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augment_deform_vae.py
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import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim
import utils
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Augment(object):
def __init__(self, target_net, aug_net, config):
# loss function
self.criterion = nn.CrossEntropyLoss().cuda()
# optimizer
# print('target net lr: {}'.format(config.lr))
# print('target optimizer momentum: {}'.format(config.momentum))
# print('target optimizer weight decay: {}'.format(config.weight_decay))
# if config.decay_type is None:
# params = target_net.parameters()
# elif config.decay_type == 'no_bn':
# params = utils.add_weight_decay(target_net, config.weight_decay)
# else:
# raise Exception('unknown decay type: {}'.format(config.decay_type))
self.target_net_optim = optim.SGD(target_net.parameters(), config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay,
nesterov=True)
for group in self.target_net_optim.param_groups:
print('target net lr: {}, weight_decay: {}, momentum: {}, nesterov: {}'
.format(group['lr'], group['weight_decay'], group['momentum'], group['nesterov']))
print('training epochs: {}'.format(config.epochs))
# lr scheduler
if config.lr_scheduler == 'cosine':
self.lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.target_net_optim,
T_max=float(config.epochs),
eta_min=0.)
else:
raise ValueError('invalid lr_schduler: {}'.format(config.lr_scheduler))
# self.network_momentum = args.momentum
self.target_net = target_net
self.aug_net = aug_net
# self.criterion = criterion
self.args = config
print('adv weight for deformation: {}'.format(self.args.adv_weight_deform))
print('reconstruction weight for deformation: {}'.format(self.args.div_weight_deform))
print('smooth weight: {}'.format(self.args.smooth_weight))
assert self.args.adv_weight_deform >= 0
assert self.args.div_weight_deform >= 0
assert self.args.smooth_weight >= 0
print('aug_net_lr: {}'.format(config.aug_net_lr))
print('aug net adam optimizer beta1: {}'.format(config.adam_beta1))
print('aug_net_weight_decay: {}'.format(config.aug_net_weight_decay))
self.aug_net_optim = torch.optim.Adam(aug_net.parameters(),
lr=config.aug_net_lr, betas=(config.adam_beta1, 0.999),
weight_decay=config.aug_net_weight_decay)
# def call_aug_net(self, data, label, require_loss=False):
# if require_loss:
# if 'vae' in self.args.exp_type and self.args.vae_type == 'condition':
# # print('using conditional vae ...')
# # exit()
# data_aug, loss = self.aug_net(data, F.one_hot(label, 10).type(torch.cuda.FloatTensor), require_loss=True)
# else:
# data_aug, loss = self.aug_net(data, require_loss=True)
#
# return data_aug, loss
# else:
# if 'vae' in self.args.exp_type and self.args.vae_type == 'condition':
# data_aug = self.aug_net(data, F.one_hot(label, 10).type(torch.cuda.FloatTensor))
# else:
# data_aug = self.aug_net(data)
#
# return data_aug
def _compute_unrolled_model(self, loss, eta):
theta = _concat(self.target_net.parameters()).data
dtheta = _concat(torch.autograd.grad(loss, self.target_net.parameters(), retain_graph=True)).data
# dtheta = _concat([v.grad for v in self.target_net.parameters()]).data
if self.args.weight_decay != 0:
dtheta.add_(self.args.weight_decay, theta)
if self.args.momentum != 0:
try:
moment = _concat(self.target_net_optim.state[v]['momentum_buffer'] for v in self.target_net.parameters()).mul_(self.args.momentum)
except:
# setting zeros is consistent with the original momentum optimizer
moment = torch.zeros_like(theta)
unrolled_model = self._construct_model_from_theta(theta.sub(eta, moment + dtheta))
return unrolled_model
# def _compute_unrolled_model(self, eta):
#
#
# theta = _concat([v.data for v in self.target_net.parameters()])
# try:
# moment = _concat(self.target_net_optim.state[v]['momentum_buffer'] for v in self.target_net.parameters()).mul_(self.args.momentum)
# except:
# moment = torch.zeros_like(theta)
# dtheta = _concat([v.grad.data for v in self.target_net.parameters()]) + self.args.weight_decay * theta
#
# unrolled_target_net = self._construct_model_from_theta(theta.sub(eta, moment+dtheta))
#
# return unrolled_target_net
def step(self, input_train, target_train, input_valid, target_valid, unrolled):
