Add

class imagepypelines.builtin_blocks.Add(term)[source]

Bases: imagepypelines.core.block_subclasses.SimpleBlock

Attributes Summary

EXTANT

Methods Summary

after_process() (optional overload)function that runs after processing for optional functionality.
before_process(data[, labels]) (optional overload)function that runs before processing for optional functionality.
label(lbl) (optional overload)retrieves the label for this datum
label_strategy(labels) calls self.label for each datum and returns a list or Nonetype
process(datum) (required overload)processes a single datum
process_strategy(data) processes each datum using self.process and return list
rename(name) Renames this block to the given name
train(data[, labels]) (optional or required overload)trains the block.

Attributes Documentation

EXTANT = {}

Methods Documentation

after_process()

(optional overload)function that runs after processing for optional functionality. intended for optional use as a cleanup function

Parameters:None
before_process(data, labels=None)

(optional overload)function that runs before processing for optional functionality. this function takes in the full data list and label list. does nothing unless overloaded

Parameters:
  • data (list) – list of datums to process
  • labels (list,None) – corresponding label for each datum, None by default (for unsupervised systems)
label(lbl)

(optional overload)retrieves the label for this datum

label_strategy(labels)

calls self.label for each datum and returns a list or Nonetype

process(datum)[source]

(required overload)processes a single datum

Parameters:datum – datum to process
Returns:datum processed by this block
Return type:processed
process_strategy(data)

processes each datum using self.process and return list

rename(name)

Renames this block to the given name

Parameters:name (str) – the new name for your Block
Returns:object reference to this block (self)
Return type:ip.Block

Note

unlike naming your block using the name parameter in instantiation, imagepypelines will not guarantee that this name will be unique. It is considered the user’s responsibility to determine that this will not cause problems in your pipeline.

train(data, labels=None)

(optional or required overload)trains the block. overloading is required if the ‘requires_training’ parameter is set to True

users are expected to save pertinent variables as instance variables

Parameters:
  • data (list) – list of datums to train on
  • labels (list,None) – corresponding label for each datum, None by default (for unsupervised systems)
Returns:

None