Validate and test the trained model

• Add a single-batched validation phase during training process, the corresponding metrics are logged onto Tensorboard so as to be compared with training metrics (same graphs) ;
• Add a model inference module, that call the test() method of ConvolutionalNeuralNetwork: it takes a trained model as an input, and infer label occurrences on a image testing set ;
• Manage the Tensorboard monitoring in a more clever way ;
• Add the possibility to gather similar labels for Mapillary dataset: by aggregating them, the number of labels decreases and the model may become easier to train. ⚠️ With this new feature, the dataset structure in json files has been modified: the labels keys are now dictionaries (instead of a lists) that link class ids (keys) and label occurrences (values), for each image.