2 = ReLU(true). setName(namePrefix + "3x3_reduce/bn/sc/relu").inputs(bn2)
val conv3 = if (config[Table](4)[String](1) == "max" && config[Table](4)[Int](2) == 0) {
SpatialConvolution(config[Table](2)(1),
config[Table](2)(2), 3, 3, 2, 2, 1, 1).setName(namePrefix + "3x3").inputs(relu2)
} else {
SpatialConvolution(config[Table](2)(1),
config[Table](2)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "3x3").inputs(relu2)
}
val bn3 = SpatialBatchNormalization(config[Table](2)(2), 1e-3)
.setName(namePrefix + "3x3/bn").inputs(conv3)
val relu4 = ReLU(true).setName(namePrefix + "3x3/bn/sc/relu").inputs(bn3)
val conv4 = SpatialConvolution(inputSize, config[Table](3)(1), 1, 1, 1, 1)
.setName(namePrefix + "double3x3_reduce").inputs(input)
val bn4 = SpatialBatchNormalization(config[Table](3)(1), 1e-3)
.setName(namePrefix + "double3x3_reduce/bn").inputs(conv4)
val relu5 = ReLU(true).setName(namePrefix + "double3x3_reduce/bn/sc/relu").inputs(bn4)
val conv5 = SpatialConvolution(config[Table](3)(1),
config[Table](3)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "double3x3a").inputs(relu5)
val bn5 = SpatialBatchNormalization(config[Table](3)(2), 1e-3)
.setName(namePrefix + "double3x3a/bn").inputs(conv5)
val relu6 = ReLU(true).setName(namePrefix + "double3x3a/bn/sc/relu").inputs(bn5)
val conv6 = if (config[Table](4)[String](1) == "max" && config[Table](4)[Int](2) == 0) {
SpatialConvolution(config[Table](3)(2),
config[Table](3)(2), 3, 3, 2, 2, 1, 1).setName(namePrefix + "double3x3b").inputs(relu6)
} else {
SpatialConvolution(config[Table](3)(2),
config[Table](3)(2), 3, 3, 1, 1, 1, 1).setName(namePrefix + "double3x3b").inputs(relu6)
}
val bn6 = SpatialBatchNormalization(config[Table](3)(2), 1e-3)
.setName(namePrefix + "double3x3b/bn").inputs(conv6)
val relu7 = ReLU(true).setName(namePrefix + "double3x3b/bn/sc/relu").inputs(bn6)
val pool = config[Table](4)[String](1) match {
case "max" =>
if (config[Table](4)[Int](2) != 0) {
SpatialMaxPooling(3, 3, 1, 1, 1, 1).ceil().setName(namePrefix + "pool").inputs(input)
} else {
SpatialMaxPooling(3, 3, 2, 2).ceil().setName(namePrefix + "pool").inputs(input)
}
case "avg" => SpatialAveragePooling(3, 3, 1, 1, 1, 1).ceil()
.setName(namePrefix + "pool").inputs(input)
case _ => throw new IllegalArgumentException
}
val reluPool = if (config[Table](4)[Int](2) != 0) {
val conv = SpatialConvolution(inputSize, config[Table](4)[Int](2), 1, 1, 1, 1)
.setName(namePrefix + "pool_proj").inputs(pool)
val bn = SpatialBatchNormalization(config[Table](4)(2), 1e-3)
.setName(namePrefix + "pool_proj/bn").inputs(conv)
ReLU(true).setName(namePrefix + "pool_proj/bn/sc/relu").inputs(bn)
} else {
pool
}
if (relu1 == null) {
JoinTable(2, 0).inputs(relu4, relu7, reluPool)
} else {
JoinTable(2, 0).inputs(relu1, relu4, relu7, reluPool)
}
}
}
object Inception_v2_NoAuxClassifier {
def apply(classNum: Int): Module[Float] = {
val model = Sequential()
model.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false)
.setName("conv1/7x7_s2"))
model.add(SpatialBatchNormalization(64, 1e-3).setName("conv1/7x7_s2/bn"))
