| --- |
| title: Data |
| --- |
| # Data: Ins and Outs |
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| Data flows through Caffe as [Blobs](net_layer_blob.html#blob-storage-and-communication). |
| Data layers load input and save output by converting to and from Blob to other formats. |
| Common transformations like mean-subtraction and feature-scaling are done by data layer configuration. |
| New input types are supported by developing a new data layer -- the rest of the Net follows by the modularity of the Caffe layer catalogue. |
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| This data layer definition |
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| layer { |
| name: "mnist" |
| # Data layer loads leveldb or lmdb storage DBs for high-throughput. |
| type: "Data" |
| # the 1st top is the data itself: the name is only convention |
| top: "data" |
| # the 2nd top is the ground truth: the name is only convention |
| top: "label" |
| # the Data layer configuration |
| data_param { |
| # path to the DB |
| source: "examples/mnist/mnist_train_lmdb" |
| # type of DB: LEVELDB or LMDB (LMDB supports concurrent reads) |
| backend: LMDB |
| # batch processing improves efficiency. |
| batch_size: 64 |
| } |
| # common data transformations |
| transform_param { |
| # feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1] |
| scale: 0.00390625 |
| } |
| } |
| |
| loads the MNIST digits. |
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| **Tops and Bottoms**: A data layer makes **top** blobs to output data to the model. |
| It does not have **bottom** blobs since it takes no input. |
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| **Data and Label**: a data layer has at least one top canonically named **data**. |
| For ground truth a second top can be defined that is canonically named **label**. |
| Both tops simply produce blobs and there is nothing inherently special about these names. |
| The (data, label) pairing is a convenience for classification models. |
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| **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition. |
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| layer { |
| name: "data" |
| type: "Data" |
| [...] |
| transform_param { |
| scale: 0.1 |
| mean_file_size: mean.binaryproto |
| # for images in particular horizontal mirroring and random cropping |
| # can be done as simple data augmentations. |
| mirror: 1 # 1 = on, 0 = off |
| # crop a `crop_size` x `crop_size` patch: |
| # - at random during training |
| # - from the center during testing |
| crop_size: 227 |
| } |
| } |
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| **Prefetching**: for throughput data layers fetch the next batch of data and prepare it in the background while the Net computes the current batch. |
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| **Multiple Inputs**: a Net can have multiple inputs of any number and type. Define as many data layers as needed giving each a unique name and top. Multiple inputs are useful for non-trivial ground truth: one data layer loads the actual data and the other data layer loads the ground truth in lock-step. In this arrangement both data and label can be any 4D array. Further applications of multiple inputs are found in multi-modal and sequence models. In these cases you may need to implement your own data preparation routines or a special data layer. |
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| *Improvements to data processing to add formats, generality, or helper utilities are welcome!* |
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| ## Formats |
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| Refer to the layer catalogue of [data layers](layers.html#data-layers) for close-ups on each type of data Caffe understands. |
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| ## Deployment Input |
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| For on-the-fly computation deployment Nets define their inputs by `input` fields: these Nets then accept direct assignment of data for online or interactive computation. |
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