def load_data_iterator(config):
tasks = ['train', 'test', 'valid']
data_iterator = {}
for task in tasks:
data_iterator[task] = create_data_loader(config,
dtype=task,
shuffle=True)
print('Data iterators have been created')
return data_iterator
#print("accuracy : {}".format(accuracy))
print(f"Got {num_correct}/{num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}")
model_net.train()
if __name__ == "__main__":
path = "/media/anand/polyglot/BraindataPY"
label_dict = {"no":1,"yes":0}
train_dirlist = dataloader.train_or_testset("train/")
test_dirlist = dataloader.train_or_testset("test/")
#extracting path,images and labels from training data
label_train = [] ###declare a list for tracking target labels for training datas
images_train = [] ####assigning a list and storing image arrays of training images
image_paths_train = [] ###This is an optional step to keep a track on the train image datas path.
dataloader.create_data_loader(train_dirlist,label_train,images_train,image_paths_train) # dirlist[:-1] includes no and yes directories
print("train_set some samples")
print(label_train[2])
print(images_train[2])
print(image_paths_train[2])
for i in images_train:
print(i)
#extracting path,images and labels from testing data
label_test = [] ###declare a list for tracking target labels for testing datas
images_test = [] ####assigning a list and storing image arrays of testing images
image_paths_test = [] ###This is an optional step to keep a track on the test image datas path
dataloader.create_data_loader(test_dirlist,label_test,images_test,image_paths_test)
print("test_set some samples")
print(label_test[2])
print(images_test[2])
print(image_paths_test[2])
def train_model(dataset=dataset, save_dir=save_dir, load_dir = load_dir, num_classes=num_classes, lr=lr,
num_epochs=nEpochs, increment = increment, save_epoch=snapshot, useTest=useTest, test_interval=nTestInterval):
"""
Args:
num_classes (int): Number of classes in the data
num_epochs (int, optional): Number of epochs to train for.
"""
classifier = Classifier(num_classes = num_classes)
generator = Modified_Generator(semantic_dim, noise_dim)
discriminator = Discriminator(input_dim=input_dim)
if args.resume_epoch is not None and args.train == 1:
checkpoint = torch.load(os.path.join(load_dir, saveName + '_increment' '_epoch-' + str(args.resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(load_dir, 'models', saveName + '_epoch-' + str(resume_epoch - 1) + '.pth.tar')))
classifier.load_state_dict(checkpoint['classifier_state_dict'])
generator.load_state_dict(checkpoint['generator_state_dict'])
discriminator.load_state_dict(checkpoint['discriminator_state_dict'])
elif args.resume_epoch is not None and args.train == 0:
checkpoint = torch.load(os.path.join(load_dir, saveName + '_increment' + '_epoch-' + str(args.resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(save_dir, 'models', saveName + '_epoch-' + str(resume_epoch - 1) + '.pth.tar')))
classifier.load_state_dict(checkpoint['classifier_state_dict'])
generator.load_state_dict(checkpoint['generator_state_dict'])
discriminator.load_state_dict(checkpoint['discriminator_state_dict'])
else:
print("Training {} from scratch...".format(modelName))
log_dir = os.path.join(save_dir)
writer = SummaryWriter(log_dir=log_dir)
iters = (total_classes - num_classes)/(increment_classes)
for i in range(int(iters)):
print('Training model on {} dataset...'.format(dataset))
if (i != 0):
num_classes = num_classes + increment_classes
checkpoint = torch.load(os.path.join(save_dir, saveName + '_increment_' + str(i-1) + '_epoch-' + 'best' + '.pth.tar'),
map_location=lambda storage, loc: storage) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(save_dir, saveName + '_increment_' + str(i-1) + '_epoch-' + 'best' + '.pth.tar')))
classifier.load_state_dict(checkpoint['classifier_state_dict'])
generator.load_state_dict(checkpoint['generator_state_dict'])
discriminator.load_state_dict(checkpoint['discriminator_state_dict'])
#model1 = deepcopy(model)
classifier1 = deepcopy(classifier)
generator1 = deepcopy(generator)
discriminator1 = deepcopy(discriminator)
classifier1, generator1, discriminator1 = classifier1.cuda(), generator1.cuda(), discriminator1.cuda()
print('Copied previous model')
in_features = classifier.classifier_out.in_features
