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74 lines
1.9 KiB
74 lines
1.9 KiB
import os
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import sys
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import torch
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sys.path.append('../')
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from core.dann import train_dann
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from core.test import test
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from models.model import AlexModel
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from utils.utils import get_data_loader, init_model, init_random_seed
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class Config(object):
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# params for path
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dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
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model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-dann'))
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# params for datasets and data loader
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batch_size = 32
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# params for source dataset
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src_dataset = "amazon31"
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src_model_trained = True
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src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
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# params for target dataset
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tgt_dataset = "webcam31"
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tgt_model_trained = True
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dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
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# params for pretrain
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num_epochs_src = 100
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log_step_src = 5
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save_step_src = 50
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eval_step_src = 10
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# params for training dann
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gpu_id = '0'
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## for office
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num_epochs = 1000
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log_step = 10 # iters
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save_step = 500
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eval_step = 10 # epochs
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manual_seed = 8888
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alpha = 0
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# params for optimizing models
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lr = 2e-4
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params = Config()
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# init random seed
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init_random_seed(params.manual_seed)
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# init device
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device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
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# load dataset
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src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size)
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tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size)
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# load dann model
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dann = init_model(net=AlexModel(), restore=None)
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# train dann model
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print("Start training dann model.")
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if not (dann.restored and params.dann_restore):
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dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader, device)
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print('done')
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