def generate_inverted_image_specific_layer(self, input_image, img_size, target_layer=3):
# Generate a random image which we will optimize
opt_img = Variable(1e-1 * torch.randn(1, 3, img_size, img_size), requires_grad=True)
# Define optimizer for previously created image
optimizer = SGD([opt_img], lr=1e4, momentum=0.9)
# Get the output from the model after a forward pass until target_layer
# with the input image (real image, NOT the randomly generated one)
input_image_layer_output = \
self.get_output_from_specific_layer(input_image, target_layer)
# Alpha regularization parametrs
# Parameter alpha, which is actually sixth norm
alpha_reg_alpha = 6
# The multiplier, lambda alpha
alpha_reg_lambda = 1e-7
# Total variation regularization parameters
# Parameter beta, which is actually second norm
tv_reg_beta = 2
# The multiplier, lambda beta
tv_reg_lambda = 1e-8
for i in range(201):
optimizer.zero_grad()
# Get the output from the model after a forward pass until target_layer
# with the generated image (randomly generated one, NOT the real image)
output = self.get_output_from_specific_layer(opt_img, target_layer)
# Calculate euclidian loss
euc_loss = 1e-1 * self.euclidian_loss(input_image_layer_output.detach(), output)
# Calculate alpha regularization
reg_alpha = alpha_reg_lambda * self.alpha_norm(opt_img, alpha_reg_alpha)
# Calculate total variation regularization
reg_total_variation = tv_reg_lambda * self.total_variation_norm(opt_img,
tv_reg_beta)
# Sum all to optimize
loss = euc_loss + reg_alpha + reg_total_variation
# Step
loss.backward()
optimizer.step()
# Generate image every 5 iterations
if i % 5 == 0:
print('Iteration:', str(i), 'Loss:', loss.data.numpy()[0])
x = recreate_image(opt_img)
cv2.imwrite('../generated/Inv_Image_Layer_' + str(target_layer) +
'_Iteration_' + str(i) + '.jpg', x)
# Reduce learning rate every 40 iterations
if i % 40 == 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= 1/10
def test_mask_same_after_update(generate_batch):
from torch.optim import SGD
unary, tags, lengths = generate_batch
h = unary.size(2)
constraint = torch.rand(h, h) < 0.5
crf = CRF(h, constraint=constraint)
opt = SGD(crf.parameters(), lr=10)
m1 = crf.constraint.numpy()
t1 = crf.transitions_p.detach().clone().numpy()
l = crf.neg_log_loss(unary, tags, lengths)
l = torch.mean(l)
l.backward()
opt.step()
m2 = crf.constraint.numpy()
t2 = crf.transitions_p.detach().numpy()
np.testing.assert_allclose(m1, m2)
with pytest.raises(AssertionError):
np.testing.assert_allclose(t1, t2)
def generate(self):
initial_learning_rate = 6
for i in range(1, 150):
# Process image and return variable
self.processed_image = preprocess_image(self.created_image)
# Define optimizer for the image
optimizer = SGD([self.processed_image], lr=initial_learning_rate)
# Forward
output = self.model(self.processed_image)
# Target specific class
class_loss = -output[0, self.target_class]
print('Iteration:', str(i), 'Loss', "{0:.2f}".format(class_loss.data.numpy()[0]))
# Zero grads
self.model.zero_grad()
# Backward
class_loss.backward()
# Update image
optimizer.step()
# Recreate image
self.created_image = recreate_image(self.processed_image)
# Save image
cv2.imwrite('../generated/c_specific_iteration_'+str(i)+'.jpg', self.created_image)
return self.processed_image
def train_siamese_distrib_margine(directory,
version,
model,
train_loader,
valid_loader,
resize,
batch_size,
margine,
exp_name='model_1',
decay=None,
lr=0.0001,
epochs=10,
momentum=0.99,
logdir='logs',
modeLoss=None,
dizionario_array=None):
print("momonetum", momentum)
print("lr", lr)
print(margine)
if not modeLoss is None:
if modeLoss == "single":
criterion = ContrastiveLoss(margine)
if not decay is None:
print("Weight_Decay", decay)
optimizer = SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=decay)
else:
optimizer = SGD(model.parameters(), lr, momentum=momentum)
if not dizionario_array is None:
optimizer.load_state_dict(dizionario_array["optimizer"])
#meters
loss_meter = AverageValueMeter()
acc_meter = AverageValueMeter()
#writer
writer = SummaryWriter(join(logdir, exp_name))
#device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
criterion.to(device)
#definiamo un dizionario contenente i loader di training e test
loader = {'train': train_loader, 'valid': valid_loader}
if not dizionario_array is None:
array_accuracy_train = dizionario_array["a_train"]
array_accuracy_valid = dizionario_array["a_valid"]
array_loss_train = dizionario_array["l_train"]
array_loss_valid = dizionario_array["l_valid"]
array_glb_train = dizionario_array["g_train"]
array_glb_valid = dizionario_array["g_valid"]
global_step = array_glb_valid[-1]
last_loss_train = array_loss_train[-1]
last_loss_val = array_loss_valid[-1]
last_acc_train = array_accuracy_train[-1]
last_acc_val = array_accuracy_valid[-1]
epoche_fatte = dizionario_array["epoche_fatte"]
epoche_avanza = dizionario_array["epoche_avanza"]
else:
array_accuracy_train = []
array_accuracy_valid = []
array_loss_train = []
array_loss_valid = []
array_glb_train = []
array_glb_valid = []
global_step = 0
last_loss_train = 0
last_loss_val = 0
last_acc_train = 0
last_acc_val = 0
#inizializziamo il global step
tempo = Timer()
start = timer()
for e in range(epochs):
print("Epoca= ", e)
#iteriamo tra due modalità: train e test
for mode in ['train', 'valid']:
loss_meter.reset()
acc_meter.reset()
model.train() if mode == 'train' else model.eval()
with torch.set_grad_enabled(
mode == 'train'): #abilitiamo i gradienti solo in training
for i, batch in enumerate(loader[mode]):
print("Num batch =", i)
I_i, I_j, l_ij, _, _ = [b.to(device) for b in batch]
#img1, img2, label12, label1, label2
#l'implementazione della rete siamese è banale:
#eseguiamo la embedding net sui due input
phi_i = model(I_i) #img 1
phi_j = model(I_j) #img2
print("Output train img1", phi_i.size())
print("Output train img2", phi_j.size())
#print("Etichetta reale",l_ij)
euclidean_distance = F.pairwise_distance(phi_i, phi_j)
euclid_tmp = torch.Tensor.numpy(
euclidean_distance.detach().cpu()) # distanza
labs = l_ij.to('cpu').numpy() # etichette reali
print(euclid_tmp)
etichette_predette = [euclid_tmp > margine]
print(etichette_predette)
etichette_predette = np.int8(etichette_predette)
etichette_predette = np.reshape(etichette_predette, -1)
print(etichette_predette)
#l_ij = l_ij.type(torch.LongTensor).to(device)
#calcoliamo la loss
l = criterion(phi_i, phi_j, l_ij)
#aggiorniamo il global_step
#conterrà il numero di campioni visti durante il training
n = I_i.shape[0] #numero di elementi nel batch
#print("numero elementi nel batch ",n)
global_step += n
if mode == 'train':
l.backward()
optimizer.step()
optimizer.zero_grad()
acc = accuracy_score(np.array(labs),
np.array(etichette_predette))
n = batch[0].shape[0]
loss_meter.add(l.item(), n)
acc_meter.add(acc, n)
#loggiamo i risultati iterazione per iterazione solo durante il training
if mode == 'train':
writer.add_scalar('loss/train',
loss_meter.value(),
global_step=global_step)
writer.add_scalar('accuracy/train',
acc_meter.value(),
global_step=global_step)
#una volta finita l'epoca (sia nel caso di training che test, loggiamo le stime finali)
if mode == 'train':
global_step_train = global_step
last_loss_train = loss_meter.value()
last_acc_train = acc_meter.value()
array_accuracy_train.append(acc_meter.value())
array_loss_train.append(loss_meter.value())
array_glb_train.append(global_step)
else:
global_step_val = global_step
last_loss_val = loss_meter.value()
last_acc_val = acc_meter.value()
array_accuracy_valid.append(acc_meter.value())
array_loss_valid.append(loss_meter.value())
array_glb_valid.append(global_step)
writer.add_scalar('loss/' + mode,
loss_meter.value(),
global_step=global_step)
writer.add_scalar('accuracy/' + mode,
acc_meter.value(),
global_step=global_step)
print("Loss TRAIN", array_loss_train)
print("Losss VALID", array_loss_valid)
print("Accuracy TRAIN", array_accuracy_train)
print("Accuracy VALID", array_accuracy_valid)
print("dim acc train", len(array_accuracy_train))
print("dim acc valid", len(array_accuracy_valid))
plt.figure(figsize=(12, 8))
plt.plot(array_glb_train, array_accuracy_train)
plt.plot(array_glb_valid, array_accuracy_valid)
plt.xlabel('samples')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['Training', 'Valid'])
plt.savefig(directory + '//plotAccuracy_' + version + '.png')
plt.show()
plt.figure(figsize=(12, 8))
plt.plot(array_glb_train, array_loss_train)
plt.plot(array_glb_valid, array_loss_valid)
plt.xlabel('samples')
plt.ylabel('loss')
plt.grid()
plt.legend(['Training', 'Valid'])
plt.savefig(directory + '//plotLoss_' + version + '.png')
plt.show()
saveArray(directory, version, array_loss_train, array_loss_valid,
array_accuracy_train, array_accuracy_valid, array_glb_train,
array_glb_valid)
saveinFileJson(start, directory, version, resize, batch_size, e, lr,
momentum, len(train_loader), array_accuracy_train[-1],
array_accuracy_valid[-1], array_loss_train[-1],
array_loss_valid[-1])
#writer.add_embedding(phi_i, batch[3], I_i, global_step=global_step, tag=exp_name+'_embedding')
#conserviamo i pesi del modello alla fine di un ciclo di training e test
net_save(epochs, model, optimizer, last_loss_train, last_loss_val,
last_acc_train, last_acc_val, global_step_train,
global_step_val, '%s.pth' % (exp_name + "_dict"))
torch.save(model, '%s.pth' % exp_name)
torch.save(
model, directory + "//" + version + "//" + '%s.pth' %
(exp_name + "_" + str(e)))
f = '{:.7f}'.format(tempo.stop())
return model, f, last_loss_train, last_loss_val, last_acc_train, last_acc_val
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, G: nn.Module,
F1: ImageClassifierHead, F2: ImageClassifierHead, optimizer_g: SGD,
optimizer_f: SGD, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':3.1f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':3.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
G.train()
F1.train()
F2.train()
end = time.time()
for i in range(args.iters_per_epoch):
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
labels_t = labels_t.to(device)
x = torch.cat((x_s, x_t), dim=0)
assert x.requires_grad is False
# measure data loading time
data_time.update(time.time() - end)
# Step A train all networks to minimize loss on source domain
optimizer_g.zero_grad()
optimizer_f.zero_grad()
g = G(x)
y_1 = F1(g)
y_2 = F2(g)
y1_s, y1_t = y_1.chunk(2, dim=0)
y2_s, y2_t = y_2.chunk(2, dim=0)
y1_t, y2_t = F.softmax(y1_t, dim=1), F.softmax(y2_t, dim=1)
loss = F.cross_entropy(y1_s, labels_s) + F.cross_entropy(y2_s, labels_s) + \
0.01 * (entropy(y1_t) + entropy(y2_t))
loss.backward()
optimizer_g.step()
optimizer_f.step()
# Step B train classifier to maximize discrepancy
optimizer_g.zero_grad()
optimizer_f.zero_grad()
g = G(x)
y_1 = F1(g)
y_2 = F2(g)
y1_s, y1_t = y_1.chunk(2, dim=0)
y2_s, y2_t = y_2.chunk(2, dim=0)
y1_t, y2_t = F.softmax(y1_t, dim=1), F.softmax(y2_t, dim=1)
loss = F.cross_entropy(y1_s, labels_s) + F.cross_entropy(y2_s, labels_s) + \
0.01 * (entropy(y1_t) + entropy(y2_t)) - classifier_discrepancy(y1_t, y2_t) * args.trade_off
loss.backward()
optimizer_f.step()
# Step C train genrator to minimize discrepancy
for k in range(args.num_k):
optimizer_g.zero_grad()
g = G(x)
y_1 = F1(g)
y_2 = F2(g)
y1_s, y1_t = y_1.chunk(2, dim=0)
y2_s, y2_t = y_2.chunk(2, dim=0)
y1_t, y2_t = F.softmax(y1_t, dim=1), F.softmax(y2_t, dim=1)
mcd_loss = classifier_discrepancy(y1_t, y2_t) * args.trade_off
mcd_loss.backward()
optimizer_g.step()
cls_acc = accuracy(y1_s, labels_s)[0]
tgt_acc = accuracy(y1_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(mcd_loss.item(), x_s.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def train(opt):
from tensorboardX import SummaryWriter
writer = SummaryWriter(path_output)
source1, source2, source3, target = taskSelect(opt.target)
dataset_s1 = dataset.DA(dir=root, name=source1, img_size=(224, 224), train=True)
dataset_s2 = dataset.DA(dir=root, name=source2, img_size=(224, 224), train=True)
dataset_s3 = dataset.DA(dir=root, name=source3, img_size=(224, 224), train=True)
dataset_t = dataset.DA(dir=root, name=target, img_size=(224, 224), train=True)
dataset_tt = dataset.DA(dir=root, name=target, img_size=(224,224), train=False,real_val=False)
dataloader_s1 = DataLoader(dataset_s1, batch_size=opt.bs, shuffle=True, num_workers=2)
dataloader_s2 = DataLoader(dataset_s2, batch_size=opt.bs, shuffle=True, num_workers=2)
dataloader_s3 = DataLoader(dataset_s3, batch_size=opt.bs, shuffle=True, num_workers=2)
dataloader_t = DataLoader(dataset_t, batch_size=opt.bs, shuffle=True, num_workers=2)
dataloader_tt = DataLoader(dataset_tt, batch_size=opt.bs, shuffle=False, num_workers=2)
# dataset_s1 = dataset.DA(dir=root, name=source1, img_size=(224, 224), train=True)
# dataset_s2 = dataset.DA(dir=root, name=source2, img_size=(224, 224), train=True)
# dataset_s3 = dataset.DA(dir=root, name=source3, img_size=(224, 224), train=True)
# dataset_t = dataset.DA(dir=root, name=target, img_size=(224, 224), train=True)
# if target == 'real':
# tmp = os.path.join(root, 'test')
# dataset_tt = dataset.DA_test(dir=tmp, img_size=(224,224))
# else:
# dataset_tt = dataset.DA(dir=root, name=target, img_size=(224, 224), train=False)
# dataloader_s1 = DataLoader(dataset_s1, batch_size=opt.bs, shuffle=True, num_workers=2)
# dataloader_s2 = DataLoader(dataset_s2, batch_size=opt.bs, shuffle=True, num_workers=2)
# dataloader_s3 = DataLoader(dataset_s3, batch_size=opt.bs, shuffle=True, num_workers=2)
# dataloader_t = DataLoader(dataset_t, batch_size=opt.bs, shuffle=True, num_workers=2)
# dataloader_tt = DataLoader(dataset_tt, batch_size=opt.bs, shuffle=False, num_workers=2)
len_data = min(len(dataset_s1), len(dataset_s2), len(dataset_s3), len(dataset_t)) # length of "shorter" domain
len_bs = min(len(dataloader_s1), len(dataloader_s2), len(dataloader_s3), len(dataloader_t))
# Define networks
feature_extractor = models.feature_extractor()
classifier_1 = models.class_classifier()
classifier_2 = models.class_classifier()
classifier_3 = models.class_classifier()
classifier_1_ = models.class_classifier()
classifier_2_ = models.class_classifier()
classifier_3_ = models.class_classifier()
# if torch.cuda.is_available():
feature_extractor = feature_extractor.to(device)
classifier_1 = classifier_1.to(device).apply(weight_init)
classifier_2 = classifier_2.to(device).apply(weight_init)
classifier_3 = classifier_3.to(device).apply(weight_init)
classifier_1_ = classifier_1_.to(device).apply(weight_init)
classifier_2_ = classifier_2_.to(device).apply(weight_init)
classifier_3_ = classifier_3_.to(device).apply(weight_init)
# Define loss
mom_loss = momentumLoss()
cl_loss = nn.CrossEntropyLoss()
disc_loss = discrepancyLoss()
# Optimizers
# Change the LR
optimizer_features = SGD(feature_extractor.parameters(), lr=0.0001,momentum=0.9,weight_decay=5e-4)
optimizer_classifier = SGD(([{'params': classifier_1.parameters()},
{'params': classifier_2.parameters()},
{'params': classifier_3.parameters()}]), lr=0.002,momentum=0.9,weight_decay=5e-4)
optimizer_classifier_ = SGD(([{'params': classifier_1_.parameters()},
{'params': classifier_2_.parameters()},
{'params': classifier_3_.parameters()}]), lr=0.002,momentum=0.9,weight_decay=5e-4)
# optimizer_features = SGD(feature_extractor.parameters(), lr=0.0001)
# optimizer_classifier = Adam(([{'params': classifier_1.parameters()},
# {'params': classifier_2.parameters()},
# {'params': classifier_3.parameters()}]), lr=0.002)
# optimizer_classifier_ = Adam(([{'params': classifier_1_.parameters()},
# {'params': classifier_2_.parameters()},
# {'params': classifier_3_.parameters()}]), lr=0.002)
if opt.pretrain is not None:
state = torch.load(opt.pretrain)
feature_extractor.load_state_dict(state['feature_extractor'])
classifier_1.load_state_dict(state['{}_classifier'.format(source1)])
classifier_2.load_state_dict(state['{}_classifier'.format(source2)])
classifier_3.load_state_dict(state['{}_classifier'.format(source3)])
classifier_1_.load_state_dict(state['{}_classifier_'.format(source1)])
classifier_2_.load_state_dict(state['{}_classifier_'.format(source2)])
classifier_3_.load_state_dict(state['{}_classifier_'.format(source3)])
# Lists
train_loss = []
acc_on_target = []
tot_loss, tot_clf_loss, tot_mom_loss, tot_s2_loss, tot_s3_loss = 0.0, 0.0, 0.0, 0.0, 0.0
n_samples, iteration = 0, 0
tot_correct = [0, 0, 0, 0, 0, 0]
saved_time = time.time()
feature_extractor.train()
classifier_1.train(), classifier_2.train(), classifier_3.train()
classifier_1_.train(), classifier_2_.train(), classifier_3_.train()
for epoch in range(opt.ep):
if epoch+1 == 5:
optimizer_classifier = SGD(([{'params': classifier_1.parameters()},
{'params': classifier_2.parameters()},
{'params': classifier_3.parameters()}]), lr=0.001,momentum=0.9,weight_decay=5e-4)
optimizer_classifier_ = SGD(([{'params': classifier_1_.parameters()},
{'params': classifier_2_.parameters()},
{'params': classifier_3_.parameters()}]), lr=0.001,momentum=0.9,weight_decay=5e-4)
if epoch+1 == 10:
optimizer_classifier = SGD(([{'params': classifier_1.parameters()},
{'params': classifier_2.parameters()},
{'params': classifier_3.parameters()}]), lr=0.0001,momentum=0.9,weight_decay=5e-4)
optimizer_classifier_ = SGD(([{'params': classifier_1_.parameters()},
{'params': classifier_2_.parameters()},
{'params': classifier_3_.parameters()}]), lr=0.0001,momentum=0.9,weight_decay=5e-4)
for i, (data_1, data_2, data_3, data_t) in enumerate(zip(dataloader_s1, dataloader_s2, dataloader_s3, dataloader_t)):
img1, lb1 = data_1
img2, lb2 = data_2
img3, lb3 = data_3
