def infer(conf_dict, args):
"""
run predict
"""
logging.info("start test process ...")
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
vocab = utils.load_vocab(args.vocab_path)
simnet_process = reader.SimNetProcessor(args, vocab)
get_infer_examples = simnet_process.get_infer_reader
infer_loader = fluid.io.DataLoader.from_generator(
capacity=16,
return_list=True,
iterable=True,
use_double_buffer=True)
infer_loader.set_sample_list_generator(
paddle.batch(get_infer_examples, batch_size=args.batch_size),
place)
conf_dict['dict_size'] = len(vocab)
conf_dict['seq_len'] = args.seq_len
net = utils.import_class("./nets", conf_dict["net"]["module_name"],
conf_dict["net"]["class_name"])(conf_dict)
model, _ = load_dygraph(args.init_checkpoint)
net.set_dict(model)
pred_list = []
if args.task_mode == "pairwise":
for left, pos_right in infer_loader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
left_feat, pos_score = net(left, pos_right)
pred = pos_score
pred_list += list(
map(lambda item: str((item[0] + 1) / 2), pred.numpy()))
else:
for left, right in infer_loader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(right, shape=[-1, 1])
left_feat, pred = net(left, right)
pred_list += map(lambda item: str(np.argmax(item)),
pred.numpy())
with io.open(args.infer_result_path, "w",
encoding="utf8") as infer_file:
for _data, _pred in zip(simnet_process.get_infer_data(),
pred_list):
infer_file.write(_data + "\t" + _pred + "\n")
logging.info("infer result saved in %s" %
os.path.join(os.getcwd(), args.infer_result_path))
def train(conf_dict, args):
"""
train process
"""
# Get device
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
# run train
logging.info("start train process ...")
def valid_and_test(pred_list, process, mode):
"""
return auc and acc
"""
pred_list = np.vstack(pred_list)
if mode == "test":
label_list = process.get_test_label()
elif mode == "valid":
label_list = process.get_valid_label()
if args.task_mode == "pairwise":
pred_list = (pred_list + 1) / 2
pred_list = np.hstack(
(np.ones_like(pred_list) - pred_list, pred_list))
metric.reset()
metric.update(pred_list, label_list)
auc = metric.eval()
if args.compute_accuracy:
acc = utils.get_accuracy(pred_list, label_list, args.task_mode,
args.lamda)
return auc, acc
else:
return auc
with fluid.dygraph.guard(place):
# used for continuous evaluation
if args.enable_ce:
SEED = 102
fluid.default_startup_program().random_seed = SEED
fluid.default_main_program().random_seed = SEED
# loading vocabulary
vocab = utils.load_vocab(args.vocab_path)
# get vocab size
conf_dict['dict_size'] = len(vocab)
conf_dict['seq_len'] = args.seq_len
# Load network structure dynamically
net = utils.import_class("./nets", conf_dict["net"]["module_name"],
conf_dict["net"]["class_name"])(conf_dict)
if args.init_checkpoint is not "":
model, _ = load_dygraph(args.init_checkpoint)
net.set_dict(model)
# Load loss function dynamically
loss = utils.import_class("./nets/losses",
conf_dict["loss"]["module_name"],
conf_dict["loss"]["class_name"])(conf_dict)
# Load Optimization method
learning_rate = conf_dict["optimizer"]["learning_rate"]
optimizer_name = conf_dict["optimizer"]["class_name"]
if optimizer_name == 'SGDOptimizer':
optimizer = fluid.optimizer.SGDOptimizer(
learning_rate, parameter_list=net.parameters())
elif optimizer_name == 'AdamOptimizer':
beta1 = conf_dict["optimizer"]["beta1"]
beta2 = conf_dict["optimizer"]["beta2"]
epsilon = conf_dict["optimizer"]["epsilon"]
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
parameter_list=net.parameters())
# load auc method
metric = fluid.metrics.Auc(name="auc")
simnet_process = reader.SimNetProcessor(args, vocab)
# set global step
global_step = 0
ce_info = []
losses = []
start_time = time.time()
train_pyreader = fluid.io.PyReader(capacity=16,
return_list=True,
use_double_buffer=True)
get_train_examples = simnet_process.get_reader("train",
epoch=args.epoch)
train_pyreader.decorate_sample_list_generator(
