def __eval(self):
# import
from mindspore import Model, load_param_into_net, load_checkpoint
from mindspore.nn.metrics import Accuracy
# load params
if self.__ckpt_path:
load_param_into_net(self.__network,
load_checkpoint(self.__ckpt_path))
else:
print(
"Warning: `ckpt_path` is None, Please call func: `set_ckpt_path($ckpt_path)`."
)
return
# loss_fn & optimizer & metrics
model = Model(self.__network,
loss_fn=self.__loss_fn,
optimizer=self.__optimizer,
metrics={"Accuracy": Accuracy()}
if self.__metrics is None else self.__metrics)
# eval
print(">>>>>>>>>>>>>>>>>>>>> eval start ... <<<<<<<<<<<<<<<<<<<<<<")
result = model.eval(self.__dataset)
print(
">>>>>>>>>>>>>>>>>>>>> eval success ~ <<<<<<<<<<<<<<<<<<<<<<: result=",
result)
def test_single_input():
""" test_single_input """
input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
context.set_context(mode=context.GRAPH_MODE)
model = Model(Net())
out = model.predict(input_data)
assert out is not None
def main(data_path,
device_target='Ascend',
summary_dir='./summary_dir',
learning_rate=0.01):
context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
momentum = 0.9
epoch_size = 1
batch_size = 32
network = LeNet5()
network.set_train()
net_loss = CrossEntropyLoss()
net_opt = nn.Momentum(network.trainable_params(), learning_rate, momentum)
model = Model(network, net_loss, net_opt)
# Init SummaryCollector callback to record summary data in model.train or model.eval
summary_collector = SummaryCollector(summary_dir=summary_dir,
collect_freq=10)
ds = create_dataset(os.path.join(data_path, "train"),
batch_size=batch_size)
print("============== Starting Training ==============")
model.train(epoch_size,
ds,
callbacks=[summary_collector],
dataset_sink_mode=False)
print("============== Train End =====================")
def test_net(network, data_path, ckpt):
"""define the evaluation method"""
print("============== Starting Testing ==============")
#load the saved model for evaluation
load_checkpoint(ckpt, net=network)
#load testing dataset
ds_eval = create_dataset(False, data_path)
# config = GPTConfig(batch_size=4,
# seq_length=1024,
# vocab_size=50257,
# embedding_size=1024,
# num_layers=24,
# num_heads=16,
# expand_ratio=4,
# post_layernorm_residual=False,
# dropout_rate=0.1,
# compute_dtype=mstype.float16,
# use_past=False)
# loss = CrossEntropyLoss(config)
net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(resnet, net_loss, metrics={"Accuracy": Accuracy()})
# model = Model(resnet, net_loss, metrics={"Accuracy": Accuracy()}, amp_level="O3")
acc = model.eval(ds_eval, dataset_sink_mode=False)
print("============== Accuracy:{} ==============".format(acc))
def __train(self):
# import
from mindspore import Model, load_param_into_net, load_checkpoint
# load params
if self.__ckpt_path and os.path.isfile(
self.__ckpt_path) and os.path.exists(self.__ckpt_path):
load_param_into_net(self.__network,
load_checkpoint(self.__ckpt_path))
# loss_fn & optimizer & metrics
model = Model(self.__network,
loss_fn=self.__loss_fn,
optimizer=self.__optimizer,
metrics=self.__metrics)
# train
print(">>>>>>>>>>>>>>>>>>>>> train start ... <<<<<<<<<<<<<<<<<<<<<<")
self.__update_cbs()
model.train(self.__epoch_size,
self.__dataset,
callbacks=self.__callbacks,
dataset_sink_mode=self.__do_sink,
sink_size=self.__sink_size)
print(">>>>>>>>>>>>>>>>>>>>> train success ~ <<<<<<<<<<<<<<<<<<<<<<")
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():
"""distributed training"""
context.set_context(mode=context.GRAPH_MODE)
parallel_dataset = FakeData()
strategy = ((2, 1), (1, 4))
context.set_auto_parallel_context(
parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
device_num=8,
strategy_ckpt_save_file="./train_strategy.ckpt")
network = Net(matmul_size=(96, 16), strategy=strategy)
net_opt = Momentum(network.trainable_params(), 0.01, 0.9)
net_loss = SoftmaxCrossEntropyWithLogits(reduction='mean')
model = Model(network=network, loss_fn=net_loss, optimizer=net_opt)
ckpt_config = CheckpointConfig(keep_checkpoint_max=1,
integrated_save=False)
global_rank_id = int(os.getenv("RANK_ID"))
ckpt_path = './rank_{}_ckpt'.format(global_rank_id)
ckpt_callback = ModelCheckpoint(prefix='parallel',
directory=ckpt_path,
config=ckpt_config)
model.train(epoch=2,
train_dataset=parallel_dataset,
callbacks=[ckpt_callback],
dataset_sink_mode=False)
context.reset_auto_parallel_context()
def test_resnet_train_tensor():
