def test_step_end_save_graph():
"""Test save checkpoint."""
train_config = CheckpointConfig(save_checkpoint_steps=16,
save_checkpoint_seconds=0,
keep_checkpoint_max=5,
keep_checkpoint_per_n_minutes=0)
cb_params = _InternalCallbackParam()
net = LossNet()
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))
net(input_data, input_label)
cb_params.train_network = net
cb_params.epoch_num = 10
cb_params.cur_epoch_num = 5
cb_params.cur_step_num = 0
cb_params.batch_num = 32
ckpoint_cb = ModelCheckpoint(prefix="test",
directory='./test_files',
config=train_config)
run_context = RunContext(cb_params)
ckpoint_cb.begin(run_context)
# import pdb;pdb.set_trace()
ckpoint_cb.step_end(run_context)
assert os.path.exists('./test_files/test-graph.meta')
if os.path.exists('./test_files/test-graph.meta'):
os.chmod('./test_files/test-graph.meta', stat.S_IWRITE)
os.remove('./test_files/test-graph.meta')
ckpoint_cb.step_end(run_context)
assert not os.path.exists('./test_files/test-graph.meta')
def test_save_checkpoint():
"""Test save checkpoint."""
train_config = CheckpointConfig(save_checkpoint_steps=16,
save_checkpoint_seconds=0,
keep_checkpoint_max=5,
keep_checkpoint_per_n_minutes=0)
cb_params = _InternalCallbackParam()
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
network_ = WithLossCell(net, loss)
_train_network = TrainOneStepCell(network_, optim)
cb_params.train_network = _train_network
cb_params.epoch_num = 10
cb_params.cur_epoch_num = 5
cb_params.cur_step_num = 0
cb_params.batch_num = 32
ckpoint_cb = ModelCheckpoint(prefix="test_ckpt",
directory='./test_files',
config=train_config)
run_context = RunContext(cb_params)
ckpoint_cb.begin(run_context)
ckpoint_cb.step_end(run_context)
if os.path.exists('./test_files/test_ckpt-model.pkl'):
os.chmod('./test_files/test_ckpt-model.pkl', stat.S_IWRITE)
os.remove('./test_files/test_ckpt-model.pkl')
def test_checkpoint_save_ckpt_seconds():
"""Test checkpoint save ckpt seconds."""
train_config = CheckpointConfig(save_checkpoint_steps=16,
save_checkpoint_seconds=100,
keep_checkpoint_max=0,
keep_checkpoint_per_n_minutes=1)
ckpt_cb = ModelCheckpoint(config=train_config)
cb_params = _InternalCallbackParam()
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
network_ = WithLossCell(net, loss)
_train_network = TrainOneStepCell(network_, optim)
cb_params.train_network = _train_network
cb_params.epoch_num = 10
cb_params.cur_epoch_num = 4
cb_params.cur_step_num = 128
cb_params.batch_num = 32
run_context = RunContext(cb_params)
ckpt_cb.begin(run_context)
ckpt_cb.step_end(run_context)
ckpt_cb2 = ModelCheckpoint(config=train_config)
cb_params.cur_epoch_num = 1
cb_params.cur_step_num = 16
ckpt_cb2.begin(run_context)
ckpt_cb2.step_end(run_context)
def _eval_process(self, valid_dataset, list_callback=None, cb_params=None):
"""
Evaluation. The data would be passed to network directly.
Args:
valid_dataset (Dataset): Dataset to evaluate the model.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
Returns:
Dict, returns the loss value & metrics values for the model in test mode.
"""
run_context = RunContext(cb_params)
list_callback.begin(run_context)
dataset_helper, _ = self._exec_preprocess(self._eval_network,
is_train=False,
phase='eval',
dataset=valid_dataset,
dataset_sink_mode=False)
for next_element in dataset_helper:
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
outputs = self._eval_network(*next_element)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
self._update_metrics(outputs)
metrics = self._get_metrics()
cb_params.metrics = metrics
list_callback.end(run_context)
return metrics
def test_loss_monitor_normal_mode():
"""Test loss monitor normal(non-sink) mode."""
cb_params = _InternalCallbackParam()
run_context = RunContext(cb_params)
loss_cb = LossMonitor(1)
cb_params.cur_epoch_num = 4
cb_params.cur_step_num = 1
cb_params.batch_num = 1
cb_params.net_outputs = Tensor(2.0)
loss_cb.begin(run_context)
loss_cb.epoch_begin(run_context)
loss_cb.step_begin(run_context)
loss_cb.step_end(run_context)
loss_cb.epoch_end(run_context)
loss_cb.end(run_context)
def test_RunContext():
"""Test RunContext."""
context_err = 666
with pytest.raises(TypeError):
RunContext(context_err)
cb_params = _InternalCallbackParam()
cb_params.member1 = 1
cb_params.member2 = "abc"
run_context = RunContext(cb_params)
run_context.original_args()
assert cb_params.member1 == 1
assert cb_params.member2 == "abc"
run_context.request_stop()
should_stop = run_context.get_stop_requested()
assert should_stop
def test_loss_monitor_sink_mode():
"""Test loss monitor sink mode."""
cb_params = _InternalCallbackParam()
cb_params.cur_epoch_num = 4
cb_params.cur_step_num = 2
cb_params.batch_num = 2
cb_params.net_outputs = Tensor(2.0)
run_context = RunContext(cb_params)
loss_cb = LossMonitor(1)
callbacks = [loss_cb]
with _CallbackManager(callbacks) as callbacklist:
callbacklist.begin(run_context)
callbacklist.epoch_begin(run_context)
callbacklist.step_begin(run_context)
callbacklist.step_end(run_context)
callbacklist.epoch_end(run_context)
callbacklist.end(run_context)
def _train_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None):
"""
Training process. The data would be passed to network directly.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
dataset_helper, _ = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=False)
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
for next_element in dataset_helper:
len_element = len(next_element)
if self._loss_fn and len_element != 2:
raise ValueError(
"when loss_fn is not None, train_dataset should"
"return two elements, but got {}".format(len_element))
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
overflow = False
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update(
):
scaling_sens = self._get_scaling_sens()
next_element = tuple(next_element) + (Tensor(
scaling_sens, mstype.float32), )
outputs = self._train_network(*next_element)
cb_params.net_outputs = outputs
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update(
):
_, overflow, _ = outputs
overflow = np.all(overflow.asnumpy())
self._loss_scale_manager.update_loss_scale(overflow)
list_callback.step_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
train_dataset.reset()
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
def _train_dataset_sink_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
iter_first_order = self._frequency - 1
iter_second_order = 1
train_dataset.__loop_size__ = iter_second_order
dataset_helper, train_network = self._exec_preprocess(
self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
iter_first_order=iter_first_order)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
loop_size = dataset_helper.loop_size()
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
has_do_dataset_init = False
switch_branch_one = True
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
list_callback.step_begin(run_context)
if switch_branch_one:
cb_params.cur_step_num += loop_size
self._train_network.add_flags_recursive(thor=True)
self._train_network.phase = 'train0'
else:
cb_params.cur_step_num += iter_first_order
self._train_network.add_flags_recursive(thor=False)
self._train_network.phase = 'train1'
if not has_do_dataset_init:
_exec_datagraph(train_dataset,
iter_first_order,
phase='train1_dataset')
has_do_dataset_init = True
switch_branch_one = not switch_branch_one
outputs = self._train_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)