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utils.py
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utils.py
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import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision import datasets
import torch.nn as nn
from scipy.spatial.distance import cdist
import numpy as np
from data_loader import mnist, svhn, usps, office31
from torch.autograd import grad
from itertools import chain
import random
def digit_load(args):
train_bs = args.batch_size
if args.trans == 's2m':
train_source = svhn.SVHN(args.dataset_root + '/svhn/', split='train', download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_source = svhn.SVHN(args.dataset_root + '/svhn/', split='test', download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
train_target = mnist.MNIST_idx(args.dataset_root + '/mnist/', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
test_target = mnist.MNIST(args.dataset_root + '/mnist/', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(32),
transforms.Lambda(lambda x: x.convert("RGB")),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif args.trans == 'u2m':
train_source = usps.USPS(args.dataset_root + '/usps/', train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_source = usps.USPS(args.dataset_root + '/usps/', train=False, download=True,
transform=transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
train_target = mnist.MNIST_idx(args.dataset_root + '/mnist/', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_target = mnist.MNIST(args.dataset_root + '/mnist/', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
elif args.trans == 'm2u':
train_source = mnist.MNIST(args.dataset_root + '/mnist/', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_source = mnist.MNIST(args.dataset_root + '/mnist/', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
train_target = usps.USPS_idx(args.dataset_root + '/usps/', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
test_target = usps.USPS(args.dataset_root + '/usps/', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
]))
dset_loaders = {}
dset_loaders["source_train"] = DataLoader(train_source, batch_size=train_bs, shuffle=True,
num_workers=args.num_workers, drop_last=False)
dset_loaders["source_test"] = DataLoader(test_source, batch_size=train_bs*2, shuffle=True,
num_workers=args.num_workers, drop_last=False)
dset_loaders["target_train"] = DataLoader(train_target, batch_size=train_bs, shuffle=True,
num_workers=args.num_workers, drop_last=False)
dset_loaders["target_train_no_shuff"] = DataLoader(train_target, batch_size=train_bs, shuffle=False,
num_workers=args.num_workers, drop_last=False)
dset_loaders["target_test"] = DataLoader(test_target, batch_size=train_bs*2, shuffle=False,
num_workers=args.num_workers, drop_last=False)
return dset_loaders
def office31_load(args):
train_bs = args.batch_size
source = args.trans.split("2")[0]
target = args.trans.split("2")[1]
dset_loaders = {}
dset_loaders["source_train"] = office31.get_office_dataloader(source,train_bs,True)
dset_loaders["source_test"] = office31.get_office_dataloader(source,train_bs,True)
dset_loaders["target_train"] = office31.get_office_dataloader(target,train_bs,True)
dset_loaders["target_train_no_shuff"] = office31.get_office_dataloader(target,train_bs,False)
dset_loaders["target_test"] = office31.get_office_dataloader(target,train_bs,True)
return dset_loaders
def init_weights_orthogonal(m):
if type(m) == nn.Conv2d:
nn.init.orthogonal_(m.weight)
if type(m) == nn.Linear:
nn.init.orthogonal_(m.weight)
def init_weights_xavier_normal(m):
if type(m) == nn.Conv2d:
nn.init.xavier_normal(m.weight)
if type(m) == nn.Linear:
nn.init.xavier_normal(m.weight)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.01)
m.bias.data.normal_(0.0, 0.01)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.01)
m.bias.data.fill_(0)
elif classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.01)
m.bias.data.normal_(0.0, 0.01)
def discrepancy(out1, out2):
return torch.mean(torch.abs(F.softmax(out1)- F.softmax(out2)))
