def create_post(node):
operation = request.GET.get("operation")
uci_model = client.get_uci_config()
parent = uci_model.find_child(node)
if isinstance(parent, uci_raw.Section):
if operation == "add-list":
form = UciRawForm(uci_raw.List, editable_key=True)
if form.validates(request.POST):
new_element = form.to_model()
elif operation == "add-option":
form = UciRawForm(uci_raw.Option, editable_key=True)
if form.validates(request.POST):
new_element = form.to_model()
else:
raise ValueError(
"Requested operation not allowed for Section node.")
elif isinstance(parent, uci_raw.Config):
form = UciRawForm(uci_raw.Section, editable_key=True)(request.POST)
if form.validates(request.POST):
new_element = form.to_model()
elif isinstance(parent, uci_raw.List):
form = UciRawForm(uci_raw.Value, editable_key=True)(request.POST)
if form.validates(request.POST):
new_element = form.to_model()
else:
raise ValueError("New node cannot be created here.")
if not form.valid:
return dict(node_path=node, form=form)
new_element.operation = "create"
parent.add(new_element)
print_model(new_element)
edit_uci_config(new_element)
bottle.redirect(reverse("uci_index"))
def create_post(node):
operation = request.GET.get("operation")
uci_model = client.get_uci_config()
parent = uci_model.find_child(node)
if isinstance(parent, uci_raw.Section):
if operation == "add-list":
form = UciRawForm(uci_raw.List, editable_key=True)
if form.validates(request.POST):
new_element = form.to_model()
elif operation == "add-option":
form = UciRawForm(uci_raw.Option, editable_key=True)
if form.validates(request.POST):
new_element = form.to_model()
else:
raise ValueError("Requested operation not allowed for Section node.")
elif isinstance(parent, uci_raw.Config):
form = UciRawForm(uci_raw.Section, editable_key=True)(request.POST)
if form.validates(request.POST):
new_element = form.to_model()
elif isinstance(parent, uci_raw.List):
form = UciRawForm(uci_raw.Value, editable_key=True)(request.POST)
if form.validates(request.POST):
new_element = form.to_model()
else:
raise ValueError("New node cannot be created here.")
if not form.valid:
return dict(node_path=node, form=form)
new_element.operation = "create"
parent.add(new_element)
print_model(new_element)
edit_uci_config(new_element)
bottle.redirect(reverse("uci_index"))
def mnist_utilizando_cnn_simples():
(X_train, y_train), (X_test, y_test) = load_mnist_dataset('mnist.npz')
# transformar para o formato [instancias][pixeis][largura][altura]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')
# normalizar os valores dos pixeis de 0-255 para 0-1
X_train = X_train / 255
X_test = X_test / 255
# transformar o label que é um inteiro em categorias binárias, o valor passa a ser o correspondente à posição
# o 5 passa a ser a lista [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# definir a topologia da rede e compilar
model = create_compile_model_cnn_simples(num_classes)
utils.print_model(model, "model_simples.png")
# treinar a rede
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=10,
batch_size=100,
verbose=2)
#print_history_accuracy(history)
utils.print_history_loss(history)
# Avaliação final com os casos de teste
scores = model.evaluate(X_test, y_test, verbose=0)
print('Scores: ', scores)
print("Erro modelo MLP: %.2f%%" % (100 - scores[1] * 100))
def ciclo_completo():
(input_attributes,
output_attributes) = read_cvs_dataset("pima-indians-diabetes.csv", 8)
model = create_model()
utils.print_model(model, "model_MLP.png")
compile_model(model)
history = fit_model(model, input_attributes, output_attributes)
utils.print_history_loss(history)
model_evaluate(model, input_attributes, output_attributes)
model_print_predictions(model, input_attributes, output_attributes)
def train(model,
v_emb,
q_emb,
groundtruth,
num_epochs,
output,
opt=None,
s_epoch=0):
lr_default = 1e-3 * 0.5
lr_decay_step = 2
lr_decay_rate = .25
lr_decay_epochs = range(10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
v_emb = v_emb.cuda()
q_emb = q_emb.cuda()
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = 0
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] *= lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
gw = model(v_emb, q_emb)
loss = gw
print(loss)
loss.backward()
optim.step()
optim.zero_grad()
def ciclo_ler_dataset_treinar_gravar():
(input_attributes,
output_attributes) = read_cvs_dataset("pima-indians-diabetes.csv", 8)
model = create_model()
utils.print_model(model, "model2.png")
compile_model(model)
history = fit_model(model, input_attributes, output_attributes)
utils.print_history_accuracy(history)
utils.print_history_loss(history)
model_evaluate(model, input_attributes, output_attributes)
utils.save_model_json(model, "model.json")
utils.save_weights_hdf5(model, "model.h5")
return (input_attributes, output_attributes)
def train_foil(model, train_loader, eval_loader, num_epochs, output, lr):
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr)
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_accuracy = 0
utils.print_model(model, logger)
logger.write('optim: adam lr=%.4f' % lr)
for epoch in range(num_epochs):
print("Epoch {}".format(epoch))
total_loss = 0
train_score = 0
t = time.time()
N = len(train_loader.dataset)
bar = progressbar.ProgressBar(max_value=N)
idx = 0
for i, (v, b, q, a) in enumerate(train_loader):
model.train(True)
bar.update(idx)
batch_size = v.size(0)
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
a = Variable(a).cuda()
idx += batch_size
pred, att = model(v, b, q, a)
loss = instance_bce_with_logits(pred, a)
optim.zero_grad()
loss.backward()
optim.step()
batch_score = compute_accuracy_with_logits(pred, a.data)
total_loss += loss.data[0] * v.size(0)
train_score += batch_score
bar.update(idx)
total_loss /= N
train_score = 100 * train_score / N
if eval_loader is not None:
model.train(False)
eval_score = evaluate_foil(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, score: %.2f' %
(total_loss, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f' % (100 * eval_score))
if eval_loader is not None and eval_score > best_eval_accuracy:
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_accuracy = eval_score
def main_quantified_TransE():
# Define some hyper-parameters for training
emb_dim = 100
lr = 0.0004
margin = 0.5
n_epochs = 1000
batch_size = 2097152
# Load dataset
data_path = "/tmp/pycharm_project_583/data/uncmtrd/agg6_202005_ALL_tv.csv"
kg_train, kg_val, kg_test = load_custom_qr(data_path=data_path)
model = TransEQuantifiedRelations(
emb_dim, kg_train.n_ent, kg_train.n_rel, dissimilarity_type="L2"
)
print_model(model) # check we only have two embedding layers - one for entity, the other for relations
dataset_name = data_path.split('/')[-1].replace('.csv', '')
curr_time = datetime.now().strftime('%Y%m%d%H%M%S')
model_prefix = os.path.join('./pretrained', f'{dataset_name}_emb{emb_dim}_lr{lr}_mgn{margin}_epch{n_epochs}_bsize{batch_size}_t{curr_time}')
criterion = MarginLoss(margin)
optimizer = Adam(model.parameters(), lr=lr, weight_decay=1e-5)
trainer = Trainer(
model,
criterion,
kg_train,
n_epochs,
batch_size,
optimizer=optimizer,
sampling_type="bern",
use_cuda=None,
)
trainer.run(kg_test=kg_test, model_prefix=model_prefix)
def LSTM_sales_data(normalizer=None):
df, scaler = get_data(normalizer=normalizer)
print("Dataset: ", df.shape)
janela = 6 #tamanho da Janela deslizante (trimestral, mensal, semestral)
X_train, y_train, X_test, y_test = split_data(df, janela)
print("X_train", X_train.shape)
print("y_train", y_train.shape)
print("X_test", X_test.shape)
print("y_test", y_test.shape)
model = build_model(janela)
model.fit(X_train, y_train, batch_size=10, epochs=300, validation_split=0.1, verbose=1) #validation 0.1 dos 0.66 usados para treino
utils.print_model(model,"lstm_model.png")
trainScore = model.evaluate(X_train, y_train, verbose=0)
print('\n Train Score: %.2f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))
testScore = model.evaluate(X_test, y_test, verbose=0)
print(' Test Score: %.2f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))
print('\n****************** UNSCALED*******************')
# Unscale Results to get real value predictions and error
trainScore = trainScore[0].reshape(-1, 1).astype('float32')
unscaled_Train = scaler.inverse_transform(trainScore)
print('\n Unscaled Train Score: %.2f MSE (%.2f RMSE)' % (unscaled_Train, math.sqrt(unscaled_Train)))
testScore = testScore[0].reshape(-1, 1).astype('float32')
unscaled_Test = scaler.inverse_transform(testScore)
print(' Unscaled Test Score: %.2f MSE (%.2f RMSE) \n' % (unscaled_Test, math.sqrt(unscaled_Test)))
p = model.predict(X_test)
predic = np.squeeze(np.asarray(p)) #para transformar uma matriz de uma coluna e n linhas em um np array de n elementos