weights = classifier.classifier_out.weight.data
print('new out features: ', num_classes + increment_classes)
classifier.classifier_out = nn.Linear(in_features, num_classes + increment_classes, bias = False)
kaiming_normal_init(classifier.classifier_out.weight)
classifier.classifier_out.weight.data[:num_classes] = weights
print('Updated Classifier With Number Of Classes %d' % (num_classes + increment_classes))
train_dataloader, test_dataloader, len_train, len_test = create_data_loader(feat_path, all_classes[num_classes:increment_classes+num_classes])
print('Classes used in the new dataset: %d to %d' % (num_classes, num_classes+increment_classes))
old_train_dataloader, old_test_dataloader, old_len_train, old_len_test = create_data_loader(feat_path, all_classes[:num_classes])
optimizer = torch.optim.Adam(classifier.parameters(), lr=lr[0], betas=(b1,b2))
optimizer_G = torch.optim.Adam(generator.parameters(), lr=lr[0], betas=(b1, b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr[0], betas=(b1, b2))
print('Classes used in the old dataset: 0 to %d' % (num_classes))
feats = sio.loadmat(att_path)
att = feats['att']
att = np.transpose(att, (1,0))
att = torch.tensor(att).cuda()
adversarial_loss = torch.nn.BCELoss().to(device)
if cuda:
classifier = classifier.to(device)
classifier1 = classifier1.to(device)
generator = generator.to(device)
generator1 = generator1.to(device)
discriminator = discriminator.to(device)
discriminator1 = discriminator.to(device)
num_epochs = num_epochs + increment
num_lr_stages = num_epochs/len(lr)
if args.train == 1:
for epoch in range(num_epochs):
start_time = timeit.default_timer()
running_old_corrects = 0.0
running_new_corrects = 0.0
classifier.train()
for (inputs, labels) in train_dataloader:
optimizer.zero_grad()
feats = Variable(inputs.to(device), requires_grad=True).float()
labels = Variable(labels.to(device), requires_grad=False).long()
loop_batch_size = len(inputs)
############### Begin Incremental Training Of Conv-LSTM Model ############################
old_labels = Variable(LongTensor(np.random.randint(0, num_classes, loop_batch_size))).long().cuda()
noise = Variable(FloatTensor(np.random.normal(0, 1, (loop_batch_size, noise_dim)))).cuda()
old_semantic = att[old_labels]
old_features = generator1(old_semantic.float(), noise)
new_features = feats
new_logits = classifier(new_features)
old_logits = classifier(old_features)
new_cls_loss = nn.CrossEntropyLoss()(new_logits, labels)
old_cls_loss = nn.CrossEntropyLoss()(old_logits, old_labels)
#loss = 10*nn.CrossEntropyLoss()(new_logits, labels) + nn.CrossEntropyLoss()(old_logits, old_labels) + nn.CrossEntropyLoss()(dataset_logits, dataset_labels) + 0.25*CustomKLDiv(new_logits[:,:num_classes], expected_logits, 0.5)
#loss = dataset_cls_loss + 100*new_cls_loss + 7.5*CustomKLDiv(new_logits[:,:num_classes], expected_logits, 0.5)
loss = 100*new_cls_loss + 10*old_cls_loss #Used for UCF101
#loss = 100*(new_cls_loss + old_cls_loss)
loss.backward()
optimizer.step()
_, old_predictions = torch.max(torch.softmax(old_logits, dim = 1), dim = 1, keepdim = False)
_, new_predictions = torch.max(torch.softmax(new_logits, dim = 1), dim = 1, keepdim = False)
running_old_corrects += torch.sum(old_predictions == old_labels.data)
running_new_corrects += torch.sum(new_predictions == labels.data)
old_epoch_acc = running_old_corrects.item() / len_train
new_epoch_acc = running_new_corrects.item() / len_train
words = 'data/old_train_acc_epoch' + str(i)
writer.add_scalar(words, old_epoch_acc, epoch)
words = 'data/new_train_acc_epoch' + str(i)
writer.add_scalar(words, new_epoch_acc, epoch)
print("Set: {} Epoch: {}/{} Train Old Acc: {} Train New Acc: {}".format(i, epoch+1, num_epochs, old_epoch_acc, new_epoch_acc))
#print("Set: {} Epoch: {}/{} Train New Acc: {}".format(i, epoch+1, num_epochs, new_epoch_acc))
if useTest and epoch % test_interval == (test_interval - 1):
#classifier.eval()
running_old_corrects = 0.0
running_new_corrects = 0.0
for (inputs, labels) in test_dataloader:
with torch.no_grad():
feats = inputs.to(device).float()
labels = labels.to(device).long()
#print(labels)
loop_batch_size = len(feats)
probs = classifier(feats)
_, predictions = torch.max(torch.softmax(probs, dim = 1), dim = 1, keepdim = False)
running_new_corrects += torch.sum(predictions == labels.data)
