def val_zsl(self, test_X, test_label, target_classes, second=False):
start = 0
ntest = test_X.size()[0]
predicted_label = torch.LongTensor(test_label.size())
# all_output = None
for i in range(0, ntest, self.batch_size):
end = min(ntest, start + self.batch_size)
if self.cuda:
output = self.model(
Variable(test_X[start:end].cuda(), volatile=True))
else:
output = self.model(Variable(test_X[start:end], volatile=True))
# if all_output is None:
# all_output = output
# else:
# all_output = torch.cat((all_output, output), 0)
_, predicted_label[start:end] = torch.max(output.data, 1)
start = end
overall_acc = self.compute_acc_avg_per_class(
util.map_label(test_label, target_classes), predicted_label,
target_classes.size(0))
acc_of_all = self.compute_each_class_acc(
util.map_label(test_label, target_classes), predicted_label,
target_classes.size(0))
# return overall_acc, predicted_label, all_output, acc_of_all
return overall_acc, acc_of_all
def fit_zsl(self):
first_acc = 0
first_accs = torch.FloatTensor(5).fill_(0)
first_all_pred = None
first_all_output = None
first_acc_of_all = None
trun = lambda x: int(x * 100)
all_length = self.test_unseen_feature.size(0)
mapped_test_label = util.map_label(self.test_unseen_label,
self.unseenclasses)
for epoch in range(self.nepoch):
for i in range(0, self.ntrain, self.batch_size):
self.model.zero_grad()
batch_input, batch_label = self.next_batch(self.batch_size)
self.input.copy_(batch_input)
self.label.copy_(batch_label)
inputv = Variable(self.input) # fake_feature
labelv = Variable(self.label) # fake_labels
output = self.model(inputv)
loss = self.criterion(
output, labelv) # 使用fake_unseen_feature和labels来训练分类器
loss.backward()
self.optimizer.step()
# using real testing data (of unseen classes) to test classifier2
# overall_acc, pred, output, acc_of_all = self.val_zsl(self.test_unseen_feature, self.test_unseen_label,
# testing only hit@1 self.unseenclasses)
# overall_acc, acc_of_all = self.val_zsl(self.test_unseen_feature, self.test_unseen_label,
# self.unseenclasses)
# testing the hit@1,2,5,..
overall_acc, overall_acc_Hit = self.val_zsl_Hit(
self.test_unseen_feature, self.test_unseen_label,
self.unseenclasses)
# get the highest evaluation result
if overall_acc > first_acc:
first_acc = overall_acc
first_accs = overall_acc_Hit
# first_all_pred = pred
# first_all_output = output
# first_acc_of_all = acc_of_all
print('First Acc: {:.2f}%'.format(first_acc * 100))
print('First Acc: ', ['{:.2f}'.format(i * 100) for i in first_accs])
# if self.args.PerClassAcc:
# # acc_per = ['{:.2f}'.format(x * 100) for x in list(first_acc_of_all)]
# # print(acc_per)
# # acc_per = collections.OrderedDict() # output is ordered by input
# first_acc_of_all = first_acc_of_all.numpy()
# for i in range(len(first_acc_of_all)):
# x = first_acc_of_all[i]
# class_name = self.unseennames[i]
# # acc_per[class_name] = np.round(x * 100, 2)
# print('{}/{}, {}, acc: {:.2f} : '.format(i+1, len(first_acc_of_all), class_name, np.round(x * 100, 2)))
# # print(acc_per)
sys.stdout.flush()
return first_acc
def val_zsl_Hit(self, test_X, test_label, target_classes, second=False):
start = 0
ntest = test_X.size()[0]
predicted_label = torch.LongTensor(test_label.size())
predicted_labels = torch.LongTensor(test_label.size(0),
target_classes.size(0))
# all_output = None
for i in range(0, ntest, self.batch_size):
end = min(ntest, start + self.batch_size)
if self.cuda:
output = self.model(
Variable(test_X[start:end].cuda(), volatile=True))
else:
