tensorlayer3/tests/pending/test_logging_hyperdash.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import unittest

import time

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import tensorflow as tf
import tensorlayer as tl

from tensorlayer.logging.contrib import hyperdash as hd

from tests.utils import CustomTestCase


class TL_Logger_Test(CustomTestCase):

    @classmethod
    def setUpClass(cls):
        cls.apikey = os.getenv('HYPERDASH_APIKEY', "test_api_key")

    def test_apikey_unset(self):

        with self.assertRaises(ValueError):
            hd.HyperDashHandler.reset_apikey()
            hd.HyperDashHandler.get_apikey()

    def test_apikey_set(self):

        with self.assertNotRaises(ValueError):
            hd.HyperDashHandler.set_apikey(self.apikey)
            hd.HyperDashHandler.get_apikey()

    def test_monitor(self):

        with self.assertNotRaises(Exception):

            hd.HyperDashHandler.set_apikey(self.apikey)

            @hd.monitor("TRAVIS 1 - dogs vs. cats")
            def train_dogs_vs_cats(exp=None):

                # Record the value of hyperparameter gamma for this experiment
                lr = exp.param("learning rate", 0.005)
                tl.logging.debug("Learning Rate: %f" % lr)

                for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
                    tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))

                    # Record a numerical performance metric
                    exp.metric(name="accuracy", value=accuracy)

                    time.sleep(0.1)

            train_dogs_vs_cats()

    def test_monitor_variant(self):

        with self.assertNotRaises(Exception):

            @hd.monitor("TRAVIS 2 - dogs vs. cats", api_key=self.apikey)
            def train_dogs_vs_cats(exp=None):

                # Record the value of hyperparameter gamma for this experiment
                lr = exp.param("learning rate", 0.005)
                tl.logging.debug("Learning Rate: %f" % lr)

                for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
                    tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))

                    # Record a numerical performance metric
                    exp.metric(name="accuracy", value=accuracy)

                    time.sleep(0.1)

            train_dogs_vs_cats()

    def test_Experiment(self):

        hd.HyperDashHandler.set_apikey(self.apikey)

        with self.assertNotRaises(Exception):

            def train_dogs_vs_cats():

                # Create an experiment with a model name, then autostart
                exp = hd.Experiment("TRAVIS 3 - dogs vs. cats")

                # Record the value of hyperparameter gamma for this experiment
                lr = exp.param("learning rate", 0.005)
                tl.logging.debug("Learning Rate: %f" % lr)

                for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
                    tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))

                    # Record a numerical performance metric
                    exp.metric(name="accuracy", value=accuracy)

                    time.sleep(0.1)

                # Cleanup and mark that the experiment successfully completed
                exp.end()

            train_dogs_vs_cats()

    def test_Experiment_variant(self):

        with self.assertNotRaises(Exception):

            def train_dogs_vs_cats():

                # Create an experiment with a model name, then autostart
                exp = hd.Experiment("TRAVIS 4 - dogs vs. cats", api_key=self.apikey)

                # Record the value of hyperparameter gamma for this experiment
                lr = exp.param("learning rate", 0.005)
                tl.logging.debug("Learning Rate: %f" % lr)

                for epoch, accuracy in enumerate([10, 30, 50, 70, 80, 90, 95, 100]):
                    tl.logging.debug("Epoch %d - Accuracy %d%%" % (epoch + 1, accuracy))

                    # Record a numerical performance metric
                    exp.metric(name="accuracy", value=accuracy)

                    time.sleep(0.1)

                # Cleanup and mark that the experiment successfully completed
                exp.end()

            train_dogs_vs_cats()


if __name__ == '__main__':

    tf.logging.set_verbosity(tf.logging.DEBUG)
    tl.logging.set_verbosity(tl.logging.DEBUG)

    unittest.main()