new_epoch_acc = running_new_corrects.item() / len_test
words = 'data/new_test_acc_epoch' + str(i)
writer.add_scalar(words, new_epoch_acc, epoch)
for (inputs, labels) in old_test_dataloader:
feats = inputs.to(device).float()
labels = labels.to(device).long()
loop_batch_size = len(inputs)
old_logits = classifier(feats)
_, old_predictions = torch.max(torch.softmax(old_logits, dim = 1), dim = 1, keepdim = False)
running_old_corrects += torch.sum(old_predictions == labels.data)
old_epoch_acc = running_old_corrects.item() / old_len_test
words = 'data/old_test_acc_epoch' + str(i)
writer.add_scalar(words, old_epoch_acc, epoch)
print("Set: {} Epoch: {}/{} Test Old Acc: {} Test New Acc: {}".format(i, epoch+1, num_epochs, old_epoch_acc, new_epoch_acc))
best_gan_acc = 0.0
for epoch in range(num_epochs):
start_time = timeit.default_timer()
running_old_corrects = 0.0
running_new_corrects = 0.0
classifier.train()
generator1.train()
generator.train()
discriminator1.train()
discriminator.train()
for (inputs, labels) in train_dataloader:
feats = Variable(inputs.to(device), requires_grad=True).float()
labels = Variable(labels.to(device), requires_grad=False).long()
loop_batch_size = len(feats)
valid = Variable(FloatTensor(loop_batch_size, 1).fill_(1.0), requires_grad=False).cuda()
fake = Variable(FloatTensor(loop_batch_size, 1).fill_(0.0), requires_grad=False).cuda()
noise = Variable(FloatTensor(np.random.normal(0, 1, (loop_batch_size, noise_dim)))).cuda()
semantic_true = att[labels].cuda()
optimizer_D.zero_grad()
############## New Dataset training #######################
true_features_2048 = feats
validity_real = discriminator(true_features_2048.detach()).view(-1)
validity_real_expected = discriminator1(true_features_2048.detach()).view(-1)
d_real_loss = adversarial_loss(validity_real, valid) + 0.25*CustomKLDiv(validity_real, validity_real_expected, 0.5, dim = 0) #Used for UCF101
#d_real_loss = 5*adversarial_loss(validity_real, valid) + 0.25*CustomKLDiv(validity_real, validity_real_expected, 0.5, dim = 0)
d_real_loss.backward(retain_graph = True)
############## All Fake Batch Training #######################
gen_imgs = generator(semantic_true.float(), noise)
validity_fake = discriminator(gen_imgs.detach()).view(-1)
validity_fake_expected = discriminator1(gen_imgs.detach()).view(-1)
d_fake_loss = adversarial_loss(validity_fake, fake) + 0.25*CustomKLDiv(validity_fake, validity_fake_expected, 0.5, dim = 0) #Used for UCF101
#d_fake_loss = 5*adversarial_loss(validity_fake, fake) + 0.25*CustomKLDiv(validity_fake, validity_fake_expected, 0.5, dim = 0)
d_fake_loss.backward(retain_graph = True)
optimizer_D.step()
############## Generator training ########################
optimizer_G.zero_grad()
validity = discriminator(gen_imgs).view(-1)
new_logits = classifier(gen_imgs)
g_loss = adversarial_loss(validity, valid) + 7.5*CustomKLDiv(gen_imgs, true_features_2048, 0.5) + 0.25*nn.CrossEntropyLoss()(new_logits, labels) + 10*nn.MSELoss()(gen_imgs, true_features)
#g_loss = adversarial_loss(validity, valid) + 7.5*CustomKLDiv(gen_imgs, true_features_2048, 0.5) + 0.25*nn.CrossEntropyLoss()(new_logits, labels) #Used for UCF101
#g_loss = 5*adversarial_loss(validity, valid) + 7.5*CustomKLDiv(gen_imgs, true_features_2048, 0.5) + 0.25*nn.CrossEntropyLoss()(new_logits, labels)
g_loss.backward(retain_graph = True)
optimizer_G.step()
############## Old Dataset Training ###########################
old_labels = Variable(LongTensor(np.random.randint(0, num_classes, loop_batch_size))).cuda()
noise = Variable(FloatTensor(np.random.normal(0, 1, (loop_batch_size, noise_dim)))).cuda()
semantic_true = att[old_labels].cuda()
optimizer_D.zero_grad()
true_features_2048 = generator1(semantic_true.float(), noise)
validity_real = discriminator(true_features_2048.detach()).view(-1)
d_real_loss = adversarial_loss(validity_real, valid)
d_real_loss.backward(retain_graph = True)
############## All Fake Batch Training #######################
gen_imgs = generator(semantic_true.float(), noise)
validity_fake = discriminator(gen_imgs.detach()).view(-1)
d_fake_loss = adversarial_loss(validity_fake, fake)
d_fake_loss.backward(retain_graph = True)
optimizer_D.step()
############## Generator training ########################
optimizer_G.zero_grad()
validity = discriminator(gen_imgs).view(-1)
