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test_trial.py
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test_trial.py
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from unittest import TestCase
from mock import MagicMock, Mock, patch, ANY, create_autospec
import torch
from torch.utils.data import DataLoader
import torchbearer
import torchbearer.callbacks as callbacks
from torchbearer import Trial, State
from torchbearer.metrics import Metric
from torchbearer.trial import deep_to, load_batch_none, load_batch_predict, load_batch_standard, load_batch_infinite, update_device_and_dtype, CallbackListInjection
class _StateMaker(object):
def __getitem__(self, keys):
if not isinstance(keys, tuple):
keys = (keys,)
assert all(isinstance(key, slice) for key in keys)
state = State()
for k in keys:
state[k.start] = k.stop
return state
make_state = _StateMaker()
class TestMockOptimizer(TestCase):
@patch('torchbearer.trial.Optimizer')
def test_mock_optimizer(self, mock_opt):
mock_opt.add_param_group = Mock()
mock_opt.load_state_dict = Mock()
mock_opt.state_dict = Mock()
mock_opt.step = Mock()
mock_opt.zero_grad = Mock()
opt = torchbearer.trial.MockOptimizer()
self.assertIsNone(opt.add_param_group({}))
mock_opt.add_param_group.assert_not_called()
self.assertIsNone(opt.load_state_dict({}))
mock_opt.load_state_dict.assert_not_called()
self.assertDictEqual(opt.state_dict(), {})
mock_opt.state_dict.assert_not_called()
self.assertIsNone(opt.step())
mock_opt.step.assert_not_called()
self.assertIsNone(opt.zero_grad())
mock_opt.zero_grad.assert_not_called()
def test_mock_optimizer_closure(self):
t = Trial(None)
closure = Mock()
opt = t.state[torchbearer.OPTIMIZER]
opt.step(closure)
self.assertTrue(closure.call_count == 1)
class TestCallbackListInjection(TestCase):
def test_pass_through(self):
mock = MagicMock()
injection = CallbackListInjection(None, mock)
# state_dict
mock.state_dict.return_value = 'test'
self.assertEqual(injection.state_dict(), 'test')
self.assertEqual(mock.state_dict.call_count, 1)
# load_state_dict
injection.load_state_dict('test')
mock.load_state_dict.assert_called_once_with('test')
# iter
mock.__iter__.return_value = ['iterator']
self.assertEqual(next(injection.__iter__()), 'iterator')
self.assertEqual(mock.__iter__.call_count, 1)
# copy
mock.copy.return_value = 'copy'
self.assertEqual(injection.copy(), 'copy')
# append
injection.append('stuff to append')
mock.append.assert_called_once_with('stuff to append')
def test_order(self):
d = {'my_number': 10}
@callbacks.on_start
def set_one(state):
d['my_number'] = 1
set_one.on_end = Mock()
@callbacks.on_start
def set_two(state):
d['my_number'] = 2
set_two.on_end = Mock()
injection = CallbackListInjection(set_one, callbacks.CallbackList([set_two]))
injection.on_end({})
self.assertEqual(set_one.on_end.call_count, 1)
self.assertEqual(set_two.on_end.call_count, 1)
injection.on_start({})
self.assertEqual(d['my_number'], 2)
class TestWithGenerators(TestCase):
def test_with_train_generator_state_filled(self):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, 1)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_GENERATOR] == generator)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == 1)
@patch('warnings.warn')
def test_with_train_generator_too_many_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, 10)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == 10)
@patch('warnings.warn')
def test_with_train_generator_inf_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, -1)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -1)
@patch('warnings.warn')
def test_with_train_generator_fractional_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, 1.5)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == 1)
@patch('warnings.warn')
def test_with_train_generator_negative_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, -2)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -2)
@patch('warnings.warn')
def test_with_train_generator_none_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == 2)
@patch('warnings.warn')
def test_with_train_generator_old_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_train_steps(100)
torchbearertrial.with_train_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == 100)
def test_with_val_generator_state_filled(self):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator(generator, 1)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_GENERATOR] == generator)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == 1)
@patch('warnings.warn')
def test_with_val_generator_too_many_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator(generator, 10)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == 10)
@patch('warnings.warn')
