def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = BatchLambda('test', self._metric_function)
self._states = [{
torchbearer.Y_TRUE: Variable(torch.FloatTensor([1])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([2]))
}, {
torchbearer.Y_TRUE: Variable(torch.FloatTensor([3])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([4]))
}, {
torchbearer.Y_TRUE: Variable(torch.FloatTensor([5])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([6]))
}]
class TestBatchLambda(unittest.TestCase):
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = BatchLambda('test', self._metric_function)
self._states = [{
torchbearer.Y_TRUE: Variable(torch.FloatTensor([1])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([2]))
}, {
torchbearer.Y_TRUE: Variable(torch.FloatTensor([3])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([4]))
}, {
torchbearer.Y_TRUE: Variable(torch.FloatTensor([5])),
torchbearer.Y_PRED: Variable(torch.FloatTensor([6]))
}]
def test_train(self):
self._metric.train()
calls = []
for i in range(len(self._states)):
self._metric.process(self._states[i])
calls.append(
call(self._states[i][torchbearer.Y_PRED].data,
self._states[i][torchbearer.Y_TRUE].data))
self._metric_function.assert_has_calls(calls)
def test_validate(self):
self._metric.eval()
calls = []
for i in range(len(self._states)):
self._metric.process(self._states[i])
calls.append(
call(self._states[i][torchbearer.Y_PRED].data,
self._states[i][torchbearer.Y_TRUE].data))
self._metric_function.assert_has_calls(calls)
class TestBatchLambda(unittest.TestCase):
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = BatchLambda('test', self._metric_function)
self._states = [{torchbearer.Y_TRUE: Variable(torch.FloatTensor([1])), torchbearer.Y_PRED: Variable(torch.FloatTensor([2]))},
{torchbearer.Y_TRUE: Variable(torch.FloatTensor([3])), torchbearer.Y_PRED: Variable(torch.FloatTensor([4]))},
{torchbearer.Y_TRUE: Variable(torch.FloatTensor([5])), torchbearer.Y_PRED: Variable(torch.FloatTensor([6]))}]
def test_train(self):
self._metric.train()
calls = []
for i in range(len(self._states)):
self._metric.process(self._states[i])
calls.append(call(self._states[i][torchbearer.Y_PRED].data, self._states[i][torchbearer.Y_TRUE].data))
self._metric_function.assert_has_calls(calls)
def test_validate(self):
self._metric.eval()
calls = []
for i in range(len(self._states)):
self._metric.process(self._states[i])
calls.append(call(self._states[i][torchbearer.Y_PRED].data, self._states[i][torchbearer.Y_TRUE].data))
self._metric_function.assert_has_calls(calls)
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)
def setUp(self):
self._metric_function = Mock(return_value='test')
self._metric = BatchLambda('test', self._metric_function)
self._states = [{torchbearer.Y_TRUE: Variable(torch.FloatTensor([1])), torchbearer.Y_PRED: Variable(torch.FloatTensor([2]))},
{torchbearer.Y_TRUE: Variable(torch.FloatTensor([3])), torchbearer.Y_PRED: Variable(torch.FloatTensor([4]))},
{torchbearer.Y_TRUE: Variable(torch.FloatTensor([5])), torchbearer.Y_PRED: Variable(torch.FloatTensor([6]))}]