256 строки
13 KiB
Python
256 строки
13 KiB
Python
#Common imports
|
|
import os
|
|
import sys
|
|
import numpy as np
|
|
import argparse
|
|
import copy
|
|
import random
|
|
import json
|
|
import pickle
|
|
|
|
#Pytorch
|
|
import torch
|
|
from torch.autograd import grad
|
|
from torch import nn, optim
|
|
from torch.nn import functional as F
|
|
from torchvision import datasets, transforms
|
|
from torchvision.utils import save_image
|
|
from torch.autograd import Variable
|
|
import torch.utils.data as data_utils
|
|
|
|
#Sklearn
|
|
from sklearn.manifold import TSNE
|
|
|
|
#robustdg
|
|
from utils.helper import *
|
|
from utils.match_function import *
|
|
|
|
#slab
|
|
from utils.slab_data import *
|
|
import utils.scripts.utils as slab_utils
|
|
import utils.scripts.lms_utils as slab_lms_utils
|
|
|
|
def get_logits(model, loader, device, label=1):
|
|
X, Y = slab_utils.extract_tensors_from_loader(loader)
|
|
L = slab_utils.get_logits_given_tensor(X, model, device=device).detach()
|
|
L = L[Y==label].cpu().numpy()
|
|
S = L[:, 1] - L[:, 0] # compute score / difference to get scalar
|
|
return S
|
|
|
|
|
|
# Input Parsing
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--dataset_name', type=str, default='slab',
|
|
help='Datasets: rot_mnist; fashion_mnist; pacs')
|
|
parser.add_argument('--method_name', type=str, default='erm_match',
|
|
help=' Training Algorithm: erm_match; matchdg_ctr; matchdg_erm')
|
|
parser.add_argument('--model_name', type=str, default='slab',
|
|
help='Architecture of the model to be trained')
|
|
parser.add_argument('--train_domains', nargs='+', type=str, default=["15", "30", "45", "60", "75"],
|
|
help='List of train domains')
|
|
parser.add_argument('--test_domains', nargs='+', type=str, default=["0", "90"],
|
|
help='List of test domains')
|
|
parser.add_argument('--out_classes', type=int, default=2,
|
|
help='Total number of classes in the dataset')
|
|
parser.add_argument('--img_c', type=int, default= 1,
|
|
help='Number of channels of the image in dataset')
|
|
parser.add_argument('--img_h', type=int, default= 224,
|
|
help='Height of the image in dataset')
|
|
parser.add_argument('--img_w', type=int, default= 224,
|
|
help='Width of the image in dataset')
|
|
parser.add_argument('--slab_data_dim', type=int, default= 2,
|
|
help='Number of features in the slab dataset')
|
|
parser.add_argument('--slab_total_slabs', type=int, default=7)
|
|
parser.add_argument('--slab_num_samples', type=int, default=1000)
|
|
parser.add_argument('--fc_layer', type=int, default= 1,
|
|
help='ResNet architecture customization; 0: No fc_layer with resnet; 1: fc_layer for classification with resnet')
|
|
parser.add_argument('--match_layer', type=str, default='logit_match',
|
|
help='rep_match: Matching at an intermediate representation level; logit_match: Matching at the logit level')
|
|
parser.add_argument('--pos_metric', type=str, default='l2',
|
|
help='Cost to function to evaluate distance between two representations; Options: l1; l2; cos')
|
|
parser.add_argument('--rep_dim', type=int, default=250,
|
|
help='Representation dimension for contrsative learning')
|
|
parser.add_argument('--pre_trained',type=int, default=0,
|
|
help='0: No Pretrained Architecture; 1: Pretrained Architecture')
|
|
parser.add_argument('--perfect_match', type=int, default=1,
|
|
help='0: No perfect match known (PACS); 1: perfect match known (MNIST)')
|
|
parser.add_argument('--opt', type=str, default='sgd',
|
|
help='Optimizer Choice: sgd; adam')
|
|
parser.add_argument('--lr', type=float, default=0.01,
|
|
help='Learning rate for training the model')
|
|
parser.add_argument('--batch_size', type=int, default=16,
|
|
help='Batch size foe training the model')
|
|
parser.add_argument('--epochs', type=int, default=15,
|
|
help='Total number of epochs for training the model')
|
|
parser.add_argument('--penalty_w', type=float, default=0.0,
|
|
help='Penalty weight for IRM invariant classifier loss')
|
|
parser.add_argument('--penalty_s', type=int, default=-1,
|
|
help='Epoch threshold over which Matching Loss to be optimised')
|
|
parser.add_argument('--penalty_irm', type=float, default=0.0,