paddle.batch(get_train_examples, batch_size=args.batch_size),
place)
if args.do_valid:
valid_pyreader = fluid.io.PyReader(capacity=16,
return_list=True,
use_double_buffer=True)
get_valid_examples = simnet_process.get_reader("valid")
valid_pyreader.decorate_sample_list_generator(
paddle.batch(get_valid_examples, batch_size=args.batch_size),
place)
pred_list = []
if args.task_mode == "pairwise":
for left, pos_right, neg_right in train_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
neg_right = fluid.layers.reshape(neg_right, shape=[-1, 1])
net.train()
global_step += 1
left_feat, pos_score = net(left, pos_right)
pred = pos_score
_, neg_score = net(left, neg_right)
avg_cost = loss.compute(pos_score, neg_score)
losses.append(np.mean(avg_cost.numpy()))
avg_cost.backward()
optimizer.minimize(avg_cost)
net.clear_gradients()
if args.do_valid and global_step % args.validation_steps == 0:
for left, pos_right in valid_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right,
shape=[-1, 1])
net.eval()
left_feat, pos_score = net(left, pos_right)
pred = pos_score
pred_list += list(pred.numpy())
valid_result = valid_and_test(pred_list, simnet_process,
"valid")
if args.compute_accuracy:
valid_auc, valid_acc = valid_result
logging.info(
"global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f"
% (global_step, valid_auc, valid_acc,
np.mean(losses)))
else:
valid_auc = valid_result
logging.info(
"global_steps: %d, valid_auc: %f, valid_loss: %f" %
(global_step, valid_auc, np.mean(losses)))
if global_step % args.save_steps == 0:
model_save_dir = os.path.join(args.output_dir,
conf_dict["model_path"])
model_path = os.path.join(model_save_dir, str(global_step))
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
fluid.dygraph.save_dygraph(net.state_dict(), model_path)
logging.info("saving infer model in %s" % model_path)
else:
for left, right, label in train_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
right = fluid.layers.reshape(right, shape=[-1, 1])
label = fluid.layers.reshape(label, shape=[-1, 1])
net.train()
global_step += 1
left_feat, pred = net(left, right)
avg_cost = loss.compute(pred, label)
losses.append(np.mean(avg_cost.numpy()))
avg_cost.backward()
optimizer.minimize(avg_cost)
net.clear_gradients()
if args.do_valid and global_step % args.validation_steps == 0:
for left, right in valid_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
right = fluid.layers.reshape(right, shape=[-1, 1])
net.eval()
left_feat, pred = net(left, right)
pred_list += list(pred.numpy())
valid_result = valid_and_test(pred_list, simnet_process,
"valid")
if args.compute_accuracy:
valid_auc, valid_acc = valid_result
logging.info(
"global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f"
% (global_step, valid_auc, valid_acc,
np.mean(losses)))
else:
valid_auc = valid_result
logging.info(
"global_steps: %d, valid_auc: %f, valid_loss: %f" %
(global_step, valid_auc, np.mean(losses)))
if global_step % args.save_steps == 0:
model_save_dir = os.path.join(args.output_dir,
conf_dict["model_path"])
model_path = os.path.join(model_save_dir, str(global_step))
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
fluid.dygraph.save_dygraph(net.state_dict(), model_path)
logging.info("saving infer model in %s" % model_path)
end_time = time.time()
ce_info.append([np.mean(losses), end_time - start_time])
# final save
logging.info("the final step is %s" % global_step)
model_save_dir = os.path.join(args.output_dir, conf_dict["model_path"])
model_path = os.path.join(model_save_dir, str(global_step))
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
fluid.dygraph.save_dygraph(net.state_dict(), model_path)
logging.info("saving infer model in %s" % model_path)
# used for continuous evaluation
if args.enable_ce:
# if True:
card_num = get_cards()
ce_loss = 0
ce_time = 0
try:
ce_loss = ce_info[-1][0]
ce_time = ce_info[-1][1]
except:
logging.info("ce info err!")
