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main.py
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main.py
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import os
import collections
import torch
import copy
import numpy as np
from tensorboardX import SummaryWriter
from utils import set_random_seed, test, load_model, weights_init
from utils import make_idx_dict, get_layer_from_idx
from models.model_builder import Model_Builder
from spectral_compression import SpectralCompression
from metrics import print_model_param_nums, print_model_param_flops
from config import *
class AdversarialCompression:
def __init__(self, args):
os.environ["CUDA_VISIBLE_DEVICES"] = args['cuda_device']
use_cuda = not args['no_cuda'] and torch.cuda.is_available()
args['device'] = torch.device("cuda" if use_cuda else "cpu")
prune_layers = {
'sleepnet_spectral': [0, 4, 7, 11, 14],
'sorsnet': [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36],
'biswalnet': [0, 7, 13, 19, 26, 32, 38, 44, 51, 57, 63, 69, 75, 81, 88, 94, 100],
'deep_residual': [3, 9, 18, 24, 33, 39, 48, 54, 63, 69, 78, 84, 93, 99, 108, 114]
}
conv_feature_size = {
'sleepnet_spectral': 1, # TODO : needs to change to a dict
'sorsnet': {
'physionet': 3,
'shhs': 4},
'biswalnet': {
'physionet': 1,
'shhs': 1},
'deep_residual': {
'physionet': 11,
'shhs': 14}, # TODO
}
self.slimming_params = {}
self.spectral_params = {}
# get current working dir
args['dir'] = os.getcwd()
# set logging params
args['run'] = args['logging_run']
args['log_dir'] = args['dir'] + '/logs{}/{}_{}'.format(args['seed'], args['dataset'], args['model'])
# set pruning params depending on model type
args['prune_layers'] = prune_layers[args['model']]
args['conv_feature_size'] = conv_feature_size[args['model']][args['dataset']]
# define paths for model persistence
args['chkpnt_dir'] = args['dir'] + '/checkpoints{}/{}/{}/'.format(args['seed'],args['dataset'], args['model'])
# decide folder structure
if args['gaussian_training']:
params = 'gt_{}/cs_{}_ortho_{}/ol_{}_sp_{}_gl_{}/'.format(args['gaussian_training'], args['train_corruption_strength'],
args['orthogonality'], args['ortho_lambda'],
args['sparsity'], args['gamma_lambda'])
elif args['orthogonality']:
params = 'rt_{}/eps_{}_ortho_{}/ol_{}_sp_{}_gl_{}/'.format(args['robust_training'], args['train_epsilon'],
args['orthogonality'], args['ortho_lambda'],
args['sparsity'], args['gamma_lambda'])
elif args['spectral_normalization']:
params = 'rt_{}/eps_{}_spn_{}/sp_{}_gl_{}/'.format(args['robust_training'], args['train_epsilon'],
args['spectral_normalization'],
args['sparsity'], args['gamma_lambda'])
else:
params = 'rt_{}/eps_{}_sp_{}_gl_{}/'.format(args['robust_training'], args['train_epsilon'],
args['sparsity'], args['gamma_lambda'])
os.makedirs(args['chkpnt_dir']+'/'+'/'.join(params.split('/')[:-1])+'/', exist_ok=True)
args['full_model_path'] = args['chkpnt_dir'] + params + 'spec_large.pt'
args['small_model_path'] = args['chkpnt_dir'] + params + 'spec_small.pt'
# paths for model def and data
args['model_dir'] = args['dir'] + '/models/'
args['data_dir'] = args['dir'] + '/data'
self.args = args
def set_params(self, model_builder):
train_loader, val_loader, test_loader = model_builder.get_loaders()
if self.args['dataset'] == 'physionet' or self.args['dataset'] == 'shhs':
clip_min, clip_max = model_builder.get_bounds()
else:
clip_min, clip_max = 0, 1
self.slimming_params = {
'prune_layers': self.args['prune_layers'],
'gamma_lambda': self.args['gamma_lambda'],
'conv_feature_size': self.args['conv_feature_size'],
'train_loader': train_loader,
'val_loader': val_loader,
'test_loader': test_loader,
}
self.spectral_params = {
# compression params
'prune_layers': self.args['prune_layers'],
'gamma_lambda': self.args['gamma_lambda'],
'conv_feature_size': self.args['conv_feature_size'],
####
# adv robustness params
'orthogonality': self.args['orthogonality'],
'ortho_lambda': self.args['ortho_lambda'],
'robust_training': self.args['robust_training'],
'attack': 'pgdInf',
'train_epsilon': self.args['train_epsilon'],
'test_epsilon': self.args['test_epsilon'],
'nb_iter': self.args['nb_iter'],
'eps_iter': self.args['step_size'],
'clip_min': clip_min,
'clip_max': clip_max,
