# Blob¶

A blob is the fundamental data representation in Mocha. It is used for both data (e.g. mini-batch of data samples) and parameters (e.g. filters of a convolution layer). Conceptually, a blob is an N-dimensional tensor.

For example, in vision, a data blob is usually a 4D-tensor. Following the vision (and Caffe) convention, the four dimensions are called width, height, channels and num. The fastest changing dimension is width and slowest changing dimension is num.

Note

The memory layout of a blob in Mocha is compatible with Caffe’s blob. So a blob (e.g. layer parameters) in Mocha can be saved to HDF5 and loaded from Caffe without doing any dimension permutation, and vise versa. However, since Julia uses the column-major convention for tensor and matrix data, and Caffe uses the row-major convention, in Mocha API, the order of the four dimensions is width, height, channels, and num, while in Caffe API, it is num, channels, height, width.

Each backend has its own blob implementation, as a subtype of Blob. For example, a blob in the CPU backend is a shallow wrapper of a Julia Array object, while a blob in the GPU backend references to a piece of GPU memory.

## Constructors and Destructors¶

A backend-dependent blob can be created with the following function:

make_blob(backend, data_type, dims)

dims is an NTuple, specifying the dimensions of the blob to be created. Currently data_type should be either Float32 or Float64.

Several helper functions are also provided:

make_blob(backend, data_type, dims...)

Spell out the dimensions explicitly.

make_blob(backend, array)

array is a Julia AbstractArray. This creates a blob with the same data type and shape as array and initializes the blob contents with array.

make_zero_blob(backend, data_type, dims)

Create a blob and initialize it with zeros.

reshape_blob(backend, blob, new_dims)

Create a reference to an existing blob with a possiblely different shape. The behavior is the same as Julia’s reshape function on an array: the new blob shares data with the existing one.

destroy(blob)

Release the resources of a blob.

Note

The resources need to be released explicitly. A Julia blob object being GC-ed does not release the underlying resource automatically.

## Accessing Properties of a Blob¶

The blob implements a simple API similar to a Julia array:

eltype(blob)

Get the element type of the blob.

ndims(blob)

Get the tensor dimension of the blob. The same as length(size(blob)).

size(blob)

Get the shape of the blob. The return value is an NTuple.

size(blob, dim)

Get the size along a particular dimension. dim can be negative. For example, size(blob, -1) is the same as size(blob)[end]. For convenience, if dim exceeds ndims(blob), the function returns 1 instead of raising an error.

length(blob)

Get the total number of elements in a blob.

get_width(blob)

The same as size(blob, 1).

get_height(blob)

The same as size(blob, 2).

get_num(blob)

The same as size(blob, -1).

get_fea_size(blob)

The the feature size in a blob, which is the same as prod(size(blob)[1:end-1]).

The wrapper get_chann was removed when Mocha upgraded from 4D-tensors to general ND-tensors, because the channel dimension is usually ambiguous for general ND-tensors.

## Accessing Data of a Blob¶

Because accessing GPU memory is costly, a blob does not have an interface to do element-wise accessing. The data can be either manipulated in a backend-dependent manner, relying on the underlying implementation details, or in a backend-independent way by copying the contents from and to a Julia array.

copy!(dst, src)

Copy the contents of src to dst. src and dst can be either a blob or a Julia array.

The following utilities can be used to initialize the contents of a blob

fill!(blob, value)

Fill every element of blob with value.

erase!(blob)

Fill blob with zeros. Depending on the implementation, erase!(blob) might be more efficient than fill!(blob, 0).