def main(cfg):
log = logging.getLogger(__name__)
# Get device
device = torch.device("cuda" if (
torch.cuda.is_available() and cfg.eval.cuda) else "cpu")
print("DEVICE : {}".format(device))
# Get task and model_name
tested_task = cfg.data.task
tested_model_name = cfg.model_name
# Find and create associated model
model_config = getattr(cfg.models, tested_model_name, None)
cfg_eval = set_format(model_config, cfg.eval)
# Enable CUDNN BACKEND
torch.backends.cudnn.enabled = cfg_eval.enable_cudnn
# Find and create associated dataset
dataset_config = cfg.data
tested_dataset_name = dataset_config.name
dataset_config.dataroot = hydra.utils.to_absolute_path(
dataset_config.dataroot)
dataset = find_dataset_using_name(tested_dataset_name,
tested_task)(dataset_config, cfg_eval)
# Find and create associated model
resolve_model(model_config, dataset, tested_task)
model_config = merges_in_sub(model_config, [cfg_eval, dataset_config])
model = find_model_using_name(model_config.architecture, tested_task,
model_config, dataset)
log.info(model)
model.eval()
if cfg_eval.enable_dropout:
model.enable_dropout_in_eval()
# Set sampling / search strategies
dataset.set_strategies(
model, precompute_multi_scale=cfg_eval.precompute_multi_scale)
model = model.to(device)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
log.info("Model size = %i", params)
tracker: BaseTracker = dataset.get_tracker(model, tested_task, dataset,
cfg.wandb, cfg.tensorboard)
checkpoint = get_model_checkpoint(model, cfg_eval.checkpoint_dir,
tested_model_name, True,
cfg_eval.weight_name, "test")
# Run training / evaluation
run(cfg, model, dataset, device, tracker, checkpoint, log)
def main(cfg):
log = logging.getLogger(__name__)
# Get device
device = torch.device("cuda" if (
torch.cuda.is_available() and cfg.training.cuda) else "cpu")
print("DEVICE : {}".format(device))
# Get task and model_name
tested_task = cfg.data.task
tested_model_name = cfg.model_name
# Find and create associated model
model_config = getattr(cfg.models, tested_model_name, None)
# Find which dataloader to use
cfg_training = set_format(model_config, cfg.training)
# Enable CUDNN BACKEND
torch.backends.cudnn.enabled = cfg_training.enable_cudnn
# Find and create associated dataset
dataset_config = cfg.data
tested_dataset_name = dataset_config.name
dataset_config.dataroot = hydra.utils.to_absolute_path(
dataset_config.dataroot)
dataset = find_dataset_using_name(tested_dataset_name,
tested_task)(dataset_config,
cfg_training)
# Find and create associated model
resolve_model(model_config, dataset, tested_task)
model_config = merges_in_sub(model_config, [cfg_training, dataset_config])
model = find_model_using_name(model_config.architecture, tested_task,
model_config, dataset)
log.info(model)
# Optimizer
lr_params = cfg_training.learning_rate
model.set_optimizer(getattr(torch.optim, cfg_training.optimizer, None),
lr_params=lr_params)
# Set sampling / search strategies
dataset.set_strategies(
model, precompute_multi_scale=cfg_training.precompute_multi_scale)
model = model.to(device)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
log.info("Model size = %i", params)
# metric tracker
if cfg.wandb.log:
import wandb
wandb.init(project=cfg.wandb.project)
# wandb.watch(model)
tracker: BaseTracker = dataset.get_tracker(model, tested_task, dataset,
cfg.wandb, cfg.tensorboard)
checkpoint = get_model_checkpoint(
model,
cfg_training.checkpoint_dir,
tested_model_name,
cfg_training.resume,
cfg_training.weight_name,
"val" if dataset.has_val_loader else "test",
)
# Run training / evaluation
run(cfg, model, dataset, device, tracker, checkpoint, log)
def _find_model_using_name(model_class, task, model_config, dataset):
resolve_model(model_config, dataset, task)
return find_model_using_name(model_class, task, model_config, dataset)