PyCS/pycs/pipeline/PipelineManager.py

from os import getcwd
from os import path

from eventlet import tpool

from pycs.pipeline.Job import Job
from pycs.pipeline.tf1.pipeline import Pipeline as TF1Pipeline
from pycs.projects.Project import Project


class PipelineManager:
    def __init__(self, project: Project):
        self.project = project
        self.pipeline = tpool.execute(self.__load_pipeline, project['pipeline']['model-distribution'])

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):
        self.pipeline.close()

    def run(self, media_file):
        # create job list
        # TODO update job progress
        job = Job('detect-faces', self.project['id'], media_file)
        result = tpool.execute(lambda p, j: p.execute(j), self.pipeline, job)

        # remove existing pipeline predictions from media_fle
        media_file.remove_pipeline_results()

        # add new predictions
        for prediction in result.predictions:
            media_file.add_result(prediction, origin='pipeline')

    def __load_pipeline(self, pipeline_identifier):
        model_distribution = self.project.parent.parent['models'][pipeline_identifier]

        if model_distribution['mode'] == 'tf1':
            model_root = path.join(getcwd(), 'models', model_distribution['name'])

            pipeline = TF1Pipeline()
            pipeline.load(model_root, model_distribution['pipeline'])

            return pipeline

    '''
    def __update(self, data):
        # get current project path
        opened_projects = list(filter(lambda x: x['status'] == 'open', data))
        if len(opened_projects) == 0:
            return

        current_project = opened_projects[0]

        # find images to predict
        if 'data' not in current_project.keys() or len(current_project['data']) == 0:
            return

        # load pipeline
        pipeline = tpool.execute(self.__load_pipeline, current_project['pipeline']['model-distribution'])

        # create job list
        for d in current_project['data']:
            print('keys:', d.keys())
            if 'result' not in d.keys():
                # TODO update job progress
                job = Job('detect-faces', current_project['id'], d)
                result = tpool.execute(lambda p, j: p.execute(j), pipeline, job)
                d['result'] = result.predictions

        # close pipeline
        pipeline.close()
    '''

    '''
    def __update(self, data):
        for current_project in data:
            print('>>>>>>>>>>')
            # find images to predict
            if 'data' not in current_project.keys() or len(current_project['data']) == 0:
                return

            # load pipeline
            pipeline = tpool.execute(self.__load_pipeline, current_project['pipeline']['model-distribution'])

            # create job list
            for d in current_project['data']:
                print('keys:', d.keys())
                if 'result' not in d.keys():
                    # TODO update job progress
                    job = Job('detect-faces', current_project['id'], d)
                    result = tpool.execute(lambda p, j: p.execute(j), pipeline, job)
                    d['result'] = result.predictions

            # close pipeline
            pipeline.close()
    '''