imgt, _ = data_t
# Prepare data
cur_batch = min(img1.shape[0], img2.shape[0], img3.shape[0], imgt.shape[0])
# print(i, cur_batch)
img1, lb1 = Variable(img1[0:cur_batch,:,:,:]).to(device), Variable(lb1[0:cur_batch]).to(device)
img2, lb2 = Variable(img2[0:cur_batch,:,:,:]).to(device), Variable(lb2[0:cur_batch]).to(device)
img3, lb3 = Variable(img3[0:cur_batch,:,:,:]).to(device), Variable(lb3[0:cur_batch]).to(device)
imgt = Variable(imgt[0:cur_batch,:,:,:]).to(device)
### STEP 1 ### train G and C pairs
# Forward
optimizer_features.zero_grad()
optimizer_classifier.zero_grad()
optimizer_classifier_.zero_grad()
# Extract Features
ft1 = feature_extractor(img1)
ft2 = feature_extractor(img2)
ft3 = feature_extractor(img3)
ft_t = feature_extractor(imgt)
# Class Prediction [bs, 345]
cl1, cl1_ = classifier_1(ft1), classifier_1_(ft1)
cl2, cl2_ = classifier_2(ft2), classifier_2_(ft2)
cl3, cl3_ = classifier_3(ft3), classifier_3_(ft3)
# Compute "momentum loss"
loss_mom = mom_loss(ft1, ft2, ft3, ft_t)
# Cross entropy loss
l1, l1_ = cl_loss(cl1, lb1), cl_loss(cl1_, lb1)
l2, l2_ = cl_loss(cl2, lb2), cl_loss(cl2_, lb2)
l3, l3_ = cl_loss(cl3, lb3), cl_loss(cl3_, lb3)
# total loss
s1loss = l1 + l2 + l3 + l1_ + l2_ + l3_ + opt.alpha * loss_mom
s1loss.backward()
optimizer_features.step()
optimizer_classifier.step()
optimizer_classifier_.step()
### STEP 2 ### fix G, and train C pairs
optimizer_classifier.zero_grad()
optimizer_classifier_.zero_grad()
# Class Prediction on each src domain
cl1, cl1_ = classifier_1(ft1.detach()), classifier_1_(ft1.detach())
cl2, cl2_ = classifier_2(ft2.detach()), classifier_2_(ft2.detach())
cl3, cl3_ = classifier_3(ft3.detach()), classifier_3_(ft3.detach())
# discrepancy on tgt domain
clt1, clt1_ = classifier_1(ft_t.detach()), classifier_1_(ft_t.detach())
clt2, clt2_ = classifier_2(ft_t.detach()), classifier_2_(ft_t.detach())
clt3, clt3_ = classifier_3(ft_t.detach()), classifier_3_(ft_t.detach())
# classification loss
l1, l1_ = cl_loss(cl1, lb1), cl_loss(cl1_, lb1)
l2, l2_ = cl_loss(cl2, lb2), cl_loss(cl2_, lb2)
l3, l3_ = cl_loss(cl3, lb3), cl_loss(cl3_, lb3)
# print(clt1.shape)
dl1 = disc_loss(clt1, clt1_)
dl2 = disc_loss(clt2, clt2_)
dl3 = disc_loss(clt3, clt3_)
# print(dl1, dl2, dl3)
# backward
s2loss = l1 + l2 + l3 + l1_ + l2_ + l3_ - dl1 - dl2 - dl3
s2loss.backward()
optimizer_classifier.step()
optimizer_classifier_.step()
### STEP 3 #### fix C pairs, train G
optimizer_features.zero_grad()
ft_t = feature_extractor(imgt)
clt1, clt1_ = classifier_1(ft_t), classifier_1_(ft_t)
clt2, clt2_ = classifier_2(ft_t), classifier_2_(ft_t)
clt3, clt3_ = classifier_3(ft_t), classifier_3_(ft_t)
dl1 = disc_loss(clt1, clt1_)
dl2 = disc_loss(clt2, clt2_)
dl3 = disc_loss(clt3, clt3_)
s3loss = dl1 + dl2 + dl3
s3loss.backward()
optimizer_features.step()
pred = torch.stack((cl1, cl2, cl3, cl1_, cl2_, cl3_), 0) # [6, bs, 345]
_, pred = torch.max(pred, dim = 2) # [6, bs]
gt = torch.stack((lb1, lb2, lb3, lb1, lb2, lb3), 0) # [6, bs]
correct = pred.eq(gt.data)
correct = torch.mean(correct.type(torch.FloatTensor), dim = 1).cpu().numpy()
tot_loss += s1loss.item() * cur_batch
tot_clf_loss += (s1loss.item() - opt.alpha * loss_mom.item()) * cur_batch
tot_s2_loss += s2loss.item() * cur_batch
tot_s3_loss += s3loss.item() * cur_batch
tot_mom_loss += loss_mom.item() * cur_batch
tot_correct += correct * cur_batch
n_samples += cur_batch
# print(cur_batch)
if iteration % opt.log_interval == 0:
current_time = time.time()
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tClfLoss: {:.4f}\tMMLoss: {:.4f}\t \
S2Loss: {:.4f}\tS3Loss: {:.4f}\t \
Accu: {:.4f}\\{:.4f}\\{:.4f}\\{:.4f}\\{:.4f}\\{:.4f}\tTime: {:.3f}'.format(\
epoch, i * opt.bs, len_data, 100. * i / len_bs, \
tot_clf_loss / n_samples,
tot_mom_loss / n_samples,
tot_s2_loss / n_samples,
tot_s3_loss / n_samples,
tot_correct[0] / n_samples,
tot_correct[1] / n_samples,
tot_correct[2] / n_samples,
tot_correct[3] / n_samples,
tot_correct[4] / n_samples,
tot_correct[5] / n_samples,
current_time - saved_time))
writer.add_scalar('Train/ClfLoss', tot_clf_loss / n_samples, iteration * opt.bs)
writer.add_scalar('Train/MMLoss', tot_mom_loss / n_samples, iteration * opt.bs)
writer.add_scalar('Train/s2Loss', tot_s2_loss / n_samples, iteration * opt.bs)
writer.add_scalar('Train/s3Loss', tot_s3_loss / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu0', tot_correct[0] / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu1', tot_correct[1] / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu2', tot_correct[2] / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu0_', tot_correct[3] / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu1_', tot_correct[4] / n_samples, iteration * opt.bs)
writer.add_scalar('Train/Accu2_', tot_correct[5] / n_samples, iteration * opt.bs)
saved_weight = torch.FloatTensor([tot_correct[0], tot_correct[1], tot_correct[2], tot_correct[3], tot_correct[4], tot_correct[5]]).to(device)
if torch.sum(saved_weight) == 0.:
saved_weight = torch.FloatTensor(6).to(device).fill_(1)/6.
else:
saved_weight = saved_weight/torch.sum(saved_weight)
saved_time = time.time()
tot_clf_loss, tot_mom_loss, tot_correct, n_samples = 0, 0, [0, 0, 0, 0, 0, 0], 0
tot_s2_loss, tot_s3_loss = 0, 0
train_loss.append(tot_loss)
# evaluation and save
if iteration % opt.eval_interval == 0 and iteration >= 0 and target != 'real':
print('weight = ', saved_weight.cpu().numpy())
evalacc = eval(saved_weight, feature_extractor, classifier_1_, classifier_2_, classifier_3_,
classifier_1, classifier_2, classifier_3, dataloader_tt)
writer.add_scalar('Test/Accu', evalacc, iteration * opt.bs)
acc_on_target.append(evalacc)
print('Eval Acc = {:.2f}\n'.format(evalacc*100))
torch.save({
'epoch': epoch,
'feature_extractor': feature_extractor.state_dict(),
'{}_classifier'.format(source1): classifier_1.state_dict(),
'{}_classifier'.format(source2): classifier_2.state_dict(),
'{}_classifier'.format(source3): classifier_3.state_dict(),
'{}_classifier_'.format(source1): classifier_1_.state_dict(),
'{}_classifier_'.format(source2): classifier_2_.state_dict(),
'{}_classifier_'.format(source3): classifier_3_.state_dict(),
'features_optimizer': optimizer_features.state_dict(),
'classifier_optimizer': optimizer_classifier.state_dict(),
'loss': tot_loss,
'saved_weight': saved_weight
}, os.path.join(path_output, target + '-{}-{:.2f}.pth'.format(epoch, evalacc*100)))
iteration += 1
pkl.dump(train_loss, open('{}train_loss.p'.format(path_output), 'wb'))
if target != 'real':
pkl.dump(acc_on_target, open('{}target_accuracy.p'.format(path_output), 'wb'))
def main():
training_size = 1 #10000
valid_size = 1 #1000
test_size = 15 #1000
epochs_num = 1 #1000
hidden_size = 60 #5
batch_size = 1 #100
data_length = 60
train_x, train_t = mkDataSet(training_size)
valid_x, valid_t = mkDataSet(valid_size)
#print(valid_t)
model = Predictor(2, hidden_size, 2)
criterion = nn.MSELoss()
optimizer = SGD(model.parameters(), lr=0.01)
for epoch in range(epochs_num):
# training
running_loss = 0.0
training_accuracy = 0.0
for i in range(int(training_size / batch_size)):
optimizer.zero_grad() # 勾配の初期化
data, label = mkRandomBatch(train_x, train_t, batch_size)
output = model(data) # 順伝播
loss = criterion(output, label) # ロスの計算
loss.backward() # 勾配の計算
optimizer.step() # パラメータの更新
running_loss += loss.item()
#training_accuracy += np.sum(np.abs((output.data - label.data).numpy()) < 0.1)
#print(label.data)
training_accuracy = mean_squared_error(np.ravel(output.data),
np.ravel(
label.data)) #MSEで誤差算出
#print('MSE Train : %.3f' % training_accuracy)
#valid
test_accuracy = 0.0
for i in range(int(valid_size / batch_size)):
offset = i * batch_size
data, label = torch.FloatTensor(
valid_x[offset:offset + batch_size]), torch.FloatTensor(
valid_t[offset:offset + batch_size])
output = model(data, None)
#test_accuracy += np.sum(np.abs((output.data - label.data).numpy()) < 10)
test_accuracy = mean_squared_error(np.ravel(output.data),
np.ravel(label.data))
#print(output.data)
#print(label.data)
#training_accuracy /= training_size
#test_accuracy /= valid_size
print('%d loss: %.3f, training_accuracy: %.5f, valid_accuracy: %.5f' %
(epoch + 1, running_loss, training_accuracy, test_accuracy))
#test
test_accuracy = 0.0
test_x, test_t = mkTestSet(test_size)
result = []
process = []
for i in range(int(
test_size /
batch_size)): #testではlabelが正解なので、output.data(出力)とlabel.dataを比較する
offset = i * batch_size
data, label = torch.FloatTensor(
test_x[offset:offset + batch_size]), torch.FloatTensor(
test_t[offset:offset + batch_size])
output = model(data, None)
test_accuracy = mean_squared_error(np.ravel(output.data),
np.ravel(label.data))
process = output.data.numpy().flatten()
result.append(process)
print('%d loss: %.3f, training_accuracy: %.5f, test_accuracy: %.5f' %
(epoch + 1, running_loss, training_accuracy, test_accuracy))
#print(test_x)
#print(type(result))
#print(result)
#print(result)
#data_np=result.numpy()
data_np = np.asarray(result).flatten()
#data_np[data_np % 2 == 0]=(data_np[data_np%2==0]+1)*1280/2
print(len(data_np))
print(data_np)
data_np = np.resize(data_np, (test_size * data_length, 2))
#print(data_np[:,([0]+1)*1280/2])
#submission = pd.Series(data_np) #name=['x','y'])
#submission.to_csv("C:\\Users\\010170243\\work\\seq2seq\\dataset\\kusakaGomiToCSV\\all\\kusaka_result.csv", header=True, index_label='id')
np.savetxt(
"C:\\Users\\010170243\\work\\seq2seq\\dataset\\kusakaGomiToCSV\\all\\kusaka_result.csv", # ファイル名
X=data_np, # 保存したい配列
delimiter=",",
fmt='%.15f',
header="x,y", # 区切り文字
)
class BaseModel(object):
def __init__(self, n_ent, n_rel, args, struct):
self.model = KGEModule(n_ent, n_rel, args, struct)
self.model.cuda()
self.n_ent = n_ent
self.n_rel = n_rel
self.time_tot = 0
self.args = args
def train(self, train_data, tester_val, tester_tst):
head, tail, rela = train_data
# useful information related to cache
n_train = len(head)
if self.args.optim == 'adam' or self.args.optim == 'Adam':
self.optimizer = Adam(self.model.parameters(), lr=self.args.lr)
elif self.args.optim == 'adagrad' or self.args.optim == 'Adagrad':
self.optimizer = Adagrad(self.model.parameters(), lr=self.args.lr)
else:
self.optimizer = SGD(self.model.parameters(), lr=self.args.lr)
scheduler = ExponentialLR(self.optimizer, self.args.decay_rate)
n_epoch = self.args.n_epoch
n_batch = self.args.n_batch
best_mrr = 0
# used for counting repeated triplets for margin based loss
for epoch in range(n_epoch):
start = time.time()
self.epoch = epoch
rand_idx = torch.randperm(n_train)
head = head[rand_idx].cuda()
tail = tail[rand_idx].cuda()
rela = rela[rand_idx].cuda()
epoch_loss = 0
for h, t, r in batch_by_size(n_batch,
head,
tail,
rela,
n_sample=n_train):
self.model.zero_grad()
loss = self.model.forward(h, t, r)
loss += self.args.lamb * self.model.regul
loss.backward()
self.optimizer.step()
self.prox_operator()
epoch_loss += loss.data.cpu().numpy()
self.time_tot += time.time() - start
scheduler.step()
if (epoch + 1) % self.args.epoch_per_test == 0:
# output performance
valid_mrr, valid_mr, valid_10 = tester_val()
test_mrr, test_mr, test_10 = tester_tst()
out_str = '%.4f\t%.4f\t\t%.4f\t%.4f\n' % (valid_mrr, valid_10,
test_mrr, test_10)
# output the best performance info
if valid_mrr > best_mrr:
best_mrr = valid_mrr
best_str = out_str
if best_mrr < self.args.thres:
print(
'\tearly stopped in Epoch:{}, best_mrr:{}'.format(
epoch + 1, best_mrr), self.model.struct)
return best_mrr, best_str
return best_mrr, best_str
def prox_operator(self, ):
for n, p in self.model.named_parameters():
if 'ent' in n:
X = p.data.clone()
Z = torch.norm(X, p=2, dim=1, keepdim=True)
Z[Z < 1] = 1
X = X / Z
p.data.copy_(X.view(self.n_ent, -1))
def test_link(self, test_data, head_filter, tail_filter):
heads, tails, relas = test_data
batch_size = self.args.test_batch_size
num_batch = len(heads) // batch_size + int(len(heads) % batch_size > 0)
head_probs = []
tail_probs = []
for i in range(num_batch):
start = i * batch_size
end = min((i + 1) * batch_size, len(heads))
batch_h = heads[start:end].cuda()
batch_t = tails[start:end].cuda()
batch_r = relas[start:end].cuda()
h_embed = self.model.ent_embed(batch_h)
r_embed = self.model.rel_embed(batch_r)
t_embed = self.model.ent_embed(batch_t)
head_scores = torch.sigmoid(self.model.test_head(r_embed,
t_embed)).data
tail_scores = torch.sigmoid(self.model.test_tail(h_embed,
r_embed)).data
head_probs.append(head_scores.data.cpu().numpy())
tail_probs.append(tail_scores.data.cpu().numpy())
head_probs = np.concatenate(head_probs) * head_filter
tail_probs = np.concatenate(tail_probs) * tail_filter
head_ranks = cal_ranks(head_probs, label=heads.data.numpy())
tail_ranks = cal_ranks(tail_probs, label=tails.data.numpy())
h_mrr, h_mr, h_h10 = cal_performance(head_ranks)
t_mrr, t_mr, t_h10 = cal_performance(tail_ranks)
mrr = (h_mrr + t_mrr) / 2
mr = (h_mr + t_mr) / 2
h10 = (h_h10 + t_h10) / 2
return mrr, mr, h10
def train(train_source_iter: ForeverDataIterator, train_target_iter: ForeverDataIterator, model: ImageClassifier,
jmmd_loss: JointMultipleKernelMaximumMeanDiscrepancy, optimizer: SGD,
lr_sheduler: StepwiseLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':4.2f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':5.4f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
jmmd_loss.train()
end = time.time()
for i in range(args.iters_per_epoch):
lr_sheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
labels_t = labels_t.to(device)
# compute output
x = torch.cat((x_s, x_t), dim=0)
y, f = model(x)
y_s, y_t = y.chunk(2, dim=0)
f_s, f_t = f.chunk(2, dim=0)
cls_loss = F.cross_entropy(y_s, labels_s)
transfer_loss = jmmd_loss(
(f_s, F.softmax(y_s, dim=1)),
(f_t, F.softmax(y_t, dim=1))
)
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
tgt_acc = accuracy(y_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(transfer_loss.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--cuda', action='store_true', help='using GPU')
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.1,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("NER")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
embedding = args.embedding
embedding_path = args.embedding_dict
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("data/alphabets/ner/", train_path, data_paths=[dev_path, test_path],
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
device = torch.device('cuda') if args.cuda else torch.device('cpu')
data_train = conll03_data.read_data_to_tensor(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
device=device)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_tensor(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
device=device)
data_test = conll03_data.read_data_to_tensor(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
device=device)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform_
if args.dropout == 'std':
network = BiRecurrentConv(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
initializer=initializer)
else:
network = BiVarRecurrentConv(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
initializer=initializer)
network = network.to(device)
lr = learning_rate
# optim = Adam(network.parameters(), lr=lr, betas=(0.9, 0.9), weight_decay=gamma)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d" %
(mode, num_layers, hidden_size, num_filters, tag_space))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data / batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_corr = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_tensor(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss, corr, _ = network.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
loss.backward()
optim.step()
with torch.no_grad():
num_tokens = masks.sum()
train_err += loss * num_tokens
train_corr += corr
train_total += num_tokens
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, acc: %.2f%%, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total,
train_corr * 100 / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, acc: %.2f%%, time: %.2fs' %
(num_batches, train_err / train_total,
train_corr * 100 / train_total, time.time() - start_time))
# evaluate performance on dev data
with torch.no_grad():
network.eval()
tmp_filename = 'tmp/%s_dev%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_dev, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
_, _, preds = network.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
pos.cpu().numpy(),
chunk.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_test, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
_, _, preds = network.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
pos.cpu().numpy(),
chunk.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename)
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def train(
cfg,
img_size=416,
resume=False,
epochs=273, # 500200 batches at bs 64, dataset length 117263
batch_size=16,
accumulate=1,
multi_scale=False,
freeze_backbone=False,
num_workers=4,
transfer=False # Transfer learning (train only YOLO layers)
):
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
if multi_scale:
img_size = 608 # initiate with maximum multi_scale size
else:
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Initialize model
model = Darknet(cfg, img_size).to(device)
# Optimizer
lr0 = 0.001 # initial learning rate
optimizer = SGD(model.parameters(),
lr=lr0,
momentum=0.9,
weight_decay=0.0005)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
yl = get_yolo_layers(model) # yolo layers
nf = int(model.module_defs[yl[0] -
