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train.py
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train.py
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import sys
import copy
import torch
from torch.nn import Sequential, Linear, ReLU, CrossEntropyLoss
import numpy as np
from datasets import loaders
from bound_layers import BoundSequential, BoundLinear, BoundConv2d, BoundDataParallel
import torch.optim as optim
# from gpu_profile import gpu_profile
import time
from datetime import datetime
# from convex_adversarial import DualNetwork
from eps_scheduler import EpsilonScheduler
from config import load_config, get_path, config_modelloader, config_dataloader, update_dict
from argparser import argparser
from pgd_eval import evaluate_pgd, evaluate_pgd_n
# sys.settrace(gpu_profile)
#DEBUGG = False True
DEBUGG = False
BREAK = False
#from multi_eval import Multi_eval
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class Logger(object):
def __init__(self, log_file = None, log_file_loss = None, log_file_grad=None, log_file_grad_norm=None, log_file_a_sign=None , log_file_cosine=None, log_file_loss_max=None):
self.log_file = log_file
self.log_file_loss = log_file_loss
self.log_file_grad = log_file_grad
self.log_file_grad_norm = log_file_grad_norm
self.log_file_a_sign = log_file_a_sign
self.log_file_cosine = log_file_cosine
self.log_file_loss_max = log_file_loss_max
def log(self, *args, **kwargs):
print(*args, **kwargs)
if self.log_file:
print(*args, **kwargs, file = self.log_file)
self.log_file.flush()
def loss(self, *args, **kwargs):
if self.log_file_loss:
print(*args, **kwargs, file = self.log_file_loss)
self.log_file_loss.flush()
def loss_max(self, *args, **kwargs):
if self.log_file_loss_max:
print(*args, **kwargs, file = self.log_file_loss_max)
self.log_file_loss_max.flush()
def grad(self, *args, **kwargs):
if self.log_file_grad:
print(*args, **kwargs, file = self.log_file_grad)
self.log_file_grad.flush()
def grad_norm(self, *args, **kwargs):
if self.log_file_grad_norm:
print(*args, **kwargs, file = self.log_file_grad_norm)
self.log_file_grad_norm.flush()
def a_sign(self, *args, **kwargs):
if self.log_file_a_sign:
print(*args, **kwargs, file = self.log_file_a_sign)
self.log_file_a_sign.flush()
def cosine(self, *args, **kwargs):
if self.log_file_cosine:
print(*args, **kwargs, file = self.log_file_cosine)
self.log_file_cosine.flush()
def Train_calloss(model, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, cal_grad = False, lower_d_list2=[],beta=1.0, kappa=0.0,cal_lb=False, frozen =5, **kwargs):
# if train=True, use training mode
# if train=False, use test mode, no back prop
num_class = 10
batch_multiplier = kwargs.get("batch_multiplier", 1)
if cal_grad:
model.train()
else:
model.eval()
# pregenerate the array for specifications, will be used for scatter
sa = np.zeros((num_class, num_class - 1), dtype = np.int32)
for ii in range(sa.shape[0]):
for j in range(sa.shape[1]):
if j < ii:
sa[ii][j] = j
else:
sa[ii][j] = j + 1
sa = torch.LongTensor(sa)
batch_size = loader.batch_size * batch_multiplier
if batch_multiplier > 1 and train:
logger.log('Warning: Large batch training. The equivalent batch size is {} * {} = {}.'.format(batch_multiplier, loader.batch_size, batch_size))
# per-channel std and mean
std = torch.tensor(loader.std).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
mean = torch.tensor(loader.mean).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
model_range = 0.0
#end_eps = eps_scheduler.get_eps(t+1, 0)
if end_eps < np.finfo(np.float32).tiny:
method = "natural"
start = time.time()
# generate specifications
c = torch.eye(num_class).type_as(data)[labels].unsqueeze(1) - torch.eye(num_class).type_as(data).unsqueeze(0)
# remove specifications to self
I = (~(labels.data.unsqueeze(1) == torch.arange(num_class).type_as(labels.data).unsqueeze(0)))
c = (c[I].view(data.size(0),num_class-1,num_class))
# scatter matrix to avoid compute margin to self
sa_labels = sa[labels]
# storing computed lower bounds after scatter
lb_s = torch.zeros(data.size(0), num_class)
ub_s = torch.zeros(data.size(0), num_class)
# FIXME: Assume unnormalized data is from range 0 - 1
if kwargs["bounded_input"]:
if norm != np.inf:
raise ValueError("bounded input only makes sense for Linf perturbation. "
"Please set the bounded_input option to false.")
data_max = torch.reshape((1. - mean) / std, (1, -1, 1, 1))
data_min = torch.reshape((0. - mean) / std, (1, -1, 1, 1))
data_ub = torch.min(data + (eps / std), data_max)
data_lb = torch.max(data - (eps / std), data_min)
else:
if norm == np.inf:
data_ub = data.cpu() + (eps / std)
data_lb = data.cpu() - (eps / std)
else:
