def test_process_final(self):
my_mock = Metric('test')
my_mock.process_final = Mock(return_value={'test': -1})
metric = MetricList([my_mock])
result = metric.process_final({'state': -1})
self.assertEqual({'test': -1}, result)
my_mock.process_final.assert_called_once_with({'state': -1})
def __init__(self, model, optimizer=None, criterion=None, metrics=[], callbacks=[], verbose=2):
if criterion is None:
def criterion(_, __):
return torch.zeros(1, device=self.state[torchbearer.DEVICE], dtype=self.state[torchbearer.DATA_TYPE], requires_grad=True)
self.verbose = verbose
self.closure = base_closure(torchbearer.X, torchbearer.MODEL, torchbearer.Y_PRED, torchbearer.Y_TRUE, torchbearer.CRITERION, torchbearer.LOSS, torchbearer.OPTIMIZER)
self.state = State()
self.state.update({
torchbearer.MODEL: model,
torchbearer.CRITERION: criterion,
torchbearer.OPTIMIZER: optimizer if optimizer is not None else MockOptimizer(),
torchbearer.METRIC_LIST: MetricList(metrics),
torchbearer.CALLBACK_LIST: CallbackList(callbacks),
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32,
torchbearer.SELF: self,
torchbearer.HISTORY: [],
torchbearer.BACKWARD_ARGS: {},
torchbearer.TRAIN_GENERATOR: None,
torchbearer.VALIDATION_GENERATOR: None,
torchbearer.TEST_GENERATOR: None,
torchbearer.TRAIN_STEPS: None,
torchbearer.VALIDATION_STEPS: None,
torchbearer.TEST_STEPS: None,
torchbearer.TRAIN_DATA: None,
torchbearer.VALIDATION_DATA: None,
torchbearer.TEST_DATA: None,
torchbearer.INF_TRAIN_LOADING: False,
torchbearer.LOADER: None
})
self.state[torchbearer.CALLBACK_LIST].on_init(self.state)
def __init__(self, model, optimizer=None, criterion=None, metrics=[], callbacks=[], pass_state=False):
if criterion is None:
def criterion(_, y_true):
return torch.zeros(1, device=y_true.device)
self.pass_state = pass_state
self.state = State()
self.state.update({
torchbearer.MODEL: model,
torchbearer.CRITERION: criterion,
torchbearer.OPTIMIZER: optimizer if optimizer is not None else MockOptimizer(),
torchbearer.METRIC_LIST: MetricList(metrics),
torchbearer.CALLBACK_LIST: CallbackList(callbacks),
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32,
torchbearer.SELF: self,
torchbearer.HISTORY: [],
torchbearer.BACKWARD_ARGS: {},
torchbearer.TRAIN_GENERATOR: None,
torchbearer.VALIDATION_GENERATOR: None,
torchbearer.TEST_GENERATOR: None,
torchbearer.TRAIN_STEPS: None,
torchbearer.VALIDATION_STEPS: None,
torchbearer.TEST_STEPS: None
})
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_default_loss(self):
metric = MetricList(['loss'])
self.assertTrue(metric.metric_list[0].name == 'loss', msg='loss not in: ' + str(metric.metric_list))
def test_default_acc(self):
metric = MetricList(['acc'])
self.assertTrue(metric.metric_list[0].name == 'acc', msg='acc not in: ' + str(metric.metric_list))
def test_list_in_list(self):
metric = MetricList(['acc', MetricList(['loss'])])
self.assertTrue(metric.metric_list[0].name == 'acc')
self.assertTrue(metric.metric_list[1].name == 'loss')
def test_reset(self):
my_mock = Metric('test')
my_mock.reset = Mock(return_value=None)
metric = MetricList([my_mock])
metric.reset({'state': -1})
my_mock.reset.assert_called_once_with({'state': -1})
def test_eval(self):
my_mock = Metric('test')
my_mock.eval = Mock(return_value=None)
metric = MetricList([my_mock])
metric.eval()
my_mock.eval.assert_called_once()
def test_train(self):
my_mock = Metric('test')
my_mock.train = Mock(return_value=None)
metric = MetricList([my_mock])
metric.train()
my_mock.train.assert_called_once()
def test_eval(self):
my_mock = Metric('test')
my_mock.eval = Mock(return_value=None)
metric = MetricList([my_mock])
metric.eval()
self.assertEqual(my_mock.eval.call_count, 1)
def test_train(self):
my_mock = Metric('test')
my_mock.train = Mock(return_value=None)
metric = MetricList([my_mock])
metric.train()
self.assertEqual(my_mock.train.call_count, 1)
def test_default_roc(self):
mlist = MetricList(['roc_auc'])
self.assertTrue(mlist.metric_list[0].name == 'roc_auc_score')
mlist = MetricList(['roc_auc_score'])
self.assertTrue(mlist.metric_list[0].name == 'roc_auc_score')