def test_train_32_100_10(self):
"""Generic functional test"""
py_args = self.generic_arguments.copy()
out = test_util.run_test_helper(run_pytorch_mnist, **py_args)
expected_accuracy = [
87.65, 88.06, 88.4, 88.43, 88.68, 88.71, 88.69, 88.89, 88.85, 88.61
]
test_util.parse_results_for_accuracy(out, expected_accuracy, 2.5)
def test_mnist_train(self):
"""Generic test on default arguments in training"""
py_args = self.generic_arguments.copy()
out = test_util.run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
97.72, 98.15, 98.51, 98.55, 98.55, 98.38, 98.34, 98.35, 98.43,
98.41
]
test_util.parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_mnist_train(self):
"""Generic test on default arguments in training"""
py_args = self.generic_arguments.copy()
out = run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
88.88, 89.63, 89.83, 90.01, 90.12, 90.22, 90.40, 90.59, 90.65,
90.70
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
def test_resnet8_bs4_4ipus(self):
out = run_popart_resnet_training(**{'--size': 8,
'--batch-size': 4,
'--norm-type': 'GROUP',
'--num-ipus': 4,
'--epochs': 5,
'--no-prng': '',
'--data-dir': './',
'--num-workers': 0})
expected_accuracy = [44.1, 53.6, 61.6, 64.6, 66.1]
parse_results_for_accuracy(out, expected_accuracy, 2)
def test_train_torchvision(self):
"""Generic functional test"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 2
py_args["--epochs"] = 10
out = run_test_helper(run_pytorch_Torchvision, **py_args)
expected_accuracy = [
16.85, 24.52, 30.77, 36.34, 40.65, 45.59, 49.07, 52.13, 55.52,
58.14
]
parse_results_for_accuracy(out, expected_accuracy, 2.5)
def test_resnet8_bs4_4ipus_pipeline(self):
out = run_popart_resnet_training(**{'--size': 8,
'--batch-size': 4,
'--norm-type': 'GROUP',
'--num-ipus': 4,
'--epochs': 5,
'--pipeline': '',
'--no-prng': '',
'--data-dir': './',
'--num-workers': 0})
expected_accuracy = [42.2, 53.9, 60.7, 63.9, 65.6]
parse_results_for_accuracy(out, expected_accuracy, 2)
def test_mnist_train_sharded_pipelined(self):
"""Generic test on default arguments in training over 2 IPUs
and pipelined"""
py_args = self.generic_arguments.copy()
py_args["--num-ipus"] = 2
py_args["--pipeline"] = ""
out = run_test_helper(run_popart_mnist_training, **py_args)
expected_accuracy = [
88.11, 88.69, 88.91, 88.94, 88.92, 88.98, 89.05, 89.14, 89.18,
89.25
]
parse_results_for_accuracy(out, expected_accuracy,
self.accuracy_tolerances)
