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ClassOSTAR.py
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ClassOSTAR.py
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import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from utils_local import evaluate_data_classifier, loop_iterable, set_requires_grad, gradient_penalty, format_list, \
compute_diff_label, set_lr, entropy_loss
from proportion_estimators import estimate_proportion
class OSTAR(object):
def __init__(self, feat_extractor, data_classifier, phi, source_data_loader, target_data_loader, div_weight=0.1,
grad_scale=1.0, cuda=False, logger=None, n_class=10, ts=1, dataset="digits", cluster_step=5,
ot_weight=1.0, domain_classifier=None, epoch_start_align=11, cluster_param="ward", epoch_start_g=30,
n_epochs=100, gamma=10, init_lr=0.001, proportion_S=None, iter_domain_classifier=10, lr_g_weight=1.0,
lr_f_weight=1.0, lr_d_weight=1.0, lr_phi_weight=1.0, eval_data_loader=None, proportion_T_gt=None, iter=0,
data_class_t=None, ent_weight=0.1, clf_t_weight=0.3, setting=10, use_div=False):
self.feat_extractor = feat_extractor
self.data_classifier = data_classifier
self.data_classifier_t = data_class_t
self.phi = phi
self.setting = setting
self.iter = iter
self.proportion_T_gt = proportion_T_gt
self.domain_classifier = domain_classifier
self.source_data_loader = source_data_loader
self.target_data_loader = target_data_loader
self.eval_data_loader = eval_data_loader
self.proportion_S = proportion_S
self.n_class = n_class
self.ent_weight = ent_weight
self.lr_g_weight = lr_g_weight
self.lr_f_weight = lr_f_weight
self.lr_d_weight = lr_d_weight
self.lr_phi_weight = lr_phi_weight
self.ot_weight = ot_weight
self.ot_weight_init = ot_weight
self.grad_scale = grad_scale
self.clf_t_weight = clf_t_weight
self.cuda = cuda
self.n_epochs = n_epochs
self.criterion = nn.CrossEntropyLoss()
self.epoch_start_align = epoch_start_align
self.epoch_start_g = epoch_start_g
self.iter_domain_classifier = iter_domain_classifier
self.gamma = gamma
self.proportion_method = "confusion"
self.logger = logger
self.cluster_step = cluster_step
self.init_lr = init_lr
self.ts = ts
self.dataset = dataset
self.cluster_param = cluster_param
self.prop_factor = 0.5
self.optimizer_feat_extractor = optim.SGD(self.feat_extractor.parameters(), lr=0.001)
self.optimizer_data_classifier = optim.SGD(self.data_classifier.parameters(), lr=0.001)
self.optimizer_data_classifier_t = optim.SGD(self.data_classifier_t.parameters(), lr=0.001)
self.optimizer_domain_classifier = optim.SGD(self.domain_classifier.parameters(), lr=0.01)
self.optimizer_phi = optim.SGD(self.phi.parameters(), lr=0.001)
self.use_div = use_div
self.div_weight = div_weight
def fit(self):
if self.cuda:
self.feat_extractor.cuda()
self.data_classifier.cuda()
self.data_classifier_t.cuda()
self.domain_classifier.cuda()
self.phi.cuda()
self.device = 'cuda'
else:
self.device = 'cpu'
k_critic = self.iter_domain_classifier
k_prop = 1
gamma = self.gamma
self.print_start = True
self.print_start_g = True
self.use_phi = False
proportion_T = torch.ones(self.n_class) / self.n_class
self.hist_proportion = proportion_T.numpy()
# Train latent space
self.logger.info("--Initialize f, g--")
for epoch in range(self.n_epochs):
self.recluster = ((self.epoch_start_g > epoch > self.epoch_start_align and ((epoch - self.epoch_start_align) % 2) == 0) or
(epoch == self.epoch_start_g) or (epoch >= self.epoch_start_g and (epoch % self.cluster_step) == 0))
self.align = (epoch >= self.epoch_start_align)
S_batches = loop_iterable(self.source_data_loader)
batch_iterator = zip(S_batches, loop_iterable(self.target_data_loader))
batch_iterator_w = zip(S_batches, loop_iterable(self.target_data_loader))
