Caffe

#Caffe | 来源: 网络整理| 查看: 265

Caffe

Deep learning framework by BAIR

Created by Yangqing Jia Lead Developer Evan Shelhamer

View On GitHub Data: Ins and Outs

Data flows through Caffe as Blobs. 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.

This data layer definition

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.

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.

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.

Transformations: data preprocessing is parametrized by transformation messages within the data layer definition.

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 } }

Prefetching: for throughput data layers fetch the next batch of data and prepare it in the background while the Net computes the current batch.

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.

Improvements to data processing to add formats, generality, or helper utilities are welcome!

Formats

Refer to the layer catalogue of data layers for close-ups on each type of data Caffe understands.

Deployment Input

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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