old_logits = classifier(gen_imgs)
g_loss = adversarial_loss(validity, valid) + 7.5*CustomKLDiv(gen_imgs, true_features_2048, 0.5) + 0.25*nn.CrossEntropyLoss()(old_logits, old_labels) + 10*nn.MSELoss()(gen_imgs, true_features_2048)
#g_loss = adversarial_loss(validity, valid) + 7.5*CustomKLDiv(gen_imgs, true_features_2048, 0.5) + 0.25*nn.CrossEntropyLoss()(old_logits, old_labels)
g_loss.backward(retain_graph = True)
optimizer_G.step()
_, old_predictions = torch.max(torch.softmax(old_logits, dim = 1), dim = 1, keepdim = False)
_, new_predictions = torch.max(torch.softmax(new_logits, dim = 1), dim = 1, keepdim = False)
running_old_corrects += torch.sum(old_predictions == old_labels.data)
running_new_corrects += torch.sum(new_predictions == labels.data)
old_epoch_acc = running_old_corrects.item() / len_train
new_epoch_acc = running_new_corrects.item() / len_train
words = 'data/gen_old_train_acc_epoch' + str(i)
writer.add_scalar(words, old_epoch_acc, epoch)
words = 'data/gen_new_train_acc_epoch' + str(i)
writer.add_scalar(words, new_epoch_acc, epoch)
print("Set: {} Epoch: {}/{} Train GAN Old Acc: {} Train GAN New Acc: {}".format(i, epoch+1, num_epochs, old_epoch_acc, new_epoch_acc))
if useTest and epoch % test_interval == (test_interval - 1):
classifier.train()
generator.train()
running_old_corrects = 0.0
running_new_corrects = 0.0
for (inputs, labels) in test_dataloader:
labels = Variable(labels.to(device), requires_grad=False).long()
loop_batch_size = len(inputs)
noise = Variable(FloatTensor(np.random.normal(0, 1, (loop_batch_size, noise_dim)))).cuda()
semantic = att[labels].cuda()
new_features = generator(semantic.float(), noise)
new_logits = classifier(new_features)
_, new_predictions = torch.max(torch.softmax(new_logits, dim = 1), dim = 1, keepdim = False)
running_new_corrects += torch.sum(new_predictions == labels.data)
new_epoch_acc = running_new_corrects.item() / len_test
words = 'data/gen_new_test_acc_epoch' + str(i)
writer.add_scalar(words, new_epoch_acc, epoch)
for (inputs, labels) in old_test_dataloader:
labels = Variable(labels.to(device), requires_grad=False).long()
loop_batch_size = len(inputs)
noise = Variable(FloatTensor(np.random.normal(0, 1, (loop_batch_size, noise_dim)))).cuda()
semantic = att[labels].cuda()
old_features = generator(semantic.float(), noise)
old_logits = classifier(old_features)
_, old_predictions = torch.max(torch.softmax(old_logits, dim = 1), dim = 1, keepdim = False)
running_old_corrects += torch.sum(old_predictions == labels.data)
old_epoch_acc = running_old_corrects.item() / old_len_test
words = 'data/gen_old_test_acc_epoch' + str(i)
writer.add_scalar(words, old_epoch_acc, epoch)
print("Set: {} Epoch: {}/{} GAN Test Old Acc: {} GAN Test New Acc: {}".format(i, epoch+1, num_epochs, old_epoch_acc, new_epoch_acc))
if ((best_gan_acc < new_epoch_acc+old_epoch_acc) and (epoch > 150)):
save_path = os.path.join(save_dir, saveName + '_increment_' + str(i) + '_epoch-' + 'best' + '.pth.tar')
torch.save({
'classifier_state_dict': classifier.state_dict(),
'generator_state_dict': generator.state_dict(),
'discriminator_state_dict': discriminator.state_dict(),
}, save_path)
best_gan_acc = new_epoch_acc + old_epoch_acc
print("Save model at {}\n".format(save_path))
else:
for epoch in range(num_epochs):
start_time = timeit.default_timer()
running_loss = 0.0
running_corrects = 0.0
#classifier.eval()
for indices, inputs, labels in test_dataloader:
inputs = inputs.permute(0,2,1,3,4)
image_sequences = Variable(inputs.to(device), requires_grad=True)
labels = Variable(labels.to(device), requires_grad=False)
model.lstm.reset_hidden_state()
loop_batch_size = len(inputs)
true_features_2048 = model(image_sequences)
true_features_2048 = true_features_2048.view(true_features_2048.size(0), -1)
logits = classifier(true_features_2048)
_, predictions = torch.max(torch.softmax(logits, dim = 1), dim = 1, keepdim = False)
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(predictions == labels.data)
epoch_loss = running_loss / len(test_dataloader)
epoch_acc = running_corrects.item() / len(test_dataloader)
writer.add_scalar('data/test_acc {}'.format(i), epoch_loss, epoch)
print("[test] Epoch: {}/{} Testing Acc: {}".format(epoch+1, num_epochs, epoch_acc))
running_loss = 0.0
running_corrects = 0.0
