def read_net_info(file_path):
f = open(file_path, 'r')
lines = f.readlines()
name_op_dict = {}
raw_info = []
op_list = []
# Units name, input tensor shapes, output tensor shapes, parent idx, children idx
for line in lines:
com = line.split(" ")
if len(com) < 3:
utils.get_logger().warn(
"net info file line length is not 3 {}".format(com))
continue
new_com = [c.strip() for c in com]
raw_info.append(new_com)
for com in raw_info:
name = com[0]
op = net_struct.Operator(name)
if com[1] != 'none':
input_tensors = com[1].split(';')
for it in input_tensors:
if it.strip() != '':
[shape, addr] = it.split('@')
op.input_tensors.append((addr, shape))
if com[2] != 'none':
output_tensors = com[2].split(';')
for ot in output_tensors:
if ot.strip() != '':
[shape, addr] = ot.split('@')
op.output_tensors.append((addr, shape))
# if com[3] != 'none':
# parents = com[3].split(';')
# for p in parents:
# if p.strip() != '':
# parents_name = raw_info[int(p.strip())][0]
# op.parents.add(parents_name)
# if com[4] != 'none':
# children = com[4].split(';')
# for c in children:
# if c.strip() != '':
# child_name = raw_info[int(c.strip())][0]
# op.children.add(child_name)
op_list.append(op)
op_list = net_struct.build_op_relationship(op_list)
for op in op_list:
name_op_dict[op.name] = op
# for op_name in name_list:
# print(name_op_dict[op_name].input_tensors)
return [op.name for op in op_list], name_op_dict
def prepare_dataset(inter_list: list,
dataset_type: str,
dataset_path: str):
logger = get_logger('prepare_dataset')
logger.info(dataset_type)
start_t = time.time()
dataset = pd.read_csv('{}/dataset_raw_{}.csv'.format(dataset_path, dataset_type), sep=';')
logger.info('Dealing with missing values, outliers, categorical features...')
# Профили
dataset['age'] = dataset['age'].fillna(dataset['age'].median())
dataset['gender'] = dataset['gender'].fillna(dataset['gender'].mode()[0])
dataset.loc[~dataset['gender'].isin(['M', 'F']), 'gender'] = dataset['gender'].mode()[0]
dataset['gender'] = dataset['gender'].map({'M': 1., 'F': 0.})
dataset.loc[(dataset['age'] > 80) | (dataset['age'] < 7), 'age'] = round(dataset['age'].median())
dataset.loc[dataset['days_between_fl_df'] < -1, 'days_between_fl_df'] = -1
# Пинги
for period in range(1, len(inter_list) + 1):
col = 'avg_min_ping_{}'.format(period)
dataset.loc[(dataset[col] < 0) |
(dataset[col].isnull()), col] = dataset.loc[dataset[col] >= 0][col].median()
# Сессии и прочее
dataset.fillna(0, inplace=True)
dataset.to_csv('{}dataset_{}.csv'.format(dataset_path, dataset_type), sep=';', index=False)
logger.info('Dataset is successfully prepared and saved to {}, run time (dealing with bad values): {}'. \
format(dataset_path, time_format(time.time() - start_t)))
def main(_):
config_file = os.path.join(FLAGS.output, 'config.json')
log_file = os.path.join(FLAGS.output, 'model.log')
config = load_config(config_file)
config['init_checkpoint'] = FLAGS.init_checkpoint
logger = get_logger(log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
map_file = os.path.join(FLAGS.output, 'maps.pkl')
with open(map_file, "rb") as f:
tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model,
os.path.join(FLAGS.output, 'checkpoint'), config,
logger)
text = "中国你好成都"
result = model.evaluate_line(sess,
input_from_line(text, FLAGS.max_seq_len,
tag_to_id),
id_to_tag,
export=True)
print(result)
def __init__(self, config):
self.logger = get_logger()
self.writer = get_tb_writer(config["Logging"]["tb_logdir"])
self.num_classes = config["Dataset"]["num_classes"]
self.batch_size = config["Train"]["batch_size"]
self.epochs = config["Train"]["epochs"]
self.input_shape = config["Train"]["image_size"]
self.channels = config["Train"]["channels"]
self.ckpt_dir = config["Logging"]["ckpt_dir"]
self.eval_per_epoch = config["Train"]["eval_per_epoch"]