print_series_prediction(y_test,predic)
print('')
print_series_prediction(y_test,predic, normalizer=scaler)
def mnist_utilizando_mlp():
(X_train, y_train), (X_test, y_test) = load_mnist_dataset('mnist.npz')
# transformar a matriz 28*28 das imagens num vector com 784 atributos para cada imagem (porque é multilayer-perceptron)
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalizar os valores dos pixeis de 0-255 para 0-1
X_train = X_train / 255
X_test = X_test / 255
# transformar o label que é um inteiro em categorias binárias, o valor passa a ser o correspondente à posição
# o 5 passa a ser a lista [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# definir a topologia da rede e compilar
model = create_compile_model_mlp(num_pixels, num_classes)
utils.print_model(model, "model.png")
# treinar a rede
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=10,
batch_size=50,
verbose=2)
#utils.print_history_accuracy(history)
utils.print_history_loss(history)
# Avaliação final com os casos de teste
scores = model.evaluate(X_test, y_test, verbose=0)
print('Scores: ', scores)
print("Erro modelo MLP: %.2f%%" % (100 - scores[1] * 100))
def debug(node):
uci_model = client.get_uci_config()
node_model = uci_model.find_child(node)
return "<pre>%s</pre>" % websafe(print_model(node_model))
def train(model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0,
logger=None,
save_one_ckpt=True):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = .25
lr_decay_epochs = range(
10, 20, lr_decay_step) if eval_loader is not None else range(
10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
dset = train_loader.dataset
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
if logger is None:
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
model_path = os.path.join(output, 'model_epoch-1.pth')
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
train_zcore = 0
total_norm = 0
count_norm = 0
n_answer_type = torch.zeros(len(dset.idx2type))
score_answer_type = torch.zeros(len(dset.idx2type))
t = time.time()
N = len(train_loader.dataset)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] *= lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for i, (v, b, q, a, c, at) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
c = c.cuda().unsqueeze(-1).float()
at = at.cuda()
answer_type = torch.zeros(v.size(0), len(dset.idx2type)).cuda()
answer_type.scatter_(1, at.unsqueeze(1), 1)
pred, conf, att = model(v, b, q, a, c)
loss = instance_bce_with_logits(pred, a)
loss.backward(retain_graph=True)
losz = instance_bce_with_logits(conf, c)
losz.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score = compute_score_with_logits(pred, a.data)
type_score = batch_score.sum(-1, keepdim=True) * answer_type
batch_score = batch_score.sum()
total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
batch_zcore = compute_zcore_with_logits(conf, c.data).sum()
train_zcore += batch_zcore.item()
n_answer_type += answer_type.sum(0).cpu()
score_answer_type += type_score.sum(0).cpu()
total_loss /= N
train_score = 100 * train_score / N
train_zcore = 100 * train_zcore / N
if None != eval_loader:
model.train(False)
eval_score, eval_zcore, bound, entropy, val_n_answer_type, val_score_answer_type = evaluate(
model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write(
'\ttrain_loss: %.2f, norm: %.4f, score: %.2f, confidence: %.2f' %
(total_loss, total_norm / count_norm, train_score, train_zcore))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
logger.write('\tconfidence: %.2f (%.2f)' % (100 * eval_zcore, 100))
if eval_loader is not None and entropy is not None:
info = ''
for i in range(entropy.size(0)):
info = info + ' %.2f' % entropy[i]
logger.write('\tentropy: ' + info)
if (eval_loader is not None and eval_score > best_eval_score) or (
eval_loader is None and epoch >= saving_epoch):
if save_one_ckpt and os.path.exists(model_path):
os.remove(model_path)
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
best_type = val_score_answer_type
if eval_loader is not None:
best_eval_score = eval_score
return best_eval_score, bound, n_answer_type, val_n_answer_type, score_answer_type / n_answer_type, best_type / val_n_answer_type
def train(model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = 1
lr_decay_epochs = range(
10, 20, lr_decay_step) if eval_loader is not None else range(
10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
woman = 0
woman_true = 0
woman_man = 0
woman_other = 0
man = 0
man_true = 0
man_woman = 0
man_other = 0
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] = 1e-3
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
import pickle as pkl
from PIL import Image, ImageDraw
lab2ans = pkl.load(open("./data/cache/trainval_label2ans.pkl", 'rb'))
'''
for i, (v, b, q, a,ques,im,g,gender) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
visual_pred, att = model(v, b, q, a)
loss = instance_bce_with_logits(visual_pred, a)
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score = compute_score_with_logits(visual_pred, a.data).sum()
total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
'''
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score, bound, _ = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
if (eval_loader is not None and eval_score > best_eval_score) or (
eval_loader is None and epoch >= saving_epoch):
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
def train(model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = 1
lr_decay_epochs = range(
10, 20, lr_decay_step) if eval_loader is not None else range(
10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
print(N)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] = 1e-3
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
'''
for i, (v, b, q, a,ques,im,g,gender) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
visual_pred, att = model(v, b, q, a)
#import pdb;pdb.set_trace()
gender=gender.squeeze(1)
weights=torch.Tensor([2.0,1.0,0.0001]).cuda()
#loss = instance_bce_with_logits(visual_pred, g.cuda())
loss=nn.CrossEntropyLoss(weights)
loss=loss(visual_pred,gender.cuda())
#import pdb;pdb.set_trace()
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score=torch.eq(visual_pred.argmax(1),gender.cuda()).sum()
#batch_score = compute_score_with_logits(visual_pred, g.cuda()).sum()
#total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
#train_score+=batch_score
'''
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score, bound, _ = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
def train(model, train_loader, eval_loader, num_epochs, output, opt=None, s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = 0.01
lr_decay_epochs = range(16,50,lr_decay_step) if eval_loader is not None else range(10,20,lr_decay_step)
gradual_warmup_steps = [0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
train_score_vqa=0
total_norm = 0
count_norm = 0
total_fair_loss=0
total_dis_loss=0
woman=0
woman_o=0
man=0
man_o=0
other=0
other_o=0
t = time.time()
N = len(train_loader.dataset)
print(N)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' % optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] =optim.param_groups[0]['lr']*lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for name,subnet in model.named_children():
if name=='w_emb' or name=='q_emb' or name=='q_att' or name=='v_att' or name=='v_net' or name=='q_net' or name=='classifier2':
print(name)
for param in subnet.parameters():
param.requires_grad=False
for i, (v, b, q, a,ques,im,g,gender) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
visual_pred, vqa_pred,att = model(v, b, q, a)
#import pdb;pdb.set_trace()
gender=gender.squeeze(1)
weights=torch.Tensor([2.0,1.0,0.001]).cuda()
vqa_loss = instance_bce_with_logits(vqa_pred, a)
loss=nn.CrossEntropyLoss(weights)
loss=loss(visual_pred,gender.cuda())
#dis_loss=torch.abs(visual_pred[:,0]-visual_pred[:,1]).mean()
#dis_loss=dis_loss.cuda()
if epoch < 12:
t_loss=vqa_loss
else:
t_loss=loss+vqa_loss
t_loss.backward()
#import pdb;pdb.set_trace()
#vp=visual_pred[:,:2].cuda()
#g=g[:,:2]
#crossloss=instance_bce_with_logits(vp,g.cuda())
#mseloss=torch.nn.functional.mse_loss(vp.softmax(1),g.cuda())
#g_swap=g[:,[1,0]].cuda()
#swap_loss=(vp.softmax(1)*g_swap).sum(1)
#swap_loss=swap_loss.sum()
for j in range(len(v)):
if gender[j]==0:
woman=woman+1
#if visual_pred[j].argmax()==0 or visual_pred[j].argmax()==1:
if visual_pred[j].argmax()==gender[j].cuda():
woman_o=woman_o+1
elif gender[j]==1:
#if visual_pred[j].argmax()==0 or visual_pred[j].argmax()==1:
man=man+1
if visual_pred[j].argmax()==gender[j].cuda():
man_o=man_o+1
else:
other=other+1
if visual_pred[j].argmax()==gender[j].cuda():