# For other norms, eps will be used instead.
data_ub = data_lb = data
if list(model.parameters())[0].is_cuda:
data = data.cuda()
data_ub = data_ub.cuda()
data_lb = data_lb.cuda()
labels = labels.cuda()
c = c.cuda()
sa_labels = sa_labels.cuda()
lb_s = lb_s.cuda()
ub_s = ub_s.cuda()
# convert epsilon to a tensor
eps_tensor = data.new(1)
eps_tensor[0] = eps
# omit the regular cross entropy, since we use robust error
output = model(data, method_opt="forward", disable_multi_gpu = (method == "natural"))
regular_ce = CrossEntropyLoss()(output, labels)
# get range statistic
model_range = output.max().detach().cpu().item() - output.min().detach().cpu().item()
if verbose or method != "natural":
if kwargs["bound_type"] == "convex-adv":
# Wong and Kolter's bound, or equivalently Fast-Lin
if kwargs["convex-proj"] is not None:
proj = kwargs["convex-proj"]
if norm == np.inf:
norm_type = "l1_median"
norm_type = "l2_normal"
else:
raise(ValueError("Unsupported norm {} for convex-adv".format(norm)))
else:
proj = None
if norm == np.inf:
norm_type = "l1"
elif norm == 2:
norm_type = "l2"
else:
raise(ValueError("Unsupported norm {} for convex-adv".format(norm)))
if loader.std == [1] or loader.std == [1, 1, 1]:
convex_eps = eps
else:
convex_eps = eps / np.mean(loader.std)
# for CIFAR we are roughly / 0.2
# FIXME this is due to a bug in convex_adversarial, we cannot use per-channel eps
if norm == np.inf:
# bounded input is only for Linf
if kwargs["bounded_input"]:
# FIXME the bounded projection in convex_adversarial has a bug, data range must be positive
assert loader.std == [1,1,1] or loader.std == [1]
data_l = 0.0
data_u = 1.0
else:
data_l = -np.inf
data_u = np.inf
else:
data_l = data_u = None
# f = DualNetwork(model, data, convex_eps, proj = proj, norm_type = norm_type, bounded_input = kwargs["bounded_input"], data_l = data_l, data_u = data_u)
lb = f(c)
elif kwargs["bound_type"] == "interval":
ub, lb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
elif kwargs["bound_type"] == "crown-full":
_, _, lb, _ = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, upper=False, lower=True, method_opt="full_backward_range")
unstable = dead = alive = relu_activity = torch.tensor([0])
elif kwargs["bound_type"] == "crown-interval":
# Enable multi-GPU only for the computationally expensive CROWN-IBP bounds,
# not for regular forward propagation and IBP because the communication overhead can outweigh benefits, giving little speedup.
ub, ilb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
if beta < 1e-5:
lb = ilb
else:
if kwargs["runnerup_only"]:
# regenerate a smaller c, with just the runner-up prediction
# mask ground truthlabel output, select the second largest class
masked_output = output.detach().scatter(1, labels.unsqueeze(-1), -100)
# location of the runner up prediction
runner_up = masked_output.max(1)[1]
# get margin from the groud-truth to runner-up only
runnerup_c = torch.eye(num_class).type_as(data)[labels]
runnerup_c.scatter_(1, runner_up.unsqueeze(-1), -1)
runnerup_c = runnerup_c.unsqueeze(1).detach()
# get the bound for runnerup_c
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range")
clb = clb.expand(clb.size(0), num_class - 1)
else:
# get the CROWN bound using interval bounds
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range",lower_d_list2=lower_d_list2)
# how much better is crown-ibp better than ibp?
diff = (clb - ilb).sum().item()
lb = clb * beta + ilb * (1 - beta)
elif kwargs["bound_type"] == "crown-interval-frozen":
# Enable multi-GPU only for the computationally expensive CROWN-IBP bounds,
# not for regular forward propagation and IBP because the communication overhead can outweigh benefits, giving little speedup.
ub, ilb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
if beta < 1e-5:
lb = ilb
else:
# get the CROWN bound using interval bounds
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range_frozen",lower_d_list2=lower_d_list2, frozen=frozen)
diff = (clb - ilb).sum().item()
lb = clb * beta + ilb * (1 - beta)
else:
raise RuntimeError("Unknown bound_type " + kwargs["bound_type"])
lb = lb_s.scatter(1, sa_labels, lb)
robust_ce = CrossEntropyLoss()(-lb, labels)
if method == "robust":
loss = robust_ce
elif method == "robust_activity":
loss = robust_ce + kwargs["activity_reg"] * relu_activity.sum()
elif method == "natural":
loss = regular_ce
robust_ce = loss
elif method == "robust_natural":
loss = (1-kappa) * robust_ce + kappa * regular_ce
else:
raise ValueError("Unknown method " + method)
if train and kwargs["l1_reg"] > np.finfo(np.float32).tiny:
reg = kwargs["l1_reg"]
l1_loss = 0.0
for name, param in model.named_parameters():
if 'bias' not in name:
l1_loss = l1_loss + torch.sum(torch.abs(param))
l1_loss = reg * l1_loss
loss = loss + l1_loss
if cal_lb:
return loss, robust_ce, clb, bias
else:
return loss, robust_ce
def Train(model, t, loader, eps_scheduler, max_eps, norm, logger, verbose, train, opt, method, cal_loss=False, n_loss=2,max_loss=4, min_loss=0.5, cal_grad = False, config = None, cal_grad_norm=False,start_beta = 1.0,start_kappa=1.0, lr_consist=False,bound_opt_eval=None, frozen=13, **kwargs):
# if train=True, use training mode
# if train=False, use test mode, no back prop
if model.bound_opts['ours'] and train is False :
torch.set_grad_enabled(True)
num_class = 10
losses = AverageMeter()
l1_losses = AverageMeter()
errors = AverageMeter()
robust_errors = AverageMeter()
regular_ce_losses = AverageMeter()
robust_ce_losses = AverageMeter()
relu_activities = AverageMeter()
bound_bias = AverageMeter()
bound_diff = AverageMeter()
