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train.py
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train.py
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"""TB-Net training."""
import os
import argparse
import moxing as mox
import math
import numpy as np
from mindspore import context, Model, Tensor
from mindspore.train.serialization import save_checkpoint
from mindspore.train.callback import Callback, TimeMonitor
import mindspore.common.dtype as mstype
from src import tbnet, config, metrics, dataset
class MyLossMonitor(Callback):
"""My loss monitor definition."""
def on_train_epoch_end(self, run_context):
"""Print loss at each epoch end."""
cb_params = run_context.original_args()
loss = cb_params.net_outputs
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
print('loss:' + str(loss))
def on_eval_epoch_end(self, run_context):
"""Print loss at each epoch end."""
cb_params = run_context.original_args()
loss = cb_params.net_outputs
if isinstance(loss, (tuple, list)):
if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
loss = loss[0]
if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
loss = np.mean(loss.asnumpy())
print('loss:' + str(loss))
def get_args():
"""Parse commandline arguments."""
parser = argparse.ArgumentParser(description='Train TBNet.')
parser.add_argument(
'--dataset',
type=str,
required=False,
default='steam',
help="'steam' dataset is supported currently"
)
parser.add_argument(
'--train_csv',
type=str,
required=False,
default='train.csv',
help="the train csv datafile inside the dataset folder"
)
parser.add_argument(
'--test_csv',
type=str,
required=False,
default='test.csv',
help="the test csv datafile inside the dataset folder"
)
parser.add_argument(
'--device_id',
type=int,
required=False,
default=0,
help="device id"
)
parser.add_argument(
'--epochs',
type=int,
required=False,
default=20,
help="number of training epochs"
)
parser.add_argument(
'--device_target',
type=str,
required=False,
default='Ascend',
choices=['GPU', 'Ascend'],
help="run code on GPU or Ascend NPU"
)
parser.add_argument(
'--data_url',
help='path to training/inference dataset folder',
default= '/data/'
)
parser.add_argument(
'--train_url',
help='model folder to save/load',
default= '/model/'
)
parser.add_argument(
'--run_mode',
type=str,
required=False,
default='graph',
choices=['graph', 'pynative'],
help="run code by GRAPH mode or PYNATIVE mode"
)
return parser.parse_args()
def train_tbnet():
"""Training process."""
args = get_args()
home = os.path.dirname(os.path.realpath(__file__))
data_dir = home
obs_data_url = args.data_url
obs_train_url = args.train_url
#将数据拷贝到训练环境
try:
mox.file.copy_parallel(obs_data_url, data_dir)
print("Successfully Download {} to {}".format(obs_data_url,
data_dir))
except Exception as e:
print('moxing download {} to {} failed: '.format(
obs_data_url, data_dir) + str(e))
os.system("python "+home+"/preprocess_dataset.py --device_target "+args.device_target)
config_path = os.path.join(home, 'data', args.dataset, 'config.json')
train_csv_path = os.path.join(home, 'data', args.dataset, args.train_csv)
test_csv_path = os.path.join(home, 'data', args.dataset, args.test_csv)
ckpt_path = os.path.join(home, 'checkpoints')
context.set_context(device_id=args.device_id)
if args.run_mode == 'graph':
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
else:
context.set_context(mode=context.PYNATIVE_MODE, device_target=args.device_target)
if not os.path.exists(ckpt_path):
os.makedirs(ckpt_path)
print(f"creating dataset from {train_csv_path}...")
net_config = config.TBNetConfig(config_path)
if args.device_target == 'Ascend':
net_config.per_item_paths = math.ceil(net_config.per_item_paths / 16) * 16
net_config.embedding_dim = math.ceil(net_config.embedding_dim / 16) * 16
train_ds = dataset.create(train_csv_path, net_config.per_item_paths, train=True).batch(net_config.batch_size)
test_ds = dataset.create(test_csv_path, net_config.per_item_paths, train=True).batch(net_config.batch_size)
print("creating TBNet for training...")
network = tbnet.TBNet(net_config)
loss_net = tbnet.NetWithLossClass(network, net_config)
if args.device_target == 'Ascend':
loss_net.to_float(mstype.float16)
train_net = tbnet.TrainStepWrap(loss_net, net_config.lr, loss_scale=True)
else:
train_net = tbnet.TrainStepWrap(loss_net, net_config.lr)
train_net.set_train()
eval_net = tbnet.PredictWithSigmoid(network)
time_callback = TimeMonitor(data_size=train_ds.get_dataset_size())
loss_callback = MyLossMonitor()
model = Model(network=train_net, eval_network=eval_net, metrics={'auc': metrics.AUC(), 'acc': metrics.ACC()})
print("training...")
for i in range(args.epochs):
print(f'===================== Epoch {i} =====================')
model.train(epoch=1, train_dataset=train_ds, callbacks=[time_callback, loss_callback])
train_out = model.eval(train_ds, dataset_sink_mode=False)
test_out = model.eval(test_ds, dataset_sink_mode=False)
print(f'Train AUC:{train_out["auc"]} ACC:{train_out["acc"]} Test AUC:{test_out["auc"]} ACC:{test_out["acc"]}')
ckpt_dir_path = os.path.join(ckpt_path, f'tbnet_epoch{i}.ckpt')
save_checkpoint(network, ckpt_dir_path)
try:
mox.file.copy_parallel(ckpt_path, obs_train_url)
print("Successfully Upload {} to {}".format(ckpt_path,
obs_train_url))
except Exception as e:
print('moxing upload {} to {} failed: '.format(ckpt_path,
obs_train_url) + str(e))
if __name__ == '__main__':
train_tbnet()