loss_epoch[idx] += print_loss
logs.append_train_loss([x / total_step for x in loss_epoch])
# validate by testing the CPC performance on the validation set
if opt.validate:
validation_loss = val_by_InfoNCELoss.val_by_InfoNCELoss(opt, model, test_loader)
logs.append_val_loss(validation_loss)
logs.create_log(model, epoch=epoch, optimizer=optimizer)
if __name__ == "__main__":
opt = arg_parser.parse_args()
arg_parser.create_log_path(opt)
# set random seeds
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
np.random.seed(opt.seed)
random.seed(opt.seed)
# load model
model, optimizer = load_audio_model.load_model_and_optimizer(opt)
# initialize logger
logs = logger.Logger(opt)
# get datasets and dataloaders
train_loader, train_dataset, test_loader, test_dataset = get_dataloader.get_libri_dataloaders(
# calculate accuracy
total += num_samples_per_speaker
correct += (svm_output == target).sum()
speaker_idx += 1
accuracy = correct / total
print("Testing Accuracy SVM: ", accuracy)
return accuracy
if __name__ == "__main__":
opt = arg_parser.parse_args()
arg_parser.create_log_path(opt, add_path_var="linear_model")
opt.SVM_training_samples = 20
# Device configuration
opt.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# random seeds
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
np.random.seed(opt.seed)
# load pretrained model
context_model, _ = load_audio_model.load_model_and_optimizer(
opt, reload_model=True
)
context_model.eval()