print("kpis\teach_step_duration_%s_card%s\t%s" %
(args.task_name, card_num, ce_time))
print("kpis\ttrain_loss_%s_card%s\t%f" %
(args.task_name, card_num, ce_loss))
if args.do_test:
# Get Feeder and Reader
test_pyreader = fluid.io.PyReader(capacity=16,
return_list=True,
use_double_buffer=True)
get_test_examples = simnet_process.get_reader("test")
test_pyreader.decorate_sample_list_generator(
paddle.batch(get_test_examples, batch_size=args.batch_size),
place)
pred_list = []
for left, pos_right in test_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
net.eval()
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
left_feat, pos_score = net(left, pos_right)
pred = pos_score
pred_list += list(pred.numpy())
test_result = valid_and_test(pred_list, simnet_process, "test")
if args.compute_accuracy:
test_auc, test_acc = test_result
logging.info("AUC of test is %f, Accuracy of test is %f" %
(test_auc, test_acc))
else:
test_auc = test_result
logging.info("AUC of test is %f" % test_auc)
def test(conf_dict, args):
"""
Evaluation Function
"""
logging.info("start test process ...")
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
vocab = utils.load_vocab(args.vocab_path)
simnet_process = reader.SimNetProcessor(args, vocab)
test_pyreader = fluid.io.PyReader(capacity=16,
return_list=True,
use_double_buffer=True)
get_test_examples = simnet_process.get_reader("test")
test_pyreader.decorate_sample_list_generator(
paddle.batch(get_test_examples, batch_size=args.batch_size), place)
conf_dict['dict_size'] = len(vocab)
conf_dict['seq_len'] = args.seq_len
net = utils.import_class("./nets", conf_dict["net"]["module_name"],
conf_dict["net"]["class_name"])(conf_dict)
model, _ = load_dygraph(args.init_checkpoint)
net.set_dict(model)
metric = fluid.metrics.Auc(name="auc")
pred_list = []
with io.open("predictions.txt", "w",
encoding="utf8") as predictions_file:
if args.task_mode == "pairwise":
for left, pos_right in test_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
left_feat, pos_score = net(left, pos_right)
pred = pos_score
# pred_list += list(pred.numpy())
pred_list += list(
map(lambda item: float(item[0]), pred.numpy()))
predictions_file.write(u"\n".join(
map(lambda item: str(
(item[0] + 1) / 2), pred.numpy())) + "\n")
else:
for left, right in test_pyreader():
left = fluid.layers.reshape(left, shape=[-1, 1])
right = fluid.layers.reshape(right, shape=[-1, 1])
left_feat, pred = net(left, right)
# pred_list += list(pred.numpy())
pred_list += list(
map(lambda item: float(item[0]), pred.numpy()))
predictions_file.write(u"\n".join(
map(lambda item: str(np.argmax(item)), pred.numpy())) +
"\n")
if args.task_mode == "pairwise":
pred_list = np.array(pred_list).reshape((-1, 1))
pred_list = (pred_list + 1) / 2
pred_list = np.hstack(
(np.ones_like(pred_list) - pred_list, pred_list))
else:
pred_list = np.array(pred_list)
labels = simnet_process.get_test_label()
metric.update(pred_list, labels)
if args.compute_accuracy:
acc = utils.get_accuracy(pred_list, labels, args.task_mode,
args.lamda)
logging.info("AUC of test is %f, Accuracy of test is %f" %
(metric.eval(), acc))
else:
logging.info("AUC of test is %f" % metric.eval())
if args.verbose_result:
utils.get_result_file(args)
logging.info("test result saved in %s" %
os.path.join(os.getcwd(), args.test_result_path))