def test_log_dir(self, mock_board):
state = {torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3))}
tboard = TensorBoardProjector(log_dir='./test', comment='torchbearer')
tboard.on_start(state)
mock_board.assert_called_once_with(log_dir=os.path.join('./test', 'Sequential_torchbearer'))
def test_log_dir(self, mock_board):
state = {torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3))}
tboard = TensorBoardProjector(log_dir='./test', comment='torchbearer')
tboard.on_start(state)
mock_board.assert_called_once_with(
log_dir=os.path.join('./test', 'Sequential_torchbearer'))
def test_writer_closed_on_end(self, mock_board):
mock_board.return_value = Mock()
mock_board.return_value.close = Mock()
state = {torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3))}
tboard = TensorBoardProjector()
tboard.on_start(state)
tboard.on_end({})
mock_board.return_value.close.assert_called_once()
def test_multi_epoch(self, mock_board):
mock_board.return_value = Mock()
mock_board.return_value.add_embedding = Mock()
state = {torchbearer.X: torch.ones(18, 3, 10, 10), torchbearer.EPOCH: 0, torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3)), torchbearer.Y_TRUE: torch.ones(18), torchbearer.BATCH: 0}
tboard = TensorBoardProjector(num_images=18, avg_data_channels=False, write_data=False, features_key=torchbearer.Y_TRUE)
tboard.on_start(state)
tboard.on_step_validation(state)
mock_board.return_value.add_embedding.assert_called_once_with(ANY, metadata=ANY, label_img=ANY, tag='features', global_step=0)
self.assertTrue(mock_board.return_value.add_embedding.call_args[0][0].size() == state[torchbearer.Y_TRUE].unsqueeze(1).size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['metadata'].size() == state[torchbearer.Y_TRUE].size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['label_img'].size() == state[torchbearer.X].size())
tboard.on_end_epoch({})
mock_board.return_value.add_embedding.reset_mock()
tboard.on_step_validation(state)
mock_board.return_value.add_embedding.assert_called_once_with(ANY, metadata=ANY, label_img=ANY, tag='features',
global_step=0)
self.assertTrue(mock_board.return_value.add_embedding.call_args[0][0].size() == state[torchbearer.Y_TRUE].unsqueeze(1).size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['metadata'].size() == state[torchbearer.Y_TRUE].size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['label_img'].size() == state[torchbearer.X].size())
def test_multi_batch_data(self, mock_board, _):
mock_board.return_value = Mock()
mock_board.return_value.add_embedding = Mock()
state = {
torchbearer.X: torch.ones(18, 3, 10, 10),
torchbearer.EPOCH: 0,
torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3)),
torchbearer.Y_TRUE: torch.ones(18),
torchbearer.BATCH: 0
}
tboard = TensorBoardProjector(num_images=45,
avg_data_channels=False,
write_data=True,
write_features=False)
tboard.on_start(state)
for i in range(3):
state[torchbearer.BATCH] = i
tboard.on_step_validation(state)
mock_board.return_value.add_embedding.assert_called_once_with(
ANY, metadata=ANY, label_img=ANY, tag='data', global_step=-1)
self.assertTrue(mock_board.return_value.add_embedding.call_args[0]
[0].size() == torch.Size([45, 300]))
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]
['metadata'].size() == torch.Size([45]))
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]
['label_img'].size() == torch.Size([45, 3, 10, 10]))
tboard.on_end({})
def test_simple_case(self, mock_board, _):
mock_board.return_value = Mock()
mock_board.return_value.add_embedding = Mock()
state = {
torchbearer.X: torch.ones(18, 3, 10, 10),
torchbearer.EPOCH: 0,
torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3)),
torchbearer.Y_TRUE: torch.ones(18),
torchbearer.BATCH: 0
}
tboard = TensorBoardProjector(num_images=18,
avg_data_channels=False,
write_data=False,
features_key=torchbearer.Y_TRUE)
tboard.on_start(state)
tboard.on_step_validation(state)
mock_board.return_value.add_embedding.assert_called_once_with(
ANY, metadata=ANY, label_img=ANY, tag='features', global_step=0)
self.assertTrue(
mock_board.return_value.add_embedding.call_args[0][0].size() ==
state[torchbearer.Y_TRUE].unsqueeze(1).size())
self.assertTrue(
mock_board.return_value.add_embedding.call_args[1]
['metadata'].size() == state[torchbearer.Y_TRUE].size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]
['label_img'].size() == state[torchbearer.X].size())
tboard.on_end(state)
def test_no_channels(self, mock_board):
mock_board.return_value = Mock()
mock_board.return_value.add_embedding = Mock()
state = {torchbearer.X: torch.ones(18, 10, 10), torchbearer.EPOCH: 0, torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3)), torchbearer.Y_TRUE: torch.ones(18), torchbearer.BATCH: 0}
tboard = TensorBoardProjector(num_images=18, avg_data_channels=False, write_data=True, write_features=False)
tboard.on_start(state)
tboard.on_step_validation(state)
mock_board.return_value.add_embedding.assert_called_once_with(ANY, metadata=ANY, label_img=ANY, tag='data', global_step=-1)
self.assertTrue(mock_board.return_value.add_embedding.call_args[0][0].size() == torch.Size([18, 100]))
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['metadata'].size() == state[torchbearer.Y_TRUE].size())
self.assertTrue(mock_board.return_value.add_embedding.call_args[1]['label_img'].size() == torch.Size([18, 1, 10, 10]))
def test_writer_closed_on_end(self, mock_board, _):
mock_board.return_value = Mock()
mock_board.return_value.close = Mock()
state = {torchbearer.MODEL: nn.Sequential(nn.Conv2d(3, 3, 3))}
tboard = TensorBoardProjector()
tboard.on_start(state)
tboard.on_end({})
self.assertEqual(mock_board.return_value.close.call_count, 1)