other_o=other_o+1
total_norm += nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
#total_fair_loss+=soft_fair_loss
#total_dis_loss+=dis_loss
batch_score=torch.eq(visual_pred.argmax(1),gender.cuda()).sum()
batch_score_vqa = compute_score_with_logits(vqa_pred, a.data).sum()
#batch_score = compute_score_with_logits(visual_pred, g.cuda()).sum()
#total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
train_score_vqa+=batch_score_vqa.item()
#train_score+=batch_score
if i==0:
print(loss)
#print(10*soft_fair_loss)
print("\n\n")
total_loss /= N
train_score = 100 * train_score / N
train_score_vqa = 100 * train_score_vqa / N
print("epoch",epoch)
woman_score=float(woman_o)/woman
man_score=float(man_o)/man
other_score=float(other_o)/other
print("woman",woman)
print("man",man)
print("other",other)
print("train_woman_score",woman_score*100)
print("train_man_score",man_score*100)
print("train_other_score",other_score*100)
print("vqa",train_score_vqa)
if None != eval_loader:
model.train(False)
eval_score, bound, _ = evaluate(model, eval_loader)
model.train(True)
#print("total_fair_loss",total_fair_loss)
#print("totla_dis_loss",total_dis_loss)
logger.write('epoch %d, time: %.2f' % (epoch, time.time()-t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, train_score))
#logger.write('\total_fair_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, total_fair_loss))
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
def train(model, train_loader, eval_loader, num_epochs, output, opt=None, s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = .25
lr_decay_epochs = range(10,20,lr_decay_step) if eval_loader is not None else range(10,20,lr_decay_step)
gradual_warmup_steps = [0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' % optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] *= lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for i, (v, b, q, a) in enumerate(train_loader):
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
a = Variable(a).cuda()
pred, att = model(v, b, q, a)
loss = instance_bce_with_logits(pred, a)
loss.backward()
total_norm += nn.utils.clip_grad_norm(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score = compute_score_with_logits(pred, a.data).sum()
total_loss += loss.data[0] * v.size(0)
train_score += batch_score
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score, bound, entropy = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time()-t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
if eval_loader is not None and entropy is not None:
info = ''
for i in range(entropy.size(0)):
info = info + ' %.2f' % entropy[i]
logger.write('\tentropy: ' + info)
if (eval_loader is not None and eval_score > best_eval_score) or (eval_loader is None and epoch >= saving_epoch):
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
def train(model, train_loader, eval_loader, args, device=torch.device("cuda")):
N = len(train_loader.dataset)
lr_default = args.base_lr
num_epochs = args.epochs
lr_decay_epochs = range(args.lr_decay_start, num_epochs,
args.lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr_default,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=args.weight_decay)
logger = utils.Logger(os.path.join(args.output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f,' %
(lr_default, args.lr_decay_step, args.lr_decay_rate) +
'grad_clip=%.2f' % args.grad_clip)
logger.write('LR decay epochs: ' +
','.join([str(i) for i in lr_decay_epochs]))
last_eval_score, eval_score = 0, 0
relation_type = train_loader.dataset.relation_type
for epoch in range(0, num_epochs):
pbar = tqdm(total=len(train_loader))
total_norm, count_norm = 0, 0
total_loss, train_score = 0, 0
count, average_loss, att_entropy = 0, 0, 0
t = time.time()
if epoch < len(gradual_warmup_steps):
for i in range(len(optim.param_groups)):
optim.param_groups[i]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[-1]['lr'])
elif (epoch in lr_decay_epochs
or eval_score < last_eval_score and args.lr_decay_based_on_val):
for i in range(len(optim.param_groups)):
optim.param_groups[i]['lr'] *= args.lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[-1]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[-1]['lr'])
last_eval_score = eval_score
mini_batch_count = 0
batch_multiplier = args.grad_accu_steps
for i, (v, norm_bb, q, q_target, target, _, _, bb, spa_adj_matrix,
sem_adj_matrix) in enumerate(train_loader):
batch_size = v.size(0)
num_objects = v.size(1)
if mini_batch_count == 0:
optim.step()
optim.zero_grad()
mini_batch_count = batch_multiplier
### Debugging ###
# with autograd.detect_anomaly():
v = Variable(v).to(device)
norm_bb = Variable(norm_bb).to(device)
q = Variable(q).to(device)
q_target = Variable(q_target).to(device)
target = Variable(target).to(device)
pos_emb, sem_adj_matrix, spa_adj_matrix = prepare_graph_variables(
relation_type, bb, sem_adj_matrix, spa_adj_matrix, num_objects,
args.nongt_dim, args.imp_pos_emb_dim, args.spa_label_num,
args.sem_label_num, device)
q_type, pred, att = model(v, norm_bb, q, pos_emb, sem_adj_matrix,
spa_adj_matrix, target)
loss = instance_bce_with_logits(
pred, target) + instance_bce_with_logits(q_type, q_target)
loss /= batch_multiplier
loss.backward()
mini_batch_count -= 1
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
args.grad_clip)
count_norm += 1
batch_score = compute_score_with_logits(pred, target, device).sum()
total_loss += loss.data.item() * batch_multiplier * v.size(0)
train_score += batch_score
pbar.update(1)
if args.log_interval > 0:
average_loss += loss.data.item() * batch_multiplier
if model.module.fusion == "ban":
current_att_entropy = torch.sum(calc_entropy(att.data))
att_entropy += current_att_entropy / batch_size / att.size(
1)
count += 1
if i % args.log_interval == 0:
att_entropy /= count
average_loss /= count
print(
"step {} / {} (epoch {}), ave_loss {:.3f},".format(
i, len(train_loader), epoch, average_loss),
"att_entropy {:.3f}".format(att_entropy))
average_loss = 0
count = 0
att_entropy = 0
total_loss /= N
train_score = 100 * train_score / N
if eval_loader is not None:
eval_score, bound, entropy = evaluate(model, eval_loader, device,
args)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
if entropy is not None:
info = ''
for i in range(entropy.size(0)):
info = info + ' %.2f' % entropy[i]
logger.write('\tentropy: ' + info)
if (eval_loader is not None)\
or (eval_loader is None and epoch >= args.saving_epoch):
logger.write("saving current model weights to folder")
model_path = os.path.join(args.output, 'model_%d.pth' % epoch)
opt = optim if args.save_optim else None
utils.save_model(model_path, model, epoch, opt)
def main():
global best_acc
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# Data
print('==> Preparing dataset %s' % args.dataset)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
if args.dataset == 'cifar10':
dataloader = datasets.CIFAR10
num_classes = 10
else:
dataloader = datasets.CIFAR100
num_classes = 100
trainset = dataloader(root='./data',
train=True,
download=True,
transform=transform_train)
trainloader = data.DataLoader(trainset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers)
testset = dataloader(root='./data',
train=False,
download=False,
transform=transform_test)
testloader = data.DataLoader(testset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers)
# Model
print("==> creating model '{}'".format(args.arch))
if args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
cardinality=args.cardinality,
num_classes=num_classes,
depth=args.depth,
widen_factor=args.widen_factor,
dropRate=args.drop,
)
elif args.arch.startswith('densenet'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
growthRate=args.growthRate,
compressionRate=args.compressionRate,
dropRate=args.drop,
)
elif args.arch.startswith('wrn'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
widen_factor=args.widen_factor,
dropRate=args.drop,
)
elif args.arch.endswith('resnet'):
model = models.__dict__[args.arch](
num_classes=num_classes,
depth=args.depth,
block_name=args.block_name,
)
else:
model = models.__dict__[args.arch](num_classes=num_classes)
print("Geometric LR: {}".format(args.geo_lr))
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
print(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
criterion = nn.CrossEntropyLoss()
param_lr = GradientRatioScheduler.get_params_base_lr(model, args.lr)
optimizer = optim.SGD(param_lr,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = GradientRatioScheduler(optimizer)
print_model(model)
input("Cont?")
# Resume
title = 'cifar-10-' + args.arch
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(
args.resume), 'Error: no checkpoint directory found!'