unstable_neurons = AverageMeter()
dead_neurons = AverageMeter()
alive_neurons = AverageMeter()
batch_time = AverageMeter()
batch_multiplier = kwargs.get("batch_multiplier", 1)
loss_points = np.logspace(np.log10(min_loss), np.log10(max_loss), n_loss, endpoint=True)
loss_points = np.concatenate((np.zeros(1),loss_points,np.ones(1)), axis=0)
ReLU_lower_bs = AverageMeter()
not_ReLU_lower_bs = AverageMeter()
A_xs = AverageMeter()
A_norms = AverageMeter()
if train:
model.train()
else:
model.eval()
# pregenerate the array for specifications, will be used for scatter
sa = np.zeros((num_class, num_class - 1), dtype = np.int32)
for i in range(sa.shape[0]):
for j in range(sa.shape[1]):
if j < i:
sa[i][j] = j
else:
sa[i][j] = j + 1
sa = torch.LongTensor(sa)
batch_size = loader.batch_size * batch_multiplier
if batch_multiplier > 1 and train:
logger.log('Warning: Large batch training. The equivalent batch size is {} * {} = {}.'.format(batch_multiplier, loader.batch_size, batch_size))
# per-channel std and mean
std = torch.tensor(loader.std).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
mean = torch.tensor(loader.mean).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
model_range = 0.0
end_eps = eps_scheduler.get_eps(t+1, 0)
if end_eps < np.finfo(np.float32).tiny:
logger.log('eps {} close to 0, using natural training'.format(end_eps))
method = "natural"
for i, (data, labels) in enumerate(loader):
if i>0 and BREAK:
break
# original_A_sign = torch.zeros_like(data)
start = time.time()
eps = eps_scheduler.get_eps(t, int(i//batch_multiplier))
crown_final_beta = kwargs['final-beta']
natural_final_factor = kwargs["final-kappa"]
beta = start_beta - (1.0-(max_eps-eps)/max_eps)*(start_beta-crown_final_beta)
kappa = start_kappa - (1.0-(max_eps-eps)/max_eps)*(start_kappa-natural_final_factor)
if train and i % batch_multiplier == 0:
opt.zero_grad()
# generate specifications
c = torch.eye(num_class).type_as(data)[labels].unsqueeze(1) - torch.eye(num_class).type_as(data).unsqueeze(0)
# remove specifications to self
I = (~(labels.data.unsqueeze(1) == torch.arange(num_class).type_as(labels.data).unsqueeze(0)))
c = (c[I].view(data.size(0),num_class-1,num_class))
# scatter matrix to avoid compute margin to self
sa_labels = sa[labels]
# storing computed lower bounds after scatter
lb_s = torch.zeros(data.size(0), num_class)
ub_s = torch.zeros(data.size(0), num_class)
# FIXME: Assume unnormalized data is from range 0 - 1
if kwargs["bounded_input"]:
if norm != np.inf:
raise ValueError("bounded input only makes sense for Linf perturbation. "
"Please set the bounded_input option to false.")
data_max = torch.reshape((1. - mean) / std, (1, -1, 1, 1))
data_min = torch.reshape((0. - mean) / std, (1, -1, 1, 1))
data_ub = torch.min(data + (eps / std), data_max)
data_lb = torch.max(data - (eps / std), data_min)
else:
if norm == np.inf:
data_ub = data + (eps / std)
data_lb = data - (eps / std)
else:
# For other norms, eps will be used instead.
data_ub = data_lb = data
if list(model.parameters())[0].is_cuda:
data = data.cuda()
data_ub = data_ub.cuda()
data_lb = data_lb.cuda()
labels = labels.cuda()
c = c.cuda()
sa_labels = sa_labels.cuda()
lb_s = lb_s.cuda()
ub_s = ub_s.cuda()
# convert epsilon to a tensor
eps_tensor = data.new(1)
eps_tensor[0] = eps
# omit the regular cross entropy, since we use robust error
output = model(data, method_opt="forward", disable_multi_gpu = (method == "natural"))
regular_ce = CrossEntropyLoss()(output, labels)
regular_ce_losses.update(regular_ce.cpu().detach().numpy(), data.size(0))
errors.update(torch.sum(torch.argmax(output, dim=1)!=labels).cpu().detach().numpy()/data.size(0), data.size(0))
# get range statistic
model_range = output.max().detach().cpu().item() - output.min().detach().cpu().item()
if verbose or method != "natural":
if kwargs["bound_type"] == "convex-adv":
# Wong and Kolter's bound, or equivalently Fast-Lin
if kwargs["convex-proj"] is not None:
proj = kwargs["convex-proj"]
if norm == np.inf:
norm_type = "l1_median"
elif norm == 2:
norm_type = "l2_normal"
else:
raise(ValueError("Unsupported norm {} for convex-adv".format(norm)))
else:
proj = None
if norm == np.inf:
norm_type = "l1"
elif norm == 2:
norm_type = "l2"
else:
raise(ValueError("Unsupported norm {} for convex-adv".format(norm)))
if loader.std == [1] or loader.std == [1, 1, 1]:
convex_eps = eps
else:
convex_eps = eps / np.mean(loader.std)
# for CIFAR we are roughly / 0.2
# FIXME this is due to a bug in convex_adversarial, we cannot use per-channel eps
if norm == np.inf:
# bounded input is only for Linf
if kwargs["bounded_input"]:
# FIXME the bounded projection in convex_adversarial has a bug, data range must be positive
assert loader.std == [1,1,1] or loader.std == [1]
data_l = 0.0
data_u = 1.0
else:
data_l = -np.inf
data_u = np.inf
else:
data_l = data_u = None
# f = DualNetwork(model, data, convex_eps, proj = proj, norm_type = norm_type, bounded_input = kwargs["bounded_input"], data_l = data_l, data_u = data_u)
lb = f(c)
elif kwargs["bound_type"] == "interval":
ub, lb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
elif kwargs["bound_type"] == "crown-full":
_, _, lb, _ = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, upper=False, lower=True, method_opt="full_backward_range")
unstable = dead = alive = relu_activity = torch.tensor([0])
elif kwargs["bound_type"] == "crown-interval":
# Enable multi-GPU only for the computationally expensive CROWN-IBP bounds,
# not for regular forward propagation and IBP because the communication overhead can outweigh benefits, giving little speedup.