#classifier.eval()
for indices, inputs, labels in old_dataloader:
inputs = inputs.permute(0,2,1,3,4)
image_sequences = Variable(inputs.to(device), requires_grad=True)
labels = Variable(labels.to(device), requires_grad=False)
model.lstm.reset_hidden_state()
loop_batch_size = len(inputs)
true_features_2048 = model(image_sequences)
true_features_2048 = true_features_2048.view(true_features_2048.size(0), -1)
logits = classifier(true_features_2048)
_, predictions = torch.max(torch.softmax(logits, dim = 1), dim = 1, keepdim = False)
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(predictions == labels.data)
epoch_loss = running_loss / len(old_dataloader)
epoch_acc = running_corrects.item() / len(old_dataloader)
writer.add_scalar('data/old_acc {}'.format(i), epoch_loss, epoch)
print("[test] Epoch: {}/{} Old Acc: {}".format(epoch+1, num_epochs, epoch_acc))
writer.close()
generator.load_state_dict(checkpoint['generator_state_dict'])
print("Training {} saved model...".format("UCF101"))
print('Total params: %.2fM' %
(sum(p.numel() for p in classifier.parameters()) / 1000000.0))
if cuda:
generator = generator.to(device)
classifier = classifier.to(device)
classifier.eval()
generator.eval()
att_path = 'ucf101_i3d/split_1/att_splits.mat'
feat_path = 'ucf101_i3d/i3d.mat'
train_dataloader, _, len_train, len_test = create_data_loader(
feat_path, range(num_class))
feats = sio.loadmat(att_path)
att = feats['att']
att = np.transpose(att, (1, 0))
att = torch.tensor(att).cuda()
start_time = timeit.default_timer()
#running_corrects = 0.0
for (inputs, labels) in train_dataloader:
feats = Variable(inputs, requires_grad=False).float().cuda()
labels = Variable(labels, requires_grad=False).long().cuda()
loop_batch_size = len(feats)
probs = feats
def test_create_datafolder4test(self):
create_data_loader(test_dirlist,label_test,images_test,image_paths_test)
assert len(image_paths_test)== 57
def test_create_dataloader4train(self):
create_data_loader(train_dirlist,label_train,images_train,image_paths_train)
assert len(label_train) == 196
batch_id += 1
step_idx += 1
scheduler.step()
train_epoch_cost = time.time() - epoch_start
ce_time.append(train_epoch_cost)
logger.info("train epoch: %d, epoch_cost: %.5f s" %
(pass_id, train_epoch_cost))
if args.save_model and rank == 0:
model_dir = os.path.join(args.save_model, "step_final")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
paddle.save(transformer.state_dict(),
os.path.join(model_dir, "transformer.pdparams"))
paddle.save(optimizer.state_dict(),
os.path.join(model_dir, "transformer.pdopt"))
if __name__ == '__main__':
# 读入参数
yaml_file = './transformer.base.yaml'
with open(yaml_file, 'rt') as f:
args = AttrDict(yaml.safe_load(f))
pprint(args)
# Define data loader
(train_loader), (eval_loader) = create_data_loader(args)
print('training the model')
do_train(args, train_loader, eval_loader)
def test_model(dataset=dataset,
load_dir=load_dir,
only_classifier=only_classifier):
if (only_classifier == 0):
generator = Modified_Generator(semantic_dim, noise_dim)
if (increment is None):
checkpoint = torch.load(os.path.join(
load_dir, saveName + '_increment' + '_epoch-' +
str(resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage
) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(
load_dir, 'models', saveName + '_epoch-' +
str(resume_epoch - 1) + '.pth.tar')))
classifier = Classifier(num_classes=num_class, bias=True)
else:
checkpoint = torch.load(os.path.join(
load_dir, saveName + '_increment_' + str(increment) +
'_epoch-' + 'best' + '.pth.tar'),
map_location=lambda storage, loc: storage
) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(load_dir, 'models',
saveName + '_epoch-' + 'best' + '.pth.tar')))
classifier = Classifier(num_classes=num_class, bias=False)
classifier.load_state_dict(checkpoint['classifier_state_dict'])
generator.load_state_dict(checkpoint['generator_state_dict'])
print("Training {} saved model...".format(modelName))
print('Total params: %.2fM' %
(sum(p.numel() for p in classifier.parameters()) / 1000000.0))
print('Training model on {} dataset...'.format(dataset))
_, test_dataloader, _, len_test = create_data_loader(
feat_path, test_classes)
if cuda:
generator = generator.to(device)
classifier = classifier.to(device)
classifier.train()
generator.train()