args.checkpoint = os.path.dirname(args.resume)
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger = Logger(os.path.join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names([
'Epoch', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.',
'Time', 'Learning Rate'
])
if args.evaluate:
print('\nEvaluation only')
test_loss, test_acc = test(testloader, model, criterion, start_epoch,
use_cuda)
print(' Test Loss: %.8f, Test Acc: %.2f' % (test_loss, test_acc))
return
# Train and val
for epoch in range(start_epoch, args.epochs):
adjust_learning_rate(scheduler, epoch)
print('\nEpoch: [%d | %d] LR: %f' %
(epoch + 1, args.epochs, max(scheduler.get_lr())))
st = time.time()
train_loss, train_acc = train(trainloader, model, criterion, optimizer,
scheduler, epoch, use_cuda)
test_loss, test_acc = test(testloader, model, criterion, epoch,
use_cuda)
# append logger file
logger.append([
epoch, train_loss, test_loss, train_acc, test_acc,
time.time() - st,
scheduler.get_lr()
])
# save model
is_best = test_acc > best_acc
best_acc = max(test_acc, best_acc)
if args.save_checkpoint_model:
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'acc': test_acc,
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
is_best,
checkpoint=args.checkpoint)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
print('Best acc:')
print(best_acc)
def debug(node):
uci_model = client.get_uci_config()
node_model = uci_model.find_child(node)
return "<pre>%s</pre>" % websafe(print_model(node_model))
def train(model, train_loader, eval_loader,output):
lr_default=0.01
grad_clip = .25
epoch=0
i_iter=0
max_iter=45071
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default)
scheduler = get_optim_scheduler(optim)
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
while i_iter<max_iter:
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
epoch=epoch+1
N = len(train_loader.dataset)
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
t=time.time()
for i, (v, b, q, a) in enumerate(train_loader):
i_iter=i_iter+1
if i_iter>max_iter:
break
scheduler.step(i_iter)
optim.zero_grad()
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
a = Variable(a).cuda()
pred= model(v, b, q, a)
loss = instance_bce_with_logits(pred, a)
loss.backward()
total_norm += nn.utils.clip_grad_norm(model.parameters(), grad_clip)
count_norm += 1
batch_score = compute_score_with_logits(pred, a.data).sum()
total_loss += loss.data[0] * v.size(0)
train_score += batch_score
#print('batch_score: %.2f' % (batch_score))
#print(train_score)
optim.step()
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch: %d, time: %.2f' % (epoch, time.time()-t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f' % (100 * eval_score))
if (eval_loader is not None and eval_score > best_eval_score):
model_path = os.path.join(output, 'model_epoch%d.pth' % (epoch))
utils.save_model(model_path, model, iter, optim)
if eval_loader is not None:
best_eval_score = eval_score
def train(model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = .25
lr_decay_epochs = range(
10, 20, lr_decay_step) if eval_loader is not None else range(
10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = 0
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] *= lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for i, (v, b, p, e, n, a, idx, types) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
p = p.cuda()
e = e.cuda()
a = a.cuda()
_, logits = model(v, b, p, e, a)
n_obj = logits.size(2)
logits.squeeze_()
merged_logit = torch.cat(
tuple(logits[j, :, :n[j][0]] for j in range(n.size(0))),
-1).permute(1, 0)
merged_a = torch.cat(
tuple(a[j, :n[j][0], :n_obj] for j in range(n.size(0))), 0)
loss = instance_bce_with_logits(merged_logit, merged_a,
'sum') / v.size(0)
N += n.sum().float()
batch_score = compute_score_with_logits(merged_logit,
merged_a.data).sum()
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
total_loss += loss.item() * v.size(0)
train_score += batch_score.item()
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score, bound, entropy = evaluate(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f/%.2f/%.2f (%.2f)' %
(100 * eval_score[0], 100 * eval_score[1],
100 * eval_score[2], 100 * bound))
eval_score = eval_score[0]
if eval_loader is not None and entropy is not None:
info = ''
for i in range(entropy.size(0)):
info = info + ' %.2f' % entropy[i]
logger.write('\tentropy: ' + info)
if (eval_loader is not None and eval_score > best_eval_score) or (
eval_loader is None and epoch >= saving_epoch):
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
def train6(model, train_loader, eval_loader, num_epochs, output,s_epoch=0):
lr_default=0.001
grad_clip = .25
utils.create_dir(output)
lr_decay_step = 2
lr_decay_rate = .5
lr_decay_epochs = range(9, 12, lr_decay_step)
gradual_warmup_steps = [0.5 * lr_default, 1.0 * lr_default,2.0*lr_default]
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default)
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' % optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0]['lr'] *= lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for i, (v, b, q, a,image_id) in enumerate(train_loader):
v = Variable(v).cuda()
b = Variable(b).cuda()
q = Variable(q).cuda()
a = Variable(a).cuda()
pred= model(v, b, q, a)
loss = instance_bce_with_logits(pred, a)
loss.backward()
total_norm += nn.utils.clip_grad_norm(model.parameters(), grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
batch_score = compute_score_with_logits(pred, a.data).sum()
total_loss += loss.data[0] * v.size(0)
train_score += batch_score
total_loss /= N
train_score = 100 * train_score / N
if None != eval_loader:
model.train(False)
eval_score,eval_loss= evaluate3(model, eval_loader)
model.train(True)
logger.write('epoch %d, time: %.2f' % (epoch, time.time()-t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f,eval loss:%.2f' % (100 * eval_score,eval_loss))
if (eval_loader is not None and eval_score > best_eval_score) or (eval_loader is None and epoch>=0):
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
def run_trainer(data_loader: dict,
model: models,
optimizer: optim,
lr_scheduler: optim.lr_scheduler,
criterion: nn,
train_epochs: int,
log_training_progress_every: int,
log_val_progress_every: int,
checkpoint_every: int,
tb_summaries_dir: str,
chkpt_dir: str,
resume_from: str,
to_device: object,
to_cpu: object,
attackers: object = None,
train_adv_periodic_ops: int = None,
*args,
**kwargs):
def mk_lr_step(loss):
lr_scheduler.step(loss)
def train_step(engine, batch):
model.train()
optimizer.zero_grad()
x, y = map(lambda _: to_device(_), batch)
if (train_adv_periodic_ops is not None) and (
engine.state.iteration % train_adv_periodic_ops == 0):
random_attacker = random.choice(list(attackers))
x = attackers[random_attacker].perturb(x, y)
y_pred = model(x)
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
return loss.item()
def eval_step(engine, batch):
model.eval()
with torch.no_grad():
x, y = map(lambda _: to_device(_), batch)
if random.choice(range(2)) % 2 == 0:
random_attacker = random.choice(list(attackers))
x = attackers[random_attacker].perturb(x, y)
y_pred = model(x)
return y_pred, y
def chkpt_score_func(engine):
val_eval.run(data_loader['val'])
y_pred, y = val_eval.state.output
loss = criterion(y_pred, y)
return np.mean(to_cpu(loss, convert_to_np=True))
# set up ignite engines
trainer = Engine(train_step)
train_eval = Engine(eval_step)
val_eval = Engine(eval_step)
@trainer.on(Events.ITERATION_COMPLETED(every=log_training_progress_every))
def log_training_results(engine):
step = True
run_type = 'train'
train_eval.run(data_loader['train'])
y_pred, y = train_eval.state.output
loss = criterion(y_pred, y)
log_results(to_cpu(y_pred, convert_to_np=True),
to_cpu(y, convert_to_np=True),
to_cpu(loss, convert_to_np=True), run_type, step,
engine.state.iteration, total_train_steps, writer)
@trainer.on(Events.ITERATION_COMPLETED(every=log_val_progress_every))
def log_val_results(engine):
step = True
run_type = 'val'
val_eval.run(data_loader['val'])
y_pred, y = val_eval.state.output
loss = criterion(y_pred, y)
mk_lr_step(loss)
log_results(to_cpu(y_pred, convert_to_np=True),
to_cpu(y, convert_to_np=True),
to_cpu(loss, convert_to_np=True), run_type, step,
engine.state.iteration, total_train_steps, writer)
# set up vars
total_train_steps = len(data_loader['train']) * train_epochs
# reporter to identify memory usage
# bottlenecks throughout network
reporter = MemReporter()
print_model(model, reporter)
# set up tensorboard summary writer
writer = create_summary_writer(model, data_loader['train'],
tb_summaries_dir)
# move model to device
model = to_device(model)
# set up progress bar
RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')
pbar = ProgressBar(persist=True, bar_format="")
pbar.attach(trainer, ['loss'])
# set up checkpoint
objects_to_checkpoint = {
'trainer': trainer,
'model': model,
'optimizer': optimizer,
'lr_scheduler': lr_scheduler
}
training_checkpoint = Checkpoint(to_save=objects_to_checkpoint,
save_handler=DiskSaver(
chkpt_dir, require_empty=False),
n_saved=3,
filename_prefix='best',
score_function=chkpt_score_func,
score_name='val_loss')