model.add(ReLU(true).setName("conv1/7x7_s2/bn/sc/relu"))
model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2"))
model.add(SpatialConvolution(64, 64, 1, 1).setName("conv2/3x3_reduce"))
model.add(SpatialBatchNormalization(64, 1e-3).setName("conv2/3x3_reduce/bn"))
model.add(ReLU(true).setName("conv2/3x3_reduce/bn/sc/relu"))
model.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1).setName("conv2/3x3"))
model.add(SpatialBatchNormalization(192, 1e-3).setName("conv2/3x3/bn"))
model.add(ReLU(true).setName("conv2/3x3/bn/sc/relu"))
model.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2"))
model.add(Inception_Layer_v2(192, T(T(64), T(64, 64), T(64, 96), T("avg", 32)),
"inception_3a/"))
model.add(Inception_Layer_v2(256, T(T(64), T(64, 96), T(64, 96), T("avg", 64)),
"inception_3b/"))
model.add(Inception_Layer_v2(320, T(T(0), T(128, 160), T(64, 96), T("max", 0)),
"inception_3c/"))
model.add(Inception_Layer_v2(576, T(T(224), T(64, 96), T(96, 128), T("avg", 128)),
"inception_4a/"))
model.add(Inception_Layer_v2(576, T(T(192), T(96, 128), T(96, 128), T("avg", 128)),
"inception_4b/"))
model.add(Inception_Layer_v2(576, T(T(160), T(128, 160), T(128, 160), T("avg", 96)),
"inception_4c/"))
model.add(Inception_Layer_v2(576, T(T(96), T(128, 192), T(160, 192), T("avg", 96)),
"inception_4d/"))
model.add(Inception_Layer_v2(576, T(T(0), T(128, 192), T(192, 256), T("max", 0)),
"inception_4e/"))
model.add(Inception_Layer_v2(1024, T(T(352), T(192, 320), T(160, 224), T("avg", 128)),
"inception_5a/"))
model.add(Inception_Layer_v2(1024, T(T(352), T(192, 320), T(192, 224), T("max", 128)),
"inception_5b/"))
model.add(SpatialAveragePooling(7, 7, 1, 1).ceil().setName("pool5/7x7_s1"))
model.add(View(1024).setNumInputDims(3))
model.add(Linear(1024, classNum).setName("loss3/classifier"))
model.add(LogSoftMax().setName("loss3/loss"))
model.reset()
model
}
def graph(classNum: Int): Module[Float] = {
val input = Input()
val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false)
.setName("conv1/7x7_s2").inputs(input)
val bn1 = SpatialBatchNormalization(64, 1e-3).setName("conv1/7x7_s2/bn").inputs(conv1)
val relu1 = ReLU(true).setName("conv1/7x7_s2/bn/sc/relu").inputs(bn1)
val pool1 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2").inputs(relu1)
val conv2 = SpatialConvolution(64, 64, 1, 1).setName("conv2/3x3_reduce").inputs(pool1)
val bn2 = SpatialBatchNormalization(64, 1e-3).setName("conv2/3x3_reduce/bn").inputs(conv2)
val relu2 = ReLU(true).setName("conv2/3x3_reduce/bn/sc/relu").inputs(bn2)
val conv3 = SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1).setName("conv2/3x3").inputs(relu2)
val bn3 = SpatialBatchNormalization(192, 1e-3).setName("conv2/3x3/bn").inputs(conv3)
val relu3 = ReLU(true).setName("conv2/3x3/bn/sc/relu").inputs(bn3)
val pool2 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2").inputs(relu3)
val layer1 = Inception_Layer_v2(pool2, 192, T(T(64), T(64, 64), T(64, 96), T("avg", 32)),
"inception_3a/")
