def test_train_eval():
"""
test_train_eval
"""
config = WideDeepConfig()
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=epochs, batch_size=batch_size,
data_type=DataType.MINDRECORD, rank_id=get_rank(), rank_size=get_group_size())
ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1, batch_size=batch_size,
data_type=DataType.MINDRECORD, rank_id=get_rank(), rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_train.ckpt")
model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback])
eval_values = list(eval_callback.eval_values)
assert eval_values[0] > 0.78
def train_and_eval(config):
"""
test_train_eval
"""
np.random.seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=epochs,
batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
ds_eval = create_dataset(data_path, train_mode=False, epochs=epochs + 1,
batch_size=batch_size, rank_id=get_rank(), rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path, config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs, ds_train,
callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
def test_train_eval():
"""
test_train_eval
"""
np.random.seed(1000)
config = WideDeepConfig()
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
expect_out0 = [0.792634, 0.799862, 0.803324]
expect_out6 = [0.796580, 0.803908, 0.807262]
if get_rank() == 0:
assert np.allclose(eval_callback.eval_values, expect_out0)
if get_rank() == 6:
assert np.allclose(eval_callback.eval_values, expect_out6)
def test_train_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
sparse = config.sparse
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
],
dataset_sink_mode=(not sparse))
def train_and_eval(config):
"""
train_and_eval
"""
set_seed(1000)
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config)
# Only save the last checkpoint at the last epoch. For saving epochs at each epoch, please
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * config.epochs,
keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' +
str(get_rank()) + '/',
config=ckptconfig)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if int(get_rank()) == 0:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
sink_size=ds_train.get_dataset_size())
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
host_device_mix = bool(config.host_device_mix)
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
de.config.set_seed(1)
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size*get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size*get_group_size(), data_type=dataset_type)
else:
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config, host_device_mix=host_device_mix)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path, config=ckptconfig)
context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_train.ckpt")
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
if not host_device_mix:
callback_list.append(ckpoint_cb)
model.train(epochs, ds_train, callbacks=callback_list, dataset_sink_mode=(not host_device_mix))
def train_and_eval(config):
"""
train_and_eval.
"""
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=config.batch_size, is_tf_dataset=config.is_tf_dataset)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def test_train(configure):
"""
test_train
"""
data_path = configure.data_path
batch_size = configure.batch_size
epochs = configure.epochs
ds_train = create_dataset(data_path,
train_mode=True,
epochs=epochs,
batch_size=batch_size)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
net_builder = ModelBuilder()
train_net, _ = net_builder.get_net(configure)
train_net.set_train()
model = Model(train_net)
callback = LossCallBack(config=configure)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=configure.ckpt_path,
config=ckptconfig)
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), callback,
ckpoint_cb
])
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, rank_id=get_rank(),
rank_size=get_group_size(), data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' + str(get_rank()) + '/',
config=ckptconfig)
out = model.eval(ds_eval)
print("=====" * 5 + "model.eval() initialized: {}".format(out))
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
if get_rank() == 0:
callback_list.append(ckpoint_cb)
model.train(epochs, ds_train,
callbacks=callback_list,
sink_size=ds_train.get_dataset_size())
def train_and_eval(config):
"""
train_and_eval.
"""
data_path = config.data_path
epochs = config.epochs
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=config.batch_size,
is_tf_dataset=config.is_tf_dataset)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
parameter_server = bool(config.parameter_server)
cache_enable = config.vocab_cache_size > 0
print("epochs is {}".format(epochs))
ds_train = create_dataset(data_path, train_mode=True, epochs=1,
batch_size=batch_size, data_type=dataset_type)
ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
batch_size=batch_size, data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size(), keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig)
callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb]
model.train(epochs, ds_train,
callbacks=callback_list,
dataset_sink_mode=(parameter_server and cache_enable))
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
ckpt_path = config.ckpt_path
if ";" in ckpt_path:
ckpt_paths = ckpt_path.split(';')
param_list_dict = {}
strategy = build_searched_strategy(config.stra_ckpt)
for slice_path in ckpt_paths:
param_slice_dict = load_checkpoint(slice_path)
for key, value in param_slice_dict.items():
if 'optimizer' in key:
continue
if key not in param_list_dict:
param_list_dict[key] = []
param_list_dict[key].append(value)
param_dict = {}
for key, value in param_list_dict.items():
if key in strategy:
merged_parameter = merge_sliced_parameter(value, strategy)
else:
merged_parameter = merge_sliced_parameter(value)
param_dict[key] = merged_parameter
else:
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
ckpt_path = config.ckpt_path
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
# data upload
print('Upload data from obs to modelarts server.')