# register events
trainer.add_event_handler(
Events.ITERATION_COMPLETED(every=checkpoint_every),
training_checkpoint)
# if resuming
if resume_from and os.path.exists(resume_from):
print(f'resume model from: {resume_from}')
checkpoint = torch.load(resume_from)
Checkpoint.load_objects(to_load=objects_to_checkpoint,
checkpoint=checkpoint)
# fire training engine
trainer.run(data_loader['train'], max_epochs=train_epochs)
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
if FLAGS.job_name == "ps":
ps_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.00001))
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index,
config=ps_config)
server.join()
elif FLAGS.job_name == "worker":
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
local_worker_device = "/job:worker/task:%d" % FLAGS.task_index
with tf.device(
tf.train.replica_device_setter(
ps_device='/job:ps/cpu:0',
worker_device=local_worker_device,
cluster=cluster)):
if FLAGS.network == 'lstm':
from models.lstm import KitModel
elif FLAGS.network == 'gru':
from models.gru import KitModel
elif FLAGS.network == 'fc':
from models.fullyconnect import KitModel
elif FLAGS.network == 'alexnet':
from models.alexnet import KitModel
elif FLAGS.network == 'vgg16':
from models.vgg16 import KitModel
elif FLAGS.network == 'vgg19' or FLAGS.network == 'vgg_e':
from models.vgg19 import KitModel
elif FLAGS.network == 'inception_v3':
from models.inception_v3 import KitModel
elif FLAGS.network == 'resnet':
from models.resnet import KitModel
elif FLAGS.network == 'seq2seq':
import models.translate.translate
from models.translate.translate import dist_train
dist_train(FLAGS, server, cluster)
sys.exit()
else:
sys.exit("Invalid network [%s]" % args.network)
this_model = KitModel(FLAGS)
this_model.build_model()
train_dir = tempfile.mkdtemp()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
],
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L1)),
gpu_options=tf.GPUOptions(visible_device_list=""))
if FLAGS.infer_shapes == True:
sess_config.graph_options.infer_shapes = FLAGS.infer_shapes
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir=train_dir,
init_op=tf.global_variables_initializer(),
global_step=this_model.global_step,
summary_writer=None,
saver=None)
if FLAGS.task_index == 0:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target,
config=sess_config,
start_standard_services=True)
print_model()
print("Start warmup %d epoch." % FLAGS.warmup)
for _ in range(FLAGS.warmup):
this_model.get_data()
sess.run(this_model.train_op, feed_dict=this_model.get_feed_dict())
current_step = 0
duration = 0
while current_step < FLAGS.epoch:
current_step += 1
this_model.get_data()
print("Start step %d" % current_step)
start_time = time.time()
_, step_loss = sess.run([this_model.train_op, this_model.cost],
feed_dict=this_model.get_feed_dict())
end_time = time.time()
print(
"Finish step %d, loss = %f, speed = %f sampes/s, duration = %f seconds"
% (current_step, step_loss, FLAGS.batch_size /
(end_time - start_time), end_time - start_time))
duration += end_time - start_time
print("Total Time = %f s." % duration)
#writer.close()
else:
sys.exit("Invalid job role name [%s]!" % args.job_name)
def train(model, train_loader, eval_loader, opt):
utils.create_dir(opt.output)
optim = torch.optim.Adam(model.parameters(), lr=opt.learning_rate, betas=(0.9, 0.999), eps=1e-08,
weight_decay=opt.weight_decay)
logger = utils.Logger(os.path.join(opt.output, 'log.txt'))
utils.print_model(model, logger)
for param_group in optim.param_groups:
param_group['lr'] = opt.learning_rate
scheduler = MultiStepLR(optim, milestones=[100], gamma=0.8)
scheduler.last_epoch = opt.s_epoch
best_eval_score = 0
for epoch in range(opt.s_epoch, opt.num_epochs):
total_loss = 0
total_norm = 0
count_norm = 0
train_score = 0
t = time.time()
N = len(train_loader.dataset)
scheduler.step()
for i, (v, b, a, _, qa_text, _, _, q_t, bias) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
a = a.cuda()
bias = bias.cuda()
qa_text = qa_text.cuda()
rand_index = random.sample(range(0, opt.train_candi_ans_num), opt.train_candi_ans_num)
qa_text = qa_text[:,rand_index,:]
a = a[:,rand_index]
bias = bias[:,rand_index]
if opt.lp == 0:
logits = model(qa_text, v, b, epoch, 'train')
loss = instance_bce_with_logits(logits, a, reduction='mean')
elif opt.lp == 1:
logits = model(qa_text, v, b, epoch, 'train')
loss_pos = instance_bce_with_logits(logits, a, reduction='mean')
index = random.sample(range(0, v.shape[0]), v.shape[0])
v_neg = v[index]
b_neg = b[index]
logits_neg = model(qa_text, v_neg, b_neg, epoch, 'train')
self_loss = compute_self_loss(logits_neg, a)
loss = loss_pos + opt.self_loss_weight * self_loss
elif opt.lp == 2:
logits, loss = model(qa_text, v, b, epoch, 'train', bias, a)
else:
assert 1==2
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
score = compute_score_with_logits(logits, a.data).sum()
train_score += score.item()
total_loss += loss.item() * v.size(0)
if i != 0 and i % 100 == 0:
print(
'training: %d/%d, train_loss: %.6f, train_acc: %.6f' %
(i, len(train_loader), total_loss / (i * v.size(0)),
100 * train_score / (i * v.size(0))))
total_loss /= N
if None != eval_loader:
model.train(False)
eval_score, bound = evaluate(model, eval_loader, opt)
model.train(True)
logger.write('\nlr: %.7f' % optim.param_groups[0]['lr'])
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write(
'\ttrain_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm / count_norm, train_score))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
if (eval_loader is not None and eval_score > best_eval_score):
if opt.lp == 0:
model_path = os.path.join(opt.output, 'SAR_top'+str(opt.train_candi_ans_num)+'_best_model.pth')
elif opt.lp == 1:
model_path = os.path.join(opt.output, 'SAR_SSL_top'+str(opt.train_candi_ans_num)+'_best_model.pth')
elif opt.lp == 2:
model_path = os.path.join(opt.output, 'SAR_LMH_top'+str(opt.train_candi_ans_num)+'_best_model.pth')
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
args.relation_type,
adaptive=args.adaptive,
pos_emb_dim=args.imp_pos_emb_dim,
dataroot=args.data_folder)
train_dset = VQAFeatureDataset('train',
dictionary,
args.relation_type,
adaptive=args.adaptive,
pos_emb_dim=args.imp_pos_emb_dim,
dataroot=args.data_folder)
# 5. Initialize ReGAT_all
print("[LOG] 5. Initializing ReGAT_all...")
model = build_regat_all(val_dset, args).to(device)
logger = utils.Logger(os.path.join(args.output, 'model_all_log.txt'))
utils.print_model(model, logger)
# 6. tfidf
# Takes around 4 minutes
print("[LOG] 6. tfidf_from_questions...")
tfidf = None
weights = None
if args.tfidf:
tfidf, weights = tfidf_from_questions(['train', 'val', 'test2015'],
dictionary)
# 7. Initialize word embeddings
print("[LOG] 7. Initializing word embeddings...")
model.w_emb.init_embedding(
join(args.data_folder, 'glove/glove6b_init_300d.npy'), tfidf, weights)
def train(model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0):
lr_default = 1e-3 if eval_loader is not None else 7e-4
lr_decay_step = 2
lr_decay_rate = 0.25
lr_decay_epochs = range(
10, 20, lr_decay_step) if eval_loader is not None else range(
10, 20, lr_decay_step)
gradual_warmup_steps = [
0.5 * lr_default, 1.0 * lr_default, 1.5 * lr_default, 2.0 * lr_default
]
saving_epoch = 3
grad_clip = .25
utils.create_dir(output)
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad, model.parameters()), lr=lr_default) \
if opt is None else opt
logger = utils.Logger(os.path.join(output, 'log.txt'))
best_eval_score = 0
utils.print_model(model, logger)
logger.write('optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f' % \
(lr_default, lr_decay_step, lr_decay_rate, grad_clip))
import pickle as pkl
lab2ans = pkl.load(open("./data/cache/trainval_label2ans.pkl", 'rb'))
woman_answer_words = [
'woman', 'women', 'female', 'girl', 'lady', 'she', 'her', 'hers',
'ladies', 'girls'
]
man_answer_words = [
'man', 'men', 'male', 'boy', 'he', 'his', 'gentleman', 'gentlemen',
'boys'
]
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
train_score_vqa = 0
total_norm = 0
count_norm = 0
total_fair_loss = 0
total_dis_loss = 0
woman = 0
woman_true = 0
man = 0
woman_man = 0
man_woman = 0
man_true = 0
other = 0.0001
other_o = 0
t = time.time()
N = len(train_loader.dataset)
print(N)
if epoch < len(gradual_warmup_steps):
optim.param_groups[0]['lr'] = gradual_warmup_steps[epoch]
logger.write('gradual warmup lr: %.4f' %
optim.param_groups[0]['lr'])
elif epoch in lr_decay_epochs:
optim.param_groups[0][
'lr'] = optim.param_groups[0]['lr'] * lr_decay_rate
logger.write('decreased lr: %.4f' % optim.param_groups[0]['lr'])
else:
logger.write('lr: %.4f' % optim.param_groups[0]['lr'])
for i, (v, b, q, a, ques, im, g, gender) in enumerate(train_loader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
a = a.cuda()
visual_pred, vqa_pred, att = model(v, b, q, a)
#import pdb;pdb.set_trace()
gender = gender.squeeze(1)
weights = torch.Tensor([2.0, 1.0, 0.001]).cuda()
vqa_loss = instance_bce_with_logits(vqa_pred, a)
loss = 0
#loss=nn.CrossEntropyLoss(weights)
#loss=loss(visual_pred,gender.cuda())
#dis_loss=torch.abs(visual_pred[:,0]-visual_pred[:,1]).mean()
#dis_loss=dis_loss.cuda()
