if __name__ == '__main__':
import utils
from models import networks
from Images import data_preprocess
from torch.backends import cudnn
torch.backends.cudnn.benchmark = True
# get dataloader
amazon_path = os.path.join(params.imgs_root_path, r'amazon\images')
dslr_path = os.path.join(params.imgs_root_path, r'dslr\images')
webcam_path = os.path.join(params.imgs_root_path, r'webcam\images')
caltech_path = os.path.join(params.imgs_root_path, r'Clatech\clatech')
# 不使用target domain
amazon_dataloader = data_preprocess.get_dataloader(amazon_path,
params.images_name)
# dslr_dataloader = data_preprocess.get_dataloader(dslr_path, params.images_name)
caltech_dataloader = data_preprocess.get_dataloader(
caltech_path, params.images_name)
# 目标域带标签的
# amazon_dataloader, dslr_dataloader = data_preprocess.get_src_tgt_dataloader(amazon_path, dslr_path, params.images_name)
# 数据均衡后的dataloader
# amazon_dataloader, dslr_dataloader = data_preprocess.data_balance_dataloader(amazon_path, dslr_path, params.images_name)
# 初始化网络
classifier = networks.Classifier(3 * params.imgs_size * params.imgs_size,
len(params.images_name)).cuda()
discriminator = networks.LargeDiscriminator(3 * params.imgs_size *
params.imgs_size).cuda()
if __name__ == '__main__':
from models import networks
from Images import data_preprocess
from torch.backends import cudnn
torch.backends.cudnn.benchmark = True
# get dataloader
amazon_path = os.path.join(params.imgs_root_path, r'amazon\images')
dslr_path = os.path.join(params.imgs_root_path, r'dslr\images')
webcam_path = os.path.join(params.imgs_root_path, r'webcam\images')
caltech_path = os.path.join(params.imgs_root_path, r'Clatech\clatech')
# 不使用target domain
amazon_dataloader = data_preprocess.get_dataloader(amazon_path,
params.images_name)
dslr_dataloader = data_preprocess.get_dataloader(dslr_path,
params.images_name)
webcam_dataloader = data_preprocess.get_dataloader(webcam_path,
params.images_name)
caltech_dataloader = data_preprocess.get_dataloader(
caltech_path, params.images_name)
# 目标域带标签的
# amazon_dataloader, dslr_dataloader = data_preprocess.get_src_tgt_dataloader(amazon_path, dslr_path, params.images_name)
# 初始化网络
classifier = networks.Classifier(3 * params.imgs_size * params.imgs_size,
len(params.images_name)).cuda()
discriminator = networks.LargeDiscriminator(3 * params.imgs_size *
params.imgs_size).cuda()
torch.save(classifier.state_dict(), '../pth/classifier_src.pth')
if __name__ == '__main__':
import utils
from models import networks
from Images import data_preprocess
from torch.backends import cudnn
torch.backends.cudnn.benchmark = True
# get dataloader
amazon_path = os.path.join(params.imgs_root_path, r'amazon\images')
dslr_path = os.path.join(params.imgs_root_path, r'dslr\images')
# 不使用target domain
amazon_dataloader = data_preprocess.get_dataloader(amazon_path,
params.images_name)
dslr_dataloader = data_preprocess.get_dataloader(dslr_path,
params.images_name)
# 目标域带标签的
# amazon_dataloader, dslr_dataloader = data_preprocess.get_src_tgt_dataloader(amazon_path, dslr_path, params.images_name)
# 初始化网络
classifier = networks.Classifier(3 * params.imgs_size * params.imgs_size,
len(params.images_name)).cuda()
discriminator = networks.LargeDiscriminator(3 * params.imgs_size *
params.imgs_size).cuda()
# if os.path.exists('../pth/classifier_src.pth'):
# classifier.load_state_dict(torch.load('../pth/classifier_src.pth'))
# else:
# train_classifier(classifier, dslr_dataloader)