本文整理汇总了Python中torchbearer.Model._test_loop方法的典型用法代码示例。如果您正苦于以下问题：Python Model._test_loop方法的具体用法？Python Model._test_loop怎么用？Python Model._test_loop使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torchbearer.Model的用法示例。
在下文中一共展示了Model._test_loop方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_test_loop_stop_training
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_test_loop_stop_training(self):
metric = Metric('test')
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 = 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.VALIDATION_STEPS: validation_steps,
torchbearer.CRITERION: criterion, torchbearer.STOP_TRAINING: True, torchbearer.METRICS: {}})
torchbearerstate = torchbearermodel._test_loop(state, callback_List, False, Model._load_batch_standard, num_steps=None)
self.assertTrue(torchbearerstate[torchbearer.MODEL].call_count == 1)示例2: test_test_loop_metrics
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
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)示例3: test_main_loop_validation_setup
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_main_loop_validation_setup(self):
metric = Metric('test')
data = [(torch.Tensor([1]), torch.Tensor([1])), (torch.Tensor([2]), torch.Tensor([2])), (torch.Tensor([3]), torch.Tensor([3]))]
generator = DataLoader(data)
valgenerator = DataLoader(data)
train_steps = 2
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])
torchbearermodel._test_loop = Mock()
torchbearerstate = torchbearermodel.fit_generator(generator, train_steps, epochs, 0, [callback],
validation_generator=valgenerator, initial_epoch=0,
pass_state=False)
self.assertTrue(torchbearerstate[torchbearer.VALIDATION_STEPS] == len(valgenerator))
self.assertTrue(torchbearerstate[torchbearer.VALIDATION_GENERATOR] == valgenerator)示例4: test_predict_generator_pass_state
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_predict_generator_pass_state(self):
torchmodel = MagicMock()
optimizer = MagicMock()
generator = MagicMock()
pass_state = False
steps = 100
torchbearermodel = Model(torchmodel, optimizer, torch.nn.L1Loss(), [])
torchbearermodel.main_state[torchbearer.FINAL_PREDICTIONS] = 1
torchbearermodel._test_loop = Mock()
torchbearermodel.predict_generator(generator, 0, steps, pass_state)
self.assertTrue(torchbearermodel._test_loop.call_args[0][2] == pass_state)示例5: test_evaluate_generator_steps
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_evaluate_generator_steps(self):
torchmodel = MagicMock()
optimizer = MagicMock()
generator = MagicMock()
pass_state = False
steps = 100
torchbearermodel = Model(torchmodel, optimizer, torch.nn.L1Loss(), [])
torchbearermodel.main_state[torchbearer.METRICS] = 1
torchbearermodel._test_loop = Mock()
torchbearermodel.evaluate_generator(generator, 0, steps, pass_state)
self.assertTrue(torchbearermodel._test_loop.call_args[0][4] == steps)示例6: test_evaluate_generator_verbose
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_evaluate_generator_verbose(self):
from torchbearer.callbacks import Tqdm
torchmodel = MagicMock()
optimizer = MagicMock()
generator = MagicMock()
pass_state = False
steps = None
torchbearermodel = Model(torchmodel, optimizer, torch.nn.L1Loss(), [])
torchbearermodel.main_state[torchbearer.METRICS] = 1
torchbearermodel._test_loop = Mock()
torchbearermodel.evaluate_generator(generator, 1, steps, pass_state)
self.assertIsInstance(torchbearermodel._test_loop.call_args[0][1].callback_list[0], Tqdm)示例7: test_predict_generator_verbose
# 需要导入模块: from torchbearer import Model [as 别名]
# 或者: from torchbearer.Model import _test_loop [as 别名]
def test_predict_generator_verbose(self):
from torchbearer.callbacks import Tqdm
torchmodel = MagicMock()
optimizer = MagicMock()
generator = MagicMock()
pass_state = False
steps = None
torchbearermodel = Model(torchmodel, optimizer, torch.nn.L1Loss(), [])
torchbearermodel.main_state[torchbearer.FINAL_PREDICTIONS] = 1
torchbearermodel._test_loop = Mock()
torchbearermodel.predict_generator(generator, 1, steps, pass_state)
self.assertIsInstance(torchbearermodel._test_loop.call_args[0][1].callback_list[1], Tqdm)
self.assertTrue(torchbearermodel._test_loop.call_args[0][2] == pass_state)
self.assertTrue(torchbearermodel._test_loop.call_args[0][4] == steps)