10.3 Pipelines
Quevedo allows you to train different neural networks (Section 10.2) to recognize different objects and features. These networks can then be composed into a pipeline to build an expert system, capable of performing a bigger task than each of the networks by themselves.
For example, a detection network can first locate the graphemes within a logogram, and then specialist networks used to classify each of the graphemes.
When using Quevedo in the command line, you can choose the pipeline
to test or execute with the -P flag (equivalent to the
-N flag for networks). In the web interface, the
pipelines are available along the trained networks and user functions
for the user to run and visualize.
Quevedo pipelines can be used to create expert systems for processing visually complex images. One such example, for the case of SignWriting, is described in Chapter 8.
10.3.1 Pipeline configuration
Pipelines are added to the config.toml file of the
dataset. Each entry is the name of the pipeline, added to the
“pipeline” table. There are a number of types of pipelines that can be
used, each with their own configuration, but there are some common
options.
10.3.1.1 Common options
Options for the pipelines often take as value the name of a neural network. Most of the time, instead of a neural network, the name of a pipeline can be used. Quevedo will search the configuration file for pipelines or networks with a matching name, so neither “network” nor “pipeline” need to be prepended.
If a “subsets” key is given to a pipeline, you will be able to use
the test command to evaluate the
performance of the pipeline on those sets. Testing pipelines follows
the same rules as for networks, and uses the configured test folds.
Training full pipelines is not yet supported, please train the
networks individually.
Pipelines can also use the extend keyword to inherit
configuration from another pipeline, making it easy to set the same
subsets to test all pipelines, or share some of the options. This
extension is recursive, so chains of pipeline configurations can be
used.
10.3.2 Logogram recognizer
A logogram recognizer pipeline has two steps. The first step uses a detector network (Section 10.2.2.2) to find graphemes in an image. An optional second step uses a classifier network (Section 10.2.2.1) or another pipeline to then extract the tags for each of the graphemes. The end result is the detected logogram but with the graphemes augmented by the classifier.
[pipeline.logograms]
detect = "detector"
classify = "classifier"detector
to
find
the
graphemes,
and
then
further
classifies
them
with
a
network
named
classifier10.3.3 Sequence classifier
A sequence classifier uses many classifier networks or pipelines to
iteratively augment the annotation of a grapheme. It has one single
option, sequence, which is a list of the sub-systems to
run.
Additionally to networks and pipelines, the steps in the sequence
can be lambda functions to run on the grapheme, or longer functions
defined in a user script. For this, place the script in the
scripts directory, and use as step
script_name.py:function_name.
[pipeline.sequence]
extend = "defaults"
sequence = [
"classifier1"
"funtions.py:error_correction",
"classifier2"
]classifier1
to
find
a
first
possible
set
of
tags
for
the
grapheme.
Then,
a
function
error_correction
in
the
functions.py
user
script
fixes
some
common
errors.
A
second
network
called
classifier2
makes
use
of
the
fixed
grapheme
to
get
a
better
prediction.10.3.4 Branching classifier
A branching pipeline can serve to classify graphemes using
different networks or pipelines according to some of the grapheme
characteristics. A criterion option sets the value to use
to choose the branch, and then the other options are the networks or
sub-pipelines for each branch. The criterion can be the name of a tag,
or a lambda function to call on the grapheme.
[pipeline.branching]
criterion = "lambda g: g.tags.get('TAG1')"
criterion = "TAG1" # equivalent to the previous one
value1 = "classifier_for_value1s"
value2 = "classifier_for_value2s"