ub, ilb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
if beta < 1e-5:
lb = ilb
else:
if kwargs["runnerup_only"]:
# regenerate a smaller c, with just the runner-up prediction
# mask ground truthlabel output, select the second largest class
masked_output = output.detach().scatter(1, labels.unsqueeze(-1), -100)
# location of the runner up prediction
runner_up = masked_output.max(1)[1]
# get margin from the groud-truth to runner-up only
runnerup_c = torch.eye(num_class).type_as(data)[labels]
# set the runner up location to -
runnerup_c.scatter_(1, runner_up.unsqueeze(-1), -1)
runnerup_c = runnerup_c.unsqueeze(1).detach()
# get the bound for runnerup_c
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range")
clb = clb.expand(clb.size(0), num_class - 1)
else:
# get the CROWN bound using interval bounds
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range")
# bound_bias.update(bias.sum() / data.size(0))
lb = clb * beta + ilb * (1 - beta)
#
elif kwargs["bound_type"] == "crown-interval-frozen":
# Enable multi-GPU only for the computationally expensive CROWN-IBP bounds,
# not for regular forward propagation and IBP because the communication overhead can outweigh benefits, giving little speedup.
ub, ilb, relu_activity, unstable, dead, alive = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="interval_range")
if beta < 1e-5:
lb = ilb
else:
_, _, clb, bias = model(norm=norm, x_U=data_ub, x_L=data_lb, eps=eps, C=c, method_opt="backward_range_frozen", frozen=frozen)
diff = (clb - ilb).sum().item()
lb = clb * beta + ilb * (1 - beta)
else:
raise RuntimeError("Unknown bound_type " + kwargs["bound_type"])
lb = lb_s.scatter(1, sa_labels, lb)
robust_ce = CrossEntropyLoss()(-lb, labels)
if kwargs["bound_type"] != "convex-adv":
relu_activities.update(relu_activity.sum().detach().cpu().item() / data.size(0), data.size(0))
unstable_neurons.update(unstable.sum().detach().cpu().item() / data.size(0), data.size(0))
dead_neurons.update(dead.sum().detach().cpu().item() / data.size(0), data.size(0))
alive_neurons.update(alive.sum().detach().cpu().item() / data.size(0), data.size(0))
if method == "robust":
loss = robust_ce
elif method == "robust_activity":
loss = robust_ce + kwargs["activity_reg"] * relu_activity.sum()
elif method == "natural":
loss = regular_ce
robust_ce = loss
elif method == "robust_natural":
loss = (1-kappa) * robust_ce + kappa * regular_ce
else:
raise ValueError("Unknown method " + method)
if train and kwargs["l1_reg"] > np.finfo(np.float32).tiny:
reg = kwargs["l1_reg"]
l1_loss = 0.0
for name, param in model.named_parameters():
if 'bias' not in name:
l1_loss = l1_loss + torch.sum(torch.abs(param))
l1_loss = reg * l1_loss
loss = loss + l1_loss
l1_losses.update(l1_loss.cpu().detach().numpy(), data.size(0))
##update a
if method != "natural" and model.bound_opts['ours'] and beta >= 1e-5:
loss, robust_ce, clb, bias=Train_calloss_ours(loss,robust_ce,model, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, beta=beta, kappa=kappa,cal_grad=True,cal_lb=True,multistep=False,frozen=frozen, **kwargs)
if (verbose or method != "natural") and kwargs["bound_type"] == "crown-interval" and beta >= 1e-5 and not kwargs["runnerup_only"]:
bound_bias.update(bias.sum() / data.size(0))
ReLU_lower_bs.update(model.ReLU_lower_b.sum(), data.size(0))
if (model.ReLU_lower_b>0).sum()>0:
print("There is positive part in the ReLU_lower_bs!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
not_ReLU_lower_bs.update(model.not_ReLU_lower_b.sum(), data.size(0))
A_norms.update(model.A_norm.sum(), data.size(0))
A_xs.update(model.A_x.sum(), data.size(0))
diff = (clb - ilb).sum().item()
bound_diff.update(diff / data.size(0), data.size(0))
lb = clb * beta + ilb * (1 - beta)
original_A_sign = model.lower_A.sign()
elif (verbose or method != "natural") and kwargs["bound_type"] == "crown-interval-frozen" and beta >= 1e-5 and not kwargs["runnerup_only"]:
diff = (clb - ilb).sum().item()
lb = clb * beta + ilb * (1 - beta)
if train:
opt.zero_grad()
loss.backward()
if cal_loss and (t*len(loader)+i)%100 ==0:
if method != "natural" and model.bound_opts['ours'] and beta >= 1e-5:
loss_max, Rloss_tmp= Train_calloss(model, t, i, data, labels, loader, max_eps, max_eps, max_eps, norm, logger, verbose, train, method,cal_grad=cal_grad, beta=beta,kappa=0, cal_lb=False,frozen=frozen,**kwargs)
loss_max, _, clb, bias=Train_calloss_ours(loss_max,Rloss_tmp,model, t, i, data, labels, loader, max_eps, max_eps, max_eps, norm, logger, verbose, train, method, beta=beta, kappa=0,cal_grad=True,cal_lb=True,multistep=False,frozen=frozen, **kwargs)
else:
loss_max, _ = Train_calloss(model, t, i, data, labels, loader, max_eps, max_eps, max_eps, norm, logger, verbose, train, method,cal_grad=cal_grad, beta=beta,kappa=0, cal_lb=False,frozen=frozen,**kwargs)
logger.loss_max(float(loss_max.data.cpu()), end='\t')
model_list = [qq for qq in model.state_dict().keys()]
dict_all_tmp = {}
lr = opt.state_dict()['param_groups'][0]['lr']
original_grad = []
for point in loss_points:
dict_all_tmp[point]={}