val layer2 = Inception_Layer_v2(layer1, 256, T(T(64), T(64, 96), T(64, 96), T("avg", 64)),
"inception_3b/")
val layer3 = Inception_Layer_v2(layer2, 320, T(T(0), T(128, 160), T(64, 96), T("max", 0)),
"inception_3c/")
val layer4 = Inception_Layer_v2(layer3, 576, T(T(224), T(64, 96), T(96, 128), T("avg", 128)),
"inception_4a/")
val layer5 = Inception_Layer_v2(layer4, 576, T(T(192), T(96, 128), T(96, 128), T("avg", 128)),
"inception_4b/")
val layer6 = Inception_Layer_v2(layer5, 576, T(T(160), T(128, 160), T(128, 160), T("avg", 96)),
"inception_4c/")
val layer7 = Inception_Layer_v2(layer6, 576, T(T(96), T(128, 192), T(160, 192), T("avg", 96)),
"inception_4d/")
val layer8 = Inception_Layer_v2(layer7, 576, T(T(0), T(128, 192), T(192, 256), T("max", 0)),
"inception_4e/")
val layer9 = Inception_Layer_v2(layer8, 1024, T(T(352), T(192, 320), T(160, 224),
T("avg", 128)), "inception_5a/")
val layer10 = Inception_Layer_v2(layer9, 1024, T(T(352), T(192, 320), T(192, 224),
T("max", 128)), "inception_5b/")
val pool = SpatialAveragePooling(7, 7, 1, 1).ceil().setName("pool5/7x7_s1").inputs(layer10)
val view1 = View(1024).setNumInputDims(3).inputs(pool)
val linear = Linear(1024, classNum).setName("loss3/classifier").inputs(view1)
val output = LogSoftMax().setName("loss3/loss").inputs(linear)
val model = Graph(input, output)
model.reset()
model
}
}
object Inception_v2 {
def apply(classNum: Int): Module[Float] = {
val features1 = Sequential()
features1.add(SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false)
.setName("conv1/7x7_s2"))
features1.add(SpatialBatchNormalization(64, 1e-3).setName("conv1/7x7_s2/bn"))
features1.add(ReLU(true).setName("conv1/7x7_s2/bn/sc/relu"))
features1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2"))
features1.add(SpatialConvolution(64, 64, 1, 1).setName("conv2/3x3_reduce"))
features1.add(SpatialBatchNormalization(64, 1e-3).setName("conv2/3x3_reduce/bn"))
features1.add(ReLU(true).setName("conv2/3x3_reduce/bn/sc/relu"))
features1.add(SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1).setName("conv2/3x3"))
features1.add(SpatialBatchNormalization(192, 1e-3).setName("conv2/3x3/bn"))
features1.add(ReLU(true).setName("conv2/3x3/bn/sc/relu"))
features1.add(SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2"))
features1.add(Inception_Layer_v2(192, T(T(64), T(64, 64), T(64, 96), T("avg", 32)),
"inception_3a/"))
features1.add(Inception_Layer_v2(256, T(T(64), T(64, 96), T(64, 96), T("avg", 64)),
"inception_3b/"))
features1.add(Inception_Layer_v2(320, T(T(0), T(128, 160), T(64, 96), T("max", 0)),
"inception_3c/"))
val output1 = Sequential()
output1.add(SpatialAveragePooling(5, 5, 3, 3).ceil().setName("pool3/5x5_s3"))
output1.add(SpatialConvolution(576, 128, 1, 1, 1, 1).setName("loss1/conv"))
output1.add(SpatialBatchNormalization(128, 1e-3).setName("loss1/conv/bn"))
output1.add(ReLU(true).setName("loss1/conv/bn/sc/relu"))
output1.add(View(128 * 4 * 4).setNumInputDims(3))
output1.add(Linear(128 * 4 * 4, 1024).setName("loss1/fc"))
output1.add(ReLU(true).setName("loss1/fc/bn/sc/relu"))