self.device = get_device(config["Train"]["device"])
self.model = load_model(config["Train"]["arch"], self.num_classes,
self.device, config["Train"]["pretrained"],
self.channels, self.logger)
self.optimizer = self.get_optimizer(
config["Train"]["optimizer"],
config["Train"]["lr_scheduler"]["lr_init"])
self.scheduler = self.get_scheduler(config["Train"]["lr_scheduler"])
self.train_loader, self.val_loader, self.classes = \
create_dataloaders(train_data_path=config["Dataset"]["train_data_path"],
val_data_path=config["Dataset"]["val_data_path"],
classes_path=config["Dataset"]["classes_path"],
img_size=self.input_shape,
batch_size=self.batch_size,
augment=config["Augmentation"],
logger=self.logger)
self.criterion = nn.CrossEntropyLoss()
def main():
config = get_config()
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
# logging to the file and stdout
logger = get_logger(config.log_dir, config.exp_name)
# fix random seed to reproduce results
set_random_seed(config.random_seed)
logger.info('Random seed: {:d}'.format(config.random_seed))
logger.info(pprint.pformat(config))
if config.method in ['src', 'jigsaw', 'rotate']:
model = AuxModel(config, logger)
elif config.method in ['cdan', 'cdan+e', 'dann']:
model = CDANModel(config, logger)
else:
raise ValueError("Unknown method: %s" % config.method)
# create data loaders
src_loader, val_loader = get_train_val_dataloader(config.datasets.src)
test_loader = get_test_dataloader(config.datasets.test)
tar_loader = None
if config.datasets.get('tar', None):
tar_loader = get_target_dataloader(config.datasets.tar)
# main loop
if config.mode == 'train':
model.train(src_loader, tar_loader, val_loader, test_loader)
elif config.mode == 'test':
model.test(test_loader)
def evaluate_one():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, target_to_id, id_to_target, feature_to_id, id_to_feature = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, BiLSTMModel, config, load_word2vec, id_to_char, logger)
while True:
try:
line = input("请输入测试句子: ")
if line == "exit":
exit(0)
features = dict()
if config["use_other_features"]:
for feature_i in config["features"]:
if feature_i == "0":
continue
features[feature_i] = input("请输入 feature_" + feature_i + " : ").split()
result = model.evaluate_one(sess, input_from_line(line, features, char_to_id, feature_to_id),
id_to_target)
print(result)
except Exception as e:
print(e)
logger.info(e)
def main():
max_mIoU = 0.0
ckpter = utils.Checkpointer(args)
logger = utils.get_logger(args.working_dir)
train_loader, test_loader = load_data(args)
model = models.__dict__[args.arch](state_dict_dir=args.baseline_dir, n_classes=args.n_classes).to(device)
if device == "cuda":
model = nn.DataParallel(model)
torch.backends.cudnn.benchmark = True
print(model)
print(model(torch.rand([2, 3, 512, 1024])).shape)
optimizer = optim.SGD(model.parameters(), lr=args.init_lr, momentum=args.momentum, weight_decay=args.weight_decay)
loss_fn = nn.CrossEntropyLoss(ignore_index=args.ignore_index)
for epoch in range(args.init_epoch, args.epochs):
train(train_loader, model, optimizer, loss_fn, epoch, logger)
mIoU = test(test_loader, model, loss_fn, logger)
is_best = max_mIoU < mIoU
max_mIoU = max(max_mIoU, mIoU)
model_state_dict = model.module.state_dict() if device == 'cuda' else model.state_dict()
state_dict = {
"epoch": epoch,
"max_mIoU": max_mIoU,
"model_state_dict": model_state_dict,
"optimizer_state_dict": optimizer.state_dict(),
}
ckpter.save_model(state_dict, epoch, is_best)
logger.info("Max mIoU: {:.3f}".format(max_mIoU))
break
def train_main(cfg):
MachineConfig(cfg["machine"])
run_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S-%f")
if "name" not in cfg:
cfg["name"] = "test" + run_id
cfg['training']['log_path'] += cfg["name"]
name = cfg['name']
print('Start', name)
expand_cfg_vars(cfg)