for v, param in enumerate(model.parameters()):
param_grad = param.grad.data.detach()
original_grad.append(param_grad.view(1,-1))
for point in loss_points:
param_tmp = param.data.detach() - point*lr*param_grad
dict_all_tmp[point][model_list[v]] = param_tmp
MSE_grad_norm = torch.zeros(1).cuda()
for point in loss_points:
if config:
global_train_config = config["training_params"]
train_config = copy.deepcopy(global_train_config)
models, model_names = config_modelloader(config)
model_loss_clean = BoundSequential.convert(models[0], train_config["method_params"]["bound_opts"])
model_loss = model_loss_clean.cuda()
model_loss.load_state_dict(dict_all_tmp[point])
opt_loss = optim.SGD(model_loss.parameters(), lr=0.00)
opt_loss.zero_grad()
if method != "natural" and model.bound_opts['ours'] and beta >= 1e-5:
loss_tmp, Rloss_tmp= Train_calloss(model_loss, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method,cal_grad=cal_grad, beta=beta,kappa=kappa, cal_lb=False,frozen=frozen,**kwargs)
loss_tmp, Rloss_tmp, clb, bias=Train_calloss_ours(loss_tmp,Rloss_tmp,model_loss, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, beta=beta, kappa=kappa,cal_grad=True,cal_lb=True,multistep=False,frozen=frozen, **kwargs)
else:
loss_tmp, Rloss_tmp= Train_calloss(model_loss, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method,cal_grad=cal_grad, beta=beta,kappa=kappa, cal_lb=False,frozen=frozen,**kwargs)
loss_tmp.backward()
logger.loss(float(loss_tmp.data.cpu()), end='\t')
model_loss_grad = []
if cal_grad:
for v, param_loss in enumerate(model_loss.parameters()):
model_loss_grad.append(param_loss.grad.data.view(1,-1))
MSE_grad_tmp = torch.nn.MSELoss(reduction='mean')(torch.cat(original_grad,dim=1),torch.cat(model_loss_grad,dim=1)).sqrt()
cosine_grad_tmp = torch.nn.CosineSimilarity(dim=1)(torch.cat(original_grad,dim=1),torch.cat(model_loss_grad,dim=1))
if method != "natural" and kwargs["bound_type"] == "crown-interval" and beta >= 1e-5:
A_sign_tmp = torch.nn.L1Loss()( original_A_sign , model_loss.lower_A.sign())
logger.a_sign(float(A_sign_tmp.data.cpu()), end='\t')
if cal_grad_norm:
MSE_grad_norm = torch.max(MSE_grad_tmp/(original_grad[0].norm()*lr*point),MSE_grad_norm)
logger.grad(float(MSE_grad_tmp.data.cpu()), end='\t')
logger.cosine(float(cosine_grad_tmp.data.cpu()), end='\t')
del model_loss
del MSE_grad_tmp, loss_tmp, Rloss_tmp
del model_loss_clean
if cal_grad_norm:
logger.grad_norm(float(MSE_grad_norm.data.cpu()), end='\t')
logger.loss('\n', end='')
logger.grad('\n', end='')
logger.cosine('\n', end='')
logger.grad_norm('\n', end='')
if method != "natural" and kwargs["bound_type"] == "crown-interval" and beta >= 1e-5:
logger.a_sign('\n', end='')
model.train()
if i % batch_multiplier == 0 or i == len(loader) - 1:
opt.step()
if cal_loss and (t*len(loader)+i)%100 ==0:
opt_loss.step()
losses.update(loss.cpu().detach().numpy(), data.size(0))
if verbose or method != "natural":
robust_ce_losses.update(robust_ce.cpu().detach().numpy(), data.size(0))
robust_errors.update(torch.sum((lb<0).any(dim=1)).cpu().detach().numpy() / data.size(0), data.size(0))
batch_time.update(time.time() - start)
all_val = ReLU_lower_bs.val+A_xs.val+not_ReLU_lower_bs.val+A_norms.val
all_avg = ReLU_lower_bs.avg+A_xs.avg+not_ReLU_lower_bs.avg+A_norms.avg
if i % 50 == 0 and train:
logger.log( '[{:2d}:{:4d}]: eps {:6f} '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Total Loss {loss.val:.4f} ({loss.avg:.4f}) '
'L1 Loss {l1_loss.val:.4f} ({l1_loss.avg:.4f}) '
'CE {regular_ce_loss.val:.4f} ({regular_ce_loss.avg:.4f}) '
'RCE {robust_ce_loss.val:.4f} ({robust_ce_loss.avg:.4f}) '
'Err {errors.val:.4f} ({errors.avg:.4f}) '
'Rob Err {robust_errors.val:.4f} ({robust_errors.avg:.4f}) '
'Uns {unstable.val:.1f} ({unstable.avg:.1f}) '
'Dead {dead.val:.1f} ({dead.avg:.1f}) '
'Alive {alive.val:.1f} ({alive.avg:.1f}) '
'Tightness {tight.val:.5f} ({tight.avg:.5f}) '
'Bias {bias.val:.5f} ({bias.avg:.5f}) '
'Diff {diff.val:.5f} ({diff.avg:.5f}) '
'R {model_range:.3f} '
'beta {beta:.3f} ({beta:.3f}) '
'kappa {kappa:.3f} ({kappa:.3f}) '
'A_x {Axs.val:.5f} ({Axs.avg:.5f}) '
'not_relu_b {not_relu_b.val:.5f} ({not_relu_b.avg:.5f}) '
'A_norm {A_norms.val:.5f} ({A_norms.avg:.5f}) '
'Relu_b {Relu_bs.val:.5f} ({Relu_bs.avg:.5f}) '
'All {all_val:.5f} ({all_avg:.5f}) '.format(
t, i, eps, batch_time=batch_time,
loss=losses, errors=errors, robust_errors = robust_errors, l1_loss = l1_losses,
regular_ce_loss = regular_ce_losses, robust_ce_loss = robust_ce_losses,
unstable = unstable_neurons, dead = dead_neurons, alive = alive_neurons,
tight = relu_activities, bias = bound_bias, diff = bound_diff,
model_range = model_range,
beta=beta, kappa = kappa,
Axs = A_xs, not_relu_b = not_ReLU_lower_bs, A_norms = A_norms, Relu_bs = ReLU_lower_bs , all_val=all_val, all_avg =all_avg
))
if(bound_opt_eval):
if bound_opt_eval.get("ours", False):
bound_opt_name="ours"
elif bound_opt_eval.get("same-slope", False):
bound_opt_name="same-slope"
elif bound_opt_eval.get("zero-lb", False):
bound_opt_name="zero-lb"
