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AdversarialFeatures_train_test.py
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AdversarialFeatures_train_test.py
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from __future__ import print_function
import random
import torch
import torch.autograd as autograd
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import networks.TFVAEGAN_model as model
import datasets.image_util as util
import classifiers.classifier_images as classifier
from config_images import opt
import os, time
import numpy as np
from torch.nn import functional as F
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
cudnn.benchmark = True
# load data
data = util.DATA_LOADER(opt)
# load model
netE = model.Encoder(opt)
netG = model.Generator(opt)
netD = model.Discriminator_D1(opt)
# Init models: Feedback module, auxillary module
netF = model.Feedback(opt)
netDec = model.AttDec(opt,opt.attSize)
netD.cuda()
netE.cuda()
netF.cuda()
netG.cuda()
netDec.cuda()
# load pretrained model
zsl_setting = ""
if opt.gzsl:
zsl_setting = "gzsl"
else:
zsl_setting = "zsl"
netG.load_state_dict(torch.load('{}/{}/{}/netG_best.pth'.format(opt.model_dir, opt.dataset, zsl_setting)))
netE.load_state_dict(torch.load('{}/{}/{}/netE_best.pth'.format(opt.model_dir, opt.dataset, zsl_setting)))
netD.load_state_dict(torch.load('{}/{}/{}/netD_best.pth'.format(opt.model_dir, opt.dataset, zsl_setting)))
netF.load_state_dict(torch.load('{}/{}/{}/netF_best.pth'.format(opt.model_dir, opt.dataset, zsl_setting)))
netDec.load_state_dict(torch.load('{}/{}/{}/netDec_best.pth'.format(opt.model_dir, opt.dataset, zsl_setting)))
netE.eval()
netG.eval()
netD.eval()
netF.eval()
netDec.eval()
if opt.gzsl:
known_classes = torch.cat((data.seenclasses, data.unseenclasses), 0)
else:
known_classes = data.unseenclasses
def compute_dec_out(netDec, test_X, new_size):
start = 0
ntest = test_X.size()[0]
feat1 = netDec(test_X)
feat2 = netDec.getLayersOutDet()
new_test_X = torch.cat([test_X,feat1,feat2],dim=1)
return new_test_X
dec_size=opt.attSize
dec_hidden_size = 4096
nclass = known_classes.size(0)
unknown_attribute = None
input_dim = opt.resSize
if netDec:
input_dim += dec_hidden_size + dec_size
def generate_syn_feature(generator, classes, attribute, num, netF=None, netDec=None):
nclass = classes.size(0)
syn_feature = torch.FloatTensor(nclass*num, opt.resSize)
syn_label = torch.LongTensor(nclass*num)
syn_att = torch.FloatTensor(num, opt.attSize)
syn_noise = torch.FloatTensor(num, opt.nz)
if opt.cuda:
syn_att = syn_att.cuda()
syn_noise = syn_noise.cuda()
for i in range(nclass):
iclass = classes[i]
iclass_att = attribute[iclass]
syn_att.copy_(iclass_att.repeat(num, 1))
syn_noise.normal_(0, 1)
syn_noisev = Variable(syn_noise)
syn_attv = Variable(syn_att)
fake = generator(syn_noisev,c=syn_attv)
if netF is not None:
dec_out = netDec(fake) # only to call the forward function of decoder
dec_hidden_feat = netDec.getLayersOutDet() #no detach layers
feedback_out = netF(dec_hidden_feat)
fake = generator(syn_noisev, a1=opt.a2, c=syn_attv, feedback_layers=feedback_out)
output = fake
syn_feature.narrow(0, i*num, num).copy_(output.data.cpu())
syn_label.narrow(0, i*num, num).fill_(iclass)
return syn_feature, syn_label
unseen_feature, unseen_label = generate_syn_feature(netG, data.unseenclasses, data.attribute, opt.syn_num, netF=netF, netDec=netDec)
zsl_cls = classifier.CLASSIFIER(unseen_feature, util.map_label(unseen_label, data.unseenclasses), data,
data.unseenclasses.size(0), opt.cuda, opt.classifier_lr, 0.5, 25,
opt.syn_num, False, netDec = netDec, dec_size = opt.attSize, dec_hidden_size=4096, test_openset = True)
first_classifier = classifier.LINEAR_LOGSOFTMAX_CLASSIFIER(input_dim,data.unseenclasses.size(0))