self.aug_net_optim.zero_grad()
if unrolled:
# self._backward_step_unrolled(input_train, target_train, input_valid, target_valid)
loss_adv, loss_div = self._backward_step_unrolled(input_train, target_train, input_valid, target_valid)
else:
self._backward_step(input_valid, target_valid)
self.aug_net_optim.step()
return loss_adv, loss_div
def _backward_step(self, input_valid, target_valid):
output_valid = self.target_net(input_valid)
loss = self.criterion(output_valid, target_valid)
loss.backward()
def _backward_step_unrolled(self, input_train, target_train, input_valid, target_valid):
# input_train_aug, div_loss = self.aug_net(input_train, require_loss=True)
input_train_aug, div_loss = self.aug_net(input_train, require_loss=True)
output_train = self.target_net(input_train_aug)
loss_train = self.criterion(output_train, target_train)
# loss_train_2 = loss_train + div_loss * self.args.div_weight
# print('div_loss: {}'.format(div_loss))
# print('loss_train: {}'.format(loss_train))
# print('loss_train_2: {}'.format(loss_train_2))
# print('self.args.div_weight: {}'.format(self.args.div_weight))
# exit()
# loss_train_2.backward()
eta = self.target_net_optim.param_groups[0]['lr']
# eta = self.lr_scheduler.get_lr()[0]
unrolled_target_net = self._compute_unrolled_model(loss_train, eta)
# # check whether the unrolled_target_net is different from the original target_net
# output2 = unrolled_target_net(input_train)
# loss2 = self.criterion(output2, target_train)
# print('loss1: {:4f}'.format(loss_train))
# print('loss2: {:4f}'.format(loss2))
# exit()
output_valid = unrolled_target_net(input_valid)
unrolled_loss = self.criterion(output_valid, target_valid)
unrolled_loss.backward()
#
loss_train_aug = -loss_train * self.args.adv_weight + div_loss * self.args.div_weight
dalpha = torch.autograd.grad(loss_train_aug, self.aug_net.parameters())
# dalpha = [per_grad.data.clamp_(min=-1, max=1) for per_grad in dalpha]
vector = [v.grad.data for v in unrolled_target_net.parameters()]
implicit_grads = self._hessian_vector_product(vector, input_train, target_train, r=self.args.val_r)
for g, ig in zip(dalpha, implicit_grads):
g.data.sub_(eta, ig.data)
for v, g in zip(self.aug_net.parameters(), dalpha):
if v.grad is None:
# print('grad is none. existing ...')
# exit()
v.grad = g.detach()
else:
v.grad.data.copy_(g.data)
return -loss_train * self.args.adv_weight, div_loss * self.args.div_weight
def _construct_model_from_theta(self, theta):
# print('type of theta: {}'.format(type(theta)))
theta = nn.Parameter(theta)
target_net_new = utils.build_model(self.args)
# .state_dict() stores all the persistent buffers (e.g. running averages), which are not included in .parameters()
model_dict = self.target_net.state_dict()
params, offset = {}, 0
for k, v in self.target_net.named_parameters():
v_length = np.prod(v.size())
params[k] = theta[offset: offset+v_length].view(v.size())
# print('type of params[k]: {}'.format(type(params[k])))
offset += v_length
assert offset == len(theta)
model_dict.update(params)
target_net_new.load_state_dict(model_dict)
return target_net_new.cuda()
def _hessian_vector_product(self, vector, input, target, r=2e-2):
R = r / _concat(vector).norm()
for p, v in zip(self.target_net.parameters(), vector):
p.data.add_(R, v)
# input_aug = self.aug_net(input)
input_aug = self.call_aug_net(input, target)
output_aug = self.target_net(input_aug)
loss = self.criterion(output_aug, target)
grads_p = torch.autograd.grad(loss, self.aug_net.parameters())
for p, v in zip(self.target_net.parameters(), vector):
p.data.sub_(2*R, v)
# input_aug = self.aug_net(input)
input_aug = self.call_aug_net(input, target)
output_aug = self.target_net(input_aug)
loss = self.criterion(output_aug, target)
grads_n = torch.autograd.grad(loss, self.aug_net.parameters())
# recover the original weights in self.target_net
for p, v in zip(self.target_net.parameters(), vector):
p.data.add_(R, v)
return [(x-y).div_(2*R) for x, y in zip(grads_p, grads_n)]