def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = EpochLambda('test', self._metric_function, step_size=3)
self._metric.reset({
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32
})
self._states = [{
torchbearer.BATCH: 0,
torchbearer.Y_TRUE: torch.LongTensor([0]),
torchbearer.Y_PRED: torch.FloatTensor([0.0]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 1,
torchbearer.Y_TRUE: torch.LongTensor([1]),
torchbearer.Y_PRED: torch.FloatTensor([0.1]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 2,
torchbearer.Y_TRUE: torch.LongTensor([2]),
torchbearer.Y_PRED: torch.FloatTensor([0.2]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 3,
torchbearer.Y_TRUE: torch.LongTensor([3]),
torchbearer.Y_PRED: torch.FloatTensor([0.3]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 4,
torchbearer.Y_TRUE: torch.LongTensor([4]),
torchbearer.Y_PRED: torch.FloatTensor([0.4]),
torchbearer.DEVICE: 'cpu'
}]
def _add_metric(self, state):
from pycm import ConfusionMatrix
def make_cm(y_pred, y_true):
_, y_pred = torch.max(y_pred, 1)
cm = ConfusionMatrix(y_true.cpu().numpy(),
y_pred.cpu().numpy(), **self.kwargs)
for handler in self._handlers:
handler(cm, state)
my_metric = EpochLambda('pycm', make_cm, False)
my_metric.reset(state)
state[torchbearer.METRIC_LIST] = MetricList(
[state[torchbearer.METRIC_LIST], my_metric])
def test_not_running(self):
metric = EpochLambda('test', self._metric_function, running=False, step_size=6)
metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32})
metric.train()
for i in range(12):
metric.process(self._states[0])
self._metric_function.assert_not_called()
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = EpochLambda('test', self._metric_function, step_size=3)
self._metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32})
self._states = [{torchbearer.BATCH: 0, torchbearer.Y_TRUE: torch.LongTensor([0]), torchbearer.Y_PRED: torch.FloatTensor([0.0]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 1, torchbearer.Y_TRUE: torch.LongTensor([1]), torchbearer.Y_PRED: torch.FloatTensor([0.1]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 2, torchbearer.Y_TRUE: torch.LongTensor([2]), torchbearer.Y_PRED: torch.FloatTensor([0.2]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 3, torchbearer.Y_TRUE: torch.LongTensor([3]), torchbearer.Y_PRED: torch.FloatTensor([0.3]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 4, torchbearer.Y_TRUE: torch.LongTensor([4]), torchbearer.Y_PRED: torch.FloatTensor([0.4]), torchbearer.DEVICE: 'cpu'}]
class TestEpochLambda(unittest.TestCase):
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = EpochLambda('test', self._metric_function, step_size=3)
self._metric.reset({
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32
})
self._states = [{
torchbearer.BATCH: 0,
torchbearer.Y_TRUE: torch.LongTensor([0]),
torchbearer.Y_PRED: torch.FloatTensor([0.0]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 1,
torchbearer.Y_TRUE: torch.LongTensor([1]),
torchbearer.Y_PRED: torch.FloatTensor([0.1]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 2,
torchbearer.Y_TRUE: torch.LongTensor([2]),
torchbearer.Y_PRED: torch.FloatTensor([0.2]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 3,
torchbearer.Y_TRUE: torch.LongTensor([3]),
torchbearer.Y_PRED: torch.FloatTensor([0.3]),
torchbearer.DEVICE: 'cpu'
}, {
torchbearer.BATCH: 4,
torchbearer.Y_TRUE: torch.LongTensor([4]),
torchbearer.Y_PRED: torch.FloatTensor([0.4]),
torchbearer.DEVICE: 'cpu'
}]
def test_train(self):
self._metric.train()
calls = [[torch.FloatTensor([0.0]),
torch.LongTensor([0])],
[
torch.FloatTensor([0.0, 0.1, 0.2, 0.3]),
torch.LongTensor([0, 1, 2, 3])
]]
for i in range(len(self._states)):
self._metric.process(self._states[i])
self.assertEqual(2, len(self._metric_function.call_args_list))
for i in range(len(self._metric_function.call_args_list)):
self.assertTrue(