output = self.model(Variable(test_X[start:end], volatile=True))
# if all_output is None:
# all_output = output
# else:
# all_output = torch.cat((all_output, output), 0)
_, predicted_label[start:end] = torch.max(output.data, 1)
_, predicted_labels[start:end] = output.data.sort(1,
descending=True)
start = end
# print("pred shape:", predicted_labels.shape)
overall_acc = self.compute_acc_avg_per_class(
util.map_label(test_label, target_classes), predicted_label,
target_classes.size(0))
overall_acc_Hit = self.compute_acc_avg_per_class_Hit(
util.map_label(test_label, target_classes), predicted_labels,
target_classes.size(0))
# print("overall acc shape:", overall_acc.shape)
# acc_of_all = self.compute_each_class_acc(util.map_label(test_label, target_classes), predicted_labels,
# target_classes.size(0))
# return overall_acc, predicted_label, all_output, acc_of_all
# return overall_acc, acc_of_all
return overall_acc, overall_acc_Hit.squeeze()
def val(self, test_X, test_label, target_classes):
start = 0
ntest = test_X.size()[0]
predicted_label = torch.LongTensor(test_label.size())
for i in range(0, ntest, self.batch_size):
end = min(ntest, start + self.batch_size)
if self.cuda:
output = self.model(Variable(test_X[start:end].cuda(), volatile=True))
else:
output = self.model(Variable(test_X[start:end], volatile=True))
_, predicted_label[start:end] = torch.max(output.data, 1)
start = end
acc = self.compute_per_class_acc(util.map_label(test_label, target_classes), predicted_label,
target_classes.size(0))
return acc
def sample():
batch_feature, batch_label, batch_sem = data.next_batch(args.BatchSize)
input_fea.copy_(batch_feature)
input_sem.copy_(batch_sem)
input_label.copy_(util.map_label(batch_label, data.seenclasses))
ones = torch.ones(disc_interpolates.size())
if args.Cuda:
ones = ones.cuda()
gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
grad_outputs=ones,
create_graph=True, retain_graph=True, only_inputs=True)[0]
# args.GP_Weight = 10
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * args.GP_Weight
return gradient_penalty
# train a classifier on seen classes, obtain \theta of Equation (4)
pretrain_cls = classifier_pretrain.CLASSIFIER(data.train_feature, util.map_label(data.train_label, data.seenclasses),
data.seenclasses.size(0), args.FeaSize, args.Cuda, 0.001, 0.5, 100, 2*args.BatchSize,
args.Pretrained_Classifier)
# freeze the classifier during the optimization
for p in pretrain_cls.model.parameters(): # set requires_grad to False
p.requires_grad = False
for epoch in range(args.Epoch):
FP = 0
mean_lossD = 0
mean_lossG = 0
for i in range(0, data.ntrain, args.BatchSize):
gradients = autograd.grad(outputs=disc_interpolates,
inputs=interpolates,
grad_outputs=ones,
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
# args.GP_Weight = 10
gradient_penalty = (
(gradients.norm(2, dim=1) - 1)**2).mean() * args.GP_Weight
return gradient_penalty
# train a classifier on seen classes, obtain \theta of Equation (4)
pretrain_cls = classifier_pretrain.CLASSIFIER(
data.train_feature, util.map_label(data.train_label, data.seenclasses),
data.seenclasses.size(0), args.FeaSize, args.Cuda, 0.001, 0.5, 100,
2 * args.BatchSize, args.Pretrained_Classifier)
# freeze the classifier during the optimization
for p in pretrain_cls.model.parameters(): # set requires_grad to False
p.requires_grad = False
for epoch in range(args.Epoch):
FP = 0
mean_lossD = 0
mean_lossG = 0
for i in range(0, data.ntrain, args.BatchSize):
# print("batch...", i)
# iteratively train the generator and discriminator