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load_model.py
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load_model.py
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from data_loader import CIFAR_Dataset
import torch
from torch.utils.data import DataLoader
from models.component import Discriminator, Classifier, Domain_Classifier
import models
import os
import argparse
from models.vaebase import DomainEncoder, ConvEncoder, ResDecoder
from torch.nn import DataParallel
keys_z = [
'encoder_z.conv1.weight', 'encoder_z.conv1.bias', 'encoder_z.bn1.weight', 'encoder_z.bn1.bias', 'encoder_z.bn1.running_mean', 'encoder_z.bn1.running_var',
'encoder_z.bn1.num_batches_tracked', 'encoder_z.conv2.weight', 'encoder_z.conv2.bias', 'encoder_z.bn2.weight', 'encoder_z.bn2.bias',
'encoder_z.bn2.running_mean', 'encoder_z.bn2.running_var', 'encoder_z.bn2.num_batches_tracked', 'encoder_z.conv3.weight', 'encoder_z.conv3.bias',
'encoder_z.bn3.weight', 'encoder_z.bn3.bias', 'encoder_z.bn3.running_mean', 'encoder_z.bn3.running_var', 'encoder_z.bn3.num_batches_tracked', 'encoder_z.fc1.weight',
'encoder_z.fc1.bias', 'encoder_z.bn1_fc.weight', 'encoder_z.bn1_fc.bias', 'encoder_z.bn1_fc.running_mean', 'encoder_z.bn1_fc.running_var', 'encoder_z.bn1_fc.num_batches_tracked',
'encoder_z.fc2.weight', 'encoder_z.fc2.bias', 'encoder_z.bn2_fc.weight', 'encoder_z.bn2_fc.bias', 'encoder_z.bn2_fc.running_mean', 'encoder_z.bn2_fc.running_var',
'encoder_z.bn2_fc.num_batches_tracked', 'encoder_z.mu_logvar_gen.weight', 'encoder_z.mu_logvar_gen.bias']
keys_d = ['encoder_d.embed.weight', 'encoder_d.bn.weight', 'encoder_d.bn.bias', 'encoder_d.bn.running_mean',
'encoder_d.bn.running_var', 'encoder_d.bn.num_batches_tracked', 'encoder_d.mu_logvar_gen.weight', 'encoder_d.mu_logvar_gen.bias']
def main(args):
device_ids = [0, 1]
test_data = CIFAR_Dataset(args=args, partition='test')
dataloader = DataLoader(test_data, batch_size=20, num_workers=6,
shuffle=False, pin_memory=True, drop_last=False)
checkpoint = torch.load("checkpoints/Threshold_0.6_D-cifar_tar-fog_A-vae_B-20_O-Pseudo_best.pth.tar")
model = models.create(args.arch, args)
model = DataParallel(model, device_ids).cuda()
# model.load_state_dict(checkpoint)
model_dict = model.state_dict()
model_vae = checkpoint['model-module']
def load_checkpoint(checkpoint, model, opt=None):
"""
load saved model accordingly
"""
if not os.path.exists(checkpoint):
raise("File does not exists {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint['model-module'])
if opt:
opt.load_state_dict(checkpoint['optim_dict'])
return checkpoint
def get_encoder(type, in_feature, num_domains, domain_in_feature):
"""
load model with a specific type of encoder
"""
if type == 'encoder_z':
encoder = ConvEncoder(in_feature)
else:
encoder = DomainEncoder(num_domains, domain_in_feature)
return encoder
def get_decoder(latent_vector):
"""
load decoder
"""
return ResDecoder(latent_vector)
def load_separately_from_model(keys_z, keys_d, keys_decoder, trained_model):
pass
# def load_decoder():
# pass
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Multi-source Open-set Domain Adaptation')
# set up dataset & backbone embedding
dataset = 'digits'
if dataset == 'digits':
unk_class = 5
max_epoch = 120
arch = 'vae'
elif dataset == 'office':
unk_class = 10
max_epoch = 360
arch = 'bigvae'
elif dataset == 'domain_net':
unk_class = 246
max_epoch = 10000
arch = 'bigvae'
elif dataset == 'pacs':
unk_class = '5'
max_epoch = 300
arch = 'bigvae'
elif dataset == 'cifar':
unk_class = 5
max_epoch = 100
arch = 'vae'
parser.add_argument('--dataset', type=str, default=dataset, choices=['digits', 'office', 'domain_net', 'pacs', 'cifar'])
parser.add_argument('-a', '--arch1', type=str, default='encoder_d', choices=['vae', 'bigvae', 'encoder_d', 'encoder_z', 'decoder'])