first_classifier.load_state_dict(zsl_cls.best_model)
first_classifier.cuda()
def to_torch(z, requires_grad=False):
return Variable(torch.FloatTensor(z), requires_grad=requires_grad).cuda()
unknown_feature = None
for i in range(0, data.unseenclasses.size(0)):
temp_class = i
ori_feature = unseen_feature[temp_class * opt.syn_num:(temp_class + 1) * opt.syn_num,:].clone()
ori_feature = torch.mean(ori_feature, dim = 0, keepdim=True)
z_0 = ori_feature.repeat(opt.cf_batch_size, 1).cuda()
for j in range(0, opt.unknown_num, opt.cf_batch_size):
z_rand = torch.FloatTensor(opt.cf_batch_size, z_0.shape[1])
z_rand.normal_(0,1)
z_value = z_rand.data.cpu().numpy()
for k in range(opt.iter_num):
netDec.eval()
z = to_torch(z_value, requires_grad=True)
fake = compute_dec_out(netDec, z, input_dim)
output, logits = first_classifier(fake)
augmented_logits = F.pad(logits, pad=(0,1))
sum_of_log = torch.sum(torch.exp(augmented_logits), dim = 1)
if opt.use_energy_cfloss:
cf_loss = opt.energy_temp * torch.logsumexp(logits / opt.energy_temp, dim=1)
else:
cf_loss = torch.log(sum_of_log + torch.ones(sum_of_log.size()).cuda())
distance_loss = torch.norm(z - z_0, dim = 1) * opt.distance_weight
total_loss = cf_loss + distance_loss
scores = F.softmax(augmented_logits, dim=1)
dc_dz = autograd.grad(total_loss, z, torch.ones(total_loss.size()).cuda())[0]
z = z - dc_dz * opt.ASE_speed
z_value = z.data.cpu().numpy()
del z
if unknown_feature is None:
unknown_feature = z_value
else:
unknown_feature = np.concatenate((unknown_feature, z_value), axis=0)
frame = unknown_feature
nan_row = np.isnan(frame[:,0])
row = np.where(nan_row==True)
frame_fix = np.delete(frame, row, axis=0)
unknown_feature = torch.Tensor(frame_fix)
unknown_label = torch.ones(unknown_feature.shape[0]) * -1
# train classifier
if opt.gzsl:
known_classes = torch.cat((data.seenclasses, data.unseenclasses), 0)
ASE_X = torch.cat((data.train_feature, unseen_feature, unknown_feature), 0)
ASE_Y = torch.cat((data.train_label, unseen_label, unknown_label), 0)
ASE_Y = util.map_label(ASE_Y, known_classes)
gzsl_X = torch.cat((data.train_feature, unseen_feature), 0)
gzsl_Y = torch.cat((data.train_label, unseen_label), 0)
gzsl_Y = util.map_label(gzsl_Y, known_classes)
ASE_cls = classifier.CLASSIFIER(ASE_X, ASE_Y, data, known_classes.size(0)+1, opt.cuda, opt.classifier_lr, 0.5, \
50, opt.syn_num, generalized=True, netDec=netDec, dec_size=opt.attSize, dec_hidden_size=4096)
gzsl_cls = classifier.CLASSIFIER(gzsl_X, gzsl_Y, data, known_classes.size(0), opt.cuda, opt.classifier_lr, 0.5, \
50, opt.syn_num, generalized=True, netDec=netDec, dec_size=opt.attSize, dec_hidden_size=4096)
else:
train_X = torch.cat((unseen_feature, unknown_feature), 0)
train_Y = torch.cat((unseen_label, unknown_label), 0)
train_Y = util.map_label(train_Y, data.unseenclasses)
ASE_cls = classifier.CLASSIFIER(train_X, train_Y, data, data.unseenclasses.size(0)+1, opt.cuda, opt.classifier_lr, 0.5, \
25, opt.syn_num, generalized=False, netDec=netDec, dec_size=opt.attSize, dec_hidden_size=4096)
zsl_cls = classifier.CLASSIFIER(unseen_feature, util.map_label(unseen_label, data.unseenclasses), data, data.unseenclasses.size(0), opt.cuda, opt.classifier_lr, 0.5, \
25, opt.syn_num, generalized=False, netDec=netDec, dec_size=opt.attSize, dec_hidden_size=4096)
if opt.gzsl:
print("gzsl_cls H is: ", gzsl_cls.H)
print("ASE_cls H is: ", ASE_cls.H)
print("gzsl_cls auroc is: ", gzsl_cls.auroc)
print("ASE_cls auroc is: ", ASE_cls.auroc)
else:
print("zsl_cls acc : {}".format(zsl_cls.acc))
print("ASE_cls acc : {}".format(ASE_cls.acc))
print("zsl_cls auroc : {}".format(zsl_cls.auroc))
print("ASE_cls auroc : {}".format(ASE_cls.auroc))