logdir = cfg['training']['log_path']
writer = SummaryWriter(log_dir=logdir, filename_suffix='.metrics')
img_writer = SummaryWriter(log_dir=logdir, filename_suffix='.tensorboardimgs')
print("RUNDIR: {}".format(logdir))
with open(logdir + "/cfg.yml", 'w') as fp:
yaml.dump(cfg, fp)
logger = get_logger(logdir)
logger.info("Let the games begin")
trainer = Trainer(cfg, writer, img_writer, logger, os.path.join(name, str(run_id)))
trainer.train()
def __init__(self, cfg, net_arch, loss_f, rank=0):
self.cfg = cfg
self.device = self.cfg.device
self.net = net_arch.to(self.device)
self.rank = rank
if self.device != "cpu" and self.cfg.dist.gpus != 0:
self.net = DDP(self.net, device_ids=[self.rank])
self.input = None
self.GT = None
self.step = 0
self.epoch = -1
self._logger = get_logger(cfg, os.path.basename(__file__))
# init optimizer
optimizer_mode = self.cfg.train.optimizer.mode
if optimizer_mode == "adam":
self.optimizer = torch.optim.Adam(
self.net.parameters(),
**(self.cfg.train.optimizer[optimizer_mode]))
else:
raise Exception("%s optimizer not supported" % optimizer_mode)
# init loss
self.loss_f = loss_f
self.log = OmegaConf.create()
def main():
args = get_args()
config = process_config(args.config)
# create the experiments dirs
create_dirs([config.cache_dir, config.model_dir,
config.log_dir, config.img_dir])
# logging to the file and stdout
logger = get_logger(config.log_dir, config.exp_name)
# Intialize wandb model
run = wandb.init(project="SemiSupervised", config=args)
run.save()
args.run_name = wandb.run.name
# fix random seed to reproduce results
random.seed(config.random_seed)
logger.info('Random seed: {:d}'.format(config.random_seed))
if config.method in ['src', 'jigsaw', 'rotate']:
model = AuxModel(config, logger)
else:
raise ValueError("Unknown method: %s" % config.method)
src_loader, val_loader = get_train_val_dataloader(config.datasets.src)
# test_loader = get_test_dataloader(config.datasets.test)
test_loader = None
tar_loader = None
if config.datasets.get('tar', None):
tar_loader = get_target_dataloader(config.datasets.tar)
if config.mode == 'train':
model.train(src_loader, tar_loader, val_loader, test_loader)
def build_trainer(cfg, experiment_name, tensorboard_in_subdir=True):
global is_first_trainer
cfg = deepcopy(cfg)
name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + cfg["general"]["tag"]
run_id = experiment_name + "_" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
base_log_dir = cfg['training']['log_path']
log_dir = f"{base_log_dir}/{experiment_name}/"
os.makedirs(log_dir, exist_ok=True)
cfg["name"] = name
cfg["training"]["log_path"] = log_dir
cfg['training']['disable_depth_estimator'] = not is_first_trainer or cfg['training'].get('disable_depth_estimator', False)
if tensorboard_in_subdir:
writer = SummaryWriter(log_dir=log_dir, filename_suffix=f'{experiment_name}.metrics')
img_writer = SummaryWriter(log_dir=log_dir, filename_suffix=f'{experiment_name}.tensorboardimgs')
else:
writer = SummaryWriter(log_dir=base_log_dir, filename_suffix=f'{experiment_name}.metrics')
img_writer = SummaryWriter(log_dir=base_log_dir, filename_suffix=f'{experiment_name}.tensorboardimgs')
logger = get_logger(log_dir)
with open(log_dir + "/cfg.yml", 'w') as fp:
yaml.dump(cfg, fp)
is_first_trainer = False
return Trainer(cfg, writer, img_writer, logger, os.path.join(name, str(run_id)))
def gather_model_profile(raw_info_file_path, data_trans_file_path, inference_latency_file_path, \
CPU_thread_index, SCALE = 1.0, CPU_little_thread_index = None):
data_trans_dict = read_data_trans(data_trans_file_path)
op_name_list, latency_dict = read_latency(inference_latency_file_path, \
CPU_thread_index, OP_LATENCY_SCALE = SCALE, CPU_little_thread_index=CPU_little_thread_index)
op_name_list, name_op_dict = read_net_info(raw_info_file_path)