|
|
help='Penalty weight for IRM invariant classifier loss')
|
|
parser.add_argument('--penalty_ws', type=float, default=0.1,
|
|
help='Penalty weight for Matching Loss')
|
|
parser.add_argument('--penalty_diff_ctr',type=float, default=1.0,
|
|
help='Penalty weight for Contrastive Loss')
|
|
parser.add_argument('--tau', type=float, default=0.05,
|
|
help='Temperature hyper param for NTXent contrastive loss ')
|
|
parser.add_argument('--match_flag', type=int, default=0,
|
|
help='0: No Update to Match Strategy; 1: Updates to Match Strategy')
|
|
parser.add_argument('--match_case', type=float, default=1.0,
|
|
help='0: Random Match; 1: Perfect Match. 0.x" x% correct Match')
|
|
parser.add_argument('--match_interrupt', type=int, default=5,
|
|
help='Number of epochs before inferring the match strategy')
|
|
parser.add_argument('--n_runs', type=int, default=3,
|
|
help='Number of iterations to repeat the training process')
|
|
parser.add_argument('--n_runs_matchdg_erm', type=int, default=1,
|
|
help='Number of iterations to repeat training process for matchdg_erm')
|
|
parser.add_argument('--ctr_model_name', type=str, default='resnet18',
|
|
help='(For matchdg_ctr phase) Architecture of the model to be trained')
|
|
parser.add_argument('--ctr_match_layer', type=str, default='logit_match',
|
|
help='(For matchdg_ctr phase) rep_match: Matching at an intermediate representation level; logit_match: Matching at the logit level')
|
|
parser.add_argument('--ctr_match_flag', type=int, default=1,
|
|
help='(For matchdg_ctr phase) 0: No Update to Match Strategy; 1: Updates to Match Strategy')
|
|
parser.add_argument('--ctr_match_case', type=float, default=0.01,
|
|
help='(For matchdg_ctr phase) 0: Random Match; 1: Perfect Match. 0.x" x% correct Match')
|
|
parser.add_argument('--ctr_match_interrupt', type=int, default=5,
|
|
help='(For matchdg_ctr phase) Number of epochs before inferring the match strategy')
|
|
parser.add_argument('--mnist_seed', type=int, default=0,
|
|
help='Change it between 0-6 for different subsets of Mnist and Fashion Mnist dataset')
|
|
parser.add_argument('--retain', type=float, default=0,
|
|
help='0: Train from scratch in MatchDG Phase 2; 2: Finetune from MatchDG Phase 1 in MatchDG is Phase 2')
|
|
parser.add_argument('--test_metric', type=str, default='acc',
|
|
help='Evaluation Metrics: acc; match_score, t_sne, mia')
|
|
parser.add_argument('--acc_data_case', type=str, default='test',
|
|
help='Dataset Train/Val/Test for the accuracy evaluation metric')
|
|
parser.add_argument('--top_k', type=int, default=10,
|
|
help='Top K matches to consider for the match score evaluation metric')
|
|
parser.add_argument('--match_func_aug_case', type=int, default=0,
|
|
help='0: Evaluate match func on train domains; 1: Evaluate match func on self augmentations')
|
|
parser.add_argument('--match_func_data_case', type=str, default='train',
|
|
help='Dataset Train/Val/Test for the match score evaluation metric')
|
|
parser.add_argument('--mia_batch_size', default=64, type=int,
|
|
help='batch size')
|
|
parser.add_argument('--mia_dnn_steps', default=5000, type=int,
|
|
help='number of training steps')
|
|
parser.add_argument('--mia_sample_size', default=1000, type=int,
|
|
help='number of samples from train/test dataset logits')
|
|
parser.add_argument('--mia_logit', default=1, type=int,
|
|
help='0: Softmax applied to logits; 1: No Softmax applied to logits')
|
|
parser.add_argument('--attribute_domain', default=1, type=int,
|
|
help='0: spur correlations as attribute; 1: domain as attribute')
|
|
parser.add_argument('--adv_eps', default=0.3, type=float,
|
|
help='Epsilon ball dimension for PGD attacks')
|
|
parser.add_argument('--logit_plot_path', default='', type=str,
|
|
help='File name to save logit/loss plots')
|
|
parser.add_argument('--ctr_abl', type=int, default=0,
|
|
help='0: Randomization til class level ; 1: Randomization completely')
|
|
parser.add_argument('--match_abl', type=int, default=0,
|
|
help='0: Randomization til class level ; 1: Randomization completely')
|
|
parser.add_argument('--cuda_device', type=int, default=0,