"""test_resnet_train_tensor"""
batch_size = 1
size = 2
context.set_context(mode=context.GRAPH_MODE)
context.reset_auto_parallel_context()
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, device_num=size,
parameter_broadcast=True)
one_hot_len = 10
dataset_types = (np.float32, np.float32)
dataset_shapes = [[batch_size, 3, 224, 224], [batch_size, one_hot_len]]
predict = Tensor(np.ones([batch_size, 3, 224, 224]).astype(np.float32) * 0.01)
label = Tensor(np.zeros([batch_size, one_hot_len]).astype(np.float32))
dataset = DatasetLenet(predict, label, 2,
size=2, batch_size=2,
np_types=dataset_types,
output_shapes=dataset_shapes,
input_indexs=(0, 1))
dataset.reset()
network = resnet9(one_hot_len)
network.set_train()
loss_fn = nn.SoftmaxCrossEntropyWithLogits()
optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), learning_rate=0.1, momentum=0.9)
model = Model(network=network, loss_fn=loss_fn, optimizer=optimizer)
model.train(epoch=2, train_dataset=dataset, dataset_sink_mode=False)
context.set_context(mode=context.GRAPH_MODE)
context.reset_auto_parallel_context()
def test_cell_list():
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(input_np)
net = Net3()
context.set_context(mode=context.GRAPH_MODE)
model = Model(net)
model.predict(input_me)
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 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 test_deeplabv3_1p():
start_time = time.time()
epoch_size = 100
args_opt = argparse.Namespace(base_size=513, crop_size=513, batch_size=2)
args_opt.base_size = config.crop_size
args_opt.crop_size = config.crop_size
args_opt.batch_size = config.batch_size
train_dataset = create_dataset(args_opt, data_url, 1, config.batch_size,
usage="eval")
dataset_size = train_dataset.get_dataset_size()
callback = LossCallBack(dataset_size)
net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
net.set_train()
model_fine_tune(net, 'layer')
loss = OhemLoss(config.seg_num_classes, config.ignore_label)
opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
model = Model(net, loss, opt)
model.train(epoch_size, train_dataset, callback)
print(time.time() - start_time)
print("expect loss: ", callback.loss)
print("expect time: ", callback.time)
expect_loss = 0.92
expect_time = 40
assert callback.loss.asnumpy() <= expect_loss
assert callback.time <= expect_time
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
ds_eval = create_dataset(data_path,
train_mode=False,
epochs=2,
batch_size=batch_size)
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_multiple_argument():
""" test_multiple_argument """
input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
input_label = Tensor(np.random.randint(0, 3, [1, 3]).astype(np.float32))
context.set_context(mode=context.GRAPH_MODE)
model = Model(LossNet())
out = model.predict(input_data, input_label)
assert out is not None
def test_value_error():
np.random.seed(6)
adv = np.random.rand(4, 4, 4).astype(np.float32)
encoder = Model(Net())
model = Model(PredNet())
detector = DivergenceBasedDetector(encoder, model, option='bad_op')
with pytest.raises(NotImplementedError):
assert detector.detect_diff(adv)
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 function_access_base(number):
""" function_access_base """
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(input_np)
if number == 2:
net = access2_net(number)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net)
model.predict(input_me)
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 resnet50_train(args):
"""Training the ResNet-50."""
epoch_size = args.epoch_size
batch_size = 32
class_num = 10
loss_scale_num = 1024
local_data_path = '/cache/data'
# set graph mode and parallel mode
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(device_id=device_id)
if device_num > 1:
context.set_auto_parallel_context(device_num=device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
init()
local_data_path = os.path.join(local_data_path, str(device_id))
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args.data_url, dst_url=local_data_path)
# create dataset
print('Create train and evaluate dataset.')
train_dataset = create_dataset(dataset_path=local_data_path, do_train=True,
repeat_num=1, batch_size=batch_size)
eval_dataset = create_dataset(dataset_path=local_data_path, do_train=False,
repeat_num=1, batch_size=batch_size)
train_step_size = train_dataset.get_dataset_size()
print('Create dataset success.')
