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Separate the reusable data processors from the data layers #244

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Separate the reusable data processors from the data layers #244

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4be8cc7
Add the data processors API
kloudkl Mar 19, 2014
cd21852
Add CroppingDataProcessor adapted from DataLayerPrefetch and test it
kloudkl Mar 21, 2014
21433e3
Test CroppingDataProcessor in CPU & GPU modes during TRAIN & TEST phase
kloudkl Mar 21, 2014
42ec06c
Add and test MirroringDataProcessor adapted from DataLayerPrefetch
kloudkl Mar 21, 2014
b76d1f2
Add and test MeanSubtractionDataProcessor following DataLayerPrefetch
kloudkl Mar 21, 2014
d00ef28
Add and test ScalingDataProcessor
kloudkl Mar 21, 2014
375eeff
Implement and test MeanZeroingDataProcessor
kloudkl Mar 21, 2014
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353 changes: 353 additions & 0 deletions examples/new_data_layer/imagenet_train.prototxt
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Original file line number Diff line number Diff line change
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name: "NewDataLayerNet"
layers {
layer {
name: "data"
type: "data_blob"
data_blob {
data_source {
name: "ilvsrc12_train_leveldb"
leveldb {
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I would add param with type: leveldb. In that case is clear that only one type is possible

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Agreed.

source: "ilvsrc12_train_leveldb"
batch_size: 256
}
}
data_processors {
mean_subtraction {
name: "mean_subtraction_ilsvrc12_imagenet_mean"
mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
}
cropping {
name: "cropping_227_227"
crop_dims {
height: 227
width: 227
}
}
mirroring {
name: "mirroring_left_right"
type: LEFT_RIGHT
}
}
}
}
top: "data"
top: "label"
}
layers {
layer {
name: "conv1"
type: "conv"
num_output: 96
kernelsize: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "data"
top: "conv1"
}
layers {
layer {
name: "relu1"
type: "relu"
}
bottom: "conv1"
top: "conv1"
}
layers {
layer {
name: "pool1"
type: "pool"
pool: MAX
kernelsize: 3
stride: 2
}
bottom: "conv1"
top: "pool1"
}
layers {
layer {
name: "norm1"
type: "lrn"
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "pool1"
top: "norm1"
}
layers {
layer {
name: "conv2"
type: "conv"
num_output: 256
group: 2
kernelsize: 5
pad: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "norm1"
top: "conv2"
}
layers {
layer {
name: "relu2"
type: "relu"
}
bottom: "conv2"
top: "conv2"
}
layers {
layer {
name: "pool2"
type: "pool"
pool: MAX
kernelsize: 3
stride: 2
}
bottom: "conv2"
top: "pool2"
}
layers {
layer {
name: "norm2"
type: "lrn"
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "pool2"
top: "norm2"
}
layers {
layer {
name: "conv3"
type: "conv"
num_output: 384
kernelsize: 3
pad: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "norm2"
top: "conv3"
}
layers {
layer {
name: "relu3"
type: "relu"
}
bottom: "conv3"
top: "conv3"
}
layers {
layer {
name: "conv4"
type: "conv"
num_output: 384
group: 2
kernelsize: 3
pad: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "conv3"
top: "conv4"
}
layers {
layer {
name: "relu4"
type: "relu"
}
bottom: "conv4"
top: "conv4"
}
layers {
layer {
name: "conv5"
type: "conv"
num_output: 256
group: 2
kernelsize: 3
pad: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "conv4"
top: "conv5"
}
layers {
layer {
name: "relu5"
type: "relu"
}
bottom: "conv5"
top: "conv5"
}
layers {
layer {
name: "pool5"
type: "pool"
kernelsize: 3
pool: MAX
stride: 2
}
bottom: "conv5"
top: "pool5"
}
layers {
layer {
name: "fc6"
type: "innerproduct"
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "pool5"
top: "fc6"
}
layers {
layer {
name: "relu6"
type: "relu"
}
bottom: "fc6"
top: "fc6"
}
layers {
layer {
name: "drop6"
type: "dropout"
dropout_ratio: 0.5
}
bottom: "fc6"
top: "fc6"
}
layers {
layer {
name: "fc7"
type: "innerproduct"
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1.
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "fc6"
top: "fc7"
}
layers {
layer {
name: "relu7"
type: "relu"
}
bottom: "fc7"
top: "fc7"
}
layers {
layer {
name: "drop7"
type: "dropout"
dropout_ratio: 0.5
}
bottom: "fc7"
top: "fc7"
}
layers {
layer {
name: "fc8"
type: "innerproduct"
num_output: 1000
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
blobs_lr: 1.
blobs_lr: 2.
weight_decay: 1.
weight_decay: 0.
}
bottom: "fc7"
top: "fc8"
}
layers {
layer {
name: "loss"
type: "softmax_loss"
}
bottom: "fc8"
bottom: "label"
}
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