# def discrepancy(out1, out2):
# p = F.softmax(out1, dim=-1)
# _kl = torch.sum(p * (F.log_softmax(out1, dim=-1)
# - F.log_softmax(out2, dim=-1)), 1)
# return torch.mean(_kl)
def discrepancy_matrix(out1, out2):
out1 = F.softmax(out1,dim=1)
out2 = F.softmax(out2,dim=1)
mul = out1.transpose(0, 1).mm(out2)
loss_dis = torch.sum(mul) - torch.trace(mul)
return loss_dis
class CrossEntropyLabelSmooth(nn.Module):
def __init__(self, num_classes, epsilon=0.1, use_gpu=True, size_average=True):
super(CrossEntropyLabelSmooth, self).__init__()
self.num_classes = num_classes
self.epsilon = epsilon
self.use_gpu = use_gpu
self.size_average = size_average
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, inputs, targets):
log_probs = self.logsoftmax(inputs)
targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).cpu(), 1)
if self.use_gpu: targets = targets.cuda()
targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
if self.size_average:
loss = (- targets * log_probs).mean(0).sum()
else:
loss = (- targets * log_probs).sum(1)
return loss
# pseudo labels
def obtain_label(loader, netE, netC1, netC2, args, c=None):
start_test = True
netE.eval()
netC1.eval()
netC2.eval()
with torch.no_grad():
iter_test = iter(loader)
for i in range(len(loader)):
data = iter_test.next()
inputs = data[0]
labels = data[1]
indexs = data[2]
inputs = inputs.cuda()
feas = netE(inputs)
outputs1 = netC1(feas)
outputs2 = netC2(feas)
outputs = outputs1 + outputs2
#torch.stack([outputs1,outputs2]).mean(dim=0)
if start_test:
all_fea = feas.float().cpu()
all_output = outputs.float().cpu()
all_label = labels.float()
start_test = False
else:
all_fea = torch.cat((all_fea, feas.float().cpu()), 0)
all_output = torch.cat((all_output, outputs.float().cpu()), 0)
all_label = torch.cat((all_label, labels.float()), 0)
all_output = nn.Softmax(dim=1)(all_output)
_, predict = torch.max(all_output, 1)
#print("all_label:",all_label.size()[0],"right:",torch.squeeze(predict).float().eq(all_label.data).sum().item())
accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
all_fea = torch.cat((all_fea, torch.ones(all_fea.size(0), 1)), 1)
all_fea = (all_fea.t() / torch.norm(all_fea, p=2, dim=1)).t()
all_fea = all_fea.float().cpu().numpy()
K = all_output.size(1)
aff = all_output.float().cpu().numpy()
initc = aff.transpose().dot(all_fea)
initc = initc / (1e-8 + aff.sum(axis=0)[:,None])
dd = cdist(all_fea, initc, 'cosine')
pred_label = dd.argmin(axis=1)
acc = np.sum(pred_label == all_label.float().numpy()) / len(all_fea)
for round in range(1):
aff = np.eye(K)[pred_label]
initc = aff.transpose().dot(all_fea)
initc = initc / (1e-8 + aff.sum(axis=0)[:,None])
dd = cdist(all_fea, initc, 'cosine')
pred_label = dd.argmin(axis=1)
acc = np.sum(pred_label == all_label.float().numpy()) / len(all_fea)
log_str = 'Only source accuracy = {:.2f}% -> After the clustering = {:.2f}%'.format(accuracy*100, acc*100)
print(log_str+'\n')
return pred_label.astype('int')
def gradient_discrepancy_loss(args, preds_s1,preds_s2, src_y, preds_t1, preds_t2, tgt_y, netE, netC1, netC2):
loss_w = Weighted_CrossEntropy
loss = nn.CrossEntropyLoss()
#CrossEntropyLabelSmooth(num_classes=args.class_num, epsilon=0.1)
total_loss = 0
c_candidate = list(range(args.class_num))
random.shuffle(c_candidate)
# gmn iterations
for c in c_candidate[0:args.gmn_N]:
# gm loss
gm_loss = 0
src_ind = (src_y == c).nonzero().squeeze()
#print("src_y,",src_y,"src_ind:",src_ind)
tgt_ind = (tgt_y == c).nonzero().squeeze()
if src_ind.shape == torch.Size([]) or tgt_ind.shape == torch.Size([]) or src_ind.shape == torch.Size([0]) or tgt_ind.shape == torch.Size([0]):
continue
p_s1 = preds_s1[src_ind]
p_s2 = preds_s2[src_ind]
p_t1 = preds_t1[tgt_ind]
p_t2 = preds_t2[tgt_ind]
s_y = src_y[src_ind]
t_y = tgt_y[tgt_ind]
#print("src_ind:",s_y,"tgt_ind:",t_y)
src_loss1 = loss(p_s1 , s_y)
tgt_loss1 = loss_w(p_t1 , t_y)
src_loss2 = loss(p_s2 , s_y)
tgt_loss2 = loss_w(p_t2 , t_y)
grad_cossim11 = []
#grad_mse11 = []
grad_cossim22 = []
#grad_mse22 = []
#netE+C1
for n, p in netC1.named_parameters():
