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=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=SUMMARY_DIR)
assert res.get('hyper_parameters', {}).get('epoch') == 14
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_get_summary_lineage(self):
"""Test the interface of get_summary_lineage."""
total_res = get_summary_lineage(data_manager=self._data_manger,
summary_dir="./run1")
expect_total_res = LINEAGE_INFO_RUN1
assert_equal_lineages(expect_total_res, total_res,
self.assertDictEqual)
def test_get_summary_lineage(self):
"""Test the interface of get_summary_lineage."""
total_res = get_summary_lineage(None, SUMMARY_DIR)
partial_res1 = get_summary_lineage(None, SUMMARY_DIR,
['hyper_parameters'])
partial_res2 = get_summary_lineage(None, SUMMARY_DIR,
['metric', 'algorithm'])
expect_total_res = LINEAGE_INFO_RUN1
expect_partial_res1 = {
'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
'hyper_parameters': {
'optimizer': 'Momentum',
'learning_rate': 0.12,
'loss_function': 'SoftmaxCrossEntropyWithLogits',
'epoch': 14,
'parallel_mode': 'stand_alone',
'device_num': 2,
'batch_size': 32
}
}
expect_partial_res2 = {
'summary_dir': os.path.join(BASE_SUMMARY_DIR, 'run1'),
'metric': {
'accuracy': 0.78
},
'algorithm': {
'network': 'ResNet'
}
}
assert_equal_lineages(expect_total_res, total_res,
self.assertDictEqual)
assert_equal_lineages(expect_partial_res1, partial_res1,
self.assertDictEqual)
assert_equal_lineages(expect_partial_res2, partial_res2,
self.assertDictEqual)
# the lineage summary file is empty
result = get_summary_lineage(None, self.dir_with_empty_lineage)
assert {} == result
# keys is empty list
expect_result = {'summary_dir': SUMMARY_DIR}
result = get_summary_lineage(None, SUMMARY_DIR, [])
assert expect_result == result
def test_get_summary_lineage_failed3(self, mock_summary, mock_valid,
mock_parser, mock_file_handler):
"""Test get_summary_lineage failed."""
mock_summary.return_value = ['/path/to/summary/file']
mock_valid.return_value = '/path/to/summary_dir'
mock_parser.return_value = None
mock_file_handler = MagicMock()
mock_file_handler.size = 1
result = get_summary_lineage(None, '/path/to/summary_dir')
assert {} == result
def test_get_summary_lineage_success(self, isdir_mock, parser_mock,
qurier_mock):
"""Test the function of get_summary_lineage."""
isdir_mock.return_value = True
parser_mock.return_value = MagicMock()
mock_querier = MagicMock()
qurier_mock.return_value = mock_querier
mock_querier.get_summary_lineage.return_value = [{
'algorithm': {
'network': 'ResNet'
}
}]
summary_dir = '/path/to/summary_dir'
result = get_summary_lineage(None, summary_dir, keys=['algorithm'])
self.assertEqual(result, {'algorithm': {'network': 'ResNet'}})
def get_dataset_graph():
"""
Get dataset graph.
Returns:
str, the dataset graph information.
Raises:
MindInsightException: If method fails to be called.
ParamValueError: If summary_dir is invalid.
Examples:
>>> GET http://xxxx/v1/mindinsight/datasets/dataset_graph?train_id=xxx
"""
summary_base_dir = str(settings.SUMMARY_BASE_DIR)
summary_dir = get_train_id(request)
try:
dataset_graph = get_summary_lineage(
DATA_MANAGER,
summary_dir=summary_dir,
keys=['dataset_graph']
)
except MindInsightException as exception:
raise MindInsightException(exception.error, exception.message, http_code=400)
if dataset_graph:
summary_dir_result = dataset_graph.get('summary_dir')
base_dir_len = len(summary_base_dir)
if summary_base_dir == summary_dir_result:
relative_dir = './'
else:
relative_dir = os.path.join(
os.curdir, summary_dir[base_dir_len + 1:]
)
dataset_graph['summary_dir'] = relative_dir
return jsonify(dataset_graph)