net_def = net_struct.NetDef()
utils.get_logger().info(data_trans_dict.keys())
# Gather three file into name_op_dict
for op_name in op_name_list:
op = name_op_dict[op_name]
op_latency = latency_dict[op_name]
op_type = op_name.split('/')[-1]
op_def = net_struct.OperatorDef()
op_def.type = op_type
# Set operator_latency transformation latency
# The data transformation latency is the sum of all the input tensor transformation latency
for (tensor_addr, tensor_shape) in op.input_tensors:
# (TODO): measure the communication latency between CPU big and little cluster
if CPU_little_thread_index != None:
op_latency.input_data_trans_latency[tensor_addr] = [0, 0]
op_latency.Transpose_latency_NCHW_to_NHWC = 0
op_latency.Transpose_latency_NHWC_to_NCHW = 0
break
# (TODO): Add support for different layout convert(NC4HW4<->Image, NHWC<->Image)
if (len(tensor_shape) >= 1):
if tensor_shape in data_trans_dict.keys():
op_latency.Transpose_latency_NCHW_to_NHWC += data_trans_dict[
tensor_shape][0]
op_latency.Transpose_latency_NHWC_to_NCHW += data_trans_dict[
tensor_shape][1]
op_latency.input_data_trans_latency[
tensor_addr] = data_trans_dict[tensor_shape]
# utils.get_logger().info("{} {} {} {}".format(op_name, tensor_addr, tensor_shape, data_trans_dict[tensor_shape]))
else:
op_latency.Transpose_latency_NCHW_to_NHWC = TRANSFORM_OVERHEAD
op_latency.Transpose_latency_NHWC_to_NCHW = TRANSFORM_OVERHEAD
op_latency.input_data_trans_latency[tensor_addr] = [
TRANSFORM_OVERHEAD, TRANSFORM_OVERHEAD
]
op_def.operator_latency = op_latency
op.op_def = op_def
name_op_dict[op_name] = op
net_def.op.append(op)
return op_name_list, name_op_dict, net_def
def main_train():
# load data sets
# sentences = [[(words11, tag11), ...], [(word21, tag21), ...], ...]
train_sentences = load_sentences(FLAGS.train_file, FLAGS.lower, FLAGS.zeros)
dev_sentences = load_sentences(FLAGS.dev_file, FLAGS.lower, FLAGS.zeros)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower, FLAGS.zeros)
# Use selected tagging scheme (IOB / IOBES)
# 更新在train_sentences和test_sentences中
update_tag_scheme(train_sentences, FLAGS.tag_schema)
update_tag_scheme(test_sentences, FLAGS.tag_schema)
# create maps if not exist
# 创建或加载字符、词、特征、target的映射字典
if not os.path.isfile(FLAGS.map_file):
# create dictionary for word
# 若存在pre-trained embedding file，则同时使用pre-trained和训练集构建字典
if FLAGS.pre_emb:
# 统计train中字，返回字典
dico_chars_train = char_mapping(train_sentences, FLAGS.lower)[0]
# 使用pre-trained的字增大训练集字的字典
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(),
FLAGS.emb_file,
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in test_sentences])
)
)
# 否则只是用训练集构建字典
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences, FLAGS.lower)
# Create a dictionary and a mapping for tags
_t, target_to_id, id_to_target = tag_mapping(train_sentences)
# 创建其他特征的映射字典，返回的三个都为dict
_f, feature_to_id, id_to_feature = feature_mapping(train_sentences, FLAGS.features)
# 存储字、target、feature的映射关系
with open(FLAGS.map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, target_to_id, id_to_target, feature_to_id, id_to_feature], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, target_to_id, id_to_target, feature_to_id, id_to_feature = pickle.load(f)
# make path for store log and model config if not exist
make_path(FLAGS)
if os.path.isfile(FLAGS.config_file):
config = load_config(FLAGS.config_file)
else:
config = create_config_model(FLAGS, char_to_id, target_to_id, feature_to_id)
logger = get_logger(FLAGS.log_file)
print_config(config, logger)
train(config, train_sentences, dev_sentences, test_sentences, char_to_id, feature_to_id, target_to_id, id_to_char,
id_to_target, logger)
def run(args):
if not os.path.exists(args.logdir):
os.makedirs(args.logdir)
logger = get_logger(os.path.join(args.logdir, 'train_sgada.log'))
