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50 lines
1.6 KiB
50 lines
1.6 KiB
from models.model import CNNModel
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from models.classifier import Classifier
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from core.dann import train_dann
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from core.test import eval, eval_src
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from core.pretrain import train_src
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import params
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from utils import get_data_loader, init_model, init_random_seed
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# init random seed
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init_random_seed(params.manual_seed)
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# load dataset
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src_data_loader = get_data_loader(params.src_dataset)
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src_data_loader_eval = get_data_loader(params.src_dataset, train=False)
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tgt_data_loader = get_data_loader(params.tgt_dataset)
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tgt_data_loader_eval = get_data_loader(params.tgt_dataset, train=False)
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# load source classifier
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src_classifier = init_model(net=Classifier(), restore=params.src_classifier_restore)
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# train source model
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print("=== Training classifier for source domain ===")
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if not (src_classifier.restored and params.src_model_trained):
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src_classifier = train_src(src_classifier, src_data_loader)
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# eval source model on both source and target domain
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print("=== Evaluating source classifier for source domain ===")
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eval_src(src_classifier, src_data_loader_eval)
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print("=== Evaluating source classifier for target domain ===")
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eval_src(src_classifier, tgt_data_loader_eval)
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# load dann model
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dann = init_model(net=CNNModel(), restore=params.dann_restore)
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# train dann model
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print("=== Training dann model ===")
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if not (dann.restored and params.dann_restore):
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dann = train_dann(dann, src_data_loader, tgt_data_loader, tgt_data_loader_eval)
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# eval dann model
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print("=== Evaluating dann for source domain ===")
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eval(dann, src_data_loader_eval)
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print("=== Evaluating dann for target domain ===")
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eval(dann, tgt_data_loader_eval)
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print('done')
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