public void TestNoInsertion2()
{
SplitLayerTest test = new SplitLayerTest();
string strInput =
"name: 'TestNetwork' " +
"layer { " +
" name: 'data' " +
" type: 'Data' " +
" top: 'data' " +
" top: 'label' " +
"} " +
"layer { " +
" name: 'data_split' " +
" type: 'Split' " +
" bottom: 'data' " +
" top: 'data_split_0' " +
" top: 'data_split_1' " +
"} " +
"layer { " +
" name: 'innerprod1' " +
" type: 'InnerProduct' " +
" bottom: 'data_split_0' " +
" top: 'innerprod1' " +
"} " +
"layer { " +
" name: 'innerprod2' " +
" type: 'InnerProduct' " +
" bottom: 'data_split_1' " +
" top: 'innerprod2' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'EuclideanLoss' " +
" bottom: 'innerprod1' " +
" bottom: 'innerprod2' " +
"} ";
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.RunInsertionTest(strInput, strInput);
}
}
finally
{
test.Dispose();
}
}
public void TestGradient()
{
SplitLayerTest test = new SplitLayerTest();
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.TestGradient();
}
}
finally
{
test.Dispose();
}
}
public void TestNoInsertionWithInPlace()
{
SplitLayerTest test = new SplitLayerTest();
string strInput =
"name: 'TestNetwork' " +
"layer { " +
" name: 'data' " +
" type: 'Data' " +
" top: 'data' " +
" top: 'label' " +
"} " +
"layer { " +
" name: 'innerprod' " +
" type: 'InnerProduct' " +
" bottom: 'data' " +
" top: 'innerprod' " +
"} " +
"layer { " +
" name: 'relu' " +
" type: 'ReLU' " +
" bottom: 'innerprod' " +
" top: 'innerprod' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'SoftmaxWithLoss' " +
" bottom: 'innerprod' " +
" bottom: 'label' " +
"} ";
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.RunInsertionTest(strInput, strInput);
}
}
finally
{
test.Dispose();
}
}
public void TestInputInsertion()
{
SplitLayerTest test = new SplitLayerTest();
string strInput =
"name: 'TestNetwork' " +
"input: 'data' " +
"input_dim: 10 " +
"input_dim: 3 " +
"input_dim: 227 " +
"input_dim: 227 " +
"layer { " +
" name: 'innerprod1' " +
" type: 'InnerProduct' " +
" bottom: 'data' " +
" top: 'innerprod1' " +
"} " +
"layer { " +
" name: 'innerprod2' " +
" type: 'InnerProduct' " +
" bottom: 'data' " +
" top: 'innerprod2' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'EuclideanLoss' " +
" bottom: 'innerprod1' " +
" bottom: 'innerprod2' " +
"} ";
string strExpectedOutput =
"name: 'TestNetwork' " +
"input: 'data' " +
"input_dim: 10 " +
"input_dim: 3 " +
"input_dim: 227 " +
"input_dim: 227 " +
"layer { " +
" name: 'data_input_0_split' " +
" type: 'Split' " +
" bottom: 'data' " +
" top: 'data_input_0_split_0' " +
" top: 'data_input_0_split_1' " +
"} " +
"layer { " +
" name: 'innerprod1' " +
" type: 'InnerProduct' " +
" bottom: 'data_input_0_split_0' " +
" top: 'innerprod1' " +
"} " +
"layer { " +
" name: 'innerprod2' " +
" type: 'InnerProduct' " +
" bottom: 'data_input_0_split_1' " +
" top: 'innerprod2' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'EuclideanLoss' " +
" bottom: 'innerprod1' " +
" bottom: 'innerprod2' " +
"} ";
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.RunInsertionTest(strInput, strExpectedOutput);
}
}
finally
{
test.Dispose();
}
}
public void TestLossInsertion()
{
SplitLayerTest test = new SplitLayerTest();
string strInput =
"name: 'UnsharedWeightsNetwork' " +
"force_backward: true " +
"layer { " +
" name: 'data' " +
" type: 'DummyData' " +
" dummy_data_param { " +
" num: 5 " +
" channels: 2 " +
" height: 3 " +
" width: 4 " +
" data_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" } " +
" top: 'data' " +
"} " +
"layer { " +
" name: 'innerproduct1' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 10 " +
" bias_term: false " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 10 " +
