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| Original file line number | Diff line number | Diff line change |
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| import unittest | ||
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| from unittest.mock import Mock, call | ||
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| from torch.autograd import Variable | ||
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| import torchbearer | ||
| from torchbearer.metrics import Std, Metric, Mean, BatchLambda, EpochLambda, ToDict | ||
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| import torch | ||
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| class TestToDict(unittest.TestCase): | ||
| def setUp(self): | ||
| self._metric = Metric('test') | ||
| self._metric.train = Mock() | ||
| self._metric.eval = Mock() | ||
| self._metric.reset = Mock() | ||
| self._metric.process = Mock(return_value='process') | ||
| self._metric.process_final = Mock(return_value='process_final') | ||
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| self._to_dict = ToDict(self._metric) | ||
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| def test_train_process(self): | ||
| self._to_dict.train() | ||
| self._metric.train.assert_called_once() | ||
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| self.assertTrue(self._to_dict.process('input') == {'test': 'process'}) | ||
| self._metric.process.assert_called_once_with('input') | ||
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| def test_train_process_final(self): | ||
| self._to_dict.train() | ||
| self._metric.train.assert_called_once() | ||
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| self.assertTrue(self._to_dict.process_final('input') == {'test': 'process_final'}) | ||
| self._metric.process_final.assert_called_once_with('input') | ||
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| def test_eval_process(self): | ||
| self._to_dict.eval() | ||
| self._metric.eval.assert_called_once() | ||
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| self.assertTrue(self._to_dict.process('input') == {'val_test': 'process'}) | ||
| self._metric.process.assert_called_once_with('input') | ||
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| def test_eval_process_final(self): | ||
| self._to_dict.eval() | ||
| self._metric.eval.assert_called_once() | ||
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| self.assertTrue(self._to_dict.process_final('input') == {'val_test': 'process_final'}) | ||
| self._metric.process_final.assert_called_once_with('input') | ||
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| def test_reset(self): | ||
| self._to_dict.reset('test') | ||
| self._metric.reset.assert_called_once_with('test') | ||
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| 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]))}] | ||
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| 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) | ||
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| 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) | ||
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| class TestEpochLambda(unittest.TestCase): | ||
| def setUp(self): | ||
| self._metric_function = Mock(return_value='test') | ||
| self._metric = EpochLambda('test', self._metric_function, step_size=3) | ||
| self._metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32}) | ||
| self._states = [{torchbearer.BATCH: 0, torchbearer.Y_TRUE: torch.LongTensor([0]), torchbearer.Y_PRED: torch.FloatTensor([0.0]), torchbearer.DEVICE: 'cpu'}, | ||
| {torchbearer.BATCH: 1, torchbearer.Y_TRUE: torch.LongTensor([1]), torchbearer.Y_PRED: torch.FloatTensor([0.1]), torchbearer.DEVICE: 'cpu'}, | ||
| {torchbearer.BATCH: 2, torchbearer.Y_TRUE: torch.LongTensor([2]), torchbearer.Y_PRED: torch.FloatTensor([0.2]), torchbearer.DEVICE: 'cpu'}, | ||
| {torchbearer.BATCH: 3, torchbearer.Y_TRUE: torch.LongTensor([3]), torchbearer.Y_PRED: torch.FloatTensor([0.3]), torchbearer.DEVICE: 'cpu'}, | ||
| {torchbearer.BATCH: 4, torchbearer.Y_TRUE: torch.LongTensor([4]), torchbearer.Y_PRED: torch.FloatTensor([0.4]), torchbearer.DEVICE: 'cpu'}] | ||
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| def test_train(self): | ||
| self._metric.train() | ||
| calls = [[torch.FloatTensor([0.0]), torch.LongTensor([0])], | ||
| [torch.FloatTensor([0.0, 0.1, 0.2, 0.3]), torch.LongTensor([0, 1, 2, 3])]] | ||
| for i in range(len(self._states)): | ||
| self._metric.process(self._states[i]) | ||
| self.assertEqual(2, len(self._metric_function.call_args_list)) | ||
| for i in range(len(self._metric_function.call_args_list)): | ||
| self.assertTrue(torch.eq(self._metric_function.call_args_list[i][0][0], calls[i][0]).all) | ||
| self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[i][0][1], -calls[i][1])), 1e-12).all) | ||
| self._metric_function.reset_mock() | ||
| self._metric.process_final({}) | ||
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| self._metric_function.assert_called_once() | ||
| self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all) | ||
| self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all) | ||
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| def test_validate(self): | ||
| self._metric.eval() | ||
| for i in range(len(self._states)): | ||
| self._metric.process(self._states[i]) | ||
| self._metric_function.assert_not_called() | ||
| self._metric.process_final_validate({}) | ||
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| self._metric_function.assert_called_once() | ||
| self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all) | ||
| self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all) | ||
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| def test_not_running(self): | ||
| metric = EpochLambda('test', self._metric_function, running=False, step_size=6) | ||
| metric.reset({torchbearer.DEVICE: 'cpu', torchbearer.DATA_TYPE: torch.float32}) | ||
| metric.train() | ||
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| for i in range(12): | ||
| metric.process(self._states[0]) | ||
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| self._metric_function.assert_not_called() |
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