def test_with_val_generator_fractional_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator(generator, 1.5)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == 1)
@patch('warnings.warn')
def test_with_val_generator_negative_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator(generator, -2)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == -2)
@patch('warnings.warn')
def test_with_val_generator_none_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == 2)
@patch('warnings.warn')
def test_with_val_generator_old_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_val_steps(100)
torchbearertrial.with_val_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == 100)
def test_with_test_generator_state_filled(self):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator(generator, 1)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_GENERATOR] == generator)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == 1)
@patch('warnings.warn')
def test_with_test_generator_too_many_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator(generator, 10)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == 10)
@patch('warnings.warn')
def test_with_test_generator_fractional_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator(generator, 1.5)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == 1)
@patch('warnings.warn')
def test_with_test_generator_negative_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator(generator, -2)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == -2)
@patch('warnings.warn')
def test_with_test_generator_none_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == 2)
@patch('warnings.warn')
def test_with_test_generator_old_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_test_steps(100)
torchbearertrial.with_test_generator(generator, None)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == 100)
@patch('warnings.warn')
def test_for_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
train_steps = 1
val_steps = 2
test_steps = 3
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
trainstep = torchbearertrial.for_train_steps = MagicMock()
valstep = torchbearertrial.for_val_steps = MagicMock()
teststep = torchbearertrial.for_test_steps = MagicMock()
torchbearertrial.for_steps(train_steps, val_steps, test_steps)
trainstep.assert_called_once_with(train_steps)
valstep.assert_called_once_with(val_steps)
teststep.assert_called_once_with(test_steps)
@patch('warnings.warn')
def test_with_generators(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
train_generator = MagicMock()
train_generator.__len__.return_value = 2
train_steps = 1
val_generator = MagicMock()
val_generator.__len__.return_value = 3
val_steps = 2
test_generator = MagicMock()
test_generator.__len__.return_value = 4
test_steps = 3
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
traingen = torchbearertrial.with_train_generator = MagicMock()
valgen = torchbearertrial.with_val_generator = MagicMock()
testgen = torchbearertrial.with_test_generator = MagicMock()
torchbearertrial.with_generators(train_generator, val_generator, test_generator, train_steps, val_steps, test_steps)
traingen.assert_called_once_with(train_generator, train_steps)
valgen.assert_called_once_with(val_generator, val_steps)
testgen.assert_called_once_with(test_generator, test_steps)
@patch('warnings.warn')
def test_for_inf_train_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_train_steps()
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -1)
@patch('warnings.warn')
def test_for_inf_val_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_val_steps()
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == -1)
@patch('warnings.warn')
def test_for_inf_test_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_test_steps()
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == -1)
@patch('warnings.warn')
def test_for_inf_steps(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_steps(True, True, True)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -1)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] == -1)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == -1)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_steps(True, False, True)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -1)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] != -1)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] == -1)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_steps(True, False, False)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] == -1)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] != -1)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] != -1)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.for_inf_steps(False, False, False)
self.assertTrue(torchbearertrial.state[torchbearer.TRAIN_STEPS] != -1)