1]['filters']) # yolo layer size (i.e. 255)
if resume: # Load previously saved model
if transfer: # Transfer learning
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict(
{
k: v
for k, v in chkpt['model'].items()
if v.numel() > 1 and v.shape[0] != 255
},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from latest.pt
chkpt = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_loss = chkpt['best_loss']
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny.cfg' in cfg:
cutoff = load_darknet_weights(model,
weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# multi gpus
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model,
device_ids=list(
range(torch.cuda.device_count())))
# Set scheduler
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[20, 40],
gamma=0.1,
last_epoch=start_epoch - 1)
# Dataset
# train_dataset = VOCDetection(root=os.path.join('~', 'data', 'VOCdevkit'), img_size=img_size, mode='train')
train_dataset = DFSignDetection(root=os.path.join('~', 'data', 'dfsign',
'dfsign_chip_voc'),
img_size=img_size,
mode='train')
# Dataloader
dataloader = DataLoader(train_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True,
pin_memory=True,
collate_fn=train_dataset.collate_fn)
# Start training
t = time.time()
# model_info(model)
nB = len(dataloader)
n_burnin = nB # burn-in batches
for epoch in range(start_epoch, epochs):
model.train()
print(
('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf',
'cls', 'total', 'nTargets', 'time'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
mloss = defaultdict(float) # mean loss
for i, (imgs, targets, _, paths) in enumerate(dataloader):
imgs = imgs.to(device)
targets = targets.to(device)
nt = len(targets)
if nt == 0: # if no targets continue
continue
# SGD burn-in
if epoch == 0 and i <= n_burnin:
lr = lr0 * (i / n_burnin)**4
for x in optimizer.param_groups:
x['lr'] = lr
optimizer.zero_grad()
# Run model
pred = model(imgs)
# Build targets
target_list = build_targets(model, targets)
# Compute loss
loss, loss_dict = compute_loss(pred, target_list)
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nB:
optimizer.step()
# Running epoch-means of tracked metrics
for key, val in loss_dict.items():
mloss[key] = (mloss[key] * i + val) / (i + 1)
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1), '%g/%g' %
(i, nB - 1), mloss['xy'], mloss['wh'], mloss['conf'],
mloss['cls'], mloss['total'], nt, time.time() - t)
t = time.time()
if i % 30 == 0:
print(s)
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
dataset.img_size = random.choice(range(10, 20)) * 32
print('multi_scale img_size = %g' % dataset.img_size)
# Update best loss
if mloss['total'] < best_loss:
best_loss = mloss['total']
# Save latest checkpoint
checkpoint = {
'epoch':
epoch,
'best_loss':
best_loss,
'model':
model.module.state_dict()
if type(model) is nn.parallel.DataParallel else model.state_dict(),
'optimizer':
optimizer.state_dict()
}
if epoch % 5 == 0:
torch.save(checkpoint, 'weights/epoch_tt100k_%03d.pt' % epoch)
# if epoch > 9 and epoch % 10 == 0:
if False:
with torch.no_grad():
APs, mAP = test.test(cfg,
weights=None,
batch_size=32,
img_size=img_size,
model=model)
pprint(APs)
print(mAP)
del checkpoint
class GoogLeNet(nn.Module):
@staticmethod
def init_weights(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
truncated_normal_(m.weight)
# nn.init.kaiming_normal_(
# tensor=m.weight,
# mode='fan_out',
# nonlinearity='relu'
# )
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self, num_classes: int, enable_aux=False, conv_type=None):
super(GoogLeNet, self).__init__()
self.criterion = None
self.optimizer = None
self.scheduler = None
self.enable_aux = enable_aux
# Input size: 224x224x3 (RGB color space w/ zero mean)
# Kernel size: 7x7
# Padding size: 3x3
# Stride = 2
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((224 - 7 + 2*3)/2) + 1 = 112
#
# Output size: 112x112x64
self.conv1 = nn.Conv2d(
in_channels=3, out_channels=64,
kernel_size=(7, 7), stride=2, padding=(3, 3)
)
# Input size: 112x112x64
# Kernel size: 3x3
# Padding size: 1x1
# Stride: 2
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((112 - 3 + 1*2)/2) + 1 = 56
#
# Output size: 56x56x64
self.maxPooling1 = nn.MaxPool2d(
kernel_size=(3, 3), stride=2, padding=(1, 1)
)
# Input size: 56x56x64
# Kernel size: 1x1
# Padding size: 0
# Stride: 1
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((56 - 1 + 0)/1) + 1 = 56
#
# Output size: 56x56x64
self.conv2_1 = nn.Conv2d(
in_channels=64, out_channels=64,
kernel_size=(1, 1), stride=1, padding=0
)
# Input size: 56x56x64
# Kernel size: 3x3
# Padding size: 1x1
# Stride: 1
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((56 - 3 + 1*2)/1) + 1 = 56
#
# Output size: 56x56x192
self.conv2_2 = nn.Conv2d(
in_channels=64, out_channels=192,
kernel_size=(3, 3), stride=1, padding=(1, 1)
)
# Input size: 56x56x192
# Kernel size: 3x3
# Padding size: 1x1
# Stride: 2
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((56 - 3 + 1*2)/2) + 1 = 28
#
# Output size: 28x28x192
self.maxPooling2 = nn.MaxPool2d(
kernel_size=(3, 3), stride=2, padding=(1, 1)
)
self.inception_3a = Inception(
in_channels=192,
ch_1x1=64, ch_3x3_reduce=96, ch_3x3=128, ch_5x5_reduce=16, ch_5x5=32, pool_proj=32,
conv_type=conv_type
)
self.inception_3b = Inception(
in_channels=256,
ch_1x1=128, ch_3x3_reduce=128, ch_3x3=192, ch_5x5_reduce=32, ch_5x5=96, pool_proj=64,
conv_type=conv_type
)
# Input size: 28x28x480
# Kernel size: 3x3
# Padding size: 1x1
# Stride: 2
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((28 - 3 + 1*2)/2) + 1 = 14
#
# Output size: 14x14x480
self.maxPooling3 = nn.MaxPool2d(
kernel_size=(3, 3), stride=2, padding=(1, 1)
)
self.inception_4a = Inception(
in_channels=480,
ch_1x1=192, ch_3x3_reduce=96, ch_3x3=208, ch_5x5_reduce=16, ch_5x5=48, pool_proj=64,
conv_type=conv_type
)
self.inception_4b = Inception(
in_channels=512,
ch_1x1=160, ch_3x3_reduce=112, ch_3x3=224, ch_5x5_reduce=24, ch_5x5=64, pool_proj=64,
conv_type=conv_type
)
self.inception_4c = Inception(
in_channels=512,
ch_1x1=128, ch_3x3_reduce=128, ch_3x3=256, ch_5x5_reduce=24, ch_5x5=64, pool_proj=64,
conv_type=conv_type
)
self.inception_4d = Inception(
in_channels=512,
ch_1x1=112, ch_3x3_reduce=144, ch_3x3=288, ch_5x5_reduce=32, ch_5x5=64, pool_proj=64,
conv_type=conv_type
)
self.inception_4e = Inception(
in_channels=528,
ch_1x1=256, ch_3x3_reduce=160, ch_3x3=320, ch_5x5_reduce=32, ch_5x5=128, pool_proj=128,
conv_type=conv_type
)
# Input size: 14x14x832
# Kernel size: 3x3
# Padding size: 1x1
# Stride: 2
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((14 - 3 + 1*2)/2) + 1 = 7
#
# Output size: 7x7x832
self.maxPooling4 = nn.MaxPool2d(
kernel_size=(3, 3), stride=2, padding=(1, 1)
)
self.inception_5a = Inception(
in_channels=832,
ch_1x1=256, ch_3x3_reduce=160, ch_3x3=320, ch_5x5_reduce=32, ch_5x5=128, pool_proj=128,
conv_type=conv_type
)
self.inception_5b = Inception(
in_channels=832,
ch_1x1=384, ch_3x3_reduce=192, ch_3x3=384, ch_5x5_reduce=48, ch_5x5=128, pool_proj=128,
conv_type=conv_type
)
# Input size: 7x7x1024
# Kernel size: 7x7
# Padding size: 0
# Stride: 1
# floor((n_h - k_h + 2*p_h)/s_h) + 1 = floor((7 - 7 + 0)/2) + 1 = 1
#
# Output size: 1x1x1024
self.avgPooling1 = nn.AvgPool2d(
kernel_size=(3, 3), stride=2, padding=0
)
self.dropout = nn.Dropout(p=0.4, inplace=True)
self.fc = nn.Linear(in_features=1024, out_features=num_classes, bias=True)
if enable_aux:
self.aux1 = AuxInception(in_channels=512, num_classes=num_classes)
self.aux2 = AuxInception(in_channels=528, num_classes=num_classes)
else:
self.aux1 = None
self.aux2 = None
def initialize(self, criterion=None, optimizer=None, scheduler=None, weight_init=None, learning_rate=1e-2) -> None:
if criterion is None:
self.criterion = nn.CrossEntropyLoss()
else:
self.criterion = criterion
if optimizer is None:
self.optimizer = SGD(self.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)
else:
self.optimizer = optimizer
if scheduler is None:
self.scheduler = torch.optim.lr_scheduler.StepLR(
optimizer=self.optimizer,
step_size=4, gamma=0.04
)
else:
self.scheduler = scheduler
if weight_init is None:
for m in self.modules():
m.apply(self.init_weights)
else:
for m in self.modules():
m.apply(weight_init)
def _forward(self, X: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
# input: 224x224x3
X = self.conv1(X)
# input: 112x112x64
X = self.maxPooling1(X)
# input 56x56x64
X = self.conv2_1(X)
X = self.conv2_2(X)
# input: 56x56x192
X = self.maxPooling2(X)
# input: 28x28x192
X = self.inception_3a(X)
# input: 28x28x256
X = self.inception_3b(X)
# input: 28x28x480
X = self.maxPooling3(X)
# input: 14x14x480
X = self.inception_4a(X)
aux1 = self.aux1(X) if (self.aux1 is not None) else None
# input: 14x14x512
X = self.inception_4b(X)
# input: 14x14x512
X = self.inception_4c(X)
# input: 14x14x512
X = self.inception_4d(X)
aux2 = self.aux2(X) if (self.aux1 is not None) else None
# input: 14x14x528
X = self.inception_4e(X)
# input: 14x14x528
X = self.maxPooling4(X)
# input: 7x7x832
X = self.inception_5a(X)
# input: 7x7x832
X = self.inception_5b(X)
# input: 7x7x1024
X = self.avgPooling1(X)
X = torch.flatten(X, 1)
# input: 1x1x1024
X = self.dropout(X)
# input: 1x1x1000
X = self.fc(X)
# output: 1 x 1 x num_classes
return X, aux1, aux2
def forward(self, X:torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]]:
X, aux1, aux2 = self._forward(X)
if self.training and self.enable_aux:
return X, aux2, aux1
else:
return X
def train(self, mode=True, data=None, epochs=10) -> 'GoogLeNet':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.to(device)
if (data is None) and (mode):
raise FileNotFoundError("\"data\" has to be a valid Dataloader object!")
self.training = mode
for m in self.modules():
m.train(mode)
if mode:
running_loss = 0.0
for epoch in range(0, epochs):
for i, datum in enumerate(data, 0):
features, labels = datum[0].to(device), datum[1].to(device)
loss = self.criterion(self(features), labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
running_loss += loss.item()
batch_split = int(len(data.dataset) / data.batch_size / 5)
batch_split = 1 if batch_split < 1 else batch_split
if i % batch_split == batch_split - 1:
if self.verbose:
print(f"[epoch {epoch + 1}, batch {i + 1}] loss: {running_loss / batch_split}")
self.scheduler.step(epoch)
running_loss = 0.0
if self.verbose:
print('Finished Training')
return self
valid_acc = 0.0
model.train()
if epoch in [30, 80]:
lr /= 3.0
for pg in opt.param_groups:
pg['lr'] = lr
for X, y in dataset:
X, y = V(X), V(y)
outputs = model(X)
epoch_acc += accuracy(outputs, y)
y = y.max(1)[1].view(-1).long()
error = loss(outputs, y)
epoch_error += error.data[0]
opt.zero_grad()
error.backward()
opt.step()
print('Train loss', epoch_error / len(dataset))
print('Train accuracy', epoch_acc / (bsz * len(dataset)))
model.eval()
for X, y in validation:
X, y = V(X, volatile=True), V(y, volatile=True)
outputs = model(X)
valid_acc += accuracy(outputs, y)
y = y.max(1)[1].view(-1).long()
error = loss(outputs, y)
valid_error += error.data[0]
print('Valid loss', valid_error / len(validation))
print('Valid accuracy', valid_acc / (bsz * len(validation)))
print('')
# Save model weights
class MaskTrainer(BaseTrainer):
def __init__(self, config):
super(MaskTrainer, self).__init__(config)
self.init_mask()
def init_mask(self):
if self.config.mask_type == 'icon':
project_path = self.config.firelab.paths.project_path
data_dir = os.path.join(project_path, self.config.data_dir)
icon = imread(os.path.join(data_dir, self.config.hp.icon_file_path))
if self.config.hp.get('should_resize_icon', False):
icon = resize(icon, self.config.hp.target_icon_size, mode='constant', anti_aliasing=True)
icon = convert_img_to_binary(icon)
self.mask = make_mask_ternary(icon)
elif self.config.mask_type == 'custom':
self.mask = np.array(self.config.mask)
elif self.config.mask_type == 'square':
self.mask = generate_square_mask(self.config.hp.square_size)
self.mask = make_mask_ternary(self.mask)
elif self.config.mask_type == 'randomly_filled_square':
self.mask = generate_square_mask(self.config.hp.square_size)
self.mask = randomly_fill_square(self.mask, self.config.hp.fill_prob)
self.mask = make_mask_ternary(self.mask)
elif self.config.mask_type == 'square_grid':
self.mask = generate_square_grid_mask(self.config.hp.n_good_cells)
self.mask = make_mask_ternary(self.mask)
else:
raise NotImplementedError('Mask type %s is not supported' % self.config.mask_type)
def init_dataloaders(self):
dataset = self.config.hp.get('dataset', 'FashionMNIST')
batch_size = self.config.hp.batch_size
project_path = self.config.firelab.paths.project_path
data_dir = os.path.join(project_path, self.config.data_dir)
if dataset == 'FashionMNIST':
data_train = FashionMNIST(data_dir, train=True, transform=transforms.ToTensor())
data_test = FashionMNIST(data_dir, train=False, transform=transforms.ToTensor())
elif dataset == 'MNIST':
data_train = MNIST(data_dir, train=True, transform=transforms.ToTensor())
data_test = MNIST(data_dir, train=False, transform=transforms.ToTensor())
elif dataset == 'CIFAR10':
train_transform = transforms.Compose([
transforms.Pad(padding=4),
transforms.RandomCrop(size=(32, 32)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.RandomErasing(p=0.5, scale=(0.25, 0.25), ratio=(1., 1.)), # Cut out 8x8 square
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
data_train = CIFAR10(data_dir, train=True, transform=train_transform)
data_test = CIFAR10(data_dir, train=False, transform=test_transform)
else:
raise NotImplementedError(f"Unknown dataset: {dataset}")
data_vis_train = Subset(data_train, random.sample(range(len(data_train)), self.config.get('n_points_for_vis', 1000)))
data_vis_test = Subset(data_test, random.sample(range(len(data_test)), self.config.get('n_points_for_vis', 1000)))
self.train_dataloader = DataLoader(data_train, batch_size=batch_size, shuffle=True)
self.val_dataloader = DataLoader(data_test, batch_size=batch_size, shuffle=False)
self.vis_train_dataloader = DataLoader(data_vis_train, batch_size=batch_size, shuffle=False)
self.vis_test_dataloader = DataLoader(data_vis_test, batch_size=batch_size, shuffle=False)
def init_models(self):
self.init_torch_model_builder()
self.model = MaskModel(
self.mask, self.torch_model_builder,
should_center_origin=self.config.hp.should_center_origin,
parametrization_type=self.config.hp.parametrization_type)
self.model = self.model.to(self.device_name)
# self.logger.info(f'Model initial orthogonality: {self.model.compute_ort_reg()}')
# self.logger.info(f'Model params: {self.config.hp.conv_model_config.to_dict()}. Parametrization: {self.config.hp.parametrization_type}')
def init_torch_model_builder(self):
if self.config.hp.model_name == 'fast_resnet':
self.torch_model_builder = lambda: FastResNet(
n_classes=10, n_input_channels=self.config.hp.get('n_input_channels', 1)).nn
elif self.config.hp.model_name == 'resnet18':
self.torch_model_builder = lambda: ResNet18(
n_classes=10, n_input_channels=self.config.hp.get('n_input_channels', 1)).nn
elif self.config.hp.model_name == "vgg":
self.torch_model_builder = lambda: VGG11(
n_input_channels=self.config.hp.get('n_input_channels', 1),
use_bn=self.config.hp.get('use_bn', True)).model
elif self.config.hp.model_name == "simple":
self.torch_model_builder = lambda: SimpleModel().nn
elif self.config.hp.model_name == "conv":
self.torch_model_builder = lambda: ConvModel(self.config.hp.conv_model_config).nn
else:
raise NotImplementedError(f"Model {self.config.hp.model_name} is not supported")
def init_criterions(self):
self.criterion = nn.CrossEntropyLoss(reduction='none')
def init_optimizers(self):
optim_type = self.config.hp.get('optim.type', 'adam').lower()
if optim_type == 'adam':
self.optim = Adam(self.model.parameters(), **self.config.hp.optim.kwargs.to_dict())
elif optim_type == 'sgd':
self.optim = SGD(self.model.parameters(), **self.config.hp.optim.kwargs.to_dict())
else:
raise NotImplementedError(f'Unknown optimizer: {optim_type}')
if not self.config.hp.optim.has('scheduler'):
self.scheduler = None
elif self.config.hp.optim.get('scheduler.type') == 'triangle_lr':
epoch_size = len(self.train_dataloader)
self.scheduler = TriangleLR(self.optim, epoch_size, **self.config.hp.optim.scheduler.kwargs.to_dict())
else:
raise NotImplementedError(f"Unknown scheduler.type: {self.config.hp.optim.get('scheduler.type')}")
def train_on_batch(self, batch):
self.optim.zero_grad()
x = batch[0].to(self.device_name)
y = batch[1].to(self.device_name)
good_losses = []
good_accs = []
bad_losses = []
bad_accs = []
good_idx = self.model.get_class_idx(1).tolist()
bad_idx = self.model.get_class_idx(-1).tolist()
num_good_points_to_use = min(len(good_idx), self.config.hp.num_good_cells_per_update)
num_bad_points_to_use = min(len(bad_idx), self.config.hp.num_bad_cells_per_update)
for i, j in random.sample(good_idx, num_good_points_to_use):
preds = self.model.run_from_weights(self.model.compute_point(i,j), x)
good_loss = self.criterion(preds, y).mean()
good_losses.append(good_loss.item())
good_loss /= num_good_points_to_use
good_loss.backward() # To make the graph free
good_accs.append((preds.argmax(dim=1) == y).float().mean().item())
for i, j in random.sample(bad_idx, num_bad_points_to_use):
preds = self.model.run_from_weights(self.model.compute_point(i,j), x)
bad_loss = self.criterion(preds, y).mean()
bad_losses.append(bad_loss.item())
bad_loss = bad_loss.clamp(0, self.config.hp.neg_loss_clip_threshold)
bad_loss /= num_bad_points_to_use
bad_loss *= self.config.hp.get('negative_loss_coef', 1.)