feats = sio.loadmat(att_path)
att = feats['att']
att = np.transpose(att, (1, 0))
att = torch.tensor(att).cuda()
start_time = timeit.default_timer()
running_corrects = 0.0
for (inputs, labels) in test_dataloader:
feats = Variable(inputs, requires_grad=False).float().cuda()
labels = Variable(labels, requires_grad=False).long().cuda()
loop_batch_size = len(feats)
probs = classifier(feats)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
running_corrects += torch.sum(predictions == labels.data)
epoch_acc = running_corrects.item() / len_test
print("[test] Classifier Testing Acc: {}".format(epoch_acc))
start_time = timeit.default_timer()
running_corrects = 0.0
for (inputs, labels) in test_dataloader:
feats = Variable(inputs, requires_grad=True).float().cuda()
labels = Variable(labels, requires_grad=False).long().cuda()
loop_batch_size = len(feats)
noise = Variable(
FloatTensor(
np.random.normal(0, 1, (loop_batch_size, noise_dim))))
semantic_true = att[labels]
with torch.no_grad():
features_2048 = generator(semantic_true.float(), noise)
probs = classifier(features_2048)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
running_corrects += torch.sum(predictions == labels.data)
real_epoch_acc = running_corrects.item() / len_test
print("[test] Epoch: Test Dataset Generator Acc: {}".format(
real_epoch_acc))
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
else:
if (increment is None):
checkpoint = torch.load(os.path.join(
load_dir, saveName + '_increment' + '_epoch-' +
str(resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage
) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(
load_dir, 'models', saveName + '_epoch-' +
str(resume_epoch - 1) + '.pth.tar')))
classifier = Classifier(num_classes=num_class, bias=False)
else:
checkpoint = torch.load(os.path.join(
load_dir, saveName + '_increment_' + str(increment) +
'_epoch-' + str(resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage
) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(
load_dir, 'models', saveName + '_epoch-' +
str(resume_epoch - 1) + '.pth.tar')))
classifier = Classifier(num_classes=num_class, bias=False)
classifier.load_state_dict(checkpoint['classifier_state_dict'])
print("Training {} saved model...".format(modelName))
print('Total params: %.2fM' %
(sum(p.numel() for p in classifier.parameters()) / 1000000.0))
print('Training model on {} dataset...'.format(dataset))
_, test_dataloader, _, len_test = create_data_loader(
feat_path, test_classes)
if cuda:
classifier = classifier.to(device)
classifier.train()
feats = sio.loadmat(att_path)
att = feats['att']
att = np.transpose(att, (1, 0))
att = torch.tensor(att).cuda()
start_time = timeit.default_timer()
running_corrects = 0.0
for (inputs, labels) in test_dataloader:
feats = Variable(inputs, requires_grad=False).float().cuda()
labels = Variable(labels, requires_grad=False).long().cuda()
loop_batch_size = len(feats)
probs = classifier(feats)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
running_corrects += torch.sum(predictions == labels.data)
epoch_acc = running_corrects.item() / len_test
print("[test] Classifier Testing Acc: {}".format(epoch_acc))
att = feats['att']
att = np.transpose(att, (1, 0))
att = torch.tensor(att).cuda()
classes = 0
for i in range(increments):
if (i == 0):
if (not os.path.exists("gen_features")):
os.mkdir("gen_features")
if (not os.path.exists(f"gen_features/{args.save_name}")):
os.mkdir(f"gen_features/{args.save_name}")
if split == 'train':
dataloader, _, _, _ = create_data_loader(
feat_path, all_classes[classes:classes + args.increment_class])
elif split == 'test':
_, dataloader, _, _ = create_data_loader(
feat_path, all_classes[classes:classes + args.increment_class])
else:
dataloader, _, _, _ = create_data_loader(
feat_path,
all_classes[classes:classes + args.increment_class],
percent=0.00001)
for j, (inputs, labels) in enumerate(dataloader):
batch_size = inputs.size(0)
labels = Variable(labels, requires_grad=False).long().cuda()
noise = Variable(
def train_model(dataset=dataset,
save_dir=save_dir,
load_dir=load_dir,
num_classes=total_classes,
lr=lr,
num_epochs=nEpochs,
save_epoch=snapshot,
useTest=useTest,
test_interval=nTestInterval):
"""
Args:
num_classes (int): Number of classes in the data
num_epochs (int, optional): Number of epochs to train for.