elif bound_opt_eval.get("one-lb", False):
bound_opt_name="one-lb"
elif bound_opt_eval.get("binary", False):
bound_opt_name="binary"
elif bound_opt_eval.get("uniform", False):
bound_opt_name="uniform"
else:
bound_opt_name="crown-ibp"
logger.log( '[{} epoch:{:2d} eps:{:.6f}]: '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Total Loss {loss.val:.4f} ({loss.avg:.4f}) '
'L1 Loss {l1_loss.val:.4f} ({l1_loss.avg:.4f}) '
'CE {regular_ce_loss.val:.4f} ({regular_ce_loss.avg:.4f}) '
'RCE {robust_ce_loss.val:.4f} ({robust_ce_loss.avg:.4f}) '
'Uns {unstable.val:.3f} ({unstable.avg:.3f}) '
'Dead {dead.val:.1f} ({dead.avg:.1f}) '
'Alive {alive.val:.1f} ({alive.avg:.1f}) '
'Tight {tight.val:.5f} ({tight.avg:.5f}) '
'Bias {bias.val:.5f} ({bias.avg:.5f}) '
'Diff {diff.val:.5f} ({diff.avg:.5f}) '
'Err {errors.val:.4f} ({errors.avg:.4f}) '
'Rob Err {robust_errors.val:.4f} ({robust_errors.avg:.4f}) '
'R {model_range:.3f} '
'beta {beta:.3f} ({beta:.3f}) '
'kappa {kappa:.3f} ({kappa:.3f}) '
'A_x {Axs.val:.5f} ({Axs.avg:.5f}) '
'not_relu_b {not_relu_b.val:.5f} ({not_relu_b.avg:.5f}) '
'A_norm {A_norms.val:.5f} ({A_norms.avg:.5f}) '
'Relu_b {Relu_bs.val:.5f} ({Relu_bs.avg:.5f}) '
'All {all_val:.5f} ({all_avg:.5f}) '.format(
bound_opt_name,t, eps, batch_time=batch_time,
loss=losses, errors=errors, robust_errors = robust_errors, l1_loss = l1_losses,
regular_ce_loss = regular_ce_losses, robust_ce_loss = robust_ce_losses,
unstable = unstable_neurons, dead = dead_neurons, alive = alive_neurons,
tight = relu_activities, bias = bound_bias, diff = bound_diff,
model_range = model_range,
kappa = kappa, beta=beta,Axs = A_xs, not_relu_b = not_ReLU_lower_bs, A_norms = A_norms, Relu_bs = ReLU_lower_bs , all_val=all_val, all_avg =all_avg
))
else:
logger.log( '[FINAL RESULT epoch:{:2d} eps:{:.6f}]: '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Total Loss {loss.val:.4f} ({loss.avg:.4f}) '
'L1 Loss {l1_loss.val:.4f} ({l1_loss.avg:.4f}) '
'CE {regular_ce_loss.val:.4f} ({regular_ce_loss.avg:.4f}) '
'RCE {robust_ce_loss.val:.4f} ({robust_ce_loss.avg:.4f}) '
'Uns {unstable.val:.3f} ({unstable.avg:.3f}) '
'Dead {dead.val:.1f} ({dead.avg:.1f}) '
'Alive {alive.val:.1f} ({alive.avg:.1f}) '
'Tight {tight.val:.5f} ({tight.avg:.5f}) '
'Bias {bias.val:.5f} ({bias.avg:.5f}) '
'Diff {diff.val:.5f} ({diff.avg:.5f}) '
'Err {errors.val:.4f} ({errors.avg:.4f}) '
'Rob Err {robust_errors.val:.4f} ({robust_errors.avg:.4f}) '
'R {model_range:.3f} '
'beta {beta:.3f} ({beta:.3f}) '
'kappa {kappa:.3f} ({kappa:.3f}) '
'A_x {Axs.val:.5f} ({Axs.avg:.5f}) '
'not_relu_b {not_relu_b.val:.5f} ({not_relu_b.avg:.5f}) '
'A_norm {A_norms.val:.5f} ({A_norms.avg:.5f}) '
'Relu_b {Relu_bs.val:.5f} ({Relu_bs.avg:.5f}) '
'All {all_val:.5f} ({all_avg:.5f}) \n'.format(
t, eps, batch_time=batch_time,
loss=losses, errors=errors, robust_errors = robust_errors, l1_loss = l1_losses,
regular_ce_loss = regular_ce_losses, robust_ce_loss = robust_ce_losses,
unstable = unstable_neurons, dead = dead_neurons, alive = alive_neurons,
tight = relu_activities, bias = bound_bias, diff = bound_diff,
model_range = model_range,
kappa = kappa, beta=beta,Axs = A_xs, not_relu_b = not_ReLU_lower_bs, A_norms = A_norms, Relu_bs = ReLU_lower_bs , all_val=all_val, all_avg =all_avg
))
for i, l in enumerate(model if isinstance(model, BoundSequential) else model.module):
if isinstance(l, BoundLinear) or isinstance(l, BoundConv2d):
norm = l.weight.data.detach().view(l.weight.size(0), -1).abs().sum(1).max().cpu()
logger.log('layer {} norm {}'.format(i, norm))
if model.bound_opts['ours'] and train is False :
torch.set_grad_enabled(False)
if method == "natural":
return errors.avg, errors.avg
else:
return robust_errors.avg, errors.avg
def Train_calloss_ours(loss,robust_ce,model, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, cal_grad = False,beta=1.0, kappa=0.0,cal_lb=False,cal_loss=False, frozen =5, multistep=False,**kwargs):
if multistep:
niters=7
lower_d_list2=model.lower_d_list
for nn in range (niters):
if nn>=1:
loss, _ = Train_calloss(model, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, lower_d_list2=lower_d_list2, beta=beta, kappa=kappa,cal_grad=True,**kwargs)
grad = torch.autograd.grad(loss,lower_d_list2,retain_graph=False, create_graph=False)
for list_i,g in enumerate(grad):
eta = g.sign()*0.1
lower_d2 = torch.clamp(lower_d_list2[list_i]-eta, min=0, max=1)
if (nn == niters-1):
lower_d2=lower_d2.detach()
lower_d_list2[list_i]=lower_d2
del g, eta
else:
lower_d_list2=[]
grad = torch.autograd.grad(loss, model.lower_d_list,retain_graph=False, create_graph=False)
for list_i,g in enumerate(grad):
eta = g.sign()*2
lower_d2 = torch.clamp(model.lower_d_list[list_i]-eta, min=0, max=1).detach()
lower_d_list2.append(lower_d2.type(torch.cuda.FloatTensor))
del g, eta
del robust_ce, grad, loss
loss, robust_ce, clb, bias = Train_calloss(model, t, i, data, labels, loader, eps,end_eps, max_eps, norm, logger, verbose, train, method, lower_d_list2=lower_d_list2, beta=beta, kappa=kappa,cal_grad=True,cal_lb=True,frozen=frozen, **kwargs)