mox.file.copy_parallel(src_url=config.data_url, dst_url=data_path)
mox.file.copy_parallel(src_url=config.ckpt_url, dst_url=ckpt_path)
tar_file = data_path + "train_demo.tar.gz"
untar(tar_file, data_path)
data_path = data_path + config.dataset_type
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(find_ckpt(ckpt_path))
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
"""Mixed Precision Tutorial
The sample can be run on GPU and Ascend 910 AI processor.
"""
import mindspore.nn as nn
from mindspore import context, Model
from mindspore.train.callback import LossMonitor
from mindspore.nn.metrics import Accuracy
from src.lenet import LeNet5
from src.datasets import create_dataset
if __name__ == "__main__":
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
ds_train = create_dataset("./datasets/MNIST_Data/train", 32)
ds_eval = create_dataset("./datasets/MNIST_Data/test", 32)
# Initialize network
network = LeNet5(10)
# Define Loss and Optimizer
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)
# amp_leval=O2 in GPU, amp_leval=O3 in Ascend, O0 is without mixed precision
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2")
# Run training
model.train(epoch=1, callbacks=[LossMonitor()], train_dataset=ds_train)
# Run training
acc = model.eval(ds_eval, dataset_sink_mode=False)
train_data_path = "./datasets/MNIST_Data/train"
eval_data_path = "./datasets/MNIST_Data/test"
model_path = "./models/ckpt/custom_debugging_info/"
net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
repeat_size = 1
network = LeNet5()
metrics = {
'accuracy': nn.Accuracy(),
'loss': nn.Loss(),
'precision': nn.Precision(),
'recall': nn.Recall(),
'f1_score': nn.F1()
}
net_opt = nn.Momentum(network.trainable_params(), lr, momentum)
config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=model_path, config=config_ck)
model = Model(network, net_loss, net_opt, metrics=metrics)
print("============== Starting Training ==============")
ds_train = create_dataset(train_data_path, repeat_size=repeat_size)
stop_cb = StopAtTime(run_time=0.6)
model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(375), stop_cb], dataset_sink_mode=False)
print("============== Starting Testing ==============")
ds_eval = create_dataset(eval_data_path, repeat_size=repeat_size)
acc = model.eval(ds_eval, dataset_sink_mode=False)
print("============== Accuracy:{} ==============".format(acc))
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
host_device_mix = bool(config.host_device_mix)
sparse = config.sparse
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
ds.config.set_seed(1)
if config.field_slice:
compute_manual_shape(config, get_group_size())
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type,
manual_shape=config.manual_shape,
target_column=config.field_size)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type,
manual_shape=config.manual_shape,
target_column=config.field_size)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
# Save strategy ckpts according to the rank id, this must be done before initializing the callbacks.
config.stra_ckpt = os.path.join(
config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config, per_print_times=20)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * epochs,
keep_checkpoint_max=5,
integrated_save=False)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=os.path.join(
config.ckpt_path,
'ckpt_' + str(get_rank())),
config=ckptconfig)
context.set_auto_parallel_context(strategy_ckpt_save_file=config.stra_ckpt)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if not host_device_mix:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
dataset_sink_mode=(not sparse))
def train_eval(config):
"""
test evaluate
"""
data_path = config.data_path + config.dataset_type
ckpt_path = config.ckpt_path
epochs = config.epochs
batch_size = config.batch_size
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
data_type=dataset_type)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
train_model = Model(train_net)
train_callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
train_model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()),
train_callback, ckpoint_cb
])
# data download
print('Download data from modelarts server to obs.')
