def setup_class(cls):
"""Setup method."""
cls.optimizer = Momentum(Tensor(0.12))
cls.loss_fn = SoftmaxCrossEntropyWithLogits()
cls.net = ResNet()
cls.run_context = dict()
cls.run_context['train_network'] = cls.net
cls.run_context['loss_fn'] = cls.loss_fn
cls.run_context['net_outputs'] = Tensor(np.array([0.03]))
cls.run_context['optimizer'] = cls.optimizer
cls.run_context['train_dataset'] = MindDataset(dataset_size=32)
cls.run_context['epoch_num'] = 10
cls.run_context['cur_step_num'] = 320
cls.run_context['parallel_mode'] = "stand_alone"
cls.run_context['device_number'] = 2
cls.run_context['batch_num'] = 32
cls.summary_record = SummaryRecord(SUMMARY_DIR)
callback = [
ModelCheckpoint(directory=SUMMARY_DIR),
SummaryStep(cls.summary_record),
TrainLineage(cls.summary_record)
]
cls.run_context['list_callback'] = _ListCallback(callback)
cls.user_defined_info = {"info": "info1", "version": "v1"}
def test_train_with_customized_network(self, *args):
"""Test train with customized network."""
args[0].return_value = 64
train_callback = TrainLineage(SUMMARY_DIR, True,
self.user_defined_info)
run_context_customized = self.run_context
del run_context_customized['optimizer']
del run_context_customized['net_outputs']
del run_context_customized['loss_fn']
net = WithLossCell(self.net, self.loss_fn)
net_cap = net
net_cap._cells = {'_backbone': self.net, '_loss_fn': self.loss_fn}
net = TrainOneStep(net, self.optimizer)
net._cells = {
'optimizer': self.optimizer,
'network': net_cap,
'backbone': self.net
}
run_context_customized['train_network'] = net
train_callback.begin(RunContext(run_context_customized))
train_callback.end(RunContext(run_context_customized))
res = get_summary_lineage(summary_dir=SUMMARY_DIR)
assert res.get('hyper_parameters', {}).get('loss_function') \
== 'SoftmaxCrossEntropyWithLogits'
assert res.get('algorithm', {}).get('network') == 'ResNet'
assert res.get('hyper_parameters', {}).get('optimizer') == 'Momentum'
def test_multiple_trains(self, *args):
"""
Callback TrainLineage and EvalLineage for multiple times.
Write TrainLineage and EvalLineage in different files under same directory.
EvalLineage log file end with '_lineage'.
"""
args[0].return_value = 10
for i in range(2):
summary_record = SummaryRecord(SUMMARY_DIR_2,
create_time=int(time.time()) + i)
eval_record = SummaryRecord(SUMMARY_DIR_2,
create_time=int(time.time() + 10) + i)
args[1].return_value = os.path.join(
SUMMARY_DIR_2,
f'train_out.events.summary.{str(int(time.time()) + 2*i)}.ubuntu_lineage'
)
train_callback = TrainLineage(summary_record, True)
train_callback.begin(RunContext(self.run_context))
train_callback.end(RunContext(self.run_context))
args[1].return_value = os.path.join(
SUMMARY_DIR_2,
f'eval_out.events.summary.{str(int(time.time())+ 2*i + 1)}.ubuntu_lineage'
)
eval_callback = EvalLineage(eval_record, True)
eval_run_context = self.run_context
eval_run_context['metrics'] = {'accuracy': 0.78 + i + 1}
eval_run_context['valid_dataset'] = self.run_context[
'train_dataset']
eval_run_context['step_num'] = 32
eval_callback.end(RunContext(eval_run_context))
file_num = os.listdir(SUMMARY_DIR_2)
assert len(file_num) == 8
def test_training_end(self, *args):
"""Test the end function in TrainLineage."""
args[0].return_value = 64
train_callback = TrainLineage(SUMMARY_DIR, True, self.user_defined_info)
train_callback.initial_learning_rate = 0.12
train_callback.end(RunContext(self.run_context))
res = get_summary_lineage(SUMMARY_DIR)
assert res.get('hyper_parameters', {}).get('epoch') == 10
run_context = self.run_context
run_context['epoch_num'] = 14
train_callback.end(RunContext(run_context))
res = get_summary_lineage(SUMMARY_DIR)
assert res.get('hyper_parameters', {}).get('epoch') == 14
def test_raise_exception_record_trainlineage(self, *args):
"""Test exception when error happened after recording training infos."""
if os.path.exists(SUMMARY_DIR_3):
shutil.rmtree(SUMMARY_DIR_3)
args[1].side_effect = MindInsightException(error=LineageErrors.PARAM_RUN_CONTEXT_ERROR,
message="RunContext error.")
train_callback = TrainLineage(SUMMARY_DIR_3, True)
train_callback.begin(RunContext(self.run_context))
full_file_name = train_callback.lineage_summary.lineage_log_path
file_size1 = os.path.getsize(full_file_name)
train_callback.end(RunContext(self.run_context))
file_size2 = os.path.getsize(full_file_name)
assert file_size2 > file_size1
eval_callback = EvalLineage(SUMMARY_DIR_3, False)
eval_callback.end(RunContext(self.run_context))
file_size3 = os.path.getsize(full_file_name)
assert file_size3 == file_size2