bad_loss *= -1 # To make it grow
bad_loss.backward() # To make the graph free
bad_accs.append((preds.argmax(dim=1) == y).float().mean().item())
good_losses = np.array(good_losses)
good_accs = np.array(good_accs)
bad_losses = np.array(bad_losses)
bad_accs = np.array(bad_accs)
# Adding regularization
if self.config.hp.parametrization_type != "up_orthogonal":
ort = self.model.compute_ort_reg()
norm_diff = self.model.compute_norm_reg()
reg_loss = self.config.hp.ort_l2_coef * ort.pow(2) + self.config.hp.norm_l2_coef * norm_diff.pow(2)
reg_loss.backward()
self.writer.add_scalar('Reg/ort', ort.item(), self.num_iters_done)
self.writer.add_scalar('Reg/norm_diff', norm_diff.item(), self.num_iters_done)
clip_grad_norm_(self.model.parameters(), self.config.hp.grad_clip_threshold)
self.optim.step()
if not self.scheduler is None:
self.scheduler.step()
self.writer.add_scalar('good/train/loss', good_losses.mean().item(), self.num_iters_done)
self.writer.add_scalar('good/train/acc', good_accs.mean().item(), self.num_iters_done)
self.writer.add_scalar('bad/train/loss', bad_losses.mean().item(), self.num_iters_done)
self.writer.add_scalar('bad/train/acc', bad_accs.mean().item(), self.num_iters_done)
self.writer.add_scalar('diff/train/loss', good_losses.mean().item() - bad_losses.mean().item(), self.num_iters_done)
self.writer.add_scalar('diff/train/acc', good_accs.mean().item() - bad_accs.mean().item(), self.num_iters_done)
self.writer.add_scalar('Stats/lengths/right', self.model.right.norm(), self.num_iters_done)
self.writer.add_scalar('Stats/lengths/up', self.model.up.norm(), self.num_iters_done)
self.writer.add_scalar('Stats/grad_norms/origin', self.model.origin.grad.norm().item(), self.num_iters_done)
self.writer.add_scalar('Stats/grad_norms/right_param', self.model.right_param.grad.norm().item(), self.num_iters_done)
self.writer.add_scalar('Stats/grad_norms/up_param', self.model.up_param.grad.norm().item(), self.num_iters_done)
self.writer.add_scalar('Stats/grad_norms/scaling', self.model.scaling_param.grad.norm().item(), self.num_iters_done)
self.writer.add_scalar('Stats/scaling', self.model.scaling_param.item(), self.num_iters_done)
def before_training_hook(self):
self.plot_mask()
self.save_mask()
self.plot_all_weights_histograms()
self.write_config()
def after_training_hook(self):
if self.is_explicitly_stopped:
self.delete_logs() # So tensorboard does not lag
else:
self.visualize_minimum(self.vis_train_dataloader, 'train')
self.visualize_minimum(self.vis_test_dataloader, 'test')
def delete_logs(self):
shutil.rmtree(self.config.firelab.paths.logs_path)
self.writer.close()
def compute_mask_scores(self, dataloader):
pad = self.config.get('solution_vis.padding', 0)
x_num_points = self.config.get('solution_vis.granularity.x', self.mask.shape[0] + 2 * pad)
y_num_points = self.config.get('solution_vis.granularity.y', self.mask.shape[1] + 2 * pad)
xs = np.linspace(-pad, self.mask.shape[0] + pad, x_num_points)
ys = np.linspace(-pad, self.mask.shape[1] + pad, y_num_points)
dummy_model = self.torch_model_builder().to(self.device_name)
scores = [[self.compute_mask_score(x, y, dummy_model, dataloader) for y in ys] for x in xs]
return xs, ys, scores
def compute_mask_score(self, x, y, dummy_model, dataloader):
w = self.model.compute_point(x, y, should_orthogonalize=True)
return validate_weights(w, dataloader, dummy_model)
def visualize_minimum(self, dataloader:DataLoader, subtitle:str):
xs, ys, scores = self.compute_mask_scores(dataloader)
self.save_minima_grid(scores, subtitle)
fig = self.build_minimum_figure(xs, ys, scores, subtitle)
self.writer.add_figure(f'Minimum_{subtitle}', fig, self.num_iters_done)
def build_minimum_figure(self, xs, ys, scores, subtitle:str):
X, Y = np.meshgrid(xs, ys)
scores = np.array(scores)
fig = plt.figure(figsize=(20, 4))
plt.subplot(141)
cntr = plt.contourf(X, Y, scores[:,:,0].T, cmap="RdBu_r", levels=np.linspace(0.3, 2.5, 30))
plt.title(f'Loss [{subtitle}]')
plt.colorbar(cntr)
plt.subplot(142)
cntr = plt.contourf(X, Y, scores[:,:,1].T, cmap="RdBu_r", levels=np.linspace(0.5, 0.9, 30))
plt.title(f'Accuracy [{subtitle}]')
plt.colorbar(cntr)
plt.subplot(143)
cntr = plt.contourf(X, Y, scores[:,:,0].T, cmap="RdBu_r", levels=100)
plt.title(f'Loss [{subtitle}]')
plt.colorbar(cntr)
plt.subplot(144)
cntr = plt.contourf(X, Y, scores[:,:,1].T, cmap="RdBu_r", levels=np.linspace(0, 1, 100))
plt.title(f'Accuracy [{subtitle}]')
plt.colorbar(cntr)
return fig
def validate(self):
self.model.is_good_mode = True
good_val_loss, good_val_acc = validate(self.model, self.train_dataloader, self.criterion)
self.model.is_good_mode = False
bad_val_loss, bad_val_acc = validate(self.model, self.train_dataloader, self.criterion)
self.model.is_good_mode = True
self.writer.add_scalar('good/val/loss', good_val_loss, self.num_epochs_done)
self.writer.add_scalar('good/val/acc', good_val_acc, self.num_epochs_done)
self.writer.add_scalar('bad/val/loss', bad_val_loss, self.num_epochs_done)
self.writer.add_scalar('bad/val/acc', bad_val_acc, self.num_epochs_done)
self.writer.add_scalar('diff/val/loss', good_val_loss - bad_val_loss, self.num_epochs_done)
self.writer.add_scalar('diff/val/acc', good_val_acc - bad_val_acc, self.num_epochs_done)
self.plot_all_weights_histograms()
if self.num_epochs_done > self.config.get('val_acc_stop_threshold_num_warmup_epochs', -1):
if good_val_acc < self.config.get('good_val_acc_stop_threshold', 0.):
self.stop(f'Good val accuracy is too low (epoch #{self.num_epochs_done}): {good_val_acc}')
elif bad_val_acc > self.config.get('bad_val_acc_stop_threshold', 1.):
self.stop(f'Bad val accuracy is too high (epoch #{self.num_epochs_done}): {bad_val_acc}')
else:
pass
if self.num_epochs_done > self.config.get('diff_threshold_num_warmup_epochs', -1):
if good_val_acc - bad_val_acc < self.config.get('good_and_bad_val_acc_diff_threshold', float('-inf')):
self.stop(f'Difference between good and val accuracies is too small '\
f'(epoch #{self.num_epochs_done}): {good_val_acc} - {bad_val_acc} = {good_val_acc - bad_val_acc}')
def plot_mask(self):
fig = plt.figure(figsize=(5, 5))
mask_img = np.copy(self.mask)
mask_img[mask_img == 2] = 0.5
plt.imshow(mask_img, cmap='gray')
self.writer.add_figure('Mask', fig, self.num_iters_done)
def save_mask(self):
save_path = os.path.join(self.config.firelab.paths.custom_data_path, 'mask.npy')
np.save(save_path, self.mask)
def plot_params_histograms(self, w, subtag:str):
dummy_model = self.torch_model_builder()
params = weight_to_param(w, param_sizes(dummy_model.parameters()))
tags = ['Weights_histogram_{}/{}'.format(i, subtag) for i in range(len(params))]
for tag, param in zip(tags, params):
self.writer.add_histogram(tag, param, self.num_iters_done)
def plot_all_weights_histograms(self):
# TODO: we do not need histograms currently...
# self.plot_params_histograms(self.model.origin + self.model.right, 'origin_right')
# self.plot_params_histograms(self.model.origin + self.model.up, 'origin_up')
# self.plot_params_histograms(self.model.origin + self.model.up + self.model.right, 'origin_up_right')
pass
def write_config(self):
config_yml = yaml.safe_dump(self.config.to_dict())
config_yml = config_yml.replace('\n', ' \n') # Because tensorboard uses markdown
self.writer.add_text('Config', config_yml, self.num_iters_done)
def save_minima_grid(self, scores, subtitle:str):
save_path = os.path.join(self.config.firelab.paths.custom_data_path, f'minima_grid_{subtitle}.npy')
np.save(save_path, scores)
class Policy(nn.Module):
def __init__(self, env):
# game params
self.board_x, self.board_y = env.get_ub_board_size()
self.action_size = env.n_actions
self.n_inputs = env.n_inputs
self.lr = args.lr
self.env = env
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
super(Policy, self).__init__()
self.conv1 = nn.Conv2d(self.n_inputs,
args.num_channels,
3,
stride=1,
padding=1).to(self.device)
self.conv2 = nn.Conv2d(args.num_channels,
args.num_channels,
3,
stride=1,
padding=1).to(self.device)
self.conv3 = nn.Conv2d(args.num_channels,
args.num_channels,
3,
stride=1).to(self.device)
self.conv4 = nn.Conv2d(args.num_channels,
args.num_channels,
3,
stride=1).to(self.device)
self.bn1 = nn.BatchNorm2d(args.num_channels).to(self.device)
self.bn2 = nn.BatchNorm2d(args.num_channels).to(self.device)
self.bn3 = nn.BatchNorm2d(args.num_channels).to(self.device)
self.bn4 = nn.BatchNorm2d(args.num_channels).to(self.device)
self.fc1 = nn.Linear(args.num_channels*(self.board_x - 4)*(self.board_y - 4) \
+ env.agent_step_dim, 1024).to(self.device)
self.fc_bn1 = nn.BatchNorm1d(1024).to(self.device)
self.fc2 = nn.Linear(1024, 512).to(self.device)
self.fc_bn2 = nn.BatchNorm1d(512).to(self.device)
self.fc3 = nn.Linear(512, self.action_size).to(self.device)
self.fc4 = nn.Linear(512, 1).to(self.device)
self.entropies = 0
self.pi_losses = AverageMeter()
self.v_losses = AverageMeter()
self.action_probs = [[], []]
self.state_values = [[], []]
self.rewards = [[], []]
self.next_states = [[], []]
if args.optimizer == 'adas':
self.optimizer = Adas(self.parameters(), lr=self.lr)
elif args.optimizer == 'adam':
self.optimizer = Adam(self.parameters(), lr=self.lr)
else:
self.optimizer = SGD(self.parameters(), lr=self.lr)
def forward(self, s, agent):
# s: batch_size x n_inputs x board_x x board_y
s = s.view(-1, self.n_inputs, self.board_x,
self.board_y) # batch_size x n_inputs x board_x x board_y
s = F.relu(self.bn1(
self.conv1(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn2(
self.conv2(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn3(self.conv3(
s))) # batch_size x num_channels x (board_x-2) x (board_y-2)
s = F.relu(self.bn4(self.conv4(
s))) # batch_size x num_channels x (board_x-4) x (board_y-4)
s = s.view(-1,
args.num_channels * (self.board_x - 4) * (self.board_y - 4))
s = torch.cat((s, agent), dim=1)
s = F.dropout(F.relu(self.fc1(s)),
p=args.dropout,
training=self.training) # batch_size x 1024
s = F.dropout(F.relu(self.fc2(s)),
p=args.dropout,
training=self.training) # batch_size x 512
pi = self.fc3(s) # batch_size x action_size
v = self.fc4(s) # batch_size x 1
return F.log_softmax(pi, dim=1), v # torch.tanh(v)
def step(self, obs, agent):
"""
Returns policy and value estimates for given observations.
:param obs: Array of shape [N] containing N observations.
:return: Policy estimate [N, n_actions] and value estimate [N] for
the given observations.
"""
obs = torch.from_numpy(obs).to(self.device)
agent = torch.from_numpy(agent).to(self.device)
pi, v = self.forward(obs, agent)
return torch.exp(pi).detach().to('cpu').numpy(), v.detach().to(
'cpu').numpy()
def store(self, player_ID, prob, state_value, reward):
self.action_probs[player_ID].append(prob)
self.state_values[player_ID].append(state_value)
self.rewards[player_ID].append(reward)
def clear(self):
self.action_probs = [[], []]
self.state_values = [[], []]
self.rewards = [[], []]
self.next_states = [[], []]
self.entropies = 0
def get_data(self):
return self.action_probs, self.state_values, self.rewards
def optimize(self):
self.optimizer.step()
def reset_grad(self):
self.optimizer.zero_grad()
def train_examples(self, examples):
"""
examples: list of examples, each example is of form (board, pi, v)
"""
for epoch in range(args.epochs):
# print('\nEPOCH ::: ' + str(epoch + 1))
self.train()
batch_count = int(len(examples) / args.batch_size)
t = tqdm(range(batch_count), desc='Training Net')
for _ in t:
sample_ids = np.random.randint(len(examples),
size=args.batch_size)
boards, agent_steps, pis, vs = list(
zip(*[examples[i] for i in sample_ids]))
boards = self.env.get_states_for_step(boards)
agent_steps = self.env.get_agents_for_step(agent_steps)
boards = torch.FloatTensor(boards.astype(np.float64)).to(
self.device)
agent_steps = torch.FloatTensor(agent_steps.astype(
np.float64)).to(self.device)
target_pis = torch.FloatTensor(np.array(pis))
target_vs = torch.FloatTensor(np.array(vs).astype(np.float64))
# predict
if self.device == 'cuda':
boards, target_pis, target_vs = boards.contiguous().cuda(
), target_pis.contiguous().cuda(), target_vs.contiguous(
).cuda()
# compute output
out_pi, out_v = self.forward(boards, agent_steps)
l_pi = self.loss_pi(target_pis, out_pi)
l_v = self.loss_v(target_vs, out_v)
total_loss = l_pi + l_v
# record loss
self.pi_losses.update(l_pi.item(), boards.size(0))
self.v_losses.update(l_v.item(), boards.size(0))
t.set_postfix(Loss_pi=self.pi_losses, Loss_v=self.v_losses)
# compute gradient and do Adas step
self.reset_grad()
total_loss.backward()
self.optimize()
self.pi_losses.plot('PolicyLoss')
self.v_losses.plot('ValueLoss')
def loss_pi(self, targets, outputs):
return -torch.sum(targets * outputs) / targets.size()[0]
def loss_v(self, targets, outputs):
return torch.sum((targets - outputs.view(-1))**2) / targets.size()[0]
def save_checkpoint(self, folder='Models', filename='model.pt'):
filepath = os.path.join(folder, filename)
if not os.path.exists(folder):
print("Checkpoint Directory does not exist! Making directory {}".
format(folder))
os.mkdir(folder)
else:
print("Checkpoint Directory exists! ")
torch.save({
'state_dict': self.state_dict(),
}, filepath)
def load_checkpoint(self, folder='Models', filename='model.pt'):
# https://github.com/pytorch/examples/blob/master/imagenet/main.py#L98
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
raise ("No model in path {}".format(filepath))
checkpoint = torch.load(filepath, map_location=self.device)
self.load_state_dict(checkpoint['state_dict'])
print('-- Load model succesfull!')
def load_colab_model(self, _dir):
self.load_state_dict(torch.load(_dir, map_location=self.device))
def save_colab_model(self, _dir):
torch.save(self.state_dict(), _dir)
def train(self,
epochs=10,
lr=0.003,
save_model=True,
save_dir='./static/models',
testing=True):
# Setup optimizers
# IT is a little bit complicated, but to match caffe implementation, it must be like this.
optimizer = SGD([
{
'params': self.model.module.conv1.weight
},
{
'params': self.model.module.conv1.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv2.weight
},
{
'params': self.model.module.conv2.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv3a.weight
},
{
'params': self.model.module.conv3a.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv3b.weight
},
{
'params': self.model.module.conv3b.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv4a.weight
},
{
'params': self.model.module.conv4a.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv4b.weight
},
{
'params': self.model.module.conv4b.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv5a.weight
},
{
'params': self.model.module.conv5a.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.conv5b.weight
},
{
'params': self.model.module.conv5b.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.fc6.weight
},
{
'params': self.model.module.fc6.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.fc7.weight
},
{
'params': self.model.module.fc7.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
{
'params': self.model.module.fc8.weight
},
{
'params': self.model.module.fc8.bias,
'lr': 2 * lr,
'weight_decay': 0.0
},
],
lr=lr,
momentum=0.9,
weight_decay=0.005)
# summary
log_dir = 'log/{}'.format(
datetime.datetime.now().strftime('%Y-%m-%d_%H:%M:%S'))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
summary = SummaryWriter(log_dir=log_dir,
comment='Training started at {}'.format(
datetime.datetime.now()))
# training
train_steps_per_epoch = len(self.loaders[0])
lr_sched = lr_scheduler.StepLR(optimizer,
step_size=4 * train_steps_per_epoch,
gamma=0.1)
for i in range(epochs):
# train
self.model.train(True)
categorical_loss = 0.0
num_iter = 0
optimizer.zero_grad()
pbar = tqdm(self.loaders[0])
for data in pbar:
num_iter += 1
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
# Compute outputs
outputs = self.model(inputs)
# compute loss
loss = nn.CrossEntropyLoss()(outputs, labels)
categorical_loss = loss.detach().item() # /steps_per_update
loss.backward()
optimizer.step()
optimizer.zero_grad()
lr_sched.step()
pbar.set_description(
'Epoch {}/{}, Iter {}, Loss: {:.8f}'.format(
i + 1, epochs, num_iter, categorical_loss))
summary.add_scalars('Train loss', {'Loss': categorical_loss},
global_step=i * train_steps_per_epoch +
num_iter)
if num_iter == train_steps_per_epoch:
if save_model:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(
self.model.module.state_dict(),
'{}/model_{:06d}.pt'.format(save_dir, i + 1))
if testing:
val_loss, top1, top5 = self.test()
summary.add_scalars(
'Validation performance', {
'Validation loss': val_loss,
'Top-1 accuracy': top1,
'Top-5 accuracy': top5,
}, i)
print(
'Epoch {}/{}: Top-1 accuracy {:.2f} %, Top-5 accuracy: {:.2f} %'
.format(i + 1, epochs, top1.item(), top5.item()))
break
class Trainer(object):
"""
Trainer encapsulates all the logic necessary for
training the Recurrent Attention Model.