self.assertTrue(torchbearertrial.state[torchbearer.VALIDATION_STEPS] != -1)
self.assertTrue(torchbearertrial.state[torchbearer.TEST_STEPS] != -1)
@patch('warnings.warn')
def test_with_inf_train_loader(self, _):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_inf_train_loader()
self.assertTrue(torchbearertrial.state[torchbearer.INF_TRAIN_LOADING])
class TestWithData(TestCase):
@patch('torchbearer.trial.TensorDataset')
@patch('torchbearer.trial.DataLoader')
def test_with_train_data(self, d, td):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
x = torch.rand(1,5)
y = torch.rand(1,5)
d.return_value = -1
steps = 4
shuffle = False
num_workers = 1
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_train_generator = MagicMock()
torchbearertrial.with_train_data(x, y, 1, shuffle=shuffle, num_workers=num_workers, steps=steps)
d.assert_called_once_with(ANY, 1, shuffle=shuffle, num_workers=num_workers)
torchbearertrial.with_train_generator.assert_called_once_with(-1, steps=4)
td.assert_called_once_with(x,y)
@patch('torchbearer.trial.TensorDataset')
@patch('torchbearer.trial.DataLoader')
def test_with_val_data(self, d, td):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
x = torch.rand(1,5)
y = torch.rand(1,5)
d.return_value = -1
steps = 4
shuffle = False
num_workers = 1
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_val_generator = MagicMock()
torchbearertrial.with_val_data(x, y, 1, shuffle=shuffle, num_workers=num_workers, steps=steps)
d.assert_called_once_with(ANY, 1, shuffle=shuffle, num_workers=num_workers)
torchbearertrial.with_val_generator.assert_called_once_with(-1, steps=4)
td.assert_called_once_with(x,y)
@patch('torchbearer.trial.TensorDataset')
@patch('torchbearer.trial.DataLoader')
def test_with_test_data(self, d, td):
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
metric = Metric('test')
criterion = None
generator = MagicMock()
generator.__len__.return_value = 2
x = torch.rand(1,5)
y = torch.rand(1,5)
d.return_value = -1
steps = 4
num_workers = 1
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric])
torchbearertrial.with_test_generator = MagicMock()
torchbearertrial.with_test_data(x, 1, num_workers=num_workers, steps=steps)
d.assert_called_once_with(ANY, 1, num_workers=num_workers)
torchbearertrial.with_test_generator.assert_called_once_with(-1, steps=4)
td.assert_called_once_with(x)
def test_with_data(self):
trial = Trial(None)
mock_train_data, mock_val_data, mock_test_data = Mock(), Mock(), Mock()
trial.with_train_data = mock_train_data
trial.with_val_data = mock_val_data
trial.with_test_data = mock_test_data
shuffle = True
batch_size = 30
one_tensor = torch.Tensor([1])
target_tensor = torch.Tensor([10])
trial.with_data(one_tensor, target_tensor, one_tensor*2, target_tensor*2, one_tensor*3, batch_size,
train_steps=100, val_steps=200, test_steps=300, shuffle=shuffle)
self.assertTrue(mock_train_data.call_args[0] == (one_tensor, target_tensor, 30, shuffle, 1, 100))
self.assertTrue(mock_val_data.call_args[0] == (one_tensor*2, target_tensor*2, 30, shuffle, 1, 200))
self.assertTrue(mock_test_data.call_args[0] == (one_tensor*3, 30, 1, 300))
class TestWithClosureAndLoader(TestCase):
def test_with_closure(self):
def closure():
return 'test'
t = Trial(None)
t.with_closure(closure)
self.assertTrue(t.closure() == 'test')
def test_with_loader(self):
def loader(state):
print('test')
t = Trial(None)
t.with_loader(loader)
self.assertTrue(t.state[torchbearer.LOADER] == loader)
class TestRun(TestCase):
def test_run_callback_calls(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([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertEqual(callback.on_start.call_count, 1)
self.assertEqual(callback.on_start_epoch.call_count, 1)
self.assertEqual(callback.on_end_epoch.call_count, 1)
self.assertEqual(callback.on_end.call_count, 1)
def test_run_epochs_ran_normal(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 = 4
callback = MagicMock()
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertTrue(torchbearertrial._fit_pass.call_count == epochs)
def test_run_epochs_ran_negative(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([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock()
torchbearertrial._validation_pass = Mock()
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertTrue(torchbearertrial._fit_pass.call_count == 0)
def test_run_epochs_history_populated(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 = 10
callback = MagicMock()
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.state[torchbearer.HISTORY] = [1,2,3,4,5]
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertTrue(torchbearertrial._fit_pass.call_count == 5)
@patch('warnings.warn')
def test_run_fit_pass_Args(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
torchmodel = 1
