dgs.models.engine.visual_sim_engine.VisualSimilarityEngine

class dgs.models.engine.visual_sim_engine.VisualSimilarityEngine(*args: Any, **kwargs: Any)

An engine class for training and testing visual similarities using visual embeddings.

For this model:

• get_data() should return the image crop

• get_target() should return the target class IDs

• train_dl contains the training data as usual

• test_dl contains the query data

• val_dl contains the gallery data

Train Params

nof_classes (int):

The number of classes in the training set.

Test Params

metric (str|callable):

The name or class of the metric used during testing / evaluation. The metric in the VisualSimilarityEngine is only used to compute the distance between the query and gallery embeddings. Therefore, a distance-based metric should be used.

It is possible to pass additional initialization kwargs to the metric by adding them to the metric_kwargs parameter.

Optional Train Params

topk_acc (list[int], optional):

The values for k for the top-k accuracy evaluation during training. Default DEF_VAL.engine.visual.topk_acc.

Optional Test Params

metric_kwargs (dict, optional):

Specific kwargs for the metric. Default DEF_VAL.engine.visual.metric_kwargs.

topk_cmc (list[int], optional):

The values for k the top-k cmc evaluation during testing / evaluation. Default DEF_VAL.engine.visual.topk_cmc.

write_embeds (list[bool, bool], optional):

Whether to write the embeddings for the Query and Gallery Dataset to the tensorboard writer. Only really feasible for smaller datasets ~1k embeddings. Default DEF_VAL.engine.visual.write_embeds.

image_key (str, optional):

Which key to use when loading the image from the state in get_data(). Default DEF_VAL.engine.visual.image_key.

__init__(config: dict[str, any], model: TorchreidVisualSimilarity, test_loader: torch.utils.data.DataLoader, val_loader: torch.utils.data.DataLoader, *, train_loader: torch.utils.data.DataLoader | None = None, **kwargs)

Methods

configure_torch_module(module[, train])	Set compute mode and send model to the device or multiple parallel devices if applicable.
evaluate()	Run tests, defined in Sub-Engine.
get_data(ds)	Get the image crop or other requested image from the state.
get_hparam_dict()	Get the hyperparameters of the current engine.
get_target(ds)	Get the target pIDs from the data.
initialize_optimizer()	Because the module might be set after the initial step, load the optimizer and scheduler at the start of the training.
load_model(path)	Load the model from a file.
predict
print_results(results)	Given a dictionary of results, print them to the console if allowed.
run()	Run the model.
save_model(epoch, metrics, optimizer, lr_sched)	Save the current model and other weights into a '.pth' file.
set_model_mode(mode)	Set model mode to train or test.
terminate()	Handle forceful termination, e.g., ctrl+c
test
train_model()	Train the given model using the given loss function, optimizer, and learning-rate schedulers.
validate_params(validations[, attrib_name])	Given per key validations, validate this module's parameters.
write_results(results, prepend)	Given a dictionary of results, use the writer to save the values.

Attributes

curr_epoch
device	Get the device of this module.
is_training	Get whether this module is set to training-mode.
module_name	Get the name of the module.
module_type
name	Get the name of the module.
name_safe	Get the escaped name of the module usable in filepaths by replacing spaces and underscores.
precision	Get the (floating point) precision used in multiple parts of this module.
train_load_image_crops	Whether to load the image crops during training.
val_dl	The torch DataLoader containing the validation (query) data.
model
metric	A metric function used to compute the embedding distance.
loss
writer
test_dl	The torch DataLoader containing the test data.
train_dl	The torch DataLoader containing the training data.