All hyperparameters are provided by the user in the
config file.
"""
def __init__(self, data_loader):
"""
Construct a new Trainer instance.
Args
----
- config: object containing command line arguments.
- data_loader: data iterator
"""
# self.config = config
# glimpse network params
self.patch_size = 16
self.glimpse_scale = 2
self.num_patches = 3
self.loc_hidden = 128
self.glimpse_hidden = 128
# core network params
self.num_glimpses = 6
self.hidden_size = 256
# reinforce params
self.std = 0.17
self.M = 10
# data params
self.train_loader = data_loader[0]
self.valid_loader = data_loader[1]
self.num_train = len(self.train_loader.sampler.indices)
self.num_valid = len(self.valid_loader.sampler.indices)
self.num_classes = 27
self.num_channels = 3
# training params
self.epochs = 25
self.start_epoch = 0
self.saturate_epoch = 150
self.init_lr = 0.001
self.min_lr = 1e-06
self.decay_rate = (self.min_lr - self.init_lr) / (self.saturate_epoch)
self.momentum = 0.5
self.lr = self.init_lr
# misc params
self.use_gpu = False
self.best = True
# self.ckpt_dir = config.ckpt_dir
# self.logs_dir = config.logs_dir
self.best_valid_acc = 0.
self.counter = 0
# self.patience = config.patience
# self.use_tensorboard = config.use_tensorboard
# self.resume = config.resume
# self.print_freq = config.print_freq
# self.plot_freq = config.plot_freq
# self.plot_dir = './plots/' + self.model_name + '/'
# if not os.path.exists(self.plot_dir):
# os.makedirs(self.plot_dir)
# configure tensorboard logging
# build RAM model
self.model = RecurrentAttention(
self.patch_size, self.num_patches, self.glimpse_scale,
self.num_channels, self.loc_hidden, self.glimpse_hidden,
self.std, self.hidden_size, self.num_classes,
)
if self.use_gpu:
self.model.cuda()
print('[*] Number of model parameters: {:,}'.format(
sum([p.data.nelement() for p in self.model.parameters()])))
# initialize optimizer and scheduler
self.optimizer = SGD(
self.model.parameters(), lr=self.lr, momentum=self.momentum,
)
self.scheduler = ReduceLROnPlateau(self.optimizer, 'min')
def reset(self):
"""
Initialize the hidden state of the core network
and the location vector.
This is called once every time a new minibatch
`x` is introduced.
"""
h_t = torch.zeros(self.batch_size, self.hidden_size)
h_t = Variable(h_t)
l_t = torch.Tensor(self.batch_size, 2).uniform_(-1, 1)
l_t = Variable(l_t)
return h_t, l_t
def train(self):
"""
Train the model on the training set.
A checkpoint of the model is saved after each epoch
and if the validation accuracy is improved upon,
a separate ckpt is created for use on the test set.
"""
# load the most recent checkpoint
# if self.resume:
# self.load_checkpoint(best=False)
print("\n[*] Train on {} samples, validate on {} samples".format(
self.num_train, self.num_valid)
)
for epoch in range(self.epochs):
print(
'\nEpoch: {}/{} - LR: {:.6f}'.format(
epoch+1, self.epochs, self.lr)
)
# train for 1 epoch
train_loss, train_acc = self.train_one_epoch(epoch)
# evaluate on validation set
valid_loss, valid_acc = self.validate(epoch)
# self.scheduler.step(valid_loss)
#
# # # decay learning rate
# # if epoch < self.saturate_epoch:
# # self.anneal_learning_rate(epoch)
#
is_best = valid_acc > self.best_valid_acc
msg1 = "train loss: {:.3f} - train acc: {:.3f} "
msg2 = "- val loss: {:.3f} - val acc: {:.3f}"
if is_best:
msg2 += " [*]"
msg = msg1 + msg2
print(msg.format(train_loss, train_acc, valid_loss, valid_acc))
# # check for improvement
# if not is_best:
# self.counter += 1
# if self.counter > self.patience:
# print("[!] No improvement in a while, stopping training.")
# return
# self.best_valid_acc = max(valid_acc, self.best_valid_acc)
# self.save_checkpoint(
# {'epoch': epoch + 1, 'state_dict': self.model.state_dict(),
# 'best_valid_acc': self.best_valid_acc,
# 'lr': self.lr}, is_best
# )
def train_one_epoch(self, epoch):
"""
Train the model for 1 epoch of the training set.
An epoch corresponds to one full pass through the entire
training set in successive mini-batches.
This is used by train() and should not be called manually.
"""
batch_time = AverageMeter()
losses = AverageMeter()
accs = AverageMeter()
tic = time.time()
for i, (x, y) in enumerate(self.train_loader):
if self.use_gpu:
x, y = x.cuda(), y.cuda()
x, y = Variable(x), Variable(y)
# plot = False
# if (epoch % self.plot_freq == 0) and (i == 0):
# plot = True
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# save images
# imgs = []
# imgs.append(x[0:9])
# extract the glimpses
locs = []
log_pi = []
baselines = []
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# store
locs.append(l_t[0:9])
baselines.append(b_t)
log_pi.append(p)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(
x, l_t, h_t, last=True
)
log_pi.append(p)
baselines.append(b_t)
# locs.append(l_t[0:9])
# convert list to tensors and reshape
baselines = torch.stack(baselines).transpose(1, 0)
log_pi = torch.stack(log_pi).transpose(1, 0)
# calculate reward
predicted = torch.max(log_probas, 1)[1]
R = (predicted.detach() == y).float()
R = R.unsqueeze(1).repeat(1, self.num_glimpses)
# compute losses for differentiable modules
loss_action = F.nll_loss(log_probas, y)
loss_baseline = F.mse_loss(baselines, R)
# compute reinforce loss
adjusted_reward = R - baselines.detach()
loss_reinforce = torch.mean(-log_pi*adjusted_reward)
# sum up into a hybrid loss
loss = loss_action + loss_baseline + loss_reinforce
# compute accuracy
correct = (predicted == y).float()
acc = 100 * (correct.sum() / len(y))
# store
losses.update(loss.item(), x.size()[0])
accs.update(acc.item(), x.size()[0])
# a = list(self.model.sensor.parameters())[0].clone()
# self.optimizer.zero_grad()
# loss_reinforce.backward()
# self.optimizer.step()
# b = list(self.model.sensor.parameters())[0].clone()
# print("Same: {}".format(torch.equal(a.data, b.data)))
# compute gradients and update SGD
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# measure elapsed time
toc = time.time()
batch_time.update(toc-tic)
# print("{:.1f}s - loss: {:.3f} - acc: {:.3f}".format(
# (toc-tic), loss.data[0], acc.data[0]
# ))
return losses.avg, accs.avg
def validate(self, epoch):
"""
Evaluate the model on the validation set.
"""
losses = AverageMeter()
accs = AverageMeter()
for i, (x, y) in enumerate(self.valid_loader):
if self.use_gpu:
x, y = x.cuda(), y.cuda()
x, y = Variable(x), Variable(y)
# duplicate 10 times
x = x.repeat(self.M, 1, 1, 1)
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# extract the glimpses
log_pi = []
baselines = []
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# store
baselines.append(b_t)
log_pi.append(p)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(
x, l_t, h_t, last=True
)
log_pi.append(p)
baselines.append(b_t)
# convert list to tensors and reshape
baselines = torch.stack(baselines).transpose(1, 0)
log_pi = torch.stack(log_pi).transpose(1, 0)
# average
log_probas = log_probas.view(
self.M, -1, log_probas.shape[-1]
)
log_probas = torch.mean(log_probas, dim=0)
baselines = baselines.contiguous().view(
self.M, -1, baselines.shape[-1]
)
baselines = torch.mean(baselines, dim=0)
log_pi = log_pi.contiguous().view(
self.M, -1, log_pi.shape[-1]
)
log_pi = torch.mean(log_pi, dim=0)
# calculate reward
predicted = torch.max(log_probas, 1)[1]
R = (predicted.detach() == y).float()
R = R.unsqueeze(1).repeat(1, self.num_glimpses)
# compute losses for differentiable modules
loss_action = F.nll_loss(log_probas, y)
loss_baseline = F.mse_loss(baselines, R)
# compute reinforce loss
adjusted_reward = R - baselines.detach()
loss_reinforce = torch.mean(-log_pi*adjusted_reward)
# sum up into a hybrid loss
loss = loss_action + loss_baseline + loss_reinforce
# compute accuracy
correct = (predicted == y).float()
acc = 100 * (correct.sum() / len(y))
# store
losses.update(loss.item(), x.size()[0])
accs.update(acc.item(), x.size()[0])
return losses.avg, accs.avg
def test(self, loader):
"""
Test the model on the held-out test data.
This function should only be called at the very
end once the model has finished training.
"""
correct = 0
self.test_loader = loader
# load the best checkpoint
# self.load_checkpoint(best=self.best)
self.num_test = len(self.test_loader.dataset)
for i, (x, y) in enumerate(self.test_loader):
# if self.use_gpu:
# x, y = x.cuda(), y.cuda()
x, y = Variable(x), Variable(y)
# duplicate 10 times
x = x.repeat(self.M, 1, 1, 1)
# initialize location vector and hidden state
self.batch_size = x.shape[0]
h_t, l_t = self.reset()
# extract the glimpses
for t in range(self.num_glimpses - 1):
# forward pass through model
h_t, l_t, b_t, p = self.model(x, l_t, h_t)
# last iteration
h_t, l_t, b_t, log_probas, p = self.model(
x, l_t, h_t, last=True
)
log_probas = log_probas.view(
self.M, -1, log_probas.shape[-1]
)
log_probas = torch.mean(log_probas, dim=0)
pred = log_probas.data.max(1, keepdim=True)[1]
correct += pred.eq(y.data.view_as(pred)).cpu().sum()
perc = (100. * correct) / (self.num_test)
print(
'[*] Test Acc: {}/{} ({:.2f}%)'.format(
correct, self.num_test, perc)
)
def anneal_learning_rate(self, epoch):
"""
This function linearly decays the learning rate
to a predefined minimum over a set amount of epochs.
"""
self.lr += self.decay_rate
# log to tensorboard
if self.use_tensorboard:
log_value('learning_rate', self.lr, epoch)
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
def save_checkpoint(self, state, is_best):
"""
Save a copy of the model so that it can be loaded at a future
date. This function is used when the model is being evaluated
on the test data.
If this model has reached the best validation accuracy thus
far, a seperate file with the suffix `best` is created.
"""
# print("[*] Saving model to {}".format(self.ckpt_dir))
filename = self.model_name + '_ckpt.pth.tar'
ckpt_path = os.path.join(self.ckpt_dir, filename)
torch.save(state, ckpt_path)
if is_best:
filename = self.model_name + '_model_best.pth.tar'
shutil.copyfile(
ckpt_path, os.path.join(self.ckpt_dir, filename)
)
def load_checkpoint(self, best=False):
"""
Load the best copy of a model. This is useful for 2 cases:
- Resuming training with the most recent model checkpoint.
- Loading the best validation model to evaluate on the test data.
Params
------
- best: if set to True, loads the best model. Use this if you want
to evaluate your model on the test data. Else, set to False in
which case the most recent version of the checkpoint is used.