####
'train_loader': train_loader,
'val_loader': val_loader,
'test_loader': test_loader,
'gaussian_training': self.args['gaussian_training']
}
def get_full_model(self, index=0):
# set seed for reproducability
set_random_seed(self.args['seed'])
if os.path.exists(self.args['full_model_path']):
full_model = load_model(self.args['full_model_path'])
else:
model_builder = Model_Builder(self.args['model'], self.args['dataset'], self.args['full_model_path'], self.args)
self.set_params(model_builder)
full_model = model_builder.get_model()
spc = SpectralCompression(self.args, self.spectral_params)
# log args
if self.args['enable_logging'] and (not index):
args_log = {k: v for (k, v) in self.args.items() if
not (isinstance(v, str) or isinstance(v, list) or isinstance(v, torch.device))}
writer = SummaryWriter(self.args['log_dir'])
writer.add_scalars('{}_large_{}/args'.format(self.args['logging_comment'], self.args['run']), args_log, 1)
if self.args['verbose'] > 0: print('\ttraining large model')
full_model = spc.train(full_model)
return full_model
def get_small_model(self, index=0):
# set seed for reproducability
set_random_seed(self.args['seed'])
if os.path.exists(self.args['small_model_path']):
small_adv_model = load_model(self.args['small_model_path'])
else:
model_builder = Model_Builder(self.args['model'], self.args['dataset'], self.args['full_model_path'], self.args)
self.set_params(model_builder)
spc = SpectralCompression(self.args, self.spectral_params)
# log args
if self.args['enable_logging'] and (not index):
args_log = {k: v for (k, v) in self.args.items() if
not (isinstance(v, str) or isinstance(v, list) or isinstance(v, torch.device))}
writer = SummaryWriter(self.args['log_dir'])
writer.add_scalars('{}_small_{}/args'.format(self.args['logging_comment'], self.args['run']), args_log, 1)
if self.args['verbose'] > 0: print('\tobtaining full model')
full_adv_model = self.get_full_model(index)
if self.args['verbose'] > 0: print('\tpruning model')
pruned_model = spc.prune_model(full_adv_model)
if self.args['verbose'] > 0: print('\tretraining pruned_model')
small_adv_model = spc.train(pruned_model, retrain=True)
return small_adv_model
def robustify(self, model):
#### need to look at full model path
model_builder = Model_Builder(self.args['model'], self.args['dataset'], self.args['full_model_path'], self.args)
self.set_params(model_builder)
spc = SpectralCompression(self.args, self.spectral_params)
robust_model = spc.train(model)
def reinit(self, model):
model.apply(weights_init)
if self.args['device'] == torch.device('cuda'):
model = model.cuda()
return model
def print_metrics(self, model, type='large', loader='test_loader'):
# set seed for reproducability
set_random_seed(self.args['seed'])
# get no. of params and no. of flops
input_res = {
'physionet': [3000, 1],
'shhs': [3750, 1]
}
if self.args['model'] == 'sorsnet':
input_res['physionet'] = [12000, 1]
input_res['shhs'] = [15000, 1]
num_params = print_model_param_nums(model) # unit of Mega
num_flops = print_model_param_flops(model.cpu(), input_res=input_res[self.args['dataset']]) # unit of Giga
if self.args['device'] == torch.device('cuda'):
model = model.cuda()
# test each model on all types of noise
noise_types = {
'physionet': ['gaussian_noise', 'shot_noise', 'none'],
'shhs': ['gaussian_noise', 'shot_noise', 'none']
}
corruption_strengths = {'physionet': [1, 2, 3],
'shhs': [1, 2, 3]}
noise_ta = collections.defaultdict(dict)
noise_f1 = collections.defaultdict(dict)
noise_cfm = collections.defaultdict(dict)
noise_metrics = collections.defaultdict(dict)
for noise in noise_types[self.args['dataset']]:
self.args['test_corruption'] = noise
for cs in corruption_strengths[self.args['dataset']]:
print('Testing on {} noise with strength {}'.format(noise, cs))
self.args['test_corruption_strength'] = cs
model_builder = Model_Builder(self.args['model'], self.args['dataset'], self.args['full_model_path'], self.args, get_hypnogram=self.args['get_hypnogram'])
self.set_params(model_builder)
_, metrics, cfm = test(self.args, model, self.args['device'], self.spectral_params[loader], type=type)
noise_ta[noise][cs] = metrics['ben_acc']
noise_f1[noise][cs] = metrics['macro avg']['f1-score']
noise_cfm[noise][cs] = cfm
