def test_reset(self):
root = Metric('test')
root.reset = Mock()
leaf = Metric('test')
leaf.reset = Mock()
tree = MetricTree(root)
tree.add_child(leaf)
tree.reset({})
root.reset.assert_called_once_with({})
leaf.reset.assert_called_once_with({})
def test_main_loop_metrics(self):
metric = Metric('test')
metric.process = Mock(return_value={'test': 0})
metric.process_final = Mock(return_value={'test': 0})
metric.reset = Mock(return_value=None)
data = [(torch.Tensor([1]), torch.Tensor([1])), (torch.Tensor([2]), torch.Tensor([2])), (torch.Tensor([3]), torch.Tensor([3]))]
generator = DataLoader(data)
train_steps = len(data)
epochs = 1
callback = MagicMock()
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
loss = torch.tensor([2], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearermodel = Model(torchmodel, optimizer, criterion, [metric])
torchbearerstate = torchbearermodel.fit_generator(generator, train_steps, epochs, 0, [callback], initial_epoch=0, pass_state=False)
torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].reset.assert_called_once()
self.assertTrue(torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].process.call_count == len(data))
torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].process_final.assert_called_once()
self.assertTrue(torchbearerstate[torchbearer.METRICS]['test'] == 0)
def test_test_loop_metrics(self):
metric = Metric('test')
metric.process = Mock(return_value={'test': 0})
metric.process_final = Mock(return_value={'test': 0})
metric.reset = Mock(return_value=None)
metric_list = MetricList([metric])
data = [(torch.Tensor([1]), torch.Tensor([1])), (torch.Tensor([2]), torch.Tensor([2])), (torch.Tensor([3]), torch.Tensor([3]))]
validation_generator = DataLoader(data)
validation_steps = len(data)
callback = MagicMock()
callback_List = torchbearer.CallbackList([callback])
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
criterion = Mock(return_value=2)
torchbearermodel = Model(torchmodel, optimizer, criterion, [metric])
state = torchbearermodel.main_state.copy()
state.update({torchbearer.METRIC_LIST: metric_list, torchbearer.VALIDATION_GENERATOR: validation_generator,
torchbearer.CallbackList: callback_List, torchbearer.MODEL: torchmodel, torchbearer.VALIDATION_STEPS: validation_steps,
torchbearer.CRITERION: criterion, torchbearer.STOP_TRAINING: False, torchbearer.METRICS: {}})
torchbearerstate = torchbearermodel._test_loop(state, callback_List, False, Model._load_batch_standard, num_steps=None)
torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].reset.assert_called_once()
self.assertTrue(torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].process.call_count == len(data))
torchbearerstate[torchbearer.METRIC_LIST].metric_list[0].process_final.assert_called_once()
self.assertTrue(torchbearerstate[torchbearer.METRICS]['test'] == 0)
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})