iterations = len(self.source_data_loader)
if self.align:
if self.print_start:
self.logger.info("--Train phi--")
self.print_start = False
if self.print_start_g and epoch >= self.epoch_start_g:
self.logger.info("--Train g--")
self.print_start_g = False
dist_loss_tot, ot_loss_tot, clf_loss_tot_s, clf_loss_tot_t, loss_tot, wass_loss_tot, ent_loss_tot = \
0, 0, 0, 0, 0, 0, 0
self.feat_extractor.train()
if self.align:
self.phi.train()
self.domain_classifier.train()
self.data_classifier_t.train()
else:
self.data_classifier.train()
if epoch == self.epoch_start_align:
self.data_classifier_t.load_state_dict(self.data_classifier.state_dict())
if self.recluster:
# Estimate proportion
self.logger.info(f"is_phi: {self.use_phi} / k_prop: {k_prop}")
proportion_T = estimate_proportion(self, k_prop=k_prop, proportion_T=proportion_T, is_phi=self.use_phi, comment=f"{self.ts}_conf_estim_{epoch}")
if epoch >= self.epoch_start_g:
k_prop += 1
elif epoch > self.epoch_start_align + 1:
k_prop = 2
self.hist_proportion = np.vstack((self.hist_proportion, proportion_T.numpy()))
compute_diff_label(self, self.proportion_T_gt, comment="pT(Y)")
self.use_phi = self.align
for batch_idx in range(iterations):
(x_s, y_s), (x_t, y_t) = next(batch_iterator)
x_s, x_t, y_s, y_t = x_s.to(self.device), x_t.to(self.device), y_s.to(self.device), y_t.to(self.device)
ent_loss, dist_loss, ot_loss, clf_s_loss, clf_t_loss, pl_loss, N_tpl = torch.zeros(1).to(self.device),\
torch.zeros(1).to(self.device), torch.zeros(1).to(self.device), torch.zeros(1).to(self.device), \
torch.zeros(1).to(self.device), torch.zeros(1).to(self.device), 0
if self.align:
# Set lr
p = (batch_idx + (epoch - self.epoch_start_align) * len(self.source_data_loader)) / (
len(self.source_data_loader) * (self.n_epochs - self.epoch_start_align))
lr = float(self.init_lr / (1. + 10 * p) ** 0.75)
set_lr(self.optimizer_domain_classifier, lr * self.lr_d_weight)
set_lr(self.optimizer_data_classifier_t, lr * self.lr_f_weight)
set_lr(self.optimizer_feat_extractor, lr * self.lr_g_weight)
set_lr(self.optimizer_phi, lr * self.lr_phi_weight)
source_weight_un = torch.zeros((y_s.size(0), 1)).to(self.device)
weight_class = torch.zeros((y_s.size(0), 1)).to(self.device)
Ns_class = torch.zeros((self.n_class, 1)).to(self.device)
Nt_class = torch.zeros((self.n_class, 1)).to(self.device)
for j in range(self.n_class):
nb_sample = y_s.eq(j).nonzero().size(0)
source_weight_un[y_s == j] = proportion_T[j].to(self.device) / nb_sample
weight_class[y_s == j] = 1 / nb_sample if nb_sample != 0 else 0
Ns_class[j] = nb_sample
Nt_class[j] = y_t.eq(j).nonzero().size(0)
#######################
# Train discriminator #
#######################
set_requires_grad(self.phi, requires_grad=False)
set_requires_grad(self.feat_extractor, requires_grad=False)
set_requires_grad(self.domain_classifier, requires_grad=True)
for _ in range(k_critic):
(x_s_w, y_s_w), (x_t_w, _) = next(batch_iterator_w)
x_s_w, x_t_w, y_s_w = x_s_w.to(self.device), x_t_w.to(self.device), y_s_w.to(self.device)
source_weight_un_w = torch.zeros((y_s_w.size(0), 1)).to(self.device)
for j in range(self.n_class):
nb_sample = y_s_w.eq(j).nonzero().size(0)
source_weight_un_w[y_s_w == j] = proportion_T[j].to(self.device) / nb_sample
with torch.no_grad():
z_w = self.feat_extractor(torch.cat((x_s_w, x_t_w), 0))
s_w = self.phi(z_w[:x_s_w.shape[0]])[0]
t_w = z_w[x_s_w.shape[0]:]
gp = gradient_penalty(self.domain_classifier, s_w, t_w, self.cuda)
critic_w = self.domain_classifier(torch.cat((s_w, t_w), 0))
wasserstein_distance_w = (critic_w[:x_s.shape[0]] * source_weight_un_w.detach()).sum() - critic_w[x_s.shape[0]:].mean()
critic_cost = - wasserstein_distance_w + gamma * gp
self.optimizer_domain_classifier.zero_grad()