if epoch < 30:
t_loss = vqa_loss
else:
t_loss = loss + vqa_loss
t_loss.backward()
#import pdb;pdb.set_trace()
#vp=visual_pred[:,:2].cuda()
#g=g[:,:2]
#crossloss=instance_bce_with_logits(vp,g.cuda())
#mseloss=torch.nn.functional.mse_loss(vp.softmax(1),g.cuda())
#g_swap=g[:,[1,0]].cuda()
#swap_loss=(vp.softmax(1)*g_swap).sum(1)
#swap_loss=swap_loss.sum()
#import pdb;pdb.set_trace()
for j in range(len(v)):
if gender[j] == 0:
woman = woman + 1
check = 0
for woman_answer in woman_answer_words:
if lab2ans[int(vqa_pred[j].argmax())] == woman_answer:
check = 1
if check == 1:
woman_true = woman_true + 1
check = 0
for man_answer in man_answer_words:
if lab2ans[int(vqa_pred[j].argmax())] == man_answer:
check = 1
if check == 1:
woman_man = woman_man + 1
check = 0
if gender[j] == 1:
man = man + 1
check = 0
for man_answer in man_answer_words:
if lab2ans[int(vqa_pred[j].argmax())] == man_answer:
check = 1
if check == 1:
man_true = man_true + 1
check = 0
for woman_answer in woman_answer_words:
if lab2ans[int(vqa_pred[j].argmax())] == woman_answer:
check = 1
if check == 1:
man_woman = man_woman + 1
check = 0
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
#total_fair_loss+=soft_fair_loss
#total_dis_loss+=dis_loss
#batch_score=torch.eq(visual_pred.argmax(1),gender.cuda()).sum()
batch_score_vqa = compute_score_with_logits(vqa_pred, a.data).sum()
#batch_score = compute_score_with_logits(visual_pred, g.cuda()).sum()
#total_loss += loss.item() * v.size(0)
#train_score += batch_score.item()
train_score_vqa += batch_score_vqa.item()
#train_score+=batch_score
if i == 50 or i == 100 or i == 500:
print(loss)
#print(10*soft_fair_loss)
print("\n\n")
total_loss /= N
train_score = 100 * train_score / N
train_score_vqa = 100 * train_score_vqa / N
#import pdb;pdb.set_trace()
print("epoch", epoch)
woman_score = float(woman_true) / woman
man_score = float(man_true) / man
#other_score=float(other_o)/other
print("woman", woman)
print("man", man)
print("other", other)
print("train_woman_score", woman_score * 100)
print("train_man_score", man_score * 100)
#print("train_other_score",other_score*100)
print("vqa", train_score_vqa)
print("\n\n")
if None != eval_loader:
model.train(False)
eval_score, bound, _ = evaluate(model, eval_loader)
model.train(True)
#print("total_fair_loss",total_fair_loss)
#print("totla_dis_loss",total_dis_loss)
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score))
#logger.write('\total_fair_loss: %.2f, norm: %.4f, score: %.2f' % (total_loss, total_norm/count_norm, total_fair_loss))
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
model_path = os.path.join(output, 'model_epoch%d.pth' % epoch)
utils.save_model(model_path, model, epoch, optim)
def train(model, train_loader, eval_loader, opt):
utils.create_dir(opt.output)
optim = torch.optim.Adam(model.parameters(),
lr=opt.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=opt.weight_decay)
logger = utils.Logger(os.path.join(opt.output, 'log.txt'))
utils.print_model(model, logger)
# load snapshot
if opt.checkpoint_path is not None:
print('loading %s' % opt.checkpoint_path)
model_data = torch.load(opt.checkpoint_path)
model.load_state_dict(model_data.get('model_state', model_data))
optim.load_state_dict(model_data.get('optimizer_state', model_data))
opt.s_epoch = model_data['epoch'] + 1
for param_group in optim.param_groups:
param_group['lr'] = opt.learning_rate
scheduler = MultiStepLR(optim,
milestones=[10, 15, 20, 25, 30, 35],
gamma=0.5)
scheduler.last_epoch = opt.s_epoch
best_eval_score = 0
for epoch in range(opt.s_epoch, opt.num_epochs):
total_loss = 0
total_bce_loss = 0
self_loss = 0
total_self_loss = 0
train_score_pos = 0
train_score_neg = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
scheduler.step()
for i, (v, b, q, a, _) in enumerate(train_loader):
v = v.cuda()
q = q.cuda()
a = a.cuda()
# for the labeled samples
if epoch < opt.pretrain_epoches:
logits_pos, _ = model(q, v, False)
if opt.ml_loss:
bce_loss_pos = instance_bce_with_logits(logits_pos,
a,
reduction='mean')
else:
bce_loss_pos = instance_bce(logits_pos, a)
loss = bce_loss_pos
else:
logits_pos, logits_neg, _, _ = model(q, v, True)
if opt.ml_loss: #use multi-label loss
bce_loss_pos = instance_bce_with_logits(logits_pos,
a,
reduction='mean')
else: #use cross-entropy loss
bce_loss_pos = instance_bce(logits_pos, a)
self_loss = compute_self_loss(logits_neg, a)
loss = bce_loss_pos + opt.self_loss_weight * self_loss
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(),
opt.grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
score_pos = compute_score_with_logits(logits_pos, a.data).sum()
train_score_pos += score_pos.item()
total_loss += loss.item() * v.size(0)
total_bce_loss += bce_loss_pos.item() * v.size(0)
if epoch < opt.pretrain_epoches: #pretrain
total_self_loss = 0
train_score_neg = 0
else: #fintune
score_neg = compute_score_with_logits(logits_neg, a.data).sum()
total_self_loss += self_loss.item() * v.size(0)
train_score_neg += score_neg.item()
if i != 0 and i % 100 == 0:
print(
'traing: %d/%d, train_loss: %.6f, bce_loss: %.6f, self_loss: %.6f, neg_train_acc: %.6f, pos_train_acc: %.6f'
%
(i, len(train_loader), total_loss /
(i * v.size(0)), total_bce_loss /
(i * v.size(0)), total_self_loss /
(i * v.size(0)), 100 * train_score_neg /
(i * v.size(0)), 100 * train_score_pos / (i * v.size(0))))
total_loss /= N
total_bce_loss /= N
total_self_loss /= N
train_score_pos = 100 * train_score_pos / N
if None != eval_loader:
model.train(False)
eval_score, bound, entropy = evaluate(model, eval_loader)
model.train(True)
logger.write('\nlr: %.7f' % optim.param_groups[0]['lr'])
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write('\ttrain_loss: %.2f, norm: %.4f, score: %.2f' %
(total_loss, total_norm / count_norm, train_score_pos))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' %
(100 * eval_score, 100 * bound))
if eval_loader is not None and entropy is not None:
info = '' + ' %.2f' % entropy
logger.write('\tentropy: ' + info)
if (eval_loader is not None and eval_score > best_eval_score):
model_path = os.path.join(opt.output, 'best_model.pth')
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
def train(args,
model,
train_loader,
eval_loader,
num_epochs,
output,
opt=None,
s_epoch=0):
device = args.device
# Scheduler learning rate
lr_default = args.lr
lr_decay_step = 2
lr_decay_rate = 0.75
lr_decay_epochs = (range(10, 20, lr_decay_step) if eval_loader is not None
else range(10, 20, lr_decay_step))
gradual_warmup_steps = [
0.5 * lr_default,
1.0 * lr_default,
1.5 * lr_default,
2.0 * lr_default,
]
saving_epoch = 15 # Start point for model saving
grad_clip = args.clip_norm
utils.create_dir(output)
# Adamax optimizer
optim = (torch.optim.Adamax(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr_default) if opt is None else opt)
# Loss function
criterion = torch.nn.BCEWithLogitsLoss(reduction="sum")
ae_criterion = torch.nn.MSELoss()
# write hyper-parameter to log file
logger = utils.Logger(os.path.join(output, "log.txt"))
logger.write(args.__repr__())
utils.print_model(model, logger)
logger.write(
"optim: adamax lr=%.4f, decay_step=%d, decay_rate=%.2f, grad_clip=%.2f"
% (lr_default, lr_decay_step, lr_decay_rate, grad_clip))
# create trainer
trainer = Trainer(args, model, criterion, optim, ae_criterion)
update_freq = int(args.update_freq)
wall_time_start = time.time()
best_eval_score = 0
# Epoch passing in training phase
for epoch in range(s_epoch, num_epochs):
total_loss = 0
train_score = 0
total_norm = 0
count_norm = 0
num_updates = 0
t = time.time()
N = len(train_loader.dataset)
num_batches = int(N / args.batch_size + 1)
if epoch < len(gradual_warmup_steps):
trainer.optimizer.param_groups[0]["lr"] = gradual_warmup_steps[
epoch]
logger.write("gradual warm up lr: %.4f" %
trainer.optimizer.param_groups[0]["lr"])
elif epoch in lr_decay_epochs:
trainer.optimizer.param_groups[0]["lr"] *= lr_decay_rate
logger.write("decreased lr: %.4f" %
trainer.optimizer.param_groups[0]["lr"])
else:
logger.write("lr: %.4f" % trainer.optimizer.param_groups[0]["lr"])
# Predicting and computing score
for i, (v, q, a, _, _, _) in enumerate(train_loader):
if args.maml:
v[0] = v[0].reshape(v[0].shape[0], 84, 84).unsqueeze(1)
if args.autoencoder:
v[1] = v[1].reshape(v[1].shape[0], 128, 128).unsqueeze(1)
v[0] = v[0].to(device)
v[1] = v[1].to(device)
q = q.to(device)
a = a.to(device)
sample = [v, q, a]
if i < num_batches - 1 and (i + 1) % update_freq > 0:
trainer.train_step(sample, update_params=False)
else:
loss, grad_norm, batch_score = trainer.train_step(
sample, update_params=True)
total_norm += grad_norm
count_norm += 1
total_loss += loss.item()
train_score += batch_score
num_updates += 1
if num_updates % int(args.print_interval / update_freq) == 0:
print(
"Iter: {}, Loss {:.4f}, Norm: {:.4f}, Total norm: {:.4f}, Num updates: {}, Wall time: {:.2f}, ETA: {}"
.format(
i + 1,
total_loss / ((num_updates + 1)),
grad_norm,
total_norm,
num_updates,
time.time() - wall_time_start,
utils.time_since(t, i / num_batches),
))
total_loss /= num_updates
train_score = 100 * train_score / (num_updates * args.batch_size)
# Evaluation
if eval_loader is not None:
print("Evaluating...")