output1.add(Linear(1024, classNum).setName("loss1/classifier"))
output1.add(LogSoftMax().setName("loss1/loss"))
val features2 = Sequential()
features2
.add(Inception_Layer_v2(576, T(T(224), T(64, 96), T(96, 128), T("avg", 128)),
"inception_4a/"))
.add(Inception_Layer_v2(576, T(T(192), T(96, 128), T(96, 128), T("avg", 128)),
"inception_4b/"))
.add(Inception_Layer_v2(576, T(T(160), T(128, 160), T(128, 160), T("avg", 96)),
"inception_4c/"))
.add(Inception_Layer_v2(576, T(T(96), T(128, 192), T(160, 192), T("avg", 96)),
"inception_4d/"))
.add(Inception_Layer_v2(576, T(T(0), T(128, 192), T(192, 256), T("max", 0)),
"inception_4e/"))
val output2 = Sequential()
output2.add(SpatialAveragePooling(5, 5, 3, 3).ceil().setName("pool4/5x5_s3"))
output2.add(SpatialConvolution(1024, 128, 1, 1, 1, 1).setName("loss2/conv"))
output2.add(SpatialBatchNormalization(128, 1e-3).setName("loss2/conv/bn"))
output2.add(ReLU(true).setName("loss2/conv/bn/sc/relu"))
output2.add(View(128 * 2 * 2).setNumInputDims(3))
output2.add(Linear(128 * 2 * 2, 1024).setName("loss2/fc"))
output2.add(ReLU(true).setName("loss2/fc/bn/sc/relu"))
output2.add(Linear(1024, classNum).setName("loss2/classifier"))
output2.add(LogSoftMax().setName("loss2/loss"))
val output3 = Sequential()
output3.add(Inception_Layer_v2(1024, T(T(352), T(192, 320), T(160, 224), T("avg", 128)),
"inception_5a/"))
output3.add(Inception_Layer_v2(1024, T(T(352), T(192, 320), T(192, 224), T("max", 128)),
"inception_5b/"))
output3.add(SpatialAveragePooling(7, 7, 1, 1).ceil().setName("pool5/7x7_s1"))
output3.add(View(1024).setNumInputDims(3))
output3.add(Linear(1024, classNum).setName("loss3/classifier"))
output3.add(LogSoftMax().setName("loss3/loss"))
val split2 = Concat(2)
split2.add(output3)
split2.add(output2)
val mainBranch = Sequential()
mainBranch.add(features2)
mainBranch.add(split2)
val split1 = Concat(2)
split1.add(mainBranch)
split1.add(output1)
val model = Sequential()
model.add(features1)
model.add(split1)
model.reset()
model
}
def graph(classNum: Int, hasDropout: Boolean = true): Module[Float] = {
val input = Input()
val conv1 = SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, false)
.setName("conv1/7x7_s2").inputs(input)
val bn1 = SpatialBatchNormalization(64, 1e-3).setName("conv1/7x7_s2/bn").inputs(conv1)
val relu1 = ReLU(true).setName("conv1/7x7_s2/bn/sc/relu").inputs(bn1)
val pool1 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool1/3x3_s2").inputs(relu1)
val conv2 = SpatialConvolution(64, 64, 1, 1).setName("conv2/3x3_reduce").inputs(pool1)
val bn2 = SpatialBatchNormalization(64, 1e-3).setName("conv2/3x3_reduce/bn").inputs(conv2)
val relu2 = ReLU(true).setName("conv2/3x3_reduce/bn/sc/relu").inputs(bn2)
val conv3 = SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1).setName("conv2/3x3").inputs(relu2)
val bn3 = SpatialBatchNormalization(192, 1e-3).setName("conv2/3x3/bn").inputs(conv3)
val relu4 = ReLU(true).setName("conv2/3x3/bn/sc/relu").inputs(bn3)
val pool2 = SpatialMaxPooling(3, 3, 2, 2).ceil().setName("pool2/3x3_s2").inputs(relu4)
