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get_lid_norm.py
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get_lid_norm.py
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import os
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from lib.adv_model import *
from lib.dataset_utils import *
from lib.dknn import DKNN, DKNNL2
from lib.lip_model import *
from lib.mnist_model import *
from lib.utils import *
from tune_mnist import Identity
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
exp_id = 0
model_name = 'train_mnist_exp%d.h5' % exp_id
net = BasicModel()
# model_name = 'train_mnist_snnl_exp%d.h5' % exp_id
# net = SNNLModel(train_it=True)
# model_name = 'train_mnist_hidden_mixup_exp%d.h5' % exp_id
# net = HiddenMixupModel()
# model_name = 'train_mnist_vae_exp%d.h5' % exp_id
# net = VAE((1, 28, 28), num_classes=10, latent_dim=20)
# net = VAE2((1, 28, 28), num_classes=10, latent_dim=20)
# model_name = 'train_mnist_cav_exp%d.h5' % exp_id
# net = ClassAuxVAE((1, 28, 28), num_classes=10, latent_dim=20)
# model_name = 'adv_mnist_exp%d.h5' % exp_id
# basic_net = BasicModel()
# # basic_net = BasicModelV2()
# config = {'epsilon': 0.3,
# 'num_steps': 40,
# 'step_size': 0.01,
# 'random_start': True,
# 'loss_func': 'xent'}
# # net = PGDModel(basic_net, config)
# net = PGDL2Model(basic_net, config)
# model_name = 'dist_mnist_exp%d.h5' % exp_id
# init_it = 1
# train_it = False
# net = NeighborModel(num_classes=10, init_it=init_it, train_it=train_it)
# orig_model = 'adv_mnist_exp2.h5'
# model_name = 'tune%d_%s' % (exp_id, orig_model)
# net = BasicModel()
# net.fc = Identity()
# model_name = 'ae_mnist_exp%d.h5' % exp_id
# net = Autoencoder((1, 28, 28), 128)
# model_name = 'adv_mnist_ae_exp%d.h5' % exp_id
# basic_net = Autoencoder((1, 28, 28), latent_dim=128)
# config = {'num_steps': 40,
# 'step_size': 0.1,
# 'random_start': True,
# 'loss_func': 'xent'}
# net = PGDL2Model(basic_net, config)
# Set all random seeds
seed = 2019
np.random.seed(seed)
torch.manual_seed(seed)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Set up model directory
save_dir = os.path.join(os.getcwd(), 'saved_models')
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
net.load_state_dict(torch.load(model_path))
net = net.module
# net = net.basic_net
net.eval()
(x_train, y_train), (x_valid, y_valid), (x_test, y_test) = load_mnist_all(
'/data', val_size=0.1, seed=seed)
net.conv1 = Identity()
net.relu1 = Identity()
net.conv2 = Identity()
net.relu2 = Identity()
net.conv3 = Identity()
net.relu3 = Identity()
net.fc = Identity()
# layers = ['relu1', 'relu2', 'relu3', 'fc']
# layers = ['relu1', 'relu2', 'relu3', 'en_mu']
# layers = ['relu1', 'relu2', 'relu3']
layers = ['relu1']
# layers = ['en_conv3']
# layers = ['en_mu']
# layers = ['maxpool1', 'maxpool2', 'relu3', 'fc2']
# layers = ['gs1', 'gs2', 'gs3', 'fc']
# net = net.cpu()
with torch.no_grad():
# dknn = DKNN(net, x_train, y_train, x_valid, y_valid, layers,
# k=75, num_classes=10)
dknn = DKNNL2(net, x_train, y_train, x_valid, y_valid, layers,
k=75, num_classes=10)
x = x_test.requires_grad_(True)[:1000]
norms = compute_spnorm(x, dknn, layers)
print(', '.join('%.4f' % i for i in norms.mean(0)))
# x = x_test[:1000]
lid = np.zeros((x.size(0), len(layers)))
reps = dknn.get_activations(x, requires_grad=False)
train_reps = dknn.get_activations(x_train, requires_grad=False)
for l, layer in enumerate(layers):
lid[:, l] = compute_lid(
reps[layer], train_reps[layer], 3000, exclude_self=False)
print(', '.join('%.4f' % i for i in lid.mean(0)))