trainer.model.train(False)
eval_score, bound = evaluate(model, eval_loader, args)
trainer.model.train(True)
logger.write("epoch %d, time: %.2f" % (epoch, time.time() - t))
logger.write("\ttrain_loss: %.2f, norm: %.4f, score: %.2f" %
(total_loss, total_norm / count_norm, train_score))
if eval_loader is not None:
logger.write("\teval score: %.2f (%.2f)" %
(100 * eval_score, 100 * bound))
# Save per epoch
if epoch >= saving_epoch:
model_path = os.path.join(output, "model_epoch%d.pth" % epoch)
utils.save_model(model_path, model, epoch, trainer.optimizer)
# Save best epoch
if eval_loader is not None and eval_score > best_eval_score:
model_path = os.path.join(output, "model_epoch_best.pth")
utils.save_model(model_path, model, epoch, trainer.optimizer)
best_eval_score = eval_score
def main():
parser = argparse.ArgumentParser(
description='PyTorch MNIST Example',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--dataset',
type=str,
default='data/mnist.npy',
help='path to dataset')
parser.add_argument('--batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for training')
parser.add_argument('--epochs',
type=int,
default=101,
metavar='N',
help='number of epochs to train')
parser.add_argument('--LR',
type=float,
default=0.01,
metavar='LR',
help='learning rate')
parser.add_argument('--L2',
type=float,
default=0.0001,
metavar='L2',
help='L2 weight decay strength')
parser.add_argument('--L1_1',
type=float,
default=5e-4,
metavar='L2',
help='L1 weight decay strength')
parser.add_argument('--L1_2',
type=float,
default=1e-5,
metavar='L2',
help='L1 weight decay strength')
parser.add_argument('--L3',
type=float,
default=0.05,
metavar='L3',
help='gradient decay strength')
parser.add_argument('--momentum',
type=float,
default=0.9,
metavar='M',
help='SGD momentum')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed')
parser.add_argument('--use_bias',
dest='use_bias',
action='store_true',
help='use biases')
parser.add_argument('--q_a',
type=int,
default=4,
metavar='S',
help='quantize activations to this number of bits')
parser.add_argument('--act_max',
type=float,
default=1.0,
help='clipping threshold for activations')
parser.add_argument('--w_max',
type=float,
default=0.,
help='clipping threshold for weights')
parser.add_argument('--stochastic',
type=float,
default=0.5,
help='stochastic quantization')
parser.add_argument('--debug',
dest='debug',
action='store_true',
help='debug')
parser.add_argument('--calculate_running',
dest='calculate_running',
action='store_true',
help='calculate_running')
parser.add_argument('--plot',
dest='plot',
action='store_true',
help='plot')
parser.add_argument('--save',
dest='save',
action='store_true',
help='save')
parser.add_argument('--bn1', dest='bn1', action='store_true', help='bn1')
parser.add_argument('--bn2', dest='bn2', action='store_true', help='bn2')
parser.add_argument('--track_running_stats',
dest='track_running_stats',
action='store_true',
help='track_running_stats')
parser.add_argument('--augment',
dest='augment',
action='store_true',
help='augment')
parser.add_argument('--triple_input',
dest='triple_input',
action='store_true',
help='triple_input')
parser.add_argument('--dropout_input',
type=float,
default=0.2,
help='dropout_input drop prob')
parser.add_argument('--dropout_act',
type=float,
default=0.4,
help='dropout_act drop prob')
parser.add_argument('--prune_weights1',
type=float,
default=0.0,
help='percentage of smallest weights to set to zero')
parser.add_argument('--prune_weights2',
type=float,
default=0.0,
help='percentage of smallest weights to set to zero')
parser.add_argument('--prune_epoch',
type=float,
default=90,
help='do pruning at the end of this epoch')
parser.add_argument('--var_name', type=str, default='', help='var_name')
parser.add_argument('--gpu', type=str, default=None, help='gpu')
parser.add_argument('--num_sims',
type=int,
default=1,
help='number of simulation runs')
args = parser.parse_args()
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
np.set_printoptions(precision=4, linewidth=200, suppress=True)
data = np.load(args.dataset, allow_pickle=True)
train_data, val_data = data
train_inputs, train_labels = train_data
test_inputs, test_labels = val_data
train_inputs = torch.from_numpy(train_inputs).cuda()
train_labels = torch.from_numpy(train_labels).cuda()
test_inputs = torch.from_numpy(test_inputs).cuda()
test_labels = torch.from_numpy(test_labels).cuda()
results = {}
if args.var_name == 'L1_1':
var_list = [
0, 1e-6, 2e-6, 3e-6, 5e-6, 7e-6, 1e-5, 2e-5, 3e-5, 4e-5, 5e-5,
7e-5, 1e-4, 2e-4
]
elif args.var_name == 'L1_2':
var_list = [
0, 1e-6, 2e-6, 3e-6, 5e-6, 7e-6, 1e-5, 2e-5, 3e-5, 4e-5, 5e-5,
7e-5, 1e-4, 2e-4
]
elif args.var_name == 'L3':
var_list = [
0, 0.001, 0.002, 0.003, 0.005, 0.007, 0.01, 0.02, 0.03, 0.04, 0.05,
0.06, 0.08, 0.1, 0.2
]
elif args.var_name == 'L2':
var_list = [
0, 5e-6, 1e-5, 2e-5, 3e-5, 4e-5, 5e-5, 7e-5, 1e-4, 2e-4, 3e-4,
4e-4, 5e-4, 0.001
]
else:
var_list = [' ']
total_list = []
for var in var_list:
if args.var_name != '':
print('\n\n********** Setting {} to {} **********\n\n'.format(
args.var_name, var))
setattr(args, args.var_name, var)
results[var] = []
best_accs = []
for s in range(args.num_sims):
model = Net(args).cuda()
optimizer = optim.SGD(model.parameters(),
lr=args.LR,
momentum=args.momentum,
weight_decay=args.L2)
num_train_batches = int(len(train_inputs) / args.batch_size)
best_acc = 0
if s == 0:
utils.print_model(model, args)
for epoch in range(args.epochs):
rnd_idx = np.random.permutation(len(train_inputs))
train_inputs = train_inputs[rnd_idx]
train_labels = train_labels[rnd_idx]
if epoch % 70 == 0 and epoch != 0:
print('\nReducing learning rate ')
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr'] / 10.
