def cpu_gpu_check():
fallback_cpu, limit_gpu_mem = cpu_gpu_reader()
if fallback_cpu is True:
cpu_fallback()
if isinstance(limit_gpu_mem, float) is True:
gpu_memory_manage(ratio=limit_gpu_mem)
else:
gpu_memory_manage()
def test_cpu_gpu_management(self):
from astroNN.shared.nn_tools import cpu_fallback
cpu_fallback(flag=True)
cpu_fallback(flag=False)
# make sure flag=2 raise error
self.assertRaises(ValueError, cpu_fallback, flag=2)
def test_cpu_gpu_management(self):
from astroNN.shared.nn_tools import cpu_fallback
cpu_fallback(flag=0)
# os environ is string
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '-1')
cpu_fallback(flag=1)
# make sure flag =1 will delete the environ
self.assertEqual(any(x == "CUDA_VISIBLE_DEVICES" for x in os.environ), False)
# make sure flag=2 raise error
self.assertRaises(ValueError, cpu_fallback, flag=2)
import unittest
import numpy as np
import numpy.testing as npt
import tensorflow as tf
from astroNN.shared.nn_tools import cpu_fallback
# make sure this test use CPU
cpu_fallback()
from astroNN.config import MAGIC_NUMBER
from astroNN.nn import reduce_var
from astroNN.nn.losses import (magic_correction_term, mean_absolute_error,
mean_squared_error, categorical_crossentropy,
binary_crossentropy, nll, mean_error,
zeros_loss, mean_percentage_error, median)
from astroNN.nn.metrics import (
categorical_accuracy,
binary_accuracy,
mean_absolute_percentage_error,
mean_squared_logarithmic_error,
)
class LossFuncTestCase(unittest.TestCase):
def test_loss_func_util(self):
# make sure custom reduce_var works
content = [1, 2, 3, 4, 5]
var_array = tf.constant(content)
self.assertEqual(reduce_var(var_array).numpy(), np.var(content))