del model.lower_d_list
return loss, robust_ce, clb, bias
def main(args):
config = load_config(args)
global_train_config = config["training_params"]
models, model_names = config_modelloader(config)
for model, model_id, model_config in zip(models, model_names, config["models"]):
train_config = copy.deepcopy(global_train_config)
if "training_params" in model_config:
train_config = update_dict(train_config, model_config["training_params"])
model = BoundSequential.convert(model, train_config["method_params"]["bound_opts"])
model.define_bound_opts(train_config["method_params"]["bound_opts"])
model_id = model_id +'_'+ train_config["method_params"]["bound_type"] +'_'+ str(train_config["train_epsilon"]) +'_'+ train_config["name"]
epochs = train_config["epochs"]
lr = train_config["lr"]
weight_decay = train_config["weight_decay"]
starting_epsilon = train_config["starting_epsilon"]
end_epsilon = train_config["epsilon"]
train_end_epsilon = train_config["train_epsilon"]
schedule_length = train_config["schedule_length"]
schedule_start = train_config["schedule_start"]
optimizer = train_config["optimizer"]
method = train_config["method"]
verbose = train_config["verbose"]
lr_decay_step = train_config["lr_decay_step"]
lr_decay_milestones = train_config["lr_decay_milestones"]
lr_decay_factor = train_config["lr_decay_factor"]
multi_gpu = train_config["multi_gpu"]
method_param = train_config["method_params"]
norm = float(train_config["norm"])
train_data, test_data = config_dataloader(config, **train_config["loader_params"])
n_loss = train_config["n_loss"]
cal_loss = train_config["cal_loss"]
max_loss = train_config["max_loss"]
min_loss = train_config["min_loss"]
cal_grad = train_config["cal_grad"]
cal_grad_norm = train_config["cal_grad_norm"]
start_beta = train_config["start_beta"]
start_kappa = train_config["start_kappa"]
lr_consist = train_config["lr_consist"]
frozen_dict = train_config["frozen_dict"]
save_model = train_config["save"]
normalize=train_config["loader_params"]["normalize_input"]
bound_eval=train_config["bound_eval"]
if bound_eval:
bound_opt_evals=[]
bound_opt_evals.append({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': True, 'uniform': False}) ##ours
bound_opt_evals.append({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': False}) ##crown ibp
bound_opt_evals.append({'same-slope': True, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': False}) ## cap
bound_opt_evals.append({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': True, 'ours': False, 'uniform': False}) ##binary
bound_opt_evals.append({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': True}) ##uniform
if train_config["method_params"]["bound_opts"]["ours"]:
bound_opt_evals.remove({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': True, 'uniform': False}) ##ours
elif train_config["method_params"]["bound_opts"]["same-slope"]:
bound_opt_evals.remove({'same-slope': True, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': False}) ## cap
elif train_config["method_params"]["bound_opts"]["binary"]:
bound_opt_evals.remove({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': True, 'ours': False, 'uniform': False}) ##binary
elif train_config["method_params"]["bound_opts"]["uniform"]:
bound_opt_evals.remove({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': True}) ##uniform
else:
bound_opt_evals.remove({'same-slope': False, 'zero-lb': False, 'one-lb': False, 'binary': False, 'ours': False, 'uniform': False}) ##crown ibp
if optimizer == "adam":
opt = optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
elif optimizer == "sgd":
opt = optim.SGD(model.parameters(), lr=lr, momentum=0.9, nesterov=True, weight_decay=weight_decay)
else:
raise ValueError("Unknown optimizer")
batch_multiplier = train_config["method_params"].get("batch_multiplier", 1)
batch_size = train_data.batch_size * batch_multiplier
num_steps_per_epoch = int(np.ceil(1.0 * len(train_data.dataset) / batch_size))
print('num_steps_per_epoch',num_steps_per_epoch)
epsilon_scheduler = EpsilonScheduler(train_config.get("schedule_type", "linear"), schedule_start * num_steps_per_epoch, ((schedule_start + schedule_length) - 1) * num_steps_per_epoch, starting_epsilon, end_epsilon, num_steps_per_epoch)
max_eps = end_epsilon
train_epsilon_scheduler = EpsilonScheduler(train_config.get("schedule_type", "linear"), schedule_start * num_steps_per_epoch, ((schedule_start + schedule_length) - 1) * num_steps_per_epoch, starting_epsilon, train_end_epsilon, num_steps_per_epoch) ##0818 JS
train_max_eps = train_end_epsilon
if lr_decay_step:
# Use StepLR. Decay by lr_decay_factor every lr_decay_step.
lr_scheduler = optim.lr_scheduler.StepLR(opt, step_size=lr_decay_step, gamma=lr_decay_factor)
lr_decay_milestones = None
elif lr_decay_milestones:
# Decay learning rate by lr_decay_factor at a few milestones.
lr_scheduler = optim.lr_scheduler.MultiStepLR(opt, milestones=lr_decay_milestones, gamma=lr_decay_factor)
else:
raise ValueError("one of lr_decay_step and lr_decay_milestones must be not empty.")