logger.info(args)
# data loaders
dataset_root = os.environ["DATASETDIR"]
source_train_loader = get_mscoco(dataset_root, args.batch_size, train=True)
target_train_loader = get_flir(dataset_root, args.batch_size, train=True)
target_val_loader = get_flir(dataset_root, args.batch_size, train=False)
target_conf_train_loader = get_flir_from_list_wdomain(dataset_root, args.batch_size, train=True)
args.classInfo = {'classes': torch.unique(torch.tensor(source_train_loader.dataset.targets)),
'classNames': source_train_loader.dataset.classes}
logger.info('SGADA training')
# train source CNN
source_cnn = CNN(in_channels=args.in_channels).to(args.device)
if os.path.isfile(args.trained):
c = torch.load(args.trained)
source_cnn.load_state_dict(c['model'])
logger.info('Loaded `{}`'.format(args.trained))
for param in source_cnn.parameters():
param.requires_grad = False
# train target CNN
target_cnn = CNN(in_channels=args.in_channels, target=True, srcTrain=False).to(args.device)
target_cnn.load_state_dict(source_cnn.state_dict())
for param in target_cnn.classifier.parameters():
param.requires_grad = False
optimizer = optim.Adam(
target_cnn.encoder.parameters(),
lr=args.lr, betas=args.betas,
weight_decay=args.weight_decay)
discriminator = Discriminator(args=args).to(args.device)
criterion = nn.CrossEntropyLoss()
d_optimizer = optim.Adam(
discriminator.parameters(),
lr=args.d_lr, betas=args.betas, weight_decay=args.weight_decay)
best_acc, best_class, classNames = train_target_cnnP_domain(
source_cnn, target_cnn, discriminator,
criterion, optimizer, d_optimizer,
source_train_loader, target_conf_train_loader, target_val_loader,
logger, args=args)
bestClassWiseDict = {}
for cls_idx, clss in enumerate(classNames):
bestClassWiseDict[clss] = best_class[cls_idx].item()
logger.info('Best acc.: {}'.format(best_acc))
logger.info('Best acc. (Classwise):')
logger.info(bestClassWiseDict)
return best_acc, bestClassWiseDict
def test_model(cfg, model, test_loader, writer):
logger = get_logger(cfg, os.path.basename(__file__))
model.net.eval()
total_test_loss = 0
test_loop_len = 0
total_test_accuracy = 0
total_test = 0
with torch.no_grad():
for model_input, target in test_loader:
model.feed_data(input=model_input, GT=target)
output = model.run_network()
loss_v = model.loss_f(output, model.GT)
_, predicted = torch.max(output.data, 1)
total_v = torch.tensor(int(target.size(0))).to('cuda')
accuracy_v = torch.tensor(
float(
(predicted == target.to('cuda')).sum().item())).to('cuda')
# print(f"loss_v: {type(loss_v)}")
# print(f"predicted: {type(accuracy_v)}")
# print(f"target: {target.device}")
# print(f"accuracy_v: {accuracy_v.device}")
if cfg.dist.gpus > 0:
# Aggregate loss_v from all GPUs. loss_v is set as the sum of all GPUs' loss_v.
torch.distributed.all_reduce(loss_v)
loss_v /= torch.tensor(float(cfg.dist.gpus))
# Aggregate accuracy_v from all GPUs. loss_v is set as the sum of all GPUs' accuracy_v.
torch.distributed.all_reduce(accuracy_v)
torch.distributed.all_reduce(total_v)
total_test += total_v.to("cpu").item()
total_test_loss += loss_v.to("cpu").item()
total_test_accuracy += accuracy_v.to("cpu").item()
test_loop_len += 1
# print(f"total_v = {total_test}")
# print(f"accuracy_v = {total_test_accuracy}")
total_test_loss /= test_loop_len
total_test_accuracy /= total_test
if writer is not None:
writer.logging_with_step(total_test_accuracy, model.step,
"test_accuracy")
writer.logging_with_step(total_test_loss, model.step, "test_loss")
writer.logging_with_epoch(total_test_accuracy, model.step,
model.epoch,
"total_test_accuracy_per_epoch")
writer.logging_with_epoch(total_test_loss, model.step, model.epoch,
"test_loss_per_epoch")
if is_logging_process():
logger.info("Test Loss %.04f at step %d" %
(total_test_loss, model.step))
def main() -> None:
utils.get_logger("bot")
