Test metrics not logging to Comet after training
🐛 Bug
When testing a model with Trainer.test metrics are not logged to Comet if the model was previously trained using Trainer.fit. While training metrics are logged correctly.
Code sample
    comet_logger = CometLogger()
    trainer = Trainer(logger=comet_logger)
    model = get_model()

    trainer.fit(model) # Metrics are logged to Comet
    trainer.test(model) # No metrics are logged to Comet

Expected behavior
Test metrics should also be logged in to Comet.
Environment
- PyTorch version: 1.3.0
Is debug build: No
CUDA used to build PyTorch: 10.1.243

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: version 3.10.2

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.168
GPU models and configuration:
GPU 0: GeForce GTX 1080 Ti
GPU 1: GeForce GTX 1080 Ti
GPU 2: GeForce GTX 1080 Ti
GPU 3: GeForce GTX 1080 Ti
GPU 4: GeForce GTX 1080 Ti
GPU 5: GeForce GTX 1080 Ti
GPU 6: GeForce GTX 1080 Ti
GPU 7: GeForce GTX 1080 Ti

Nvidia driver version: 418.67
cuDNN version: /usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.1

Versions of relevant libraries:
[pip3] numpy==1.16.4
[pip3] pytorch-lightning==0.6.0
[pip3] torch==1.3.0
[pip3] torchvision==0.4.1
[conda] Could not collect

Additional context
I believe the issue is caused because at the end of the training routine, logger.finalize("success") is called. This in turn calls experiment.end() inside the logger and the Experiment object doesn't expect to send more information after this.
An alternative is to create another Trainer object, with another logger but this means that the metrics will be logged into a different Comet experiment from the original. This issue can be solved using the ExistingExperiment object form the Comet SDK, but the solution seems a little hacky and the CometLogger currently doesn't support this kind of experiment.