torchbearertrial = Trial(torchmodel, None, None, [], callbacks=[])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertEqual(torchbearertrial._fit_pass.call_count, 1)
def test_run_stop_training(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 = 10
callback = MagicMock()
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.state[torchbearer.STOP_TRAINING] = True
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertEqual(callback.on_start_epoch.call_count, 1)
self.assertTrue(callback.on_end_epoch.call_count == 0)
self.assertEqual(callback.on_end.call_count, 1)
def test_run_stop_training_second(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 = 10
callback = MagicMock()
@torchbearer.callbacks.on_end_epoch
def stop_callback(state):
state[torchbearer.STOP_TRAINING] = True
torchmodel = MagicMock()
torchmodel.forward = Mock(return_value=1)
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[stop_callback, callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial._validation_pass = Mock(return_value={torchbearer.METRICS: {}})
torchbearertrial.with_train_generator(generator, steps=train_steps)
torchbearertrial.run(epochs=epochs, verbose=0)
self.assertEqual(callback.on_start_epoch.call_count, 1)
self.assertEqual(callback.on_end_epoch.call_count, 1)
self.assertEqual(callback.on_end.call_count, 1)
def test_run_history_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([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
torchbearertrial = Trial(torchmodel, optimizer, criterion, [metric], callbacks=[callback])
torchbearertrial._fit_pass = Mock(return_value={torchbearer.METRICS: {'fit_test': 1}})
torchbearertrial._validation_pass = Mock(return_value={'val_test': 2})
torchbearertrial.with_train_generator(generator, steps=train_steps)
history = torchbearertrial.run(epochs=epochs, verbose=0)
self.assertDictEqual(history[0], {'train_steps': train_steps, 'validation_steps': None, 'fit_test': 1, 'val_test': 2})
class TestFitPass(TestCase):
@patch('torchbearer.CallbackListInjection')
def test_fit_train_called(self, mock_inj):
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
torchmodel = MagicMock()
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
metric_list = MagicMock()
callback_list = MagicMock()
mock_inj.return_value = callback_list
state = make_state[
torchbearer.MAX_EPOCHS: epochs, torchbearer.STOP_TRAINING: False, torchbearer.MODEL: torchmodel, torchbearer.CRITERION: criterion, torchbearer.OPTIMIZER: optimizer,
torchbearer.METRIC_LIST: metric_list, torchbearer.CALLBACK_LIST: callback_list, torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float,
torchbearer.HISTORY: [], torchbearer.TRAIN_GENERATOR: generator, torchbearer.TRAIN_STEPS: train_steps, torchbearer.EPOCH: 0, torchbearer.INF_TRAIN_LOADING: False,
torchbearer.BACKWARD_ARGS: {},
]
torchbearertrial = Trial(torchmodel, optimizer, criterion, [], callbacks=[])
torchbearertrial.train = Mock()
torchbearertrial.state = {torchbearer.TRAIN_GENERATOR: generator, torchbearer.CALLBACK_LIST: callback_list,
torchbearer.TRAIN_DATA: (generator, train_steps), torchbearer.INF_TRAIN_LOADING: False, torchbearer.LOADER: None}
torchbearertrial._fit_pass(state)
self.assertEqual(torchbearertrial.train.call_count, 1)
@patch('torchbearer.CallbackListInjection')
def test_fit_metrics_reset(self, mock_inj):
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
torchmodel = MagicMock()
optimizer = MagicMock()
loss = torch.tensor([2.0], requires_grad=True)
criterion = Mock(return_value=loss)
metric_list = MagicMock()
callback_list = MagicMock()
mock_inj.return_value = callback_list
state = make_state[
torchbearer.MAX_EPOCHS: epochs, torchbearer.STOP_TRAINING: False, torchbearer.MODEL: torchmodel, torchbearer.CRITERION: criterion, torchbearer.OPTIMIZER: optimizer,
torchbearer.METRIC_LIST: metric_list, torchbearer.CALLBACK_LIST: callback_list, torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float,
torchbearer.HISTORY: [], torchbearer.TRAIN_GENERATOR: generator, torchbearer.TRAIN_STEPS: train_steps, torchbearer.EPOCH: 0, torchbearer.INF_TRAIN_LOADING: False,
torchbearer.BACKWARD_ARGS: {},
]
torchbearertrial = Trial(torchmodel, optimizer, criterion, [], callbacks=[])
torchbearertrial.train = Mock()
torchbearertrial.state = {torchbearer.TRAIN_GENERATOR: generator, torchbearer.CALLBACK_LIST: callback_list,
torchbearer.TRAIN_DATA: (generator, train_steps), torchbearer.INF_TRAIN_LOADING: False, torchbearer.LOADER: None}
torchbearertrial._fit_pass(state)
self.assertEqual(metric_list.reset.call_count, 1)
@patch('torchbearer.CallbackListInjection')
def test_fit_callback_calls(self, mock_inj):
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
torchmodel = MagicMock()
optimizer = MagicMock()
optimizer.step = lambda closure: closure()
loss = torch.tensor([2.0], requires_grad=True)