# Terminate if the config failed to load.
if not utils.load_config():
return
# Set logging level to debug if verbose is true.
if g.config["verbose"]:
g.log.setLevel(logging.DEBUG)
g.log.handlers[0].setLevel(logging.DEBUG)
try:
g.exchanges = exchanges.get_exchanges()
g.db = Database()
image.init()
Twitter(callback)
except Exception as e:
g.log.critical(f"{type(e).__name__}: {e}")
g.log.critical(traceback.format_exc())
def evaluate_line():
# 加载意图识别模型
id_to_cat = get_id_to_cat('{}/categories.txt'.format(data_path))
print(
"==========================Loading the Intention Classification model....=========================="
)
model_1 = ImportGraph('{}/model_cnn'.format(model_path))
model_2 = ImportGraph('{}/model_rnn'.format(model_path))
print("Model loaded..")
flag = 0
# 加载命名实体识别模型
print(
"==========================Loading the NER model....=========================="
)
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
# 循环识别
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
# 获取测试句子
text = input("请输入要进行识别的句子：")
# intent 识别
id_text = process_text(text, '{}/vocab.txt'.format(data_path))
pred_1 = model_1.run(id_text, 1.0)
pred_2 = model_2.run(id_text, 1.0)
pred = pred_1 + pred_2
res = id_to_cat[int(np.argmax(pred))]
print(res)
# NER 识别
result = model.evaluate_line(sess,
input_from_line(text, char_to_id),
id_to_tag)
print(result)
def get_ops_total_latency(op, name_op_dict):
"""Get the op's execution latency on CPU and GPU
based on the device placement result of op's parents
considering the communication latency
"""
to_CPU_transpose_latency = 0.0
to_GPU_transpose_latency = 0.0
utils.get_logger().info(
"op name: {}, input tensors:{}, data trans dict {}".format(
op.name, op.input_tensors,
op.op_def.operator_latency.input_data_trans_latency))
for op_parent_name in op.parents:
op_parent = name_op_dict[op_parent_name]
for child_tensor_addr, child_tensor_shape in op.input_tensors:
for parent_tensor_addr, parent_tensor_shape in op_parent.output_tensors:
if child_tensor_addr == parent_tensor_addr:
utils.get_logger().info("{} {} {}".format(
op.name, op_parent.name, parent_tensor_shape))
if op_parent.op_def.device_type == net_struct.DeviceType.CPU:
to_GPU_transpose_latency += op.op_def.operator_latency.input_data_trans_latency[
child_tensor_addr][1]
elif op_parent.op_def.device_type == net_struct.DeviceType.GPU:
to_CPU_transpose_latency += op.op_def.operator_latency.input_data_trans_latency[
child_tensor_addr][0]
utils.get_logger().info("{} {} {} {} {}".format(op.name, \
op.op_def.operator_latency.CPU_latency, op.op_def.operator_latency.GPU_latency,\
to_CPU_transpose_latency, to_GPU_transpose_latency))
CPU_latency = op.op_def.operator_latency.CPU_latency + to_CPU_transpose_latency
GPU_latency = op.op_def.operator_latency.GPU_latency + to_GPU_transpose_latency
return CPU_latency, GPU_latency
def main():
logger = utils.get_logger(__name__)
texts, tags = read_wnut('../example_data/wnut17train.conll')
#mlflow.set_tracking_uri(uri=settings.MLFLOW_URI)
train_texts, test_texts, train_tags, test_tags = model_utils.train_test_split(texts, tags, test_size=.2)
unique_tags = set(tag for doc in tags for tag in doc)
tag2id = {tag: id for id, tag in enumerate(unique_tags)}
id2tag = {id: tag for tag, id in tag2id.items()}
params = bert.BertParams(unique_tags=unique_tags, tag2id=tag2id, id2tag=id2tag, epochs=100)
trainer = bert.BertTokenTrainer(params=params, num_labels=len(unique_tags))
trainer.train(train_texts, train_tags)
def train_model(cfg, model, train_loader, writer):
logger = get_logger(cfg, os.path.basename(__file__))
model.net.train()
for model_input, model_target in train_loader:
model.optimize_parameters(model_input, model_target)
loss = model.log.loss_v
model.step += 1
if is_logging_process() and (loss > 1e8 or math.isnan(loss)):
logger.error("Loss exploded to %.02f at step %d!" %
(loss, model.step))
raise Exception("Loss exploded")
if model.step % cfg.log.summary_interval == 0:
if writer is not None:
writer.logging_with_step(loss, model.step, "train_loss")
