Workspace Class

    Code: workspace.py| API Docs:

    Workspace is a class that holds all the related objects created during runtime:

    • all blobs, and
    • all instantiated networks. It is the owner of all these objects and deals with the scaffolding logistics.

    A workspace is created for you whenever you create nets or handle blobs of data with Caffe2. Calling initializes an empty workspace with the given root folder. For any operators that are going to interface with the file system, such as load operators, they will write things under this root folder given by the workspace.

    You will notice that the CreateNet methods require a net. These are NetDefs that are created from data from datasets, pre-trained models, protobuf data describing the nets and models need to be instantiated as prototype objects inheriting the characteristics from Caffe2’s protobuf spec. These will then be managed in the workspace. Also, note that we’ve loaded the init_net first, which will setup references to blobs that should be filled by predict_net once you load that net.

    For more examples of the basics of workspaces, check out the basics tutorial.

    CreateNet

    Creates an empty net unless blobs are passed in.

    Outputs
    net object 
    1. workspace.CreateNet(net_def, input_blobs)

    FeedBlob

    Inputs
    name the name of the blob
    arr either a TensorProto object or a numpy array object to be fed into the workspace
    device_option (optional) the device option to feed the data with
    Returns
    True or False, stating whether the feed is successful.

      FetchBlob

      Fetches a blob from the workspace.

      Inputs
      name the name of the blob - a string or a BlobReference
      Returns
      Fetched blob (numpy array or string) if successful 
      1. workspace.FetchBlob(name)

      FetchBlobs

      Fetches a list of blobs from the workspace.

      Inputs
      names list of names of blobs - strings or BlobReferences
      Returns
      list of fetched blobs 
      1. workspace.FetchBlob(name)

      Returns the current namescope string. To be used to fetch blobs.

      Outputs
      namescope

      InferShapesAndTypes

      Infers the shapes and types for the specified nets.

      Inputs
      nets the list of nets
      blob_dimensions (optional) a dictionary of blobs and their dimensions. If not specified, the workspace blobs are used.

      ResetWorkSpace

      Resets the workspace, and if root_folder is empty it will keep the current folder setting.

      Inputs
      root_folder string
      Outputs
      workspace object 
      1. workspace.ResetWorkspace(root_folder)

      RunNet

      Runs a given net.

      Inputs
      name the name of the net, or a reference to the net.
      num_iter number of iterations to run, defaults to 1
      Returns
      True or an exception. 
      1. workspace.RunNet(name, num_iter)

      RunNetOnce

      Inputs
      net 
      1. workspace.RunNetOnce(net)

      Will execute a single operator.

      Inputs
      operator 
      1. workspace.RunOperatorOnce(operator)

      RunOperatorsOnce

      Will execute a set of operators.

      Inputs
      operators list
      Outputs
      Boolean on success
      False if any op fails

      RunPlan

      Construct a plan of multiple execution steps to run multiple different networks.Use RunPlan to execute this plan.

      Inputs
      plan_or_step 
      1. workspace.RunPlan(plan_or_step)

      StartMint

      Starts a mint instance.Note: this does not work well under ipython yet. According to https://github.com/ipython/ipython/issues/5862

      Inputs
      root_folder string
      port int
      Output
      mint instance 
      1. workspace.StartMint(root_folder, port)

      StringifyBlobName

      Returns the name of a blob.

      Inputs
      name
      Outputs
      name, “BlobReference” 
      1. workspace.StringifyBlobName(name)

      Returns the name of a net.

      Inputs
      name
      Outputs
      name, “Net” 
      1. workspace.StringifyNetName(name)

      StringifyProto

      Inputs
      obj a protocol buffer object, or a Pycaffe2 object that has a Proto() function.
      Outputs
      string the output protobuf string.
      Raises
      AttributeError if the passed in object does not have the right attribute.