" } " +
" } " +
" param { name: 'unsharedweights1' } " +
" bottom: 'data' " +
" top: 'innerproduct1' " +
" loss_weight: 2.5 " +
"} " +
"layer { " +
" name: 'innerproduct2' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 10 " +
" bias_term: false " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 10 " +
" } " +
" } " +
" param { name: 'unsharedweights2' } " +
" bottom: 'data' " +
" top: 'innerproduct2' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'EuclideanLoss' " +
" bottom: 'innerproduct1' " +
" bottom: 'innerproduct2' " +
"} ";
string strExpectedOutput =
"name: 'UnsharedWeightsNetwork' " +
"force_backward: true " +
"layer { " +
" name: 'data' " +
" type: 'DummyData' " +
" dummy_data_param { " +
" num: 5 " +
" channels: 2 " +
" height: 3 " +
" width: 4 " +
" data_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" } " +
" top: 'data' " +
"} " +
"layer { " +
" name: 'data_data_0_split' " +
" type: 'Split' " +
" bottom: 'data' " +
" top: 'data_data_0_split_0' " +
" top: 'data_data_0_split_1' " +
"} " +
"layer { " +
" name: 'innerproduct1' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 10 " +
" bias_term: false " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 10 " +
" } " +
" } " +
" param { name: 'unsharedweights1' } " +
" bottom: 'data_data_0_split_0' " +
" top: 'innerproduct1' " +
"} " +
"layer { " +
" name: 'innerproduct1_innerproduct1_0_split' " +
" type: 'Split' " +
" bottom: 'innerproduct1' " +
" top: 'innerproduct1_innerproduct1_0_split_0' " +
" top: 'innerproduct1_innerproduct1_0_split_1' " +
" loss_weight: 2.5 " +
" loss_weight: 0 " +
"} " +
"layer { " +
" name: 'innerproduct2' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 10 " +
" bias_term: false " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 10 " +
" } " +
" } " +
" param { name: 'unsharedweights2' } " +
" bottom: 'data_data_0_split_1' " +
" top: 'innerproduct2' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'EuclideanLoss' " +
" bottom: 'innerproduct1_innerproduct1_0_split_1' " +
" bottom: 'innerproduct2' " +
"} ";
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.RunInsertionTest(strInput, strExpectedOutput);
}
}
finally
{
test.Dispose();
}
}
public void TestNoInsertionImageNet()
{
SplitLayerTest test = new SplitLayerTest();
string strInput =
"name: 'CaffeNet' " +
"layer { " +
" name: 'data' " +
" type: 'Data' " +
" data_param { " +
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " +
" batch_size: 256 " +
" } " +
" transform_param { " +
" crop_size: 227 " +
" mirror: true " +
" mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " +
" } " +
" top: 'data' " +
" top: 'label' " +
"} " +
"layer { " +
" name: 'conv1' " +
" type: 'Convolution' " +
" convolution_param { " +
" num_output: 96 " +
" kernel_size: 11 " +
" stride: 4 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 0. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'data' " +
" top: 'conv1' " +
"} " +
"layer { " +
" name: 'relu1' " +
" type: 'ReLU' " +
" bottom: 'conv1' " +
" top: 'conv1' " +
"} " +
"layer { " +
" name: 'pool1' " +
" type: 'Pooling' " +
" pooling_param { " +
" pool: MAX " +
" kernel_size: 3 " +
" stride: 2 " +
" } " +
" bottom: 'conv1' " +
" top: 'pool1' " +
"} " +
"layer { " +
" name: 'norm1' " +
" type: 'LRN' " +
" lrn_param { " +
" local_size: 5 " +
" alpha: 0.0001 " +
" beta: 0.75 " +
" } " +
" bottom: 'pool1' " +
" top: 'norm1' " +
"} " +
"layer { " +
" name: 'conv2' " +
" type: 'Convolution' " +
" convolution_param { " +
" num_output: 256 " +
" group: 2 " +
" kernel_size: 5 " +