torch.eq(self._metric_function.call_args_list[i][0][0],
calls[i][0]).all)
self.assertTrue(
torch.lt(
torch.abs(
torch.add(
self._metric_function.call_args_list[i][0][1],
-calls[i][1])), 1e-12).all)
self._metric_function.reset_mock()
self._metric.process_final({})
self.assertEqual(self._metric_function.call_count, 1)
self.assertTrue(
torch.eq(self._metric_function.call_args_list[0][0][1],
torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(
torch.lt(
torch.abs(
torch.add(self._metric_function.call_args_list[0][0][0],
-torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))),
1e-12).all)
def test_validate(self):
self._metric.eval()
for i in range(len(self._states)):
self._metric.process(self._states[i])
self._metric_function.assert_not_called()
self._metric.process_final_validate({})
self.assertEqual(self._metric_function.call_count, 1)
self.assertTrue(
torch.eq(self._metric_function.call_args_list[0][0][1],
torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(
torch.lt(
torch.abs(
torch.add(self._metric_function.call_args_list[0][0][0],
-torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))),
1e-12).all)
def test_not_running(self):
metric = EpochLambda('test',
self._metric_function,
running=False,
step_size=6)
metric.reset({
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32
})
metric.train()
for i in range(12):
metric.process(self._states[0])
self._metric_function.assert_not_called()
def build(self):
if on_epoch:
return EpochLambda(name, metric_function)
else:
return BatchLambda(name, metric_function)
def decorator(metric_function):
if on_epoch:
return EpochLambda(name, metric_function)
else:
return BatchLambda(name, metric_function)
class TestEpochLambda(unittest.TestCase):
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = EpochLambda('test', self._metric_function, step_size=3)
self._metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32})
self._states = [{torchbearer.BATCH: 0, torchbearer.Y_TRUE: torch.LongTensor([0]), torchbearer.Y_PRED: torch.FloatTensor([0.0]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 1, torchbearer.Y_TRUE: torch.LongTensor([1]), torchbearer.Y_PRED: torch.FloatTensor([0.1]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 2, torchbearer.Y_TRUE: torch.LongTensor([2]), torchbearer.Y_PRED: torch.FloatTensor([0.2]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 3, torchbearer.Y_TRUE: torch.LongTensor([3]), torchbearer.Y_PRED: torch.FloatTensor([0.3]), torchbearer.DEVICE: 'cpu'},
{torchbearer.BATCH: 4, torchbearer.Y_TRUE: torch.LongTensor([4]), torchbearer.Y_PRED: torch.FloatTensor([0.4]), torchbearer.DEVICE: 'cpu'}]
def test_train(self):
self._metric.train()
calls = [[torch.FloatTensor([0.0]), torch.LongTensor([0])],
[torch.FloatTensor([0.0, 0.1, 0.2, 0.3]), torch.LongTensor([0, 1, 2, 3])]]
for i in range(len(self._states)):
self._metric.process(self._states[i])
self.assertEqual(2, len(self._metric_function.call_args_list))
for i in range(len(self._metric_function.call_args_list)):
self.assertTrue(torch.eq(self._metric_function.call_args_list[i][0][0], calls[i][0]).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[i][0][1], -calls[i][1])), 1e-12).all)
self._metric_function.reset_mock()
self._metric.process_final({})
self._metric_function.assert_called_once()
self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all)
def test_validate(self):
self._metric.eval()
for i in range(len(self._states)):
self._metric.process(self._states[i])
self._metric_function.assert_not_called()
self._metric.process_final_validate({})
self._metric_function.assert_called_once()
self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all)
def test_not_running(self):
metric = EpochLambda('test', self._metric_function, running=False, step_size=6)
metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32})
metric.train()
for i in range(12):
metric.process(self._states[0])
self._metric_function.assert_not_called()