Load a language model and decode with it during evaluation

Based on !346 (merged)

Closes #252 (closed)

docs/usage/train/config.md

@@ -80,16 +80,6 @@ folder/
 | `training.load_epoch`    | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str`        | `"last"` |
 | `training.lr_schedulers` | Learning rate schedulers.                                                   | custom class |          |
 
-### Data
-
-| Name                           | Description                                                | Type   | Default                                              |
-| ------------------------------ | ---------------------------------------------------------- | ------ | ---------------------------------------------------- |
-| `training.data.batch_size`     | Mini-batch size for the training loop.                     | `int`  | `2`                                                  |
-| `training.data.load_in_memory` | Load all images in CPU memory.                             | `bool` | `True`                                               |
-| `training.data.worker_per_gpu` | Number of parallel processes per gpu for data loading.     | `int`  | `4`                                                  |
-| `training.data.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#data-preprocessing))       |
-| `training.data.augmentation`   | Whether to use data augmentation on the training set.      | `bool` | `True` (see [dedicated section](#data-augmentation)) |
-
 ### Device
 
 | Name                       | Description                                                                                                                 | Type   | Default |
@@ -100,7 +90,7 @@ folder/
 | `training.device.nb_gpu`   | Number of GPUs to train DAN. Set to `null` to use all GPUs available.                                                       | `int`  |         |
 | `training.device.force`    | Use a specific device if available. Use `cpu` to train on CPU (for debugging) or `cuda`/`cuda:$gpu_device` to train on GPU. | `str`  |         |
 
-- To train on several GPUs, simply set the `training.device.use_ddp` parameter to `True`. By default, the model will use all available GPUs. To restrict access to fewer GPUs, one can modify the `training.device.nb_gpu` parameter.
+To train on several GPUs, simply set the `training.device.use_ddp` parameter to `True`. By default, the model will use all available GPUs. To restrict access to fewer GPUs, one can modify the `training.device.nb_gpu` parameter.
 
 ### Optimizers
 
@@ -117,7 +107,7 @@ folder/
 | `training.validation.eval_on_valid_interval` | Interval (in epochs) to evaluate during training.                          | `int`  | `5`     |
 | `training.validation.set_name_focus_metric`  | Dataset to focus on to select best weights.                                | `str`  |         |
 
-- During the validation stage, the batch size is set to 1. This avoids problems associated with image sizes that can be very different inside batches and lead to significant padding, resulting in performance degradations.
+During the validation stage, the batch size is set to 1. This avoids problems associated with image sizes that can be very different inside batches and lead to significant padding, resulting in performance degradations.
 
 ### Metrics
 
@@ -141,7 +131,17 @@ folder/
 | `training.transfer_learning.encoder` | Model to load for the encoder \[state_dict_name, checkpoint_path, learnable, strict\]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, True]`  |
 | `training.transfer_learning.decoder` | Model to load for the decoder \[state_dict_name, checkpoint_path, learnable, strict\]. | `list` | `["decoder", "pretrained_models/dan_rimes_page.pt", True, False]` |
 
-### Data preprocessing
+### Data
+
+| Name                           | Description                                                | Type   | Default                                         |
+| ------------------------------ | ---------------------------------------------------------- | ------ | ----------------------------------------------- |
+| `training.data.batch_size`     | Mini-batch size for the training loop.                     | `int`  | `2`                                             |
+| `training.data.load_in_memory` | Load all images in CPU memory.                             | `bool` | `True`                                          |
+| `training.data.worker_per_gpu` | Number of parallel processes per gpu for data loading.     | `int`  | `4`                                             |
+| `training.data.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#preprocessing))       |
+| `training.data.augmentation`   | Whether to use data augmentation on the training set.      | `bool` | `True` (see [dedicated section](#augmentation)) |
+
+#### Preprocessing
 
 Preprocessing is applied before training the network (see the [dedicated references](../../ref/ocr/managers/dataset.md)). The list of accepted transforms is defined in the [dedicated references](../../ref/ocr/transforms.md#dan.ocr.transforms.Preprocessing).
 
@@ -196,7 +196,7 @@ Usage:
 ]
 ```
 
-### Data augmentation
+#### Augmentation
 
 Augmentation transformations are applied on-the-fly during training to artificially increase data variability.