if is_logging_process():
logger.info("Train Loss %.04f at step %d" % (loss, model.step))
def setup_method(self):
# set log/checkpoint dir
self.TEST_DIR = pathlib.Path(TEST_DIR)
self.working_dir = self.TEST_DIR
chkpt_dir = (self.TEST_DIR / "chkpt").resolve()
os.makedirs(self.TEST_DIR, exist_ok=True)
os.makedirs(chkpt_dir, exist_ok=True)
# set cfg
with initialize(config_path="../config"):
self.cfg = compose(
config_name="default", overrides=[f"working_dir={self.working_dir}"]
)
self.cfg.device = "cpu"
self.cfg.log.chkpt_dir = str(chkpt_dir)
self.cfg.log.use_wandb = False
self.cfg.log.use_tensorboard = False
# load job_logging_cfg
project_root_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
hydra_conf = OmegaConf.load(
os.path.join(project_root_path, "config/default.yaml")
)
job_logging_name = None
for job_logging in hydra_conf.defaults:
job_logging_name = job_logging.get("hydra/job_logging")
if job_logging_name is not None:
break
job_logging_cfg_path = os.path.join(
project_root_path,
"config/hydra/job_logging",
str(job_logging_name) + ".yaml",
)
if os.path.exists(job_logging_cfg_path):
job_logging_cfg = OmegaConf.load(job_logging_cfg_path)
else:
job_logging_cfg = dict()
with open_dict(self.cfg):
self.cfg.job_logging_cfg = job_logging_cfg
self.cfg.job_logging_cfg.handlers.file.filename = str(
(self.working_dir / "trainer.log").resolve()
)
# set logger
self.logger = get_logger(self.cfg, os.path.basename(__file__))
def __init__(self, cfg, net_arch, loss_f, rank=0):
self.cfg = cfg
self.device = self.cfg.device
self.net = net_arch.to(self.device)
self.rank = rank
if self.device != "cpu" and self.cfg.dist.gpus != 0:
self.net = DDP(self.net, device_ids=[self.rank])
self.input = None
self.GT = None
self.step = 0
self.epoch = -1
self._logger = get_logger(cfg, os.path.basename(__file__))
self.optimizer = get_optimizer(cfg, self.net)
self.scheduler = get_scheduler(cfg, self.optimizer)
# init loss
self.loss_f = loss_f
self.log = OmegaConf.create()
def evaluate_line():
config = load_config(args.config_file)
logger = get_logger(args.log_file)
# limit GPU memory 限制GPU的内存大小
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(args.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, intent_to_id, id_to_intent = pickle.load(
f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, args.ckpt_path, load_word2vec,
config, id_to_char, logger)
while True:
try:
line = input("请输入测试句子:")
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag, id_to_intent)
print(result)
except Exception as e:
logger.info(e)
def main():
logger = get_logger('make_prediction')
with open('{}/baseline_xgb.pcl'.format(settings.MODEL_PATH), 'rb') as f:
model = pickle.load(f)
X = pd.read_csv('{}dataset_test.csv'.format(settings.DATASET_PATH),
sep=';')
X_mm = X.drop(['user_id'], axis=1)
X_mm = MinMaxScaler().fit_transform(X_mm)
predict_test = model.predict(X_mm)
X_pred = pd.DataFrame(X['user_id'])
X_pred['is_churned'] = predict_test
X_pred.to_csv('{}dataset_pred.csv'.format(settings.PREDICTION_PATH),
sep=';',
index=False)
logger.info('Prediction is successfully saved to {}'.format(
settings.PREDICTION_PATH))
def __init__(self, args, embeddings, tag2label, vocab, paths, config):
self.batch_size = args.batch_size
self.epoch_num = args.epoch
self.hidden_dim = args.hidden_dim
self.embeddings = embeddings
self.CRF = args.CRF
self.update_embedding = args.update_embedding
self.dropout_keep_prob = args.dropout
self.optimizer = args.optimizer
self.lr = args.lr
self.clip_grad = args.clip
self.tag2label = tag2label
self.num_tags = len(tag2label)
self.vocab = vocab
self.shuffle = args.shuffle
self.model_path = paths['model_path']
self.summary_path = paths['summary_path']
self.logger = get_logger(paths['log_path'])
self.result_path = paths['result_path']
self.test_result_path = paths['test_result_path']
self.config = config
def evaluate_test():
config = load_config(args.config_file)
logger = get_logger(args.log_file)
with open(args.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, intent_to_id, id_to_intent = pickle.load(