# create model
net = resnet50(class_num=class_num)
# reduction='mean' means that apply reduction of mean to loss
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
lr = Tensor(get_lr(global_step=0, total_epochs=epoch_size, steps_per_epoch=train_step_size))
opt = Momentum(net.trainable_params(), lr, momentum=0.9, weight_decay=1e-4, loss_scale=loss_scale_num)
loss_scale = FixedLossScaleManager(loss_scale_num, False)
# amp_level="O2" means that the hybrid precision of O2 mode is used for training
# the whole network except that batchnoram will be cast into float16 format and dynamic loss scale will be used
# 'keep_batchnorm_fp32 = False' means that use the float16 format
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}, amp_level="O2", keep_batchnorm_fp32=False,
loss_scale_manager=loss_scale)
# define performance callback to show ips and loss callback to show loss for every epoch
performance_cb = PerformanceCallback(batch_size)
loss_cb = LossMonitor()
cb = [performance_cb, loss_cb]
print(f'Start run training, total epoch: {epoch_size}.')
model.train(epoch_size, train_dataset, callbacks=cb)
if device_num == 1 or device_id == 0:
print(f'Start run evaluation.')
output = model.eval(eval_dataset)
print(f'Evaluation result: {output}.')
def logical_operator_base(symbol):
""" logical_operator_base """
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(input_np)
logical_operator = {"and": 1, "or": 2}
x = logical_operator[symbol]
net = logical_Net(x)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net)
model.predict(input_me)
def test_distribute_predict_auto_parallel():
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, full_batch=True)
inputs = Tensor(np.ones([32, 64, 128]).astype(np.float32))
net = Net()
model = Model(net)
predict_map = model.infer_predict_layout(inputs)
output = model.predict(inputs)
context.reset_auto_parallel_context()
return predict_map, output
def run_test(netclass, count):
context.set_context(mode=context.GRAPH_MODE)
net = netclass()
model = Model(net)
for _ in range(count):
input_np = np.random.randn(2, 3).astype(np.float32)
input_ms = Tensor(input_np)
output_np = net.construct(input_np) # run python
output_ms = model.predict(input_ms) # run graph
assert np.shape(output_np) == np.shape(output_ms.asnumpy())
def test_graph_summary_callback2():
dataset = get_dataset()
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optim = Momentum(net.trainable_params(), 0.1, 0.9)
context.set_context(mode=context.GRAPH_MODE)
model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_GRAPH",
network=net) as test_writer:
summary_cb = SummaryStep(test_writer, 1)
model.train(2, dataset, callbacks=summary_cb)
def train_net(data_dir,
cross_valid_ind=1,
epochs=400,
batch_size=16,
lr=0.0001,
run_distribute=False,
cfg=None):
if run_distribute:
init()
group_size = get_group_size()
parallel_mode = ParallelMode.DATA_PARALLEL
context.set_auto_parallel_context(parallel_mode=parallel_mode,
device_num=group_size,
gradients_mean=False)
net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
if cfg['resume']:
param_dict = load_checkpoint(cfg['resume_ckpt'])
load_param_into_net(net, param_dict)
criterion = CrossEntropyWithLogits()
train_dataset, _ = create_dataset(data_dir, epochs, batch_size, True,
cross_valid_ind, run_distribute)
train_data_size = train_dataset.get_dataset_size()
print("dataset length is:", train_data_size)
ckpt_config = CheckpointConfig(
save_checkpoint_steps=train_data_size,
keep_checkpoint_max=cfg['keep_checkpoint_max'])
ckpoint_cb = ModelCheckpoint(prefix='ckpt_unet_medical_adam',
directory='./ckpt_{}/'.format(device_id),
config=ckpt_config)
optimizer = nn.Adam(params=net.trainable_params(),
learning_rate=lr,
weight_decay=cfg['weight_decay'],
loss_scale=cfg['loss_scale'])
loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(
cfg['FixedLossScaleManager'], False)
model = Model(net,
loss_fn=criterion,
loss_scale_manager=loss_scale_manager,
optimizer=optimizer,
amp_level="O3")
print("============== Starting Training ==============")
model.train(
1,
train_dataset,
callbacks=[StepLossTimeMonitor(batch_size=batch_size), ckpoint_cb],
dataset_sink_mode=False)
print("============== End Training ==============")
def test_mag_net_divergence_transform():
"""
Compute mindspore result.
