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83 lines
2.6 KiB
83 lines
2.6 KiB
import os
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import sys
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import datetime
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from tensorboardX import SummaryWriter
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import torch
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sys.path.append('../')
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from models.model import SVHNmodel
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from core.train import train_dann
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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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model_name = "svhn-mnist"
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model_base = '/home/wogong/models/pytorch-dann'
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model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
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note = 'paper-structure'
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model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
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os.makedirs(model_root)
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config = os.path.join(model_root, 'config.txt')
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finetune_flag = False
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lr_adjust_flag = 'simple'
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src_only_flag = False
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# params for datasets and data loader
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batch_size = 128
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# params for source dataset
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src_dataset = "svhn"
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src_image_root = os.path.join('/home/wogong/datasets', 'svhn')
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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 = "mnist"
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tgt_image_root = os.path.join('/home/wogong/datasets', 'mnist')
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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 training dann
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gpu_id = '0'
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## for digit
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num_epochs = 200
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log_step = 50
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save_step = 100
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eval_step = 1
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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 = 5 # epochs
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manual_seed = None
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alpha = 0
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# params for optimizing models
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lr = 0.01
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momentum = 0.9
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weight_decay = 1e-6
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params = Config()
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logger = SummaryWriter(params.model_root)
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device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
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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, params.src_image_root, params.batch_size, train=True)
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src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False)
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tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True)
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tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False)
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# load dann model
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dann = init_model(net=SVHNmodel(), restore=None)
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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, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger)
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