def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset svhn_data_loader = get_svhn(split='train', download=True) svhn_data_loader_eval = get_svhn(split='test', download=True) mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) # Model init WDGRL tgt_encoder = model_init(Encoder(), params.encoder_wdgrl_path) critic = model_init(Discriminator(in_dims=params.d_in_dims, h_dims=params.d_h_dims, out_dims=params.d_out_dims), params.disc_wdgrl_path) clf = model_init(Classifier(), params.clf_wdgrl_path) # Train critic to optimality print("====== Training critic ======") if not (critic.pretrained and params.model_trained): critic = train_critic_wdgrl(tgt_encoder, critic, svhn_data_loader, mnist_data_loader) # Train target encoder print("====== Training encoder for both SVHN and MNIST domains ======") if not (tgt_encoder.pretrained and clf.pretrained and params.model_trained): tgt_encoder, clf = train_tgt_wdgrl(tgt_encoder, clf, critic, svhn_data_loader, mnist_data_loader, robust=False) # Eval target encoder on test set of target dataset print("====== Evaluating classifier for encoded SVHN and MNIST domains ======") print("-------- SVHN domain --------") eval_tgt(tgt_encoder, clf, svhn_data_loader_eval) print("-------- MNIST adaption --------") eval_tgt(tgt_encoder, clf, mnist_data_loader_eval)
def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) usps_data_loader = get_usps(train=True, download=True) usps_data_loader_eval = get_usps(train=False, download=True) # Model init Revgard tgt_encoder = model_init(Encoder(), params.tgt_encoder_revgrad_path) critic = model_init(Discriminator(), params.disc_revgard_path) clf = model_init(Classifier(), params.clf_revgrad_path) # Train models print("====== Training source encoder and classifier in MNIST and USPS domains ======") if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained and params.model_trained): tgt_encoder, clf, critic = train_revgrad(tgt_encoder, clf, critic, mnist_data_loader, usps_data_loader, robust=False) # Eval target encoder on test set of target dataset print("====== Evaluating classifier for encoded MNIST and USPS domain ======") print("-------- MNIST domain --------") eval_tgt(tgt_encoder, clf, mnist_data_loader_eval) print("-------- USPS adaption --------") eval_tgt(tgt_encoder, clf, usps_data_loader_eval)
def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) usps_data_loader = get_usps(train=True, download=True) usps_data_loader_eval = get_usps(train=False, download=True) # Model init WDGRL tgt_encoder = model_init(Encoder(), params.encoder_wdgrl_rb_path) critic = model_init(Discriminator(), params.disc_wdgrl_rb_path) clf = model_init(Classifier(), params.clf_wdgrl_rb_path) # Train target encoder print("====== Robust Training encoder for both MNIST and USPS domains ======") if not (tgt_encoder.pretrained and clf.pretrained and params.model_trained): tgt_encoder, clf = train_tgt_wdgrl(tgt_encoder, clf, critic, mnist_data_loader, usps_data_loader, usps_data_loader_eval, robust=True) # Eval target encoder on test set of target dataset print("====== Evaluating classifier for encoded MNIST and USPS domains ======") print("-------- MNIST domain --------") eval_tgt_robust(tgt_encoder, clf, mnist_data_loader_eval) print("-------- USPS adaption --------") eval_tgt_robust(tgt_encoder, clf, usps_data_loader_eval)
def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset svhn_data_loader = get_svhn(split='train', download=True) svhn_data_loader_eval = get_svhn(split='test', download=True) mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) # Model init ADDA src_encoder = model_init(Encoder(), params.src_encoder_adda_rb_path) tgt_encoder = model_init(Encoder(), params.tgt_encoder_adda_rb_path) critic = model_init( Discriminator(in_dims=params.d_in_dims, h_dims=params.d_h_dims, out_dims=params.d_out_dims), params.disc_adda_rb_path) clf = model_init(Classifier(), params.clf_adda_rb_path) # Train source model for adda print( "====== Robust training source encoder and classifier in SVHN domain ======" ) if not (src_encoder.pretrained and clf.pretrained and params.model_trained): src_encoder, clf = train_src_robust(src_encoder, clf, svhn_data_loader) # Eval source model print("====== Evaluating classifier for SVHN domain ======") eval_tgt_robust(src_encoder, clf, svhn_data_loader_eval) # Train target encoder print("====== Robust training encoder for MNIST domain ======") # Initialize target encoder's weights with those of the source encoder if not tgt_encoder.pretrained: tgt_encoder.load_state_dict(src_encoder.state_dict()) if not (tgt_encoder.pretrained and critic.pretrained and params.model_trained): tgt_encoder = train_tgt_adda(src_encoder, tgt_encoder, critic, svhn_data_loader, mnist_data_loader, robust=True) # Eval target encoder on test set of target dataset print("====== Ealuating classifier for encoded MNIST domain ======") print("-------- Source only --------") eval_tgt_robust(src_encoder, clf, mnist_data_loader_eval) print("-------- Domain adaption --------") eval_tgt_robust(tgt_encoder, clf, mnist_data_loader_eval)
def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset svhn_data_loader = get_svhn(split='train', download=True) svhn_data_loader_eval = get_svhn(split='test', download=True) mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) # Model init DANN tgt_encoder = model_init(Encoder(), params.tgt_encoder_dann_rb_path) critic = model_init( Discriminator(in_dims=params.d_in_dims, h_dims=params.d_h_dims, out_dims=params.d_out_dims), params.disc_dann_rb_path) clf = model_init(Classifier(), params.clf_dann_rb_path) # Train models print( "====== Training source encoder and classifier in SVHN and MNIST domains ======" ) if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained and params.model_trained): tgt_encoder, clf, critic = train_dann(tgt_encoder, clf, critic, svhn_data_loader, mnist_data_loader, mnist_data_loader_eval, robust=True) # Eval target encoder on test set of target dataset print( "====== Evaluating classifier for encoded SVHN and MNIST domains ======" ) print("-------- SVHN domain --------") eval_tgt_robust(tgt_encoder, clf, svhn_data_loader_eval) print("-------- MNIST adaption --------") eval_tgt_robust(tgt_encoder, clf, mnist_data_loader_eval)
def main(): # init random seed init_random_seed(params.manual_seed) # Load dataset mnist_data_loader = get_mnist(train=True, download=True) mnist_data_loader_eval = get_mnist(train=False, download=True) usps_data_loader = get_usps(train=True, download=True) usps_data_loader_eval = get_usps(train=False, download=True) # Model init DANN tgt_encoder = model_init(Encoder(), params.tgt_encoder_dann_rb_path) critic = model_init(Discriminator(), params.disc_dann_rb_path) clf = model_init(Classifier(), params.clf_dann_rb_path) # Train models print( "====== Robust Training source encoder and classifier in MNIST and USPS domains ======" ) if not (tgt_encoder.pretrained and clf.pretrained and critic.pretrained and params.model_trained): tgt_encoder, clf, critic = train_dann(tgt_encoder, clf, critic, mnist_data_loader, usps_data_loader, usps_data_loader_eval, robust=False) # Eval target encoder on test set of target dataset print( "====== Evaluating classifier for encoded MNIST and USPS domains ======" ) print("-------- MNIST domain --------") eval_tgt_robust(tgt_encoder, clf, critic, mnist_data_loader_eval) print("-------- USPS adaption --------") eval_tgt_robust(tgt_encoder, clf, critic, usps_data_loader_eval) print("====== Pseudo labeling on USPS domain ======") pseudo_label(tgt_encoder, clf, "usps_train_pseudo", usps_data_loader) # Init a new model tgt_encoder = model_init(Encoder(), params.tgt_encoder_path) clf = model_init(Classifier(), params.clf_path) # Load pseudo labeled dataset usps_pseudo_loader = get_usps(train=True, download=True, get_pseudo=True) print("====== Standard training on USPS domain with pseudo labels ======") if not (tgt_encoder.pretrained and clf.pretrained): train_src_adda(tgt_encoder, clf, usps_pseudo_loader, mode='ADV') print("====== Evaluating on USPS domain with real labels ======") eval_tgt(tgt_encoder, clf, usps_data_loader_eval) tgt_encoder = model_init(Encoder(), params.tgt_encoder_rb_path) clf = model_init(Classifier(), params.clf_rb_path) print("====== Robust training on USPS domain with pseudo labels ======") if not (tgt_encoder.pretrained and clf.pretrained): train_src_robust(tgt_encoder, clf, usps_pseudo_loader, mode='ADV') print("====== Evaluating on USPS domain with real labels ======") eval_tgt(tgt_encoder, clf, usps_data_loader_eval)