f)
test_sentences = load_sentences(args.test_file, args.lower, args.zeros)
update_tag_scheme(test_sentences, args.tag_schema)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
intent_to_id, args.lower)
test_manager = BatchManager(test_data, 100)
# limit GPU memory 限制GPU的内存大小
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, args.ckpt_path, load_word2vec,
config, id_to_char, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def main(wandb):
# create the experiments dirs
create_dirs([
config.cache_dir, config.model_dir, config.log_dir,
config.best_model_dir
])
# logging to the file and stdout
logger = get_logger(config.log_dir, config.dataset)
# fix random seed to reproduce results
logger.info('Random seed: {:d}'.format(config.random_seed))
# model = get_model(config)
model = AuxModel(config, logger, wandb)
src_loader, tar_loader, val_loader, test_loader = get_loaders(config)
if config.mode == 'train':
model.train(src_loader, tar_loader, val_loader, None)
elif config.mode == 'val':
model.test(val_loader)
elif config.mode == 'test':
model.test(test_loader)
def test_model(cfg, model, test_loader, writer):
logger = get_logger(cfg, os.path.basename(__file__))
model.net.eval()
total_test_loss = 0
test_loop_len = 0
with torch.no_grad():
for model_input, model_target in test_loader:
output = model.inference(model_input)
loss_v = model.loss_f(output, model_target.to(cfg.device))
if cfg.dist.gpus > 0:
# Aggregate loss_v from all GPUs. loss_v is set as the sum of all GPUs' loss_v.
torch.distributed.all_reduce(loss_v)
loss_v /= torch.tensor(float(cfg.dist.gpus))
total_test_loss += loss_v.to("cpu").item()
test_loop_len += 1
total_test_loss /= test_loop_len
if writer is not None:
writer.logging_with_step(total_test_loss, model.step, "test_loss")
if is_logging_process():
logger.info("Test Loss %.04f at step %d" %
(total_test_loss, model.step))
def evaluate_line_ner():
config = load_config(FLAGS.config_file)
logger = get_logger(FLAGS.log_file)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
while True:
# try:
# line = input("请输入测试句子:")
# result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
# print(result)
# except Exception as e:
# logger.info(e)
line = input("请输入测试句子:")
result = model.evaluate_line(sess,
input_from_line(line, char_to_id),
id_to_tag)
print(result)
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import xml.etree.ElementTree as ET
from jinja2 import Environment, FileSystemLoader
from utils import utils
logger = utils.get_logger('Summarize')
def parse_arguments():
parser = argparse.ArgumentParser()
xml_help = """
Paths to xml summary files from layer tests.
In case of entries intersection, results will
be merged basing on timestamp - entry from latest
report is be kept.
"""
out_help = "Path where to save html report"
report_tag = "Report tag"
output_filename_help = "Output report filename"
conformance_mode_help = "Allow to align test number"
parser.add_argument("--xml", help=xml_help, nargs="*", required=True)
parser.add_argument("--out", help=out_help, default="")
parser.add_argument("--output_filename",
import os
from itertools import tee
from gensim.models import Word2Vec
from utils.utils import get_logger
_logger = get_logger(__name__)
def _train_model(tokenized_lines, params):
params_str = '_w' + str(params['win_size']) + '_m' + str(params['min_w_num']) + '_v' + str(params['vect_size'])
_logger.info('Word2Vec model will be trained now. It can take long, so relax and have fun')
_logger.info('Parameters for training: %s' % params_str)
tokenized_lines_for_voc, tokenized_lines_for_train = tee(tokenized_lines)
model = Word2Vec(window=int(params['win_size']), min_count=int(params['min_w_num']), size=int(params['vect_size']),
workers=int(params['workers_num']))
model.build_vocab(tokenized_lines_for_voc)
model.train(tokenized_lines_for_train)
return model
def _save_model(model, model_filename):
_logger.info('Trained model will now be saved as %s for later use' % model_filename)
model.save(model_filename)