noise_metrics[noise][cs] = metrics
# test model on all types of epsilon
epsilons = {'physionet': [2.0, 6.0, 12.0],
'shhs': [2.0, 6.0, 12.0]} # TODO
adv_ta = collections.defaultdict(dict)
adv_f1 = collections.defaultdict(dict)
adv_cfm = collections.defaultdict(dict)
adv_metrics = collections.defaultdict(dict)
for epsilon in epsilons[self.args['dataset']]:
print('Adversarial testing with epsilon {}'.format(epsilon))
self.args['test_epsilon'] = epsilon
model_builder = Model_Builder(self.args['model'], self.args['dataset'], self.args['full_model_path'], self.args, get_hypnogram=self.args['get_hypnogram'])
self.set_params(model_builder)
spc = SpectralCompression(self.args, self.spectral_params)
_, _, ben_metrics, a_metrics, a_cfm = spc.test_robustness(model, self.spectral_params['test_loader'], type=type)
adv_ta[epsilon] = a_metrics['adv_acc']
adv_f1[epsilon] = a_metrics['macro avg']['f1-score']
adv_cfm[epsilon] = a_cfm
adv_metrics[epsilon] = a_metrics
result_str = 'Size & Flops\n'
result_str += '\t Size {} M\n'.format(num_params)
result_str += '\t Flops {} G\n'.format(num_flops)
result_str += '\n\nNoise accuracy with {}\n'.format(loader)
for cs in corruption_strengths[self.args['dataset']]:
result_str += '\t========= corruption strength {} ============\n'.format(cs)
for noise_type in noise_types[self.args['dataset']]:
result_str += '\t{} : {:0.2f} (ACC), {:0.2f} (F1)\n'.format(noise_type,noise_ta[noise_type][cs],noise_f1[noise_type][cs])
result_str += '\n\nAdversarial Accuracy with {}\n'.format(loader)
for epsilon in epsilons[self.args['dataset']]:
result_str += '\ttrain_epsilon {:0.2f} test_epsilon {:0.2f} : {:0.2f} (ACC), {:0.2f} (F1)\n'.format(self.args['train_epsilon'], epsilon, adv_ta[epsilon], adv_f1[epsilon])
result_str += '\n\nNoise confusion matrices with {}\n'.format(loader)
for cs in corruption_strengths[self.args['dataset']]:
result_str += '\t========= corruption strength {} ============\n'.format(cs)
for noise_type in noise_types[self.args['dataset']]:
result_str += 'confusion matrix for noisy eeg ({} {})\n'.format(noise_type,cs)
for i, row in enumerate(noise_cfm[noise_type][cs]):
result_str += '\t{} : {} {} {} {} {} : pre {:0.2f} rec {:0.2f} f1 {:0.2f}\n'.format(
self.args['classes'][i], row[0], row[1], row[2], row[3], row[4],
noise_metrics[noise_type][cs][self.args['classes'][i]]['precision'],
noise_metrics[noise_type][cs][self.args['classes'][i]]['recall'],
noise_metrics[noise_type][cs][self.args['classes'][i]]['f1-score'])
result_str += '\n\nAdversarial confusion matrices with {}\n'.format(loader)
for epsilon in epsilons[self.args['dataset']]:
result_str += 'confusion matrix for adv eeg (train_eps : {})\n'.format(epsilon)
for i, row in enumerate(adv_cfm[epsilon]):
result_str += '\t{} : {} {} {} {} {} : pre {:0.2f} rec {:0.2f} f1 {:0.2f}\n'.format(
self.args['classes'][i], row[0], row[1], row[2], row[3], row[4],
adv_metrics[epsilon][self.args['classes'][i]]['precision'],
adv_metrics[epsilon][self.args['classes'][i]]['recall'],
adv_metrics[epsilon][self.args['classes'][i]]['f1-score'])
# print
if self.args['verbose'] > 0:
print(result_str)
if self.args['enable_logging']:
# save results to file
file_path = '{}/{}_{}_{}/result_info_sparsity_{}_train_noisestrength_{}_train_epsilon_{}_ortholambda_{}_gammalambda_{}.txt'.format( self.args['log_dir'],
self.args['logging_comment'],
type, self.args['run'],
self.args['sparsity'],
self.args['train_corruption_strength'],
self.args['train_epsilon'],
self.args['ortho_lambda'],
self.args['gamma_lambda'])
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(file_path, 'w') as file:
file.write(result_str)
def visualize_eigenvalues(self, model):
# idx_dict maps layer_idx to the layer in t
ctr, self.idx_dict = make_idx_dict(model, -1, [], {})
for layer_idx in self.args['prune_layers']:
layer = get_layer_from_idx(model, copy.deepcopy(self.idx_dict), layer_idx)
weight = layer.weight.data.cpu().numpy()
W = np.reshape(weight, [weight.shape[0], np.prod(weight.shape[1:])])
eval, evec = np.linalg.eig(np.matmul(W.transpose(), W))
print(eval)
def main():
args = args_sors_physionet
ac = AdversarialCompression(args)
model = ac.get_full_model(0)
ac.print_metrics(model, type='large')
if __name__ == '__main__':
main()