model_name = get_path(config, model_id, "model", load = False)
best_model_name = get_path(config, model_id, "best_model", load = False)
model_log = get_path(config, model_id, "train_log")
loss_log = model_log+'loss'
grad_log = model_log+'grad'
grad_norm_log = model_log+'grad_norm'
a_sign_log = model_log+'a_sign'
cosine = model_log+'cosine'
loss_max = model_log+'loss_max'
logger = Logger(open(model_log, "w"),open(loss_log, "w"),open(grad_log, "w"),open(grad_norm_log, "w"),open(a_sign_log, "w"),open(cosine, "w"),open(loss_max,'w'))
logger.log(model_name)
logger.log("Command line:", " ".join(sys.argv[:]))
logger.log("training configurations:", train_config)
logger.log("Model structure:")
logger.log(str(model))
logger.log("data std:", train_data.std)
best_err = np.inf
recorded_pgd_err=np.inf
recorded_clean_err = np.inf
timer = 0.0
if multi_gpu:
logger.log("\nUsing multiple GPUs for computing CROWN-IBP bounds\n")
model = BoundDataParallel(model)
model = model.cuda()
frozen = 13
for t in range(epochs):
train_epoch_start_eps = train_epsilon_scheduler.get_eps(t, 0)
train_epoch_end_eps = train_epsilon_scheduler.get_eps(t+1, 0)
epoch_start_eps = epsilon_scheduler.get_eps(t, 0)
epoch_end_eps = epsilon_scheduler.get_eps(t+1, 0)
logger.log("Epoch {}, learning rate {}, epsilon {:.6g} - {:.6g}".format(t, lr_scheduler.get_lr(), train_epoch_start_eps, train_epoch_end_eps))
start_time = time.time()
for ff, (frozen_t,frozen_value) in enumerate(zip(frozen_dict.keys(),frozen_dict.values())):
if t >= int(frozen_t):
frozen = frozen_value
if train_config["method_params"]["bound_type"] == "crown-interval-frozen":
logger.log("Frozen value {}".format(frozen))
Train(model,t, train_data, train_epsilon_scheduler, train_max_eps, norm, logger, verbose, True, opt, method, cal_loss=cal_loss, n_loss=n_loss,max_loss=max_loss, min_loss=min_loss, cal_grad = cal_grad, config = config,cal_grad_norm=cal_grad_norm, start_beta=start_beta,start_kappa=start_kappa,lr_consist=lr_consist, frozen=frozen, **method_param)
if lr_consist ==False:
if lr_decay_step:
lr_scheduler.step(epoch=max(t - (schedule_start + schedule_length - 1) + 1, 0))
elif lr_decay_milestones:
lr_scheduler.step()
epoch_time = time.time() - start_time
timer += epoch_time
logger.log('Epoch time: {:.4f}, Total time: {:.4f}'.format(epoch_time, timer))
logger.log("Evaluating...")
with torch.no_grad():
if bound_eval and t >= (schedule_start):
for _,bound_opt_eval in enumerate(bound_opt_evals):
model.convert_bounds(bound_opt_eval)
Train(model,t, test_data, EpsilonScheduler("linear", 0, 0, epoch_end_eps, epoch_end_eps, 1), max_eps, norm, logger, verbose, False, None, method, start_beta=start_beta,start_kappa=start_kappa,bound_opt_eval=bound_opt_eval,frozen=frozen, **method_param)
model.convert_bounds(train_config["method_params"]['bound_opts'])
err, clean_err = Train(model,t, test_data, EpsilonScheduler("linear", 0, 0, epoch_end_eps, epoch_end_eps, 1), max_eps, norm, logger, verbose, False, None, method, start_beta=start_beta,start_kappa=start_kappa,frozen=frozen, **method_param)
logger.log('saving to', model_name)
torch.save({
'state_dict' : model.module.state_dict() if multi_gpu else model.state_dict(),
'epoch' : t,
}, model_name)
# save the best model after we reached the schedule
if t >= (schedule_start + schedule_length):
if err <= best_err:
best_err = err
recorded_clean_err = clean_err
if normalize:
pgd_err = evaluate_pgd_n(loader=test_data,model=model,norm=norm, epsilon=max_eps, alpha=max_eps/4, niters=100)
else:
pgd_err = evaluate_pgd(loader=test_data,model=model,norm=norm, epsilon=max_eps, alpha=max_eps/4, niters=100)
recorded_pgd_err = pgd_err
if(recorded_pgd_err>best_err):
print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
logger.log('Saving best model {} with error {}, pgd {}, clean {}'.format(best_model_name, best_err,recorded_pgd_err,recorded_clean_err))
torch.save({
'state_dict' : model.module.state_dict() if multi_gpu else model.state_dict(),
'robust_err' : err,
'pgd_err' : pgd_err,
'clean_err' : clean_err,
'epoch' : t,
}, best_model_name)
if save_model:
if t%save_model ==0:
torch.save({
'state_dict' : model.module.state_dict() if multi_gpu else model.state_dict(),
'epoch' : t,
}, model_name+str(t))
logger.log('Total Time: {:.4f}'.format(timer))
logger.log('Model {} best err {}, pgd_err {:.4f}, clean err {}'.format(model_id, best_err, recorded_pgd_err,recorded_clean_err))
logger.log('{:.4f}/{:.4f}/{:.4f}'.format(recorded_clean_err, recorded_pgd_err, best_err))
if __name__ == "__main__":
args = argparser()
print('torch version: ',torch.__version__)
main(args)