"""
print("[*] Loading model from {}".format(self.ckpt_dir))
filename = self.model_name + '_ckpt.pth.tar'
if best:
filename = self.model_name + '_model_best.pth.tar'
ckpt_path = os.path.join(self.ckpt_dir, filename)
ckpt = torch.load(ckpt_path)
# load variables from checkpoint
self.start_epoch = ckpt['epoch']
self.best_valid_acc = ckpt['best_valid_acc']
self.lr = ckpt['lr']
self.model.load_state_dict(ckpt['state_dict'])
if best:
print(
"[*] Loaded {} checkpoint @ epoch {} "
"with best valid acc of {:.3f}".format(
filename, ckpt['epoch']+1, ckpt['best_valid_acc'])
)
else:
print(
"[*] Loaded {} checkpoint @ epoch {}".format(
filename, ckpt['epoch']+1)
)
def train(config):
loader = DataLoader(TrainEvalDataset(
config.dataset(split='train', **config.dataset_parameter), config),
config.batch_size,
True,
num_workers=num_processor)
test_loader = DataLoader(TrainEvalDataset(
config.dataset(split='test', **config.dataset_parameter), config),
config.batch_size,
False,
num_workers=num_processor)
net = NetModel(config.net)
loss_calculator = LossCalculator(config.net.loss)
# net = nn.DataParallel(net)
logger.info(config.net.pre_train)
logger.info(type(config.net.pre_train))
if config.net.pre_train is not None and os.path.exists(
config.net.pre_train):
unused, unused1 = net.load_state_dict(
{(('base_net.' + k) if not k.startswith('base_net') else k): v
for k, v in torch.load(config.net.pre_train).items()},
strict=False)
logger.info(unused)
logger.info(unused1)
net = net.to(device)
optimizer = SGD(net.parameters(), config.lr, 0.9, weight_decay=0.0005)
# optimizer = Adam(net.parameters(), config.lr, weight_decay=0.0005)
exp_lr_scheduler = lr_scheduler.ExponentialLR(optimizer, 0.30)
storage_dict = SqliteDict(f'{config.output_dir}/dcl_snap.db')
start_epoach = 0
if len(storage_dict) > 0:
kk = list(storage_dict.keys())
# net.load_state_dict(
# torch.load(BytesIO(storage_dict[38])))
net.load_state_dict(torch.load(BytesIO(storage_dict[kk[-1]])))
start_epoach = int(kk[-1]) + 1
logger.info(f'loading from epoach{start_epoach}')
global_step = 0
for epoach in (range(start_epoach, config.max_it)):
net.train()
for batch_cnt, batch in tqdm(enumerate(loader), total=len(loader)):
image, label = batch
if isinstance(image, torch.Tensor):
image = image.to(device)
elif isinstance(image, dict):
for k, v in image.items():
if isinstance(v, torch.Tensor):
image[k] = image[k].to(device)
elif isinstance(image, list):
for v in image:
v.to(device)
for k, v in label.items():
if isinstance(v, torch.Tensor):
label[k] = label[k].to(device)
optimizer.zero_grad()
net_out = net(image)
loss_sum, loss_map = loss_calculator(net_out, label)
loss_sum.backward()
optimizer.step()
global_step += 1
wtire_summary(loss_map, 'train', global_step)
exp_lr_scheduler.step(epoach)
logger.debug(f'saving epoach {epoach}')
buffer = BytesIO()
torch.save(net.state_dict(), buffer)
buffer.seek(0)
storage_dict[epoach] = buffer.read()
storage_dict.commit()
test(config, net, test_loader, epoach, loss_calculator)
next_pred, _ = n_copy(input, (h.detach(), c.detach()))
target = input[0, 0, 0].detach() + gamma * next_pred.detach().item()
# print(gt_target, target)
real_error = (target - value_prediction)**2
gt_error = (gt_target - value_prediction)**2
if sum_of_error is None:
sum_of_error = real_error
else:
sum_of_error = sum_of_error + real_error
running_error = running_error * 0.9999 + gt_error.detach().item() * 0.0001
if (i % args["truncation"] == 0):
opti.zero_grad()
sum_of_error.backward()
opti.step()
h = h.detach()
c = c.detach()
sum_of_error = None
if (i % 50000 == 20000):
error_list.append([str(rank), str(i), str(running_error)])
if (i % 100000 == 4):
my_experiment.insert_values("predictions", predictions_table_keys,
predictions_list)
predictions_list = []
my_experiment.insert_values("error_table", error_table_keys,
error_list)
error_list = []
if (i % 100000 == 0):
batch_size = 256
trans_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = MNIST('./data/mnist/', train=True, download=True, transform=trans_mnist)
test_dataset = MNIST('./data/mnist/', train=False, download=True, transform=trans_mnist)
train_loader = DataLoader(train_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
model = LeNet().to(args.device)
sgd = SGD(model.parameters(), lr=1e-1)
cross_error = CrossEntropyLoss()
epoch = 100
writer = SummaryWriter('./runs/t_centerlize')
for _epoch in range(epoch):
epoch_loss = []
for idx, (train_x, train_label) in enumerate(train_loader):
train_x, train_label = train_x.to(args.device), train_label.to(args.device)
#label_np = np.zeros((train_label.shape[0], 10))
sgd.zero_grad()
predict_y = model(train_x.float())
_error = cross_error(predict_y, train_label.long())
_error.backward()
sgd.step()
epoch_loss.append(_error)
avg_epoch = sum(epoch_loss) / len(epoch_loss)
writer.add_scalar("train_loss", avg_epoch, _epoch)
print('Round {:3d}, Average loss {:.3f}'.format(_epoch, avg_epoch))
acc_test, loss_test = test_img(model, test_dataset, args)
print("Testing accuracy: {:.2f}".format(acc_test))
class LunaTrainingApp():
def __init__(self, sys_argv=None):
if sys_argv is None:
sys_argv = sys.argv[1:]
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size',
help="Batch size to use for training",
default=32,
type=int)
parser.add_argument(
'--num-workers',
help="Number of worker processes for background data loading",
default=8,
type=int)
parser.add_argument('--epochs',
help="Number of epochs to train for",
default=1,
type=int)
self.cli_args = parser.parse_args(sys_argv)
self.time_str = datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
def main(self):
log.info("Starting {}, {}".format(type(self).__name__, self.cli_args))
self.use_cuda = torch.cuda.is_available()
self.device = torch.device("cuda" if self.use_cuda else "cpu")
self.model = LunaModel()
if self.use_cuda:
if torch.cuda.device_count() > 1:
self.model = nn.DataParallel(self.model)
self.model = self.model.to(self.device)
self.optimizer = SGD(self.model.parameters(), lr=0.01, momentum=0.9)
train_dl = DataLoader(
LunaDataset(test_stride=10, isTestSet_bool=False),
batch_size=self.cli_args.batch_size *
(torch.cuda.device_count() if self.use_cuda else 1),
num_workers=self.cli_args.num_workers,
pin_memory=self.use_cuda)
test_dl = DataLoader(
LunaDataset(test_stride=10, isTestSet_bool=True),
batch_size=self.cli_args.batch_size *
(torch.cuda.device_count() if self.use_cuda else 1),
num_workers=self.cli_args.num_workers,
pin_memory=self.use_cuda)
for epoch_ndx in range(1, self.cli_args.epochs + 1):
log.info("Epoch {} of {}, {}/{} batches of size {}*{}".format(
epoch_ndx, self.cli_args.epochs, len(train_dl), len(test_dl),
self.cli_args.batch_size,
(torch.cuda.device_count() if self.use_cuda else 1)))
# Trainig loop, very similar to below
self.model.train()
trainingMetrics_tensor = torch.zeros(3, len(train_dl.dataset), 1)
batch_iter = enumerateWithEstimate(train_dl,
"E{} Traning".format(epoch_ndx),
start_ndx=train_dl.num_workers)
for batch_ndx, batch_tup in batch_iter:
self.optimizer.zero_grad()
loss_var = self.computeBatchLoss(batch_ndx, batch_tup,
train_dl.batch_size,
trainingMetrics_tensor)
loss_var.backward()
self.optimizer.step()
del loss_var
# Testing loop, very similar to above, but simplified
with torch.no_grad():
self.model.eval()
testingMetrics_tensor = torch.zeros(3, len(test_dl.dataset), 1)
batch_iter = enumerateWithEstimate(
test_dl,
"E{} Testing".format(epoch_ndx),
start_ndx=test_dl.num_workers)
for batch_ndx, batch_tup in batch_iter:
self.computeBatchLoss(batch_ndx, batch_tup,
test_dl.batch_size,
testingMetrics_tensor)
self.logMetrics(epoch_ndx, trainingMetrics_tensor,
testingMetrics_tensor)
def computeBatchLoss(self, batch_ndx, batch_tup, batch_size,
metrics_tensor):
input_tensor, label_tensor, _series_list, _center_list = batch_tup
input_devtensor = input_tensor.to(self.device)
label_devtensor = label_tensor.to(self.device)
prediction_devtensor = self.model(input_devtensor)
loss_devsensor = nn.MSELoss(reduction='none')(prediction_devtensor,
label_devtensor)
start_ndx = batch_ndx * batch_size
end_ndx = start_ndx + label_tensor.size(0)
metrics_tensor[METRICS_LABEL_NDX, start_ndx:end_ndx] = label_tensor
metrics_tensor[METRICS_PRED_NDX, start_ndx:end_ndx] = \
prediction_devtensor.to('cpu')
metrics_tensor[METRICS_LOSS_NDX, start_ndx:end_ndx] = \
loss_devsensor
return loss_devsensor.mean()
def logMetrics(self,
epoch_ndx,
trainingMetrics_tensor,
testingMetrics_tensor,
classificationThreshold_float=0.5):
log.info("E{} {}".format(epoch_ndx, type(self).__name__))
for mode_str, metrics_tensor in [('trn', trainingMetrics_tensor),
('tst', testingMetrics_tensor)]:
metrics_ary = metrics_tensor.detach().numpy()[:, :, 0]
assert np.isfinite(metrics_ary).all()
benLabel_mask = metrics_ary[METRICS_LABEL_NDX] <= \
classificationThreshold_float
benPred_mask = metrics_ary[METRICS_PRED_NDX] <= \
classificationThreshold_float
malLabel_mask = ~benLabel_mask
malPred_mask = ~benPred_mask
benLabel_count = benLabel_mask.sum()
malLabel_count = malLabel_mask.sum()
benCorrect_count = (benLabel_mask & benPred_mask).sum()
malCorrect_count = (malLabel_mask & malPred_mask).sum()
metrics_dict = {}
metrics_dict['loss/all'] = metrics_ary[METRICS_LOSS_NDX].mean()
metrics_dict['loss/ben'] = metrics_ary[METRICS_LOSS_NDX,
benLabel_mask].mean()
metrics_dict['loss/mal'] = metrics_ary[METRICS_LOSS_NDX,
malLabel_mask].mean()
metrics_dict['correct/all'] = (malCorrect_count + benCorrect_count) \
/ metrics_ary.shape[1] * 100
metrics_dict[
'correct/ben'] = benCorrect_count / benLabel_count * 100
metrics_dict[
'correct/mal'] = malCorrect_count / malLabel_count * 100
log.info(("E{} {:8} {loss/all:.4f} loss, {correct/all:-5.1f}% "
"correct").format(epoch_ndx, mode_str, **metrics_dict))
log.info(("E{} {:8} {loss/ben:.4f} loss, {correct/ben:-5.1f}% "
"correct").format(epoch_ndx, mode_str + '_ben',
**metrics_dict))
log.info(("E{} {:8} {loss/mal:.4f} loss, {correct/mal:-5.1f}% "
"correct").format(epoch_ndx, mode_str + 'mal',
**metrics_dict))
def train_class(directory,
version,
model,
train_loader,
valid_loader,
resize,
batch_size,
exp_name='experiment',
lr=0.01,
epochs=10,
momentum=0.99,
logdir='logs',
dizionario=None):
print("Taining classifacation")
criterion = nn.CrossEntropyLoss()
optimizer = SGD(model.parameters(), lr, momentum=momentum)
#meters
loss_meter = AverageValueMeter()
acc_meter = AverageValueMeter()
#writer
writer = SummaryWriter(join(logdir, exp_name))
#device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
#definiamo un dizionario contenente i loader di training e test
loader = {'train': train_loader, 'valid': valid_loader}
array_accuracy_train = []
array_accuracy_valid = []
array_loss_train = []
array_loss_valid = []
array_glb_train = []
array_glb_valid = []
last_loss_train = 0
last_loss_val = 0
last_acc_train = 0
last_acc_val = 0
#inizializziamo il global step
global_step = 0
tempo = Timer()
start = timer()
start_epoca = 0
if dizionario is not None:
print("Inizializza")
array_accuracy_train = dizionario["a_train"]
array_accuracy_valid = dizionario["a_valid"]
array_loss_train = dizionario["l_train"]
array_loss_valid = dizionario["l_valid"]
array_glb_train = dizionario["g_train"]
array_glb_valid = dizionario["g_valid"]
global_step = dizionario["g_valid"][-1]
start_epoca = dizionario["epoche_fatte"] + 1 # indice epoca di inizio
print("global step", global_step)
print("a_acc_train", array_accuracy_train)
print("a_acc_valid", array_accuracy_valid)
print("loss_train", array_loss_train)
print("loss_valid", array_loss_valid)
print("glb_train", array_glb_train)
print("glb_valid", array_glb_valid)
print("epoca_start_indice ", start_epoca)
start = timer()
print("Num epoche", epochs)
for e in range(start_epoca, epochs):
print("Epoca= ", e)
#iteriamo tra due modalità: train e test
for mode in ['train', 'valid']:
loss_meter.reset()
acc_meter.reset()
model.train() if mode == 'train' else model.eval()
with torch.set_grad_enabled(
mode == 'train'): #abilitiamo i gradienti solo in training
for i, batch in enumerate(loader[mode]):
print(batch['label'])
#x, y = [b.to(device) for b in batch]
x = batch['image'].to(
device) #"portiamoli sul device corretto"
y = batch['label'].to(device)
output = model(x)
#aggiorniamo il global_step
#conterrà il numero di campioni visti durante il training
n = x.shape[0] #numero di elementi nel batch
print("numero elementi nel batch ", n)
global_step += n
l = criterion(output, y)
if mode == 'train':
l.backward()
optimizer.step()
optimizer.zero_grad()
print("Etichette predette", output.to('cpu').max(1)[1])
acc = accuracy_score(y.to('cpu'),
output.to('cpu').max(1)[1])
loss_meter.add(l.item(), n)
acc_meter.add(acc, n)
#loggiamo i risultati iterazione per iterazione solo durante il training
if mode == 'train':
writer.add_scalar('loss/train',
loss_meter.value(),
global_step=global_step)
writer.add_scalar('accuracy/train',
acc_meter.value(),
global_step=global_step)
print("Accuracy Train=", acc_meter.value())
#una volta finita l'epoca (sia nel caso di training che test, loggiamo le stime finali)
if mode == 'train':
global_step_train = global_step
last_loss_train = loss_meter.value()
last_acc_train = acc_meter.value()
print("Accuracy Train=", acc_meter.value())
array_accuracy_train.append(acc_meter.value())
array_loss_train.append(loss_meter.value())
array_glb_train.append(global_step)
else:
global_step_val = global_step
last_loss_val = loss_meter.value()
last_acc_val = acc_meter.value()
print("Accuracy Valid=", acc_meter.value())
array_accuracy_valid.append(acc_meter.value())
array_loss_valid.append(loss_meter.value())
array_glb_valid.append(global_step)
writer.add_scalar('loss/' + mode,
loss_meter.value(),
global_step=global_step)
writer.add_scalar('accuracy/' + mode,
acc_meter.value(),
global_step=global_step)
print("Loss TRAIN", array_loss_train)
print("Losss VALID", array_loss_valid)
print("Accuracy TRAIN", array_accuracy_train)
print("Accuracy VALID", array_accuracy_valid)
print("dim acc train", len(array_accuracy_train))
print("dim acc valid", len(array_accuracy_valid))
figure = plt.figure(figsize=(12, 8))
plt.plot(array_glb_train, array_accuracy_train)
plt.plot(array_glb_valid, array_accuracy_valid)
plt.xlabel('samples')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['Training', 'Valid'])
plt.savefig(directory + '//plotAccuracy_' + version + '.png')
plt.clf()
plt.close(figure)
figure = plt.figure(figsize=(12, 8))
plt.plot(array_glb_train, array_loss_train)
plt.plot(array_glb_valid, array_loss_valid)
plt.xlabel('samples')
plt.ylabel('loss')
plt.grid()
plt.legend(['Training', 'Valid'])
plt.savefig(directory + '//plotLoss_' + version + '.png')
plt.clf()
plt.close(figure)
#conserviamo i pesi del modello alla fine di un ciclo di training e test
net_save(epochs,
model,
optimizer,
last_loss_train,
last_loss_val,
last_acc_train,
last_acc_val,
global_step_train,
global_step_val,
'%s.pth' % (exp_name + "_dict"),
dict_stato_no=True)
#conserviamo i pesi del modello alla fine di un ciclo di training e test
torch.save(
model, directory + "//" + version + "//" + '%s.pth' %
(exp_name + "_" + str(e)))
torch.save(model, '%s.pth' % (exp_name))
saveArray(directory, version, array_loss_train, array_loss_valid,
array_accuracy_train, array_accuracy_valid, array_glb_train,
array_glb_valid)
saveinFileJson(start, directory, version, resize, batch_size, e, lr,
momentum, len(train_loader), array_accuracy_train[-1],
array_accuracy_valid[-1], array_loss_train[-1],
array_loss_valid[-1])
f = '{:.7f}'.format(tempo.stop())
return model, f, last_loss_train, last_loss_val, last_acc_train, last_acc_val
fitness_shaping)
train_writer.add_scalar('fitness', raw_fitness.mean(), i)
train_writer.add_scalar('fitness/std', raw_fitness.std(), i)
for p_idx, p in enumerate(population.parameters()):
train_writer.add_histogram('grads/%d' % p_idx, p.grad, i)
for k, p in population.mixing_logits.items():
train_writer.add_histogram(
"entropy/%s" % k,
t.distributions.Categorical(logits=p).entropy(), i)
means = population.component_means # (480, 5)
dist = ((means.unsqueeze(0) - means.unsqueeze(1))**2).sum(
dim=2).sqrt() # (1, 480, 5,) - (480, 1, 5) = (480, 480, 5)
train_writer.add_histogram("dist", dist, i)
optim.step()
sched.step()
population.std *= 0.999
mean_fit = raw_fitness.mean().item()
pbar.set_description("avg fit: %.3f, std: %.3f" %
(mean_fit, raw_fitness.std().item()))
all_params = population.parameters()
t.save(all_params, 'last.t')
if mean_fit > best_so_far:
best_so_far = mean_fit
t.save(all_params, 'best.t')
util.upload_results('best.t')
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, model: ImageClassifier,
domain_adv: ConditionalDomainAdversarialLoss, optimizer: SGD,
lr_sheduler: StepwiseLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':3.1f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':3.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
domain_accs = AverageMeter('Domain Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, domain_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
domain_adv.train()
end = time.time()
for i in range(args.iters_per_epoch):
lr_sheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, _ = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
# compute output
x = torch.cat((x_s, x_t), dim=0)
y, f = model(x)
y_s, y_t = y.chunk(2, dim=0)
f_s, f_t = f.chunk(2, dim=0)
cls_loss = F.cross_entropy(y_s, labels_s)
transfer_loss = domain_adv(y_s, f_s, y_t, f_t)
domain_acc = domain_adv.domain_discriminator_accuracy
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
domain_accs.update(domain_acc.item(), x_s.size(0))
trans_losses.update(transfer_loss.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
# Choose different scheduler to test
scheduler = StepLR(optim, step_size=10, gamma=0.1)
scheduler = MultiStepLR(optim, milestones=[3, 6, 9], gamma=0.1)
scheduler = ReduceLROnPlateau(optim,
threshold=0.99,
mode='min',
patience=2,
cooldown=5)
scheduler = WarmupLR(scheduler,
init_lr=0.01,
num_warmup=3,
warmup_strategy='cos')
# this zero gradient update is needed to avoid a warning message, issue #8.
optim.zero_grad()
optim.step()
# The wrapper doesn't affect old scheduler api
# Simply plug and play
for epoch in range(1, 20):
# step with pseudo loss if we're using reducelronplateau
if isinstance(scheduler._scheduler, ReduceLROnPlateau):
pseudo_loss = 20 - epoch
scheduler.step(pseudo_loss)
print('Epoch: {} LR: {:.3f} pseudo loss: {:.2f}'.format(
epoch, optim.param_groups[0]['lr'], pseudo_loss))
# step without any parameters
else:
scheduler.step()
print(epoch, optim.param_groups[0]['lr'])
optim.step() # backward pass (update network)
def main():
# Arguments parser
parser = argparse.ArgumentParser(
description='Tuning with DNN Model for NER')
# Model Hyperparameters
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
default='LSTM')
parser.add_argument('--encoder_mode',
choices=['cnn', 'lstm'],
help='Encoder type for sentence encoding',
default='lstm')
parser.add_argument('--char_method',
choices=['cnn', 'lstm'],
help='Method to create character-level embeddings',
required=True)
parser.add_argument(
'--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN for sentence level')
parser.add_argument('--char_hidden_size',
type=int,
default=30,
help='Output character-level embeddings size')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--dropout',
choices=['std', 'weight_drop'],
help='Dropout method',
default='weight_drop')
parser.add_argument('--p_em',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input of RNN model')
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
# Data loading and storing params
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--dataset_name',
type=str,
default='alexa',
help='Which dataset to use')
parser.add_argument('--train',
type=str,
required=True,
help='Path of train set')
parser.add_argument('--dev',
type=str,
required=True,
help='Path of dev set')
parser.add_argument('--test',
type=str,
required=True,
help='Path of test set')
parser.add_argument('--results_folder',
type=str,
default='results',
help='The folder to store results')
parser.add_argument('--tmp_folder',
type=str,
default='tmp',
help='The folder to store tmp files')
parser.add_argument('--alphabets_folder',
type=str,
default='data/alphabets',
help='The folder to store alphabets files')
parser.add_argument('--result_file_name',
type=str,
default='hyperparameters_tuning',
help='File name to store some results')
parser.add_argument('--result_file_path',
type=str,
default='results/hyperparameters_tuning',
help='File name to store some results')
# Training parameters
parser.add_argument('--cuda',
action='store_true',
help='whether using GPU')
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Base learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.95,
help='Decay rate of learning rate')
parser.add_argument('--schedule',
type=int,
default=3,
help='schedule for learning rate decay')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for l2 regularization')
parser.add_argument('--max_norm',
type=float,
default=1.,
help='Max norm for gradients')
parser.add_argument('--gpu_id',
type=int,
nargs='+',
required=True,
help='which gpu to use for training')
# Misc
parser.add_argument('--embedding',
choices=['glove', 'senna', 'alexa'],
help='Embedding for words',
required=True)
parser.add_argument('--restore',
action='store_true',
help='whether restore from stored parameters')
parser.add_argument('--save_checkpoint',
type=str,
default='',
help='the path to save the model')
parser.add_argument('--o_tag',
type=str,
default='O',
help='The default tag for outside tag')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--evaluate_raw_format',
action='store_true',
help='The tagging format for evaluation')
args = parser.parse_args()
logger = get_logger("NERCRF")
# rename the parameters
mode = args.mode
encoder_mode = args.encoder_mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
char_hidden_size = args.char_hidden_size
char_method = args.char_method
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
max_norm = args.max_norm
schedule = args.schedule
dropout = args.dropout
p_em = args.p_em
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
dataset_name = args.dataset_name
result_file_name = args.result_file_name
evaluate_raw_format = args.evaluate_raw_format
o_tag = args.o_tag
restore = args.restore
save_checkpoint = args.save_checkpoint
gpu_id = args.gpu_id
results_folder = args.results_folder
tmp_folder = args.tmp_folder
alphabets_folder = args.alphabets_folder
use_elmo = False
p_em_vec = 0.