"""
generator = Modified_Generator(semantic_dim, noise_dim)
discriminator = Discriminator(input_dim=input_dim)
classifier = Classifier(num_classes=num_classes)
if args.resume_epoch is not None:
checkpoint = torch.load(os.path.join(
load_dir, saveName + '_increment'
'_epoch-' + str(args.resume_epoch - 1) + '.pth.tar'),
map_location=lambda storage, loc: storage
) # Load all tensors onto the CPU
print("Initializing weights from: {}...".format(
os.path.join(
save_dir, 'models',
saveName + '_epoch-' + str(resume_epoch - 1) + '.pth.tar')))
classifier.load_state_dict(checkpoint['classifier_state_dict'])
generator.load_state_dict(checkpoint['generator_state_dict'])
discriminator.load_state_dict(checkpoint['discriminator_state_dict'])
print("Training {} saved model...".format(modelName))
else:
print("Training {} from scratch...".format(modelName))
print('Total params: %.2fM' %
(sum(p.numel() for p in classifier.parameters()) / 1000000.0))
log_dir = os.path.join(save_dir)
writer = SummaryWriter(log_dir=log_dir)
print('Training model on {} dataset...'.format(dataset))
train_dataloader, test_dataloader, len_train, len_test = create_data_loader(
feat_path, all_classes)
trainval_loaders = {'train': train_dataloader, 'test': test_dataloader}
if cuda:
generator = generator.to(device)
discriminator = discriminator.to(device)
classifier = classifier.to(device)
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=lr,
betas=(b1, b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=lr,
betas=(b1, b2))
optimizer = torch.optim.Adam(list(classifier.parameters()), lr=lr)
adversarial_loss = torch.nn.BCELoss().to(device)
feats = sio.loadmat(att_path)
att = feats['att']
att = np.transpose(att, (1, 0))
att = torch.tensor(att).cuda()
if args.train == 1:
if args.only_gan == 0:
for epoch in range(num_epochs):
start_time = timeit.default_timer()
running_loss = 0.0
running_corrects = 0.0
classifier.train()
for (inputs, labels) in (trainval_loaders["train"]):
feats = Variable(inputs, requires_grad=True).float().cuda()
labels = Variable(labels,
requires_grad=False).long().cuda()
optimizer.zero_grad()
#model.lstm.reset_hidden_state()
loop_batch_size = len(feats)
probs = classifier(feats)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
loss = nn.CrossEntropyLoss()(probs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * feats.size(0)
running_corrects += torch.sum(predictions == labels.data)
epoch_loss = running_loss / len_train
epoch_acc = running_corrects.item() / len_train
writer.add_scalar('data/train_acc_epoch_benchmark', epoch_acc,
epoch)
print(
"[train] Epoch: {}/{} Training Acc: {} Training Loss: {}".
format(epoch + 1, num_epochs, epoch_acc, epoch_loss))
if useTest and epoch % test_interval == (test_interval - 1):
classifier.train()
start_time = timeit.default_timer()
running_corrects = 0.0
for (inputs, labels) in (trainval_loaders["test"]):
feats = Variable(inputs,
requires_grad=True).float().cuda()
labels = Variable(labels,
requires_grad=False).long().cuda()
loop_batch_size = len(feats)
with torch.no_grad():
probs = classifier(feats)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
running_corrects += torch.sum(
predictions == labels.data)
#print(len_test)
real_epoch_acc = running_corrects.item() / len_test
writer.add_scalar('data/test_acc_epoch_benchmark',
real_epoch_acc, epoch)
print("[test] Epoch: {}/{} Test Dataset Acc: {}".format(
epoch + 1, num_epochs, real_epoch_acc))
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) +
"\n")
if (epoch % save_epoch == save_epoch - 1):
save_path = os.path.join(
save_dir, saveName + '_increment'
'_epoch-' + str(epoch) + '.pth.tar')
torch.save(
{
'generator_state_dict': generator.state_dict(),
'discriminator_state_dict':
discriminator.state_dict(),
'classifier_state_dict': classifier.state_dict(),
}, save_path)
print("Save model at {}\n".format(save_path))
for epoch in range(num_epochs):
start_time = timeit.default_timer()
running_d_loss = 0.0
running_g_loss = 0.0
gen_running_corrects = 0.0
generator.train()
discriminator.train()
classifier.train()
