diff --git a/NewtonOutput/best_model.pt b/NewtonOutput/best_model.pt deleted file mode 100644 index 167726c23986185dda1a51d573d2474d56e2580c..0000000000000000000000000000000000000000 Binary files a/NewtonOutput/best_model.pt and /dev/null differ diff --git a/NewtonOutput/confiance_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/confiance_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png deleted file mode 100644 index d6937f22056ece86bb71eb76ce91e4355d8981ae..0000000000000000000000000000000000000000 Binary files a/NewtonOutput/confiance_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png and /dev/null differ diff --git a/NewtonOutput/confusion_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/confusion_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png deleted file mode 100644 index 330930a0ab064fea346aa2af03feb0ede15f48fc..0000000000000000000000000000000000000000 Binary files a/NewtonOutput/confusion_matrix_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png and /dev/null differ diff --git a/NewtonOutput/training_plot_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png b/NewtonOutput/training_plot_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png deleted file mode 100644 index ec8d79d7b5ed5a56e8636801a9f59810963218dc..0000000000000000000000000000000000000000 Binary files a/NewtonOutput/training_plot_model_ResNet18_noise_0_lr_0.001_optim_Adam_betas_0.938_0.9928.png and /dev/null differ diff --git a/dataset/dataset.py b/dataset/dataset.py index f064c404c9126088164e11b1040bdf5a93362d41..0ea51076a6b3d6382b5c9b6d18902bbb6098e096 100644 --- a/dataset/dataset.py +++ b/dataset/dataset.py @@ -252,7 +252,7 @@ def load_data_duo(base_dir, batch_size, args, shuffle=True): dataset=train_dataset, batch_size=batch_size, shuffle=shuffle, - num_workers=8, + num_workers=1, collate_fn=None, pin_memory=False, ) @@ -260,7 +260,7 @@ def load_data_duo(base_dir, batch_size, args, shuffle=True): dataset=val_dataset, batch_size=batch_size, shuffle=shuffle, - num_workers=8, + num_workers=1, collate_fn=None, pin_memory=False, ) diff --git a/main.py b/main.py index 54886332f556978232ca685f985f1d66fe7f70d6..2cd28d3050f8ab7ae463989b269f1b2bfc129394 100644 --- a/main.py +++ b/main.py @@ -254,10 +254,10 @@ def run_duo(args): plt.plot(train_loss) plt.plot(train_loss) - plt.savefig(f'NewtonOutput/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png') + plt.savefig(f'output/Atraining_plot_model_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png') #load and evaluate best model load_model(model, args.save_path) - make_prediction_duo(model,data_test, f'NewtonOutput/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png') + make_prediction_duo(model,data_test, f'output/Amodel_{args.model}_noise_{args.noise_threshold}_lr_{args.lr}_optim_{args.optim + ("_momentum_"+str(args.momentum) if args.optim=="SGD" else "_betas_" + str(args.beta1)+ "_" +str(args.beta2))}.png') return best_loss,best_acc def make_prediction_duo(model, data, f_name):