parser.add_argument('-a', '--arch2', type=str, default='encoder_z', choices=['vae', 'bigvae', 'encoder_d', 'encoder_z', 'decoder'])
parser.add_argument('--discriminator', type=bool, default=False)
parser.add_argument('--loss', type=str, default='VAE', choices=["VAE", "betaH", "betaB", "factor", "btcvae"])
parser.add_argument('--CE_loss', type=str, default='CE', choices=['angular', 'focal', 'CE', 'momentum'])
parser.add_argument('--dist', type=str, default='normal', choices=['normal', 'laplace', 'flow'])
parser.add_argument('--method', type=str, default='MSDA')
parser.add_argument('--unk_class', type=int, default=unk_class, help="after which classes will be unknown")
parser.add_argument('--strict_setting', type=bool, default=True)
parser.add_argument('--open_method', type=str, default='Pseudo', choices=['OSBP', 'OSVM', 'Pseudo'])
if dataset == 'digits':
parser.add_argument('--target_domain', type=str, default='syn',
choices=['mnistm', 'mnist', 'usps', 'svhn', 'syn'])
elif dataset == 'office':
parser.add_argument('--target_domain', type=str, default='webcam', metavar='N',
choices=['amazon', 'dslr', 'webcam'], help='target domain dataset')
elif dataset == 'domain_net':
parser.add_argument('--target_domain', type=str, default='clipart', metavar='N',
choices=['clipart', 'infograph', 'painting', 'quickdraw', 'real', 'sketch'],
help='target domain dataset')
elif dataset == 'pacs':
parser.add_argument('--target_domain', type=str, default='photo', metavar='N',
choices=['art_painting', 'cartoon', 'photo', 'sketch'], help='target domain dataset')
elif dataset == 'cifar':
parser.add_argument('--target_domain', type=str, default='fog', metavar='N',
choices=['brightness', 'contrast', 'fog', 'defocus_blur', 'frost'], help='target domain dataset')
# set up path
working_dir = os.path.dirname(os.path.abspath(__file__))
parser.add_argument('--data_dir', type=str, metavar='PATH',
default=os.path.join(working_dir, 'data/'))
parser.add_argument('--logs_dir', type=str, metavar='PATH',
default=os.path.join(working_dir, 'logs/'))
parser.add_argument('--checkpoints_dir', type=str, metavar='PATH',
default=os.path.join(working_dir, 'checkpoints/'))
# verbose setting
parser.add_argument('--resume', type=bool, default=False)
parser.add_argument('--max_epoch', type=int, default=max_epoch, metavar='N', help='how many epochs')
parser.add_argument('--log_step', type=int, default=100)
parser.add_argument('--log_epoch', type=int, default=1)
parser.add_argument('--eval_log_step', type=int, default=1)
# @note 1500
parser.add_argument('--test_interval', type=int, default=1500)
# hyper-parameters
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('-b', '--batch_size', type=int, default=20)
# parser.add_argument('--threshold', type=float, default=0.1)
parser.add_argument('--dropout', type=float, default=0.7)
# optimizer
parser.add_argument('--lr', type=float, default=0.0002)
parser.add_argument('--weight_decay', type=float, default=5e-4)
# reconstruction specific
parser.add_argument('--rec_dist', type=str, default='gaussian', choices=['bernoulli', 'gaussian', 'laplace'])
parser.add_argument('--steps_anneal', type=int, default=1000, help='Number of annealing steps where gradually adding '
'the regularization')
parser.add_argument('--reg_anneal', type=int, default=1000)
# loss-specific
parser.add_argument('--btcvae-A', type=float,
default=1.,
help="Weight of the MI term (alpha in the paper).")
parser.add_argument('--btcvae-G', type=float,
default=1.,
help="Weight of the dim-wise KL term (gamma in the paper).")
parser.add_argument('--btcvae-B', type=float,
default=6.,
help="Weight of the TC term (beta in the paper).")
parser.add_argument('--in_features', type=int, default=2048)
parser.add_argument('--domain_in_features', type=int, default=100) ## vae中间层
main(parser.parse_args())