for (inputs, labels) in (trainval_loaders["train"]):
feats = Variable(inputs, requires_grad=True).float().cuda()
labels = Variable(labels, requires_grad=False).long().cuda()
loop_batch_size = len(feats)
valid = Variable(FloatTensor(loop_batch_size, 1).fill_(1.0),
requires_grad=False)
fake = Variable(FloatTensor(loop_batch_size, 1).fill_(0.0),
requires_grad=False)
noise = Variable(
FloatTensor(
np.random.normal(0, 1, (loop_batch_size, noise_dim))))
semantic_true = att[labels]
optimizer_D.zero_grad()
############## All real batch training #######################
true_features_2048 = feats
validity_real = discriminator(true_features_2048).view(-1)
d_real_loss = adversarial_loss(validity_real, valid)
d_real_loss.backward(retain_graph=True)
############## All Fake Batch Training #######################
gen_imgs = generator(semantic_true.float(), noise)
validity_fake = discriminator(gen_imgs.detach()).view(-1)
d_fake_loss = adversarial_loss(validity_fake, fake)
d_fake_loss.backward(retain_graph=True)
optimizer_D.step()
############## Generator training ########################
optimizer_G.zero_grad()
validity = discriminator(gen_imgs).view(-1)
generated_preds = classifier(gen_imgs)
gen_adv = adversarial_loss(validity, valid)
KL_loss = CustomKLDiv(gen_imgs, true_features_2048, 0.5)
#l1_loss = nn.L1Loss()(gen_imgs, true_features_2048)
mse_loss = nn.MSELoss()(gen_imgs, true_features_2048)
cls_loss = nn.CrossEntropyLoss()(generated_preds, labels)
g_loss = gen_adv + 7.5 * KL_loss + 0.25 * cls_loss + 200 * mse_loss
#g_loss = gen_adv + 7.5*KL_loss + 0.25*cls_loss + 1000*l1_loss
#g_loss = gen_adv + 7.5*KL_loss + 0.25*cls_loss
#g_loss = gen_adv + 100*l1_loss
#g_loss = gen_adv + 7.5*KL_loss
g_loss.backward(retain_graph=True)
optimizer_G.step()
_, gen_predictions = torch.max(torch.softmax(generated_preds,
dim=1),
dim=1,
keepdim=False)
running_d_loss = running_d_loss + (
d_real_loss.item() + d_fake_loss.item()) * feats.size(0)
running_g_loss = running_g_loss + g_loss.item() * feats.size(0)
gen_running_corrects += torch.sum(
gen_predictions == labels.data)
epoch_d_loss = running_d_loss / len_train
epoch_g_loss = running_g_loss / len_train
gen_epoch_acc = gen_running_corrects.item() / len_train
writer.add_scalar('data/gen_train_acc_epoch_benchmark',
gen_epoch_acc, epoch)
writer.add_scalar('data/d_loss_epoch_benchmark', epoch_d_loss,
epoch)
writer.add_scalar('data/g_loss_epoch_benchmark', epoch_g_loss,
epoch)
print(
"[train] Epoch: {}/{} Generator Loss {} Discriminator Loss {} Generator Acc: {} "
.format(epoch + 1, num_epochs, epoch_g_loss, epoch_d_loss,
gen_epoch_acc))
if useTest and epoch % test_interval == (test_interval - 1):
classifier.train()
generator.train()
start_time = timeit.default_timer()
running_corrects = 0.0
for (inputs, labels) in (trainval_loaders["test"]):
feats = Variable(inputs, requires_grad=True).float().cuda()
labels = Variable(labels,
requires_grad=False).long().cuda()
loop_batch_size = len(feats)
noise = Variable(
FloatTensor(
np.random.normal(0, 1,
(loop_batch_size, noise_dim))))
semantic_true = att[labels]
with torch.no_grad():
features_2048 = generator(semantic_true.float(), noise)
probs = classifier(features_2048)
_, predictions = torch.max(torch.softmax(probs, dim=1),
dim=1,
keepdim=False)
running_corrects += torch.sum(predictions == labels.data)
real_epoch_acc = running_corrects.item() / len_test
writer.add_scalar('data/gen_test_acc_epoch_benchmark',
real_epoch_acc, epoch)
print("[test] Epoch: {}/{} Test Dataset Generator Acc: {}".
format(epoch + 1, num_epochs, real_epoch_acc))
stop_time = timeit.default_timer()
print("Execution time: " + str(stop_time - start_time) + "\n")
if (epoch % save_epoch == save_epoch - 1):
save_path = os.path.join(
save_dir, saveName + '_increment'
'_epoch-' + str(epoch) + '.pth.tar')
torch.save(
{
'epoch': epoch + 1,
'generator_state_dict': generator.state_dict(),
'discriminator_state_dict': discriminator.state_dict(),
'classifier_state_dict': classifier.state_dict(),
}, save_path)
print("Save model at {}\n".format(save_path))
writer.close()