mox.file.copy_parallel(src_url=config.ckpt_path, dst_url=config.train_url)
param_dict = load_checkpoint(find_ckpt(ckpt_path))
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
eval_model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(eval_model, ds_eval, auc_metric, config)
eval_model.eval(ds_eval, callbacks=eval_callback)
def train_and_eval(config):
"""
test_train_eval
"""
set_seed(1000)
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
if config.dataset_type == "tfrecord":
dataset_type = DataType.TFRECORD
elif config.dataset_type == "mindrecord":
dataset_type = DataType.MINDRECORD
else:
dataset_type = DataType.H5
parameter_server = bool(config.parameter_server)
if cache_enable:
config.full_batch = True
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
ds.config.set_seed(1)
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size * get_group_size(),
data_type=dataset_type)
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size(),
data_type=dataset_type)
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
if cache_enable:
config.stra_ckpt = os.path.join(
config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
context.set_auto_parallel_context(
strategy_ckpt_save_file=config.stra_ckpt)
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
if _is_role_worker():
if cache_enable:
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size() * epochs,
keep_checkpoint_max=1,
integrated_save=False)
else:
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
else:
ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
keep_checkpoint_max=1)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path + '/ckpt_' +
str(get_rank()) + '/',
config=ckptconfig)
callback_list = [
TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback
]
if get_rank() == 0:
callback_list.append(ckpoint_cb)
model.train(epochs,
ds_train,
callbacks=callback_list,
dataset_sink_mode=bool(parameter_server and cache_enable))
def train_and_eval(config):
"""
test_train_eval
"""
data_path = config.data_path
batch_size = config.batch_size
epochs = config.epochs
print("epochs is {}".format(epochs))
if config.full_batch:
context.set_auto_parallel_context(full_batch=True)
de.config.set_seed(1)
ds_train = create_dataset(data_path,
train_mode=True,
epochs=epochs,
batch_size=batch_size * get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=epochs + 1,
batch_size=batch_size * get_group_size())
else:
ds_train = create_dataset(data_path,
train_mode=True,
epochs=epochs,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=epochs + 1,
batch_size=batch_size,
rank_id=get_rank(),
rank_size=get_group_size())
print("ds_train.size: {}".format(ds_train.get_dataset_size()))
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
train_net.set_train()
auc_metric = AUCMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(
save_checkpoint_steps=ds_train.get_dataset_size(),
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path,
config=ckptconfig)
context.set_auto_parallel_context(
strategy_ckpt_save_file="./strategy_train.ckpt")
model.train(epochs,
ds_train,
callbacks=[
TimeMonitor(ds_train.get_dataset_size()), eval_callback,
callback, ckpoint_cb
])
model_file = model_path + "checkpoint_lenet-1_1875.ckpt"
os.system('rm -f {0}*.ckpt {0}*.meta {0}*.pb'.format(model_path))
network = LeNet5()
net_opt = nn.Momentum(network.trainable_params(), lr, momentum)
net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
config_ck = CheckpointConfig(save_checkpoint_steps=1875,
keep_checkpoint_max=16)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
directory=model_path,
config=config_ck)
train_dataset = create_dataset("./datasets/MNIST_Data/train")
eval_dataset = create_dataset("./datasets/MNIST_Data/test")
print("===============start training===============")
model.train(epoch_size,
train_dataset,
callbacks=[ckpoint_cb, LossMonitor()],
dataset_sink_mode=False)
print(
"===============saving models in mindir and onnx formats==============="
)
output_file_formats(model_file, network, [32, 1, 32, 32],
"checkpoint_lenet", "MINDIR")
output_file_formats(model_file, network, [32, 1, 32, 32],
"checkpoint_lenet", "ONNX")