|
|
help='Select the cuda device by id among the avaliable devices' )
|
|
parser.add_argument('--os_env', type=int, default=0,
|
|
help='0: Code execution on local server/machine; 1: Code execution in docker/clusters' )
|
|
|
|
args = parser.parse_args()
|
|
|
|
#GPU
|
|
cuda= torch.device("cuda:" + str(args.cuda_device))
|
|
if cuda:
|
|
kwargs = {'num_workers': 1, 'pin_memory': False}
|
|
else:
|
|
kwargs= {}
|
|
|
|
args.kwargs= kwargs
|
|
|
|
#List of Train; Test domains
|
|
train_domains= args.train_domains
|
|
test_domains= args.test_domains
|
|
|
|
#Initialize
|
|
final_acc= []
|
|
final_auc= []
|
|
final_s_auc= []
|
|
final_sc_auc= []
|
|
base_res_dir=(
|
|
"results/" + args.dataset_name + '/' + args.method_name + '/' + args.match_layer
|
|
+ '/' + 'train_' + str(args.train_domains)
|
|
)
|
|
if not os.path.exists(base_res_dir):
|
|
os.makedirs(base_res_dir)
|
|
|
|
#Checks
|
|
if args.method_name == 'matchdg_ctr' and args.test_metric == 'acc':
|
|
raise ValueError('Match DG during the contrastive learning phase cannot be evaluted for test accuracy metric')
|
|
sys.exit()
|
|
|
|
if args.perfect_match == 0 and args.test_metric == 'match_score' and args.match_func_aug_case==0:
|
|
raise ValueError('Cannot evalute match function metrics when perfect match is not known')
|
|
sys.exit()
|
|
|
|
#Execute the method for multiple runs ( total args.n_runs )
|
|
for run in range(args.n_runs):
|
|
|
|
#Seed for reproducability
|
|
np.random.seed(run*10)
|
|
torch.manual_seed(run*10)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed_all(run*10)
|
|
|
|
#DataLoader
|
|
train_dataset= torch.empty(0)
|
|
val_dataset= torch.empty(0)
|
|
test_dataset= torch.empty(0)
|
|
# if args.test_metric == 'match_score':
|
|
# if args.match_func_data_case== 'train':
|
|
# train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
|
|
# elif args.match_func_data_case== 'val':
|
|
# val_dataset= get_dataloader( args, run, train_domains, 'val', 1, kwargs )
|
|
# elif args.match_func_data_case== 'test':
|
|
# test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
|
|
# elif args.test_metric == 'acc':
|
|
# if args.acc_data_case== 'train':
|
|
# train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
|
|
# elif args.acc_data_case== 'test':
|
|
# test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
|
|
# elif args.test_metric in ['mia', 'privacy_entropy', 'privacy_loss_attack']:
|
|
# train_dataset= get_dataloader( args, run, train_domains, 'train', 1, kwargs )
|
|
# test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
|
|
# elif args.test_metric == 'attribute_attack':
|
|
# train_dataset= get_dataloader( args, run, train_domains + test_domains, 'train', 1, kwargs )
|
|
# test_dataset= get_dataloader( args, run, train_domains + test_domains, 'test', 1, kwargs )
|
|
# else:
|
|
# test_dataset= get_dataloader( args, run, test_domains, 'test', 1, kwargs )
|
|
|
|
# print('Train Domains, Domain Size, BaseDomainIdx, Total Domains: ', train_domains, total_domains, domain_size, training_list_size)
|
|
|
|
#Import the testing module
|
|
from evaluation.base_eval import BaseEval
|
|
test_method= BaseEval(
|
|
args, train_dataset, val_dataset,
|
|
test_dataset, base_res_dir,
|
|
run, cuda
|
|
)
|
|
|
|
test_method.get_model()
|
|
model= test_method.phi
|
|
|
|
for train_domain in train_domains:
|
|
spur_prob= float(train_domain)
|
|
data, temp1, _, _= get_data(args.slab_num_samples, spur_prob, args.slab_total_slabs)
|
|
|
|
# compute logit scores
|
|
std_log = get_logits(model, data['te_dl'], cuda)
|
|
break
|
|
|
|
# plot logit distributions
|
|
kw = dict(kde=False, bins=20, norm_hist=True,
|
|
hist_kws={"histtype": "step", "linewidth": 2,
|
|
"alpha": 0.8, "ls": '-'})
|
|
|
|
fig, ax = plt.subplots(1,1,figsize=(6,4))
|
|
ax = sns.distplot(std_log, label='Standard Logits', **kw)
|
|
|
|
slab_utils.update_ax(ax, 'Logit Distributions of Positive Data', 'Logits', 'Density',
|
|
ticks_fs=13, label_fs=13, title_fs=16, legend_fs=14, legend_loc='upper left')
|
|
plt.savefig('results/slab_train_logit_plot/' + str(args.method_name)+ '_' + str(args.penalty_ws) + '_' + str(run) + '.jpg')
|