# if len(p.shape) == 1: continue
real_grad = grad([src_loss1],
[p],
create_graph=True,
only_inputs=True,
allow_unused=False)[0]
fake_grad = grad([tgt_loss1],
[p],
create_graph=True,
only_inputs=True,
allow_unused=False)[0]
if len(p.shape) > 1:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=1).mean()
else:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=0)
#_mse = F.mse_loss(fake_grad, real_grad)
grad_cossim11.append(_cossim)
#grad_mse.append(_mse)
grad_cossim1 = torch.stack(grad_cossim11)
gm_loss1 = (1.0 - grad_cossim1).sum()
#grad_mse1 = torch.stack(grad_mse)
#gm_loss1 = (1.0 - grad_cossim1).sum() * args.Q + grad_mse1.sum() * args.Z
#netE+C2
for n, p in netC2.named_parameters():
# if len(p.shape) == 1: continue
real_grad = grad([src_loss2],
[p],
create_graph=True,
only_inputs=True)[0]
fake_grad = grad([tgt_loss2],
[p],
create_graph=True,
only_inputs=True)[0]
if len(p.shape) > 1:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=1).mean()
else:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=0)
#_mse = F.mse_loss(fake_grad, real_grad)
grad_cossim22.append(_cossim)
#grad_mse.append(_mse)
grad_cossim2 = torch.stack(grad_cossim22)
#grad_mse2 = torch.stack(grad_mse)
#gm_loss2 = (1.0 - grad_cossim2).sum() * args.Q + grad_mse2.sum() * args.Z
gm_loss2 = (1.0 - grad_cossim2).sum()
gm_loss = (gm_loss1 + gm_loss2)/2.0
total_loss += gm_loss
return total_loss/args.gmn_N
def gradient_discrepancy_loss_margin(args, p_s1,p_s2, s_y, p_t1, p_t2, t_y, netE, netC1, netC2):
loss_w = Weighted_CrossEntropy
loss = nn.CrossEntropyLoss()
#CrossEntropyLabelSmooth(num_classes=args.class_num, epsilon=0.1)
# gm loss
gm_loss = 0
#print("src_ind:",s_y,"tgt_ind:",t_y)
src_loss1 = loss(p_s1 , s_y)
tgt_loss1 = loss_w(p_t1 , t_y)
src_loss2 = loss(p_s2 , s_y)
tgt_loss2 = loss_w(p_t2 , t_y)
grad_cossim11 = []
#grad_mse11 = []
grad_cossim22 = []
#grad_mse22 = []
#netE+C1
for n, p in netC1.named_parameters():
# if len(p.shape) == 1: continue
real_grad = grad([src_loss1],
[p],
create_graph=True,
only_inputs=True,
allow_unused=False)[0]
fake_grad = grad([tgt_loss1],
[p],
create_graph=True,
only_inputs=True,
allow_unused=False)[0]
if len(p.shape) > 1:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=1).mean()
else:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=0)
#_mse = F.mse_loss(fake_grad, real_grad)
grad_cossim11.append(_cossim)
#grad_mse.append(_mse)
grad_cossim1 = torch.stack(grad_cossim11)
gm_loss1 = (1.0 - grad_cossim1).mean()
#grad_mse1 = torch.stack(grad_mse)
#gm_loss1 = (1.0 - grad_cossim1).sum() * args.Q + grad_mse1.sum() * args.Z
#netE+C2
for n, p in netC2.named_parameters():
# if len(p.shape) == 1: continue
real_grad = grad([src_loss2],
[p],
create_graph=True,
only_inputs=True)[0]
fake_grad = grad([tgt_loss2],
[p],
create_graph=True,
only_inputs=True)[0]
if len(p.shape) > 1:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=1).mean()
else:
_cossim = F.cosine_similarity(fake_grad, real_grad, dim=0)
#_mse = F.mse_loss(fake_grad, real_grad)
grad_cossim22.append(_cossim)
#grad_mse.append(_mse)
grad_cossim2 = torch.stack(grad_cossim22)
#grad_mse2 = torch.stack(grad_mse)
#gm_loss2 = (1.0 - grad_cossim2).sum() * args.Q + grad_mse2.sum() * args.Z
gm_loss2 = (1.0 - grad_cossim2).mean()
gm_loss = (gm_loss1 + gm_loss2)/2.0
return gm_loss
def Entropy_div(input_):
epsilon = 1e-5
input_ = torch.mean(input_, 0) + epsilon
entropy = input_ * torch.log(input_)
entropy = torch.sum(entropy)
return entropy
def Entropy_condition(input_):
bs = input_.size(0)
entropy = -input_ * torch.log(input_ + 1e-5)
entropy = torch.sum(entropy, dim=1).mean()
return entropy
def Entropy(input_):
return Entropy_condition(input_) + Entropy_div(input_)
def Weighted_CrossEntropy(input_,labels):
input_s = F.softmax(input_)
entropy = -input_s * torch.log(input_s + 1e-5)
entropy = torch.sum(entropy, dim=1)
weight = 1.0 + torch.exp(-entropy)
weight = weight / torch.sum(weight).detach().item()
#print("cross:",nn.CrossEntropyLoss(reduction='none')(input_, labels))
return torch.mean(weight * nn.CrossEntropyLoss(reduction='none')(input_, labels))