critic_cost.backward()
self.optimizer_domain_classifier.step()
wass_loss_tot += wasserstein_distance_w.item()
#############
# Train phi #
#############
set_requires_grad(self.phi, requires_grad=True)
set_requires_grad(self.feat_extractor, requires_grad=False)
set_requires_grad(self.domain_classifier, requires_grad=False)
with torch.no_grad():
z_nograd = self.feat_extractor(torch.cat((x_s, x_t), 0))
zt_nograd = z_nograd[x_s.shape[0]:]
phi_z_s_nograd, rs_nograd = self.phi(z_nograd[:x_s.shape[0]].detach())
# Alignment
critic = self.domain_classifier(torch.cat((phi_z_s_nograd, zt_nograd.detach()), 0))
critic_s = critic[:x_s.shape[0]]
critic_t = critic[x_s.shape[0]:]
dist_loss = self.grad_scale * ((critic_s * source_weight_un.detach()).sum() - critic_t.mean())
# OT penalization
ot_loss = sum([torch.sum((torch.abs(r) ** 2) * weight_class.detach()) for r in rs_nograd])
loss = dist_loss + ot_loss * self.ot_weight
self.optimizer_phi.zero_grad()
loss.backward()
self.optimizer_phi.step()
###############
# Train g, fT #
###############
self.train_g = False if (self.epoch_start_g > epoch >= self.epoch_start_align) else True
set_requires_grad(self.data_classifier, requires_grad=False)
set_requires_grad(self.data_classifier_t, requires_grad=True)
set_requires_grad(self.feat_extractor, requires_grad=self.train_g)
set_requires_grad(self.phi, requires_grad=False)
z = self.feat_extractor(torch.cat((x_s, x_t), 0))
z_s, z_t = z[:x_s.shape[0]], z[x_s.shape[0]:]
phi_z_s = self.phi(z_s)[0]
# Phi
self.criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(proportion_T / self.proportion_S).to(self.device))
clf_t_loss = self.criterion(self.data_classifier_t(phi_z_s.detach()), y_s)
clf_t_loss *= self.clf_t_weight
# Entropy on target
output_class_t = self.data_classifier_t(z_t)
ent_loss = self.ent_weight * entropy_loss(output_class_t)
if self.use_div:
msoftmax = nn.Softmax(dim=1)(output_class_t).mean(dim=0)
ent_loss -= self.div_weight * torch.sum(-msoftmax * torch.log(msoftmax + 1e-5))
if self.train_g:
# Source
self.criterion = nn.CrossEntropyLoss()
source_preds = self.data_classifier(z_s)
clf_s_loss = self.criterion(source_preds, y_s)
loss = clf_s_loss + clf_t_loss + ent_loss
if self.train_g:
self.optimizer_feat_extractor.zero_grad()
self.optimizer_data_classifier_t.zero_grad()
loss.backward()
if self.train_g:
self.optimizer_feat_extractor.step()
self.optimizer_data_classifier_t.step()
else:
set_requires_grad(self.data_classifier, requires_grad=True)
set_requires_grad(self.feat_extractor, requires_grad=True)
z = self.feat_extractor(torch.cat((x_s, x_t), 0))
source_preds = self.data_classifier(z[:x_s.shape[0]])
self.criterion = nn.CrossEntropyLoss()
clf_s_loss = self.criterion(source_preds, y_s)
loss = clf_s_loss
self.optimizer_feat_extractor.zero_grad()
self.optimizer_data_classifier.zero_grad()
loss.backward()
self.optimizer_feat_extractor.step()
self.optimizer_data_classifier.step()
loss_tot += loss.item()
clf_loss_tot_s += clf_s_loss.item()
clf_loss_tot_t += clf_t_loss.item()
dist_loss_tot += dist_loss.item()
ot_loss_tot += ot_loss.item()
ent_loss_tot += ent_loss.item()
self.logger.info(
'{} OSTAR {} s{} Iter {} Epoch {}/{} \tTotal: {:.6f} L_S: {:.6f} L_T: {:.6f} DistL:{:.6f} WassL:{:.6f} '
'OTL:{:.6f} H:{:.6f}'.format(self.ts, self.dataset, self.setting, self.iter, epoch, self.n_epochs,
loss_tot, clf_loss_tot_s, clf_loss_tot_t, dist_loss_tot, wass_loss_tot, ot_loss_tot, ent_loss_tot))
if (epoch + 1) % 5 == 0:
evaluate_data_classifier(self, self.source_data_loader, is_target=False, verbose=False)
evaluate_data_classifier(self, self.eval_data_loader, is_target=True, is_ft=self.align)
self.logger.info(f"pT(Y) {self.proportion_method}: {format_list(proportion_T, 4)}")