val layer1 = Inception_Layer_v2(pool2, 192, T(T(64), T(64, 64), T(64, 96), T("avg", 32)),
"inception_3a/")
val layer2 = Inception_Layer_v2(layer1, 256, T(T(64), T(64, 96), T(64, 96), T("avg", 64)),
"inception_3b/")
val features1 = Inception_Layer_v2(layer2, 320, T(T(0), T(128, 160), T(64, 96), T("max", 0)),
"inception_3c/")
val pool2_1 = SpatialAveragePooling(5, 5, 3, 3).ceil().setName("pool3/5x5_s3").inputs(features1)
val conv2_1 = SpatialConvolution(576, 128, 1, 1, 1, 1).setName("loss1/conv").inputs(pool2_1)
val bn2_1 = SpatialBatchNormalization(128, 1e-3).setName("loss1/conv/bn").inputs(conv2_1)
val relu2_1 = ReLU(true).setName("loss1/conv/bn/sc/relu").inputs(bn2_1)
val view2_1 = View(128 * 4 * 4).setNumInputDims(3).inputs(relu2_1)
val linear2_1 = Linear(128 * 4 * 4, 1024).setName("loss1/fc").inputs(view2_1)
val relu2_2 = ReLU(true).setName("loss1/fc/bn/sc/relu").inputs(linear2_1)
val linear2_2 = Linear(1024, classNum).setName("loss1/classifier").inputs(relu2_2)
val output1 = LogSoftMax().setName("loss1/loss").inputs(linear2_2)
val layer3_1 = Inception_Layer_v2(features1, 576, T(T(224), T(64, 96), T(96, 128),
T("avg", 128)), "inception_4a/")
val layer3_2 = Inception_Layer_v2(layer3_1, 576, T(T(192), T(96, 128), T(96, 128),
T("avg", 128)), "inception_4b/")
val layer3_3 = Inception_Layer_v2(layer3_2, 576, T(T(160), T(128, 160), T(128, 160),
T("avg", 96)), "inception_4c/")
val layer3_4 = Inception_Layer_v2(layer3_3, 576, T(T(96), T(128, 192), T(160, 192),
T("avg", 96)), "inception_4d/")
val features2 = Inception_Layer_v2(layer3_4, 576, T(T(0), T(128, 192), T(192, 256),
T("max", 0)), "inception_4e/")
val pool3_1 = SpatialAveragePooling(5, 5, 3, 3).ceil().setName("pool4/5x5_s3").inputs(features2)
val conv3_1 = SpatialConvolution(1024, 128, 1, 1, 1, 1).setName("loss2/conv").inputs(pool3_1)
val bn3_1 = SpatialBatchNormalization(128, 1e-3).setName("loss2/conv/bn").inputs(conv3_1)
val relu3_1 = ReLU(true).setName("loss2/conv/bn/sc/relu").inputs(bn3_1)
val view3_1 = View(128 * 2 * 2).setNumInputDims(3).inputs(relu3_1)
val linear3_1 = Linear(128 * 2 * 2, 1024).setName("loss2/fc").inputs(view3_1)
val relu3_2 = ReLU(true).setName("loss2/fc/bn/sc/relu").inputs(linear3_1)
val linear3_2 = Linear(1024, classNum).setName("loss2/classifier").inputs(relu3_2)
val output2 = LogSoftMax().setName("loss2/loss").inputs(linear3_2)
val rayer5_1 = Inception_Layer_v2(features2, 1024, T(T(352), T(192, 320), T(160, 224),
T("avg", 128)), "inception_5a/")
val layer5_2 = Inception_Layer_v2(rayer5_1, 1024, T(T(352), T(192, 320), T(192, 224),
T("max", 128)), "inception_5b/")
val pool5_1 = SpatialAveragePooling(7, 7, 1, 1).ceil().setName("pool5/7x7_s1").inputs(layer5_2)
val view5_1 = View(1024).setNumInputDims(3).inputs(pool5_1)
val linear5_1 = Linear(1024, classNum).setName("loss3/classifier").inputs(view5_1)
val output3 = LogSoftMax().setName("loss3/loss").inputs(linear5_1)
val split2 = JoinTable(2, 0).inputs(output3, output2, output1)
val model = Graph(input, split2)
model.reset()
model
}
}