" pad: 2 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 1. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'norm1' " +
" top: 'conv2' " +
"} " +
"layer { " +
" name: 'relu2' " +
" type: 'ReLU' " +
" bottom: 'conv2' " +
" top: 'conv2' " +
"} " +
"layer { " +
" name: 'pool2' " +
" type: 'Pooling' " +
" pooling_param { " +
" pool: MAX " +
" kernel_size: 3 " +
" stride: 2 " +
" } " +
" bottom: 'conv2' " +
" top: 'pool2' " +
"} " +
"layer { " +
" name: 'norm2' " +
" type: 'LRN' " +
" lrn_param { " +
" local_size: 5 " +
" alpha: 0.0001 " +
" beta: 0.75 " +
" } " +
" bottom: 'pool2' " +
" top: 'norm2' " +
"} " +
"layer { " +
" name: 'conv3' " +
" type: 'Convolution' " +
" convolution_param { " +
" num_output: 384 " +
" kernel_size: 3 " +
" pad: 1 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 0. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'norm2' " +
" top: 'conv3' " +
"} " +
"layer { " +
" name: 'relu3' " +
" type: 'ReLU' " +
" bottom: 'conv3' " +
" top: 'conv3' " +
"} " +
"layer { " +
" name: 'conv4' " +
" type: 'Convolution' " +
" convolution_param { " +
" num_output: 384 " +
" group: 2 " +
" kernel_size: 3 " +
" pad: 1 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 1. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'conv3' " +
" top: 'conv4' " +
"} " +
"layer { " +
" name: 'relu4' " +
" type: 'ReLU' " +
" bottom: 'conv4' " +
" top: 'conv4' " +
"} " +
"layer { " +
" name: 'conv5' " +
" type: 'Convolution' " +
" convolution_param { " +
" num_output: 256 " +
" group: 2 " +
" kernel_size: 3 " +
" pad: 1 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 1. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'conv4' " +
" top: 'conv5' " +
"} " +
"layer { " +
" name: 'relu5' " +
" type: 'ReLU' " +
" bottom: 'conv5' " +
" top: 'conv5' " +
"} " +
"layer { " +
" name: 'pool5' " +
" type: 'Pooling' " +
" pooling_param { " +
" kernel_size: 3 " +
" pool: MAX " +
" stride: 2 " +
" } " +
" bottom: 'conv5' " +
" top: 'pool5' " +
"} " +
"layer { " +
" name: 'fc6' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 4096 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.005 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 1. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'pool5' " +
" top: 'fc6' " +
"} " +
"layer { " +
" name: 'relu6' " +
" type: 'ReLU' " +
" bottom: 'fc6' " +
" top: 'fc6' " +
"} " +
"layer { " +
" name: 'drop6' " +
" type: 'Dropout' " +
" dropout_param { " +
" dropout_ratio: 0.5 " +
" } " +
" bottom: 'fc6' " +
" top: 'fc6' " +
"} " +
"layer { " +
" name: 'fc7' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 4096 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.005 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 1. " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'fc6' " +
" top: 'fc7' " +
"} " +
"layer { " +
" name: 'relu7' " +
" type: 'ReLU' " +
" bottom: 'fc7' " +
" top: 'fc7' " +
"} " +
"layer { " +
" name: 'drop7' " +
" type: 'Dropout' " +
" dropout_param { " +
" dropout_ratio: 0.5 " +
" } " +
" bottom: 'fc7' " +
" top: 'fc7' " +
"} " +
"layer { " +
" name: 'fc8' " +
" type: 'InnerProduct' " +
" inner_product_param { " +
" num_output: 1000 " +
" weight_filler { " +
" type: 'gaussian' " +
" std: 0.01 " +
" } " +
" bias_filler { " +
" type: 'constant' " +
" value: 0 " +
" } " +
" } " +
" param { " +
" lr_mult: 1 " +
" decay_mult: 1 " +
" } " +
" param { " +
" lr_mult: 2 " +
" decay_mult: 0 " +
" } " +
" bottom: 'fc7' " +
" top: 'fc8' " +
"} " +
"layer { " +
" name: 'loss' " +
" type: 'SoftmaxWithLoss' " +
" bottom: 'fc8' " +
" bottom: 'label' " +
"} ";
try
{
foreach (ISplitLayerTest t in test.Tests)
{
t.RunInsertionTest(strInput, strInput);
}
}
finally
{
test.Dispose();
}
}