result_file_path = args.result_file_path
score_file = "%s/score_gpu_%s" % (tmp_folder, '-'.join(map(str, gpu_id)))
if not os.path.exists(results_folder):
os.makedirs(results_folder)
if not os.path.exists(tmp_folder):
os.makedirs(tmp_folder)
if not os.path.exists(alphabets_folder):
os.makedirs(alphabets_folder)
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, ner_alphabet = conll03_data.create_alphabets(
"{}/{}/".format(alphabets_folder, dataset_name),
train_path,
data_paths=[dev_path, test_path],
embedd_dict=embedd_dict,
max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
device = torch.device('cuda') if args.cuda else torch.device('cpu')
print(device)
data_train = conll03_data.read_data_to_tensor(train_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_tensor(dev_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
data_test = conll03_data.read_data_to_tensor(test_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
writer = CoNLL03Writer(word_alphabet, char_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform_
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
elif args.dropout == 'var':
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiWeightDropRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
network = network.to(device)
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
# optim = Adam(network.parameters(), lr=lr, weight_decay=gamma, amsgrad=True)
nn.utils.clip_grad_norm_(network.parameters(), max_norm)
logger.info("Network: %s, encoder_mode=%s, num_layer=%d, hidden=%d, char_hidden_size=%d, char_method=%s, tag_space=%d, crf=%s" % \
(mode, encoder_mode, num_layers, hidden_size, char_hidden_size, char_method, tag_space, 'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data // batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
best_test_f1 = 0.0
best_test_acc = 0.0
best_test_precision = 0.0
best_test_recall = 0.0
best_test_epoch = 0.0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
_, word, char, labels, masks, lengths = conll03_data.get_batch_tensor(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(_, word, char, labels, mask=masks)
loss.backward()
optim.step()
with torch.no_grad():
num_inst = word.size(0)
train_err += loss * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
with torch.no_grad():
network.eval()
tmp_filename = '%s/gpu_%s_dev' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_dev, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename, score_file,
evaluate_raw_format, o_tag)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = '%s/gpu_%s_test' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_test, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename, score_file, evaluate_raw_format, o_tag)
if best_test_f1 < test_f1:
best_test_acc, best_test_precision, best_test_recall, best_test_f1 = test_acc, test_precision, test_recall, test_f1
best_test_epoch = epoch
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
print(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
with open(result_file_path, 'a') as ofile:
ofile.write(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
ofile.write(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
ofile.write(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n\n"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
print('Training finished!')
x = model.module.features[2](x)
x = model.module.features[3](x)
#x = x.view(x.size(0), model.module.nfscat*3, model.module.nspace, model.module.nspace)
loss = -1.0* x[0, f_num, 2, 2]
#https://towardsdatascience.com/pytorch-implementation-of-perceptual-losses-for-real-time-style-transfer-8d608e2e9902
#reg_loss = REGULARIZATION * (
#torch.sum(torch.abs(im_as_var[:, :, :-1] - im_as_var[ :, :, 1:])) +
#torch.sum(torch.abs(im_as_var[ :, :-1, :] - im_as_var[:, 1:, :]))
#)
reg_loss = 0
loss = loss + reg_loss
loss.backward()
optimizer.step()
recreated_im = copy.copy(im_as_var.data.cpu().numpy()[0]).transpose(2,1,0)
#recreated_im = recreated_im[11:22,11:22,:]
minned = recreated_im - np.min(recreated_im)
ax1 = fig.add_subplot(num_rows, num_cols, importance + 1)
ax1.imshow(minned/np.max(minned))
ax1.axis('off')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
ax1.set_title("{0:.2f}".format(allFilters - scores[f_num]))
plt.subplots_adjust(wspace=1.0, hspace=0.1)
plt.savefig("deep_dream_alexnet_l2.png")
plt.close()
def train(opt):
# set device to cpu/gpu
if opt.use_gpu:
device = torch.device("cuda", opt.gpu_id)
else:
device = torch.device("cpu")
# Data transformations for data augmentation
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.RandomErasing(),
])
transform_val = transforms.Compose([
transforms.ToTensor(),
])
# get CIFAR10/CIFAR100 train/val set
if opt.dataset == "CIFAR10":
alp_lambda = 0.5
lambda_loss = [0.005, 0.001]
train_set = CIFAR10(root="./data", train=True,
download=True, transform=transform_train)
val_set = CIFAR10(root="./data", train=True,
download=True, transform=transform_val)
else:
alp_lambda = 0.5
lambda_loss = [0.005, 0.001]
train_set = CIFAR100(root="./data", train=True,
download=True, transform=transform_train)
val_set = CIFAR100(root="./data", train=True,
download=True, transform=transform_val)
num_classes = np.unique(train_set.targets).shape[0]
# set stratified train/val split
idx = list(range(len(train_set.targets)))
train_idx, val_idx, _, _ = train_test_split(
idx, train_set.targets, test_size=opt.val_split, random_state=42)
# get train/val samplers
train_sampler = SubsetRandomSampler(train_idx)
val_sampler = SubsetRandomSampler(val_idx)
# get train/val dataloaders
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=opt.batch_size,
num_workers=opt.num_workers)
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=opt.batch_size,
num_workers=opt.num_workers)
data_loaders = {"train": train_loader, "val": val_loader}
print("Dataset -- {}, Metric -- {}, Train Mode -- {}, Backbone -- {}".format(opt.dataset,
opt.metric, opt.train_mode, opt.backbone))
print("Train iteration batch size: {}".format(opt.batch_size))
print("Train iterations per epoch: {}".format(len(train_loader)))
# get backbone model
if opt.backbone == "resnet18":
model = resnet18(pretrained=False)
else:
model = resnet34(pretrained=False)
# set metric loss function
in_features = model.fc.in_features
model.fc = Softmax(in_features, num_classes)
model.to(device)
if opt.use_gpu:
model = DataParallel(model).to(device)
criterion = CrossEntropyLoss()
mse_criterion = MSELoss()
cent_criterion = CenterLoss(num_classes, in_features, device)
# set optimizer and LR scheduler
if opt.optimizer == "sgd":
optimizer = SGD([{"params": model.parameters()}],
lr=opt.lr, weight_decay=opt.weight_decay, momentum=0.9)
cent_optimizer = SGD([{"params": cent_criterion.parameters()}],
lr=opt.lr, weight_decay=opt.weight_decay, momentum=0.9)
else:
optimizer = Adam([{"params": model.parameters()}],
lr=opt.lr, weight_decay=opt.weight_decay)
cent_optimizer = Adam([{"params": cent_criterion.parameters()}],
lr=opt.lr, weight_decay=opt.weight_decay)
if opt.scheduler == "decay":
scheduler = lr_scheduler.StepLR(
optimizer, step_size=opt.lr_step, gamma=opt.lr_decay)
else:
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer, factor=0.1, patience=10)
# train/val loop
for epoch in range(opt.epoch):
for phase in ["train", "val"]:
total_examples, total_correct, total_loss = 0, 0, 0
if phase == "train":
model.train()
else:
model.eval()
start_time = time.time()
for ii, data in enumerate(data_loaders[phase]):
# load data batch to device
images, labels = data
images = images.to(device)
labels = labels.to(device).long()
# perform adversarial attack update to images
if opt.train_mode == "at" or opt.train_mode == "alp":
adv_images = pgd(
model, images, labels, 8. / 255, 2. / 255, 7)
else:
pass
# at train mode
if opt.train_mode == "at":
# get feature embedding and logits from resnet
features, predictions = model(images, labels)
adv_features, adv_predictions = model(adv_images, labels)
# get center loss
cent_loss = cent_criterion(features, labels)
cent_loss = cent_loss + \
cent_criterion(adv_features, labels)
# get feature norm loss
norm = features.mm(features.t()).diag()
adv_norm = adv_features.mm(adv_features.t()).diag()
norm_loss = (torch.sum(norm) + torch.sum(adv_norm)) / \
(features.size(0) + adv_features.size(0))
# get cross-entropy loss
ce_loss = criterion(predictions, labels)
ce_loss = ce_loss + criterion(adv_predictions, labels)
# combine cross-entropy loss, center loss and feature norm loss using lambda weights
loss = ce_loss + lambda_loss[0] * \
cent_loss + lambda_loss[1] * norm_loss
optimizer.zero_grad()
cent_optimizer.zero_grad()
# for result accumulation
predictions = adv_predictions
# alp train mode
elif opt.train_mode == "alp":
# get feature embedding and logits from resnet
features, predictions = model(images, labels)
adv_features, adv_predictions = model(adv_images, labels)
# get center loss
cent_loss = cent_criterion(features, labels)
cent_loss = cent_loss + \
cent_criterion(adv_features, labels)
# get feature norm loss
norm = features.mm(features.t()).diag()
adv_norm = adv_features.mm(adv_features.t()).diag()
norm_loss = (torch.sum(norm) + torch.sum(adv_norm)) / \
(features.size(0) + adv_features.size(0))
# get cross-entropy loss
ce_loss = criterion(predictions, labels)
ce_loss = ce_loss + criterion(adv_predictions, labels)
# get alp loss
alp_loss = mse_criterion(adv_predictions, predictions)
# combine cross-entropy loss, center loss and feature norm loss using lambda weights
loss = ce_loss + lambda_loss[0] * \
cent_loss + lambda_loss[1] * norm_loss
# combine loss with alp loss
loss = loss + alp_lambda * alp_loss
optimizer.zero_grad()
cent_optimizer.zero_grad()
# for result accumulation
predictions = adv_predictions
# clean train mode
else:
# get feature embedding and logits from resnet
features, predictions = model(images, labels)
# get center loss
cent_loss = cent_criterion(features, labels)
# get feature norm loss
norm = features.mm(features.t()).diag()
norm_loss = torch.sum(norm) / features.size(0)
# get cross-entropy loss
ce_loss = criterion(predictions, labels)
# combine cross-entropy loss, center loss and feature norm loss using lambda weights
loss = ce_loss + lambda_loss[0] * \
cent_loss + lambda_loss[1] * norm_loss
optimizer.zero_grad()
cent_optimizer.zero_grad()
# only take step if in train phase
if phase == "train":
loss.backward()
optimizer.step()
cent_optimizer.step()
# accumulate train or val results
predictions = torch.argmax(predictions, 1)
total_examples += predictions.size(0)
total_correct += predictions.eq(labels).sum().item()
total_loss += loss.item()
# print accumulated train/val results at end of epoch
if ii == len(data_loaders[phase]) - 1:
end_time = time.time()
acc = total_correct / total_examples
loss = total_loss / len(data_loaders[phase])
print("{}: Epoch -- {} Loss -- {:.6f} Acc -- {:.6f} Time -- {:.6f}sec".format(
phase, epoch, loss, acc, end_time - start_time))
if phase == "train":
loss = total_loss / len(data_loaders[phase])
scheduler.step(loss)
else:
print("")
# save model after training for opt.epoch
save_model(model, opt.dataset, opt.metric, opt.train_mode, opt.backbone)
class AR1(BaseStrategy):
"""
The AR1 strategy with Latent Replay.
This implementations allows for the use of both Synaptic Intelligence and
Latent Replay to protect the lower level of the model from forgetting.
While the original papers show how to use those two techniques in a mutual
exclusive way, this implementation allows for the use of both of them
concurrently. This behaviour is controlled by passing proper constructor
arguments).
"""
def __init__(self,
criterion=None,
lr: float = 0.001,
momentum=0.9,
l2=0.0005,
train_epochs: int = 4,
init_update_rate: float = 0.01,
inc_update_rate=0.00005,
max_r_max=1.25,
max_d_max=0.5,
inc_step=4.1e-05,
rm_sz: int = 1500,
freeze_below_layer: str = "lat_features.19.bn.beta",
latent_layer_num: int = 19,
ewc_lambda: float = 0,
train_mb_size: int = 128,
eval_mb_size: int = 128,
device=None,
plugins: Optional[Sequence[StrategyPlugin]] = None,
evaluator: EvaluationPlugin = default_logger,
eval_every=-1):
"""
Creates an instance of the AR1 strategy.
:param criterion: The loss criterion to use. Defaults to None, in which
case the cross entropy loss is used.
:param lr: The learning rate (SGD optimizer).
:param momentum: The momentum (SGD optimizer).
:param l2: The L2 penalty used for weight decay.
:param train_epochs: The number of training epochs. Defaults to 4.
:param init_update_rate: The initial update rate of BatchReNorm layers.
:param inc_update_rate: The incremental update rate of BatchReNorm
layers.
:param max_r_max: The maximum r value of BatchReNorm layers.
:param max_d_max: The maximum d value of BatchReNorm layers.
:param inc_step: The incremental step of r and d values of BatchReNorm
layers.
:param rm_sz: The size of the replay buffer. The replay buffer is shared
across classes. Defaults to 1500.
:param freeze_below_layer: A string describing the name of the layer
to use while freezing the lower (nearest to the input) part of the
model. The given layer is not frozen (exclusive).
:param latent_layer_num: The number of the layer to use as the Latent
Replay Layer. Usually this is the same of `freeze_below_layer`.
:param ewc_lambda: The Synaptic Intelligence lambda term. Defaults to
0, which means that the Synaptic Intelligence regularization
will not be applied.
:param train_mb_size: The train minibatch size. Defaults to 128.
:param eval_mb_size: The eval minibatch size. Defaults to 128.
:param device: The device to use. Defaults to None (cpu).
:param plugins: (optional) list of StrategyPlugins.
:param evaluator: (optional) instance of EvaluationPlugin for logging
and metric computations.
:param eval_every: the frequency of the calls to `eval` inside the
training loop.
if -1: no evaluation during training.
if 0: calls `eval` after the final epoch of each training
experience.
if >0: calls `eval` every `eval_every` epochs and at the end
of all the epochs for a single experience.
"""
warnings.warn("The AR1 strategy implementation is in an alpha stage "
"and is not perfectly aligned with the paper "
"implementation. Please use at your own risk!")
if plugins is None:
plugins = []
# Model setup
model = MobilenetV1(pretrained=True, latent_layer_num=latent_layer_num)
replace_bn_with_brn(model,
momentum=init_update_rate,
r_d_max_inc_step=inc_step,
max_r_max=max_r_max,
max_d_max=max_d_max)
fc_name, fc_layer = get_last_fc_layer(model)
if ewc_lambda != 0:
# Synaptic Intelligence is not applied to the last fully
# connected layer (and implicitly to "freeze below" ones.
plugins.append(
SynapticIntelligencePlugin(ewc_lambda,
excluded_parameters=[fc_name]))
self.cwr_plugin = CWRStarPlugin(model,
cwr_layer_name=fc_name,
freeze_remaining_model=False)
plugins.append(self.cwr_plugin)
optimizer = SGD(model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=l2)
if criterion is None:
criterion = CrossEntropyLoss()
self.ewc_lambda = ewc_lambda
self.freeze_below_layer = freeze_below_layer
self.rm_sz = rm_sz
self.inc_update_rate = inc_update_rate
self.max_r_max = max_r_max
self.max_d_max = max_d_max
self.lr = lr
self.momentum = momentum
self.l2 = l2
self.rm = None
self.cur_acts: Optional[Tensor] = None
self.replay_mb_size = 0
super().__init__(model,
optimizer,
criterion,
train_mb_size=train_mb_size,
train_epochs=train_epochs,
eval_mb_size=eval_mb_size,
device=device,
plugins=plugins,
evaluator=evaluator,
eval_every=eval_every)
def before_training_exp(self, **kwargs):
self.model.eval()
self.model.end_features.train()
self.model.output.train()
if self.training_exp_counter > 0:
# In AR1 batch 0 is treated differently as the feature extractor is
# left more free to learn.
# This if is executed for batch > 0, in which we freeze layers
# below "self.freeze_below_layer" (which usually is the latent
# replay layer!) and we also change the parameters of BatchReNorm
# layers to a more conservative configuration.
# "freeze_up_to" will freeze layers below "freeze_below_layer"
# Beware that Batch ReNorm layers are not frozen!
freeze_up_to(self.model,
freeze_until_layer=self.freeze_below_layer,
layer_filter=AR1.filter_bn_and_brn)
# Adapt the parameters of BatchReNorm layers
change_brn_pars(self.model,
momentum=self.inc_update_rate,
r_d_max_inc_step=0,
r_max=self.max_r_max,
d_max=self.max_d_max)
# Adapt the model and optimizer
self.model = self.model.to(self.device)
self.optimizer = SGD(self.model.parameters(),
lr=self.lr,
momentum=self.momentum,
weight_decay=self.l2)
# super()... will run S.I. and CWR* plugin callbacks
super().before_training_exp(**kwargs)
# Update cur_j of CWR* to consider latent patterns
if self.training_exp_counter > 0:
for class_id, count in examples_per_class(self.rm[1]).items():
self.model.cur_j[class_id] += count
self.cwr_plugin.cur_class = [
cls for cls in set(self.model.cur_j.keys())
if self.model.cur_j[cls] > 0
]
self.cwr_plugin.reset_weights(self.cwr_plugin.cur_class)
def make_train_dataloader(self, num_workers=0, shuffle=True, **kwargs):
"""
Called after the dataset instantiation. Initialize the data loader.
For AR1 a "custom" dataloader is used: instead of using
`self.train_mb_size` as the batch size, the data loader batch size will
be computed ad `self.train_mb_size - latent_mb_size`. `latent_mb_size`
is in turn computed as:
`
len(train_dataset) // ((len(train_dataset) + len(replay_buffer)
// self.train_mb_size)
`
so that the number of iterations required to run an epoch on the current
batch is equal to the number of iterations required to run an epoch
on the replay buffer.
:param num_workers: number of thread workers for the data loading.
:param shuffle: True if the data should be shuffled, False otherwise.