train_acc = train(args, model, num_train_batches, train_inputs,
train_labels, optimizer)
val_acc = test(model, test_inputs, test_labels)
if (args.prune_weights1 > 0 or args.prune_weights2 > 0
) and epoch % args.prune_epoch == 0 and epoch != 0:
print('\n\nAccuracy before pruning: {:.2f}\n\n'.format(
val_acc))
sparsities = prune_weights(args, model)
val_acc = test(model, test_inputs, test_labels)
print('\n\nAccuracy after pruning: {:.2f}\n\n'.format(
val_acc))
else:
sparsities = [
p.data[torch.abs(p.data) < 0.01 *
p.data.max()].numel() / p.data.numel() * 100.0
for _, p in model.named_parameters()
]
print(
'Epoch {:>2d} train acc {:>.2f} test acc {:>.2f} LR {:.4f} sparsity {:>3.1f} {:>3.1f}'
.format(epoch, train_acc, val_acc,
optimizer.param_groups[0]['lr'], sparsities[0],
sparsities[1]))
if val_acc > best_acc:
best_acc = val_acc
if epoch > 80 and (args.save or args.plot):
sparsities = prune_weights(args, model)
val_acc = test(model, test_inputs, test_labels)
print('\n\nAccuracy after pruning: {:.2f}\n\n'.format(
val_acc))
w_pos = model.fc1.weight.clone()
w_pos[w_pos < 0] = 0
w_neg = model.fc1.weight.clone()
w_neg[w_neg >= 0] = 0
pos = F.linear(model.quantized_input, w_pos)
neg = F.linear(model.quantized_input, w_neg)
sep1 = torch.cat((neg, pos), 0)
w_pos = model.fc2.weight.clone()
w_pos[w_pos < 0] = 0
w_neg = model.fc2.weight.clone()
w_neg[w_neg >= 0] = 0
pos = F.linear(model.act, w_pos)
neg = F.linear(model.act, w_neg)
sep2 = torch.cat((neg, pos), 0)
dict_names = [
'input', 'fc1_weights', 'preact', 'diff_preact',
'act', 'fc2_weights', 'output', 'diff_output'
]
tensors = [
model.quantized_input, model.fc1.weight,
model.preact, sep1, model.act, model.fc2.weight,
model.output, sep2
]
shapes = [list(t.shape) for t in tensors]
arrays = [
t.detach().cpu().half().numpy() for t in tensors
]
mlp_dict = {
key: value
for key, value in zip(dict_names, shapes)
}
if args.save:
print('\n\nSaving MLP:\n{}\n'.format(mlp_dict))
# np.save('mlp.npy', arrays[1:])
# scipy.io.savemat('chip_plots/mnist_val.mat', mdict={key: value for key, value in zip(names[:], values[:])})
# scipy.io.savemat('chip_plots/mnist_labels.mat', mdict={'mnist_test_labels': test_labels.detach().cpu().numpy()})
# print('\nLabels:', test_labels.detach().cpu().numpy().shape, test_labels.detach().cpu().numpy()[:20], '\n\n')
scipy.io.savemat(
'chip_plots/mlp.mat',
mdict={
key: value
for key, value in zip(
dict_names[1:], arrays[1:])
})
# scipy.io.savemat('chip_plots/mlp_first_layer_q4_act_1_acc_.mat', mdict={dict_names[2]: arrays[2], dict_names[3]: arrays[3]})
if args.plot:
names = [
'input', 'weights', 'output', 'diff_output'
]
layers = []
layer = []
print('\n\nlen(arrays) // len(names):',
len(arrays), len(names),
len(arrays) // len(names), '\n\n')
num_layers = len(arrays) // len(names)
for k in range(num_layers):
print('layer', k, names)
for j in range(len(names)):
layer.append([arrays[len(names) * k + j]])
layers.append(layer)
layer = []
info = []
neuron_inputs = []
for n, p in model.named_parameters():
if 'weight' in n:
neuron_inputs.append(np.prod(p.shape[1:]))
for idx in range(len(neuron_inputs)):
temp = []
temp.append('{:d} neuron inputs '.format(
neuron_inputs[idx]))
#if args.plot_power:
#temp.append('{:.2f}mW '.format(self.power[idx][0]))
info.append(temp)
if args.plot:
print('\nPlotting {}\n'.format(names))
plot_layers(num_layers=len(layers),
models=['chip_plots/'],
epoch=epoch,
i=0,
layers=layers,
names=names,
var='',
vars=[''],
infos=info,
pctl=99.9,
acc=val_acc)
#plot_grid([[[v] for v in values]], ['input', 'quantized_input', 'weights', 'output'], path='chip_plots/epoch_' + str(epoch), filename='_mlp_histograms.png')
#layers = [[[a1, aa1], [a2, aa2]]]
#raise(SystemExit)
if args.plot and os.path.exists('chip_plots/mlp.mat'):
os.rename(
r'chip_plots/mlp.mat',
r'chip_plots/mlp_act_max_{:.1f}_w_max_{:.1f}_L2_{:.4f}_L3_{:.1f}_drop_{:.2f}_{:.2f}_LR_{:.3f}_acc_{:.2f}.mat'
.format(args.act_max, args.w_max, args.L2, args.L3,
args.dropout_input, args.dropout_act, args.LR,
best_acc))
print('\nSimulation {:d} Best Accuracy: {:.2f}\n\n'.format(
s, best_acc))
best_accs.append(best_acc)
total_list.append(
(np.mean(best_accs), np.min(best_accs), np.max(best_accs)))
print('\n{:d} runs: {} {} {:.2f} ({:.2f}/{:.2f})\n'.format(
args.num_sims, args.var_name, var, *total_list[-1]))
print('\n\n')
for var, (mean, min, max) in zip(var_list, total_list):
print('{} {:>5} acc {:.2f} ({:.2f}/{:.2f})'.format(
args.var_name, var, mean, min, max))
print('\n\n')
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
if FLAGS.job_name == "ps":
ps_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.01))
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index,
config=ps_config)
server.join()
elif FLAGS.job_name == "worker":
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc+verbs",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir)
#####################################
# Select the preprocessing function #
#####################################
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
FLAGS.network, is_training=True)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(FLAGS.network,
FLAGS.num_classes,
is_training=True)
if FLAGS.dataset_name != "synthetic":
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image = image_preprocessing_fn(image,
network_fn.default_image_size,
network_fn.default_image_size)
images, labels = tf.train.batch([image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
else:
images = random_ops.random_uniform(
(FLAGS.batch_size, network_fn.default_image_size,
network_fn.default_image_size, 3),
maxval=1)
labels = random_ops.random_uniform((FLAGS.batch_size, ),
maxval=FLAGS.num_classes - 1,
dtype=tf.int32)
with tf.device(
tf.train.replica_device_setter(
ps_device='/job:ps/cpu:0',
worker_device=("/job:worker/task:%d" % FLAGS.task_index),
cluster=cluster)):
global_step = tf.contrib.framework.get_or_create_global_step()
#images, labels = cifar.distorted_inputs(FLAGS)
logits, end_points = network_fn(images)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels)
cost = tf.reduce_mean(loss)
train_op = tf.train.AdagradOptimizer(0.01).minimize(
cost, global_step=global_step)
print_model()
train_dir = tempfile.mkdtemp()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
],
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L1)),
gpu_options=tf.GPUOptions(visible_device_list=""))
if FLAGS.infer_shapes == True:
sess_config.graph_options.infer_shapes = FLAGS.infer_shapes
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir=train_dir,
init_op=tf.global_variables_initializer(),
global_step=global_step,
summary_writer=None,
saver=None)
if FLAGS.task_index == 0:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target,
config=sess_config,
start_standard_services=True)
print("Start warmup %d epoch." % FLAGS.warmup)
for _ in range(FLAGS.warmup):
sess.run(train_op)
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
current_step = 0
duration = 0
while current_step < FLAGS.epoch:
current_step += 1
start_time = time.time()
_, step_loss = sess.run([train_op, cost],
options=options,
run_metadata=run_metadata)
end_time = time.time()
print(
"Finish step %d, loss = %f, speed = %f sampes/s, duration = %f seconds"
% (current_step, step_loss, FLAGS.batch_size /
(end_time - start_time), end_time - start_time))
duration += end_time - start_time
if current_step == 3:
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open('timeline.json', 'w') as f:
f.write(chrome_trace)
print("Total Time = %f s." % duration)
#writer.close()
else:
sys.exit("Invalid job role name [%s]!" % args.job_name)
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
if FLAGS.job_name == "ps":
ps_config = tf.ConfigProto(
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.00001
))
# Create and start a server for the local task.
server = tf.train.Server(cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index,
config = ps_config)
server.join()
elif FLAGS.job_name == "worker":
maybe_download_and_extract(FLAGS.data_dir, FLAGS.data_url)
cifar.modify_flags(FLAGS)
print (FLAGS.data_dir)
# Create and start a server for the local task.
server = tf.train.Server(cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
local_worker_device = "/job:worker/task:%d" % FLAGS.task_index
with tf.device(tf.train.replica_device_setter(
ps_device='/job:ps/cpu:0',
worker_device=local_worker_device,
cluster=cluster)):
if FLAGS.network == 'fc':
from models.fullyconnect import KitModel
elif FLAGS.network == 'cifar':
from models.cifar import KitModel
elif FLAGS.network == 'alexnet':
from models.alexnet import KitModel
elif FLAGS.network == 'vgg19' or FLAGS.network == 'vgg_e':
from models.vgg19 import KitModel
elif FLAGS.network == 'inception_v3' :
from models.inception_v3 import KitModel
elif FLAGS.network == 'resnet':
from models.resnet import KitModel
else:
sys.exit("Invalid network [%s]" % FLAGS.network)
this_model = KitModel(FLAGS)
images, labels = cifar.distorted_inputs(FLAGS)
logits = this_model.inference(images)
loss = this_model.loss(labels)
train_op = this_model.train()
train_dir = tempfile.mkdtemp()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps", "/job:worker/task:%d" % FLAGS.task_index],
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1)
),
gpu_options=tf.GPUOptions(
visible_device_list=""
)
)
if FLAGS.infer_shapes == True:
sess_config.graph_options.infer_shapes = FLAGS.infer_shapes
sv = tf.train.Supervisor(
is_chief = (FLAGS.task_index == 0),
logdir = train_dir,
init_op = tf.global_variables_initializer(),
global_step = this_model.global_step,
summary_writer = None,
saver = None)
if FLAGS.task_index == 0:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." % FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target, config = sess_config, start_standard_services = True)
print_model()
print ("Start warmup for %d mini-batch." % FLAGS.warmup)
for _ in range(FLAGS.warmup):
sess.run(this_model.train_op)
current_step = 0
current_epoch = 1
duration = 0
FLAGS.epoch = FLAGS.epoch * NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
print ("Start Training for %d mini-batch." % FLAGS.epoch)
while current_step < FLAGS.epoch:
current_step += 1
start_time = time.time()
_, step_loss = sess.run([this_model.train_op, this_model.cost])
end_time = time.time()
# print("Finish step %d, loss = %f, speed = %f sampes/s, duration = %f seconds" % (current_step, step_loss, FLAGS.batch_size / (end_time - start_time), end_time - start_time))
duration += end_time - start_time
print("Time: %f seconds, step_loss: %f" % (duration, step_loss))
if current_step * FLAGS.batch_size > current_epoch * NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN:
print ("Finish epoch %d" % (current_epoch))
current_epoch += 1
print ("Total Time = %f s." % duration)
#writer.close()
else:
sys.exit("Invalid job role name [%s]!" % FLAGS.job_name)