"""
np.random.seed(6)
adv = np.random.rand(4, 4, 4).astype(np.float32)
encoder = Model(Net())
model = Model(PredNet())
detector = DivergenceBasedDetector(encoder, model)
adv_trans = detector.transform(adv)
assert np.any(adv_trans != adv)
def run_test(netclass, count, dev):
context.set_context(mode=context.GRAPH_MODE, device_target=dev)
net = netclass()
model = Model(net)
for _ in range(count):
input_np = np.random.randn(2, 3).astype(np.float32)
input_ms = Tensor(input_np)
output_np = net.construct(input_np) # run python
output_ms = model.predict(input_ms) # run graph
np.testing.assert_array_almost_equal(output_np,
output_ms.asnumpy(),
decimal=3)
def train_net(data_dir, seg_dir, run_distribute, config=None):
if run_distribute:
init()
rank_id = get_rank()
rank_size = get_group_size()
parallel_mode = ParallelMode.DATA_PARALLEL
context.set_auto_parallel_context(parallel_mode=parallel_mode,
device_num=rank_size,
gradients_mean=True)
else:
rank_id = 0
rank_size = 1
# train_dataset = create_dataset(data_path=data_dir, seg_path=seg_dir, config=config, \
# rank_size=rank_size, rank_id=rank_id, is_training=True)
train_dataset = create_dataset_diy()
# for item in train_dataset:
# print(item)
# exit(0)
train_data_size = train_dataset.get_dataset_size()
print("train dataset length is:", train_data_size)
network = UNet3d(config=config)
loss = SoftmaxCrossEntropyWithLogits()
# loss = nn.DiceLoss()
lr = Tensor(dynamic_lr(config, train_data_size), mstype.float32)
optimizer = nn.Adam(params=network.trainable_params(), learning_rate=lr)
scale_manager = FixedLossScaleManager(config.loss_scale,
drop_overflow_update=False)
network.set_train()
model = Model(network,
loss_fn=loss,
optimizer=optimizer,
loss_scale_manager=scale_manager,
amp_level='O3')
time_cb = TimeMonitor(data_size=train_data_size)
loss_cb = LossMonitor(per_print_times=2)
ckpt_config = CheckpointConfig(
save_checkpoint_steps=train_data_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix='{}'.format(config.model),
directory='./ckpt_{}/'.format(rank_size),
config=ckpt_config)
callbacks_list = [loss_cb, time_cb, ckpoint_cb]
print("============== Starting Training ==============")
model.train(config.epoch_size,
train_dataset,
callbacks=callbacks_list,
dataset_sink_mode=False)
print("============== End Training ==============")
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 run_eval():
"""eval method"""
if not os.path.exists(config.output_path):
os.makedirs(config.output_path)
context.set_context(mode=context.GRAPH_MODE,
device_target="Davinci",
save_graphs=False,
device_id=get_device_id())
layers = config.layers
num_factors = config.num_factors
topk = rconst.TOP_K
num_eval_neg = rconst.NUM_EVAL_NEGATIVES
ds_eval, num_eval_users, num_eval_items = create_dataset(
test_train=False,
data_dir=config.data_path,
dataset=config.dataset,
train_epochs=0,
eval_batch_size=config.eval_batch_size)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
ncf_net = NCFModel(num_users=num_eval_users,
num_items=num_eval_items,
num_factors=num_factors,
model_layers=layers,
mf_regularization=0,
mlp_reg_layers=[0.0, 0.0, 0.0, 0.0],
mf_dim=16)
param_dict = load_checkpoint(config.checkpoint_file_path)
load_param_into_net(ncf_net, param_dict)
loss_net = NetWithLossClass(ncf_net)
train_net = TrainStepWrap(loss_net)
eval_net = PredictWithSigmoid(ncf_net, topk, num_eval_neg)
ncf_metric = NCFMetric()
model = Model(train_net,
eval_network=eval_net,
metrics={"ncf": ncf_metric})
ncf_metric.clear()
out = model.eval(ds_eval)
eval_file_path = os.path.join(config.output_path, config.eval_file_name)
eval_file = open(eval_file_path, "a+")
eval_file.write("EvalCallBack: HR = {}, NDCG = {}\n".format(
out['ncf'][0], out['ncf'][1]))
eval_file.close()
print("EvalCallBack: HR = {}, NDCG = {}".format(out['ncf'][0],
out['ncf'][1]))
print("=" * 100 + "Eval Finish!" + "=" * 100)