"""
current_batch_mb_size = self.train_mb_size
if self.training_exp_counter > 0:
train_patterns = len(self.adapted_dataset)
current_batch_mb_size = train_patterns // (
(train_patterns + self.rm_sz) // self.train_mb_size)
current_batch_mb_size = max(1, current_batch_mb_size)
self.replay_mb_size = max(0,
self.train_mb_size - current_batch_mb_size)
# AR1 only supports SIT scenarios (no task labels).
assert len(self.adapted_dataset.keys()) == 1
curr_data = list(self.adapted_dataset.values())[0]
self.current_dataloader = DataLoader(curr_data,
num_workers=num_workers,
batch_size=current_batch_mb_size,
shuffle=shuffle)
def training_epoch(self, **kwargs):
for self.mb_it, (self.mb_x, self.mb_y, _) in \
enumerate(self.current_dataloader):
self.before_training_iteration(**kwargs)
self.optimizer.zero_grad()
self.mb_x = self.mb_x.to(self.device)
self.mb_y = self.mb_y.to(self.device)
if self.training_exp_counter > 0:
lat_mb_x = self.rm[0][self.mb_it *
self.replay_mb_size:(self.mb_it + 1) *
self.replay_mb_size]
lat_mb_x = lat_mb_x.to(self.device)
lat_mb_y = self.rm[1][self.mb_it *
self.replay_mb_size:(self.mb_it + 1) *
self.replay_mb_size]
lat_mb_y = lat_mb_y.to(self.device)
self.mb_y = torch.cat((self.mb_y, lat_mb_y), 0)
else:
lat_mb_x = None
# Forward pass. Here we are injecting latent patterns lat_mb_x.
# lat_mb_x will be None for the very first batch (batch 0), which
# means that lat_acts.shape[0] == self.mb_x[0].
self.before_forward(**kwargs)
self.logits, lat_acts = self.model(self.mb_x,
latent_input=lat_mb_x,
return_lat_acts=True)
if self.epoch == 0:
# On the first epoch only: store latent activations. Those
# activations will be used to update the replay buffer.
lat_acts = lat_acts.detach().clone().cpu()
if self.mb_it == 0:
self.cur_acts = lat_acts
else:
self.cur_acts = torch.cat((self.cur_acts, lat_acts), 0)
self.after_forward(**kwargs)
# Loss & Backward
# We don't need to handle latent replay, as self.mb_y already
# contains both current and replay labels.
self.loss = self.criterion(self.logits, self.mb_y)
self.before_backward(**kwargs)
self.loss.backward()
self.after_backward(**kwargs)
# Optimization step
self.before_update(**kwargs)
self.optimizer.step()
self.after_update(**kwargs)
self.after_training_iteration(**kwargs)
def after_training_exp(self, **kwargs):
h = min(self.rm_sz // (self.training_exp_counter + 1),
self.cur_acts.size(0))
curr_data = self.experience.dataset
idxs_cur = torch.randperm(self.cur_acts.size(0))[:h]
rm_add_y = torch.tensor(
[curr_data.targets[idx_cur] for idx_cur in idxs_cur])
rm_add = [self.cur_acts[idxs_cur], rm_add_y]
# replace patterns in random memory
if self.training_exp_counter == 0:
self.rm = rm_add
else:
idxs_2_replace = torch.randperm(self.rm[0].size(0))[:h]
for j, idx in enumerate(idxs_2_replace):
idx = int(idx)
self.rm[0][idx] = rm_add[0][j]
self.rm[1][idx] = rm_add[1][j]
self.cur_acts = None
# Runs S.I. and CWR* plugin callbacks
super().after_training_exp(**kwargs)
@staticmethod
def filter_bn_and_brn(param_def: LayerAndParameter):
return not isinstance(param_def.layer, (_NormBase, BatchRenorm2D))
def train_model(model_name,
model,
lr=LEARNING_RATE,
epochs=EPOCHS,
momentum=MOMENTUM,
weight_decay=0,
train_loader=training_set_loader,
test_loader=validation_set_loader):
if not os.path.exists(RESULTS_PATH + "/" + model_name):
os.makedirs(RESULTS_PATH + "/" + model_name)
criterion = nn.CrossEntropyLoss()
optimizer = SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=weight_decay)
loaders = {'train': train_loader, 'test': test_loader}
losses = {'train': [], 'test': []}
accuracies = {'train': [], 'test': []}
#testing variables
y_testing = []
preds = []
if USE_CUDA and cuda_available:
model = model.cuda()
for e in range(epochs):
for mode in ['train', 'test']:
if mode == 'train':
model.train()
else:
model.eval()
epoch_loss = 0
epoch_acc = 0
samples = 0
try:
for i, batch in enumerate(loaders[mode]):
# convert tensor to variable
x = Variable(batch['image'],
requires_grad=(mode == 'train'))
y = Variable(batch['label'])
if USE_CUDA and cuda_available:
x = x.cuda()
y = y.cuda()
output = model(x)
l = criterion(output, y) # loss
if mode == 'train':
l.backward()
optimizer.step()
optimizer.zero_grad()
else:
y_testing.extend(y.data.tolist())
preds.extend(output.max(1)[1].tolist())
if USE_CUDA and cuda_available:
acc = accuracy_score(
y.data.cuda().cpu().numpy(),
output.max(1)[1].cuda().cpu().numpy())
else:
acc = accuracy_score(y.data, output.max(1)[1])
epoch_loss += l.data.item() * x.shape[0] # l.data[0]
epoch_acc += acc * x.shape[0]
samples += x.shape[0]
print ("\r[%s] Epoch %d/%d. Iteration %d/%d. Loss: %0.2f. Accuracy: %0.2f" % \
(mode, e+1, epochs, i, len(loaders[mode]), epoch_loss/samples, epoch_acc/samples))
if DEBUG and i == 2:
break
except Exception as err:
print("\n\n######### ERROR #######")
print(str(err))
print("\n\n######### batch #######")
print(batch['img_name'])
print("\n\n")
epoch_loss /= samples
epoch_acc /= samples
losses[mode].append(epoch_loss)
accuracies[mode].append(epoch_acc)
print ("\r[%s] Epoch %d/%d. Iteration %d/%d. Loss: %0.2f. Accuracy: %0.2f" % \
(mode, e+1, epochs, i, len(loaders[mode]), epoch_loss, epoch_acc))
torch.save(
model.state_dict(),
str(RESULTS_PATH) + "/" + str(model_name) + "/" + str(model_name) +
".pt")
return model, (losses, accuracies), y_testing, preds
batch_loader.chars_vocab_size)
neg_loss = NEG_loss(params.word_vocab_size, params.word_embed_size)
if args.use_cuda:
neg_loss = neg_loss.cuda()
# NEG_loss is defined over two embedding matrixes with shape of [params.word_vocab_size, params.word_embed_size]
optimizer = SGD(neg_loss.parameters(), 0.1)
for iteration in range(args.num_iterations):
input_idx, target_idx = batch_loader.next_embedding_seq(args.batch_size)
input = Variable(t.from_numpy(input_idx).long())
target = Variable(t.from_numpy(target_idx).long())
if args.use_cuda:
input, target = input.cuda(), target.cuda()
out = neg_loss(input, target, args.num_sample).mean()
optimizer.zero_grad()
out.backward()
optimizer.step()
if iteration % 500 == 0:
out = out.cpu().data.numpy()[0]
print('iteration = {}, loss = {}'.format(iteration, out))
word_embeddings = neg_loss.input_embeddings()
np.save('data/word_embeddings.npy', word_embeddings)
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, classifier: ImageClassifier,
mdd: MarginDisparityDiscrepancy, optimizer: SGD,
lr_scheduler: LambdaLR, epoch: int, args: argparse.Namespace):
batch_time = AverageMeter('Time', ':3.1f')
data_time = AverageMeter('Data', ':3.1f')
losses = AverageMeter('Loss', ':3.2f')
trans_losses = AverageMeter('Trans Loss', ':3.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
tgt_accs = AverageMeter('Tgt Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, trans_losses, cls_accs, tgt_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
classifier.train()
mdd.train()
criterion = nn.CrossEntropyLoss().to(device)
end = time.time()
for i in range(args.iters_per_epoch):
optimizer.zero_grad()
x_s, labels_s = next(train_source_iter)
x_t, labels_t = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
labels_t = labels_t.to(device)
# measure data loading time
data_time.update(time.time() - end)
# compute output
x = torch.cat((x_s, x_t), dim=0)
outputs, outputs_adv = classifier(x)
y_s, y_t = outputs.chunk(2, dim=0)
y_s_adv, y_t_adv = outputs_adv.chunk(2, dim=0)
# compute cross entropy loss on source domain
cls_loss = criterion(y_s, labels_s)
# compute margin disparity discrepancy between domains
# for adversarial classifier, minimize negative mdd is equal to maximize mdd
transfer_loss = -mdd(y_s, y_s_adv, y_t, y_t_adv)
loss = cls_loss + transfer_loss * args.trade_off
classifier.step()
cls_acc = accuracy(y_s, labels_s)[0]
tgt_acc = accuracy(y_t, labels_t)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
tgt_accs.update(tgt_acc.item(), x_t.size(0))
trans_losses.update(transfer_loss.item(), x_s.size(0))
# compute gradient and do SGD step
loss.backward()
optimizer.step()
lr_scheduler.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def train(**kwargs):
opt.parse(kwargs)
images, tags, labels = load_data(opt.data_path)
pretrain_model = load_pretrain_model(opt.pretrain_model_path)
y_dim = tags.shape[1]
X, Y, L = split_data(images, tags, labels)
print('...loading and splitting data finish')
img_model = ImgModule(opt.bit, pretrain_model)
txt_model = TxtModule(y_dim, opt.bit)
if opt.use_gpu:
img_model = img_model.cuda()
txt_model = txt_model.cuda()
train_L = torch.from_numpy(L['train'])
train_x = torch.from_numpy(X['train'])
train_y = torch.from_numpy(Y['train'])
query_L = torch.from_numpy(L['query'])
query_x = torch.from_numpy(X['query'])
query_y = torch.from_numpy(Y['query'])
retrieval_L = torch.from_numpy(L['retrieval'])
retrieval_x = torch.from_numpy(X['retrieval'])
retrieval_y = torch.from_numpy(Y['retrieval'])
num_train = train_x.shape[0]
F_buffer = torch.randn(num_train, opt.bit)
G_buffer = torch.randn(num_train, opt.bit)
if opt.use_gpu:
train_L = train_L.cuda()
F_buffer = F_buffer.cuda()
G_buffer = G_buffer.cuda()
Sim = calc_neighbor(train_L, train_L)
B = torch.sign(F_buffer + G_buffer)
batch_size = opt.batch_size
lr = opt.lr
optimizer_img = SGD(img_model.parameters(), lr=lr)
optimizer_txt = SGD(txt_model.parameters(), lr=lr)
learning_rate = np.linspace(opt.lr, np.power(10, -6.), opt.max_epoch + 1)
result = {'loss': []}
ones = torch.ones(batch_size, 1)
ones_ = torch.ones(num_train - batch_size, 1)
unupdated_size = num_train - batch_size
max_mapi2t = max_mapt2i = 0.
for epoch in range(opt.max_epoch):
# train image net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0:batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
image = Variable(train_x[ind].type(torch.float))
if opt.use_gpu:
image = image.cuda()
sample_L = sample_L.cuda()
ones = ones.cuda()
ones_ = ones_.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
cur_f = img_model(image) # cur_f: (batch_size, bit)
F_buffer[ind, :] = cur_f.data
F = Variable(F_buffer)
G = Variable(G_buffer)
theta_x = 1.0 / 2 * torch.matmul(cur_f, G.t())
logloss_x = -torch.sum(S * theta_x -
torch.log(1.0 + torch.exp(theta_x)))
quantization_x = torch.sum(torch.pow(B[ind, :] - cur_f, 2))
balance_x = torch.sum(
torch.pow(cur_f.t().mm(ones) + F[unupdated_ind].t().mm(ones_),
2))
loss_x = logloss_x + opt.gamma * quantization_x + opt.eta * balance_x
loss_x /= (batch_size * num_train)
optimizer_img.zero_grad()
loss_x.backward()
optimizer_img.step()
# train txt net
for i in tqdm(range(num_train // batch_size)):
index = np.random.permutation(num_train)
ind = index[0:batch_size]
unupdated_ind = np.setdiff1d(range(num_train), ind)
sample_L = Variable(train_L[ind, :])
text = train_y[ind, :].unsqueeze(1).unsqueeze(-1).type(torch.float)
text = Variable(text)
if opt.use_gpu:
text = text.cuda()
sample_L = sample_L.cuda()
# similar matrix size: (batch_size, num_train)
S = calc_neighbor(sample_L, train_L) # S: (batch_size, num_train)
cur_g = txt_model(text) # cur_f: (batch_size, bit)
G_buffer[ind, :] = cur_g.data
F = Variable(F_buffer)
G = Variable(G_buffer)
# calculate loss
# theta_y: (batch_size, num_train)
theta_y = 1.0 / 2 * torch.matmul(cur_g, F.t())
logloss_y = -torch.sum(S * theta_y -
torch.log(1.0 + torch.exp(theta_y)))
quantization_y = torch.sum(torch.pow(B[ind, :] - cur_g, 2))
balance_y = torch.sum(
torch.pow(cur_g.t().mm(ones) + G[unupdated_ind].t().mm(ones_),
2))
loss_y = logloss_y + opt.gamma * quantization_y + opt.eta * balance_y
loss_y /= (num_train * batch_size)
optimizer_txt.zero_grad()
loss_y.backward()
optimizer_txt.step()
# update B
B = torch.sign(F_buffer + G_buffer)
# calculate total loss
loss = calc_loss(B, F, G, Variable(Sim), opt.gamma, opt.eta)
print('...epoch: %3d, loss: %3.3f, lr: %f' %
(epoch + 1, loss.data, lr))
result['loss'].append(float(loss.data))
if opt.valid:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x,
query_y, retrieval_y, query_L, retrieval_L)
print(
'...epoch: %3d, valid MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f'
% (epoch + 1, mapi2t, mapt2i))
if mapt2i >= max_mapt2i and mapi2t >= max_mapi2t:
max_mapi2t = mapi2t
max_mapt2i = mapt2i
img_model.save(img_model.module_name + '.pth')
txt_model.save(txt_model.module_name + '.pth')
lr = learning_rate[epoch + 1]
# set learning rate
for param in optimizer_img.param_groups:
param['lr'] = lr
for param in optimizer_txt.param_groups:
param['lr'] = lr
print('...training procedure finish')
if opt.valid:
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' %
(max_mapi2t, max_mapt2i))
result['mapi2t'] = max_mapi2t
result['mapt2i'] = max_mapt2i
else:
mapi2t, mapt2i = valid(img_model, txt_model, query_x, retrieval_x,
query_y, retrieval_y, query_L, retrieval_L)
print(' max MAP: MAP(i->t): %3.4f, MAP(t->i): %3.4f' %
(mapi2t, mapt2i))
result['mapi2t'] = mapi2t
result['mapt2i'] = mapt2i
write_result(result)
def train(model, state, path, annotations, val_path, val_annotations, resize, max_size, jitter, batch_size, iterations, val_iterations, mixed_precision, lr, warmup, milestones, gamma, is_master=True, world=1, use_dali=True, verbose=True, metrics_url=None, logdir=None):
'Train the model on the given dataset'
print("This is train.py, lr = ", lr)
# Prepare model
nn_model = model
stride = model.stride
model = convert_fixedbn_model(model)
if torch.cuda.is_available():
model = model.cuda()
# Setup optimizer and schedule
optimizer = SGD(model.parameters(), lr=lr, weight_decay=0.0001, momentum=0.9)
model, optimizer = amp.initialize(model, optimizer,
opt_level = 'O2' if mixed_precision else 'O0',
keep_batchnorm_fp32 = True,
loss_scale = 128.0,
verbosity = is_master)
print("This is train.py/train, optimizer param_groups, before: ")
print(optimizer.state_dict()['param_groups'])
if world > 1:
model = DistributedDataParallel(model)
model.train()
if 'optimizer' in state:
#print("This is state['optimizer']")
#print(state['optimizer'])
optimizer.load_state_dict(state['optimizer'])
for g in optimizer.param_groups:
g['lr'] = lr
g['initial_lr'] = lr
print("This is train.py/train, optimizer param_groups, after: ")
print(optimizer.state_dict()['param_groups'])
#print(optimizer.param_groups)
def schedule(train_iter):
if warmup and train_iter <= warmup:
return 0.9 * train_iter / warmup + 0.1
return gamma ** len([m for m in milestones if m <= train_iter])
scheduler = LambdaLR(optimizer, schedule)
# Prepare dataset
if verbose: print('Preparing dataset...')
data_iterator = (DaliDataIterator if use_dali else DataIterator)(
path, jitter, max_size, batch_size, stride,
world, annotations, training=True)
if verbose: print(data_iterator)
if verbose:
print(' device: {} {}'.format(
world, 'cpu' if not torch.cuda.is_available() else 'gpu' if world == 1 else 'gpus'))
print(' batch: {}, precision: {}'.format(batch_size, 'mixed' if mixed_precision else 'full'))
print('Training model for {} iterations...'.format(iterations))
# Create TensorBoard writer
if logdir is not None:
from tensorboardX import SummaryWriter
if is_master and verbose:
print('Writing TensorBoard logs to: {}'.format(logdir))
writer = SummaryWriter(logdir=logdir)
profiler = Profiler(['train', 'fw', 'bw'])
iteration = state.get('iteration', 0)
while iteration < iterations:
cls_losses, box_losses = [], []
for i, (data, target) in enumerate(data_iterator):
scheduler.step(iteration)
# Forward pass
profiler.start('fw')
optimizer.zero_grad()
cls_loss, box_loss = model([data, target])
del data
profiler.stop('fw')
# Backward pass
profiler.start('bw')
with amp.scale_loss(cls_loss + box_loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
# Reduce all losses
cls_loss, box_loss = cls_loss.mean().clone(), box_loss.mean().clone()
if world > 1:
torch.distributed.all_reduce(cls_loss)
torch.distributed.all_reduce(box_loss)
cls_loss /= world
box_loss /= world
if is_master:
cls_losses.append(cls_loss)
box_losses.append(box_loss)
if is_master and not isfinite(cls_loss + box_loss):
raise RuntimeError('Loss is diverging!\n{}'.format(
'Try lowering the learning rate.'))
del cls_loss, box_loss
profiler.stop('bw')
iteration += 1
profiler.bump('train')
if is_master and (profiler.totals['train'] > 60 or iteration == iterations):
focal_loss = torch.stack(list(cls_losses)).mean().item()
box_loss = torch.stack(list(box_losses)).mean().item()
learning_rate = optimizer.param_groups[0]['lr']
if verbose:
msg = '[{:{len}}/{}]'.format(iteration, iterations, len=len(str(iterations)))
msg += ' focal loss: {:.3f}'.format(focal_loss)
msg += ', box loss: {:.3f}'.format(box_loss)
msg += ', {:.3f}s/{}-batch'.format(profiler.means['train'], batch_size)
msg += ' (fw: {:.3f}s, bw: {:.3f}s)'.format(profiler.means['fw'], profiler.means['bw'])
msg += ', {:.1f} im/s'.format(batch_size / profiler.means['train'])
msg += ', lr: {:.2g}'.format(learning_rate)
print(msg, flush=True)
if logdir is not None:
writer.add_scalar('focal_loss', focal_loss, iteration)
writer.add_scalar('box_loss', box_loss, iteration)
writer.add_scalar('learning_rate', learning_rate, iteration)
del box_loss, focal_loss
if metrics_url:
post_metrics(metrics_url, {
'focal loss': mean(cls_losses),
'box loss': mean(box_losses),
'im_s': batch_size / profiler.means['train'],
'lr': learning_rate
})
# Save model weights
state.update({
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
})
with ignore_sigint():
nn_model.save(state)
profiler.reset()
del cls_losses[:], box_losses[:]
if val_annotations and (iteration == iterations or iteration % val_iterations == 0):
infer(model, val_path, None, resize, max_size, batch_size, annotations=val_annotations,
mixed_precision=mixed_precision, is_master=is_master, world=world, use_dali=use_dali, is_validation=True, verbose=False)
model.train()
if iteration == iterations:
break
if logdir is not None:
writer.close()